ALBERT

Description: Urbanization is increasingly becoming a widespread phenomenon at all scales of development around the globe. Be it developing or developed nations, all are witnessing urbanization at very high pace. In order to study its impacts, various methodologies and techniques are being implemented to measure growth of urban extents over spatial and temporal domains. But urbanization being a very dynamic phenomenon has been facing ambiguities regarding methods to study its dynamism. This paper aims at quantifying urban expansion in Delhi, the capital city of India. The process has been studied using urban land cover pattern derived from Landsat TM/ETM satellite data for two decades (1998–2011). These maps show that built-up increased by 417 ha in first time period (1998–2003) and 6,633 ha during next period (2003–2011) of study. For quantification of metrics for urban expansion, the Urban Landscape Analysis Tool (ULAT) was employed. Land cover mapping was done with accuracy of 92.67 %, 93.3 % and 96 % respectively for years 1998, 2003 and 2011. Three major land covers classes mapped are; (i) built-up, (ii) water and (iii) other or non-built-up. The maps were then utilized to extract degree of urbanization based on spatial density of built-up area consisting of seven classes, (i) Urban built-up, (ii) Suburban built-up,(iii) Rural built-up, (iv) Urbanized open land, (v) Captured open land, (vi) Rural open land and (vii) Water. These classes were demarcated based on the urbanness of cells. Similarly urban footprint maps were generated. The two time maps were compared to qualitatively and quantitatively capture the dynamics of urban expansion in the city. Along with urbanized area and urban footprint maps, the new development areas during the study time periods were also identified. The new development areas consisted of three major categories of developments, (i) infill, (ii) extension and (iii) leapfrog.

Description: With the availability of very high resolution multispectral imagery, it is possible to identify small features in urban environment. Because of the multiscale feature and diverse composition of land cover types found within the urban environment, the production of accurate urban land cover maps from high resolution satellite imagery is a difficult task. This paper demonstrates the potential of 8 bands capability of World View 2 satellite for better automated feature extraction and discrimination studies. Multiresolution segmentation and object based classification techniques were then applied for discrimination of urban and vegetation features in a part of Dehradun, Uttarakhand, India. The study demonstrates that scale, colour, shape, compactness and smoothness have a significant influence on the quality of image objects achieved, which in turn governs the classified result. The object oriented analysis is a valid approach for analyzing high spatial and spectral resolution images. World View 2 imagery with its rich spatial and spectral information content has very high potential for discrimination of the less varied varieties of vegetation.

Description: Rice crop occupies an important aspect of food security and also contributes to global warming via GHGs emission. Characterizing rice crop using spatial technologies holds the key for addressing issues of global warming and food security as different rice ecosystems respond differently to the changed climatic conditions. Remote sensing has become an important tool for assessing seasonal vegetation dynamics at regional and global scale. Bangladesh is one of the major rice growing countries in South Asia. In present study we have used remote sensing data along with GIS and ancillary map inputs in combination to derive seasonal rice maps, rice phenology and rice cultural types of Bangladesh. The SPOT VGT S10 NDVI data spanning Aus, Aman and Boro crop season (1 st May 2008 to 30 th April 2009) were used, first for generating the non-agriculture mask through ISODATA clustering and then to generate seasonal rice maps during second classification. The spectral rice profiles were modelled and phenological parameters were derived. NDVI growth profiles were modelled and crop calendar was derived. To segregate the rice cultural types of Bangladesh into IPCC rice categories, we used elevation, irrigated area, interpolated rainfall maps and flood map through logical modelling in GIS. The results indicated that the remote sensing derived rice area was 9.99 million ha as against the reported area of 11.28 million ha. The wet and dry seasons accounted for 64% and 36 % of the rice area, respectively. The flood prone, drought prone and deep water categories account for 7.5%, 5.56% and 2.03%, respectively. The novelty of current findings lies in the spatial outcome in form of seasonal and rice cultural type maps of Bangladesh which are helpful for variety of applications.

Description: Gravity Recovery And Climate Experiment (GRACE) satellites were launched on March 17 2002 to derive with unprecedented accuracy, estimates of the global high resolution model of the earth’s gravity field. Local gravity changes with change in mass or mass redistribution. The mass changes can be due to hydrological events, seismic events or postglacial rebound, majorly. GRACE is sensitive to changes at large spatial scale since the resolution of GRACE is 400 km. Hydrological activities over basins provide sufficient mass changes to be detected by GRACE. In this research paper the discussion would be about two major flooding events in India, one being the 2005 monsoon flooding in Mumbai and nearby states and other being flood experienced by Bihar in 2008. The GRACE data is in the form of matrix consisting spherical harmonic coefficients. These coefficients are processed to obtain mass changes in terms of equivalent water height at a spatial scale of 400 km. The strategy of analysis is also discussed which need to be followed depending upon limitations of GRACE observation and requirement of application, here in this case application is flood induced mass change detection. Time-series and residual plots are generated and they show the flooding events for the concerned area as outliers. Better visualisation is obtained by residual plot, if there is a trend or systematic behaviour in time-series. This work points towards the qualitative capability of GRACE to detect flooding events at large spatial scale. Quantitative analysis requires in-situ data over the period of GRACE which is not possible for the cases discussed here.

Description: Biodiversity maps are crucial to conservation management. The present study assesses the accuracy of detecting tree diversity in an Italian forest site by combining mid-resolution images from Landsat-TM or Advanced Land Observation Satellite (ALOS)’s Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) sensors with environmental data namely elevation, slope, aspect and solar radiation in an artificial Neural Network (NN) classifier. The map accuracies obtained for Landsat-TM and ALOS images are 60 % and 53 % respectively. Use of environmental data increases accuracies to 91 % and 81 % respectively. Landsat-TM detects tree diversity more accurately than ALOS. Both the coarser pixel size and finer spectral resolution of Landsat-TM contributed to its higher accuracy.

Description: Spatial and temporal distribution of chlorophyll a (chl a ) and Total Suspended Matter (TSM) and inter comparison of Ocean Color Monitor-2 (OCM-2) and Moderate Resolution Imaging Spectro-radiometer (MODIS-Aqua) derived chlorophyll a and TSM was made along the southwest Bay of Bengal (BoB). The in-situ chl a and TSM concentration measured during different seasons were ranged from 0.09 to 10.63 μgl −1 and 11.04–43.75 mgl −1 respectively. OCM-2 and MODIS derived chl a showed the maximum (6–8 μgl −1 ) at nearshore waters and the minimum (0–1 μgl −1 ) along the offshore waters. OCM-2 derived TSM imageries showed the maximum (50–60 mgl −1 ) along the nearshore waters of Palk Strait and the moderate concentration (2–5 mgl −1 ) was observed in the offshore waters. MODIS derived minimum TSM concentration (13.244 mgl −1 ) was recorded along the offshore waters, while the maximum concentration of 15.78 mgl −1 was found along the Kodiakarai region. The inter-comparison of OCM-2 and MODIS chl a data ( R 2  = 0.549, n  = 49, p  〈 0.001, SEE = ±0.117) indicate that MODIS data overestimates chl a concentration in the nearshore waters of the southern BoB compared to the OCM-2. The correlation between OCM-2 and MODIS-Aqua TSM data ( R 2  = 0.508, N  = 53, P  〈 0.001 and SEE = ±0.024) confirms that variation in the range of values measured by OCM-2 (2–60 mgl −1 ) and the MODIS (13–16 mgl −1 ) derived TSM values. Despite problems in range of measurements, persistent cloud cover etc., the launch of satellites like OCM-2 with relatively high spatial resolutions makes job easier and possible to monitor chl a distribution and sediment discharges on day to day basis in the southwest BoB.

Description: Road extraction from remotely sensed images has always been a challenging problem. In this paper we present an approach for road extraction based on topological derivative and mathematical morphology. The road extraction scheme has three main steps: image segmentation using topological derivative, road cluster identification and road cluster filtering using mathematical morphology.

Description: The study evaluates and compares Digital Elevation Model (DEM) data of various grid spacing derived using high resolution Cartosat 1 stereo data for hydrologic applications. DEM is essential in modeling different environmental processes which depend on surface elevation. The accuracy of derived DEM varies with grid spacing and source. The CartoDEM is the photogrammetric DEM derived from stereo pairs. Damanganga basin lying in the Western Ghats was analysed using 11 Carto stereo pairs. The process of triangulation resulted in RMSE of 0.42. DEM was extracted at 10 m, 20 m, 30 m, 40 m, 50 m and 90 m grid spacing and compared with ASTER GDEM (30 m) and SRTM DEM (90 m). DEM accuracy was checked with Root Mean Square Error (RMSE) statistic for random points generated in different elevation zones. Extracted stream networks were compared based on Correctness Index and Figure of Merit index, calculated for all the Digital Elevation Models at varying cell sizes. In order to further evaluate the DEM’s, a simple flood simulation with no water movement and no consideration of real time precipitation data was carried out and relationship between heights of flood stage and inundation area for each Digital Elevation Model was also established.

Description: Hydrologic analysis of microwatersheds is essential for water resources planning at large scale. Space based input for decentralized planning at panchayat level use high resolution DEM. Drainage and slope play important role in planning and Digital Elevations Models (DEM) are widely being used for estimation of hydrologic parameters which are useful as input for hydrologic models. The estimates vary as per resolution and type of DEM. This paper evaluates the suitability of DEM derived through Cartosat-1 satellite stereo data(CartoDEM) for hydrologic parameter estimation of microwatersheds and compares the results with Airborne Laser Terrain Mapper (ALTM) based DEM data. Comparison is based on the hydrologic parameters delineated in Geographical Information System. Microwatersheds are delineated and drainage length extracted using two different cell sizes for both DEMs. Correctness Index, Figure of Merit, visual comparison, Percent within buffer and Junction comparison method, compared extracted river network. Average watershed slope is calculated using three different methods. CartoDEM derived drainage is comparable with ALTM derived drainage. There is high correlation between Carto5 and Caro10 DEMs in terms of drainage delineation and slope calculation. Average watershed slope vary as per calculation methods but average channel slope value (S3) although less, is comparable across DEMs.

Description: The present study focuses on the growing human needs which drive the native boatmen of the Kashmir valley (Hanjis) that bring the interchanges of land use/cover classes in Dal lake and its environs of Srinagar city. Further to assesses the effects of land transformation on lake water quality. The results suggested that the significant land use changes have been occurred during the past of 30 years (1981–2011). Besides this, interchange of land has taken place between different land uses classes, which has resulted into lake water pollution due the addition of various nutrients/pollutant discharged from Hanjis activities. The study concludes that the land transformation has converted the once fresh water lake much deteriorated pond.

Description: The Lonar crater in India provides an ample opportunity to in-depth analysis of crater morphology. This paper focuses on the topographical mapping of Lonar crater with detailed study on slope, regional analysis and its rim signature. The slope of the crater (inner wall region) reveals that the northern part is steep and southern part is gentle, while, on the outer region, the northern part is flat and the later shows abrupt variations. On regional topographical mapping (~4 crater radii) around the Lonar crater, it was observed that the terrain descends from NE to SW. An elevation difference of ~20 m was observed between the N and S part, infers that the pre-impact terrain is a descending one. The crater northern rim was elevated ~10 m to ~15 m, whereas southern rim was elevated ~50 m above the average regional surface. We found that the topographically lower southern region was abruptly changed and the rim has been uplifted to an elevation of ~604 m above the average regional elevation (~555 m). This result infers that the post-impact topography was abruptly altered along the S side. The crater rim signature extracted from highest point all along the rim shows a near flat surface on north, whereas the V-shaped protrusion shows active erosion and degradation on the west. Thus, DEM based topographic study has opened a new insight about the Lonar crater, from differential rim uplift, alteration along the rim and finally revealed that the impact crater formed on a descending terrain.

Description: Present study was designed to determine the effect of various growing environments on sucking pest population dynamics in cotton and to work out their relation with spectral indices. Crop spectral reflectance in four IRS bands was measured with ground truth radiometer during 1000–1200 h in all the treatment combinations. Incidence of sucking pest in cotton was found out to be highly influenced by growing environments. The leafhopper and whitefly population was highest in 15 May sown cotton crop and was lowest in 15 April sown crop. Cultivar HS-6 was highly affected by both the sucking pest than the other cultivar H-1226. The spectral indices (SR, NDVI and TVI) were highest in 15 April sown crop at all the phenophases and were lowest in 15 May sown crop. The cultivar H-1226 showed higher values of spectral indices as compared to HS-6. The relationship of pests’ population with various spectral indices was established. Multiple regression models based on spectral indices can be used for prediction of sucking pest population more than 69 and 74 % accuracy in leafhopper and whitefly, respectively in cotton crop.

Description: Aerosol and water vapour are very important element in the Earth’s climate system which has direct role in the Earth’s radiation budget. In this paper the seasonality, latitudinal distribution and the relationship of aerosol optical thickness (AOD) and water vapour (WV) using MODIS Level 3 monthly data from 2001 to 2008 are analysed. The analysis shows that AOD (0.55 μm) values reach maximum during southwest monsoon and remain minimum during northeast monsoon period. The Equatorial Indian Ocean shows minimum AOD (0.115 to 0.153) throughout the year compared to Arabian Sea (0.208 to 0.613) and Bay of Bengal (0.214 to 0.351). Arabian Sea shows high variation and maximum value of AOD compared to Bay of Bengal and Equatorial Indian Ocean. During southwest monsoon WV over Bay of Bengal was found higher in concentration compared to Arabian Sea and Equitorial Indian Ocean throughout the study period. Comparison between Arabian Sea (2.98 cm to 5.07 cm) and Bay of Bengal (3.49 cm to 5.94 cm) shows that WV concentration is less in Arabian Sea throughout the year. The analysis of correlation between WV and AOD was found to be inconsistent. However, AOD and WV shows a strong positive correlation for whole year (Mean R 2 =0.90) in the Equitorial Indian Ocean region except in the months of January, February and March. In general, the correlation between WV and AOD is found to be strongly positive for oceanic aerosol (sea salt) in low water vapour condition.

Description: Soil nutrient maps based on intensive soil sampling are useful to adopt site-specific management practices. Geostatistical methods have been widely used to determine the spatial correlation and the range of spatial dependence at different sampling scales. If spatial dependence is detected, the modeled semivariograms can then be used to map the interested variable by kriging, an interpolation method that produces unbiased estimates with minimal estimation variance. The objectives of this paper were to examine and map the spatial distribution of the soil micronutrients Cu, Zn, Fe and Mn on an agricultural area in Kupwara, J&K, under temperate climatic conditions. The ordinary kriging was first used to determine the values for the non-sampled locations, and then the indicator approach was used to transform the micronutrient content values into binary values having the mean values of each nutrient as the threshold content. All four elements analyzed showed spatial dependence using the indicator semivariograms. The strength of spatial dependence was assessed using the values of nugget effect and range from the semivariogram, the fitted range values decreased in the order Zn 〉 Cu 〉 Mn 〉 Fe. The spatial dependence of the combination of two or more of the studied micronutrients was also examined using indicator semivariograms. In opposition to spatial analysis of individual microelements, indicator semivariograms obtained for the binary coding of the variables showed a great nugget effect value or a low proportion of sill. The maps for each nutrient obtained using indicator kriging showed some similarity in the spatial distribution, suggesting the delimitation of uniform management areas.

