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Changes in the surface area of lakes can indicate climate change and human activity, and there are few reports on the characteristics of such changes in North Xinjiang, China. The normalized difference water index (NDWI) was modified by using the middle infrared band (MIR) instead of the near infrared band (NIR). The modified NDWI (MNDWI) can effectively extract the lake boundary. Changes in the number and area of lakes (surface area > 1 km2) were examined using topographic maps (1:100,000) and Land Remote-Sensing Satellite (System, Landsat) series images (multispectral scanner, thematic mapper, enhanced thematic mapper plus, and operational land imager) for the period from 1960 to 2018. The results showed that more than 85.00% of the lakes were identified in the Erqis River Basin (ErRB), Ulungur River Basin (URB), and Central North Tianshan (CNT) between 1960 and 1980, few lakes were observed in the Ebinur Lake Basin (ELB), Jimunai River Basin (JRB), Emin River Basin (EmRB), Guerbantonggute Desert Area (GDA), and Ili River Basin (IRB), and no lakes were observed in the Eastern North Tianshan (ENT). The surface area and numbers of lakes in North Xinjiang were increased by 795.99 km2 (35.54%) and 39 (195.00%), respectively, between 1960 and 2018. Multiple factors influenced the changes observed for different types of lakes. The number and surface area of mountain lakes fluctuated and increased mainly in response to precipitation and temperature. The number of oasis lakes remained unchanged until 1990, but then increased in response to precipitation and temperature. The fluctuations in the surface area of oasis lakes, which were affected by climate and groundwater development, were relatively small between 1960 and 1990, but then became larger. However, the number and surface area of plain lakes increased between 1960 and 2018 and were mainly influenced by climate change and human activity, such as water diversion for irrigation, ecological restoration projects, and the implementation of the river chief system. [ABSTRACT FROM AUTHOR]

We present a long-term assessment (1965–2018) of recessional and area change patterns as well as the recent decade surface velocity ( υ ‾ , 2000–2001 and 2017–2018) and elevation change ( d h / d t , 2003–2016) of the Parvati Glacier, north-western Himalaya. Multi-temporal high (Corona, Hexagon, and PlanetScope) to medium (Land Remote-Sensing Satellite (System, Landsat) Thematic Mapper (TM)/Enhanced Thematic Mapper+ (ETM+)/Operational Land Imager (OLI), Sentinel 2A- Multispectral Instrument (MSI), and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)) resolution images from 1965 to 2018 were analysed to evaluate multi-parameters of glacier dynamics. We estimated an average annual recessional rate of 37.5 ± 0.5 m and total area loss of 2.2 ± 0.01 km2 for the Parvati Glacier over the past half-century (i.e. 1965–2018). During 1973–1989, the Parvati Glacier experienced a significant recession (70.9 ± 3.1 m a−1) that resultant into disintegrated of the glacier in two separate glaciers between 1981 and 1989. We observed a higher average recession rate (28.4 ± 1.6 m a−1) of the detached glacier after its disintegrated from the Parvati Glacier (18.7 ± 1.6 m a−1) between 1989 and 2018. However, the debris-covered ice area of the Parvati Glacier (about 12%) increased twice that of the detached glacier (about 6.1%) during 1994–2018. The estimated υ ‾ reveals a substantial slowdown by about 27.7% and about 20% for the Parvati and detached glacier, respectively, during 2000–2001 and 2017–2018. Overall the annual average surface lowering rate (−0.7 ± 0.5 m a−1) for both the glacier has been almost similar during 2003–2016 but varied significantly at altitudinal. Our study shows inconsistency in previously reported recessional rate and observed advance phases in 1976–1989 and 2009–2013 for the Parvati Glacier during almost the same duration of the study. It is mainly ascribed to the misinterpretation of glacier boundaries using the Survey of India (SoI) topographic map of 1962 where glacier boundaries more generalized and by using coarser resolution of Landsat Multispectral Scanner (MSS) images (1970s), particularly for the debris-covered area. Overall, the Parvati Glacier shows a significant recession and frontal area change; and in the recent decades, extensive surface lowering, along with an observable slowdown in glacier υ ‾ and appearance of ice-cliffs in the ablation area collectively point towards substantial glacier degeneration over the period of our analysis. [ABSTRACT FROM AUTHOR]

Surface water maps are essential for many environmental applications. Waterbody delineation from satellite images remains a challenging task due to sensor limitations, the presence of clouds, the low albedo surfaces in urban areas, topographic, and atmospheric conditions. In this paper, a model based on the Supported Vector Machine (SVM) classifier was adopted for waterbody extraction from Sentinel-2, Landsat 8 Operational Land Imager (OLI) and RapidEye satellite images. As well, the accuracy of two other sources (OpenStreetMapping (OSM) and Military Geographic Institute (MGI)) was tested. The free images from Sentinel-2 and Landsat 8 OLI were more accurate (Kappa (KHAT):0.89, 0.88) data sources than commercial RapidEye images (KHAT: 0.79). Regarding the performance between Sentinel-2 and Landsat 8 OLI, Sentinel-2 obtained the most accurate results (overall accuracy 94.49 vs. 94.17, commission error 1.34 vs. 1.87). Due to the variable spatial resolution of OSM and MGI data, it was not possible to detect small waterbodies with these sources, and therefore high values of omission error and a strong underestimation of the area of surface water were obtained. This study demonstrates the suitability of free images for mapping and monitoring of surface waterbodies, including small water bodies. [ABSTRACT FROM AUTHOR]

