ALBERT

Description: The use of remote sensing for environmental policy development is now quite common and well-documented, as images from remote sensing platforms are often used to focus attention on emerging environmental issues and spur debate on potential policy solutions. However, its use in policy implementation and evaluation has not been examined in much detail. Here we examine the use of remote sensing to support the implementation and enforcement of policies regarding the conservation of forests and wetlands in the USA. Specifically, we focus on the “Roadless Rule” and “Travel Management Rules” as enforced by the US Department of Agriculture Forest Service on national forests, and the “No Net Loss” policy and Clean Water Act for wetlands on public and private lands, as enforced by the US Environmental Protection Agency and the US Army Corps of Engineers. We discuss several national and regional examples of how remote sensing for forest and wetland conservation has been effectively integrated with policy decisions, along with barriers to further integration. Some of these barriers are financial and technical (such as the lack of data at scales appropriate to policy enforcement), while others are political.

Description: This study used an affordable ground-based portable LiDAR system to provide an understanding of the structural differences between old-growth and secondary-growth Southeastern pine. It provided insight into the strengths and weaknesses in the structural determination of portable systems in contrast to airborne LiDAR systems. Portable LiDAR height profiles and derived metrics and indices (e.g., canopy cover, canopy height) were compared among plots with different fire frequency and fire season treatments within secondary forest and old growth plots. The treatments consisted of transitional season fire with four different return intervals: 1-yr, 2-yr, 3-yr fire return intervals, and fire suppressed plots. The remaining secondary plots were treated using a 2-yr late dormant season fire cycle. The old growth plots were treated using a 2-yr growing season fire cycle. Airborne and portable LiDAR derived canopy cover were consistent throughout the plots, with significantly higher canopy cover values found in 3-yr and fire suppressed plots. Portable LiDAR height profile and metrics presented a higher sensitivity in capturing subcanopy elements than the airborne system, particularly in dense canopy plots. The 3-dimensional structures of the secondary plots with varying fire return intervals were dramatically different to old-growth plots, where a symmetrical distribution with clear recruitment was visible. Portable LiDAR, even though limited to finer spatial scales and specific biases, is a low-cost investment with clear value for the management of forest canopy structure.

Description: The goal of the two year Ground European Network for Earth Science Interoperations-Digital Repositories (GENESI-DR) project was to build an open and seamless access service to Earth science digital repositories for European and world-wide science users. In order to showcase GENESI-DR, one of the developed technology demonstrators focused on fast search, discovery, and access to remotely sensed imagery in the context of post-disaster building damage assessment. This paper describes the scenario and implementation details of the technology demonstrator, which was developed to support post-disaster damage assessment analyst activities. Once a disaster alert has been issued, response time is critical to providing relevant damage information to analysts and/or stakeholders. The presented technology demonstrator validates the GENESI-DR project data search, discovery and security infrastructure and integrates the rapid urban area mapping and the near real-time orthorectification web processing services to support a post-disaster damage needs assessment analysis scenario. It also demonstrates how the GENESI-DR SOA can be linked to web processing services that access grid computing resources for fast image processing and use secure communication to ensure confidentiality of information.

Description: In this paper, two main approaches for automatic building detection and localization using high spatial resolution imagery and LiDAR data are compared and evaluated: thresholding-based and object-based classification. The thresholding-based approach is founded on the establishment of two threshold values: one refers to the minimum height to be considered as building, defined using the LiDAR data, and the other refers to the presence of vegetation, which is defined according to the spectral response. The other approach follows the standard scheme of object-based image classification: segmentation, feature extraction and selection, and classification, here performed using decision trees. In addition, the effect of the inclusion in the building detection process of contextual relations with the shadows is evaluated. Quality assessment is performed at two different levels: area and object. Area-level evaluates the building delineation performance, whereas object-level assesses the accuracy in the spatial location of individual buildings. The results obtained show a high efficiency of the evaluated methods for building detection techniques, in particular the thresholding-based approach, when the parameters are properly adjusted and adapted to the type of urban landscape considered.

Description: The urban heat island effect is linked to the built environment and threatens human health during extreme heat events. In this study, we analyzed whether characteristic land uses within an urban area are associated with higher or lower surface temperatures, and whether concentrations of “hot” land uses exacerbate this relationship. Zonal statistics on a thermal remote sensing image for the City of Toronto revealed statistically significant differences between high average temperatures for commercial and resource/industrial land use (29.1 °C), and low average temperatures for parks and recreational land (25.1 °C) and water bodies (23.1 °C). Furthermore, higher concentrations of either of these land uses were associated with more extreme surface temperatures. We also present selected neighborhoods to illustrate these results. The paper concludes by recommending that municipal planners and decision-makers formulate policies and regulations that are specific to the problematic land uses, in order to mitigate extreme heat.

Description: The objective of this study is to validate an approach based on the change detection in multitemporal TerraSAR images (X-band) for mapping soil moisture in the Sahelian area. In situ measurements were carried out simultaneously with TerraSAR-X acquisitions on two study sites in Niger. The results show the need for comparing the difference between the rainy season image and a reference image acquired in the dry season. The use of two images enables a reduction of the roughness effects. The soils of plateaus covered with erosion crusts are dry throughout the year while the fallows show more significant moisture during the rainy season. The accuracy on the estimate of soil moisture is about 2.3% (RMSE) in comparison with in situ moisture contents.

Description: Forest management planning in Finland is currently adopting a new-generation forest inventory method, which is based on interpretation of airborne laser scanning data and digital aerial images. The inventory method is based on a systematic grid, where the grid elements serve as inventory units, for which the laser and aerial image data are extracted and the forest variables estimated. As an alternative or a complement to the grid elements, image segments can be used as inventory units. The image segments are particularly useful as the basis for generation of the silvicultural treatment and cutting units since their boundaries should follow the actual stand borders, whereas when using grid elements it is typical that some of them cover parts of several forest stands. The proportion of the so-called mixed cells depends on the size of the grid elements and the average size and shape of the stands. In this study, we carried out automatic segmentation of two study areas on the basis of laser and aerial image data with a view to delineating micro-stands that are homogeneous in relation to their forest attributes. Further, we extracted laser and aerial image features for both systematic grid elements and segments. For both units, the feature set used for estimating the forest attributes was selected by means of a genetic algorithm. Of the features selected, the majority (61–79%) were based on the airborne laser scanning data. Despite the theoretical advantages of the image segments, the laser and aerial features extracted from grid elements seem to work better than features extracted from image segments in estimation of forest attributes. We conclude that estimation should be carried out at grid level with an area-specific combination of features and estimates for image segments to be derived on the basis of the grid-level estimates.

Description: Narrow channels located in the longwave IR CO2 absorption region between approximately 13.2 and 14.5 μm, the well known CO2 slicing channels, have been proven to be quite effective for the estimates of cloud heights and effective cloud amounts as well as atmospheric temperature profiles. The designs of some of the near-future multi-channel earth observing satellite sensors cannot accommodate these longwave IR channels. Based on the analysis of the multi-channel imaging data collected with the NASA Moderate Resolution Imaging SpectroRadiometer (MODIS) instrument and on theoretical cloud radiative transfer modeling, we have found that narrow channels located at the midwave IR region between approximately 4.2 and 4.55 μm, where the combined CO2 and N2O absorption effects decrease rapidly with increasing wavelength, have similar properties as the longwave IR CO2 slicing channels. The scattering of solar radiation by clouds on the long wavelength side of the 4.3 μm CO2 absorption makes only a small contribution to the upwelling radiances. In order to retain the crucial cloud and temperature sensing capabilities, future satellite sensors should consider including midwave IR CO2 and N2O slicing channels if the longwave IR channels cannot be implemented on the sensors. The hyperspectral data covering the 3.7-15.5 mm wavelength range and measured with the Infrared Atmospheric Sounding Interferometer (IASI) can be used to further assess the utility of midwave IR channels for satellite remote sensing.

Description: Landsat MSS and ETM+ images were analyzed to detect 25-year land-cover change (1976–2001) in the critical Taguibo Watershed in Mindanao Island, Southern Philippines. This watershed has experienced historical modifications of its land-cover due to the presence of logging industries in the 1950s, and continuous deforestation due to illegal logging and slash-and-burn agriculture in the present time. To estimate the impacts of land-cover change on watershed runoff, land-cover information derived from the Landsat images was utilized to parameterize a GIS-based hydrologic model. The model was then calibrated with field-measured discharge data and used to simulate the responses of the watershed in its year 2001 and year 1976 land-cover conditions. The availability of land-cover information on the most recent state of the watershed from the Landsat ETM+ image made it possible to locate areas for rehabilitation such as barren and logged-over areas. We then created a “rehabilitated” land-cover condition map of the watershed (re-forestation of logged-over areas and agro-forestation of barren areas) and used it to parameterize the model and predict the runoff responses of the watershed. Model results showed that changes in land-cover from 1976 to 2001 were directly related to the significant increase in surface runoff. Runoff predictions showed that a full rehabilitation of the watershed, especially in barren and logged-over areas, will be likely to reduce the generation of a huge volume of runoff during rainfall events. The results of this study have demonstrated the usefulness of multi-temporal Landsat images in detecting land-cover change, in identifying areas for rehabilitation, and in evaluating rehabilitation strategies for management of tropical watersheds through its use in hydrologic modeling.

Description: Remote sensing has become the most important data source for the digital elevation model (DEM) generation. DEM analyses can be applied in various fields and many of them require appropriate DEM visualization support. Analytical hill-shading is the most frequently used relief visualization technique. Although widely accepted, this method has two major drawbacks: identifying details in deep shades and inability to properly represent linear features lying parallel to the light beam. Several authors have tried to overcome these limitations by changing the position of the light source or by filtering. This paper proposes a new relief visualization technique based on diffuse, rather than direct, illumination. It utilizes the sky-view factor—a parameter corresponding to the portion of visible sky limited by relief. Sky-view factor can be used as a general relief visualization technique to show relief characteristics. In particular, we show that this visualization is a very useful tool in archaeology as it improves the recognition of small scale features from high resolution DEMs.

Description: This research presents a time-effective approach for mapping streambed and riparian zone extent from high spatial resolution LiDAR derived products, i.e., digital terrain model, terrain slope and plant projective cover. Geographic object based image analysis (GEOBIA) has proven useful for feature extraction from high spatial resolution image data because of the capacity to reduce effects of reflectance variations of pixels making up individual objects and to include contextual and shape information. This functionality increases the likelihood of developing transferable and automated mapping approaches. LiDAR data covered parts of the Werribee Catchment in Victoria, Australia, which is characterized by urban, agricultural, and forested land cover types. Field data of streamside vegetation structure and physical form properties were used for both calibration of the mapping routines and validation of the mapping results. To improve the transferability of the rule set, the GEOBIA approach was developed for an area representing different riparian zone environments, i.e., urbanized, agricultural and hilly forested areas. Results show that mapping streambed extent (R2 = 0.93, RMSE = 3.6 m, n = 35) and riparian zone extent (R2 = 0.74, RMSE = 3.9, n = 35) from LiDAR derived products can be automated using GEOBIA to enable derivation of spatial information in an accurate and time-effective manner suited for natural resource management agencies.

Description: The importance of landscape and heritage recording and documentation with optical remote sensing sensors is well recognized at international level. The continuous development of new sensors, data capture methodologies and multi-resolution 3D representations, contributes significantly to the digital 3D documentation, mapping, conservation and representation of landscapes and heritages and to the growth of research in this field. This article reviews the actual optical 3D measurement sensors and 3D modeling techniques, with their limitations and potentialities, requirements and specifications. Examples of 3D surveying and modeling of heritage sites and objects are also shown throughout the paper.

Description: Within urban areas, green spaces play a critically important role in the quality of life. They have remarkable impact on the local microclimate and the regional climate of the city. Quantifying the ‘greenness’ of urban areas allows comparing urban areas at several levels, as well as monitoring the evolution of green spaces in urban areas, thus serving as a tool for urban and developmental planning. Different categories of vegetation have different impacts on recreation potential and microclimate, as well as on the individual perception of green spaces. However, when quantifying the ‘greenness’ of urban areas the reliability of the underlying information is important in order to qualify analysis results. The reliability of geo-information derived from remote sensing data is usually assessed by ground truth validation or by comparison with other reference data. When applying methods of object based image analysis (OBIA) and fuzzy classification, the degrees of fuzzy membership per object in general describe to what degree an object fits (prototypical) class descriptions. Thus, analyzing the fuzzy membership degrees can contribute to the estimation of reliability and stability of classification results, even when no reference data are available. This paper presents an object based method using fuzzy class assignments to outline and classify three different classes of vegetation from GeoEye imagery. The classification result, its reliability and stability are evaluated using the reference-free parameters Best Classification Result and Classification Stability as introduced by Benz et al. in 2004 and implemented in the software package eCognition (www.ecognition.com). To demonstrate the application potentials of results a scenario for quantifying urban ‘greenness’ is presented.

Description: Wildland fires are a yearly recurring phenomenon in many terrestrial ecosystems. Accurate fire severity estimates are of paramount importance for modeling fire-induced trace gas emissions and rehabilitating post-fire landscapes. We used high spatial and high spectral resolution MODIS/ASTER (MASTER) airborne simulator data acquired over four 2007 southern California burns to evaluate the effectiveness of 19 different spectral indices, including the widely used Normalized Burn Ratio (NBR), for assessing fire severity in southern California chaparral. Ordinal logistic regression was used to assess the goodness-of-fit between the spectral index values and ordinal field data of severity. The NBR and three indices in which the NBR is enhanced with surface temperature or emissivity data revealed the best performance. Our findings support the operational use of the NBR in chaparral ecosystems by Burned Area Emergency Rehabilitation (BAER) projects, and demonstrate the potential of combining optical and thermal data for assessing fire severity. Additional testing in more burns, other ecoregions and different vegetation types is required to fully understand how (thermally enhanced) spectral indices relate to fire severity.

