ALBERT

Description: With continued climate change, coastal areas are subjected to undesirable situations in the form of sea-level rise and its adverse outcomes like storm surge, flooding and erosion. Being a unique geographic location, low topography, relatively higher population density as well as overwhelming dependence on natural resources, Bangladesh is one of the vulnerable countries exposed to the impacts of global warming and climate change. Bhola in southern Bangladesh represents the world’s most dynamic estuary is potentially vulnerable to accelerated sea level rise and associated  calamities. The present study aims to develop a coastal vulnerability index (CVI) using eight parameters namely (a) geomorphology, (b) slope, (c) relative sea level change rate, (d) mean tide range (e) shoreline erosion and accretion, (f) population (g) bathymetry and (h) coastal flooding which were addressed as the relative risk variable for the study area using geospatial techniques i.e., Remote Sensing and GIS. The aforementioned parameters were ranked on the basis of their potential contribution to physical changes on the coast, as sea-level rises, and the final calculation was done over 263.87 km shoreline by the square root of the mean values of the ranked variables. According to the vulnerability index, about 22 % corresponding to 57.23 km of the entire coast is under very high-risk and another 29 % representing 75.26 km is under high-risk. Again, 25 % encompassing 67.69 km shoreline is at moderate risk and 24 %, that is 63.69 km shoreline is found to be at low risk. The most vulnerable coastal regions are found mainly along the western coast of Char Fasson and northern and southwestern coast of Bhola Sadar of Bhola Island.

Description: Monitoring new changes in cities adjacent to dynamic sand dunes requires precise classifier technique. Unlike traditional techniques of supervised classification which use training sites, the integration of image transformation tasseled cap and automatic feature extraction module based on spectral signatures has provided to be sensitive and realistic techniques with time and cost effective. The proposed module was applied to Al Ain district, United Arab Emirates (UAE). The module consists of four steps in terms of segmentation, thresholding and clustering and computing attributes. The obtained greenness and classified maps were then enhanced by applying a 3 × 3 Sobel filter. The new changes were detected by combining the multi-temporal greenness and classification maps. Accuracy assessment and quantitative analysis were performed using confusion matrix and ground truthing. The results showed significant increasing in urban and agricultural areas from the year from 1990 to 2000 compared with the period of time from the year 2000 to 2006. The image difference showed that the vegetation and building classes had increased 7.58 and 20.28 km 2 respectively. This study showed that image difference and fuzzy logic approach are the most sensitive techniques for detecting new changes in areas adjacent to dynamic sand dunes.

Description: Exploitation of groundwater and frequent failure of monsoons has produced rapid water table depletion in present days. Rainwater is discharged as surface runoff without replenishing the groundwater table. Identification of potential zones for groundwater recharge has become an important event to replenish the groundwater resources. The recharge potential is governed by various features of surface, subsurface and hydrometeorological parameters. Remote sensing technique is found to be very effective tool for the integration of various features of these factors. In this work, an attempt has been made to identify groundwater recharge potential zones in Namakkal district of Tamilnadu state, India. Weighted index overlay analysis (WIOA) was carried out in this study using the software ArcGIS by intergrating the thematic layers of soil, geology, geomorphology, lineament, land use/land cover, rainfall, groundwater level and slope. The groundwater potentials were delineated as Very Good, Good, Moderate to Good, Moderate and Poor zones. Good category of groundwater recharge potential zones constitutes 62.21 % in the study area. GIS based output results were validated with the water level data of Public Works Department (PWD).

Description: To maximize the non-gaussianity in sphered satellite data, many authors have proposed different independent component analysis (ICA) based approaches to classify images by reducing the mixing effect in classes. In multispectral data, few heterogeneous classes have little variation in spectral resolution. Even though, a classified image should exhibit high spectral variance among different classes, while it should be less within a particular class. To improve the classification accuracy in the presence of mixed classes i.e., having similar spectral characteristics, a novel method improved fixed point independent component analysis (IFPICA) is proposed. This method segregates the objects from mixed classes on maximizing the approximation of negentropy, which reduces the effect of quite similar spectral characteristics among different classes. It can easily estimate the independent component of this non-gaussian distribution of data with the help of nonlinearity. Therefore, this nonlinearity helps to optimize the performance of this approach, which minimizes the variance among similar classes. Due to the presence of neural algorithms, it is quite robust, computationally simple and has very fast convergence, in respect to the spectral distributions of satellite images. Hence, this proposed IFPICA approach plays a major role in the classification of satellite images such as road, vegetation, buildings and grassland area. The images used in the study doesn’t have any initial or additive noise, which would obstruct the process of IFPICA algorithm used in the work, therefore preprocessing is not required for noise suppression in this work. The post-processing, e.g., deflation, denoising, filtering, etc. are also not required due to similar reason.

Description: There is no doubt that every classification technique involves advantages and drawbacks like any people thoughts, and as gathering different thoughts aids people to ensure and support for a beneficial decision, the combining of different classification techniques helps to construct strong classification system supplying better results. Ensemble classification techniques are applied today in most image classification fields, and remote sensing image classification is one of those areas giving a growing number of trust with this kind of classification system. In this paper, we used and combined three popular classification techniques using majority vote method. First will be the unsupervised segmentation technique k-means that we follow by SVM (support vector machine) regularization. The last technique will be the supervised neuronal technique LVQ (linear vector quantization). The main contribution of this work is to combine three different kinds of classification methods; the unsupervised k-means technique to get an initial view of the image site that will helps us to choose the best training area for SVM classification system which is the second kind. The last kind is the neuronal supervised LVQ technique. Finally, we used in this specific work a LANDSAT image of a well known south Algerian region which is the Ouargla oasis acquired on December, 20 in 2000.

Description: A cross-calibration model is developed for calibrating the reflectance of HJ-1A/CCD1 sensor to MODIS/Terra sensor in the post-launch era. The vicarious cross-calibration models are initialized using the image pairs of HJ-1A/CCD1 and MODIS/Terra sensors collected from two testing sites, Nam Co Lake and Qinghai Lake, China. Our results show the cross-calibration model produced good performance in calibrating reflectance of HJ-1A/CCD1 sensor to MODIS/Terra sensor, whose mean relative error do not exceed 8.5 % for three visible channels of HJ-1A/CCD1 sensor. Moreover, the theoretical difference between the MODIS/Terra and HJ-1A/CCD1 reflectance are simulated using the 6S code. The results indicate that it would cause a pronounced difference. Therefore, it necessary to reinitialize the coefficients of the water color remote sensing models, when those models with HJ-1A/CCD1 band were applied to the vicarious cross-calibration data.

Description: In the present study, attempt has been made to understand the structural controls on preservation and dynamics of coal fires by establishing spatial relations of these fires with the faults, in the Jharia coal field. This is carried out, using recent distribution of the coal fires mapped from Landsat 8 TIRS data and historical coal fire distribution data derived from earlier studies (2003, 2006 and 2012). The major mapped fires are restricted to five different locales in the coalfield, namely Kujama, Kusunda, Katras-Mudidih, Shatabdi and Nadhkarkee-Jayramdih. Spatial analysis of different coal fire locales with respect to the geological map indicates that the fires may be associated with faults. Areal extension of fires coincide with the trend of faults (NW-SE around Kusunda and E-W around Katras). Further, the temporal preservation of the fires are also controlled by these faults. Fires associated with faults sustain for longer time than the fires that are not in proximity to the faults. This indicates that, these faults may have acted as conduits in supplying oxygen for preserving the coal fires over a period of time. Further, these faults permit the propagation and spread of the fire, through the arealy exposed fractures developed along their trends.

