Analysis of VNIR (400–1100 nm) spectral signatures for estimation of soil organic matter in tropical soils of Thailand

The widely available laboratory spectrometers detect targets at spectral regions restricted to visible and near-infrared (VNIR). The spectral response of soils in this region is predominantly featureless and obstructs the exploitation of absorption features as diagnostic criterion. In this study, polynomial based modelling was developed as an alternative method of estimating soil organic matter (OM) from VNIR spectral region. Forty-one core samples, collected from Lop Buri, Thailand, were subjected to chemical and radiometric analysis. Computations were made across four categories of synthesized bandwidths. The selection procedure identified bands at 960, 1100 and 520?nm as OM sensitive. The widening interval of bandwidth has corresponded with diminishing predictive power, termed ‘bandwidth decay effect’. The use of polynomial models and their validations showed a higher performance than the analysis made with multiple regressions analysis. The polynomial based approach offers a fresh opportunity for modelling other non-photoactive soil nutrient parameters. Furthermore, it may form the basis for integration of spectrometers and satellite sensors, aimed at mapping of non-vegetated soils.     

Accuracy assessment of MODIS,NOAA and IRS data in snow cover mapping under Himalayan conditions

Snow cover information is an essential parameter for a wide variety of scientific studies and management applications, especially in snowmelt runoff modelling. Until now NOAA and IRS data were widely and effectively used for snow‐covered area (SCA) estimation in several Himalayan basins. The suit of snow cover products produced from MODIS data had not previously been used in SCA estimation and snowmelt runoff modelling in any Himalayan basin. The present study was conducted with the aim of assessing the accuracy of MODIS, NOAA and IRS data in snow cover mapping under Himalayan conditions. The total SCA was estimated using these three datasets for 15 dates spread over 4 years. The results were compared with ground‐based estimation of snow cover. A good agreement was observed between satellite‐based estimation and ground‐based estimation. The influence of aspect in SCA estimation was analysed for the three satellite datasets and it was observed that MODIS produced better results. Snow mapping accuracy with respect to elevation was tested and it was observed that at higher elevation MODIS sensed more snow and proved better at mapping snow under mountain shadow conditions. At lower elevation, IRS proved better in mapping patchy snow cover due to higher spatial resolution. The temporal resolution of MODIS and NOAA data is better than IRS data, which means that the chances of getting cloud‐free scenes is higher. In addition, MODIS has an automated snow‐mapping algorithm, which reduces the time and errors incorporated during processing satellite data manually. Considering all these factors, it was concluded that MODIS data could be effectively used for SCA estimation under Himalayan conditions, which is a vital parameter for snowmelt runoff estimation.     

Applicability of the Mix–Unmix Classifier in percentage tree and soil cover mapping

The Mix–Unmix Classifier is a simple novel method developed to address the problem of under‐determination in linear spectral unmixing. This paper tests the applicability of the Mix–Unmix Classifier in percentage mapping of tree cover and different soil types from single bands of satellite imagery. Various transformations were executed on African Moderate Resolution Imaging Spectroradiometer (MODIS) data bands 1, 2, 3, 4, 6 and 7. The equatorial rainforest is most distinguishable under skewness. The skewness transformation band is unmixed into two endmembers: tree (endmember of interest) and non‐tree (background). The resulting percentage tree cover map was compared with a University of Maryland percentage tree cover map of the continent, giving a correlation coefficient of 0.87. Fraction images of three soil types were generated from Japanese Earth Resources Satellite (JERS) synthetic aperture radar (SAR) L‐band data covering a section of Jordan. The soil types considered were hardpan topsoil, Qaa topsoil, and topsoil of herbaceous layer. The correlation coefficients of the Mix–Unmix Classifier‐derived fraction images versus reference fraction images for the three soil types were 0.89, 0.87 and 0.89, respectively.     