Description: Recent interest in use of satellite remote sensing for environmental compliance and remediation assessment has been heightened by growing policy requirements and the need to provide more rapid and efficient monitoring and enforcement mechanisms. However, remote sensing solutions are attractive only to the extent that they can deliver environmentally relevant information in a meaningful and time-sensitive manner. Unfortunately, the extent to which satellite-based remote sensing satisfies the demands for compliance and remediation assessment under the conditions of an actual environmental accident or calamity has not been well documented. In this study a remote sensing solution to the problem of site remediation and environmental compliance assessment was introduced based on the use of the RDX anomaly detection algorithm and vegetation indices developed from the Tasseled Cap Transform. Results of this analysis illustrate how the use of standard vegetation transforms, integrated into an anomaly detection strategy, enable the time-sequenced tracking of site remediation progress. Based on these results credible evidence can be produced to support compliance evaluation and remediation assessment following major environmental disasters.

Description: Remotely sensed multi-spectral and -spatial data facilitates the study of mosquito-borne disease vectors and their response to land use and cover composition in the urban environment. In this study we assess the feasibility of integrating remotely sensed multispectral reflectance data and LiDAR (Light Detection and Ranging)-derived height information to improve land use and land cover classification. Classification and Regression Tree (CART) analyses were used to compare and contrast the enhancements and accuracy of the multi-sensor urban land cover classifications. Eight urban land-cover classes were developed for the city of Tucson, Arizona, USA. These land cover classes focus on pervious and impervious surfaces and microclimate landscape attributes that impact mosquito habitat such as water ponds, residential structures, irrigated lawns, shrubs and trees, shade, and humidity. Results show that synergistic use of LiDAR, multispectral and the Normalized Difference Vegetation Index data produced the most accurate urban land cover classification with a Kappa value of 0.88. Fusion of multi-sensor data leads to a better land cover product that is suitable for a variety of urban applications such as exploring the relationship between neighborhood composition and adult mosquito abundance data to inform public health issues.

Description: We demonstrate the efficacy of a commercial portable 2D laser scanner (operating at a wavelength close to 1,000 nm) deployed from a fixed-wing aircraft for measuring the sea surface topography and wave profiles over coastal waters. The LiDAR instrumentation enabled simultaneous measurements of the 2D laser scanner with two independent inertial navigation units, and also simultaneous measurements with a more advanced 2D laser scanner (operating at a wavelength near 1,500 nm). The latter scanner is used routinely for accurately measuring terrestrial topography and was used as a benchmark in this study. We present examples of sea surface topography and wave profiles based on low altitude surveys ( 〈 ~300 m) over coastal waters in the vicinity of Cape de Couedic, Kangaroo Island, South Australia and over the surf zone adjacent to the mouth of the Murray River, South Australia. Relative wave heights in the former survey are shown to be consistent with relative wave heights observed from a waverider buoy located near Cape de Couedic during the LiDAR survey. The sea surface topography of waves in the surf zone was successfully mapped with both laser scanners resolving relative wave height variations and fine structure of the sea surface to within approximately 10 cm. A topographic map of the sea surface referenced to the airborne sensor frame transforms to an accurate altimetry map which may be used with airborne electromagnetic instrumentation to provide an averaged altimetry covering a portion of the larger electromagnetic footprint. This averaged altimetry is deemed to be significantly more reliable as a measurement of altimetry than spot measurements using a nadir-looking laser altimeter and would therefore improve upon the use of airborne electromagnetic methods for bathymetric mapping in surf-zone waters. The aperture range of the scanner does not necessarily determine the swath. We observed that instead, the maximum swath at a given altitude was limited by the angle of incidence of the laser at the water surface.

Description: Peer-reviewed journals are a pillar of modern science. Their aim is to achieve highest scientific standards by carrying out a rigorous peer review that is, as a minimum requirement, supposed to be able to identify fundamental methodological errors or false claims. Unfortunately, as many climate researchers and engaged observers of the climate change debate pointed out in various internet discussion fora, the paper by Spencer and Braswell [1] that was recently published in Remote Sensing is most likely problematic in both aspects and should therefore not have been published. After having become aware of the situation, and studying the various pro and contra arguments, I agree with the critics of the paper. Therefore, I would like to take the responsibility for this editorial decision and, as a result, step down as Editor-in-Chief of the journal Remote Sensing. [...]

Description: Indonesian peatlands are one of the largest near-surface pools of terrestrial organic carbon. Persistent logging, drainage and recurrent fires lead to huge emission of carbon each year. Since tropical peatlands are highly inaccessible, few measurements on peat depth and forest biomass are available. We assessed the applicability of quality filtered ICESat/GLAS (a spaceborne LiDAR system) data to measure peatland topography as a proxy for peat volume and to estimate peat swamp forest Above Ground Biomass (AGB) in a thoroughly investigated study site in Central Kalimantan, Indonesia. Mean Shuttle Radar Topography Mission (SRTM) elevation was correlated to the corresponding ICESat/GLAS elevation. The best results were obtained from the waveform centroid (R2 = 0.92; n = 4,186). ICESat/GLAS terrain elevation was correlated to three 3D peatland elevation models derived from SRTM data (R2 = 0.90; overall difference = −1.0 m, ±3.2 m; n = 4,045). Based on the correlation of in situ peat swamp forest AGB and airborne LiDAR data (R2 = 0.75, n = 36) an ICESat/GLAS AGB prediction model was developed (R2 = 0.61, n = 35). These results demonstrate that ICESat/GLAS data can be used to measure peat topography and to collect large numbers of forest biomass samples in remote and highly inaccessible peatland forests.

Description: This paper presents the findings of the impact of atmospheric effects when applied on satellite images intended for supporting archaeological research. The study used eleven multispectral Landsat TM/ETM+ images from 2009 until 2010, acquired over archaeological and agricultural areas. The modified Darkest Pixel (DP) atmospheric correction algorithm was applied, as it is considered one of the most simple and effective atmospheric corrections algorithm. The NDVI equation was applied and its values were evaluated before and after the application of atmospheric correction to satellite images, to estimate its possible effects. The results highlighted that atmospheric correction has a significant impact on the NDVI values. This was especially true in seasons where the vegetation has grown. Although the absolute impact on NDVI, after applying the DP, was small (0.06), it was considered important if multi-temporal time series images need to be evaluated and cross-compared. The NDVI differences, before and after atmospheric correction, were assessed using student’s t-test and the statistical differences were found to be significant. It was shown that relative NDVI difference can be as much as 50%, if atmosphere effects are ignored. Finally, the results had proven that atmospheric corrections can enhance the interpretation of satellite images (especially in cases where optical thickness of water vapour is minimized ≈ 0). This fact can assist in the detection and identification of archaeological crop marks. Therefore, removal of atmospheric effects, for archaeological purposes, was found to be of great importance in improving the image enhancement and NDVI values.

Description: The SMOS satellite mission, launched in 2009, allows global soil moisture estimations to be made using the L-band Microwave Emission of the Biosphere (L-MEB) model, which simulates the L-band microwave emissions produced by the soil–vegetation layer. This model was calibrated using various sources of in situ and airborne data. In the present study, we propose to evaluate the L-MEB model on the basis of a large set of airborne data, recorded by the CAROLS radiometer during the course of 20 flights made over South West France (the SMOSMANIA site), and supported by simultaneous soil moisture measurements, made in 2009 and 2010. In terms of volumetric soil moisture, the retrieval accuracy achieved with the L-MEB model, with two default roughness parameters, ranges between 8% and 13%. Local calibrations of the roughness parameter, using data from the 2009 flights for different areas of the site, allowed an accuracy of approximately 5.3% to be achieved with the 2010 CAROLS data. Simultaneously we estimated the vegetation optical thickness (t) and we showed that, when roughness is locally adjusted, MODIS NDVI values are correlated (R2 = 0.36) to t. Finally, as a consequence of the significant influence of the roughness parameter on the estimated absolute values of soil moisture, we propose to evaluate the relative variability of the soil moisture, using a default soil roughness parameter. The soil moisture variations are estimated with an uncertainty of approximately 6%.

Description: Indirect Time-of-Flight (I-TOF) cameras can be implemented in a number of ways, each with specific characteristics and performances. In this paper a comprehensive analysis of the implementation possibilities is developed in order to model the main performances with a high level of abstraction. After the extraction of the main characteristics for the high-level model, several figures of merit (FoM) are defined with the purpose of obtaining a common metric: noise equivalent distance, correlated and uncorrelated power responsivity, and background light rejection ratio. The obtained FoMs can be employed for the comparison of different implementations of range cameras based on the I-TOF method: specifically, they are applied for several different sensors developed by the authors in order to compare their performances.

Description: Using unmanned aircraft systems (UAS) as remote sensing platforms offers the unique ability for repeated deployment for acquisition of high temporal resolution data at very high spatial resolution. Multispectral remote sensing applications from UAS are reported in the literature less commonly than applications using visible bands, although light-weight multispectral sensors for UAS are being used increasingly. . In this paper, we describe challenges and solutions associated with efficient processing of multispectral imagery to obtain orthorectified, radiometrically calibrated image mosaics for the purpose of rangeland vegetation classification. We developed automated batch processing methods for file conversion, band-to-band registration, radiometric correction, and orthorectification. An object-based image analysis approach was used to derive a species-level vegetation classification for the image mosaic with an overall accuracy of 87%. We obtained good correlations between: (1) ground and airborne spectral reflectance (R2 = 0.92); and (2) spectral reflectance derived from airborne and WorldView-2 satellite data for selected vegetation and soil targets. UAS-acquired multispectral imagery provides quality high resolution information for rangeland applications with the potential for upscaling the data to larger areas using high resolution satellite imagery.

Description: Air pollution has been a major topic of debate in highly developed areas over the last quarter century and therefore mitigation of poor air quality for health and environmental reasons has been a primary focus for local governments. Particulate matter, especially finer particles (PM2.5), is detrimental to human health, and urban expansion is thought to be a contributing factor to enhanced levels of PM2.5. However, there is limited research on the connection between land use and land cover change (LULC) and PM2.5 emissions. Using high resolution LANDSAT imagery from the past 12 years along with ground observations of PM2.5 mass concentrations in the Birmingham, AL region, we explore the links between the PM2.5 mass concentrations and LULC trends. Utilization of GIS allowed us to seamlessly analyze county-based patterns of LULC change and PM2.5 concentrations and display them in an easy to interpret manner. We found a moderate-to-strong correlation between PM2.5 observations and the urban area surrounding monitoring sites in 1998 and 2010. We also discuss factors such as local climate and topography and EPA imposed standards that can confound these comparisons. Finally, we determine the next steps that are required to fully quantify the cause and effect between LULC and air quality.

Description: The need to monitor the Earth’s surface over a range of spatial and temporal scales is fundamental in ecosystems planning and management. Change-Vector Analysis (CVA) is a bi-temporal method of change detection that considers the magnitude and direction of change vector. However, many multispectral applications do not make use of the direction component. The procedure most used to calculate the direction component using multiband data is the direction cosine, but the number of output direction cosine images is equal to the number of original bands and has a complex interpretation. This paper proposes a new approach to calculate the spectral direction of change, using the Spectral Angle Mapper and Spectral Correlation Mapper spectral-similarity measures. The chief advantage of this approach is that it generates a single image of change information insensitive to illumination variation. In this paper the magnitude component of the spectral similarity was calculated in two ways: as the standard Euclidean distance and as the Mahalanobis distance. In this test the best magnitude measure was the Euclidean distance and the best similarity measure was Spectral Angle Mapper. The results show that the distance and similarity measures are complementary and need to be applied together.

Description: The analysis of vegetation dynamics is essential in semi-arid regions, in particular because of the frequent occurrence of long periods of drought. In this paper, multi-temporal series of the Normalized Difference of Vegetation Index (NDVI), derived from SPOT-VEGETATION satellite data between September 1998 and June 2010, were used to analyze the vegetation dynamics over the semi-arid central region of Tunisia. A study of the persistence of three types of vegetation (pastures, annual agriculture and olive trees) is proposed using fractal analysis, in order to gain insight into the stability/instability of vegetation dynamics. In order to estimate the state of vegetation cover stress, we propose evaluating the properties of an index referred to as the Vegetation Anomaly Index (VAI). A positive VAI indicates high vegetation dynamics, whereas a negative VAI indicates the presence of vegetation stress. The VAI is tested for the above three types of vegetation, during the study period from 1998 to 2010, and is compared with other drought indices. The VAI is found to be strongly correlated with precipitation.

Description: Mangroves provide valuable ecosystem goods and services such as carbon sequestration, habitat for terrestrial and marine fauna, and coastal hazard mitigation. The use of satellite remote sensing to map mangroves has become widespread as it can provide accurate, efficient, and repeatable assessments. Traditional remote sensing approaches have failed to accurately map fringe mangroves and true mangrove species due to relatively coarse spatial resolution and/or spectral confusion with landward vegetation. This study demonstrates the use of the new Worldview-2 sensor, Object-based image analysis (OBIA), and support vector machine (SVM) classification to overcome both of these limitations. An exploratory spectral separability showed that individual mangrove species could not be spectrally separated, but a distinction between true and associate mangrove species could be made. An OBIA classification was used that combined a decision-tree classification with the machine-learning SVM classification. Results showed an overall accuracy greater than 94% (kappa = 0.863) for classifying true mangroves species and other dense coastal vegetation at the object level. There remain serious challenges to accurately mapping fringe mangroves using remote sensing data due to spectral similarity of mangrove and associate species, lack of clear zonation between species, and mixed pixel effects, especially when vegetation is sparse or degraded.