This article proposes an automated method for detection and mapping of land surface elevation changes from unmanned aerial vehicle (UAV)-based imagery. In contrast to conventional methods, which require a significant human effort to succeed, the novel approach, based on multi-temporal digital terrain model analysis, detects and outlines zones of mass deposition or mass remotion with no manual input. Main components of the proposed method are: (i) a normalized difference elevation index and (ii) an automated edge-detection procedure. Performance of method is evaluated for topographic change detection in a quarry zone using a multi-temporal UAV-based imagery dataset. It is demonstrated that the proposed method allows for a rapid and automatic elevation change mapping and that results are as accurate as conventional methods based on time-consuming thresholding and visual interpretation. [ABSTRACT FROM PUBLISHER]

An altimeter’s mounting and pointing biases must be computed so that the altimeter footprints can be geolocated precisely. Whereas normal methods for computing biases require a comparison of the altimeter footprints’ elevations over a previously surveyed site with the elevations from that survey, the intra-scan method can be computed with any subset of data from an altimeter with a scan pattern that intersects itself several times within a few seconds. This study assumes an altimeter whose footprints are in a self-intersecting ellipsoidal pattern, such as those from the Airborne Topographic Mapper (ATM). Differences in the derived elevations at the intersections are used to estimate the mounting and pointing angle biases; since differences are used, the range bias cannot be computed with this method. As data over any surface can theoretically be used to estimate biases, the intra-scan method has significantly greater temporal resolution than the ground survey method; this allows the detection of changes in the biases. The biases estimated from the intra-scan method during the Operation IceBridge Greenland 2013 campaign were typically within less than 0.1° of those estimated from the ground survey method. [ABSTRACT FROM AUTHOR]

The Manimahesh and Tal Glaciers are located in the Budhil fifth-order sub-basin of the Ravi, Himachal Himalaya, Northwestern Himalaya (India). These glaciers were analysed using high- (Corona KH-4A) to medium- (Landsat TM/ETM+/OLI, ASTER) spatial resolution satellite data between 1971 and 2013, along with extensive field measurements (2011–2014) of frontal changes. The results show that the Manimahesh and Tal Glaciers retreated by 157 ± 34 m (4 ± 1 m year–1) and 45 ± 34 m (1 ± 1 m year–1), respectively, whereas, the total area lost is estimated at 0.21 ± 0.01 km2(0.005 km2year–1) and 0.010 ± 0.003 km2(0.0002 km2year–1), respectively, between 1971 and 2013. The rate of retreat is significantly lower than that previously reported. Our field measurements (2011–2014) also suggest a retreating trend and validate the measured glacier changes using remotely sensed temporal data. [ABSTRACT FROM AUTHOR]

The volume of water in lakes is commonly estimated by combining data of water level variations with accurate bathymetry and shore topographic maps. However, bathymetry and shore topography data are often difficult to acquire, due to high costs for labour and equipment. This article presents an innovative method for calculating lake water volumes by using long-term time series remote-sensing data. Multi-spectral satellite remote-sensing images were used to map a lake’s water surface area. The lake water surface boundaries for each year were combined with field-observed water levels to generate a description of the underwater terrain. The lake water volume was then calculated from the water surface area and the underwater terrain data using a constructed TIN (triangulated irregular network) volume model. Lake Baiyangdian, the largest shallow freshwater lake in the North China Plain, was chosen as the case study area. For the last 40 years the water levels of Lake Baiyangdian have reflected multiple dry and wet periods, which provide a good data series for the study of the proposed method. Archived Landsat MSS/TM/ETM+ and HJ-1A/B images from 1973 to 2011 were used as the basic data. The NDWI (normalized difference water index) and MNDWI (modified NDWI) were used to map the water surface of the lake, and the lake water volumes were calculated with the 3D Analyst tool of ArcMap 9.3. The results show that the estimated water volumes from remote-sensing images were very consistent with the volumes derived from the fitted equation of the lake storage capacity curve based on observed data. [ABSTRACT FROM AUTHOR]

The integration of spectral, textural, and topographic information using a random forest classifier for land-cover mapping in the rugged Nujiang Grand Canyon was investigated in this study. Only a few land-cover categories were accurately discriminated using spectral information exclusively, with an overall accuracy of 0.56 and a kappa coefficient of 0.51. The inclusion of topographic information as additional bands provided higher overall accuracy (0.69) and kappa coefficient (0.65) than topographic correction (overall accuracy, 0.57–0.58; kappa coefficient range, 0.52–0.53), which failed to markedly improve classification accuracy. In contrast with the exclusive use of spectral bands, most of the included land-cover categories were correctly classified using textural features exclusively (overall accuracy, 0.67–0.88; kappa coefficient, 0.63–0.87). In particular, classification based on geostatistical features led to slightly more accurate results than did grey-level co-occurrence matrix parameters. The window size selected for texture calculation markedly affected the texture-based classification accuracy: larger window size yielded higher classification accuracy. However, no optimal window size exists. The inclusion of the topographic bands in the texture images led to an increase in the overall accuracy of 1.1–9.0%, and to an increase in the kappa coefficient of 0.0–10.9%. Thus, for the Nujiang Grand Canyon, topographic information was more important for the discrimination of some land-cover types than spectral and textural information. Among the Landsat Thematic Mapper (TM) spectral bands, bands 6 and 4 were of greatest importance. The relative importance of textural features generally increased with window size, and a few textural features were of consistently high importance. Although a random forest classifier does not overfit, undertaking feature selection analysis prior to classification may still be valuable. [ABSTRACT FROM PUBLISHER]