Description: Crop residue has become an increasingly important factor in agriculture management. It assists in the reduction of soil erosion and is an important source of soil organic carbon (soil carbon sequestration). In recent past, remote sensing, especially narrowband, data have been explored for crop residue assessment. In this context, a study was carried out to identify different narrow-bands and evaluate the performance of SWIR region based spectral indices for crop residue discrimination. Ground based hyperspectral data collected for wheat crop residue was analyzed using Stepwise Discriminant Analysis (SDA) technique to select significant bands for discrimination. Out of the seven best bands selected to discriminate between matured crop, straw heap, combine-harvested field with stubbles and soil, four bands were from SWIR (1980, 2030, 2200, 2440 nm) region. Six spectral indices were computed, namely CAI, LCA, SINDRI, NDSVI, NDI5 and hSINDRI for crop residue discrimination. LCA and CAI showed to be best ( F  〉 115) in discriminating above classes, while LCA and SINDRI were best ( F  〉 100) among all indices in discriminating crop residue under different harvesting methods. Comparison of different spectral resolution (from 1 nm to 150 nm) showed that for crop residue discrimination a resolution of 100 nm at 2100–2300 m region would be sufficient to discriminate crop residue from other co-existing classes.

Description: Mixed pixel is a key issue in medium to coarse resolution remote sensing image, and it seriously restricts the remote sensing classification. This paper presents an Independent component analysis (ICA) algorithm based on the variational Bayesian (VB) methods, named VBICA, for spectral unmixing in multispectral remote sensing image. The model assumes that the mixed pixels to be separated are given as linear mixtures. The matrixes of linear mixtures are assumed to be unknown. In the Bayesian framework, the endmember and abundance have finally been achieved with Bayesian inference and approximate variational algorithm. The proposed method is evaluated and tested on a numerical simulative image from the noise resistance, area size, pixel purity, estimated number of endmembers and real multispectral remote sensing image of 100 × 100 pixels. Experimental results on simulated image demonstrated that compared to the Fast ICA algorithm, the proposed algorithm can give more accurate results, and the validity of the proposed algorithm is verified by the real multispectral remote sensing image of the similarity on spectral curves, average similarity and ground objects distribution maps.

Description: There is a need for timely information about changes in the air pollution levels in cities for adopting precautionary measures. Keeping this in view, an attempt has been made to develop a model which will be useful to obtain air quality information directly from remotely sensed data easily and quickly. For this study pixel values, vegetation indices and urbanization index from IRS P6 LISS IV and Landsat ETM+ images were used to develop regression based models with Air Pollution Index (API), which were calculated from in-situ air pollutant information. It was found that among the 12 parameters of IRS, highest correlation exists between pixel values in NIR (Near Infra-Red) band (Pearson correlation −0.77) and Normalized Difference Vegetation Index (NDVI) (Pearson correlation −0.68) and both have inverse relationship with API. In case of Landsat, the highest correlation was observed in SWIR (Short Wave Infra-Red) band (Pearson correlation −0.83) and NIR (Pearson correlation −0.78). Both single and multivariate regression models were calibrated from best correlated variables from IRS and Landsat. Among all the models, multivariate regression model from Landsat with four most correlated variables gave the most accurate air pollution image. On comparison between the API modeled and API interpolated images, 90.5 % accuracy was obtained.

Description: Identification of suitable site for urban development in hilly areas is one of the critical issues of planning. Site suitability analysis has become inevitable for delineating appropriate site for various developmental initiatives, especially in the undulating terrain of the hills. The study illustrates the use of geographic information system (GIS) and multicriteria evaluation (MCE) technique for selection of suitable sites for urban development in Mussoorie municipal area, Dehradun district, Uttarakhand. For this purpose Toposheet and IKONOS satellite data were used to generate various thematic layers using ArcGIS software. Criteria using five parameters, i.e. slope, road proximity, land use/land cover, land values and geological formation were used for site suitability analysis following land evaluation. The generated thematic maps of these criteria were standardized using pairwise comparison matrix known as analytical hierarchy process (AHP). A weight for each criterion was generated by comparing them with each other according to their importance. With the help of these weights and criteria, final site suitability map was prepared.

Description: The Ahar area is located in East Azarbaijan province, and covers an area of about 2,500 km 2 . Spectral mapping techniques were applied on VNIR and SWIR of ASTER data for discriminating between hydrothermal alteration zones and the identification of high potential mineralized lithological unit associated with hydrothermal porphyry copper mineralization in the Ahar. In this research to remove atmospheric and topographic effects from ASTER data, the log-residual method (LRM) was used. Four methods, Relative Band Depth Ratios (RBD), Principal Component Analysis (PCA), Minimum Noise Fraction (MNF) and matched filtering (MF), were used to processing and interpretation of remote sensing data in the study area. Results show that ASTER images provide preliminary mineralogy information and geo-referenced alteration maps at low cost and with high accuracy for reconnaissance porphyry copper mineralizations.

Description: Ocean colour and thermal data from earth orbiting satellites are being used in fishery resource applications. The location of ocean fronts and circulation features are considered as prospective fishing grounds. Ocean frontal zones, as resolved by strong gradients in the surface temperature distribution, has historically been important as indicators for fishing localities over all the oceans. In the present study, the sea surface temperature and chlorophyll concentration map along with the primary productivity map are used to evaluate the usefulness of the above data in fishery forecasting. In the present study, North Andhra Fishing Zone and Maharastra Fishing Zone, India have been taken for investigation. The analysis of thermal front, chlorophyll front with fish catch indicates a nonlinear relationship. The analysis of sea surface temperature (SST) with fish catch also shows a nonlinear relationship. The analysis of chlorophyll concentration with fish catch shows a linear relationship. However, the coefficient of determination (r 2 ) in each case is found to be inconsistent between the two fishing zones. The analysis of primary productivity front with fish catch shows a non linear relationship. The analysis of primary productivity and fish catch shows a linear relationship. The r 2 values in both the cases are found to be closer to each other between the two fishing zones. Therefore, it is suggested to include primary productivity map for improved fishing forecasting.

Description: In this paper, we propose a novel scheme to improve the accuracy of remote sensing image classification by integrating data fusion, multiple feature combination and ensemble learning. Intensity-Hue-Saturation (IHS), Gram-Schmidt (GS), Brovey and wavelet fusion methods are first performed to obtain the optimal fusion images of high resolution and multispectral images. Support Vector Machine (SVM) classifier is then adopted to classify the fused image with different feature sets, and ensemble learning algorithm based on dynamic classifier selection (DCS) is finally used to integrate multiple classification maps. The proposed classification scheme is implemented with three remote sensing data sets, obtaining the highest overall accuracy and kappa coefficient in all cases (92.63% and 0.8917 for BJ-1 data set, 81.89% and 0.7513 for Landsat TM and SPOT4 data set, 92.21% and 0.8838 for ALOS data set respectively). The experimental results show that the integration of data fusion, feature combination and ensemble learning improves the classification performance obviously and has great potential in practical uses.

Description: The name ‘Seven Pagodas’ has served as a nickname for the south Indian port of Mahabalipuram since the early European explorers used it as landmark for navigation as they could see summits of seven temples from the sea. There are many theories concerning the name Seven Pagodas. The present study has compared coastline and adjacent seven monuments illustrated in a 17th century Portolan Chart (maritime map) with recent remote sensing data. This analysis throws new light on the name “Seven Pagodas” for the city. This study has used DEM of the site to simulate the coastline which is similar to the one depicted in the old portolan chart. Through this, the then sea level and corresponding flooding extent according to topography of the area and their effect on monuments could be analyzed. Most importantly this work has in the process identified possibly the seven monuments that constituted the name Seven Pagodas and this provides an alternative explanation to one of the mysteries of history. This work has demonstrated unique method of studying coastal archaeological sites. As large numbers of heritage sites around the world are on coastlines, this methodology has potential to be very useful for coastal heritage preservation and management.

Description: The present paper discusses the impact of topography on accuracy for land cover classification and “from-to class change using improved spectral change vector analysis suggested by Chen et al. ( 2003 ). Two AWiFS sensor images of different dates are used. Double Window Flexible Pace Search (DFPS) is used to estimate threshold of change magnitude for change/no change classes. The topographic corrections show accuracy of 90% (Kappa coefficient 0.7811) for change/no change area as compared to 82% (Kappa coefficient 0.6512) in uncorrected satellite data. Direction cosines of change vector for determining change direction in n-dimensional spectral space is used for image classification with a minimum distance categorizing technique. The results of change detection are compared (i) Improved CVA with conventional two bands CVA and (ii) Improved CVA before and after topographic corrections. The improved CVA with topographic correction consideration using slope match show maximum accuracy of 90% (Kappa coefficient 0.83) as compared to conventional CVA which show maximum accuracy of 82% (Kappa coefficient 0.6624). The overall accuracy of ”from- to class using improved CVA increases from 86% (Kappa coefficient 0.7817) to 90% (Kappa coefficient 0.83) after topographic corrections. The improved CVA with proper topographic corrections is found to be effective for change detection analysis in the rugged Western Himalayan terrain.

Description: To understand the scale effects on chlorophyll-a (chl- a ) concentration retrieved from satellite images, the two-band algorithm (TA) and three-band algorithm (TBA) were constructed for estimating chl- a from satellite images. Two synchronous images of Advanced Wide-Field Sensor (AWiFS) and Linear Imaging Self-Scanner (LISS) of Indian remote sensing satellite were used to assess and validate the scale errors of these two algorithms. They were collected at local time 02:55:46:471 and 02:58:25:053 on October 8, 2005 in Yellow River Estuary, and their spatial resolutions are 24 m and 56 m, respectively. From the results of this study, it was found that: (1) the relative scale error (RSE) of TA and TBA, caused by scale changing from LISS to AWiFS, varied from 0% to 100%; (2) the RSE was correlated with the spatial non-homogeneous degree of chl- a distribution; and (3) using TBA to estimate chl- a concentration in Yellow River Estuary decreased 2.55% of model uncertainty, but increased 4.97% of scale errors, in comparison with TA. Additionally, the study indicated that the performance of algorithms for chl-a estimation was greatly affected by the scale error. If the scale effects of chl- a retrieval algorithm were taken into consideration, TA had a superior performance to the TBA in this study.

Description: Urban areas are the most dynamic region on earth. Their size has been constantly increased during the past and this process will go on in the future. Since there is no standard policy and guidelines for construction of buildings and urban planning, cities tend to have irregular growth. Many cities in the world face the problem of urban sprawl in its suburbs. So issues of urban sprawl need to be settled with the help of technologies such as satellite remote sensing and automated change detection. This paper presents a wavelet based post classification change detection technique that is applied to 1996 and 2004 MSS images of Madurai City, South India to determine the urban growth. The classification stage of the technique uses coilflet wavelet filter to correlate with the MSS land cover images of Madurai city to derive texture feature vector and this feature vector is inputted to a fuzzy-c means classifier, an unsupervised classification procedure. The post classification change detection technique is employed for identifying the newly developed urban fringe of the study area. The error matrix analysis is used to assess the accuracy of the change map. The performance of the presented technique is found superior than that of classical change detection methods such as image differencing, change vector analysis and principal component analysis.

Description: Investigations were carried out to develop precision nutrient management techniques for sugarcane. The study area (800 ha) comprised of Bijapur, Bilgi and Jamakhandi talukas that lie between 16° 34′–28° 10′ N latitudes and 75° 33′–75° 37′ E longitudes and located around Nandi Sahakari Sakkare Karkhane (NSSK) Niyamit, Galagali. The soils are medium to deep black with pH and EC ranging from 7.32 to 8.36 and 0.17 to 1.13 dS/m, respectively. The soils are low to medium in available nitrogen, medium in available phosphorus and high in available potassium content. Crop condition assessment was made through analysis of LISS-III satellite images using Erdas Imagine software. Fertigation with 300 kg N and 195 kg K per ha at fortnightly interval and soil application of 32 kg P per ha as basal, recorded higher sugarcane yield (167 Mg ha −1 ) as compared to 124 Mg ha −1 obtained with soil application of 250 kg N, 32 kg P and 156 kg K per ha and flood irrigation as per the package recommended by the University(POP). Fertigation of N and K at weekly interval recorded highest NDVI value (0.354) and soil application of nutrients as per POP resulted in the lowest NDVI of 0.219.

Description: Estimation of infrastructural growth is the key issue for planning and resource management. In this regard it is highly required to have a proper database and documentation. Remotely sensed data and its processing techniques are most important parameter to achieve this goal. In developing countries, the planning and resource management is still dependent on traditional methods, but integration of satellite data of high resolution and of multiple spectral bands with appropriate processing techniques, makes it possible to get optimal result in limited fiscal resources. The merging of multi resolution sensor data can be the best option instead of using costly data for low budget planning and development. This study aims to analyze the potentials of image fusion of multispectral and panchromatic satellite data with high ground resolution images and evaluating their significance in infrastructural classification. While the accuracy assessment tests of classification result, suggest the appropriate classification techniques. The Relevance of image fusion in auto vectorization has also been discussed in this paper.

Description: Field experiment was conducted during 2009–10 and 2010–11 rabi season at research farm of IARI, New Delhi for assessing the aphid infestation in mustard. In aphid infested plant the LAI was 67 to 94% lower than healthy plant. Chlorophyll concentration decreased to 50% in infested plant as compared to healthy plant. Infestation was more severe in late sown crop and due to aphid infestation the percentage oil content and yield was reduced significantly. The spectral reflectance of aphid infested canopy and healthy canopy taken in the laboratory had significant difference in NIR region. In the visible region, the reflectance peak occurred in healthy canopy at around 550–560 nm while this peak was lower by 31% in the aphid infested canopy. The reflectance for healthy crop was found to be more in visible as well as NIR region as compared to aphid infested canopy. The most significant spectral bands for the aphid infestation in mustard are in visible (550–560 nm) and near infrared regions (700–1250 nm and 1950–2450 nm). The different level of aphid infestation can be identified in 1950–2450 nm spectral regions. Spectral indices viz NDVI, RVI, AI and SIPI had significant correlation with aphid infestation. Hence these indices could be used for identifying aphid infestation in mustard.