Description: Observations from Aerosol Robotic Network (AERONET) radiometers during 2003–2012 in Beijing are assessed and used to characterize a series of aerosol properties and their variations. Some distinctive features are found. (1) Aerosol optical depth contributed by fine particles at 440 nm (AOD440-F) in spring showed a decreasing trend in the decade; compared with 2003 it decreased about 54 % in the spring of 2012. The annual averaged AOD440-F increased from 2003 to 2007 and decreased after Beijing Olympics (2008). (2) The spectrum of single-scattering albedo (SSA) followed a rapid increasing pattern from 440 to 675 nm and a slow decreasing or neutral pattern from 675 to 1020 nm, but in summer it was nearly wavelength independent. After Beijing Olympics, in addition to summer in which the SSA remained almost unchanged, in the other seasons the SSA increased, this suggests the decline of absorbing aerosols loading. (3) The imaginary part of aerosol refractive indices k(λ) showed a wintertime maximum and summertime minimum associated with the variation of black carbon content. After Beijing Olympics k(λ) significantly decreased. A good correlation between k(λ) and SSA was observed. However, different from the electromagnetic theory predicted, high (low) real part n(λ) sometimes corresponded to low (high) SSA. (4) The volume median radius of fine mode (R vf ) and aerosol optical depth (AOD) showed a good correlation, but the volume median radius of coarse mode (R vc ) and AOD exhibited a minor and negative correlation which different from previous studies.

Description: Snow physical properties, snow cover and glacier facies are important parameters which are used to quantify snowpack characteristics, glacier mass balance and seasonal snow and glacier melt. This study has been done using C-band synthetic aperture radar (SAR) data of Indian radar imaging satellite, radar imaging satellite-1 (RISAT)-1, to estimate the seasonal snow cover and retrieve snow physical properties (snow wetness and snow density), and glacier radar zones or facies classification in parts of North West Himalaya (NWH), India. Additional SAR data used are of Radarsat-2 (RS-2) satellite, which was used for glacier facies classification of Smudra Tapu glacier in Himachal Pradesh. RISAT-1 based snow cover area (SCA) mapping, snow wetness and snow density retrieval and glacier facies classification have been done for the first time in NWH region. SAR-based inversion models were used for finding out wet and dry snow dielectric constant, dry and wet SCA, snow wetness and snow density. RISAT-1 medium resolution scan-SAR mode (MRS) in HV polarization was used for first time in NWH for deriving time series of SCA maps in Beas and Bhagirathi river basins for years 2013–2014. The SAR-based inversion models were implemented separately for RISAT-1 quad pol. FRS2, for wet snow and dry snow permittivity retrieval. Masks for layover and shadow were considered in estimating final snow parameters. The overall accuracy in terms of R 2 value comes out to be 0.74 for snow wetness and 0.72 for snow density based on the limited ground truth data for subset area of Manali sub-basin of Beas River up to Manali for winter of 2014. Accuracy for SCA was estimated to be 95 % when compared with optical remote sensing based SCA maps with error of ±10 %. The time series data of RISAT-1 MRS and hybrid data in RH/RV mode based decompositions were also used for glacier radar zones classification for Gangotri and Samudra Tapu glaciers. The various glaciers radar zones or facies such as debris covered glacier ice, clean or bare glacier ice radar zone, percolation/refreeze radar zone and wet snow, ice wall etc., were identified. The accuracy of classified maps was estimated using ground truth data collected during 2013 and 2014 glacier field work to Samudra Tapu and Gangotri glaciers and overall accuracy was found to be in range of 82–90 %. This information of various glacier radar zones can be utilized in marking firn line of glaciers, which can be helpful for glacier mass balance studies.

Description: In this paper, we propose an adaptive filtering technique for Synthetic Aperture Radar (SAR) images. A new windowing technique is introduced where the total window is divided into five equal sized overlapping sub-windows. The pixel to be filtered is a part of each of these sub-windows. A weighted mean of all sub-windows is computed for the pixel under consideration. The weights are accounted from a measure of heterogeneity calculated for each sub-windows. The filter is able to adapt automatically and adjust the speckle suppression strength based on local statistics. This allows the filter to preserve edges while strongly suppressing speckle over homogeneous areas. The proposed filter was compared with some well known SAR filtering techniques in terms of speckle suppression and edge preservation ability. Several experiments were performed on datasets acquired from both air-borne and space-borne SAR platforms. Some well known indices were used for quantitative comparison with other filters. Among the filters compared, the proposed filter shows good speckle suppression ability while still exhibiting reasonable edge preservation ability.

Description: Synthetic aperture radar (SAR) is a day and night, all weather satellite imaging technology. Inherent property of SAR image is speckle noise which produces granular patterns in the image. Speckle noise occurs due to the interference of backscattered echo from earth’s rough surface. There are various speckle reduction techniques in spatial domain and transform domain. Non local means filtering (NLMF) is the technique used for denoising which uses Gaussian weights. In NLMF algorithm, the filtering is performed by taking the weighted mean of all the pixels in a selected search area. The weight given to the pixel is based on the similarity measure calculated as the weighted Euclidean distance over the two windows. Non local means filtering smoothes out homogeneous areas but edges are not preserved. So a discontinuity adaptive weight is used in order to preserve heterogeneous areas like edges. This technique is called as discontinuity adaptive non local means filtering and is well-adapted and robust in the case of Additive White Gaussian Noise (AWGN) model. But speckle is a multiplicative random noise and hence Euclidean distance is not a good choice. This paper presents evaluation results of using different distance measures for improving the accuracy of the Non local means filtering technique. The results are verified using real and synthetic images and from the results it can be concluded that the usage of Manhattan distance improves the accuracy of NLMF technique. Non local approach is used as a preprocessing or post processing technique for many denoising algorithms. So improving NLMF technique would help improving many of the existing denoising techniques.

Description: Chlorophyll fluorescence is an indicator of plant photosynthetic activity and has been used to monitor the health status of vegetation. Several studies have exploited the application of red/far-red chlorophyll fluorescence ratio in detecting the impact of various types of stresses in plants. Recently, sunlight-induced chlorophyll fluorescence imaging has been used to detect and discriminate different stages of mosaic virus infection in potted cassava plants with a multi-spectral imaging system (MSIS). In this study, the MSIS is used to investigate the impact of drought and herbicide stress in field grown crop plants. Towards this control and treatment groups of colocasia and sweet potato plants were grown in laterite soil beds and the reflectance images of these crop plants were recorded up to 14-days of treatment at the Fraunhofer lines of O 2 B at 687 nm and O 2 A at 759.5 nm and the off-lines at 684 and 757.5 nm. The recorded images were analyzed using the Fraunhofer Line Discrimination technique to extract the sunlight-induced chlorophyll fluorescence component from the reflectance images of the plant leaves. As compared to the control group, the chlorophyll fluorescence image ratio ( F 687 / F 760 ) in the treatment groups of both the plant varieties shows an increasing trend with increase in the extent of stress. Further, the F 687 / F 760 ratio was found to correlate with the net photosynthetic rate (Pn) and stomatal conductance (g s ) of leaves. The correlation coefficient ( R 2 ) for the relationship of F 687 / F 760 ratio with Pn were found to be 0.78, 0.79 and 0.78, respectively for the control, herbicide treated and drought treated colocasia plants, while these were 0.77, 0.86 and 0.88, respectively for sweet potato plants. The results presented show the potential of proximal remote sensing and the application F 687 / F 760 fluorescence image ratio for effective monitoring of stress-induced changes in field grown plants.