Remote sensing and geographic information systems (GISs) are increasingly being used in protected area monitoring and habitat suitability studies. In this article, Erdas Imagine's Expert Classifier tool was used to assess the specific trajectories of habitat suitability change during 1994–2007 in the Sonitpur elephant habitat, India. Sonitpur has been witness to increasing human–elephant conflict in the past decade. The suitability analysis took into account information relating to forest type and density, elevation, slope, source of water, human activities in terms of settlement, agriculture, tea plantations, roads, and railways. Satellite imagery, data from topographical maps, digital elevation data, and global positioning system readings formed the major data inputs that were incorporated into a GIS. Various decision rules were created and confidence levels assigned to the input layers to generate high, medium, and low habitat suitability. The area witnessed a sharp decline in suitability from 63% in 1994 to less than 38% by 2007. The high-suitability area declined by more than 50% during this period. The unique natural protected areas of Sonitpur, which are a mix of reserved forests, wildlife sanctuaries, and a national park, urgently need to be protected from further habitat degradation. [ABSTRACT FROM PUBLISHER]

Clustering algorithms have been an important area of research in the domain of computer science for data mining of patterns in various kinds of data. This process can identify major patterns or trends without any supervisory information such as data labels. Broadly specified, it divides a set of objects into clusters each of which is a representative of a meaningful sub-population. In this article, we first carry out an assessment of available categories of clustering techniques and find that hierarchical- and density-based algorithms are apt for clustering light detection and ranging (lidar) data. Then, we adapt and examine the effect of two algorithms, namely density-based spatial clustering of applications with noise (DBSCAN) and ordering of points to identify the clustering structure (based on perimeter of triangles) (OPTICS (BOPT)) found in the literature in the area of knowledge discovery in databases, on lidar data. The performances of the algorithms are evaluated with respect to execution time and comparison of clustering outputs with respect to a manually classified data set of. DBSCAN performs better in both respects. The efficacy of DBSCAN is also demonstrated for detecting clusters of complex shapes for two different data sets, each of areal dimension of. [ABSTRACT FROM AUTHOR]

Historical to recent spatial variability in surface waterlogging was evaluated using a topographical map of 1925 and satellite images of 1975, 1988, 1999 and 2006. A visual satellite image interpretation technique was used to delineate waterlogged areas and associated infrastructure details. Areas of 31, 73.89, 163.86, 143.61 and 102.59 km2 were found to be affected by surface waterlogging during 1925, 1975, 1988, 1999 and 2006, respectively. A sharp increase in waterlogging from 1925 to 1975 is attributed to the functioning of irrigation projects in the region. The very high areas under waterlogging during 1988 and 1999 reflect intensification of agricultural activity. A substantial decrease in waterlogging during 2006 is attributed to the implementation of government wasteland reclamation measures. The area statistics of waterlogging computed for the buffer zone along select features showed that the maximum waterlogging occurs along canals and railways followed by rivers and the least along roads. [ABSTRACT FROM AUTHOR]

The digital elevation model (DEM) produced by the Shuttle Radar Topographic Mission (SRTM) has provided important fundamental data for topographic analysis in many fields. The recently released global digital elevation model (GDEM) produced by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) has higher spatial resolution and wider coverage than the SRTM3 DEM, and thus may be of more value to researchers. Taking two typical study areas—the Loess Plateau and the North China Plain of China—as an example, this article assesses the accuracy of the SRTM3 DEM and ASTER GDEM by collecting ground control points from topographical maps. It is found that both the SRTM3 DEM and the ASTER GDEM are far more accurate for the North China Plain than for the Loess Plateau. For the Loess Plateau, the accuracy of the ASTER GDEM is similar to that of the SRTM3 DEM; whereas for the North China Plain, it is much worse than that of the SRTM3 DEM. Considering the negative bias of the ASTER GDEM for flat or gentle regions, we improve its accuracy by adding the difference of the mean value between the SRTM3 DEM and ASTER GDEM for the North China Plain; then, the root mean square error (RMSE) of ±7.95 m from the original ASTER GDEM is improved to ±5.26 m, which demonstrates that it is a simple but useful way to improve the accuracy of the ASTER GDEM in flat or gentle regions. [ABSTRACT FROM AUTHOR]