Description: CartoDEM is an Indian National DEM generated from Cartosat-1 stereo data. Cartosat-1, launched in May, 2005, is an along track (aft −5°, Fore +26°) stereo with 2.5 m GSD, give base-height ratio of 0.63 with 27 km swath. The operational procedure of DEM generation comprises stereo strip triangulation of 500 × 27 km segment with 10 m posting along with 2.5 m resolution ortho image and free—access posting of 30 m has been made available (bhuvan.nrsc.gov.in). A multi approach evaluation of CartoDEM comprising (a) absolute accuracy with respect to ground control points for two sites namely Jagatsinghpur -flat and Dharamshala- hilly; second site i.e. Alwar-plain and hilly with high resolution aerial DEM, (b) relative difference between SRTM and ASTERDEM (c) absolute accuracy with ICESat GLAS for two sites namely Jagatsinghpur-plain and Netravathi river, Western Ghats-hilly (d) relative comparison of drainage delineation with respect to ASTERDEM is reported here. The absolute height accuracy in flat terrain was 4.7 m with horizontal accuracy of 7.3 m, while in hilly terrain it was 7 m height with a horizontal accuracy of 14 m. While comparison with ICESat GLAS data absolute height difference of plain and hilly was 5.2 m and 7.9 m respectively. When compared to SRTM over Indian landmass, 90 % of pixels reported were within ±8 m difference. The drainage delineation shows better accuracy and clear demarcation of catchment ridgeline and more reliable flow-path prediction in comparison with ASTER. The results qualify Indian DEM for using it operationally which is equivalent and better than the other publicly available DEMs like SRTM and ASTERDEM.

Description: The potential of quad polarization radar data for the target discrimination has been analyzed. Quad polarization data of the RADARSAT-2 fine resolution mode has been utilized. Class separability analysis has been carried out on different polarization combinations using Transformed Divergence (TD) method and it is observed that HH-HV/VH-VV polarization combination gives better class separability when compared to other polarization combinations. Classification has been carried out on the optimized polarization combination using Maximum likelihood (MLC) and Support Vector Machine (SVM) classifiers. It is observed that SVM classification gives better classification accuracy compared to MLC. Overall classification accuracy is 93.03% for SVM and 88.78% for MLC. Class separability and classification accuracy comparison results are presented.

Description: In this paper we report chlorophyll measurements made during an ocean colour validation cruise in April 2011 of the research vessel, Sagar Paschimi in the coastal waters of Northern Bay of Bengal. The chlorophyll- a concentration in these waters range from 0.2 to 4.0 mg/m 3 . Chlorophyll- a concentration from OCM-2 was estimated using the global ocean colour algorithms namely, OC2, OC3, OC4 and Chl- a algorithms respectively. OCM data was processed using the global SeaWiFS Data Analysis System (SeaDAS) in which all the above mentioned algorithms are embedded for estimating the chlorophyll- a concentration. A comparative study was made between and in-situ and satellite derived chlorophyll- a concentration. Although the matchups between in-situ and satellite data from OCM-2 were sparse, it indicates that direct application of the standard SeaWiFS algorithm-the OC4-V4 algorithm—in the coastal waters of the Bay of Bengal will underestimate chlorophyll- a by up to 30%. The results show a good correlation with an R value of 0.61 using OC2 algorithm. However, all the other global algorithms over estimate the chlorophyll- a concentration even in low chlorophyll concentration range. The comparison between in-situ and all the existing chlorophyll algorithms shows the efficiency of these algorithms for quantification of chlorophyll in coastal waters and hence the need to develop regional algorithms and fluorescence based algorithms for better quantification.

Description: Physical habitat of any aquatic ecosystem is an integral part upon which the biological structures of resident communities are built. Degradation of the physical habitat has serious consequences on aquatic communities and is among the leading causes of stream impairment worldwide. Therefore, a sound habitat assessment approach is necessary to assess the condition of running water and to determine if habitat degradation is responsible for any degradation in biological condition. The present study was focused on Physical Habitat Assessment of Denwa River, a sub tributary of river Narmada in central India, for generating a Habitat Suitability map. The Denwa River originates from Satpura ranges in central India and flows through entire Pachmarhi plateau supporting a diversity of habitats for aquatic flora and fauna. A survey was carried out to assess the physical characteristic of Denwa river (84 kms) from its origin to the confluence point of Tawa Denwa river. Six reaches have been identified in study area on the basis of their physiographic conditions. For Habitat Assessment measurement, Rapid Bioassessment Protocols for Stream and Wadeable rivers by United States Environmental Protection Agency (USEPA, 841-B-99-002) have been used. Seven parameters from this protocol have been chosen for physical characterization. GIS techniques have been used to develop a Habitat Suitability Map of the study area based on scores to illustrate its suitability to support aquatic life. The present paper discusses in detail the suitability of the different reaches of the River Denwa for supporting the aquatic biodiversity.

Description: Cyclone is one of the major coastal hazards affecting the Indian coastline bringing floods and widespread damage due to rain, storm surges and heavy winds. Hence, coastal risk assessment is essential to identify the threats to the land and population, from cyclones. The risk assessment has been carried out in northern coast of Tamil Nadu, India, for the stretch from Kattivakkam to Kovalam. Risk is the probability of expected losses from a given hazard and it varies according to the vulnerability of the region. The risk assessment has been structured in four components, namely, Environmental Vulnerability, Social Vulnerability, Hazard Potential and Mitigation Capacity. The thematic layers and hazard maps are created using Satellite data and Geographic Information System. Coastal risk indices are generated for each component of risk using Analytic Hierarchy Process. Based on the index value, the level of risk is mapped and the results help in faster and appropriate decision making.

Description: Remote sensing has been extensively used for water delineation and has played an important role in water quality evaluation and environmental management strategies. Suspended sediments are important determinants of water quality in coastal zones. Remote sensing enables the effective monitoring of total suspended sediments (TSS) and the detection of areas with critical water quality issues. This study aims to develop and implement regression models for estimating and mapping TSS concentrations from Advanced Land Observation Satellite (ALOS) images over the coastal waters of Langkawi Island, Malaysia. The algorithm was developed based on the water reflectance model, which is a function of the inherent optical properties of water. Such properties can then be linked to TSS concentration. In this study, an ALOS Advanced Visible and Near Infrared Radiometer type 2 device was used as the imaging sensor system. Concurrent complementary in-situ water samples were collected within the area coverage of the sensor, and digital numbers (DN) for each band corresponding to the sea-truth locations were determined. The extracted DN values were converted into reflectance values and then regressed with their respective sea-truth data. An algorithm was proposed to obtain the regression coefficient. This algorithm can estimate TSS concentrations with a high correlation coefficient (R 2  = 0.96) and low root-mean-square error (RMSE = 1.98 mg/l). Finally, a map of the TSS concentration was generated by using the proposed algorithm. This study found that TSS mapping can be conducted by using ALOS data over the coastal waters of Langkawi Island, Malaysia.

Description: This paper proposes a new approach to 3D modeling on the basis of laser scanner data. It presents the attempt to automatically detect two planes and an edge in one processing algorithm. The algorithm is based on the M split estimation, which is a recently developed regression method of multi functional models. Plane equations in three dimensional space are derived on the basis of the presented method. M split estimation divides the functional model of least squares adjustment in particular into two groups. Both simulated and real data from airborne laser scanning is tested to present the results. In each case the mehod gave good results which encourages for further work on the application of this method for 3D modeling.

Description: Selection of a suitable landfill site for Solid Waste Management (SWM) forms an important component of urban planning. The problem of SWM has assumed significant proportion for the municipal authorities in the wake of rapid industrialization, urbanization and resultant pressure on existing resources. Many criteria such as distance from residential locations, transport connectivity, presence of water-bodies (drains, ponds, rivers etc.) and forests, ground water table and geology are taken into consideration while planning for suitable sites. Spatial Analyst Tool along with Analytic Hierarchy Process (AHP) model is extremely useful in such multicriteria decision making process. The present study, based on these tools/techniques, endeavours to identify a suitable location for landfill site in a part of the National Capital Territory of Delhi. The study identifies six potential sites out of which one has been proposed and recommended as the best suitable site.

Description: Detecting fires, which are at their early stages is the first component of effective fire fighting. To date, several algorithms have been proposed to detect fire spots using remote sensing data. Nevertheless, in order to be able to accurately detect small and cool fires, which are very important at the regional scale, most of these algorithms need to be adjusted and improved. In this paper, an agent-based algorithm is presented for regional forest fire detection using bi-temporal MODIS data. The algorithm is designed to be so self-adaptive and consistent that it could be applied to the different pairs of consecutive images taken by the same satellite platform and at the same daytime. The results clearly show that compared with the MODIS contextual algorithm (version 4), the proposed method is more sensitive to small and cool forest fires in Iran.

Description: Digha coastal region in the northeastern part of the Bay of Bengal is potentially vulnerable to erosional hazard. The present study assessed the coastal erosion vulnerability along this 65 km long coastal stretch located between Rasulpur (Midnapur) and Subarnarekha (Balasore) estuarine complex, which had been subjected to anthropogenic intervention. Multi-resolution Landsat satellite imagery were used for shoreline change study from 1972 to 2010. During this period, accretion was recorded updrift of artificial structures, viz, seawall, groin, pylons and jetties; while, extensive erosion was recorded in downdrift areas of these structures. Assessment was subsequently divided into four categories ranging from “high erosion” to “accretion”. Data from several sources were compiled to map landuse and human activities in the coastal zone. This map was divided into four categories, ranging from “very high capital” to “no capital” landuse. Population density map of the surrounding coastal villages was generated using census data, and divided into four categories ranging from “high density area” to “very low density area”. Subsequently, coastal erosion vulnerability was assessed by combining coastal retreat with landuse type and population density in this study area using simple vector algebraic technique. Zones of vulnerability of different magnitude (viz., very high, high, moderate, and low) have been identified. Furthermore, calculation of “imminent collapse zone (ICZ)” shows that maximum values are around artificial structures and anthropogenic activities. The coastal erosion vulnerability map prepared from this study can be used for proper planning and management of this coastal region.

Description: Road network extraction from high resolution satellite images is one of the most important aspects. In the present paper, research experimentation is carried out in order to extract the roads from the high resolution satellite image using image segmentation methods. The segmentation technique is implemented using adaptive global thresholding and morphological operations. Global thresholding segments the image to fix the boundaries. To compute the appropriate threshold values several problems are also analyzed, for instance, the illumination conditions, the different type of pavement material, the presence of objects such as vegetation, vehicles, buildings etc. Image segmentation is performed using morphological approach implemented through dilation of similar boundaries and erosion of dissimilar and irrelevant boundaries decided on the basis of pixel characteristics. The roads are clearly identifiable in the final processed image, which is obtained by superimposing the segmented image over the original enhanced image. The experimental results proved that proposed approach can be used in reliable way for automatic detection of roads from high resolution satellite image. The results can be used in automated map preparation, detection of network in trajectory planning for unmanned aerial vehicles. It also has wide applications in navigation, computer vision as a predictor-corrector algorithm for estimating the road position to simulate dynamic process of road extraction. Although an expert can label road pixels from a given satellite image but this operation is prone to errors. Therefore, an automated system is required to detect the road network in a high resolution satellite image in a robust manner.

Description: Albeit the advent of fast computing facilities, digital image classification of remotely sensed data is still remain the topic of research. This might be due to the reason that the ancillary information such as texture and topography is absent in image classification. Since two decades, texture is widely applied in image classification but there is no explicit icon in most popularly used remote sensing software. Hence the aim of this study is to classify the Landsat ETM+ captured in 2000 using spectral information, topographic information and texture information. This study helps to throw light into statistical texture analysis i.e., the effect window size i.e., 3 × 3 to 9 × 9, on image classification. The ability of Grey Run Length Matrix (GRLM), which is computationally complex compared to industrially well-known Grey Level Co-occurrence Matrix (GLCM) but encompasses greater potential to discriminate between two classes, is explored. Eight spectral bands, 11 texture parameters extracted from Landsat ETM+ data and elevation, slope, aspect extracted from DEM data are classified individually using Artificial Neural Network (ANN) and the individually classified information is integrated using endorsement theory. Validations of classified results are performed using Google Maps and Landmap services updated in 2009. The results are compared with Maximum Likelihood classification (MLC) and hence all the evidence (spectral, texture and topography) with 5 × 5 texture window provided maximum classification accuracy of 70.44 %.

Description: The present study demonstrated the methodology to assess agro-climatic suitability of the soybean crop through integration of crop suitability based on FAO framework of land evaluation and biophysical (water limited) yield potential in the rainfed agro-ecosystem. A long term climatic database (1980–2003) was prepared to compute decadal rainfall and temperature variations of 13 IMD stations in part of Madhya Pradesh state. The climatic database was used in soil water balance software–BUDGET to compute crop specific length of growing period (LGP) and biophysical production potential such as water limited crop yield potential of each soil types for soybean crop. Water limited crop yield potential of soils were found to be varied from 33 to 100 and LGP ranged from 65 to 180 days in the area. FAO based land suitability was analyzed in association with the water limited yield potential for better appraisal of land potential and assess their suitability in rainfed area. FAO based land suitability indicated 2.45 % area as highly suitable and 57.49 % area as moderately suitable. However, integration of water limited crop yield potential with FAO based land suitability lead to agro-climatic suitability analysis indicated 17.60 % and 40.03 % area, respectively as highly suitable and moderately suitable. FAO based land evaluation showed 88.13 % of plains as moderately suitable whereas agro-climatic suitability indicated only 47.79 %. Agro-climatic suitability analysis revealed undulating plateau and undulating plains as most suitable for soybean crop.

Description: A novel approach to study vegetation dynamics is introduced, using the Empirical Mode Decomposition (EMD) to analyze NDVI time series. The NDVI time series which is nonlinear and nonstationary can be decomposed by EMD into components called intrinsic mode functions (IMFs), based on inherent temporal scales. The highest frequency component which has been found to represent noise is subtracted from the original NDVI series; thus smoothing the noisy signal. The different key features describing vegetation phenology have been extracted by analyzing the noise free signal. The lowest frequency component (last IMF) is the trend in the NDVI series. The trend in the series has been identified finding the Sen’s slope of last IMF, and the non-parametric seasonal Mann–Kendall test has been used to confirm the significance of the observed trend. The method has been applied on per–pixel basis to the SPOT Vegetation NDVI product covering Northeast India and surrounding regions for the time span of 1998–2009. Results show that the method has performed well in identifying the pixel clusters with significant trends. Hotspot regions with severe vegetation degeneration have been identified, and the relationship of the observed trends with the expected causative variables such as land use and land cover, topographic relief, and anthropogenic causes has been explored. The spatial locations of these critical regions closely matches with the findings of the previous studies carried out locally in the region, mainly indicating the shifting cultivation practice to be the main cause for land cover change.

Description: The present study is aimed to determine the bio-optical characteristics of oceanic waters during South west monsoon in Bay of Bengal using hyperspectral radiometer. The variability of diffuse attenuation coefficient, K d (λ), with chlorophyll a showed a good relation at shorter wavelengths, indicating the effect of phytoplankton on K d (λ). The determination coefficient, R 2 at 412, 443, 490 and 555 nm were greater than 0.931. A good linear relation between K d (490) and K d (λ) was observed at shorter wavelengths. These relationships of K d (λ) provides a platform to study the underwater light field during Southwest monsoon in Bay of Bengal.