Description: Information on highways is an essential input for various geospatial applications, including car navigation, forensic analysis on highway geometries, and intelligent transportation systems. Semi-automatic and automatic extractions of highways are critical for the regular updating of municipal databases and for highway maintenance. This study presents a semi-automatic data processing approach for extracting highways from high-resolution airborne LiDAR height information and aerial orthophotos. The method was developed based on two data sets. Experimental results for the first testing site showed that the accuracy of the proposed method for highway extraction was 74.50 % for completeness and 73.13 % for correctness. Meanwhile, the completeness and correctness for the second testing site were 71.20 and 70.72 %, respectively. The proposed method was compared with an object-based approach on a different data set. The accuracy for highway extraction of the object-based approach was 64.29 % for completeness and 63.11 % for correctness, whereas that of the proposed method was 67.14 % for completeness and 65.08 % for correctness. This research aims to promote semi-automatic highway extraction from LiDAR data and orthophotos by proposing a new approach and a multistep post-processing technique. The proposed method provides an accurate final output that is valuable for a wide range of geospatial applications.

Description: Immovable cultural properties constituting cultural heritage centre upon regions that have the level of natural features required for basic human needs. Such regions constitute a space with characteristics of an eco-cultural subregion and they do not have any administrative or geographical boundaries. Aims of this study are to introduce the concept of eco-cultural subregion, which is the holistic expression of natural features together with cultural heritage, to the literature; to present a method in identifying geographical boundaries of subregions; and to formulate a classification system. The research was conducted in the Phrygian Valley, which is one of the most important centres of ancient Phrygians in Western Anatolia, using a method based on GIS-MCDM (Geographic Information System-Multiple Criteria Decision Making). The selected MCDM technique was Simos Procedure. Natural features and cultural heritage were considered together and the entire Phrygian Valley was approached as both an ecological and a cultural region. Ecological features were analysed using 7 main and 17 subfactors, while cultural heritage was analysed using 2 main factors and 15 subfactors. Simos Procedure was employed in the assignment of weight for main factors and subfactors. First 17 ecological subregions with similar characteristics under 4 levels were identified. Considering their cultural heritage values, a classification system was formulated and eco-cultural subregions were expressed in level/degree in this system. In consequence of this procedure boundaries of eco-cultural subregions were drawn and mapped. This classification also enabled to identify the concept of eco-cultural subregion which is not found in the literature. It is expected that eco-cultural subregions will provide to discuss ecological features and cultural heritage holistically in conservation and planning studies.

Description: Detailed and enhanced land use land cover (LULC) feature extraction is possible by merging the information extracted from two different sensors of different capability. In this study different pixel level image fusion algorithms (PCA, Brovey, Multiplicative, Wavelet and combination of PCA & IHS) are used for integrating the derived information like texture, roughness, polarization from microwave data and high spectral information from hyperspectral data. Span image which is total intensity image generated from Advanced Land observing Satellite-Phase array L-band SAR (ALOS-PALSAR) quad polarization data and EO-1 Hyperion data (242 spectral bands) were used for fusion. Overall PCA fused images had shown better result than other fusion techniques used in this study. However, Brovey fusion method was found good for differentiating urban features. Classification using support vector machines was conducted for classifying Hyperion, ALOS PALSAR and fused images. It was observed that overall classification accuracy and kappa coefficient with PCA fused images was relatively better than other fusion techniques as it was able to discriminate various LULC features more clearly.

Description: In the present study, parameters derived from Ice, Cloud, and land Elevation Satellite/Geoscience Laser Altimeter System (GLAS) full waveform were used for land cover classification in western part of Doon valley, Uttarakhand, India. Three parameters, viz, height , front slope angle ( afslope ) and canopy return ratio ( rCanopy ) were extracted from the returned full waveform signals. k-means (KM), partitioning around medoids (PAM), and fuzzy c-means (FCM) with different cluster sizes were used for classifying the land cover types with the help of GLAS-derived parameters. Among the clustering methods, KM performed the best. The overall accuracy (89.41 %) of all methods were quite significant with cluster size three i.e. with three classes forest, mango orchard and other class including agriculture, barren/fallow land, settlement, dry river bed, etc. The accuracy of the PAM (60 %) and the FCM (68.4 %) decreased drastically at four clusters with the separation of agriculture from barren/fallow land. The accuracy of the PAM and the FCM further decreased with increase in the number of clusters whereas KM showed reliable results for all clusters. KM with five clusters was able to distinguish five different land covers, viz, forest, mango orchard, agriculture and barren/fallow land and other class including settlement, dry river bed, etc. with an overall classification accuracy of 72.93 %. The study presents a method for classifying land cover types using GLAS full waveform data.

Description: The single-frequency Radar Imaging Satellite-1 (RISAT-1) Synthetic Aperture Radar (SAR) (launched in April, 2012) is designed to provide a wide variety of imaging options compared to the prevailing SAR missions. The scale of the calibration task for the RISAT-1 mission is increased with the inclusion of multiple beams and polarizations (single, dual and quad) along with circular/hybrid polarization capabilities, for the first time from space. Significant efforts have been made in the evolution of SAR processor for the provision of radiometrically and geometrically calibrated products to the users. After the commissioning phase, RISAT-1 was put into routine operations in Oct. 2012. Since then extensive beam coverage of Amazon rainforest in various modes was carried out to evaluate the calibration parameters and SAR processor performance. Subsequently, the processor version 1.2.0 was improved to version 1.3.0 and with further modifications to version 1.4.0. This article details the analysis carried out at each processor version, by quality evaluation team, to quantify the parameter behaviour and to give feedback on the processor performance to the SAR processor team. The various radiometric quality parameters evaluated and monitored are shape of the antenna pattern, gamma-naught, backscattering coefficient (sigma-naught), speckle index, radiometric resolution and Noise Equivalent Sigma Zero (NESZ). Data studied for the analysis is in Single Look Complex (SLC) for stripmap mode and multilook (Ground range) for ScanSAR and stripmap mode in dual and circular polarizations. The performance of version 1.4.0 for all the beams (near-to-far) is found to be consistent and meeting the mission specifications for the product.

Description: Kachchh basin is a Mesozoic rift basin under the influence of many active faults. This in turn gives rise to marked structural complexity and associated seismicity. Remote Sensing study of geomorphic evidences of these faults has been carried out using satellite images and is validated using morphometric analysis and digital elevation model data. Satellite images not only help in identifying expression of active faults and active tectonics on a macroscopic scale, but also provide the image characteristics of active faults directly. A few faults along with nature of lateral displacement could be identified from the Kachchh area. Morphometric analysis viz., sinuosity, asymmetry factor and hypsometry indicated affected streams and drainage basins due to fault activity.

Description: The deformed sedimentary sequence exposed in the core of Precambrian Kaladgi Basin, North Karnataka India, has given rise to prominent regional basinal structures with intervening anticlines. Deformation ceases away from the core part. The basinal structures are recognised on satellite images, aerial photographs and toposheets because of resistant segmented ridges of quartzite. A closure view of Bhuvan images around Yadwad village, North Karnataka, the core portion of Kaladgi Basin also revealed presence of folded geometry in the older carbonates of Bagalkot Group seen along the margins of the regional folds. Though folding in the sequence exposed in the region around Yadwad and further west is described by many authors, no detailed analysis of structures is available in published literature. An attempt is made here to map, analyse and integrate the structures in carbonates of Yadwad region on different scales observed in the field to find out their possible origin. It is suggested that the structures are syntectonic in origin formed in the presence of circulating fluids in high pressure and low temperature during the post depositional phase the Bagalkot sequence in which gravity played an important role in the formation of regional structures. The sequence also has recorded low grade of metamorphism as seen from the thin section observations.