Description: In this study, individual tree height (TH), crown base height (CBH), crown area (CA) and crown volume (CV), which were considered as essential parameters for individual stem volume and biomass estimation, were estimated by both an airborne laser scanner (ALS) and a terrestrial laser scanner (TLS). These ALS- and TLS-derived tree parameters were compared because TLS has been introduced as an instrument to measure objects more precisely. ALS-estimated TH was extracted from the highest value within a crown boundary delineated with the crown height model (CHM). The ALS-derived CBH of individual trees was estimated by k-means clustering method using the ALS data within the boundary. The ALS-derived CA was calculated simply with the crown boundary, after which CV was computed automatically using the crown geometric volume (CGV). On the other hand, all TLS-derived parameters were detected manually and precisely except the TLS-derived CGV. As a result, the ALS-extracted TH, CA, and CGV values were underestimated whereas CBH was overestimated when compared with the TLS-derived parameters. The coefficients of determination (R2) from the regression analysis between the ALS and TLS estimations were approximately 0.94, 0.75, 0.69 and 0.58, and root mean square errors (RMSEs) were approximately 0.0184 m, 0.4929 m, 2.3216 m2 and 13.2087 m3 for TH, CBH, CA and CGV, respectively. Thereby, the error rate decreased to 0.0089, 0.0727 and 0.0875 for TH, CA and CGV via the combination of ALS and TLS data.

Description: Downed logs on the forest floor provide habitat for species, fuel for forest fires, and function as a key component of forest nutrient cycling and carbon storage. Ground-based field surveying is a conventional method for mapping and characterizing downed logs but is limited. In addition, optical remote sensing methods have not been able to map these ground targets due to the lack of optical sensor penetrability into the forest canopy and limited sensor spectral and spatial resolutions. Lidar (light detection and ranging) sensors have become a more viable and common data source in forest science for detailed mapping of forest structure. This study evaluates the utility of discrete, multiple return airborne lidar-derived data for image object segmentation and classification of downed logs in a disturbed forested landscape and the efficiency of rule-based object-based image analysis (OBIA) and classification algorithms. Downed log objects were successfully delineated and classified from lidar derived metrics using an OBIA framework. 73% of digitized downed logs were completely or partially classified correctly. Over classification occurred in areas with large numbers of logs clustered in close proximity to one another and in areas with vegetation and tree canopy. The OBIA methods were found to be effective but inefficient in terms of automation and analyst’s time in the delineation and classification of downed logs in the lidar data.

Description: Light Detection and Ranging (LiDAR) remote sensing has demonstrated potential in measuring forest biomass. We assessed the ability of LiDAR to accurately estimate forest total above ground biomass (TAGB) on an individual stem basis in a conifer forest in the US Pacific Northwest region using three different computer software programs and compared results to field measurements. Software programs included FUSION, TreeVaW, and watershed segmentation. To assess the accuracy of LiDAR TAGB estimation, stem counts and heights were analyzed. Differences between actual tree locations and LiDAR-derived tree locations using FUSION, TreeVaW, and watershed segmentation were 2.05 m (SD 1.67), 2.19 m (SD 1.83), and 2.31 m (SD 1.94), respectively, in forested plots. Tree height differences from field measured heights for FUSION, TreeVaW, and watershed segmentation were −0.09 m (SD 2.43), 0.28 m (SD 1.86), and 0.22 m (2.45) in forested plots; and 0.56 m (SD 1.07 m), 0.28 m (SD 1.69 m), and 1.17 m (SD 0.68 m), respectively, in a plot containing young conifers. The TAGB comparisons included feature totals per plot, mean biomass per feature by plot, and total biomass by plot for each extraction method. Overall, LiDAR TAGB estimations resulted in FUSION and TreeVaW underestimating by 25 and 31% respectively, and watershed segmentation overestimating by approximately 10%. LiDAR TAGB underestimation occurred in 66% and overestimation occurred in 34% of the plot comparisons.

Description: This paper focuses on the Ice, Cloud and land Elevation Satellite (ICESat) Geoscience Laser Altimeter System (GLAS) data availability over the 2 million km2 Cerrado, the Brazilian central savanna biome and one of the world’s biodiversity hotspots. Overall, about 2.5 million laser shots, distributed along the seven years of ICESat operation (2003–2009) and comprising three major seasonal domains, were acquired, from which, 206,026 and 176,035 screened footprints are coincident with the remnant vegetation and cultivated pasture areas (the dominant land-use form in the Cerrado). Although these points are well distributed over the entire Cerrado, the ICESat track data collection results in substantial data gaps. In relation to the 15,612 Cerrado watersheds (6th order Otto basin system), 8,369 and 4,415 watersheds are completely deprived of data points over their remnant vegetation and pasture covers, respectively. Light Detection and Ranging (LIDAR) availability was also evaluated in relation to specific targets of interest, including both fully-protected conservation units as well as areas impacted by fire and deforestation. In spite of the very few occurrences, our assessments indicate that enough LIDAR data is available for retrieving structural and functional properties of a variety of Cerrado physiognomies, as well as to assess how these physiognomies respond to anthropogenic induced changes. In fact, the comprehensive data availability analysis conducted in this study corroborate the potential of GLAS LIDAR waveforms for the retrieval of biophysical properties at both local and regional scales, particularly concerning remnant carbon stocks and pasture conditions, key information for the conservation of the fast-changing and severely threatened Cerrado.

Description: The benefits of terrestrial remote sensing in the environmental sciences are clear across a range of applications, and increasingly remote sensing analyses are being integrated into public health research. This integration has largely been in two areas: first, through the inclusion of continuous remote sensing products such as normalized difference vegetation index (NDVI) or moisture indices to answer large-area questions associated with the epidemiology of vector-borne diseases or other health exposures; and second, through image classification to map discrete landscape patches that provide habitat to disease-vectors or that promote poor health. In this second arena, new improvements in object-based image analysis (or “OBIA”) can provide advantages for public health research. Rather than classifying each pixel based on its spectral content alone, the OBIA approach first segments an image into objects, or segments, based on spatially connected pixels with similar spectral properties, and then these objects are classified based on their spectral, spatial and contextual attributes as well as by their interrelations across scales. The approach can lead to increases in classification accuracy, and it can also develop multi-scale topologies between objects that can be utilized to help understand human-disease-health systems. This paper provides a brief review of what has been done in the public health literature with continuous and discrete mapping, and then highlights the key concepts in OBIA that could be more of use to public health researchers interested in integrating remote sensing into their work.

Description: Numerous attempts have been made to constrain climate sensitivity with observations [1-10] (with [6] as LC09, [8] as SB11). While all of these attempts contain various caveats and sources of uncertainty, some efforts have been shown to contain major errors and are demonstrably incorrect. For example, multiple studies [11-13] separately addressed weaknesses in LC09 [6]. The work of Trenberth et al. [13], for instance, demonstrated a basic lack of robustness in the LC09 method that fundamentally undermined their results. Minor changes in that study’s subjective assumptions yielded major changes in its main conclusions. Moreover, Trenberth et al. [13] criticized the interpretation of El Niño-Southern Oscillation (ENSO) as an analogue for exploring the forced response of the climate system. In addition, as many cloud variations on monthly time scales result from internal atmospheric variability, such as the Madden-Julian Oscillation, cloud variability is not a deterministic response to surface temperatures. Nevertheless, many of the problems in LC09 [6] have been perpetuated, and Dessler [10] has pointed out similar issues with two more recent such attempts [7,8]. Here we briefly summarize more generally some of the pitfalls and issues involved in developing observational constraints on climate feedbacks. [...]

Description: Mapping ecosystem services (ESs) is an important tool for providing the quantitative information necessary for the optimal use and protection of ecosystems and biodiversity. A common mapping approach is to apply established empirical relationships to ecosystem property maps. Often, ecosystem properties that provide services to humanity are strongly related to the land use and land cover, where the spatial allocation of the land cover in the landscape is especially important. Land use and land cover maps are, therefore, essential for ES mapping. However, insight into the uncertainties in land cover maps and how these propagate into ES maps is lacking. To analyze the effects of these uncertainties, we mapped pollination efficiency as an example of an ecosystem function, using two continental-scale land cover maps and two global-scale land cover maps. We compared the outputs with maps based on a detailed national-scale map. The ecosystem properties and functions could be mapped using the GLOBCOVER map with a reasonable to good accuracy. In homogeneous landscapes, an even coarser resolution map would suffice. For mapping ESs that depend on the spatial allocation of land cover in the landscape, a classification of satellite images using fractional land cover or mosaic classes is an asset.

Description: A method is presented for estimating subaqueous integrated backscatter using near-nadir viewing satellite lidar. The algorithm takes into account specular reflection of laser light, laser scattering by wind-generated foam as well as sun glint and solar scattering from foam. The formulation is insensitive to the estimate of wind speed but sensitive to the estimate of transmittance used in the atmospheric correction. As a case study, CALIOP data over Tampa Bay were compared to MODIS 645 nm remote sensing reflectance, which previously has been shown to be nearly linearly related to turbidity. The results indicate good correlation on nearly all CALIOP cloud-free dates during the period 2006 through 2007, particularly those with relatively high atmospheric transmittance. The correlation decreases when data are composited over all dates but is still statistically significant, a possible indication of variability in the biogeochemical composition in the water. Overall, the favorable results show promise for the application of satellite lidar integrated backscatter in providing information about subsurface backscatter properties, which can be extracted using appropriate models.

Description: We developed a ground truth database for urban areas from the Global Rural-Urban Mapping Project (GRUMP) Settlement Points gazetteer of populated place names by visually interpreting 3,734 urban points on satellite images, thus acquiring 2,144 urban and 1,388 non-urban data points. Our database contained many more urban data points than the existing databases, which had only 0 to 11 ground truth data points. We used our database in combination with the Degree Confluence Project database to assess the accuracy of eight satellite-derived urban area maps, among which the MODIS Terra + Aqua Land Cover Type Yearly L3 Global 500 m SIN Grid was the most accurate (84% overall accuracy; kappa coefficient, 0.63). Moreover, the most recently published maps were not necessarily the most accurate. We compared the accuracy assessment results of our database with those of another database and found that ours detected more errors of commission but included less chance agreement.

Description: Coastlines are constantly changing due to both natural and anthropogenic forces, and climate change and associated sea level rise will continue to reshape coasts in the future. Erosion is not only apparent along oceanfront areas; shoreline dynamics in sheltered water bodies have also gained greater attention. Additional estuarine shoreline studies are needed to better understand and protect coastal resources. This study uses a point-based approach to analyze estuarine shoreline change and associated parameters, including fetch, wave energy, elevation, and vegetation, in the Neuse River Estuary (NRE) at two contrasting scales, Regional (whole estuary) and Local (estuary partitioned into eight sections, based on orientation and exposure). With a mean shoreline-change rate of –0.58 m yr−1, the majority (93%) of the NRE study area is eroding. Change rates show some variability related to the land-use land-cover classification of the shoreline. Although linear regression analysis at the Regional Scale did not find significant correlations between shoreline change and the parameters analyzed, trends were determined from Local Scale data. Specifically, erosion rates, fetch, and wave exposure increase in the down-estuary direction, while elevation follows the opposite trend. Linear regression analysis between mean fetch and mean shoreline-change rates at the Local Scale provide a first-order approach to predict shoreline-change rates. The general trends found in the Local Scale data highlight the presence of underlying spatial patterns in shoreline-change rates within a complex estuarine system, but Regional Scale analysis suggests shoreline composition also has an important influence.

Description: Visual image interpretation and digital image classification have been used to map and monitor mangrove extent and composition for decades. The presence of a high-spatial resolution hyperspectral sensor can potentially improve our ability to differentiate mangrove species. However, little research has explored the use of pixel-based and object-based approaches on high-spatial hyperspectral datasets for this purpose. This study assessed the ability of CASI-2 data for mangrove species mapping using pixel-based and object-based approaches at the mouth of the Brisbane River area, southeast Queensland, Australia. Three mapping techniques used in this study: spectral angle mapper (SAM) and linear spectral unmixing (LSU) for the pixel-based approaches, and multi-scale segmentation for the object-based image analysis (OBIA). The endmembers for the pixel-based approach were collected based on existing vegetation community map. Nine targeted classes were mapped in the study area from each approach, including three mangrove species: Avicennia marina, Rhizophora stylosa, and Ceriops australis. The mapping results showed that SAM produced accurate class polygons with only few unclassified pixels (overall accuracy 69%, Kappa 0.57), the LSU resulted in a patchy polygon pattern with many unclassified pixels (overall accuracy 56%, Kappa 0.41), and the object-based mapping produced the most accurate results (overall accuracy 76%, Kappa 0.67). Our results demonstrated that the object-based approach, which combined a rule-based and nearest-neighbor classification method, was the best classifier to map mangrove species and its adjacent environments.

Description: The intensity information from terrestrial laser scanners (TLS) has become an important object of study in recent years, and there are an increasing number of applications that would benefit from the addition of calibrated intensity data to the topographic information. In this paper, we study the range and incidence angle effects on the intensity measurements and search for practical correction methods for different TLS instruments and targets. We find that the range (distance) effect is strongly dominated by instrumental factors, whereas the incidence angle effect is mainly caused by the target surface properties. Correction for both effects is possible, but more studies are needed for physical interpretation and more efficient use of intensity data for target characterization.