Description: The paper deals with the application of Remote Sensing and Geographical Information System (GIS) technique for a watershed development program. For this study, the WRJ-2 watershed falling under Narkhed and Katol Tahsils of Nagpur district, Maharashtra, India is investigated. Various thematic maps (i.e. drainage, geology, soil, geomorphology and land use/ land cover) have been prepared using the remote sensing and GIS techniques. Initially, differential weightage values are assigned to all the thematic maps as per their runoff characteristics. Subsequently, the maps are integrated in GIS environment to identify potential sites for water conservation measures like gully plugs, earthen check dams, continuous contour trenches, percolation tanks, cement bandhara, afforestration and farm ponds, etc. The study depicts that the GIS technique facilitates integration of thematic maps and thereby helps in an identification of micro-zones each with unique characters in-terms of hydrogeology, thus amenable to specific water conservation techniques. It is therefore concluded that, the GIS technique is suitable for an identification of water conservation structures.

Description: Satellite remote sensing images play an important role in environmental monitoring for mining industry. There are a number of environmental variables, soil and surface variables, associated with mineral activities, that are to some extent detectable easily with satellite earth observation data. The aim of this paper is to detect the quarrying-mining activities are located in seismically active regions of Turkey using satellite images. Because mining-quarrying blasts have been observed and listed as earthquakes in the seismicity catalogue by seismic networks. The presence of mines-quarries in an active seismic zone can cause errors during the analysis of the distribution of microseismicity and the editing of seismic catalogs. Therefore, this study is a meaningful analysis for seismic networks interested in tectonic researches, because it highlights areas where need to pay careful attention is advisable for identification of mining-quarrying blasts. The new digital database was created using the satellite images of mining and quarrying areas taken from the Google Earth program ( http://maps.google.com ). In the study, approximately 721 known and illegal mining-quarrying sources have been detected. That were organized in an informational atlas includes information on locations, geographic coordinates and satellite images of the mining-quarrying sources in Turkey which can be distributed as a CD or on the WEB. Kekovali et al. (International Journal of the Physical Sciences 6(15), 3784–3794, 2011 ) estimated potential mining and quarry areas of Turkey from the Kandilli Observatory Earthquake Research Institute & National Earthquake Monitoring Center (KOERI-NEMC) seismic catalogs using daytime to nighttime ratio analysis (Qm). In this study, the correlation between the estimated satellite locations of the mining and quarrying activities and the areas with Qm ≥ 2.0 values taken from the previous study was examinated. The result of the study, the important potential mining-quarrying sources were estimated for Turkey includes: Kütahya, Muğla, Manisa, Balıkesir, İstanbul, İzmit, Edirne, Bursa, Bilecik, Tekirdağ, Ankara, Konya, Eskişehir, Malatya, Yozgat, Kırıkkale, Malatya, Şanlıurfa, Sinop, Trabzon. Monitoring and controlling mining-quarrying activities through traditional methods is quite difficult due to high costs and lengthy time in obtaining accurate and updated maps. The use of satellite images is an inexpensive and effective tool for mapping large mining-quarrying activity areas that can be also used to supplement data from environmental studies. In the future work, the satellite database can be processed and analyzed in order to produce a proper GIS database that includes important mining-quarrying sources of Turkey.

Description:    Estimation of crop variables is necessary for crop type monitoring as well as crop yield forecast. At the present era artificial neural network methodology are widely used to the remote sensing domain for numerous applications like crop yield forecasting and crop type classification. In the present work, two neural network models namely general regression neural network (GRNN) and radial basis function neural network (RBFNN) are used to estimate crop variables: leaf area index (LAI), biomass (BM), plant height (PH) and soil moisture (SM) by using ground based X-band scatterometer data. The both networks are trained and tested with X-band scatterometer data. The performance of the GRNN and RBFNN networks are found that the radial basis approach is more suitable for crop variable estimation in comparison to the GRNN approach. This work presents the applicability of neural network as an estimator and method employed could be useful to estimate the crop variables of other crops. Content Type Journal Article Category Short Note Pages 1-7 DOI 10.1007/s12524-011-0197-9 Authors Abhishek Pandey, Department of Applied Physics, Institute of Technology, Banaras Hindu University, Varanasi, 221005 India Khem B. Thapa, Department of Physics, University Institute of Engineering and Technology, CSJM University, Kanpur, 208024 India R. Prasad, Department of Applied Physics, Institute of Technology, Banaras Hindu University, Varanasi, 221005 India K. P. Singh, Department of Electronics Engineering, Institute of Technology, Banaras Hindu University, Varanasi, 221005 India Journal Journal of the Indian Society of Remote Sensing Online ISSN 0974-3006 Print ISSN 0255-660X

Description:    In the present study, landforms and soils have been characterized in Borgaon Manju watershed of basaltic terrain located in Akola district, Maharashtra, Central India. Terrain characterization using Shuttle Radar Topography Mission (SRTM) elevation data (90 m) and IRS-P6 LISS IV data in conjunction with adequate field surveys shows nine distinct landforms. Soil resource inventory shows fourteen soil series in the study area. Soils formed on gently sloping (3–8 %) subdued plateau are very shallow (23 cm), moderately well drained, moderate (15–40 %) surface stoniness, severely eroded, clayey and slightly alkaline in reaction, whereas, the soils formed on level to nearly level (0–1 %) slope in the main valley are very deep (〉150 cm), well drained, very slight (〈3 %) surface stoniness, moderately eroded with clayey surface and moderately alkaline in reaction. Soils in the watershed are grouped into Lithic Ustorthents, Vertic Haplustepts, Calcic Haplustepts, Typic Haplustepts, Typic Haplusterts and Sodic Calciusterts. The study demonstrates that the analysis of SRTM elevation data and IRS P6–IV data in Geographic Information System (GIS) with adequate field surveys helps in characterization of landforms and soils in analysis of landscape-soil relationship. Content Type Journal Article Category Research Article Pages 1-13 DOI 10.1007/s12524-012-0240-5 Authors G. P. Obi Reddy, National Bureau of Soil Survey & Land Use Planning (ICAR), Amravati Road, Nagpur, 440 033 India M. S. S. Nagaraju, National Bureau of Soil Survey & Land Use Planning (ICAR), Amravati Road, Nagpur, 440 033 India I. K. Ramteke, National Bureau of Soil Survey & Land Use Planning (ICAR), Amravati Road, Nagpur, 440 033 India Dipak Sarkar, National Bureau of Soil Survey & Land Use Planning (ICAR), Amravati Road, Nagpur, 440 033 India Journal Journal of the Indian Society of Remote Sensing Online ISSN 0974-3006 Print ISSN 0255-660X

Description:    One of the major after effect of Bhuj Earthquake which occurred on January 26, 2001 was wide spread appearance of liquefaction of soil in the Rann of Kachchh and the coastal areas of Kandla port covering an area of more than tens of thousands of kilometers. Remote sensing data products allow us to explore the land surface parameters at different spatial scales. In this work, an attempt has been made to identify the liquefied soil area using conventional indices from IRS-1D temporal images. The same has been investigated and compared with Class Based Sensor Independent (CBSI) spectral indices, while applying fuzzy based noise classification as soft computing approach using supervised classification. Seven spectral indices have been investigated to identify liquefied soil areas using temporal multi-spectral images. The result shows that the temporal variations can be accounted by using appropriate remote sensing based spectral indices. It is found that CBSI based TNDVI using temporal data yields the best results for identification of liquefied soil areas, while CBSI based SR gives best results for water body identification. Content Type Journal Article Category Research Article Pages 1-9 DOI 10.1007/s12524-012-0239-y Authors S. S. Sengar, Indian Institute of Technology, Roorkee, India A. Kumar, Indian Institute of Remote Sensing, Dehradun, India S. K. Ghosh, Indian Institute of Technology, Roorkee, India H. R. Wason, Indian Institute of Technology, Roorkee, India Journal Journal of the Indian Society of Remote Sensing Online ISSN 0974-3006 Print ISSN 0255-660X

Description:    The paper deals with the application of Remote Sensing and Geographical Information System (GIS) technique for a watershed development program. For this study, the WRJ-2 watershed falling under Narkhed and Katol Tahsils of Nagpur district, Maharashtra, India is investigated. Various thematic maps (i.e. drainage, geology, soil, geomorphology and land use/ land cover) have been prepared using the remote sensing and GIS techniques. Initially, differential weightage values are assigned to all the thematic maps as per their runoff characteristics. Subsequently, the maps are integrated in GIS environment to identify potential sites for water conservation measures like gully plugs, earthen check dams, continuous contour trenches, percolation tanks, cement bandhara, afforestration and farm ponds, etc. The study depicts that the GIS technique facilitates integration of thematic maps and thereby helps in an identification of micro-zones each with unique characters in-terms of hydrogeology, thus amenable to specific water conservation techniques. It is therefore concluded that, the GIS technique is suitable for an identification of water conservation structures. Content Type Journal Article Category Research Article Pages 1-12 DOI 10.1007/s12524-012-0232-5 Authors Abhay M. Varade, PG Department of Geology, RTM Nagpur University, Amravati Road, Nagpur, MS 440001, India Y. D. Khare, Maharashtra Remote Sensing Application Agency (MRSAC), Nagpur, MS, India N. C. Mondal, Indo- French Centre for Groundwater Research, CSIR-National Geophysical Research Institute, Hyderabad, 500 007 India Sarang Muley, Groundwater Surveys and Development Agency, Nagpur, MS, India Pawan Wankawar, PG Department of Geology, RTM Nagpur University, Amravati Road, Nagpur, MS 440001, India Priti Raut, PG Department of Geology, RTM Nagpur University, Amravati Road, Nagpur, MS 440001, India Journal Journal of the Indian Society of Remote Sensing Online ISSN 0974-3006 Print ISSN 0255-660X

Description:    Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) SWIR bands are used in identification of alteration zones which have developed during hydrothermal activity. Among the available methods of hyperspectral data analysis, PCA and RBD techniques are found to be useful in delineation of clay alteration and iron oxide zones. ASTER data analysis by PCA and RBD of (B5+B7)/B6 shows delineation of two distinct alteration zones with characteristic mineral assemblages viz. propylitic zone (chlorite, epidote, montmorillonite and calcite) and phyllic zone (illite, kaolinite, white mica and quartz). Iron oxide rich zones (gossans) have been delineated using ASTER band ratio technique (B2/B1). Geochemical dispersion of soil samples shows that Pb and Zn concentration is higher in phyllic and propylitic zones around Sawar and Malpura area respectively. Thus, ASTER data shows the potential in discrimination of metasedimentary rocks and delineation of alteration zones for targeting base metals around Sawar-Malpura area in central Rajasthan. Content Type Journal Article Category Research Article Pages 1-14 DOI 10.1007/s12524-012-0237-0 Authors B. K. Bhadra, Regional Remote Sensing Centre (West), NRSC/ISRO, CAZRI Campus, Jodhpur, 342003 India Suparn Pathak, Regional Remote Sensing Centre (West), NRSC/ISRO, CAZRI Campus, Jodhpur, 342003 India G. Karunakar, Hindustan Zinc Ltd, Udaipur, 313004 India J. R. Sharma, Regional Remote Sensing Centre (West), NRSC/ISRO, CAZRI Campus, Jodhpur, 342003 India Journal Journal of the Indian Society of Remote Sensing Online ISSN 0974-3006 Print ISSN 0255-660X

Description:    There is a need for timely information about changes in the air pollution levels in cities for adopting precautionary measures. Keeping this in view, an attempt has been made to develop a model which will be useful to obtain air quality information directly from remotely sensed data easily and quickly. For this study pixel values, vegetation indices and urbanization index from IRS P6 LISS IV and Landsat ETM+ images were used to develop regression based models with Air Pollution Index (API), which were calculated from in-situ air pollutant information. It was found that among the 12 parameters of IRS, highest correlation exists between pixel values in NIR (Near Infra-Red) band (Pearson correlation −0.77) and Normalized Difference Vegetation Index (NDVI) (Pearson correlation −0.68) and both have inverse relationship with API. In case of Landsat, the highest correlation was observed in SWIR (Short Wave Infra-Red) band (Pearson correlation −0.83) and NIR (Pearson correlation −0.78). Both single and multivariate regression models were calibrated from best correlated variables from IRS and Landsat. Among all the models, multivariate regression model from Landsat with four most correlated variables gave the most accurate air pollution image. On comparison between the API modeled and API interpolated images, 90.5 % accuracy was obtained. Content Type Journal Article Category Research Article Pages 1-9 DOI 10.1007/s12524-012-0235-2 Authors Chitrini Mozumder, Asian Institute of Technology, Khlong Luang, Pathum Thani, Thailand K. Venkata Reddy, National Institute of Technology, Warangal, Andhra Pradesh, India Deva Pratap, National Institute of Technology, Warangal, Andhra Pradesh, India Journal Journal of the Indian Society of Remote Sensing Online ISSN 0974-3006 Print ISSN 0255-660X

Description:    Proper monitoring and assessment of the tropical deforestation or degradation is a prerequisite for judicious management and planning activities at a regional or global level. Satellite remote sensing has proved a cost effective means of mapping and monitoring environmental changes in terms of vegetation and other ecological issues. In this study attempt has been made to monitor the forest deforestation or degradation in a natural forest stand of Assam, north east India using Forest Canopy Density (FCD) Mapping and monitoring Model. The present study reveals a massive decline in forest area between the period 1987 to 2010 i.e., total area under deforestation is found to be 843.76 km 2 which account 26.28 % whereas the area under regrowth is found to be 227.07 km 2 (i.e., 7.07 %) of the total study area. Accuracy assessment of the 2010 classified image indicates overall accuracy of 84.0 % with kappa coefficient of 0.77. The results show the ability of FCD model to detect the temporal change in forest canopy density. This study also reveals the potentiality of Landsat data in monitoring the changes in the terrestrial ecosystems. Content Type Journal Article Category Short Note Pages 1-7 DOI 10.1007/s12524-012-0224-5 Authors Jyotishman Deka, Department of Forestry, North Eastern Regional Institute of Science and Technology, Deemed University, Nirjuli, Arunachal Pradesh 791109, India Om Prakash Tripathi, Department of Forestry, North Eastern Regional Institute of Science and Technology, Deemed University, Nirjuli, Arunachal Pradesh 791109, India Mohamed Latif Khan, Department of Forestry, North Eastern Regional Institute of Science and Technology, Deemed University, Nirjuli, Arunachal Pradesh 791109, India Journal Journal of the Indian Society of Remote Sensing Online ISSN 0974-3006 Print ISSN 0255-660X