This article presents 3D surface deformation mapping results derived from satellite synthetic aperture radar (SAR) data acquired over underground coal mines. Both ENVISAT Advanced Synthetic Aperture Radar (ASAR) and Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) data were used in this study. The quality of the 3D deformation mapping results due to underground mining is mainly limited by two factors. (1) Differential interferometric synthetic aperture radar (DInSAR) is less sensitive to displacement along the north–south direction in the case of the current SAR satellite configurations. (2) The mining-induced displacement is continuous and nonlinear; and the accuracy of the 3D DInSAR measurement is severely affected by the similar but non-identical temporal overlaps of the InSAR pairs. The simulation and real data analyzes have shown that it would be more practical to use DInSAR pairs with the assumption of negligible northing displacement to derive the displacements in the easting and vertical directions. The northing displacement could then be estimated from the residuals. This limitation could be overcome in the future with the launch of more radar satellites, which would provide better viewing geometry. [ABSTRACT FROM AUTHOR]

In this article, wave refraction and shoaling in coastal areas were investigated and used to derive the bathymetry. With its high spatial resolution, which can achieve up to 1 m in SpotLight mode, and its low cut-off wavelength, the TerraSAR-X satellite provides images that are particularly suitable for the observation of wave behaviour in transient and shallow waters. By computing the two-dimensional (2D) spectra, shoaling waves were tracked from the open sea to the shoreline. The observed wave refraction and shoaling were compared with wave refraction laws and first-order wave theory (Airy theory). The retrieved bathymetry was compared against depth data from other sources such as ETOPO1, the US Coastal Relief Model and sea charts from the British Admiralty. A further aim of this article was the investigation of breaking waves showing up as near-shore image patterns. A theory is presented of how to derive the height of breaking waves by use of this pattern. Synthetic aperture radar (SAR) images with azimuth as well as range travelling waves were investigated. As test sites, we chose the entrance of Port Phillip near Melbourne (Australia) and the Duck Research Pier in North Carolina (USA). [ABSTRACT FROM PUBLISHER]

Many countries still lack national digital elevation models (DEMs) and have to rely on global datasets, which can negatively influence the reliability of flood model results. Mozambique is considered the most risk prone country for floods in Southern Africa. In this study a quality and accuracy assessment of two global DEMs (Shuttle Radar Topography Mission (SRTM) and HYDRO1K) is presented for a simple static flood inundation model of lower Limpopo Basin. This is accomplished with a local fit and vertical accuracy assessment of global datasets on a local scale as well as simulations of flood extent in the floodplain carried out by filling the DEMs with water according to the 2000 flood event. The results from the vertical accuracy assessment show that global DEMs can be used on a local scale. However, flood simulations performed on original DEMs contain inadequacies and are misleading with both under- and overestimation of the flooded area, while simulation performed on locally fitted DEMs shows a better agreement with the actual event. This study clearly shows that DEMs with questionable accuracy and resolution should be used with great caution in flood inundation modelling because they could result in deceptive model predictions, and lead to devastating after-effects in risk prone areas. [ABSTRACT FROM AUTHOR]

The present study evaluates the usability of available and low-cost geodata, including topographic maps, remote sensing and geographic information system (GIS) data, for high mountain research activities. The challenges for geospatial technologies in a high mountain environment are discussed. We present two case studies of high mountain environments in Khumbu Himal and Mount Aconcagua (Argentina/Chile), where the applicability and integration of various geodata sets have been applied. Important topics documented and discussed include topographic accuracy, glacier information extraction, multitemporal investigations, digital elevation model (DEM) and image map generation. The study indicates that accuracy analyses of available geodata are mandatory for achieving the requirements of geoscience research. [ABSTRACT FROM AUTHOR]

Geometric high-resolution satellite imagery (HRSI) is being used increasingly for generating large-scale topographic maps. The detection of object shapes has become easier and more accurate with improved geometric resolution. The grey value range and spectral resolution are also important for the identification and classification of objects. The nominal ground sampling distance (GSD) must not be the same as the effective GSD corresponding to the information content. In addition, the topographic conditions, object contrast, sun elevation and azimuth and atmospheric conditions influence the object identification. The information content of panchromatic and multispectral satellite images (Landsat 7 ETM+, ASTER, TK-350, KVR-1000, SPOT-5, IRS-1C, IKONOS, QuickBird and OrbView-3) available for the Zonguldak test field were investigated with respect to the generation of large-scale topographic maps. The rule of thumb for topographic mapping of at least 0.1 mm GSD in the map scale with the limit of a maximum of 5 m GSD also for smaller map scales has been confirmed. [ABSTRACT FROM AUTHOR]

Twelve normal, active faults transverse to the regional Ganga and Yamuna longitudinal faults have been identified in the western part of the gently sloping Upper Gangetic Plain from the interpretation of digital elevation models (DEMs), prepared by manual digitization of spot heights from 1 : 50 000 scale topographic maps. High vertical exaggeration of these DEMs reveals artefactual morphostructures such as 'cliffs' and 'significant breaks in slopes' that are indicative of faults. Convergent drainage on the upthrown blocks, initiation of new streams on the downthrown blocks, offsetting of drainage and anomalous sinuosity of streams close to faults have provided additional corroborating evidence of the faults. Detailed field investigations indicate relatively steeper slopes close to the inferred faults than the adjoining areas. Erosion of the upthrown blocks and deposition of sediments on the downthrown blocks in the form of terminal fans under a semi-arid climate formed features typical of many such faults. [ABSTRACT FROM AUTHOR]