Description: In India, Jharia Coalfield (JCF) has one of the densest congregations of surface-subsurface coal fires known worldwide. Systematic investigation and quantification of actual scenario of coal fires in JCF is always necessary to plan sustainable mining, industrial growth and environmental remediation on a long term basis. The present approach involves evaluation and mapping of coal fire using ASTER (Advanced Spaceborne Thermal Emission and Reflection) data. Mapping reveals that the area located around western, eastern and south-eastern parts of JCF covering territories of Shatabdi opencast, Barora; Sijua opencast; Godhar colliery; Kusunda; Bokapahari; Kujama and Lodna are under intense fire with cumulative coverage of 6.23 km 2 . The ASTER derived Land Surface Temperature (LST) of the anomalous areas have been subsequently validated by the field observations, carried out in JCF in February, 2010. The methodology adopted in the present study would provide precise evaluation and monitoring of coal fire in Jharia.

Description: Snow cover monitoring in the Qinghai-Tibetan Plateau is very important to global climate change research. Because of the geographic distribution of ground meteorological stations in Qinghai-Tibetan Plateau is too sparse, satellite remote sensing became the only choice for snow cover monitoring in Qinghai-Tibetan Plateau. In this paper, multi-channel data from Visible and Infrared Radiometer (VIRR) on Chinese polar orbiting meteorological satellites Fengyun-3(FY-3) are utilized for snow cover monitoring, in this work, the distribution of snow cover is extracted from the normalized difference snow index(NDSI), and the multi-channel threshold from the brightness temperature difference in infrared channels. Then, the monitoring results of FY-3A and FY-3B are combined to generate the daily composited snow cover product. Finally, the snow cover products from MODIS and FY-3 are both verified by snow depth of meteorological station observations, result shows that the FY-3 products and MODIS products are basically consistent, the overall accuracy of FY-3 products is higher than MODIS products by nearly 1 %. And the cloud coverage rate of FY-3 products is less than MODIS by 2.64 %. This work indicates that FY-3/VIRR data can be reliable data sources for monitoring snow cover in the Qinghai-Tibetan Plateau.

Description: In this research paper, a new framework is proposed to increase the total number of correct matches for stereo correspondence using tri-stereo images. The research work investigates some of the less explored properties of Disparity Space Image (DSI), and considers the local maxima in addition to the global maximum of the cost function and propose a new tri-stereo matching method as Modified Dual Winner Takes All (MDWTA) using edges of disparity space image. Conventionally using a single DSI from a stereo pair optimization techniques are applied to get the disparity, whereas the proposed approach uses Winner Takes All (WTA) approach using two DSIs from a triplet. The dual property of DSI that is the manner in which it stores cost for rows and columns for forward and reverse matching is introduced. This property is applied to check consistency for forward and reverse matching in a single pass, which gives initial correct matches. Next a left-centre-right consistency check is applied to discard inconsistent disparities obtained from these three views. Subsequently to obtain disparity for gaps thus generated a rule is formulated using local maxima, edges and adjacent global maximum to find correct disparity. Evaluation of the results obtained is carried by comparing them with block matching WTA method and other recent methods such as, Dynamic Programming (DP) and Semi-Global Matching (SGM) applied on images from Terrain Mapping Camera of Chandrayaan-1 and PRISM sensor of the ALOS mission. Methodology proposed is also verified using standard Middlebury stereo dataset. Experimental results show that the proposed method gives an increased number of correct matches by 10–15 % as compared to basic WTA and DP. However the results obtained are better to SGM for certain regions and features.

Description: Image fusion techniques are widely used for remote sensing data. A special application is for using low resolution multi-spectral image with high resolution panchromatic image to obtain an image having both spectral and spatial information. Alignment of images to be fused is a step prior to image fusion. This is achieved by registering the images. This paper proposes the methods involving Fast Approximate Nearest Neighbor (FANN) for automatic registration of satellite image (reference image) prior to fusion of low spatial resolution multi-spectral QuickBird satellite image (sensed image) with high spatial resolution panchromatic QuickBird satellite image. In the registration steps, Scale Invariant Feature Transform (SIFT) is used to extract key points from both images. The keypoints are then matched using the automatic tuning algorithm, namely, FANN. This algorithm automatically selects the most appropriate indexing algorithm for the dataset. The indexed features are then matched using approximate nearest neighbor. Further, Random Sample Consensus (RanSAC) is used for further filtering to obtain only the inliers and co-register the images. The images are then fused using Intensity Hue Saturation (IHS) transform based technique to obtain a high spatial resolution multi-spectral image. The results show that the quality of fused images obtained using this algorithm is computationally efficient.

Description: Estimating the water budgets of large basins is a challenge because of the lack of data and information. It becomes more complicated in endorheic basins that consist of separate land and water phases. The application of remotely-sensed data is one solution in this regard. The present study addresses this issue and develops a modeling framework to evaluate a water budget based on remotely-sensed data for endorheic basins. To explore the methodology, Lake Urmia basin was selected as a case study. The lake water level has declined steeply since 1995 and stakeholders have agreed to allocate 3100 MCM of water per year to the lake. This makes it necessary to monitor river inflow into the lake to fulfill the agreement. Gauging stations have been employed around the lake, but they could not account for shortages such as water uptake below the stations. To do this, separate water budgets for the water body and the land were required. More specifically, it was necessary to estimate actual evapotranspiration ( ET a ) from freshwater ( E f ) and saltwater ( E s ) estimated using the SEBAL model. Different methods were applied to estimate soil moisture, groundwater exploitation, and surface-groundwater inflow into the lake. A comparison of the observed and estimated amounts showed good agreement. For instance, the coefficient of determination for the observed/reported and estimated ET a and E f were 0.83 and 0.84, respectively. The average annual inflow was estimated to be 2.2 BCM/year for 2002–2008 using the RS model, which is about 84 % of the total inflow from the last recording stations before the lake and shows influence of water exploitation after these stations. Future study should focus on increasing temporal and spatial resolution of the method

Description: The studies on forest cover change can reveal the status of forests and facilitate for its conservation planning. Idukki is the largest district in the state of Kerala having a total geographical area of 5019 km 2 . The objectives of the present study are to map forest cover in Idukki district using multi-temporal remote sensing data (1975, 1990, 2001 and 2012) and topographical maps (1925), to analyze the trends in deforestation and land use changes. Overall statistics for the period of 1925 indicate that about 4675.7 km 2 (93.2 %) of the landscape was under forest. The forest cover in 2012 was estimated as 2613.4 km 2 (52.1 %). Recently, due to the implementation of policies and protection efforts, the rate of deforestation was greatly reduced. The commencement of hydroelectric projects during 1925–1990 responsible for an increase of area under water bodies by inundating other land uses. The long term analysis shows agricultural area been decreasing and commercial plantations been increasing in the district. There has been a significant increase in the area of plantations from 1236.2 km 2 (1975) to 1317.3 km 2 (2012).

Description: One of the greatest challenges for optical satellite images applications is the presence of shadows. In stereo correspondence of images for example, shadows obstruct the correct extraction of objects that degrades the quality of stereo matching results. The aim of this research is to present a new, simple and efficient shadow detection and removal approach. The proposed approach first detects shadows by operating directly in red, green and blue color space using a new method including spectral and spatial properties of shadow. Secondly, shadows are removed by supplying more light to the shadow’s region using an energy minimization concept. The edges of shadows are removed or attenuated using some filters. The experimental results show that the proposed shadow detection and removal approach can generate accurate and efficient recovered pairs of satellite images. Furthermore, we demonstrate its reliability on the application of a Hopfield neural matching by comparing the correspondence of images before and after shadow removal.