Description:    In this paper we report chlorophyll measurements made during an ocean colour validation cruise in April 2011 of the research vessel, Sagar Paschimi in the coastal waters of Northern Bay of Bengal. The chlorophyll- a concentration in these waters range from 0.2 to 4.0 mg/m 3 . Chlorophyll- a concentration from OCM-2 was estimated using the global ocean colour algorithms namely, OC2, OC3, OC4 and Chl- a algorithms respectively. OCM data was processed using the global SeaWiFS Data Analysis System (SeaDAS) in which all the above mentioned algorithms are embedded for estimating the chlorophyll- a concentration. A comparative study was made between and in-situ and satellite derived chlorophyll- a concentration. Although the matchups between in-situ and satellite data from OCM-2 were sparse, it indicates that direct application of the standard SeaWiFS algorithm-the OC4-V4 algorithm—in the coastal waters of the Bay of Bengal will underestimate chlorophyll- a by up to 30%. The results show a good correlation with an R value of 0.61 using OC2 algorithm. However, all the other global algorithms over estimate the chlorophyll- a concentration even in low chlorophyll concentration range. The comparison between in-situ and all the existing chlorophyll algorithms shows the efficiency of these algorithms for quantification of chlorophyll in coastal waters and hence the need to develop regional algorithms and fluorescence based algorithms for better quantification. Content Type Journal Article Category Research Article Pages 1-9 DOI 10.1007/s12524-012-0203-x Authors P. V. Nagamani, National Remote Sensing Centre, ISRO, Hyderabad, 500 625 India M. I. Hussain, S V P College of Engineering, Visakhapatnam, India S. B. Choudhury, National Remote Sensing Centre, ISRO, Hyderabad, 500 625 India C. R. Panda, Institute of Metals and Minerals Technology, CSIR, Bhubaneswar, 751 013 USA P. Sanghamitra, Institute of Metals and Minerals Technology, CSIR, Bhubaneswar, 751 013 USA R. N. Kar, Institute of Metals and Minerals Technology, CSIR, Bhubaneswar, 751 013 USA A. Das, Institute of Metals and Minerals Technology, CSIR, Bhubaneswar, 751 013 USA I. V. Ramana, National Remote Sensing Centre, ISRO, Hyderabad, 500 625 India K. H. Rao, National Remote Sensing Centre, ISRO, Hyderabad, 500 625 India Journal Journal of the Indian Society of Remote Sensing Online ISSN 0974-3006 Print ISSN 0255-660X

Description:    This paper proposes a new approach to 3D modeling on the basis of laser scanner data. It presents the attempt to automatically detect two planes and an edge in one processing algorithm. The algorithm is based on the M split estimation, which is a recently developed regression method of multi functional models. Plane equations in three dimensional space are derived on the basis of the presented method. M split estimation divides the functional model of least squares adjustment in particular into two groups. Both simulated and real data from airborne laser scanning is tested to present the results. In each case the mehod gave good results which encourages for further work on the application of this method for 3D modeling. Content Type Journal Article Category Research Article Pages 1-5 DOI 10.1007/s12524-012-0213-8 Authors Artur Janowski, Department of Geodesy, Faculty of Civil and Environmental Engineering, Gdansk University of Technology, Gdansk, Poland Jacek Rapinski, Institute of Geodesy, University of Warmia and Mazury in Olsztyn, Olsztyn, Poland Journal Journal of the Indian Society of Remote Sensing Online ISSN 0974-3006 Print ISSN 0255-660X

Description: From the Guest Editor’s Desk Content Type Journal Article Category Preface Pages 279-279 DOI 10.1007/s12524-011-0173-4 Authors Sushma Panigrahy, ABHG/EPSA, Space Applications Centre, ISRO, Ahmedabad, 380015 Gujarat, India Journal Journal of the Indian Society of Remote Sensing Online ISSN 0974-3006 Print ISSN 0255-660X Journal Volume Volume 39 Journal Issue Volume 39, Number 3

Description:    Crop residue has become an increasingly important factor in agriculture management. It assists in the reduction of soil erosion and is an important source of soil organic carbon (soil carbon sequestration). In recent past, remote sensing, especially narrowband, data have been explored for crop residue assessment. In this context, a study was carried out to identify different narrow-bands and evaluate the performance of SWIR region based spectral indices for crop residue discrimination. Ground based hyperspectral data collected for wheat crop residue was analyzed using Stepwise Discriminant Analysis (SDA) technique to select significant bands for discrimination. Out of the seven best bands selected to discriminate between matured crop, straw heap, combine-harvested field with stubbles and soil, four bands were from SWIR (1980, 2030, 2200, 2440 nm) region. Six spectral indices were computed, namely CAI, LCA, SINDRI, NDSVI, NDI5 and hSINDRI for crop residue discrimination. LCA and CAI showed to be best ( F  〉 115) in discriminating above classes, while LCA and SINDRI were best ( F  〉 100) among all indices in discriminating crop residue under different harvesting methods. Comparison of different spectral resolution (from 1 nm to 150 nm) showed that for crop residue discrimination a resolution of 100 nm at 2100–2300 m region would be sufficient to discriminate crop residue from other co-existing classes. Content Type Journal Article Category Research Article Pages 1-8 DOI 10.1007/s12524-012-0226-3 Authors Rimjhim Bhatnagar Singh, Agriculture, Terrestrial Biosphere & Hydrology Group (ABHG), EPSA, Space Applications Centre, Ahmedabad, India S. S. Ray, Mahalanobis National Crop Forecast Centre, Pusa Campus, New Delhi, 110012 India S. K. Bal, Punjab Agricultural University, Ludhiana, Punjab, India B. S. Sekhon, Punjab Agricultural University, Ludhiana, Punjab, India G. S. Gill, Punjab Agricultural University, Ludhiana, Punjab, India Sushma Panigrahy, Agriculture, Terrestrial Biosphere & Hydrology Group (ABHG), EPSA, Space Applications Centre, Ahmedabad, India Journal Journal of the Indian Society of Remote Sensing Online ISSN 0974-3006 Print ISSN 0255-660X

Description:    Groundwater exploration in the Western Doon valley has been carried out to delineate the groundwater potential and groundwater quality zones suitable for domestic purposes based on the integrated use of Remote Sensing and Geographical Information Systems (GIS). The Western Doon Valley, occupying broad synclinal troughs in the evolving fold-thrust system of sub-Himalaya, which is filled by post-Siwalik fluvial and debris flow deposits in the late Quaternary-Holocene. The Western Doon Valley area is bounded by the Mussoorie range in the north with 1800–2800 m elevation and in the south by young topographic relief of the frontal Siwalik range with ~800 m average elevation. Groundwater quality of Western Doon valley through pictorially representation in the GIS environment, it is inferred that calcium, magnesium, total hardness and nitrate at some locations above the desirable limit. The groundwater prospects map has been prepared by integrating the hydrogeomorphologic, land use/land cover from satellite data (IRS-ID, LISS-III data) slope, soil, drainage density, depth to water table of pre-monsoon and post-monsoon periods (unconfined aquifer), water table fluctuation, static water level (confined to semi-confined aquifers), specific capacity, discharge and drawdown maps using index overlay method in the GIS environment. The groundwater prospects are depicted in five categories Very high, high, moderate, low and very low (runoff zone) integrated with the groundwater quality zones which have been prepared from hydrochemical data. The results indicated that 16.82 % of the area is under Very high potential zone category with 16.11 % and 0.71 % of desirable and undesirable quality of groundwater and 18.65 %, 42.06 %, 6.96 % and 15.46 % classified as high, moderate, low and very low potential zones with desirable and undesirable quality of groundwater for domestic purposes. This study be useful for designing the groundwater prospects and management plan for the sustainable development of study area. Content Type Journal Article Category Research Article Pages 1-13 DOI 10.1007/s12524-012-0220-9 Authors A. S. Jasrotia, P.G. Department of Geology, University of Jammu, Jammu, 180 006 India B. D. Bhagat, P.G. Department of Geology, University of Jammu, Jammu, 180 006 India Ajay Kumar, P.G. Department of Geology, University of Jammu, Jammu, 180 006 India Rajesh Kumar, P.G. Department of Geology, University of Jammu, Jammu, 180 006 India Journal Journal of the Indian Society of Remote Sensing Online ISSN 0974-3006 Print ISSN 0255-660X

Description:    Identification of suitable site for urban development in hilly areas is one of the critical issues of planning. Site suitability analysis has become inevitable for delineating appropriate site for various developmental initiatives, especially in the undulating terrain of the hills. The study illustrates the use of geographic information system (GIS) and multicriteria evaluation (MCE) technique for selection of suitable sites for urban development in Mussoorie municipal area, Dehradun district, Uttarakhand. For this purpose Toposheet and IKONOS satellite data were used to generate various thematic layers using ArcGIS software. Criteria using five parameters, i.e. slope, road proximity, land use/land cover, land values and geological formation were used for site suitability analysis following land evaluation. The generated thematic maps of these criteria were standardized using pairwise comparison matrix known as analytical hierarchy process (AHP). A weight for each criterion was generated by comparing them with each other according to their importance. With the help of these weights and criteria, final site suitability map was prepared. Content Type Journal Article Category Research Article Pages 1-8 DOI 10.1007/s12524-012-0221-8 Authors Manish Kumar, NRDMS, Department of Geography, Kumaun University, SSJ Campus, Almora, 263601 India Vasim Riyasat Shaikh, NRDMS, Department of Geography, Kumaun University, SSJ Campus, Almora, 263601 India Journal Journal of the Indian Society of Remote Sensing Online ISSN 0974-3006 Print ISSN 0255-660X

Description:    Severity of flood damage is highly dependent on topography. In case of wetland, which includes altered water level regimes and linked with river system, estimation of flood is very important. However, substantial uncertainty exists in the precise estimation of river discharge when catchment is ungauged or partially gauged. This paper explores the river discharge of a partially gauged catchment contributing to Loktak Lake, an important wetland of North East India. Usually for the estimation of runoff response of an ungauged catchment resulting from a rainfall event, the geomorphological instantaneous unit hydrograph (GIUH) approach is very popular. In this paper, firstly an analysis has been performed to explore the geomorphologic parameters of the mentioned catchment and later on these geomorphological parameters coupled with Nakagami- m distribution to derive of GIUHs for various dynamic velocities. Moreover, these GIUHs are compared with another synthetic unit hydrograph (SUH) developed by Central Water Commission, Govt. of India (CWC) approach to find out the exact one as any recorded storm event data was unavailable for validation. Furthermore, a sensitivity analysis of the proposed model has been conducted. Content Type Journal Article Category Research Article Pages 1-13 DOI 10.1007/s12524-012-0219-2 Authors K. J. Singh, Department of Civil Engineering, NERIST, Nirjuli, 791109 Arunachal Pradesh, India Shibayan Sarkar, Department of Mechanical Engineering and Mining Machinery Engineering, Indian School of Mines Dhanbad, Dhanbad, 826004 Jharkhand, India Journal Journal of the Indian Society of Remote Sensing Online ISSN 0974-3006 Print ISSN 0255-660X

Description:    The Lonar crater in India provides an ample opportunity to in-depth analysis of crater morphology. This paper focuses on the topographical mapping of Lonar crater with detailed study on slope, regional analysis and its rim signature. The slope of the crater (inner wall region) reveals that the northern part is steep and southern part is gentle, while, on the outer region, the northern part is flat and the later shows abrupt variations. On regional topographical mapping (~4 crater radii) around the Lonar crater, it was observed that the terrain descends from NE to SW. An elevation difference of ~20 m was observed between the N and S part, infers that the pre-impact terrain is a descending one. The crater northern rim was elevated ~10 m to ~15 m, whereas southern rim was elevated ~50 m above the average regional surface. We found that the topographically lower southern region was abruptly changed and the rim has been uplifted to an elevation of ~604 m above the average regional elevation (~555 m). This result infers that the post-impact topography was abruptly altered along the S side. The crater rim signature extracted from highest point all along the rim shows a near flat surface on north, whereas the V-shaped protrusion shows active erosion and degradation on the west. Thus, DEM based topographic study has opened a new insight about the Lonar crater, from differential rim uplift, alteration along the rim and finally revealed that the impact crater formed on a descending terrain. Content Type Journal Article Category Research Article Pages 1-10 DOI 10.1007/s12524-012-0216-5 Authors S. Vijayan, Department of Information Science and Technology, Anna University, Chennai, India K. Vani, Department of Information Science and Technology, Anna University, Chennai, India S. Sanjeevi, Department of Geology, Anna University, Chennai, India Journal Journal of the Indian Society of Remote Sensing Online ISSN 0974-3006 Print ISSN 0255-660X

Description:    In this paper, we propose a novel scheme to improve the accuracy of remote sensing image classification by integrating data fusion, multiple feature combination and ensemble learning. Intensity-Hue-Saturation (IHS), Gram-Schmidt (GS), Brovey and wavelet fusion methods are first performed to obtain the optimal fusion images of high resolution and multispectral images. Support Vector Machine (SVM) classifier is then adopted to classify the fused image with different feature sets, and ensemble learning algorithm based on dynamic classifier selection (DCS) is finally used to integrate multiple classification maps. The proposed classification scheme is implemented with three remote sensing data sets, obtaining the highest overall accuracy and kappa coefficient in all cases (92.63% and 0.8917 for BJ-1 data set, 81.89% and 0.7513 for Landsat TM and SPOT4 data set, 92.21% and 0.8838 for ALOS data set respectively). The experimental results show that the integration of data fusion, feature combination and ensemble learning improves the classification performance obviously and has great potential in practical uses. Content Type Journal Article Category Research Article Pages 1-10 DOI 10.1007/s12524-012-0205-8 Authors Peijun Du, Department of Geographical Information Science, Nanjing University, Nanjing, 210093 China Yu Chen, Key Laboratory for Land Environment and Disaster Monitoring of SBSM, China University of Mining and Technology, Xuzhou, 221116 China Junshi Xia, Key Laboratory for Land Environment and Disaster Monitoring of SBSM, China University of Mining and Technology, Xuzhou, 221116 China Kun Tan, Key Laboratory for Land Environment and Disaster Monitoring of SBSM, China University of Mining and Technology, Xuzhou, 221116 China Journal Journal of the Indian Society of Remote Sensing Online ISSN 0974-3006 Print ISSN 0255-660X