Description: Monitoring mine waste from sulfide deposits by hyperspectral remote sensing can be used to predict surface water quality by quantitatively estimating acid drainage and metal contamination on a yearly basis. In addition, analysis of the mineralogy of surface crusts rich in soluble salts can provide a record of annual humidity and temperature. In fact, temporal monitoring of salt efflorescence from mine wastes at a mine site in the Iberian Pyrite Belt (Huelva, Spain) has been achieved using hyperspectral airborne Hymap data. Furthermore, climate variability estimates are possible based on oxidation stages derived from well-known sequences of minerals, by tracing sulfide oxidation intensity using archive spectral libraries. Thus, airborne and spaceborne hyperspectral remote sensing data can be used to provide a short-term record of climate change, and represent a useful set of tools for assessing environmental geoindicators in semi-arid areas. Spectral and geomorphological indicators can be monitored on a regular basis through image processing, supported by field and laboratory spectral data. In fact, hyperspectral image analysis is one of the methods selected by the Joint Research Centre of the European Community (Ispra, Italy) to study abandoned mine sites, in order to assess the enforcement of the European Mine Waste Directive (2006/21/EC of the European Parliament and of the Council 15 March 2006) on the management of waste from extractive industries (Official Journal of the European Union, 11 April 2006). The pyrite belt in Andalucia has been selected as one of the core mission test sites for the PECOMINES II program (Cracow, November 2005), using imaging spectroscopy; and this technique is expected to be implemented as a monitoring tool by the Environmental Net of Andalucía (REDIAM, Junta de Andalucía, Spain).

Description: Urban forest ecosystems provide a range of social and ecological services, but due to the heterogeneity of these canopies their spatial extent is difficult to quantify and monitor. Traditional per-pixel classification methods have been used to map urban canopies, however, such techniques are not generally appropriate for assessing these highly variable landscapes. Landsat imagery has historically been used for per-pixel driven land use/land cover (LULC) classifications, but the spatial resolution limits our ability to map small urban features. In such cases, hyperspatial resolution imagery such as aerial or satellite imagery with a resolution of 1 meter or below is preferred. Object-based image analysis (OBIA) allows for use of additional variables such as texture, shape, context, and other cognitive information provided by the image analyst to segment and classify image features, and thus, improve classifications. As part of this research we created LULC classifications for a pilot study area in Seattle, WA, USA, using OBIA techniques and freely available public aerial photography. We analyzed the differences in accuracies which can be achieved with OBIA using multispectral and true-color imagery. We also compared our results to a satellite based OBIA LULC and discussed the implications of per-pixel driven vs. OBIA-driven field sampling campaigns. We demonstrated that the OBIA approach can generate good and repeatable LULC classifications suitable for tree cover assessment in urban areas. Another important finding is that spectral content appeared to be more important than spatial detail of hyperspatial data when it comes to an OBIA-driven LULC.

Description: Invasive species’ phenologies often contrast with those of native species, representing opportunities for detection of invasive species with multi-temporal remote sensing. Detection is especially critical for ecosystem-transforming species that facilitate changes in disturbance regimes. The African C4 grass, Pennisetum ciliare, is transforming ecosystems on three continents and a number of neotropical islands by introducing a grass-fire cycle. However, previous attempts at discriminating P. ciliare in North America using multi-spectral imagery have been unsuccessful. In this paper, we integrate field measurements of hyperspectral plant species signatures and canopy cover with multi-temporal spectral analysis to identify opportunities for detection using moderate-resolution multi-spectral imagery. By applying these results to Landsat TM imagery, we show that multi-spectral discrimination of P. ciliare in heterogeneous mixed desert scrub is feasible, but only at high abundance levels that may have limited value to land managers seeking to control invasion. Much higher discriminability is possible with hyperspectral shortwave infrared imagery because of differences in non-photosynthetic vegetation in uninvaded and invaded landscapes during dormant seasons but these spectra are unavailable in multispectral sensors. Therefore, we recommend hyperspectral imagery for distinguishing invasive grass-dominated landscapes from uninvaded desert scrub.

Description: The objective of this study is to compare WorldView-2 (WV-2) and QuickBird-2-simulated (QB-2) imagery regarding their potential for object-based urban land cover classification. Optimal segmentation parameters were automatically found for each data set and the obtained results were quantitatively compared and discussed. Four different feature selection algorithms were used in order to verify to which data set the most relevant object-based features belong to. Object-based classifications were performed with four different supervised algorithms applied to each data set and the obtained accuracies and model performances indexes were compared. Segmentation experiments carried out involving bands exclusively available in the WV-2 sensor generated segments slightly more similar to our reference segments (only about 0.23 discrepancy). Fifty seven percent of the different selected features and 53% of all the 80 selections refer to features that can only be calculated with the additional bands of the WV-2 sensor. On the other hand, 57% of the most relevant features and 63% of the second most relevant features can also be calculated considering only the QB-2 bands. In 10 out of 16 classifications, higher Kappa values were achieved when features related to the additional bands of the WV-2 sensor were also considered. In most cases, classifications carried out with the 8-band-related features generated less complex and more efficient models than those generated only with QB-2 band-related features. Our results lead to the conclusion that spectrally similar classes like ceramic tile roofs and bare soil, as well as asphalt and dark asbestos roofs can be better distinguished when the additional bands of the WV-2 sensor are used throughout the object-based classification process.

Description: Cloud properties and the Earth’s radiation budget are defined as essential climate variables by Global Climate Observing System (GCOS). The cloud albedo is a measure for the portion of solar radiation reflected back to space by clouds. This information is essential for the analysis and interpretation of the Earth’s radiation budget and the solar surface irradiance. We present and discuss a method for the production of the effective cloud albedo and the solar surface irradiance based on the visible channel (0.45–1 μm) on-board of the Meteosat satellites. This method includes a newly developed self-calibration approach and has been used to generate a 23-year long (1983–2005) continuous and validated climate data record of the effective cloud albedo and the solar surface irradiance. Using these records we demonstrate the ability of the method to provide these essential variables in high accuracy and homogeneity. Further on, we discuss the role of the cloud albedo within climate monitoring and analysis. We found trends with opposite sign in the observed effective cloud albedo resulting in positive trends in the solar surface irradiance over ocean and partly negative trends over land. Ground measurements are scarce over the ocean and thus satellite-derived effective cloud albedo and solar surface irradiance constitutes a unique observational data source. Within this scope it has to be considered that the ocean is the main energy reservoir of the Earth, which emphasises the role of satellite-observed effective cloud albedo and derived solar surface irradiance as essential climate variables for climate monitoring and analysis.

Description: Roughness is an important input parameter for modeling of natural hazards such as floods, rock falls and avalanches, where it is basically assumed that flow velocities decrease with increasing roughness. Seeing roughness as a multi-scale level concept (i.e., ranging from fine-scale soil characteristics to description of understory and lower tree layer) various roughness raster products were derived from the original full-waveform airborne laser scanning (FWF-ALS) point cloud using two different types of roughness parameters, the surface roughness (SR) and the terrain roughness (TR). For the calculation of the SR, ALS terrain points within a defined height range to the terrain surface are considered. For the parameterization of the SR, two approaches are investigated. In the first approach, a geometric description by calculating the standard deviation of plane fitting residuals of terrain points is used. In the second one, the potential of the derived echo widths are analyzed for the parameterization of SR. The echo width is an indicator for roughness and the slope of the target. To achieve a comparable spatial resolution of both SR layers, the calculation of the standard deviation of detrended terrain points requires a higher terrain point density than the SR parameterization using the echo widths. The TR describes objects (i.e., point clusters) close but explicitly above the terrain surface, with 20 cm defined as threshold height value for delineation of the surface layer (i.e., forest floor layer). Two different empirically defined vegetation layers below the canopy layer were analyzed (TR I: 0.2 m to 1.0 m; TR II: 0.2 m to 3.0 m). A 1 m output grid cell size was chosen for all roughness parameters in order to provide consistency for further integration of high-resolution optical imagery. The derived roughness parameters were then jointly classified, together with a normalized Digital Surface Model (nDSM) showing the height of objects (i.e., trees) above ground. The presented approach enables the classification of forested areas in patches of different vegetation structure (e.g., varying soil roughness, understory, density of natural cover). For validation purposes in situ reference data were collected and cross-checked with the classification results, positively confirming the general feasibility of the proposed vertical concept of integrated roughness mapping on various vertical levels. Results can provide valuable input for forest mapping and monitoring, in particular with regard to natural hazard modeling.

Description: Greece is a high risk Mediterranean country with respect to wildfires. This risk has been increasing under the impact of climate change, and in summer 2007 approximately 200,000 ha of vegetated land were burnt. The SEVIRI sensor, on board the Meteosat Second Generation (MSG) geostationary satellite, is the only spaceborne sensor providing five and 15-minute observations of Europe in 12 spectral channels, including a short-wave infrared band sensitive to fire radiative temperature. In August 2007, when the bulk of the destructive wildfires started in Greece, the receiving station, operated by the Institute for Space Applications and Remote Sensing, provided us with a time series of MSG-SEVIRI images. These images were processed in order to test the reliability of a real‑time detection and tracking system and its complementarity to conventional means provided by the Fire Brigade. EUMETSAT’s Active Fire Monitoring (FIR) image processing algorithm for fire detection and monitoring was applied to SEVIRI data, then fine-tuned according to Greek conditions, and evaluated. Alarm announcements from the Fire Brigade’s archives were used as ground truthing data in order to assess detection reliability and system performance. During the examined period, MSG-SEVIRI data successfully detected 82% of the fire events in Greek territory with less than 1% false alarms.

Description: With the progression of LiDAR (Light Detection and Ranging) towards a mainstream resource management tool, it has become necessary to understand how best to process and analyze the data. While most ground surface identification algorithms remain proprietary and have high purchase costs; a few are openly available, free to use, and are supported by published results. Two of the latter are the multiscale curvature classification and the Boise Center Aerospace Laboratory LiDAR (BCAL) algorithms. This study investigated the accuracy of these two algorithms (and a combination of the two) to create a digital terrain model from a raw LiDAR point cloud in a semi-arid landscape. Accuracy of each algorithm was assessed via comparison with 〉7,000 high precision survey points stratified across six different cover types. The overall performance of both algorithms differed by only 2%; however, within specific cover types significant differences were observed in accuracy. The results highlight the accuracy of both algorithms across a variety of vegetation types, and ultimately suggest specific scenarios where one approach may outperform the other. Each algorithm produced similar results except in the ceanothus and conifer cover types where BCAL produced lower errors.

Description: Dual pushbroom hyperspectral sensors consist of two different instruments (covering different wavelengths) that are usually mounted on the same optical bench. This configuration leads to problems, such as co-registration of pixels and squint of the field of view, known as the boresight effect. Determination of image-orientation parameters is due to the combination of an inertial measurement system (IMU) and global position system (GPS). The different positions of the IMU, the GPS antenna and the imaging sensors cause the orientation and boresight effect. Any small change in the correction of the internal orientation affects the co-registration between images extracted from the two instruments. Correcting the boresight effect is a key and almost automatic task performed by all dual-system users to better analyze the full spectral information of a given pixel. Thus, the boresight effect is considered to be noise in the system and a problem that needs to be corrected prior to any (thematic) data analysis. We propose using the boresight effect, prior to its correction, as a tool to monitor and detect spectral phenomena that can provide additional information not present in the corrected images. The advantage of using this effect was investigated with the AISA-Dual sensor, composed of an EAGLE sensor for the VIS-NIR (VNIR) region (400–970 nm) and HAWK for the SWIR region (980–2,450 nm). During the course of more than six years of operating this sensor, we have found that the boresight effect provides a new capacity to analyze hyperspectral data, reported herein. Accordingly, we generated a protocol to use this effect for three applications: (1) enhancing the shadow effect; (2) generating a 3-D view; and (3) better detecting spectral/spatial anomalies based on sub-pixel edge detection. This paper provides examples of these applications and suggests possible uses from an airborne platform.

Description: In Washington State, USA, mountain goats (Oreamnos americanus) have experienced a long-term population decline. To assist management, we created annual and seasonal (summer and winter) habitat models based on 2 years of data collected from 38 GPS-collared (GPS plus collar v6, Vectronic-Aerospace GmbH, Berlin, Germany) mountain goats in the western Cascades. To address GPS bias of position acquisition, we evaluated habitat and physiographic effects on GPS collar performance at 543 sites in the Cascades. In the western Cascades, total vegetation cover and the quadratic mean diameter of trees were shown to effect GPS performance. In the eastern Cascades, aspect and total vegetation cover were found to influence GPS performance. To evaluate the influence of bias correction on the analysis of habitat selection, we created resource selection functions with and without bias correction for mountain goats in the western Cascades. We examined how well the resultant habitat models performed with reserved data (25% of fixes from 38 study animals) and with data from 9 other GPS-collared mountain goats that were both temporally and spatially independent. The statistical properties of our GPS bias correction model were similar to those previously reported explaining between 20 and 30% of the variation, however, application of bias correction improved the accuracy of the mountain goat habitat model by only 1–2% on average and did not alter parameter estimates in a meaningful, or consistent manner. Despite statistical limitations, our habitat models, most notably during the winter, provided the widest extent and most detailed models of the distribution of mountain goat habitat in the Cascades yet developed.

Description: The use of marine spatial planning for zoning multi-use areas is growing in both developed and developing countries. Comprehensive maps of marine resources, including those important for local fisheries management and biodiversity conservation, provide a crucial foundation of information for the planning process. Using a combination of field and high spatial resolution satellite data, we use an empirical procedure to create a bathymetric map (RMSE 1.76 m) and object-based image analysis to produce accurate maps of geomorphic and benthic coral reef classes (Kappa values of 0.80 and 0.63; 9 and 33 classes, respectively) covering a large (〉260 km2) traditional fisheries management area in Fiji. From these maps, we derive per-pixel information on habitat richness, structural complexity, coral cover and the distance from land, and use these variables as input in models to predict fish species richness, diversity and biomass. We show that random forest models outperform five other model types, and that all three fish community variables can be satisfactorily predicted from the high spatial resolution satellite data. We also show geomorphic zone to be the most important predictor on average, with secondary contributions from a range of other variables including benthic class, depth, distance from land, and live coral cover mapped at coarse spatial scales, suggesting that data with lower spatial resolution and lower cost may be sufficient for spatial predictions of the three fish community variables.