A new vegetation index derived from the pattern decomposition method applied to Landsat‐7/ETM+ images in Mongolia

The goal of this study was to estimate vegetation coverage and map the land‐cover in an experimental field (60×60 km) near Mandalgobi, Mongolia using Landsat‐7/ETM+ data for ground truthing in the Advanced Earth Observing Satellite II (ADEOS‐II) Mongolian Plateau Experiment (AMPEX). We measured soil moisture, vegetation coverage, and vegetation moisture in the field at 49 grid points around the time that the Aqua satellite passed over the area. We also surveyed the land‐cover in the field. Using ground‐based data and characteristics of spectral reflectance, we attempted to extract vegetation information from satellite data using the pattern decomposition method, which is a type of spectral mixture analysis. This method uses normalized spectral shapes as endmembers, which do not change between scenes. We defined an index using the pattern decomposition coefficients to analyse sparsely vegetated areas. The index showed a linear relationship with vegetation coverage. The vegetation coverage was estimated for the study site, and the average coverage at the site was 21.4%. Land‐cover types were classified using the index and the pattern decomposition coefficients; the kappa coefficient was 0.75. The index was useful for estimating vegetation coverage and land‐cover mapping for semiarid areas.     

Pine plantation structure mapping using WorldView-2 multispectral image

Optical images of different spectral and spatial resolutions continue to provide a reliable source of data for estimating forest inventory parameters. WorldView-2 launched in October 2009 is the first commercial optical satellite to provide high spatial resolution images with eight spectral bands, some of which are new and require investigation for estimation of forest structure parameters. In this study, a WorldView-2 multispectral image has been investigated for mapping pine plantation structural parameters including stand volume, basal area, stocking, mean diameter at breast height (mean DBH), and mean height of trees over a Pinus radiata plantation in New South Wales, Australia. Spectral derivatives including reflectance bands, band ratios, principal components (PCs), and several vegetation indices (VIs) were calculated using four typical bands, including blue, green, red, and near-infrared (NIR1), and all eight bands. Moreover, textural information, including 11 grey-level co-occurrence matrix (GLCM) indices, was extracted using four window sizes and orientations. Several models were developed using the extracted attributes separately to compare the efficiency of the models derived from the attributes of four typical bands and eight bands, as well as to compare between the capability of spectral-based and textural-based models for estimating structural parameters. The results showed that models derived from textural attributes of eight spectral bands provide the best estimates compared to those derived from four typical bands and the models derived from spectral derivatives. Moreover, the mean height and mean DBH with 8% and 13.7% error of estimation, respectively, were estimated more accurately than basal area, stand volume, and stocking, where the error of estimation is up to 30%.     

Sub‐pixel reflectance unmixing in estimating vegetation water content and dry biomass of corn and soybeans cropland using normalized difference water index (NDWI) from satellites

Estimating vegetation cover, water content, and dry biomass from space plays a significant role in a variety of scientific fields including drought monitoring, climate modelling, and agricultural prediction. However, getting accurate and consistent measurements of vegetation is complicated very often by the contamination of the remote sensing signal by the atmosphere and soil reflectance variations at the surface. This study used Landsat TM/ETM+ and MODIS data to investigate how sub‐pixel atmospheric and soil reflectance contamination can be removed from the remotely sensed vegetation growth signals. The sensitivity of spectral bands and vegetation indices to such contamination was evaluated. Combining the strengths of atmospheric models and empirical approaches, a hybrid atmospheric correction scheme was proposed. With simplicity, it can achieve reasonable accuracy in comparison with the 6S model. Insufficient vegetation coverage information and poor evaluation of fractional sub‐pixel bare soil reflectance are major difficulties in sub‐pixel soil reflectance unmixing. Vegetation coverage was estimated by the Normalized Difference Water Index (NDWI). Sub‐pixel soil reflectance was approximated from the nearest bare soil pixel. A linear reflectance mixture model was employed to unmix sub‐pixel soil reflectance from vegetation reflectance. Without sub‐pixel reflectance contamination, results demonstrate the true linkage between the growth of sub‐pixel vegetation and the corresponding change in satellite spectral signals. Results suggest that the sub‐pixel soil reflectance contamination is particularly high when vegetation coverage is low. After unmixing, the visible and shortwave infrared reflectances decrease and the near‐infrared reflectances increase. Vegetation water content and dry biomass were estimated using the unmixed vegetation indices. Superior to the NDVI and the other NDWIs, the SWIR (1650 nm) band‐based NDWI showed the best overall performance. The use of the NIR (1240 nm), which is a unique band of MODIS, was also discussed.     