High-resolution satellite rainfall products, at daily accumulation and 0.25° spatial resolution, are evaluated using station networks located over two different parts of Africa. The first site is located over Ethiopia with a very complex terrain. The second site, located over Zimbabwe, has a less rugged topography. The evaluated satellite rainfall products are the NOAA-CPC African rainfall estimation algorithm (RFE), TRMM-3B42, the CPC morphing technique (CMORPH), PERSIANN, and the Naval Research Laboratory's blended product. These products perform reasonably well over both regions in detecting the occurrence of rainfall. However, performances are poor in estimating the amount of rainfall in each pixel. The correlation coefficients are low and random errors high. The performance was better over Zimbabwe as compared with Ethiopia. Comparing the different products, CMORPH and TRMM-3B42 showed a better performance over Ethiopia, while RFE, CMORPH, and TRMM-3B42 preformed relatively better over Zimbabwe. [ABSTRACT FROM AUTHOR]

It is useful to have a disaggregated population database at uniform grid units in disaster situations. This study presents a method for settlement location probability and population density estimations at a 90 m resolution for northern Iraq using the Shuttle Radar Topographic Mission (SRTM) digital terrain model and Landsat Enhanced Thematic Mapper satellite imagery. A spatial model each for calculating the probability of settlement location and for estimating population density is described. A randomly selected subset of field data (equivalent to 50%) is first analysed for statistical links between settlement location probability and population density; and various biophysical features which are extracted from Landsat or SRTM data. The model is calibrated using this subset. Settlement location probability is attributed to the distance from roads and water bodies and land cover. Population density can be estimated based upon land cover and topographic features. The Landsat data are processed using a segmentation and subsequent feature-based classification approach making this method robust to seasonal variations in imagery and therefore applicable to a time series of images regardless of acquisition date. The second half of the field data is used to validate the model. Results show a reasonable estimate of population numbers (r = 0.205, p<0.001) for both rural and urban settlements. Although there is a strong overall correlation between the results of this and the LandScan model (r = 0.464, p<0.001), this method performs better than the 1 km resolution LandScan grid for settlements with fewer than 1000 people, but is less accurate for estimating population numbers in urban areas (LandScan rural r = 0.181, p<0.001; LandScan urban r = 0.303, p<0.001). The correlation between true urban population numbers is superior to that of LandScan however when the 90 m grid values are summed using a filter which corresponds to the LandScan spatial resolution (r = 0.318, p<0.001). [ABSTRACT FROM AUTHOR]

Mountain landscapes are characterized by great spatial diversity. One basic problem is that there are few high-resolution data for secluded mountain areas. We present a new approach towards topographic mapping and vegetation monitoring: low-altitude remote sensing using Kite Aerial Photography (KAP). The study was conducted in the Norwegian mountains above the treeline. We assessed this approach under specific alpine circumstances. Following the collection of data, we derived a digital elevation model (DEM) from two overlapping images. The model was evaluated by the statistical correlation of 265 random field points and extracted heights from (i) linear contour line interpolation of a topographic map of scale 1 : 50 000, (ii) photogrammetric analysis of kite aerial photographs, and (iii) kriging interpolation of approximately 1000 measured field points. Finally, the vegetation was classified, using both supervised and unsupervised methods. The accuracy of the classification results was evaluated by comparing 265 random points, derived from terrestrial mappings, to classified vegetation types by an error matrix. The generation of derived data compared well with data obtained from high-resolution field surveys and was better than data derived from public-domain government cartography and moderate-scale satellite remote sensing data. Our results demonstrate the economic and logistic advantages of this new KAP-based methodology. The flexibility and outstanding high resolution of our new low-altitude remote sensing approach proved to be particularly suitable for closing the gap between terrestrial investigations and high-altitude remote sensing. Hence, our KAP approach addresses the challenge of multiscale research in mountain landscapes. [ABSTRACT FROM AUTHOR]

This paper describes how remote sensing techniques were used to study the effect of mangroves and other woody coastal vegetation as a protective measure against the 2004 Indian Ocean Tsunami. Remote sensing made it possible to compare pre- and post-Tsunami images of large areas. A study site was selected based on medium resolution Landsat imagery and existing topographic maps. Selection criteria included substantial damages reported, presence of woody vegetated and non-vegetated shorelines, homogeneous bathymetry and good coverage of pre- and post-Tsunami satellite imagery. The Pichawaram mangrove, Tamil Nadu, India, matched these criteria. Pre- and post-Tsunami Ikonos and QuickBird images were compared through the visual interpretation of pre-Tsunami coastal vegetation and post-Tsunami damage. The results were validated in the field. The analysis showed that mangrove forests and coastal shelterbelts provided protection from the Tsunami. This was concluded from analysing the spatial distribution of damage relative to woody vegetation along the coast as well as transects detailing the amount of damage behind the coastline and the coastal woody vegetation. [ABSTRACT FROM AUTHOR]