Description: With the appearance of low-cost and high-resolution consumer-grade digital cameras, a practical three-dimensional (3D) measurement system using a consumer-grade digital camera is greatly anticipated in various fields. In these circumstances, the authors have been concentrating on developing a practical 3D measurement system that includes photogrammetric software called the Image Based Integrated Measurement (IBIM) system. The IBIM system device consists of full/half-mirrors, a consumer-grade digital camera, and a laser distance meter. The most remarkable advantage of the system is its ability to calculate exterior orientation parameters, interior orientation parameters, and pseudo ground control points (GCPs) without using scale bars or the GCPs in the object field. The system has the ability to calibrate multiple cameras of different resolutions using a camera-variant parameter set. However, there remains one issue that needs to be resolved before this system can be effectively used, namely, improvement of the system which does not depend on the IBIM system device. With this motive, a practical photogrammetry method using a consumer-grade digital cameras and a hand-held laser distance meter is proposed. To test our proposed method, the bundle of distances from the center camera position to the feature points in the object field were measured individually at archaeological sites in Greece. In order to evaluate the possibility and practicability of the proposed photogrammetry method, this paper describes and evaluates the camera calibration techniques using images from multiple cameras of different resolutions and a bundle of distances.

Description: Hyperspectral image analysis is gaining momentum in a wealth of natural resources and agricultural applications facilitated by the increased availability of low-cost imaging systems. In this study, we demonstrate the development of the Vegetation Mobile Mapping System (VMMS), a low-cost hyperspectral sensing system that is supported by consumer-grade digital camera(s). The system was developed using off-the-shelf imaging and navigation components mainly for ground-based applications. The system integrates a variety of components including timing and positioning GPS receivers and an Inertial Measurement Unit (IMU). The system was designed to be modular and interoperable allowing the imaging components to be used with different navigation systems. The technique used for synchronizing captured images with GPS time was presented. A relative radiometric calibration technique utilizing images of homogeneous targets to normalize pixel gain and offset parameters was used. An empirical spectral calibration method was used to assign wavelengths to image bands. Data acquisition parameters to achieve appropriate spatial coverage were presented. The system was tested in ground-based data collection and analysis experiments that included water quality and vegetation studies.

Description: The Arctic is in transition from climate-driven thawing of permafrost. We investigate satellite-derived water equivalent mass changes, snow water equivalent with in situ measurements of runoff and ground-survey derived geoid models from 1999 through 2009. The Alaskan Arctic coastal plain groundwater storage (including wetland bog, thaw pond and lake) is increasing by 1.15 ± 0.65 km3/a (area-average 1.10 ± 0.62 cm/a), and Yukon River watershed groundwater storage is decreasing by 7.44 ± 3.76 km3/a (area‑average 0.79 ± 0.40 cm/a). Geoid changes show increases within the Arctic coastal region and decreases within the Yukon River watershed. We hypothesize these changes are linked to the development of new predominately closed- and possibly open-talik in the continuous permafrost zone under large thaw lakes with increases of lakes and new predominately open-talik and reduction of permafrost extent in the discontinuous and sporadic zones with decreases of thaw lakes.

Description: Conventional discrete return airborne lidar systems, used in the commercial sector for efficient generation of high quality spatial data, have been considered for the past decade to be an ideal choice for various mapping applications. Unlike two-dimensional aerial imagery, the elevation component of airborne lidar data provides the ability to represent vertical structure details with very high precision, which is an advantage for many lidar applications focusing on the analysis of elevated features such as 3D vegetation mapping. However, the use of conventional airborne discrete return lidar systems for some of these applications has often been limited, mostly due to relatively coarse vertical resolution and insufficient number of multiple measurements in vertical domain. For this reason, full waveform airborne sensors providing more detailed representation of target vertical structure have often been considered as a preferable choice in some areas of 3D vegetation mapping application, such as forestry research. This paper presents an overview of the specific features of airborne lidar technology concerning 3D mapping applications, particularly vegetation mapping. Certain key performance characteristics of lidar sensors important for the quality of vegetation mapping are discussed and illustrated by the advanced capabilities of the ALTM-Orion, a new discrete return sensor manufactured by Optech Incorporated. It is demonstrated that advanced discrete return sensors with enhanced 3D mapping capabilities can produce data of enhanced quality, which can represent complex structures of vegetation targets at the level of details equivalent in some aspects to the content of full waveform data. It is also shown that recent advances in conventional airborne lidar technology bear the potential to create a new application niche, where high quality dense point clouds, enhanced by fully recorded intensity for multiple returns, may provide sufficient information for modeling and analysis, which have traditionally been applied mostly to full waveform data.

Description: The global characteristics of retrievals of the column-averaged CO2 dry air mole fraction, XCO2, from shortwave infrared observations has been studied using the expected measurement performance of the NASA Orbiting Carbon Observatory-2 (OCO-2) mission. This study focuses on XCO2 retrieval precision and averaging kernels and their sensitivity to key parameters such as solar zenith angle (SZA), surface pressure, surface type and aerosol optical depth (AOD), for both nadir and sunglint observing modes. Realistic simulations have been carried out and the single sounding retrieval errors for XCO2 have been derived from the formal retrieval error covariance matrix under the assumption that the retrieval has converged to the correct answer and that the forward model can adequately describe the measurement. Thus, the retrieval errors presented in this study represent an estimate of the retrieval precision. For nadir observations, we find single-sounding retrieval errors with values typically less than 1 part per million (ppm) over most land surfaces for SZAs less than 70° and up to 2.5 ppm for larger SZAs. Larger errors are found over snow/ice and ocean surfaces due to their low albedo in the spectral regions of the CO2 absorption bands and, for ocean, also in the O2 A band. For sunglint observations, errors over the ocean are significantly smaller than in nadir mode with values in the range of 0.3 to 0.6 ppm for small SZAs which can decrease to values as small as 0.15 for the largest SZAs. The vertical sensitivity of the retrieval that is represented by the column averaging kernel peaks near the surface and exhibits values near unity throughout most of the troposphere for most anticipated scenes. Nadir observations over dark ocean or snow/ice surfaces and observations with large AOD and large SZA show a decreased sensitivity to near-surface CO2. All simulations are carried out for a mid-latitude summer atmospheric profile, a given aerosol type and vertical distribution, a constant windspeed for ocean sunglint and by excluding the presence of thin cirrus clouds. The impact of these parameters on averaging kernels and XCO2 retrieval errors are studied with sensitivity studies. Systematic biases in retrieved XCO2, as can be introduced by uncertainties in the spectroscopic parameters, instrument calibration or deficiencies in the retrieval algorithm itself, are not included in this study. The presented error estimates will therefore only describe the true retrieval errors once systematic biases are eliminated. It is expected that it will be possible to retrieve XCO2 for cloud free observations and for low AOD (here less than 0.3 for the wavelength region of the O2 A band) with sufficient accuracy for improving CO2 surface flux estimates and we find that on average 18% to 21% of all observations are sufficiently cloud-free with only few areas suffering from the presence of persistent clouds or high AOD. This results typically in tens of useful observations per 16 day ground track repeat cycle at a 1° × 1° resolution. Averaging observations acquired along ~1° intervals for individual ground tracks will significantly reduce the random component of the errors of the XCO2 average product for ingestion into data assimilation/inverse models. If biases in the XCO2 retrieval of the order of a few tenth ppm can be successfully removed by validation or by bias-correction in the flux inversion, then it can be expected that OCO-2 XCO2 data can lead to tremendous improvements in estimates of CO2 surface-atmosphere fluxes.

Description: The introduction of web based mapping facilities that use satellite imagery, offers local people the possibility to map their environment. However, maps need to be accurate, which is the reason why map making is assigned to professionals. In this paper, we investigated the classification accuracy of road infrastructure from high resolution satellite imagery of an urban area in western Kenya achieved by surveyors and non-surveyors alike, with and without local knowledge. Those with local knowledge classified roads with over 92% accuracy on average, irrespective of surveying background. Professional surveyors and laymen without local knowledge achieved lower accuracies of 67.7% and 42.9% respectively. We argue that local knowledge is also likely to improve the classification accuracy of many other attributes featured in topographic maps and thus conclude that there is reason to consider engaging local expertise in the production and updating of topographic maps.

Description: This paper is focused on spaceborne Differential Interferometric SAR (DInSAR) for land deformation measurement and monitoring. In the last two decades several DInSAR data analysis procedures have been proposed. The objective of this paper is to describe the DInSAR data processing and analysis tools developed at the Institute of Geomatics in almost ten years of research activities. Four main DInSAR analysis procedures are described, which range from the standard DInSAR analysis based on a single interferogram to more advanced Persistent Scatterer Interferometry (PSI) approaches. These different procedures guarantee a sufficient flexibility in DInSAR data processing. In order to provide a technical insight into these analysis procedures, a whole section discusses their main data processing and analysis steps, especially those needed in PSI analyses. A specific section is devoted to the core of our PSI analysis tools: the so-called 2+1D phase unwrapping procedure, which couples a 2D phase unwrapping, performed interferogram-wise, with a kind of 1D phase unwrapping along time, performed pixel-wise. In the last part of the paper, some examples of DInSAR results are discussed, which were derived by standard DInSAR or PSI analyses. Most of these results were derived from X-band SAR data coming from the TerraSAR-X and CosmoSkyMed sensors.

Description: Meteosat satellite images are processed to yield values of the incoming surface solar irradiance (SSI), one of the Essential Climate Variables. Two HelioClim databases, HC-1 and HC-3, were constructed covering Europe, Africa and the Atlantic Ocean, and contain daily and monthly means of SSI. The HC-1 database spans from 1985 to 2005; HC‑3 began in 2004 and is updated daily. Their quality and limitations in retrieving monthly means of SSI have been studied by a comparison between eleven stations offering long time-series of measurements. A good agreement was observed for each site: bias was less than 10 W/m² in absolute value (5% in relative value) for HC-3. HC-1 offers a similar quality, though it underestimates the SSI for latitudes greater than 45° and less than −45°. Time-series running from 1985 to date can be created by concatenating the HC-1 and HC-3 values and could help in assessing SSI and its changes.

Description: Through the application and use of geospatial data, this study aimed to detect and characterize some of the key environmental drivers contributing to landscape-scale vegetation response patterns in Central Asia. The objectives of the study were to identify the variables driving the year-to-year vegetation dynamics in three regional landscapes (desert, steppe, and mountainous); and to determine if the identified environmental drivers can be used to explain the spatial-temporal variability of these spatio-temporal dynamics over time. It was posed that patterns of change in terrestrial phenology, derived from the 8 km bi-weekly time series of Normalized Difference Vegetation Index (NDVI) data acquired by the Advanced Very High Resolution Radiometer (AVHRR) satellites (1981–2008), can be explained through a multi-scale analysis of a suite of environmental drivers. Multiple linear stepwise regression analyses were used to test the hypotheses and address the objectives of the study. The annually computed phenological response variables or pheno-metricstime (season start, season length, and an NDVI-based productivity metric) were modeled as a function of ten environmental factors relating to soil, topography, and climate. Each of the three studied regional landscapes was shown to be governed by a distinctive suite of environmental drivers. The phenological responses of the steppe landscapes were affected by the year-to-year variation in temperature regimes. The phenology of the mountainous landscapes was influenced primarily by the elevation gradient. The phenological responses of desert landscapes were demonstrated to have the greatest variability over time and seemed to be affected by soil carbon content and year-to-year variation of both temperature regimes and winter precipitation patterns. Amounts and scales of observed phenological variability over time (measured through coefficient of variation for each pheno-metrictime) in each of the regional landscapes were interpreted in terms of their resistance and resilience capacities under existing and projected environmental settings.

Description: The objective of the research is to characterize the surface spectral reflectance of the nearshore waters using atmospheric correction code—Tafkaa for retrieval of the marine water constituent concentrations from hyperspectral data. The study area is the nearshore waters of New York/New Jersey considered as a valued ecological, economic and recreational resource within the New York metropolitan area. Comparison of the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) measured radiance and in situ reflectance measurement shows the effect of the solar source and atmosphere in the total upwelling spectral radiance measured by AVIRIS. Radiative transfer code, Tafkaa was applied to remove the effects of the atmosphere and to generate accurate reflectance (R(0)) from the AVIRIS radiance for retrieving water quality parameters (i.e., total chlorophyll). Chlorophyll estimation as index of phytoplankton abundance was optimized using AVIRIS band ratio at 675 nm and 702 nm resulting in a coefficient of determination of R2 = 0.98. Use of the radiative transfer code in conjunction with bio optical model is the main tool for using ocean color remote sensing as an operational tool for monitoring of the key nearshore ecological communities of phytoplankton important in global change studies.

Description: Japanese Knotweed s.l. taxa are amongst the most aggressive vascular plant Invasive Alien Species (IAS) in the world. These taxa form dense, suppressive monocultures and are persistent, pervasive invaders throughout the more economically developed countries (MEDCs) of the world. The current paper utilises the Object-Based Image Analysis (OBIA) approach of Definiens Imaging Developer software, in combination with very high spatial resolution (VHSR) colour infra-red (CIR) and visible‑band (RGB) aerial photography in order to detect Japanese Knotweed s.l. taxa in Wales (UK). An algorithm was created using Definiens in order to detect these taxa, using variables found to effectively distinguish them from landscape and vegetation features. The results of the detection algorithm were accurate, as confirmed by field validation and desk‑based studies. Further, these results may be incorporated into Geographical Information Systems (GIS) research as they are readily transferable as vector polygons (shapefiles). The successful detection results developed within the Definiens software should enable greater management and control efficacy. Further to this, the basic principles of the detection process could enable detection of these taxa worldwide, given the (relatively) limited technical requirements necessary to conduct further analyses.