Description:    The name ‘Seven Pagodas’ has served as a nickname for the south Indian port of Mahabalipuram since the early European explorers used it as landmark for navigation as they could see summits of seven temples from the sea. There are many theories concerning the name Seven Pagodas. The present study has compared coastline and adjacent seven monuments illustrated in a 17th century Portolan Chart (maritime map) with recent remote sensing data. This analysis throws new light on the name “Seven Pagodas” for the city. This study has used DEM of the site to simulate the coastline which is similar to the one depicted in the old portolan chart. Through this, the then sea level and corresponding flooding extent according to topography of the area and their effect on monuments could be analyzed. Most importantly this work has in the process identified possibly the seven monuments that constituted the name Seven Pagodas and this provides an alternative explanation to one of the mysteries of history. This work has demonstrated unique method of studying coastal archaeological sites. As large numbers of heritage sites around the world are on coastlines, this methodology has potential to be very useful for coastal heritage preservation and management. Content Type Journal Article Category Short Note Pages 1-8 DOI 10.1007/s12524-012-0210-y Authors M. B. Rajani, NIIT University (NU), Neemrana, NH-8, Delhi- Jaipur Highway, District Alwar, Rajasthan 301705, India K. Kasturirangan, National Institute of Advanced Studies (NIAS), Indian Institute of Science Campus, Bangalore, 560012 India Journal Journal of the Indian Society of Remote Sensing Online ISSN 0974-3006 Print ISSN 0255-660X

Description:    This paper discusses a statistical and band transformation based approach to select bands for hyperspectral image analysis. Hyperspectral images contain large number of spectral bands with redundant information about the spectral classes in the image scene. It is necessary to reduce the high dimensionality of the data for the processing of hyperspectral data. We report a feature selection technique that removes correlated spectral bands using band decorrelation technique and obtains maximum variance image bands based on factor analysis. Factor analysis method of band selection technique is also validated against existing methods of band selection. The study is carried out for the agriculturally rich area of Musiri region of South India that has varied landcover types. Evaluation of the band selection procedure is done using signature separability measures such as Euclidean distance, Divergence, Transformed divergence and Jeffries Matusita distance. Results indicated that selected bands exhibited maximum separability and also occurred predominantly at wavelength 700 nm, 850, 1000 nm, 1200 nm, 1648 nm and 2200 nm. Content Type Journal Article Category Research Article Pages 1-13 DOI 10.1007/s12524-012-0214-7 Authors A. Lavanya, Department of Geology, Anna University, Chennai, 600 025 India S. Sanjeevi, Department of Geology, Anna University, Chennai, 600 025 India Journal Journal of the Indian Society of Remote Sensing Online ISSN 0974-3006 Print ISSN 0255-660X

Description: Erratum to: Reservoir Impact Assessment on Land Use/Land Cover and Infrastructure—A Case Study on Polavaram Project Content Type Journal Article Category Erratum Pages 603-603 DOI 10.1007/s12524-011-0164-5 Authors Ramprasad Naik Desavathu, Department of Geo-engineering, College of engineering, Andhra University, Visakhapatnam, 530 003 India Suryaprakasarao Bosukonda, Gandi Institute of Technology and Management, Pinagadi, Visakaptanam, 531 173 India Mrutyunjayareddy Kamireddy, Andhra Pradesh State Remote sensing Center, 2nd floor, DES campus, Kairatabad, Hyderabad, 500 004 Andhra Pradesh, India Journal Journal of the Indian Society of Remote Sensing Online ISSN 0974-3006 Print ISSN 0255-660X Journal Volume Volume 39 Journal Issue Volume 39, Number 4

Description:    Driven by significant technological developments in the hyperspectral imaging, material mapping using reference spectra has received renewed interest of the remote sensing community. The applicability of reference spectral signatures in image classification depends mainly on the material type and its spectral signature behaviour. Identification and spectral characterization of materials which exhibit unique spectral behaviour is the first step in this approach. Consequently there have been active researches for the identification of surface materials which exhibit unique spectral signatures. The uniqueness of reflectance signature of winter rape relative to its co-occurring crop species was reported in this study. Reflectance spectral libraries constructed from field spectral reflectance measurements collected over five agricultural crops (alfalfa, winter barley, winter rape, winter rye, and winter wheat) during four subsequent growing seasons were classified by the linear discriminant analysis (LDA). Further, the reference field spectral database was used for the spectral feature fitting and classification of a historical HyMAP airborne hyperspectral imagery acquired at a separate site, by spectral library search. Results indicate the existence of a meaningful spectral matching between image and field spectra for winter rape and demonstrate the potential for transferring spectral library for hyperspectral image classification. The observed consistency in the discrimination of winter rape demonstrates experimentally the fundamental principle of remote sensing which suggests the theoretical existence of unique spectral signatures for materials which can be incorporated as reference spectral signatures for hyperspectral image classification. Content Type Journal Article Category Research Article Pages 1-14 DOI 10.1007/s12524-012-0211-x Authors Rama Rao Nidamanuri, Department of Earth and Space Sciences, Indian Institute of Space Science and Technology, Valiyamala, Thiruvananthapuram, 695 547 Kerala, India Bernd Zbell, Institute for Landscape Systems Analysis, Leibniz – Centre for Agricultural Landscape Research (ZALF), 15374 Müncheberg, Germany Journal Journal of the Indian Society of Remote Sensing Online ISSN 0974-3006 Print ISSN 0255-660X

Description:    A genetic algorithm based approach is used in this paper for the selection of a subset from the combination of Wavelet Packet Statistical and Wavelet Packet Co-occurrence textural feature sets to classify the LISS IV satellite images using neural networks. Generally, adding a new feature increases the complexity of training and classification. Hence there is a need to differentiate between those features that contribute ample information and others. Many current feature reduction techniques such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) involve linear transformations of the original pattern vectors to new vectors of lower dimensions. Hence a multi-objective Genetic Algorithm has been employed to reduce the complexity and increase the accuracy of classification. Four indices - user’s accuracy, producer’s accuracy, overall accuracy and kappa co-efficient are used to assess the accuracy of the classified data. Experimental results show that the proposed Genetic Algorithm approach with lesser number of optimal features produces comparable results with that of our earlier approach using more features. Content Type Journal Article Category Research Article Pages 1-12 DOI 10.1007/s12524-012-0208-5 Authors S. Rajesh, Department of MCA, Mepco Schlenk Engineering College, Sivakasi, 626005 India S. Arivazhagan, Department of ECE, Mepco Schlenk Engineering College, Sivakasi, 626005 India K. Pratheep Moses, Department of Planning, School of Architecture and Planning, Anna University, Chennai, Tamil Nadu, India R. Abisekaraj, Scientist, Naval Science and Technological Laboratory, DRDO, Vishakapatanam, 530 627 India Journal Journal of the Indian Society of Remote Sensing Online ISSN 0974-3006 Print ISSN 0255-660X

Description:    The present paper discusses the impact of topography on accuracy for land cover classification and “from-to class change using improved spectral change vector analysis suggested by Chen et al. ( 2003 ). Two AWiFS sensor images of different dates are used. Double Window Flexible Pace Search (DFPS) is used to estimate threshold of change magnitude for change/no change classes. The topographic corrections show accuracy of 90% (Kappa coefficient 0.7811) for change/no change area as compared to 82% (Kappa coefficient 0.6512) in uncorrected satellite data. Direction cosines of change vector for determining change direction in n-dimensional spectral space is used for image classification with a minimum distance categorizing technique. The results of change detection are compared (i) Improved CVA with conventional two bands CVA and (ii) Improved CVA before and after topographic corrections. The improved CVA with topographic correction consideration using slope match show maximum accuracy of 90% (Kappa coefficient 0.83) as compared to conventional CVA which show maximum accuracy of 82% (Kappa coefficient 0.6624). The overall accuracy of ”from- to class using improved CVA increases from 86% (Kappa coefficient 0.7817) to 90% (Kappa coefficient 0.83) after topographic corrections. The improved CVA with proper topographic corrections is found to be effective for change detection analysis in the rugged Western Himalayan terrain. Content Type Journal Article Category Research Article Pages 1-13 DOI 10.1007/s12524-011-0180-5 Authors J. K. Sharma, Rayat Institute of Engineering and Information Technology, Railmajra, Near Ropar, Distt., Nawanshahr, Punjab 144533, India V. D. Mishra, Snow and Avalanche Study Establishment (SASE), Him Parisar, Sector 37A, Chandigarh, 160036 India R. Khanna, Electronics and Communication Engineering, Thapar University, Patiala, 147004 India Journal Journal of the Indian Society of Remote Sensing Online ISSN 0974-3006 Print ISSN 0255-660X

Description: The present study was carried out to evaluate agricultural capability of a watershed located in Khuzestan; a province in southern Iran. It is aimed to examine the applicability of Multi Criteria Decision-Making (MCDM) methods in site selection process. Accordingly, the ecological resources of the watershed were initially identified. To specify the criteria required for agricultural land evaluation, Delphi method was applied. After selecting the criteria, they were weighted using Analytical Hierarchy Process (AHP) Method. Weighted Overlay (WO) Method was also used to overlay the map layers in the GIS environment. Afterwards, sensitivity analysis was performed using Weights Sensitivity Analysis (WSA) method to show the impressibility rate of the results against a certain changes in the inputs. The results revealed that out of 6591.2 ha of the total watershed area, 50.8 % has unsuitable potentiality while 27.32 % has a poor suitability for irrigated agriculture. It was also determined that only 6.96 % of the whole study area has a suitable potential for this purpose. Besides, the findings indicated that 23.38 % of the total watershed area is unsuitable for rain-fed farming. the results also showed that 31.78 % and 19.12 % of the entire study area has moderate and high potentials for rain-fed agriculture, respectively. In a general overview, this study could present how MCDM is effective in handling land capability studies.

Description: Extraction of accurate spatial information from high-resolution satellite imagery is becoming increasingly important for a variety of tasks. In this study, three-dimensional architectural data were extracted from QuickBird images using Barista’s monoplotting function. We evaluated the accuracy of the Rational Polynomial Coefficients bundle adjustment and extracted building heights. We obtained accuracies of one-pixel in geo-positioning and 2.66 m in building height. The height accuracy is 0.16 m greater than the estimated error for a one-pixel image measurement. The presence of roof overhangs is one primary factors affecting height accuracy. The application of three-dimensional architectural data represents well the vertical extension of urban growth in Tiexi District from 2002 to 2008.

Description: The corresponding points are prerequisite to the registration of Terrestrial Laser Scanning data (TLS). The exceptional corresponding points will direct impact the quality of registration. The interest in this paper is in the so-called residuals iteration correction algorithm, which focused on a new procedure for correcting the exceptional corresponding points. The kernel of the procedure is the Affine proposed by Berger ( 1987 ). This paper describes the three main steps of residuals iteration correction algorithm based on Affine, namely the decomposition of exceptional corresponding points, the propagation of registration residuals, and the correction of exceptional corresponding points. The paper outlines the key advantages of the proposed approach, such as the capability to correct exceptional corresponding point automatically according to the point precision. Furthermore, it illustrates the performance of proposed approach with a validation experiment where two exceptional corresponding points were simulated and “3S” statue TLS data in Wuhan University was acquired. From the analysis of this experiment, the result shows that the validation of correction of exceptional corresponding points based on residuals iteration.

Description: One of the most widely used outputs of remote sensing technology is Hyperspectral image. This large amount of information can increase classification accuracy. But at the same time, conventional classification techniques are facing the problem of statistical estimation in high-dimensional space. Recently in remote sensing, support vector machines (SVMs) have shown very suitable performance in classifying high dimensionality problem. Another strategy that has recently been used in remote sensing is multiple classifier system (MCS). It can also improve classification accuracy by combining different classifier methods or by a diversity of the same classifier. This paper aims to classify a Hyperspectral data using the most common methods of multiple classifier systems i.e. adaboost and bagging and a MCS based on SVM. The data used in the paper is an AVIRIS data with 224 spectral bands. The final results show the high capability of SVMs and MCSs in classifying high dimensionality data.

Description: The present study is focussing on that how Landsat 7 ETM + can be effectively used for estimation of Trophic State Index for Sukhna Lake. Sukhna Lake in Chandigarh has undergone lot of changes in last few decades. The depth and area both reduced tremendously. The shrinkage of the lake is due to the siltation and inadequate water volumes flowing to it. The Trophic State Index has been estimated by using secchi disk transparency and Landsat 7 ETM + data. The in situ observations for parameters like pH, DO were measured by using multiparameter water quality instrument TROLL 9500. The best tested interpolation technique has been used to generate in situ images. The results have shown that the lake is in Hypereutrophic condition since 2000.

Description: Three-dimensional (3D) spatial information is crucial for improving the quality of human life through urban planning and management, and it is widely utilized due to its rapid, periodic and inexpensive acquisition. In this context, extraction of digital surface and elevation models (DSM and DEM) is a significant research topic for space-borne optical and synthetic aperture radar (SAR) remote sensing. The DSMs include visible features on the earth’s surface such as vegetation, forest and elevated man-made objects, while DEMs contain only the bare ground. In this paper, using TerraSAR-X (TSX) high resolution Spotlight (HS) images, high-resolution interferometric DEM generation in a part of Istanbul urban area is aimed. This is not an easy task because of SAR imaging problems in complex geometry of urban settlements. The interferometric processing steps for DSM generation were discussed including critical parameters and thresholds to improve the quality of the final product and a 3 m gridded DSM was generated. The DSM-DEM conversion was performed by filtering and the quality of generated DEM was verified against a reference DEM from stereo photogrammetry with 3 m original grid spacing. The achieved root mean square error of height differences (RMSZ) varies from 7.09 to 8.11 m, depending on the terrain slope. The differential DEM, illustrates the height differences between generated DEM and the reference DEM, was generated to show the correlation between height differences and the coherence map. Finally, a perspective view of test area was created draping extracted DEM and a high-resolution IKONOS panchromatic image.

Description: Haryana has emerged as an important state for Rice & Wheat production in India contributing significantly in the central pool. Mechanized combine harvesting technologies, which have become common in Rice Wheat System (RWS) in India, leave behind large quantities of straw in the field for open burning of residue. Besides causing pollution, the burning kills the useful micro flora of the soil causing soil degradation. There is no field survey (Girdawari) data available with the Government for the areas where stubble burning is taking place. The present paper describes the methodology and results of wheat and rice residue burning areas for three districts of Haryana namely Kaithal, Kurukshetra and Karnal for the year 2010 using complete enumeration approach of multi-date IRS-P6 AWiFS and LISS-III data. In season ground truth was collected using hand held GPS and used to identify area of burnt wheat/rice residues, associated crops and land features. After geo-referencing the satellite images, district images were masked-out and multi-date image data stacks were created. Normalized Difference Vegetation Index (NDVI) of each date was generated and used at the time of classification along with other spectral bands. The non-agricultural classes in the image included: forest, wasteland, water bodies, urban/settlement and permanent vegetation etc. The vector of these non-agriculture classes were extracted from the land use, imported and mask was generated. During the classification non-agriculture area was excluded by using mask of these classes. From this the agricultural area could be separated out. The area was estimated by computing pixels under the classified image mask. In season multi-date AWiFS data along with available single-date LISS-III data between third week of April to last week of May are found to be useful for estimation of wheat residue burning areas estimation. The data between second week of October to last week of November is useful for estimation of rice residue burning areas estimation at district level.