Evaluation of data sources for mapping glacial meltwater features

The meltwater system of disintegrating ice sheets provides an important source of information for the reconstruction of ice-retreat patterns during deglaciation. Recent method development in glacial geomorphology, using satellite imagery and digital elevation models (DEMs) for glacial landform mapping, has predominantly been focused on the identification of lineation and other large-scale accumulation features. Landforms created by meltwater have often been neglected in these efforts. Meltwater features such as channels, deltas and fossil shorelines were traditionally mapped using stereo interpretation of aerial photographs. However, during the transition into the digital era, driven by a wish to cover large areas more economically, meltwater features were lost in most mapping surveys. We have evaluated different sets of satellite images and DEMs for their suitability to map glacial meltwater features (lateral meltwater channels, eskers, deltas, ice-dammed lake drainage channels and fossil shorelines) in comparison with the traditional mapping from aerial photographs. Several sets of satellite images and DEMs were employed to map the landform record of three reference areas, located in northwestern Scotland, northeastern Finland and western Sweden. The employed satellite imagery consisted of Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Satellite Pour l'Observation de la Terre (SPOT) 5 and Indian Remote Sensing (IRS) 1C, and the DEMs used were from NEXTMap Britain, Panorama, National elevation data set of Sweden and National Land Survey of Finland. ASTER images yielded better results than the panchromatic band of Landsat 7 ETM+?in all three regions, despite the same spatial resolution of the data. In agreement with previous studies, this study shows that DEMs display accumulation features such as eskers suitably well. Satellite images are shown to be insufficiently detailed for the interpretation of smaller features such as meltwater channels. Hence, satellite imagery and DEMs of intermediate resolution contain meltwater system information only at a general level that allows for the identification of landforms of medium to large sizes. It is therefore pertinent that data with an appropriate spatial and spectral resolution are accessed to fulfil the need of a particular mapping effort. Stereo interpretation of aerial photographs continues to be an advisable method for local meltwater system reconstructions; alternatively, it can be replaced by mapping from high-resolution DEMs such as NEXTMap Britain. For regional to sub-continental reconstructions, the use of ASTER satellite imagery is recommended, because it provides both spectral and spatial resolutions suitable for the identification of meltwater features on a medium to large scale.     

Spectral analysis and classification accuracy of coffee crops using Landsat and a topographic‐environmental model

Coffee is an extremely important cash crop, yet previous work indicates that satellite mapping of coffee has produced low classification accuracy. This research examines spectral band combinations and ancillary data for evaluating the classification accuracy and the nature of spectral confusion between coffee and other cover types in a Costa Rican study area. Supervised classification using Landsat Enhanced Thematic Mapper (ETM+) with only red, near‐infrared, and mid‐infrared bands had significantly lower classification accuracy compared to datasets that included more spectral bands and ancillary data. The highest overall accuracy achieved was 65%, including a coffee environmental stratification model (CESM). Producer's and user's accuracy was highest for shade coffee plantations (91.8 and 61.1%) and sun coffee (86.2 and 68.4%) with band combination ETM+ 34567, NDVI, cos (i), and including the use of the CESM. Post‐classification stratification of the optimal coffee growing zone based on elevation and precipitation data did not show significant improvement in land cover classification accuracy when band combinations included both the thermal band and NDVI. A forward stepwise discriminant analysis indicated that ETM+ 5 (mid‐infrared band) had the highest discriminatory power. The best discriminatory subset for all woody cover types including coffee excluded ETM+ 3 and 7; however, the land cover accuracy assessment indicated that overall accuracy, as well as producer's and user's accuracy of shade and sun coffee, were slightly improved with the inclusion of these bands. Although spectral separation between coffee crops and woodland areas was only moderately successful in the Costa Rica study, the overall accuracy, as well as the sun and shade coffee producer's and user's accuracy, were higher than reported in previous research.     

The application of satellite remote sensing for soil studies in upland areas of Southern Italy

Spectral data can supply useful information on soil spatial variability even when the soil surface is partially masked by vegetation. This paper reports the first results of research aiming to assess the possibilities and limits of satellite remote sensing data for studying soils in the Apennine Mountains of Southern Italy. This region is characterized by the presence of large areas of bare soils during certain times of the year, but also by the dissected terrain which strongly influences the spectral response of soils. The results presented show the potential of satellite remotely-sensed data to broadly predict certain soil parameters, such as organic matter (OM) and calcium carbonate content (CACAR), from radiance values. However, the results of a sub-scene spectral classification, illustrate a greater potential for satellite data to provide useful reconnaisance soil mapping information, which can be tested by limited ground checks     

Evaluation of the potential of various spectral indices and textural features derived from satellite images for surficial deposits mapping