Description: In this paper, the effect of urban heat island is analyzed using the Landsat TM data and ASTER data in 2005 as a case study in Hong Kong. Two algorithms were applied to retrieve the land surface temperature (LST) distribution from the Landsat TM and ASTER data. The spatial pattern of LST in the study area is retrieved to characterize their local effects on urban heat island. In addition, the correlation between LST and the normalized difference vegetation index (NDVI), the normalized difference build-up index (NDBI) is analyzed to explore the impacts of the green land and the build-up land on the urban heat island by calculating the ecological evaluation index of sub-urban areas. The results indicate that the effect of urban heat island in Hong Kong is mainly located in three sub-urban areas, namely, Kowloon Island, the northern Hong Kong Island and Hong Kong International Airport. The correlation between LST and NDVI, NDBI also indicates that the negative correlation of LST and NDVI suggests that the green land can weaken the effect on urban heat island, while the positive correlation between LST and NDBI means that the built-up land can strengthen the effect of urban heat island in our case study. Although satellite data (e.g., Landsat TM and ASTER thermal bands data) can be applied to examine the distribution of urban heat islands in places such as Hong Kong, the method still needs to be refined with in situ measurements of LST in future studies.

Description: The objective was to investigate the error sources of the airborne laser scanning based individual tree detection (ITD), and its effects on forest management planning calculations. The investigated error sources were detection of trees (etd), error in tree height prediction (eh) and error in tree diameter prediction (ed). The effects of errors were analyzed with Monte Carlo simulations. etd was modeled empirically based on a tree’s relative size. A total of five different tree detection scenarios were tested. Effect of eh was investigated using 5% and 0% and effect of ed using 20%, 15%, 10%, 5%, 0% error levels, respectively. The research material comprised 15 forest stands located in Southern Finland. Measurements of 5,300 trees and their timber assortments were utilized as a starting point for the Monte Carlo simulated ITD inventories. ITD carried out for the same study area provided a starting point (Scenario 1) for etd. In Scenario 1, 60.2% from stem number and 75.9% from total volume (Vtotal) were detected. When the only error source was etd (tree detection varying from 75.9% to 100% of Vtotal), root mean square errors (RMSEs) in stand characteristics ranged between the scenarios from 32.4% to 0.6%, 29.0% to 0.5%, 7.8% to 0.2% and 5.4% to 0.1% in stand basal area (BA), Vtotal, mean height (Hg) and mean diameter (Dg), respectively. Saw wood volume RMSE varied from 25.1% to 0.2%, as pulp wood volume respective varied from 37.8% to 1.0% when errors stemmed only from etd. The effect of ed was most significant for Vtotal and BA and the decrease in RMSE was from 12.0% to 0.6% (BA) and from 10.9% to 0.5% (Vtotal) in the most accurate tree detection scenario when ed varied from 20% to 0%. The effect of increased accuracy in tree height prediction was minor for all the stand characteristics. The results show that the most important error source in ITD is tree detection. At stand level, unbiased predictions for tree height and diameter are enough, given the present tree detection accuracy.

Description: The Hyperspectral Infrared Imager (HyspIRI) is a proposed NASA satellite remote sensing system combining a visible to shortwave infrared (VSWIR) imaging spectrometer with over 200 spectral bands between 0.38 and 2.5 μm and an 8-band thermal infrared (TIR) multispectral imager, both at 60 m spatial resolution. Short Wave Infrared (SWIR) (2.0–2.5 μm) simulation results are described here using Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data in preparation for the future launch. The simulated data were used to assess the effect of the HyspIRI 60 m spatial resolution on the ability to identify and map minerals at hydrothermally altered and geothermal areas. Mineral maps produced using these data successfully detected and mapped a wide variety of characteristic minerals, including jarosite, alunite, kaolinite, dickite, muscovite-illite, montmorillonite, pyrophyllite, calcite, buddingtonite, and hydrothermal silica. Confusion matrix analysis of the datasets showed overall classification accuracy ranging from 70 to 92% for the 60 m HyspIRI simulated data relative to 15 m spatial resolution data. Classification accuracy was lower for similar minerals and smaller areas, which were not mapped well by the simulated 60 m HyspIRI data due to blending of similar signatures and spectral mixing with adjacent pixels. The simulations demonstrate that HyspIRI SWIR data, while somewhat limited by their relatively coarse spatial resolution, should still be useful for mapping hydrothermal/geothermal systems, and for many other geologic applications requiring mineral mapping.

Description: The sensitivity of the climate system to an imposed radiative imbalance remains the largest source of uncertainty in projections of future anthropogenic climate change. Here we present further evidence that this uncertainty from an observational perspective is largely due to the masking of the radiative feedback signal by internal radiative forcing, probably due to natural cloud variations. That these internal radiative forcings exist and likely corrupt feedback diagnosis is demonstrated with lag regression analysis of satellite and coupled climate model data, interpreted with a simple forcing-feedback model. While the satellite-based metrics for the period 2000–2010 depart substantially in the direction of lower climate sensitivity from those similarly computed from coupled climate models, we find that, with traditional methods, it is not possible to accurately quantify this discrepancy in terms of the feedbacks which determine climate sensitivity. It is concluded that atmospheric feedback diagnosis of the climate system remains an unsolved problem, due primarily to the inability to distinguish between radiative forcing and radiative feedback in satellite radiative budget observations.

Description: In remote sensing, traditional methodologies for image classification consider the spectral values of a pixel in different image bands. More recently, classification methods have used neighboring pixels to provide more information. In the present study, we used these more advanced techniques to discriminate between mangrove and non‑mangrove regions in the Gulf of California of northwestern Mexico. A maximum likelihood algorithm was used to obtain a spectral distance map of the vegetation signature characteristic of mangrove areas. Receiver operating characteristic (ROC) curve analysis was applied to this map to improve classification. Two classification thresholds were set to determine mangrove and non-mangrove areas, and two performance statistics (sensitivity and specificity) were calculated to express the uncertainty (errors of omission and commission) associated with the two maps. The surface area of the mangrove category obtained by maximum likelihood classification was slightly higher than that obtained from the land cover map generated by the ROC curve, but with the difference of these areas to have a high level of accuracy in the prediction of the model. This suggests a considerable degree of uncertainty in the spectral signatures of pixels that distinguish mangrove forest from other land cover categories.

Description: The automatic extraction of buildings from true color stereo aerial imagery in a dense built-up area is the main focus of this paper. Our approach strategy aimed at reducing the complexity of the image content by means of a three-step procedure combining reliable geospatial image analysis techniques. Even if it is a rudimentary first step towards a more general approach, the method presented proved useful in urban sprawl studies for rapid map production in flat area by retrieving indispensable information on buildings from scanned historic aerial photography. After the preliminary creation of a photogrammetric model to manage Digital Surface Model and orthophotos, five intermediate mask-layers data (Elevation, Slope, Vegetation, Shadow, Canny, Shadow, Edges) were processed through the combined use of remote sensing image processing and GIS software environments. Lastly, a rectangular building block model without roof structures (Level of Detail, LoD1) was automatically generated. System performance was evaluated with objective criteria, showing good results in a complex urban area featuring various types of building objects.

Description: Spatial monitoring tools are necessary to respond to the threat of global biodiversity loss. At the European scale, remote sensing tools for NATURA 2000 habitat monitoring have been requested by the European Commission to fulfill the obligations of the EU Habitats Directive. This paper introduces a method by which swath events in semi-natural grasslands can be detected from multi-temporal TerraSAR-X data. The investigated study sites represent rare and endangered habitats (NATURA 2000 codes 6410, 6510), located in the Döberitzer Heide nature conservation area west of Berlin. We analyzed a time series of 11 stripmap images (HH-polarization) covering the vegetation period affected by swath (June to September 2010) at a constant 11-day acquisition rate. A swath detection rule was established to extract the swath events for the NATURA 2000 habitats as well as for six contrasting pasture sites not affected by swath. All swath events observed in the field were correctly allocated. The results indicate the potential to allocate semi-natural grassland swath events to 11-day-periods using TerraSAR-X time series. Since the conservation of semi-natural grassland habitats requires compliance with specific swath management rules, the detection of swath events may thus provide new parameters for the monitoring of NATURA 2000 grassland habitats.

Description: Eelgrass (Zostera marina) is a keystone component of inter- and sub-tidal ecosystems. However, anthropogenic pressures have caused its populations to decline worldwide. Delineation and continuous monitoring of eelgrass distribution is an integral part of understanding these pressures and providing effective coastal ecosystem management. A proposed tool for such spatial monitoring is remote imagery, which can cost- and time-effectively cover large and inaccessible areas frequently. However, to effectively apply this technology, an understanding is required of the spectral behavior of eelgrass and its associated substrates. In this study, in situ hyperspectral measurements were used to define key spectral variables that provide the greatest spectral separation between Z. marina and associated submerged substrates. For eelgrass classification of an in situ above water reflectance dataset, the selected variables were: slope 500–530 nm, first derivatives (R’) at 566 nm, 580 nm, and 602 nm, yielding 98% overall accuracy. When the in situ reflectance dataset was water-corrected, the selected variables were: 566:600 and 566:710, yielding 97% overall accuracy. The depth constraint for eelgrass identification with the field spectrometer was 5.0 to 6.0 m on average, with a range of 3.0 to 15.0 m depending on the characteristics of the water column. A case study involving benthic classification of hyperspectral airborne imagery showed the major advantage of the variable selection was meeting the sample size requirements of the more statistically complex Maximum Likelihood classifier. Results of this classifier yielded eelgrass classification accuracy of over 85%. The depth limit of eelgrass spectral detection for the AISA sensor was 5.5 m.

Description: For many coastal waters, total suspended matter (TSM) plays a major role in key biological, chemical and geological processes. Effective mapping and monitoring technologies for TSM are therefore needed to support research investigations and environmental assessment and management efforts. Although several investigators have demonstrated that TSM or suspended sediments can be successfully mapped using MODIS 250 m data for relatively large water bodies, MODIS 250 m data is of more limited use for smaller estuaries and bays or aquatic systems with complex shoreline geometry. To adequately examine TSM in the Albemarle-Pamlico Estuarine System (APES) of North Carolina, the large-scale synoptic view of MODIS and the higher spatial resolution of other sensors are required. MODIS, Landsat 7 ETM+ and FORMOSAT-2 remote sensing instrument (RSI) data were collected on 8 November, 24 November and 10 December, 2010. Using TSM images (mg/L) derived from MODIS 250 m band 1 (620–670 nm) data, Landsat 7 ETM+ 30 m band 3 (630–690 nm) and FORMOSAT-2 RSI 8 m band 3 (630−690 nm) atmospherically corrected images were calibrated to TSM for select areas of the APES. There was a significant linear relationship between both Landsat 7 ETM+ (r2 = 0.87, n = 599, P 〈 0.001) and FORMOSAT-2 RSI (r2 = 0.95, n = 583, P 〈 0.001) reflectance images and MODIS-derived TSM concentrations, thus providing consistent estimates of TSM at 250, 30 and 8 m pixel resolutions. This multi-sensor approach will support a broad range of investigations on the water quality of the APES and help guide sampling schemes of future field campaigns.

Description: Significant instrumental systematic errors are known to exist in data captured with range cameras using lock-in pixel technology. Because they are independent of the imaged object scene structure, these errors can be rigorously estimated in a self-calibrating bundle adjustment procedure. This paper presents a review and a quantitative comparison of three methods for range camera self-calibration in order to determine which, if any, is superior. Two different SwissRanger range cameras have been calibrated using each method. Though differences of up to 2 mm (in object space) in both the observation precision and accuracy measures exist between the methods, they are of little practical consequence when compared to the magnitude of these measures (12 mm to 18 mm). One of the methods was found to underestimate the principal distance but overestimate the rangefinder offset in comparison to the other two methods whose estimates agreed more closely. Strong correlations among the rangefinder offset, periodic error terms and the camera position co-ordinates are indentified and their cause explained in terms of network geometry and observation range.

Description: Solar surface irradiance (SIS) is an essential variable in the radiation budget of the Earth. Climate data records (CDR’s) of SIS are required for climate monitoring, for climate model evaluation and for solar energy applications. A 23 year long (1983–2005) continuous and validated SIS CDR based on the visible channel (0.45–1 μm) of the MVIRI instruments onboard the first generation of Meteosat satellites has recently been generated using a climate version of the well established Heliosat method. This version of the Heliosat method includes a newly developed self-calibration algorithm and an improved algorithm to determine the clear sky reflection. The climate Heliosat version is also applied to the visible narrow-band channels of SEVIRI onboard the Meteosat Second Generation Satellites (2004–present). The respective channels are observing the Earth in the wavelength region at about 0.6 μm and 0.8 μm. SIS values of the overlapping time period are used to analyse whether a homogeneous extension of the MVIRI CDR is possible with the SEVIRI narrowband channels. It is demonstrated that the spectral differences between the used visible channels leads to significant differences in the solar surface irradiance in specific regions. Especially, over vegetated areas the reflectance exhibits a high spectral dependency resulting in large differences in the retrieved SIS. The applied self-calibration method alone is not able to compensate the spectral differences of the channels. Furthermore, the extended range of the input values (satellite counts) enhances the cloud detection of the SEVIRI instruments resulting in lower values for SIS, on average. Our findings have implications for the application of the Heliosat method to data from other geostationary satellites (e.g., GOES, GMS). They demonstrate the need for a careful analysis of the effect of spectral and technological differences in visible channels on the retrieved solar irradiance.