Using the temporal and multi‐spectral signature of the soil wetness regions formed by monsoon storms in Indian river catchments, an analysis of time‐series VEGETATION images is proposed here to map the wetness regions and their spatial distribution for 1999, 2000 and 2001. The spatial distribution of the mapped regions was then compared with the corresponding distribution of topographic index range in 41 watersheds of the River Mahanadi. The comparison shows that there was a significant relationship (n = 41, r2 = 0.65) between the two. Extending the same analysis for all other Indian river catchments, similar relationships were obtained, except in the Brahmaputra catchment, where the temporal and spatial patterns of monsoon storms are different from those of the other river catchments. [ABSTRACT FROM AUTHOR]

Digital elevation models of Gepatschferner in Northern Tyrol, Austria were obtained with digital photogrammetry from high altitude stereo photo pairs and by digitizing an analogue topographic glacier map, for 1990 and 1971, respectively. A difference map was calculated to identify regions of glacier elevation increases and decreases corresponding to glacier mass gain and loss. While the glacier tongue below 2600 m showed mainly increases in glacier surface elevation as compared to 1971, the remaining glacier thickness experienced both increases and decreases. The accumulation zone above 3200 m contained several extreme cases of morphological features with altitude changes>+30 m. While the increased ice thickness of the glacier tongue can be traced to a well documented period of mass accumulation in the 1970s, changes in glacier surface elevation in the higher zones may be linked either to the morphology of the glacier bed or to differences in insolation on the glacier surface. Between 1971 and 1990 the glacier experienced a net loss of 26×106 m3 corresponding to approximately 0.9% of the entire glacier volume. [ABSTRACT FROM AUTHOR]

Qualified ground control points (GCPs) are crucial in the geolocation of a remotely sensed image. If a region has no published map, the geographical coordinates of GCPs must be obtained indirectly. Although these can be re‐constructed from a synthetic aperture radar (SAR) image using sensor position and velocity, this conventional method does not provide accurate GCPs owing to the variable elevations of actual topography. We propose an inverse geolocation method for GCPs by SAR simulation. This improves the accuracy of extracted GCPs by accommodating topographic effects, and requires a high‐resolution digital elevation model (DEM) and SAR with precise orbit data. The errors in the northing derive mainly from the imaging pulse time, and were improved by applying an azimuth time correction. In this study ERS SAR data with precise Delft orbit and shuttle radar topography mission (SRTM) DEM were used. Mean values for the planimetric distance error in the GCPs were 4.1 and 5.4 m with standard deviations of 10.6 and 16.9 m in northing and easting, respectively. The obtained GCPs were applied to an IRS image for geo‐rectification, and the result was mean image positional errors of 3.6 and 2.7 m with standard deviations of 8.4 and 15.4 m in northing and easting, respectively. The root mean square errors are 9.0 and 15.3 m in northing and easting, respectively. [ABSTRACT FROM AUTHOR]

Operational use of remote sensing as a tool for post-fire, Mediterranean forest management has been limited by problems of classification accuracy arising from confusion of burned and non-burned areas. Frequently, this occurs as a result of slope illumination and shadowing effects caused by the complex topography encountered in many forested areas. Cloud shadows can also be a problem. The aim of this work was to investigate how image classification results could be improved by removing the illumination effects of topography from satellite images. This was achieved by applying supervised classification to both uncorrected and topographically corrected LANDSAT TM data for a site on the Greek island of Thasos. The classification methodology included atmospheric and geometric correction, field-based training, seperability/contingency analysis and maximum likelihood processing. The classification scheme was determined on the basis of consultation with the Greek Forest Service. Overlay of the resulting class maps enabled comparison of the total burned area and its spatial extent using the two different approaches to processing. The results of each approach were compared with the forest perimeter map generated by the Forest Service using traditional survey methods. Accuracy assessment and error analysis clearly indicated that the removal of the topographic effect from the satellite image before its classification resulted in more accurate mapping of the burned area. It is concluded that operational use of satellite remote sensing for forest fire management depends on accurate, robust, widely available and proven techniques. Topographic correction should now be regarded as an essential element of any classification methodology which will be used for operational, post-fire management of forests in complex Mediterranean landscapes. [ABSTRACT FROM AUTHOR]

In monitoring land cover change by overlay of two maps from different dates, the rate of change is frequently overestimated. This is due to three sources of uncertainty: (1) semantic differences in class definitions between two maps, (2) positional errors and (3) classification errors. In this study, four methods are proposed that use the Bayes theorem to update prior estimates of land cover change with information on the probabilities with which land cover classes are mistaken for each other. The methods were illustrated for two case studies. In the first case study, the real change was 1.4%, and by overlay of the two maps, 7.4% was predicted. The estimates by the four methods were 6.3%, 15.3%, 6.7% and 1.6%. In the second study, these percentages were 48% and 36%, and with our four methods: 39.2%, 54.1%, 50.8% and 53.0%. Two of the methods account for the correlation in classification accuracy between maps of two dates. Where this correlation was high (study area 1), the methods that accounted for correlation yielded change estimates closer to the real change than the methods that did not account for this correlation. [ABSTRACT FROM AUTHOR]