Description: The normal compositional model (NCM) is a well-known and powerful model in hyperspectral unmixing which represents endmembers as independent Gaussian vectors to capture endmember variability. However, the assumption of independent endmembers diminishes the model accuracy because the high degree of correlation between endmembers of a scene and identical sources of variability demonstrate that the endmembers are dependent. This paper proposes a new hyperspectral unmixing algorithm which represents endmembers using dependent Gaussian vectors to estimate abundance fractions. To overcome the higher complexity caused by dependence assumption, this algorithm introduces new independent Gaussian vectors named Base Vectors to represent different endmembers by a weighted linear combination. Also, the proposed unmixing algorithm uses maximum likelihood method to estimate weight coefficients of Base Vectors which are used to represent mixed pixel. Finally, abundance estimation can be done using the new representation for endmembers and mixed pixel. The proposed algorithm is evaluated and compared with other state-of-the-art unmixing algorithms using simulated and real hyperspectral images. Experimental results demonstrate that the proposed unmixing algorithm can unmix pixels composed of correlated endmembers in hyperspectral images in the presence of spectral variability more accurately than previous methods.

Description: A phytoplankton bloom was monitored in coastal waters of Bay of Bengal and its influence in water column properties was investigated. Significant draw down of CO 2 was noted within the vicinity of the bloom associated with high chlorophyll biomass. Microscopic analysis revealed diatoms as the dominant population. Skeletonema costatum a diatom, reached cell density of 36,898 cells l −1 within the bloom. The lowest surface pCO 2 observed was 287 µatm at the southern end of the transect covarying with surface chlorophyll of 1.090 µg l −1 . At the northern end the surface pCO 2 went as low as 313 µatm. The pCO 2 levels below the mixed layer increased twice of that of surface value (~600 µatm). The chlorophyll values observed by Ocean Colour Monitor-2 were modestly related with the in situ measurements. The primary productivity derived from growth rate, assimilation number and maximum surface chlorophyll was 160.6 mg C m −2  day −1 leading to a modest sequestration ~of 0.08 Gg of carbon per day by the surface waters. Our observations reflects the potential role of diatom blooms on coastal carbon dynamics therefore should be carefully monitored in realm of anthropogenic changes.

Description: Accurate simulation of rice yield is very important and vital for agriculture and food security. This study has analyzed the applicability of the RS-P-YEC (the remote-sensing-photosynthesis-yield estimation for crops) model for the rice yield simulation of the Middle and Lower Reaches of Yangtze River (MLRYR) in China. The simulated rice yield was compared with the actual statistical dataset, so as to obtain the accuracy of the model results. The results showed that the correlation coefficients ( R ) between simulated rice yield and statistical data is 0.708 ( P  〈 0.01), the average relative errors were 9, 6.5, 7.2 %, and the root mean square errors were 777.5, 606.4, 693.4 kg/ha in 2007, 2008 and 2009, respectively. It indicated that the RS-P-YEC model can be used to estimate rice yield in the MLRYR region of China.

Description: In this work, the genetic algorithm is utilised for automatic detection of oil spills under wind speed conditions larger than 20 ms −1 . The procedure is implemented using sequences of RADARSAT-2 SAR ScanSAR Narrow single beam data acquired in the Gulf of Mexico. The study demonstrates that implementing crossover allows for generation of accurate oil spills pattern. This conclusion is confirmed by the receiver–operating characteristic (ROC) curve. The ROC curve indicates that the existence of oil slick footprints can be identified with the area under the ROC curve and the no-discrimination line of 85 %, which is greater than that of other surrounding environmental features. In conclusion, the genetic algorithm can be used as a tool for the automatic detection of oil spills and the ScanSAR Narrow single beam mode serves as an excellent sensor for oil spill detection and surveying under wind speed larger than 20 ms −1 .

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: Cloud masking is one of the primary and important steps in the atmospheric correction procedure in particular to coastal ocean waters. Cloud masking for ocean colour data processing is based on the assumption that the water reflectance is close to zero in the near-infrared (NIR). This is valid over the open ocean, but coastal waters may have a higher NIR reflectance due to the presence of suspended sediment concentration and non-maritime aerosols. As the result, more scattering is observed in the water leaving radiance and hence sometimes the cloud-free pixels are also classified as clouds, leading to data loss. In this present study, we have analysed cloud albedo value for masking the clouds using the default cloud masking threshold (0.027) set for processing the global ocean colour sensors like SeaWiFS and MODIS and now OCM-2 in the SeaWiFS Data Analysis System (SeaDAS). The default cloud masking value of SeaDAS over masks the clouds and sometimes the turbid coastal water pixels are also getting masked when implemented over the COM-2 imageries. Hence, an exercise has been carried out to set the cloud albedo value for OCM-2 sensor and the albedo values were tested with the lower and higher values ranging from 0.015 to 5.0 as the threshold and from the analysis it is found that 0.07 is the optimum value for Ocean Colour Monitor-2 (OCM-2) sensor for masking the clouds. Using this threshold, maximum valid ocean pixels are achieved compared to the default cloud albedo mask with the value 0.027. This has been checked with both the Local Area Coverage (LAC) and Global Area Coverage (GAC) products of OCM-2 and the results were also compared with the MODIS data. Hence, it is recommended that 0.07 is the optimum cloud masking threshold for OCM-2 sensor.

Description: Hydrothermal alteration zones have significant role in prospecting of porphyry mineral deposits. In this research, Image processing techniques were applied on the digital subset ETM + data covered the Siah-Jangal area. These techniques generated several products of enhanced satellite imagery, such as colour composite images, ratio images and principal component analysis images. These techniques have been successfully used in mapping of hydrothermal alteration and mineralization zones in the study area. The colour composite and band ratio methods show very clearly the hydrothermal altered zones of clay minerals and iron oxides in and around the Siah-Jangal area. The principal component analysis using the Crosta technique also enabled us to represent undoubtedly the altered hydroxyl and iron-oxide mineral zones as well. The Crosta method has been used on selected 6 (standard method of PCA), 4 and 3 bands. There is no significant difference of the hydroxyl images in the Band ratio and PC methods. But the best results, in terms of mapping the distribution of iron oxide alteration, were obtained using the band ratio and Crosta methods on 3 bands. The results indicate that methods is promising for identifying alteration zones and can assist exploration geologists to find new prospects of porphyry copper and gold deposits in the other virgin regions before costly detailed ground investigations.

Description: Various satellite data are currently used to detect ships on the sea surface. However, no study on the use of Gaofen-1 (GF-1) data to monitor ships on the surface of inland rivers has been reported. Therefore, we proposed a method to extract inland river-surface ships from GF-1 imagery. The Normalized Differential Water Index was calculated to enhance the contrast between water and non-water areas after the preprocessing procedure. The multi-resolution segmentation method and object-oriented classification rule sets were used to detect the ships in the image. Results show that most of the ships, whose length-to-width ratio ranges from 3.0 to 7.2, could be identified correctly regardless of their size. The results also indicate that detecting ships on inland rivers using GF-1 imagery is feasible.