Description: Regional studies of land cover change are often limited by available data and in terms of comparability across regions, by the transferability of methods. This research addresses the role of roads and infrastructure improvements across a tri-national frontier region with similar climatic and biophysical conditions but very different trajectories of forest clearing. The standardization of methodologies and the extensive spatial and temporal framework of the analysis are exciting as they allow us to monitor a dynamic region with global significance as it enters an era of increased road connectivity and massive potential forest loss. Our study region is the “MAP” frontier, which covers Madre de Dios in Peru, Acre in Brazil, and Pando in Bolivia. This tri-national frontier is being integrated into the global economy via the paving of the Inter-Oceanic Highway which links the region to ports in the Atlantic and Pacific, constituting a major infrastructure change within just the last decade. Notably, there are differences in the extent of road paving among the three sides of the tri-national frontier, with paving complete in Acre, underway in Madre de Dios, and incipient in Pando. Through a multi-temporal analysis of land cover in the MAP region from 1986 to 2005, we found that rates of deforestation differ across the MAP frontier, with higher rates in Acre, followed by Madre de Dios and the lowest rates in Pando, although the dominant land cover across the region is still stable forest cover (89% overall). For all dates in the study period, deforestation rates drop with distance from major roads although the distance before this drop off appears to relate to development, with Acre influencing forests up to around 45 km out, Madre de Dios to about 18 km out and less of a discernable effect or distance value in Pando. As development occurs, the converted forest areas saturate close to roads, resulting in increasing rates of deforestation at further distances and patch consolidation of clearings over time. We can use this trend as a basis for future change predictions, with Acre providing a guide to likely future development for Madre de Dios, and in time potentially for Pando. Given the correspondence of road paving to deforestation, our findings imply that as road paving increases connectivity, flows of people and goods will accelerate across this landscape, increasing the likelihood of dramatic future changes on all sides of the tri‑national frontier.

Description: Two remote-sensing optical algorithms for the retrieval of the water quality components (WQCs) in the Albemarle-Pamlico Estuarine System (APES) were developed and validated for chlorophyll a (Chl). Both algorithms were semi-empirical because they incorporated some elements of optical processes in the atmosphere, water, and air/water interface. One incorporated a very simple atmospheric correction and modified quasi-single-scattering approximation (QSSA) for estimating the spectral Gordon’s parameter, and the second estimated WQCs directly from the top of atmosphere satellite radiance without atmospheric corrections. A modified version of the Global Meteorological Database for Solar Energy and Applied Meteorology (METEONORM) was used to estimate directional atmospheric transmittances. The study incorporated in situ Chl data from the Ferry-Based Monitoring (FerryMon) program collected in the Neuse River Estuary (n = 633) and Pamlico Sound (n = 362), along with Medium Resolution Imaging Spectrometer (MERIS) satellite imagery collected (2006–2009) across the APES; providing quasi-coinciding samples for Chl algorithm development and validation. Results indicated a coefficient of determination (R2) of 0.70 and mean-normalized root-mean-squares errors (NRMSE) of 52% in the Neuse River Estuary and R2 = 0.44 (NRMSE = 75 %) in the Pamlico Sound—without atmospheric corrections. The simple atmospheric correction tested provided on performance improvements. Algorithm performance demonstrated the potential for supporting long-term operational WQCs satellite monitoring in the APES.

Description: The determination of the orthometric height from geometric leveling has practical difficulties that, despite a number of scientific and technological advances, passed a century without substantial modifications or advances. Currently, the Global Navigation Satellite System (GNSS) has been used with reasonable success for orthometric height determination. With a sufficient number of benchmarks with known horizontal and vertical coordinates, it is often possible to adjust using the least squares method mathematical expressions that allow interpolation of geoid heights. The objective of this study is to present an alternative method to interpolate geoid heights based on the technique of Artificial Neural Networks (ANNs). The study area is the Brazilian state of São Paulo, and for training the ANN the authors have used geoid height information from the EGM08 gravity model with a grid spacing of 10 minutes of arc. The efficiency of the model was tested at 157 points with known geoid heights distributed across the study area. The results were also compared with the Brazilian Geoid Model (MAPGEO2004). Based on those 157 benchmarks it was possible to verify that the model generated by ANNs provided a mean absolute error of 0.24 m in obtaining a geoid height value. Statistical tests have shown that there was no difference between the means from known geoid heights and geoid heights provided by the neural model for a significance level of 5%. It was also found that ANNs provided an improvement of 2.7 times in geoid height estimates when compared with the MAPGEO2004 geoid model.

Description: The overall objective of this study was to use field spectro-radiometers for finding possible spectral regions in which chlorophyll-a (Chl-a) and particulate organic carbon (POC) could be identified so as to assist the assessment and monitoring of water quality using satellite remote sensing technology. This paper presents the methodology adopted in this study which is based on the application of linear regression analysis between the mean reflectance values (measured with the GER1500 field spectro-radiometer) across the spectrum and the concentrations of chlorophyll-a (µg/L) and POC (µg/L) acquired simultaneously on the same day and time in the Lower Thames Valley in West London (U.K.) from old campaigns. Each regression model (512 in total) corresponded to a measured wavelength of the GER1500 field spectro-radiometer. The achieved correlations presented as r2 against wavelength, indicate the regions with high correlation values for both water quality variables. Based on the results from this study and by matching the spectral bands of the field spectro-radiometer with those of the Landsat TM satellite sensor (or any other sensor), it has been found that suitable spectral regions for monitoring water quality in water treatment reservoirs are the following: for chlorophyll-a, the spectral region of 0.45–0.52 μm (TM band 1), and for POC, the region 0.52–0.60 μm (TM bands 1 and 2). Then 12 atmospheric corrected Landsat TM/ETM+ band 1 images acquired from 2001 to 2010 were used for validation purposes to retrieve the Chl-a concentrations.

Description: Urbanization reflects how human-activities affect natural climate system. Accurately assessing the urban system by comparing it with the nearby rural regions helps to identify the impacts of urbanization. This work uses the recent satellite observed aerosol, skin temperature, land cover, albedo, cloud fraction and water vapor measurements to reveal how the city of Shanghai, one of the biggest, dense urban areas in East Asia, affects land surface and atmosphere conditions. In addition, the National Aeronautics and Space Administration (NASA) ground observations from AErosol RObotic NETwork (AERONET) is also used to reveal diurnal, seasonal, and interannual variations of the heavy aerosol load over Shanghai region. Furthermore, Shanghai reduces surface albedo, total column water vapor, cloud fraction and increases land skin temperature than rural region. These observations prove that Shanghai significantly modifies local and regional land surface physical properties as well as physical processes, which lead to the urban heat island effect (UHI).

Description: In Finland, forest site types are used to assess the need of silvicultural operations and the growth potential of the forests and, therefore, provide important inventory information. This study introduces airborne laser scanner (ALS) data and the k-NN classifier data analysis technique applicable to the site quality assessment of mature forests. Both the echo height and the intensity value percentiles of different echo types of ALS data were used in the analysis. The data are of 274 mature forest stands of different sizes, belonging to five forest site types, varying from very fertile to poor forests, in Koli National Park, eastern Finland. The k-NN classifier was applied with values of k varying from 1 to 5. The best overall classification accuracy achieved for all the forest site types and for a single type, were 58% and 73%, respectively. The conclusion is that when conducting large-scale forest inventories ALS-data based analysis would be a useful technology for the identification of mature boreal site types. However, the technique could still be improved and further studies are needed to ensure its applicability under different local conditions and with data representing earlier stages of stand development.

Description: Snowfall detection and measurement represent highly difficult problems in modern hydrometeorology. Ground measurements are complicated due to detection technology limitations, snow drift and accumulation issues, and error definition. The snowfall detection from space is in turn affected by all detection limitations that characterize the measurement of rainfall with the addition of several complications, such as the indirect character of remote sensing precipitation estimation, the presence of frozen or snow-covered terrain, and the unknown vertical distribution of hydrometeors in the cloud column. Several methods for the retrieval of snowfall intensity from satellite have been proposed in recent times using passive and active sensors. No satisfactory answer to the general problem of quantitative snowfall intensity determination has been found to date, but several studies contribute to delineate a working framework for the future operational retrieval algorithms.

Description: Image registration is widely used in remote-sensing applications. The existing automatic image registration techniques fall into two categories: Intensity-based and feature-based; the latter (which extracts structures from both images) being more suitable for multi-sensor fusion, detection of temporal changes and image mosaicking. Conventional image registration algorithms have proven to be inaccurate, time-consuming, and unfeasible due to image complexity which makes it cumbersome or even impossible to discern the appropriate control points. In this study, we propose a novel method for automatic image registration based on topology (AIRTop) for change detection and multi‑sensor (airborne and spaceborne) fusion. In this algorithm, we first apply image‑processing methods (SURF—Speeded-Up Robust Features) to extract the landmark structures (roads and buildings) and convert them to a features (vector) map. The following stages are applied in GIS (Geographic Information System), where topology rules, which define the permissible spatial relationships between features, are defined. The relationships between features are established by weight-based topological map-matching algorithm (tMM). The suggested algorithm presents a robust method for image registration. The main focus in this study is on scale and image rotation, when the quality of the scanning system is constant. These seem to offer a good compromise between feature complexity and robustness to commonly occurring deformations. The skew and the anisotropic scaling are assumed to be second-order effects that are covered to some degree by the overall robustness of the sensor.

Description: Urbanization reflects how human-activities affect natural climate system. Accurately assessing the urban system by comparing it with the nearby rural regions helps to identify the impacts of urbanization. This work uses the recent satellite observed aerosol, skin temperature, land cover, albedo, cloud fraction and water vapor measurements to reveal how the city of Shanghai, one of the biggest, dense urban areas in East Asia, affects land surface and atmosphere conditions. In addition, the National Aeronautics and Space Administration (NASA) ground observations from AErosol RObotic NETwork (AERONET) is also used to reveal diurnal, seasonal, and interannual variations of the heavy aerosol load over Shanghai region. Furthermore, Shanghai reduces surface albedo, total column water vapor, cloud fraction and increases land skin temperature than rural region. These observations prove that Shanghai significantly modifies local and regional land surface physical properties as well as physical processes, which lead to the urban heat island effect (UHI).

Description: In Finland, forest site types are used to assess the need of silvicultural operations and the growth potential of the forests and, therefore, provide important inventory information. This study introduces airborne laser scanner (ALS) data and the k-NN classifier data analysis technique applicable to the site quality assessment of mature forests. Both the echo height and the intensity value percentiles of different echo types of ALS data were used in the analysis. The data are of 274 mature forest stands of different sizes, belonging to five forest site types, varying from very fertile to poor forests, in Koli National Park, eastern Finland. The k-NN classifier was applied with values of k varying from 1 to 5. The best overall classification accuracy achieved for all the forest site types and for a single type, were 58% and 73%, respectively. The conclusion is that when conducting large-scale forest inventories ALS-data based analysis would be a useful technology for the identification of mature boreal site types. However, the technique could still be improved and further studies are needed to ensure its applicability under different local conditions and with data representing earlier stages of stand development.

Description: The active Séchilienne landslide (Isère, France) has been continuously monitored by tacheometry, radar and extensometry devices for 25 years. Indeed, if the 3 mil. m3 of rocks in the active zone named ―Ruines‖ fell down, the debris would dam the Romanche valley. The breaking of the dam by overtopping and rapid erosion would bring a catastrophic flood and other dramatic consequences throughout the valley. Given the rockfall hazard in the most active zone, it is impossible to use targets in this area: Only reflectorless remote sensing techniques can provide information. A time-series of seven Terrestrial Laser Scanner (TLS) point clouds acquired between 2004 and 2007 enable us to monitor the 3D displacements of the whole scanned area, although point coverage is not homogeneous. From this sequential monitoring, the volume of registered collapses can be deduced and the landslide movement along the main geological structures can be inferred. From monitoring associated subsidence and toppling observed on TLS data, it can be deduced that blocks rearrangements are linked to structural settings and that the Séchilienne landslide is complex. To conclude, TLS point clouds enable an accurate monitoring of the evolution of the inaccessible "Ruines" area and, proven its ability to provide reliable kinematic information, even in areas where on-site instrumentation is infeasible.

Description: In this letter, the urban heat island effects on the temperature time series of Uccle (Brussels, Belgium) during the summers months 1960–1999 was estimated using both ground-based weather stations and remote sensing imagery, combined with a numerical land surface scheme including state-of-the-art urban parameterization, the Town Energy Balance Scheme. Analysis of urban warming based on remote sensing method reveals that the urban bias on minimum temperature is rising at a higher rate, 2.5 times (2.85 ground-based observed) more, than on maximum temperature, with a linear trend of 0.15 °C (0.19 °C ground-based observed) and 0.06 °C (0.06 °C ground-based observed) per decade respectively. The results based on remote sensing imagery are compatible with estimates of urban warming based on weather stations. Therefore, the technique presented in this work is a useful tool in estimating the urban heat island contamination in long time series, countering the drawbacks of a ground-observational approach.

Description: Most remote sensing studies in deserts focus solely on vegetation monitoring to assess the extent of desertification. However, the application of sand dune encroachment into such studies would greatly improve the accuracy in the prediction criteria of risk-prone areas. This study applies the latter methodology for tracking desertification using sand dunes in the Kelso Dunes (in Newberry-Baker, CA, USA). The approach involves the comparison of spectral characteristics of the dunes in Landsat Thematic Mapper (TM) images over a 24-year period (1982, 1988, 1994, 2000, and 2006). During this 24-year period, two El Niño events occurred (1983 and 1993); it was concluded that despite the shift in predominant winds, the short-term variation in wind direction did not make a noticeable change in dune formation, but greatly influences vegetation cover. Therefore, relying solely on vegetation monitoring to assess desertification can lead to overestimations in prediction analysis. Results from this study indicate that the Kelso Dunes are experiencing an encroachment rate of approximately 5.9 m3/m/yr over the 24-year period. While quantifying the Kelso Dunes or any natural dynamic system is subject to uncertainties, the encroachment rate approach reflects the highly heterogeneous nature of the sand dunes (in regards to spectral variability in brightness) at Kelso Dunes and serves as an exemplar for future research.