Description: SIFT (scale invariant feature transform) is one of the most robust and widely used image matching algorithms based on local features. However, its computational complexity is high. In order to reduce the matching time, an improved feature matching algorithm is proposed in this paper under the premise of stable registration accuracy. This paper proposed a normalized cross-correlation with SIFT combination of remote sensing image matching algorithm. The basic idea of the algorithm is performing the space geometry transformation of the input image with reference to the base image. Then the normalized cross-correlation captures the relevant part of the remote sensing images. By this way, we can reduce the matching range. So some unnecessary calculations are properly omitted. By utilizing the SIFT algorithm, we match the preprocessed remote sensing images, and get the registration points. This can shorten the matching time and improve the matching accuracy. Its robustness is increased correspondingly. The experimental results show that the proposed Normalized cross-correlation plus SIFT algorithm is more rapid than the standard SIFT algorithm while the performance is favorably compared to the standard SIFT algorithm when matching among structured scene images. The experiment results confirm the feasibility of our methods.

Description: Thar desert spreads in western part of Rajasthan, northern part of Gujarat, and some parts of Punjab and Haryana. The terrain is dominated by slightly sloping plains, broken by some dunes and low barren hills. The area is characterized by low average annual rainfall which is erratic in distribution and intensity. Drought will remain a major hindrance for agricultural production in Thar desert. Due to water stress condition, many watershed based development activities has been adopted by government and non-government organizations for the growth and sustainable development of this region. The need of this hour is preparation of a national level watershed atlas of 1:50,000 scale because majority of thematic maps are being produced presently on same or 1:10,000 scale. The manual delineation of watershed boundary in flat terrain based on topographic map will be time consuming and less accurate in the absence of prominent contour lines. Automated approach for watershed delineation using Digital Elevation Model (DEM) along a suitable algorithm has the advantage because the output is not only less time consuming but also independent from human decisions. Hence, a case study has been carried out in Churu sub-basin part of Indus basin which is located in Thar desert region. Depression less DEM with different spatial resolutions was used as input in hydrology tool of ArcGIS spatial analyst function for characterization of watersheds. The Churu sub-basin has been divided into various numbers of watersheds with an average size of 600 km 2 . These watershed boundaries have been validated with respect to high resolution satellite imageries (IRS P6 LISS IV), Survey of India toposheets, ancillary data and limited field checks.

Description: A new classification parameter is developed using 1535 ERS-2 wave mode synthetic aperture radar (SAR) test imagettes to better differentiate homogeneous and inhomogeneous imagettes. The comparison between the new parameter (Min) and the previous one ( Inhomo ) (Schulz-Stellenfleth and Lehner, 2004 ) was done under varied threshold values of Inhomo . It is concluded that the performance of ‘Min’ is much better than ‘ Inhomo ’ when applying to the 1535 test imagettes. Furthermore, both Min and Inhomo are applied to nearly 1 million imagettes collected for the period from 1 September 1998 to 30 November 2000. The comparisons of the global inhomogeneous distribution between ‘Min’ and ‘Inhomo’ reveal that both the areas and percentage of inhomogeneity calculated by ‘Min’ are larger than that calculated by ‘ Inhomo ’. By analyzing the low wind speed distribution of HOAPS data, we found that low wind speed over the ocean is one of the key reasons for the inhomogeneity of SAR imagettes.

Description: The cellular automata (CA) model is an important tool in land use change studies. Swift increases in population and long-term expectations of rapid urbanization have led to extensive land use change, and normal living conditions have affected the natural resources of the land. This paper highlights and analyzes the historical urban changes in Kirkuk City, Iraq, considering repeated changes undergone by the state such change as government infrastructures, wars, and economic blockade. In this paper, an integrated model, built-in multi regression model, and multi-criteria evaluation were considered to improve the representation of CA transition rules. Environmental and socioeconomic factors were used to produce Suitable Maps (SMs). These SMs were practicalities to create factor layers and weight usage, rating method process for variance expert decision-making groups, and geographic information systems for the periods 1984, 1990, 2000, and 2010. The roots of the equation (R 2 ) values are compared and these values are chosen to produce a good model of suitable maps. The approach used in this study provides a mechanism for monitoring suitability maps in Kirkuk. Furthermore, the model Markov CA is implemented and evaluated. The results indicate that the model, its related concepts performs sufficiency

Description: The homegardens represent an important component of the trees outside forests (TOF) in the rural ecosystem which fulfill a range of subsistence and economic needs besides providing many environmental services. The present work was focused on the identification and mapping of rural homegardens as a component of the trees outside forests in the larger landscape of the three districts—Cachar, Hailakandi and Karimganj, of Barak Valley, Assam, northeast India. Mapping and identification of homegardens and other dominant land use/land cover classes was done with IRS-P6 LISS-IV data using on-screen visual interpretation technique in a geographic information system environment. Two major TOF classes could be identified from the satellite data and homegardens were found to be the dominant TOF class with the highest percentage coverage of the total geographical area in the three districts. The study reveals that high resolution satellite data of IRS-P6 LISS-IV can be successfully used for classification and mapping of different land use/land cover classes including the homegardens with an overall classification accuracy of 91 %. The land use/land cover map generated for the three districts shows the distribution of the homegardens in relation to other land use/land cover classes and can be used in future for proper identification of homegardens and resource management planning.

Description: Satellite imagery analysis has played a key role in environmental monitoring and modeling over the past few decades. Remotely sensed multitemporal, multisensor data are often required in Earth observation applications. A common problem associated with the use of multisource image data is the gray value differences caused by non-surface factors such as different illumination, atmospheric or sensor conditions. Radiometric corrections serve to remove the effects that alter the spectral characteristics of land features, except for actual changes in ground target, becoming mandatory in multi-sensor, multi-date studies. This paper presents methodology for correction of Landsat TM images in Radon domain. We propose radiometric correction using Radon Transform based regression method. The transform domain method statistically determines correction values based on contrast between spectral properties of various homogeneous areas. TM band 1 is the band most affected by atmospheric scattering whereas TM band 5 is less affected by atmospheric scattering effect. Hence TM Band 5 is taken as the reference image. Results are assessed statistically and compared with results of popular spatial domain Regression Line Method. The application of the methods for vegetation analyses is also shown in this paper. Test results show that the method gives improved results in removal of atmospheric influence.

Description: Feature selection methods play an important role in Hyperspectral Remote Sensing applications, especially in classification. This paper proposed a new Feature selection strategy for Hyperspectral dataset. This strategy was designed to help refine vegetation classification of 4 categories with 13 species vegetation which are the most common species in central China. An ASD field spectrometer (Analytical Spectral Device) was used to collect spectrum information of plant leaves from each species through 400 nm to 900 nm with 1 nm spectral resolution. Firstly, correlation between the physical/chemical characteristics of the leaves and the separability of each vegetation species was tested. Then, two feature selection methods, spectral angle and spectral distance, and the feature parameters extracted from spectral curves (FPESC) were used to build the feature space which would be the input space for the classifiers. At last, two linear classifiers, mahalanobis distance (MDC), and fisher linear discriminate analysis (FLDA), and a quadratic classifier, maximum likelihood (MLC), were used for vegetation species refine classification. The results showed that (1) there were no significant differences among 13 species on the leaf dry weight (physical parameter) and leaf chlorophyll content (chemical parameter); (2) FPESC of 13 species have distinctive differences and could be ideal features to discriminate these species; (3) The linear classifiers, MDC and FLDA, have better classification results in the experiments compared to the quadratic classifier MLC, where MDC has the highest classification accuracy which is above 96.2 %.

Description: In this study chlorophyll measurements were made during March 2012 in the estuarine waters of Off Kakinada and Yanam coast, Bay of Bengal onboard a coastal vessel. In-situ water samples and optical data was collected at 21 stations (surface to 150 m depth) using Underwater radiometer (Hyperpro-II). In-vivo chlorophyll profiles were collected using wet labs fluorometer integrated with underwater Hyperspectral radiometer. Chlorophyll-a concentrations were estimated using HPLC by collecting the water samples at each sampling location. And also chlorophyll-a concentrations were retrieved from the OCM-2 data of OCEANSAT-2 satellite, processed using SeaDAS v.6.2 with the available global ocean colour algorithms namely, OC2 and OC4V4. A total of 33 samples used covering all the stations for chlorophyll-a estimation, and surface water samples of all the stations only being used for direct comparison among chlorophyll concentrations of HPLC, in-situ (fluorometrically integrated to Hyperpro-II) and retrieved from OCM-2. A good correlation found between the Fluorometer derived and HPLC measured chlorophyll-a concentration with an R 2 value of 0.78. The relation between Chlorophyll-a concentration measured from HPLC and retrieved from OCM-2 (OC2 and OC4V4 algorithms) using SeaDASv.6.2 for 10 samples has been compared for validation and obtained an R 2 value of 0.6. Also comparisons done with the in-situ measured (fluorometer) Chlorophyll-a concentration with OCM-2 chlorophyll data (OC4-V4 and OC2 algorithms) and validation with 10 concurrent in-situ surface measurements showed a significant overestimation by OCM-2 at low chlorophyll-a concentrations and underestimation at high chlorophyll-a concentrations.

Description: Farming is a risky business but, from the food security point of view, it is important that farmers continue to grow crops so that people get food to eat. Although natural calamities cannot be eliminated, its impact can be reduced through implementation of pro-active and pro-poor risk management policy programs. Remote sensing, with capabilities of synoptic coverage, multi-spectral and multi-temporal observations, is ideally suited for in-season monitoring the progress of crop. Normalized Differential Vegetation Index (NDVI) is the primary index for monitoring vegetation status and its temporal behavior captures the dynamic response of vegetation cover to prevailing physical conditions. The present study offers a methodology for making multiple inseason assessment of the crop growth vis-a-vis its normal performance. This is treated by use of conditional distribution. Present analysis reports the performance in deriving spectral maxima (Gmax) from complete profile of validation season and one which was derived from conditional mean approach. It was observed that in more than 90 % of the cases, the difference in Gmax was less than 3 %. Thus the performance of methodology can be termed as very good.

Description: Entropy is the measure of uncertainty in any data and is adopted for maximisation of mutual information in many remote sensing operations. The availability of wide entropy variations motivated us for an investigation over the suitability preference of these versions to specific operations. The popular available versions like Tsalli’s, Shannon’s, and Renyi’s entropies have been analysed in context of various remote sensing operations namely thresholding, clustering and registration. These methodologies have been evaluated with reference to the study area using different statistical parameters. Renyi’s entropy has been found to be suitable for image registration purpose followed by Tsalli’s and Shannon; whereas Tsalli’s entropy has been found preferable for thresholding and clustering.

Description: Remote sensing offers a wide variety of image data with different characteristics in terms of spatial and spectral resolutions. For optical sensor systems, imaging systems have a trade-off between high spatial and high spectral resolution, and no single system offers both. Hence, in the remote sensing application, an image with ‘greater quality’ often means higher spatial and higher spectral resolution. It is, therefore, necessary and very useful to merge images with higher spectral information and higher spatial information. Pansharpening combines spatial information from the high-resolution panchromatic image and color information from multispectral bands to create a high-resolution color image. Here we propose Discrete Cosine Transform (DCT) based pansharpening algorithm using Adaptive Linear model which preserves spectral information from Multispectral image and retains spatial resolution of Panchromatic image.

Description: Because of the difficulty of monitoring and measuring snow cover in mountainous watersheds, satellite images are used as an alternative to mapping snow cover to replace the ground operations in the watershed. Snow cover is one of the most important data in simulation snowmelt runoff. The daily snow cover maps are received from Moderate Resolution Imaging Spectroradiometer (MODIS), and are used in deriving the snow depletion curve, which is one of the input parameters of the snowmelt runoff model (SRM). Simulating Snowmelt runoff is presented using SRM model as one of the major applications of satellite images processing and extracting snow cover in the Ghara - Chay watershed. The first results of modeling process show that MODIS snow covered area product can be used for simulation and forecast of snowmelt runoff in Ghara - Chay watershed. The studies found that the SCA results were more reliable in the study area.

Description: A spectral library of commonly occurring Himalayan plant species has been developed. The 512-channel ASD handheld Fieldspec®Pro, 2000 Spectroradiometer with 3 nm spectral resolution and spectral range of 325 to 1,075 nm has been used for recording the leaf, branch or canopy spectra of plant species. Simultaneous measurements of crop biochemical parameters were estimated following standard methods, and vegetation indices were calculated for all the observations. The photographs of the plant species as well as their general descriptions were also detailed in the library. The spectral library has been developed in .Net programming environment. The GUI of library aids in viewing of all the information related to plant species such as spectral details, spectral graphs, general information of species, observation details, plant photographs, species spectral narrowband indices, species biochemical parameters, export options and help through menus and sub menus. The spectra and other information can be exported for further use by the user. The spectral reflectance can be used during classification of Hyperspectral images. The information provided in the library may also be used to explore the application potential of Hyperspectral images in studying chemical constituents, growth behavior, and ambient ecology of plants on a regional scale in Himalayan region. The basic objective of the work was to standardize the technique for vegetation spectral library development and make the data available for comparison by the user.

Description: Landslide hazards are a major natural disaster that affects most of the hilly regions around the world. In India, significant damages due to earthquake induced landslides have been reported in the Himalayan region and also in the Western Ghat region. Thus there is a requirement of a quantitative macro-level landslide hazard assessment within the Indian subcontinent in order to identify the regions with high hazard. In the present study, the seismic landslide hazard for the entire state of Karnataka, India was assessed using topographic slope map, derived from the Digital Elevation Model (DEM) data. The available ASTER DEM data, resampled to 50 m resolution, was used for deriving the slope map of the entire state. Considering linear source model, deterministic seismic hazard analysis was carried out to estimate peak horizontal acceleration (PHA) at bedrock, for each of the grid points having terrain angle 10° and above. The surface level PHA was estimated using nonlinear site amplification technique, considering B-type NEHRP site class. Based on the surface level PHA and slope angle, the seismic landslide hazard for each grid point was estimated in terms of the static factor of safety required to resist landslide, using Newmark’s analysis. The analysis was carried out at the district level and the landslide hazard map for all the districts in the Karnataka state was developed first. These were then merged together to obtain a quantitative seismic landslide hazard map of the entire state of Karnataka. Spatial variations in the landslide hazard for all districts as well as for the entire state Karnataka is presented in this paper. The present study shows that the Western Ghat region of the Karnataka state is found to have high landslide hazard where the static factor of safety required to resist landslide is very high.