Description: Terrestrial Laser Scanner (TLS) is an important device to provide three-dimensional geometric information with respect to known reference coordinates system. The data from TLS are often called point clouds due to their high density. They are unstructured and do not have any explicit information about the object, except 3D positional and intensity information to each point. Data segmentation and feature extraction are fundamental problems in the point cloud processing step. These are important prerequisites for object recognition and environment understanding. This paper describes a methodology to use the geometrical information of the scanned object, such as a normal vector to extract and segment object edges. The edge based segmentation technique will be studied and improved. The proposed method is programmed and used to extract different region boundaries for two real TLS data, and the quality of the results shows the effectiveness of the proposed algorithm.

Description: Snow depth parameter inversion in the farmland using passive microwave remote sensing is of great significance to the agricultural production in Northeast China. Firstly, the Helsinki University of Technology (HUT) snow emission model was validated in the farmland based on microwave radiation imager (MWRI) onboard FengYun-3B satellite (FY-3B). The results showed that there was a big difference between the brightness temperature of HUT model simulation and MWRI for 18.7 GHz horizontal polarization (18.7 H) and 36.5 GHz horizontal polarization (36.5 H). To improve HUT model, the empirical parameter in the model was localized. Then the localized HUT (LHUT) model was built, where the extinction coefficient was calculated by the new extinction coefficient formula. Next, LHUT model was validated based on MWRI data and compared with HUT model. The results showed that LHUT underestimates slightly the brightness temperature with 0.91 and 4.19 K for 18.7 and 36.5 H respectively, and LHUT is superior to HUT model. Finally, the genetic algorithm (GA) was used to invert snow depth based on LHUT. The results showed that snow depth was underestimated with 6.79 cm based on LHUT. The inverted snow depth based on LHUT model is in better agreement with the measured snow depth.

Description: Extraction of the magnitude and direction of deformation is an important issue. Deformation is typically extracted by comparing data related to a finite number of control point. However, such a method can only extract the magnitude of deformation, but not the direction of deformation. In this paper, the improved ICP model is exploited for extracting the deformation. The main idea of this method is the construction of improved ICP and the determination of the relationship between extraction of six deformation parameters and local matching. This proposed deformation extraction method is particularly suited for scenarios where the deformation area is 3D rigid-body. The performance of the proposed method is extensively evaluated numerically and experimentally according to the 3D rigid-body board deformation. It is important to note that the conclusions were achieved under non-ideal conditions, e.g. using non-calibrated TLS point cloud and non-special targets. Besides the simulation experiment, the validation results achieved on bridge test site are briefly discussed.

Description: In order to determine current tree condition and predict future growth using LiDAR data, tree height, diameter at breast height, diameter 4 m above the ground, tree volume, tree volume growth, diameter at breast height growth and diameter growth 4 m above ground for individual trees were estimated from various crown height metrics and measurements obtained using a small footprint airborne laser scanner flown over a planted forest in Japan. Ground-truth values for tree height, diameter at breast height, diameter 4 m above ground, tree volume, and volume and diameter growth were collected. The actual values were compared with the laser-derived crown height metrics, including: percentiles, maximum, mean, coefficient of variation and crown density, all for the first and last crown height laser pulses. The regressions explained 75–79 % of the variability in ground-truth tree height, diameter at breast height, diameter 4 m above ground and tree volume. Cross-validation of the regressions revealed standard deviations of the differences between predicted and ground-truth values of 1.30 m (6.7 %), 5.2 cm (22.2 %), 3.8 cm (18.7 %) and 0.22 m 3 (43.3 % of ground-truth mean), respectively. The regressions also explained 69–77 % of the variability in ground-truth averages. Cross-validation of the regressions revealed standard deviations of the differences between predicted and ground-truth values of 0.15 cm yr −1 (43.7 %), 0.1 cm yr −1 (31.0 %) and 0.008 m 3  yr −1 (58.5 % of ground-truth mean), respectively. The study confirms that it may be possible to predict individual tree growth based on LiDAR data.

Description: The main objective of this study is to integrate satellite derived forest burnt areas over a multi-decadal scale to understand fire frequencies in Silent Valley National Park, Kerala, India. There are nine vegetation types distinguished i.e. wet evergreen forest, shola, semi evergreen forest, moist deciduous forest, dry deciduous forest, savannah, reed brakes, scrub and grasslands using Resoursesat-2 LISS III data. The spatial extent of burnt area found to be highly varying and has been estimated as 7.95 km 2 in 1973, 17.19 km 2 in 1985, 5.51 km 2 in 1989, 9.55 km 2 in 1990, 4.55 km 2 in 1994, 6.89 km 2 in 1996 and 5.80 km 2 in 1999. Analysis of burnt area coverage for recent decade based on 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013 and 2014 reveals fire affected area as 8.10, 4.20, 12.21, 5.52, 3.19, 0.62, 1.81, 2.03, 3.00 and 1.36 km 2 area respectively. Spatial analysis indicates, an area of 47.51 km 2 (19.5 % of total vegetation cover) affected by varying frequencies of fires from 1973 to 2014. High frequencies of fires have been observed in dry deciduous forests and grasslands. Buffer zone of national park has undergone frequent fires which may degrade climax forest vegetation and affects the regeneration and adaptability of indigenous species. Spatial tracking reveals 58.1 % grid cells were affected by forest fires. Overall, there is a declining trend of forest fires indicates management effectiveness in Silent Valley National Park.

Description: Fractional vegetation cover (FVC) is an important ecological parameter and indication for ecological environment change. As the main part of Qinghai-Tibet Plateau, Qinghai Province is a more sensitive area of climate change and fragile zone of ecology environment. In this study, multi-temporal satellite data were analyzed to estimate FVC from 2000 to 2013 in Qinghai Province using no-dense & dense model. When 1 〈 Leaf Area Index (LAI) 〈 3 , the no-dense model was used and dense model was used under other conditions. This study explored the application of MODIS data in FVC estimation. Normalized Difference Vegetation Index (NDVI) data, Fraction of Photosynthetically Active Radiation (FPAR) and Leaf Area Index (LAI) products were acquired from MOD13A1 and MOD15A2. FVC highly related to LAI, light extinction coefficient and FPAR was estimated using no-dense & dense model. Slope map was calculated by multiple years FVC results. The increased tendency of FVC in this area illustrated the improving ecological environment. However, ecological and environmental problems of Qinghai Lake are still needed to be solved.

Description: High local groundwater flow into rock tunnels through the fracture zones is an important factor influencing excavation time and costs. Therefore, detection of geological features for prediction of high local groundwater flow is essential before excavation of tunnels. Observations during excavation suggest that open fractures in the limestone layers of Li3 unit are the main reason for the high water inflow. The limestone thick layers outcrop in form of cliffs constituting the upper parts of the cuesta. Moreover, Li3 unit as a recharge source of the springs has the highest potential for groundwater inflow throughout the tunnel. To reduce the costs and time of field works, remote sensing investigations can be a good solution. The main aim of this study is to present a methodology for digital processing of ASTER satellite imagery data to detect mall-scale fractures of cuesta-forming limestone layers. Experimental results provided for the Long Zagros tunnel show that, fractures in direction of cliff limestone unites can be detected using colour and textural characteristics of the ASTER image. Results also show that, Li3 unite can be detected on the image based on lack of small scale folding, have a vertical outcrop, interruption in edge direction and without clay cover.