Description: Savanna ecosystems are an important component of dryland regions and yet are exceedingly difficult to study using satellite imagery. Savannas are composed are varying amounts of trees, shrubs and grasses and typically traditional classification schemes or vegetation indices cannot differentiate across class type. This research utilizes object based classification (OBC) for a region in Namibia, using IKONOS imagery, to help differentiate tree canopies and therefore woodland savanna, from shrub or grasslands. The methodology involved the identification and isolation of tree canopies within the imagery and the creation of tree polygon layers had an overall accuracy of 84%. In addition, the results were scaled up to a corresponding Landsat image of the same region, and the OBC results compared to corresponding pixel values of NDVI. The results were not compelling, indicating once more the problems of these traditional image analysis techniques for savanna ecosystems. Overall, the use of the OBC holds great promise for this ecosystem and could be utilized more frequently in studies of vegetation structure.

Description: We used twelve Landsat scenes from the 1980s–2009 and regional 2000–2009 MODIS data to examine the long-term trend in the normalized difference vegetation index (NDVI) within unburned areas of the Alaskan boreal forest. Our analysis shows that there has been a declining trend in NDVI in this region, with the strongest “browning trend” occurring in eastern Alaska where the climate during the growing season is relatively dry and warm. Possible reasons for the "browning trend" are decreased vegetation due to temperature-induced drought stress and increased infestations of insect pests.

Description: This paper evaluates the performance of spatial methods to estimate leaf area index (LAI) fields from ground-based measurements at high-spatial resolution over a cropland landscape. Three geostatistical model variants of the kriging technique, the ordinary kriging (OK), the collocated cokriging (CKC) and kriging with an external drift (KED) are used. The study focused on the influence of the spatial sampling protocol, auxiliary information, and spatial resolution in the estimates. The main advantage of these models lies in the possibility of considering the spatial dependence of the data and, in the case of the KED and CKC, the auxiliary information for each location used for prediction purposes. A high-resolution NDVI image computed from SPOT TOA reflectance data is used as an auxiliary variable in LAI predictions. The CKC and KED predictions have proven the relevance of the auxiliary information to reproduce the spatial pattern at local scales, proving the KED model to be the best estimator when a non-stationary trend is observed. Advantages and limitations of the methods in LAI field predictions for two systematic and two stratified spatial samplings are discussed for high (20 m), medium (300 m) and coarse (1 km) spatial scales. The KED has exhibited the best observed local accuracy for all the spatial samplings. Meanwhile, the OK model provides comparable results when a well stratified sampling scheme is considered by land cover.

Description: There are difficulties associated with near-real time or frequent pavement monitoring, because it is time consuming and costly. This study aimed to develop a binary logit model for the evaluation of highway riding quality, which could be used to monitor pavement conditions. The model was applied to investigate the influence of backscattering values of Phase Array type L-band Synthetic Aperture Radar (PALSAR). Training data obtained during 3–7 May 2007 was used in the development process, together with actual international roughness index (IRI) values collected along a highway in Ayutthaya province, Thailand. The analysis showed that an increase in the backscattering value in the HH or the VV polarization indicated the poor condition of the pavement surface and, of the two, the HH polarization is more suitable for developing riding quality evaluation. The model developed was applied to analyze highway number 3467, to demonstrate its capability. It was found that the assessment accuracy of the prediction of the highway level of service was 97.00%. This is a preliminary study of the proposed technique and more intensive investigation must be carried out using ALOS/PALSAR images in various seasons.

Description: In the westernmost region of the rapidly widening Corinth rift, Greece, extensive development of roads, bridges and other human infrastructure has caused continuous environmental change over the past twenty years. River networks, the land surface and the coastal environment, have been altered, especially in the areas corresponding to deltaic fans. In this paper we use earth observation systems that have captured these environmental changes, particularly medium (Landsat TM and ETM+) and high (Quickbird) resolution satellite images, to identify environmental changes between the periods 1992, 2000, 2002, and 2005. Six pseudo-color multi-temporal images in different spectral areas were created in order to detect changes to the terrestrial and coastal environment caused mainly by direct or indirect human impact. This methodology provided new data for quantifying significant alterations in the environment on different scales. In many cases this revealed their sequence during the time of observation.

Description: Evaluating ecological patterns and processes is crucial for the conservation of ecosystems [1]. In this view, remote sensing is a powerful tool for monitoring their status and change. This involves several tasks like biodiversity estimate, landscape ecology, and species distribution modeling, to name a few [2]. Due to the difficulties associated with field-based data collection [3], the use of remote sensing for estimating ecological status and change is promising since it provides a synoptic view of an area with a high temporal resolution [4]. Of course in some cases remote sensing should be viewed as a help to plan a field survey rather than a replacement of it. Further, its improper use may lead to pitfalls and misleading results. [...]

Description: Previous analyses of the Earth’s annual cycle and its trends have utilized surface temperature data sets. Here we introduce a new analysis of the global and hemispheric annual cycle using a satellite remote sensing derived data set during the period 1979–2009, as determined from the lower tropospheric (LT) channel of the MSU satellite. While the surface annual cycle is tied directly to the heating and cooling of the land areas, the tropospheric annual cycle involves additionally the gain or loss of heat between the surface and atmosphere. The peak in the global tropospheric temperature in the 30 year period occurs on 10 July and the minimum on 9 February in response to the larger land mass in the Northern Hemisphere. The actual dates of the hemispheric maxima and minima are a complex function of many variables which can change from year to year thereby altering these dates.Here we examine the time of occurrence of the global and hemispheric maxima and minima lower tropospheric temperatures, the values of the annual maxima and minima, and the slopes and significance of the changes in these metrics.  The statistically significant trends are all relatively small. The values of the global annual maximum and minimum showed a small, but significant trend. Northern and Southern Hemisphere maxima and minima show a slight trend toward occurring later in the year. Most recent analyses of trends in the global annual cycle using observed surface data have indicated a trend toward earlier maxima and minima.

Description: The Atmospheric Infrared Sounder (AIRS) on EOS/Aqua platform provides a measurement of global methane (CH4) in the mid-upper troposphere since September, 2002. As a thermal infrared sounder, the most sensitivity of AIRS to atmospheric CH4 is in the mid-upper troposphere with the degree of freedom of ~1.0. Validation of AIRS CH4 product versus thousands of aircraft profiles (convolved using the AIRS averaging kernels) demonstrates that its RMS error (RMSE) is mostly less than 1.5%, and its quality is pretty stable from 2003 to 2009. For scientific analysis of the spatial and temporal variation of mid-upper tropospheric CH4 (MUT-CH4) in the High Northern Hemisphere (HNH), it is more valuable to use the AIRS retrieved CH4 in a layer of about 100 hPa below tropopause (“Representative Layer”) than in a fixed pressure layer. Further analysis of deseasonalized time-series of AIRS CH4 in both a fixed pressure layer and the “Representative Layer” of AIRS (only for the HNH) from 2003 to 2009 indicates that, similar to the CH4 in the marine boundary layer (MBL) that was found to increase in 2007–2008, MUT-CH4 was also observed to have a recent increase but the most significant increase occurred in 2008. MUT-CH4 continued to increase in 2009, especially in the HNH. Moreover, the trend of MUT-CH4 from 2006 to 2008 is lower than the trend of CH4 in the MBL by 30–40% in both the southern hemisphere and HNH. This delay for the MUT-CH4 increase of about one year than CH4 in the MBL as well as the smaller increase trend for MUT-CH4 suggest that surface emission is likely a major driver for the recent CH4 increase. It is also found that the seasonal cycle of MUT-CH4 is different from CH4 in the MBL due to the impact of transport, in addition to the surface emission and the photochemical loss.

Description: Changes in vegetation phenology are recognized as being possibly the most important early indicator of the impact of climate change on ecosystems. Time series of image data are accepted as being the best way to map these changes, if we can derive suitable indices from the huge volumes of time series data that make the interpretation of these time series difficult. The problem is thus to derive a set of indices from the time series of image data that depict changes in vegetation phenology in a way that is easy to analyze and use. This problem has been solved by deriving a set of six Phenological Change Indices that measure the five ways that the phenological curve of vegetation can change over time. These indices were then tested using simulated data based on sample phenological profiles for a set of land covers and showed that four of the indices measured four of the ways that the phenological profile can change, and two of the indices gave similar results in measuring the last way that the phenological profiles can change. A time series of image data was then used to map the Phenological Change Indices for Eurasia and a sample of land covers was used to relate the changes in phenology to location for each land cover. This work showed that the detected changes in phenology are similar to those found in other papers. The benefit of these indices is that we can now analyze changes in phenology in a much more detailed and accurate way than has been possible until now.

Description: Giant Salvinia (Salvinia molesta) is one of the world’s most invasive aquatic weeds. We evaluated the accuracy of airborne multispectral digital video imagery for separating giant salvinia from other aquatic and terrestrial features at a study site located in northeast, Texas. The five-band multispectral digital video imagery was subjected to an unsupervised computer analysis to derive a thematic map of the infested area. User’s and producer’s accuracies of the giant salvinia class were 74.6% and 87.2%, respectively. Aerial multispectral digital videography has potential as a remote sensing tool for differentiating giant salvinia from other terrestrial and aquatic features.

Description: We evaluated the use of the Green-Red Vegetation Index (GRVI) as a phenological indicator based on multiyear stand-level observations of spectral reflectance and phenology at several representative ecosystems in Japan. The results showed the relationships between GRVI values and the seasonal change of vegetation and ground surface with high temporal resolution. We found that GRVI has the following advantages as a phenological indicator: (1) “GRVI = 0” can be a site-independent single threshold fordetection of the early phase of leaf green-up and the middle phase of autumn coloring, and (2) GRVI can show a distinct response to subtle disturbance and the difference of ecosystem types.

Description: Accurate geospatial information on the extent of irrigated land improves our understanding of agricultural water use, local land surface processes, conservation or depletion of water resources, and components of the hydrologic budget. We have developed a method in a geospatial modeling framework that assimilates irrigation statistics with remotely sensed parameters describing vegetation growth conditions in areas with agricultural land cover to spatially identify irrigated lands at 250-m cell size across the conterminous United States for 2002. The geospatial model result, known as the Moderate Resolution Imaging Spectroradiometer (MODIS) Irrigated Agriculture Dataset (MIrAD-US), identified irrigated lands with reasonable accuracy in California and semiarid Great Plains states with overall accuracies of 92% and 75% and kappa statistics of 0.75 and 0.51, respectively. A quantitative accuracy assessment of MIrAD-US for the eastern region has not yet been conducted, and qualitative assessment shows that model improvements are needed for the humid eastern regions where the distinction in annual peak NDVI between irrigated and non-irrigated crops is minimal and county sizes are relatively small. This modeling approach enables consistent mapping of irrigated lands based upon USDA irrigation statistics and should lead to better understanding of spatial trends in irrigated lands across the conterminous United States. An improved version of the model with revised datasets is planned and will employ 2007 USDA irrigation statistics.

Description: In this contribution, a hybrid multi-contextual Markov model for unsupervised near real-time flood detection in multi-temporal X-band synthetic aperture radar (SAR) data is presented. It incorporates scale-dependent, as well as spatio-temporal contextual information, into the classification scheme, by combining hierarchical marginal posterior mode (HMPM) estimation on directed graphs with noncausal Markov image modeling related to planar Markov random fields (MRFs). In order to increase computational performance, marginal posterior-based entropies are used for restricting the iterative bi-directional exchange of spatio-temporal information between consecutive images of a time sequence to objects exhibiting a low probability, to be classified correctly according to the HMPM estimation. The Markov models, originally developed for inference on regular graph structures of quadtrees and planar lattices, are adapted to the variable nature of irregular graphs, which are related to information driven image segmentation. Entropy based confidence maps, combined with spatio-temporal relationships of potentially inundated bright scattering vegetation to open water areas, are used for the quantification of the uncertainty in the labeling of each image element in flood possibility masks. With respect to accuracy and computational effort, experiments performed on a bi-temporal TerraSAR-X ScanSAR data-set from the Caprivi region of Namibia during flooding in 2009 and 2010 confirm the effectiveness of integrating hierarchical as well as spatio-temporal context into the labeling process, and of adapting the models to irregular graph structures.

Description: As catastrophic phenomena, landslides often cause large-scale socio-economic destruction including loss of life, economic collapse, and human injury. In addition, landslides can impair the functioning of critical infrastructure and destroy cultural heritage and ecological systems. In order to build a more landslide resistant and resilient society, an original GIS-based decision support system is put forth in order to help emergency managers better prepare for and respond to landslide disasters. The GIS-based landslide monitoring and management system includes a Central Repository System (CRS), Disaster Data Processing Modules (DDPM), a Command and Control System (CCS) and a Portal Management System (PMS). This architecture provides valuable insights into landslide early warning, landslide risk and vulnerability analyses, and critical infrastructure damage assessments. Finally, internet-based communications are used to support landslide disaster modelling, monitoring and management.

Description: Updated tropical lower tropospheric temperature datasets covering the period 1979–2009 are presented and assessed for accuracy based upon recent publications and several analyses conducted here. We conclude that the lower tropospheric temperature (TLT) trend over these 31 years is +0.09 ± 0.03 °C decade−1. Given that the surface temperature (Tsfc) trends from three different groups agree extremely closely among themselves (~ +0.12 °C decade−1) this indicates that the “scaling ratio” (SR, or ratio of atmospheric trend to surface trend: TLT/Tsfc) of the observations is ~0.8 ± 0.3. This is significantly different from the average SR calculated from the IPCC AR4 model simulations which is ~1.4. This result indicates the majority of AR4 simulations tend to portray significantly greater warming in the troposphere relative to the surface than is found in observations. The SR, as an internal, normalized metric of model behavior, largely avoids the confounding influence of short-term fluctuations such as El Niños which make direct comparison of trend magnitudes less confident, even over multi-decadal periods.