This paper addresses semantic accuracy in relation to images obtained with remote sensing. Semantic accuracy is defined in terms of map complexity. Complexity metrics are applied as a metric to measure complexity. The idea is that a homogeneous map of a low complexity is of a high semantic accuracy. In this study, complexity metrics like aggregation index, fragmentation index and patch size are applied on two images with different objectives, one from an agricultural area in the Netherlands, and one from a rural area in Kazakhstan. Images are segmented first using region merging segmentation. Effects on metrics and semantic accuracy are discussed. On the basis of well‐defined subsets, we conclude that the complexity metrics are suitable to quantify the semantic accuracy of the map. Segmentation is the most useful for an agricultural area including various agricultural fields. Metrics are mutually comparable being highly correlated, but showing some different aspects in quantifying map homogeneity and identifying objects of a high semantic accuracy. [ABSTRACT FROM AUTHOR]

Land cover mapping is perhaps the most important application of remote sensing data. The abundance of mixed pixels (representing uncertainties in class allocation), particularly in coarse spatial resolution images, has always been known to lead to difficulties in producing accurate land cover classifications. Soft classification methods may help in quantifying uncertainties in areas of transition between various types of land cover. This study aims to estimate and accommodate uncertainties in all stages of a supervised classification process (i.e. training, allocation and testing) so as to produce accurate and meaningful land cover classifications. Three soft classification methods have been used—a probabilistic maximum likelihood classifier and the two classifiers based on fuzzy set theory (fuzzy c ‐means and possibilistic c ‐means). Uncertainty and accuracy measures based on a fuzzy error matrix have been adopted to evaluate each classifier. All of the classifiers show an increase in classification accuracy when uncertainty is appropriately accounted for in all stages of the supervised classification. In particular, the possibilistic c ‐means classifier produced the highest map and individual class accuracy and has been found to be more robust to the existence of uncertainties in the datasets. The approach suggested in this paper can be used to generate accurate land cover maps, even in the presence of uncertainties in the form of mixed pixels in remote sensing images. [ABSTRACT FROM AUTHOR]

A crop map of The Netherlands was created using a methodology that integrates multi-temporal and multi-sensor satellite imagery, statistical data on crop area and parcel boundaries from a 1 : 10 000 digital topographic map. In the first phase a crop field database was created by extracting static parcel boundaries from the digital topographic map and by adding dynamic crop boundaries using on-screen digitizing. In the next phase the crop type was determined from the spectral and phenological properties of each field. The resulting crop map has an accuracy larger than 80% for most individual crops and an overall accuracy of 90%. By comparing cost and man-hours it was demonstrated that per-field classification is more efficient than per-pixel classification and decreased the effort for classification from 1500 to 500 man-hours, but the effort for creating the crop field database was estimated at 2300 man-hours. The use of image segmentation techniques for deriving the crop field database was discussed. It was concluded that image segmentation cannot replace the use of a large-scale topographic map but, in the future, image segmentation may be used to map the dynamic crop boundaries within the topographic parcels. [ABSTRACT FROM AUTHOR]

The aim of this study is to evaluate the hazard of landslides at Boun, Korea, using a Geographic Information System (GIS) and remote sensing. Landslide locations were identified in the Boun area from interpretation of aerial photographs and field surveys. The topographic, soil, forest, geologic, lineament and land cover data were collected, processed and constructed into a spatial database using GIS and remote sensing data. The factors that influence landslide occurrence, such as slope, aspect and curvature of the topography, were calculated from the topographic database. Texture, material, drainage and effective soil thickness were extracted from the soil database, and type, age, diameter and density of timber were extracted from the forest database. The lithology was extracted from the geological database and lineaments were detected from Indian Remote Sensing (IRS) satellite images. The land cover was classified based on the Landsat Thematic Mapper (TM) satellite image. Landslide hazard areas were analysed and mapped, using the landslide-occurrence factors, by the probability-likelihood ratio method. The results of the analysis were verified using actual landslide location data. The validation results showed satisfactory agreement between the hazard map and the existing data on landslide locations. [ABSTRACT FROM AUTHOR]

Geometric correction accuracy of IRS-1D panchromatic imagery was investigated using GPS- and 1 : 25 000 scale topographic map-derived control points. The differentially corrected GPS-derived coordinates provided markedly more accurate results than did uncorrected handheld GPS- and map-derived GCPs. The rms error value of differentially corrected GPS-derived control points based on second-degree polynomial was in the order of ±3 m. Geometric corrections made with second-degree polynomials, using both the map- and uncorrected handheld GPS-derived control points, yielded rms error values in the order of ±5 m. The results revealed that the uncorrected handheld GPS-derived control points can be a valuable alternative to planimetric control for geometric correction of IRS-1C/D panchromatic imagery with one-pixel size accuracy. [ABSTRACT FROM AUTHOR]