Description: The study was carried out to evaluate the global soil moisture product of Advanced Microwave Scanning Radiometer-2 (AMSR-2), Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E), Soil Moisture and Ocean Salinity (SMOS) and Tropical Rainfall Measuring Mission’s (TRMM) Microwave Imager (TMI) in central region of India for monsoon period of 2011 and 2013. The evaluation was done using 100, 75 in-situ soil moisture measurements of 2011, 2013 respectively. The in-situ measurements were spread across ~10,000 sq.km area which is roughly equal to 12 pixels of SMOS, AMSR-2 AMSR-E and TMI data. The result shows reasonable correlation of r 2  = 0.506, 0.467 between SMOS and in-situ soil moisture for 2011, 2013 respectively. TMI exhibits good correlation of r 2  = 0.55 in 2013 but failed to do the same in 2011. AMSR-2 soil moisture products shows moderate correlation r 2  = 0.32 in 2011 but poorly correlated in 2013.

Description: On the subject of informal sprawl in recent Egypt, this research takes Greater Cairo Region (GCR) as a case and puts forward that informal urban sprawl can be estimated from spatial disposition, informal sprawl efficacy and outer influences; and then evolves a geo-spatial indicators system for quantifying sprawl. So as to estimating these indicators, various data sources are selected, including land use maps, digitized map of the highways and town centers, and population statistical data, etc. GIS spatial analysis methods are used to spatialize these indicators into 120 m × 120 m cells. In addition, an incorporated sprawl index (ISI) is estimated by weight sum of these indicators. The results demonstrated that geo-spatial indicators system can captivate most of the exemplary attributes and interior differentia of urban sprawl. Building land in Cairo has kept quick growing with considerable amount of low efficacy and dysfunctional spatial disposition. The subsequent sprawl attributes are determined by each indicator’s exemplary spatial disposition of urban sprawling, conspicuous fragmentation and unevenness of landscape due to ineffective implementation of land use planning, improper pattern of exemplary discontinuous development, strip development and leapfrog development, low efficacy of sprawl, low development density, low population density output in newly developed areas; and unfavorable influences on agriculture and environment and population life.

Description: The article provides analysis of the existing methods of situational adaptation and spatial data models used in ground-based and on-board geographic information systems (GIS); examines their advantages and disadvantages. A new method based on a model in which spatial data are represented as a set of successive approximations is suggested; it allows to adapt them by sampling at the required level of detail. The results of the simulation based on the data generated using the proposed method, are shown. The efficiency of its application for on-board geographic information systems and solutions is proved. It is concluded that the method is suitable to use for solving real-time resource-intensive GIS-tasks on the on-board equipment with limited computational power.

Description: In this study, we used an object-oriented method for merging pixel-based classification and image segments to get an optimal classification result for an urban land-cover classification which is one of the important applications of Polarimetric SAR (PolSAR) remote-sensing. Because of the nature of PolSAR images, various features can be extracted and used in a classification. To achieve an improved classification accuracy, an optimal subset of features should be used. For this purpose, we used a class-based multiple classifier with a support vector machine (SVM) as a pixel-based classification with the class accuracy as the criterion in feature selection. We proposed the SVM margin as a new distance criterion for feature selection. In addition, to overcome the degradation of pixel-based classification results due to presence of speckle noise in PolSAR images, thematic features were used in image segmentation. In general, the proposed method consisted of three steps: feature selection, pixel-based classification, and polarimetric spatial classification. The pixel-based classification result was merged with a set of segments that were obtained from multi-resolution segmentation and the overall accuracy of the results was evaluated. The objectives of the study were to improve the accuracy of classification and to introduce margin as the new criterion for feature selection. Our results showed that the overall accuracy of the proposed method (90.07 %) was improved compared with the single SVM classifier (80.46 %) and pixel-based multiple classifier (83.61 %). Lastly, the SVM margin distance as well as class accuracy could be considered as appropriate criteria in feature selection.

Description: All pixels in satellite data are essentially mixture pixels that composed of many sub-pixels with different bio-optical properties due to the continuum of variation of water dynamic conditions and the intrinsic mixed nature of water body. Generally, it is more possible to successfully find the “clear water” from data with high spatial resolution than these with low spatial resolution, because the mixed pixel in low spatial resolution data is more complicated and mixing than these in high spatial resolution data. To account for this, an improved cross-platform atmospheric correction model (ICAC) has been developed for removing the atmospheric effects from the Landsat-5 image. The accuracy and stable of ICAC model is evaluated through comparison between the satellite-derived and synchronized field-measured remote sensing reflectance. The results indicate that use of ICAC model can produce 7.19, 8.76, 5.28, and 14.42 % uncertainty in deriving remote sensing reflectance at three visible and one near-infrared bans, respectively, from Landsat-5 data. By comparison, using the ICAC algorithm in removing atmospheric influence on Landat-5 data in Taihu Lake could decrease by 6.32, 12.80, 25.45, and 32.96 %, respectively, at three visible bands and one NIR band to traditional cross-platform atmospheric correction algorithm. The improvements are very significant.

Description: This paper presents a new method for reconstruction of scattering mechanism contributions in quad-polarimetric SAR (PolSAR) data. Scattering mechanisms of one pixel are reconstructed using a comparison among polarimetric signatures of this pixel with polarimetric signatures of four canonical objects including trihedral (sphere or flat plate), dihedral, helix, and dipole. Pattern recognition matching methods are utilized in this comparison. In this research, a full polarimetric Radarsat-2 image was chosen for evaluation of the proposed method. The study area included various land cover classes e.g., different forest species, urban, water, and ground vegetation. The eight features corresponding to four canonical objects and two signatures (co-polarized and cross-polarized) provided by the proposed method were analyzed in various classes. The results of SVM classifier using these features were compared with results obtained from SVM classifiers using features provided by Freeman, Van Zyl, and Yamaguchi decomposition methods. Results showed that proposed feature set extracts new concepts from the images which are different from the concept extracting by other features presented in previous studies. Also, the accuracy of the proposed method in recognition of forest species was better than the other methods.

Description: In this paper pixel-based and object-oriented classifications were investigated for land-cover mapping in an urban area. Since the image fusion methods are playing a useful role in supplying classification different fusion approaches such as Gram-Schmidt Transform (GS), Principal Component Transform (PC), Haar wavelet, and À Trous Wavelet Transform (ATWT) algorithms have been used and the fused image with the best quality has been assessed on its respected classification. A Hyperion image and IRS-PAN image covering a region near Tehran, Iran have been used to demonstrate the enhancement and accuracy assessment of fused image over the initial images. The evaluation results of fused images showed that the Haar wavelet approach has good quality in preserving spectral information as well as spatial information. Classification results were compared to evaluate the effectiveness of the two classification approaches. Result of the pan-sharpened image classifications displayed that the object-oriented procedure presented more accurate outcomes (90.47 %) than those obtained by pixel-based classification method (77.33 %).

Description: Some of the major metropolitan centers in the world are highly susceptible to flash floods and major disruptions, owing to sudden and excessive rainfall events. The city of Mumbai, India’s financial capital, suffered one such event on 19 June, 2015. This was a second event of such nature, following the landmark event of 26 July, 2005. Such extreme rainfall events are often brought about by certain rapidly developing, local disturbances, which if actively monitored, may be provide important information that can be of great use for early warning to civic authorities and emergency planners. In this paper, we have analyzed a number of different meteorological and remotely sensed parameters, a few days before the actual event, to track the development and eventual culmination of a “perfect storm” that affected Mumbai and left the city tattered. We show how regional upper layer disturbance patterns are developed, induced by warming of sea-surface temperature (SST) and sustained by instability in the atmospheric boundary layers to quickly develop into massive cyclonic storms.