Description: Himalayan glaciers and their mass balance are poorly sampled through direct mass balance measurements. Thus, based on Landsat datasets of ETM + (2000), ETM + (2006) and TM (2011), mass balance studies of 32 glaciers was carried out using accumulation area ratio (AAR) method in the Tirungkhad river basin, a tributary of Satluj River, located in western Himalayan region. The overall specific mass balance was negative varying from −27 cm (2000) to −41 cm (2011). Out of 32 glaciers, 27 glaciers (81.2 %) showed negative mean mass balance and 5 glaciers (18.7 %) showed positive mean mass balance. Mean of specific mass balance for the year 2000, 2006 and 2011 was found to be −48 cm, −55 cm and −0.61 cm respectively, in case of glaciers with negative mass balance while in case of glaciers with positive mass balance, it was 0.67 cm (2000), 0.56 cm (2006) and 0.47 cm (2011). The investigations suggested a loss of −0.034 km 3 of glacial ice for 2000, 0.036 km 3 for 2006 and 0.038 km 3 for 2011 respectively. The negative mass balance of the glaciers since 2000 correlates well with the increasing trend of annual mean temperature and decreasing trend of precipitation (snow water equivalent (SWE) and rainfall). Based on Mann Kendall test the temperature and SWE trends were significant at 95 % confidence level, however, the rainfall trend was insignificant.

Description: Land is the basic resource that is needed by man in order to survive: It provides humans with living space, nutrition and energy resources. The rapid growth of the human population, climate change and pollution on a catastrophic scale has caused the quality of land resources to be compromised. Remote sensing is a useful tool in land cover change detection providing information to decision makers. The aim of this study was to evaluate land cover changes in the Mtunzini area in South Africa over the past 18 years; determine why changes have occurred and predict land cover patterns for future years. In this study a supervised classification was used to detect land cover classes of the Mtunzini area from 1992 to 2009 using four Landsat images in the time series analysis. The supervised classification had an accuracy of 80.80 % which was used to model land cover changes. Commercial sugar cane and forest plantation classes increased throughout the time series. It was estimated in the modelling procedure that bushland (42.11 %) and bare soil (35 %) would be changed to commercial sugar cane. This is indicative of the expanding agriculture sector in Mtunzini. Natural vegetation is predicted to be disturbed: 18 % of bushland and 15.07 % of dense bush are expected to be replaced by rural dwellings. This is owing to a potential increase in the rural population and a reduced local economic growth. This study highlights the need for increased vigilance of the forestry industry and commercial sugar cane farms which may be encroaching on natural vegetation and livelihoods of local residents. Strategic planning and proper management of natural vegetation types is needed as these land cover types are decreasing rapidly.

Description: The real-time operational use of atmospheric motion vectors (AMVs) at numerical weather prediction (NWP) centers in India are being adversely affected due to inaccurate height assignment of cloud tracers, especially in thin semi-transparent clouds. In India, the operational derivation of AMVs from the Indian geostationary satellite Kalpana-1 began few years ago. A statistical empirical method (SEM) of height assignment, based on a genetic algorithm, is currently used to estimate the height of the retrieved vectors from Kalpana-1. This method has many limitations. In this paper, attempts have been made to implement the widely used and well tested height assignment methods such as the infrared window (WIN) technique, the H 2 O intercept, and the cloud base method in the Kalpana-1 AMV retrieval algorithm. The new height assignment algorithm significantly improves the statistics of the retrieved winds when compared to radiosondes, especially in high and mid levels winds.

Description: Lunar surface exploration is increasing rapidly with priority being given to precision of lunar soft landing. Lunar soft landing is achieved when craters and grabens are used as navigational landmarks. Grabens are formed from localized tensional stress fields or from near-surface dike emplacement. These tectonic features tend to have consistently straight or accurate parallel-striking walls bounded by steep, inward-dipping normal faults. Aiming at navigational application, a novel approach for automatic graben detection based on Hessian technique has been implemented on Digital Terrain Model (DTM) of lunar image. The Hessian technique uses gradient change as a key parameter to identify grabens. Adaptive Binarization using Otsu method is used to extract graben features from the Hessian image. Features such as small grabens and craters are removed using morphological operations, resulting in significant appearance of grabens. The experiment is conducted in different DTM images of lunar surface and the results indicate 90 % of the grabens are detected. The statistical results are evaluated based on visual interpretation, for both automatic and manual graben detection. It is observed that the proposed automatic graben detection technique gives better results than the manual detection.

Description: Objective of this study was to identify stripe rust affected areas of wheat crop as well as evaluation of remote sensing (RS) derived indices. Moderately low temperature and high humidity favour the growth of yellow rust. Most affected areas of Punjab are the foothill districts such as Gurdaspur, Hoshiarpur and Ropar. Occurrence of yellow rust is possible when maximum temperature for day is below 15 °C and Temperature difference of day’s maximum and minimum temperature is less than 5 °C during the early growth of wheat. Forecast of the infestation was done using 3 days forecast of weather data obtained from Weather Research and Forecasting (WRF) model at 5 km resolution. Weather forecast used was obtained from Meteorological and Oceanographic Satellite Data Archival System (MOSDAC) site and post infestation, identification of specific locations were done using multi-date IRS AWiFS data. It is an attempt for early detection through 3 days advance forewarning of weather which will be handy tool for planners to expedite relief measures in case of epidemic with a more focused zones of infestation as well as for crop insurers to know the location and extent of damage affected areas.

Description: Sandy beaches of the eastern coast zone in Eastern Laizhou Bay represent the most popular tourist, recreational destinations and constitute some of the most valuable restates in China. This paper presents the detection of shoreline changes in Laizhou Bay East Bank using an automatic histogram thresholding algorithm on the basis of multi-temporal Landsat images. Shoreline change rates (SCR) and shoreline change areas (SCA) were retrieved using the statistical approach and zonal change detection method, respectively. Results showed that during 1979–2010 a large portion (over 59.8 %) of shoreline are dominated by a retreating process with an average rate of −2.01 m/year, while other parts of shoreline exhibited a seaward advancing trend due to intense land reclamation activities. It is our anticipation that the result of this work would support sandy beaches protection and management in China coast.

Description: Drainage and lineaments play an important role in the flow of groundwater. The objective of this study is to assess the groundwater level and its relation to drainage and lineaments in a hard rock region of a part of Nalgonda district, Andhra Pradesh, southern India. The region predominantly comprise of granites and gneisses. Groundwater level was measured in 42 representative wells in this study area from March 2008 to January 2010 once in every two months. Observed groundwater levels were compared with drainage and dyke density. Groundwater level fluctuation in low drainage density region is generally greater than those in moderate and high drainage density regions. The dykes do not act as barriers for groundwater flow as they are highly weathered. The quantity and flow of groundwater in this region is predominantly controlled by drainage density, intensity of weathering and presence of fractures. Thus the study indicate that the drainage density play a major role in groundwater level fluctuation and as the dykes are weathered, they do not affect the groundwater flow in this shallow unconfined aquifer.

Description:    To understand the scale effects on chlorophyll-a (chl- a ) concentration retrieved from satellite images, the two-band algorithm (TA) and three-band algorithm (TBA) were constructed for estimating chl- a from satellite images. Two synchronous images of Advanced Wide-Field Sensor (AWiFS) and Linear Imaging Self-Scanner (LISS) of Indian remote sensing satellite were used to assess and validate the scale errors of these two algorithms. They were collected at local time 02:55:46:471 and 02:58:25:053 on October 8, 2005 in Yellow River Estuary, and their spatial resolutions are 24 m and 56 m, respectively. From the results of this study, it was found that: (1) the relative scale error (RSE) of TA and TBA, caused by scale changing from LISS to AWiFS, varied from 0% to 100%; (2) the RSE was correlated with the spatial non-homogeneous degree of chl- a distribution; and (3) using TBA to estimate chl- a concentration in Yellow River Estuary decreased 2.55% of model uncertainty, but increased 4.97% of scale errors, in comparison with TA. Additionally, the study indicated that the performance of algorithms for chl-a estimation was greatly affected by the scale error. If the scale effects of chl- a retrieval algorithm were taken into consideration, TA had a superior performance to the TBA in this study. Content Type Journal Article Category Research Article Pages 1-12 DOI 10.1007/s12524-012-0204-9 Authors Jun Chen, The Key Laboratory of Marine Hydrocarbon Resources and Geology, Qingdao, 266071 China Xunfei Hu, Geomatics & Survey Center of Taihu Zhou, Taizhou, 318000 China Wenting Quan, Shaanxi Remote Sensing Information Center for Agriculture, Xi’an, 710000 China Journal Journal of the Indian Society of Remote Sensing Online ISSN 0974-3006 Print ISSN 0255-660X

Description:    Cyclone is one of the major coastal hazards affecting the Indian coastline bringing floods and widespread damage due to rain, storm surges and heavy winds. Hence, coastal risk assessment is essential to identify the threats to the land and population, from cyclones. The risk assessment has been carried out in northern coast of Tamil Nadu, India, for the stretch from Kattivakkam to Kovalam. Risk is the probability of expected losses from a given hazard and it varies according to the vulnerability of the region. The risk assessment has been structured in four components, namely, Environmental Vulnerability, Social Vulnerability, Hazard Potential and Mitigation Capacity. The thematic layers and hazard maps are created using Satellite data and Geographic Information System. Coastal risk indices are generated for each component of risk using Analytic Hierarchy Process. Based on the index value, the level of risk is mapped and the results help in faster and appropriate decision making. Content Type Journal Article Category Research Article Pages 1-20 DOI 10.1007/s12524-011-0198-8 Authors V. Poompavai, Department of Earth and Space Sciences, Indian Institute of Space Science and Technology, Thiruvananthapuram, 695 547 India M. Ramalingam, Institute of Remote Sensing, Anna University Chennai, Chennai, 600 025 India Journal Journal of the Indian Society of Remote Sensing Online ISSN 0974-3006 Print ISSN 0255-660X

Description:    Estimation of infrastructural growth is the key issue for planning and resource management. In this regard it is highly required to have a proper database and documentation. Remotely sensed data and its processing techniques are most important parameter to achieve this goal. In developing countries, the planning and resource management is still dependent on traditional methods, but integration of satellite data of high resolution and of multiple spectral bands with appropriate processing techniques, makes it possible to get optimal result in limited fiscal resources. The merging of multi resolution sensor data can be the best option instead of using costly data for low budget planning and development. This study aims to analyze the potentials of image fusion of multispectral and panchromatic satellite data with high ground resolution images and evaluating their significance in infrastructural classification. While the accuracy assessment tests of classification result, suggest the appropriate classification techniques. The Relevance of image fusion in auto vectorization has also been discussed in this paper. Content Type Journal Article Category Short Article Pages 1-7 DOI 10.1007/s12524-012-0206-7 Authors Jyoti Sarup, Department of Civil Engineering, Maulana Azad National Institute of Technology, Bhopal, India Akinchan Singhai, Centre for Remote Sensing and GIS, Department of Civil Engineering, Maulana Azad National Institute of Technology, Bhopal, India Journal Journal of the Indian Society of Remote Sensing Online ISSN 0974-3006 Print ISSN 0255-660X

Description:    CartoDEM is an Indian National DEM generated from Cartosat-1 stereo data. Cartosat-1, launched in May, 2005, is an along track (aft −5°, Fore +26°) stereo with 2.5 m GSD, give base-height ratio of 0.63 with 27 km swath. The operational procedure of DEM generation comprises stereo strip triangulation of 500 × 27 km segment with 10 m posting along with 2.5 m resolution ortho image and free—access posting of 30 m has been made available (bhuvan.nrsc.gov.in). A multi approach evaluation of CartoDEM comprising (a) absolute accuracy with respect to ground control points for two sites namely Jagatsinghpur -flat and Dharamshala- hilly; second site i.e. Alwar-plain and hilly with high resolution aerial DEM, (b) relative difference between SRTM and ASTERDEM (c) absolute accuracy with ICESat GLAS for two sites namely Jagatsinghpur-plain and Netravathi river, Western Ghats-hilly (d) relative comparison of drainage delineation with respect to ASTERDEM is reported here. The absolute height accuracy in flat terrain was 4.7 m with horizontal accuracy of 7.3 m, while in hilly terrain it was 7 m height with a horizontal accuracy of 14 m. While comparison with ICESat GLAS data absolute height difference of plain and hilly was 5.2 m and 7.9 m respectively. When compared to SRTM over Indian landmass, 90 % of pixels reported were within ±8 m difference. The drainage delineation shows better accuracy and clear demarcation of catchment ridgeline and more reliable flow-path prediction in comparison with ASTER. The results qualify Indian DEM for using it operationally which is equivalent and better than the other publicly available DEMs like SRTM and ASTERDEM. Content Type Journal Article Category Research Article Pages 1-13 DOI 10.1007/s12524-012-0212-9 Authors S. Muralikrishnan, National Remote Sensing Centre, Indian Space Research Organization, Hyderabad, India Abhijit Pillai, National Remote Sensing Centre, Indian Space Research Organization, Hyderabad, India B. Narender, National Remote Sensing Centre, Indian Space Research Organization, Hyderabad, India Shashivardhan Reddy, National Remote Sensing Centre, Indian Space Research Organization, Hyderabad, India V. Raghu Venkataraman, National Remote Sensing Centre, Indian Space Research Organization, Hyderabad, India V. K. Dadhwal, National Remote Sensing Centre, Indian Space Research Organization, Hyderabad, India Journal Journal of the Indian Society of Remote Sensing Online ISSN 0974-3006 Print ISSN 0255-660X

Description:    Large scale adoption of input intensive rice–wheat cropping system in the centrally located Jalandhar district of Indian Punjab has led to over-exploitation of ground water resources, intensive use of chemical fertilizers and deterioration of soil health. To overcome these shortfalls, in the present study, agricultural area diversification plan has been generated from agricultural area and crop rotation maps derived from remote sensing data (IRS P6-AWiFS and RADARSAT ScanSAR) along with few agro-physical parameters in GIS environment. Cropping system indices (area diversity, multiple cropping and cultivated land utilization) were also worked out from remote sensing data .Analysis of remote sensing data (2004–05) revealed that rice and wheat individually remained the dominant crops, occupy 57.8% and 64.9% of total agricultural area (TAA), respectively. Therefore, in the diversified plan, it is suggested that at least 39% of the current 40% TAA under rice–wheat rotation should be replaced by other low water requiring, high value and soil enriching crops, particularly in coarse textured alluvial plain having good quality ground water zones with low annual rainfall(〈700 mm). This will reduce water requirement to the tune of 15,660 cm depth while stabilizing the production and profitability by crop area diversification without further degradation of natural resources. Content Type Journal Article Category Research Article Pages 1-12 DOI 10.1007/s12524-011-0194-z Authors B. U. Choudhury, Division of Soil Science, ICAR Research Complex for NEH Region, Umiam, Meghalaya 793103, India Anil Sood, Punjab Remote Sensing Centre, Ludhiana, 141004 India S. S. Ray, Agricultural Resources Group, Space Applications Centre, ISRO, Ahmedabad, 380015 India P. K. Sharma, Punjab Remote Sensing Centre, Ludhiana, 141004 India S. Panigrahy, Agricultural Resources Group, Space Applications Centre, ISRO, Ahmedabad, 380015 India Journal Journal of the Indian Society of Remote Sensing Online ISSN 0974-3006 Print ISSN 0255-660X