For deciphering a greater amount of geological information from satellite remote sensor data, high spectral resolution 'multi-spectral optical' remote sensor data and high spatial resolution 'panchromatic optical' or 'radar microwave' remote sensor data are conventionally merged by data fusion. In a mature topographic terrain, topographic expression, i.e. variation in surface relief over the area, plays an important role in reflecting lithological variation and exposed/subsurface structural features. If the topographic information from such a terrain is merged with high spectral resolution multi-spectral and higher spatial resolution panchromatic satellite remote sensor data by an appropriate data fusion method, the visual interpretability of geological information in the final image product will be significantly improved. This method may also be useful in identifying linear features that are reflected through subtle variation in topographic elevation, but suppressed in the satellite data due to parallelism of the sun azimuth with the trends of these features or due to high sun elevations. In the present work, surface relief data derived from the existing Survey of India topographic base map have been merged with high spectral resolution Landsat-5 thematic mapper (TM) data and higher spatial resolution IRS-1C panchromatic data of Holenarsipur Supracrustal Belt, Karnataka, India, using a data fusion method. The method employs mainly the Intensity-Hue-Saturation (IHS) technique incorporating the concept of the High-Pass-Filter (HPF) technique of data fusion. In this technique, although the spatial details of the original panchromatic data are not being exploited to their fullest potential, the geological information reflected through subtle topographic expression are highlighted well, for example, boundaries of resistant rock formations, regional fold closures and folded layers in resistant rock formations, intrusive granitic masses and mafic-ultramafic intrusive bodies, and regional trends of the planar structural fabrics (regional foliation, fault and joint planes). [ABSTRACT FROM AUTHOR]

This article presents a method to study and correct radiometric distortions caused by topography in SAR images. The method is easy to implement and requires neither sophisticated software nor code-level programming. It also considers the case of a flat surface having an elevation different from the one for which calibration parameters were derived. An ortho-image of the slant range distance is used with a Digital Elevation Model to generate images of the local incident angle along the range and azimuth directions. The method compensates for variations in the terrain area of each pixel and for the angular dependence of backscatter, allowing the choice of either an empirical or semi-empirical scattering model. The method is applied to high-resolution C-SAR subsets of an agricultural area in the Central Cordillera of Costa Rica. The removal of topographic features appears excellent for local incident angles up to 80°, but small-scale structures have pronounced effects on the radar return for higher local incident angles and are not adequately corrected. [ABSTRACT FROM AUTHOR]

The topographic features formed by factors such as altitude, tectonic movement, lithology and others were analysed quantitatively by a new technique in this study on rift valleys in western Turkey. This technique calls for the determination of the altitude and vertical geologic movements in long-wavelength components after the separation of the short- and long-wavelength components of the two-dimensional Fast Fourier Transform (FFT) based on a digital terrain model (DTM). The location of active faults can be inferred from the magnitude of the anomalies of altitudinal dispersion and the position of the anomalies, and it is also inferred that the values of the anomalies are related to the displacement of the faults. The results have been checked in the field and it was shown that this technique is very effective for structural analysis. [ABSTRACT FROM AUTHOR]

A pixel block intensity modulation (PRIM) method has been developed to add spatial detail to Landsat Thematic Mapper (TM) thermal band TM6 images in regions with sufficient topography. The method uses 30 m resolution TM reflective spectral band images (TM1-5 and 7) to modulate the relevant TM6 image on the basis of its 120m resolution thermal pixel blocks. Topographic detail in each 120m resolution pixel block of the TM6 image is thus recovered, without altering the average thermal digital number level of the block, by the spatial information recorded in the reflective spectral bands at 30m resolution. Tests confirm that the PBIM can effectively integrate topographic spatial detail from reflective spectral bands with TM6 images while retaining the fidelity of the original thermal spectral information. PBIM is also applicable, as a general method, for data fusion of multispectral and panchromatic images with different spatial resolutions. Bearing in mind that, for space-borne remote sensing, the spatial resolution of the thermal band will continue to be lower than that of VISSWIR and panchromatic bands in the multispectral sensor systems of the next generation, such as Enhanced Thematic Mapper Plus, the PBIM method will remain a useful technique for enhancing thermal imagery data for some time. [ABSTRACT FROM AUTHOR]

Rapid changes, natural and anthropogenic, across the Damietta promontory of the Nile delta have been analyzed by undertaking comparisons of Landsat satellite image data, acquired in 1978, 1983, 1990, 1993 and 1995. These images were utilized together with a series of topographic maps dated 1909, 1922, 1944 and 1955 to cover a 86-year monitoring period. Changes documented in this study include: areas of shoreline erosion and accretion, evolution of Damietta coastal spit and changes in the lagoon margin and contiguous islands. The analysis describes areas of erosion along the promontory tip adjacent to the river mouth (-0.51 km2 yr-1) reverted to downcoast accretion along the promontory flank (0.30 km2 yr-1). Part of this sand is carried south-west along the shoreline as a seaward sand spit. This spit is actively migrating to the south-east (0.17 km yr-1) and is accompanied by changes in width (0.06km yr-1). Local areas of erosion and accretions are also detected along the Manzala lagoon sand barrier. The pattern of erosion versus accretion along the coast of the Damietta promontory is attributed to the construction of dams on the upper Nile River, first the Aswan Low Dam in 1909 and particularly the Aswan High Dam in 1964 that cut-off almost all water discharge from the river and the delivery of sediments to the coast. In the absence of a sediment supply, the continued transport of beach sand by waves and currents toward the east has resulted in beach erosion of the promontory close to the river mouth, while shoreline accretion has continued further to the east. An analysis of changes of the total area of Manzala lagoon has indicated that the lagoon has progressively shrunk in size with an average rate of 5.2km2 yr-1, while its embeded islands have accreted at 2.93km2 yr-1. The changes in the lagoon area and islands have resulted from extensive reclamation of the lagoon margin, weed growth and siltation processes. [ABSTRACT FROM AUTHOR]