Description: 〈h3〉Abstract〈/h3〉 〈p〉The present study aims to explore and compare the potential of different Artificial Intelligence-based Soft Computing (AISC) techniques to prepare surface Soil Moisture Content (SMC) map using fine-resolution (~ 5 m), quad-polarized Synthetic Aperture Radar (SAR) data obtained from Radar Imaging Satellite 1 (RISAT1). Potential of three different AISC techniques, i.e. Support Vector Machine (SVM), Random Forest (RF) and Genetic Programming (GP), is explored. The estimated surface SMC is validated with the field soil moisture values in both bare and vegetated lands (〈 30 cm height). Different techniques have their own merits and demerits; however, we recommend GP to be most useful due to its other features. For example, GP provides the mathematical relationship, importance and sensitivity of each individual input to the surface SMC. This helps us to quantify the contribution of quad-polarized backscattering coefficients and soil texture information. It is noticed that the use of only SAR data without soil texture information may be acceptable with reasonable accuracy with an enormous benefit of its applicability to the locations without soil texture information. Using this, an exemplary fine-resolution (~ 5 m) SMC map is developed. Such high-resolution maps for large spatial extent are expected to be highly useful in many applications.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Ongoing demands for irrigation, drinking water supplies and hydropower generation encourage human beings to construct dams and reservoirs on rivers. Such engineering structures together with embankments may protect downstream areas from flooding, can give short-term benefits but may degrade the natural or pristine condition of a river by altering the longitudinal and lateral connectivity of water and sediments. This study analyses the human-induced changes in the channel belt and floodplain morphology of the Yamuna River between Wazirabad and new Okhla barrages using topographic maps and satellite image. In the pre-dam condition (1867–1868), the Yamuna River had a highly braided channel pattern compared to the post-dam situation. After construction of the Tajewala, Hathnikund and Wazirabad barrages, the longitudinal connectivity of sediments and discharge were disturbed so that bar areas were reduced and values of the braided index declined. Dam-induced moderation of peak discharges, rapidly growing population, increasing length of embankments and roads in the floodplain are the major reasons behind the urbanization of Yamuna floodplain in Delhi. This study also shows that the increasing length of engineering structure caused a reduction in channel belt area and its width. Results of the present study are useful for the environmentalists, policy makers and earth scientists working on the restoration and management of floodplain between Wazirabad and new Okhla barrage.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Applications of high-resolution remote sensing satellite data are becoming increasingly extensive. China has launched a series of high-resolution optical satellites: GaoFen-1 (GF-1), GaoFen-2, etc., and published several remote sensing image products. However, validation of the long time series of these satellite geometric products is lacking. Therefore, this study selects 2013–2016 GaoFen-1 satellite geometry products for the Beijing area over four years, obtaining a total of 164 images, and uses ZY-3 satellite products to verify the accuracy of the data set. Moreover, the positioning accuracy of the geometric products of two camera types carried by GF-1 (wide field of view, WFV; panchromatic and multispectral, PMS) is analyzed. The results yield the following conclusions. (1) GF-1 satellite geometric products have obvious systematic errors, and the error value varies significantly with the orbit time. (2) The internal distortion of the GF-1 satellite image is well controlled, and the geometric offset magnitude and direction of ground control points (GCPs) are very similar in the interior of each scene. (3) In a short period of time (20 days), the offset direction of the four WFV cameras is very similar, but there is a small difference in the mean plane offset (RMSE〈sub〉2D〈/sub〉). The RMSE〈sub〉2D〈/sub〉 and offset direction of the two PMS cameras are very similar. (4) In the long term, the relative offset of the WFV and PMS cameras varies with the satellite orbit time. At the first year of the satellite orbit time (in 2014), the relative offsets of four WFV cameras increased to maximum (168.96 m, 171.12 m, 226.65 m, and 207.04 m). From 2015 to 2016, the offsets decreased to a relatively stable state (within 100 m). (5) This validation method based on reference data is shown to be feasible. This study provides an important reference for the application of high-resolution remote sensing satellite products.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Precipitation plays an omni-important role in a developing country like India where the lion’s share of the economy is dependent on agriculture which in turn is dependent on the rains. However, with unplanned urbanization and climate change, the precipitation pattern has changed a lot. Present work studies the distribution, pattern and trend analysis (seasonal and annual) of precipitation for 100 years of upper Godavari basin which is an important agricultural belt of India. Various statistical methods like Mann–Kendall, Sen’s slope estimator and linear regression were employed to understand the nature and rate of spatial–temporal change. Except monsoon season, there has been a decrease in the amount of rainfall during the other seasons. The purpose of the trend analysis is to help the policy makers for efficient and sustainable water resource management taking in view the grass root realities. The effect of environmental change on the spatial and temporal dissemination of precipitation is clearly visible in the analysis. In general, the study area shows a decline in annual precipitation.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉The timely precise information of land use land cover (LULC) in the canal command area can help in managing irrigation water according to the crop water requirement. A study was conducted to map the LULC of irrigated command area of three distributaries in the lower Chenab canal system, Pakistan, namely Mungi, Killianwala and Khurrianwala for the estimation of canal water deficit (CWD). Multispectral images of LANDSAT-7 were used for Rabi season of 2009–2010 and 2010–2011. Normalized difference vegetation index-based unsupervised classification was performed for the formation of LULC of the commands area. During the initial classification, totally nine clusters were created with maximum likelihood. These clusters were then merged into final four classes on the basis of field knowledge. Accuracy assessment was performed using error matrix; producer and user accuracies were estimated for each class with overall accuracy of 84% and 86% for the Rabi season 2009–2010 and 2010–2011, respectively. For the assessment of the irrigation water demand, potential evapotranspiration was estimated using the Penman–Monteith equation. Crop water requirement was estimated based on the 10-day Kc value of the mapped crop from the LULC. Irrigation water demand for cropped area was estimated from the LULC and crop evapotranspiration. Canal water deficit was estimated from the available canal water supply and irrigation water requirement in the Rabi season. During the Rabi seasons, average CWD was 64%, 72% and 32% (2009–2010) and 33%, 46% and 36% (2010–2011) for Khurrianwala, Killianwala and Mungi distributary, respectively.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Temporal evolution of biophysical properties of Bay of Bengal (BoB) was measured using an Argo float equipped with fluorescence sensor (BGC-float) during the winter monsoon of 2013–2014. This season was subjected to intense vertical mixing due to the passage of several cyclonic storms beginning with ‘〈em〉Phailin〈/em〉’ in October to ‘〈em〉Madi〈/em〉’ in December 2013. These events had resulted in anomalous chlorophyll bloom (〉 0.9 mg/l) together with an increase of ~  1 PSU in the surface salinity and a drop-in temperature (~  1 °C) in the southwestern BoB. The event was well captured by a BGC-float (WMO ID 2902086) present in the region close to the tracks of cyclones ‘〈em〉Leher〈/em〉’ and ‘〈em〉Madi〈/em〉’. The presence of a strong cold-core eddy as observed from the sea level anomaly data played a vital role in spreading the bloom over a wider area and maintaining it for nearly two months from November 2013 to January 2014. This was also evident from the satellite observations of surface chlorophyll-〈em〉a〈/em〉 concentration and Ekman pumping velocity (~  2 × 10〈sup〉–3〈/sup〉 m/s). The intense mixing caused by the cyclonic storms has resulted in eroding the stratification and assisting the entrainment of nutrients into the euphotic layer, which was subsequently sustained by the mesoscale eddy. The present study reveals the strong synergistic application potential of BGC-float data together with satellite observations in monitoring the changes in the vertical structure of chlorophyll during the cyclonic storms and other mesoscale processes. 〈/p〉