The present paper deals with the relevance of spectral and textural indices to surficial deposits identification and mapping. The study area is located in the Cochabamba valley in central Bolivia. Potential of SPOT‐4, Landsat‐7 and Radarsat‐1 data were compared for surficial deposits mapping. Different spectral indices including NDVI (normalized difference vegetation index) and TSAVI (transformed soil adjusted vegetation index) and textural features (mean, standard deviation, angular second moment, entropy, etc.) were extracted from these datasets and used in the mapping process. The results showed that indices exhibit different level of sensitivities according to surficial deposit types. A discriminant analysis was conducted to extract the most significant indices, which were then used in a three‐step linear combination mathematical model to map surficial deposits. We achieved an overall classification rate of 74% using spectral data of land use map in step 1. By adding information on vegetation and soils obtained from evaluation of spectral indices, this rate was improved to 82% during step 2. Finally, it was further slightly improved to 83% by adding textural data in the final step.     

Mapping burned areas in Mediterranean countries using spectral mixture analysis from a uni‐temporal perspective

The main aim of this study was to evaluate the usefulness of spectral mixture analysis (SMA) for mapping forest areas burned by fires in the Mediterranean area using low and medium spatial resolution satellite sensor data. A methodology requiring only one single post‐fire image was used to carry out the study (uni‐temporal techniques). This methodology is based on the contextual classification of the fraction images obtained after applying SMA to the original post‐fire image. The results showed that the proposed method, using only one image acquired post‐fire, could accurately identify the burned surface area (Kappa coefficient>0.8). The spatial resolution of the satellite images had practically no influence on the accuracy of the burned area estimate but did affect the possibility of detecting areas inside the perimeter of the burned area which were only slightly damaged.     

Mapping coffee crops in southeastern Brazil using spectral mixture analysis and data mining classification

The task of mapping coffee crops using multispectral data sets is not yet a trivial routine. This is because coffee fields are extremely heterogeneous in terms of spectral reflectance. This study therefore aims to contribute to the mapping of coffee crops using multispectral imagery with 23.5 m spatial resolution taken by the Linear Imaging Self Scanner (LISS III) instrument on board the Indian Remote Sensing (IRS) satellite system. The section of land covered by this study is a traditional coffee-producing province located in the south of the State of Minas Gerais, southeastern Brazil. Whereas the pixel mixture effect was managed using spectral mixture analysis (SMA), the classification was carried out using data mining (DM) techniques. The decision tree (DT) outcomes were evaluated using a simple and qualitative method based on the elements of photointerpretation. In total, eight land-use and land-cover (LULC) types were mapped, including three classes of coffee-growing land expressing different phenological conditions and management. These were named ‘Production Coffee’, ‘Mixed Coffee’, and ‘Old/Pruned Coffee’. The results showed that the methodology was effective for mapping LULC types, as the workflow adopted simplified image interpretation and offered improvements in the classification performance. Despite the coffee-cultivation classes having a large spectral variability, which increases the chances of classification errors, not many confusions were observed involving the three coffee classes mapped with other categories of use. This therefore shows that the method was efficient in isolating the coffee classes (with an accuracy greater than 70%) from other categories of use. Comparing the results obtained in this work with a conventional maximum-likelihood (ML) classification, the results revealed that when using the methodology described, the confusions between classes were less dispersed and an improvement of approximately 10% was observed in the mapping of the Production Coffee class.     

Multitemporal spectral analysis for cheatgrass (Bromus tectorum) classification

Operational satellite remote sensing data can provide the temporal repeatability necessary to capture phenological differences among species. This study develops a multitemporal stacking method coupled with spectral analysis for extracting information from Landsat imagery to provide species‐level information. Temporal stacking can, in an approximate mathematical sense, effectively increase the ‘spectral’ resolution of the system by adding spectral bands of several multitemporal images. As a demonstration, multitemporal linear spectral unmixing is used to successfully delineate cheatgrass (Bromus tectorum) from soil and surrounding vegetation (77% overall accuracy). This invasive plant is an ideal target for exploring multitemporal methods because of its phenological differences with other vegetation in early spring and, to a lesser degree, in late summer. The techniques developed in this work are directly applicable for other targets with temporally unique spectral differences.     

A simple method of full spectrum reconstruction by a five-band approach for ocean colour applications

Ground truth measurements are necessary for the validation of remotely sensed data. Rapid ship or aircraft spectral measurements of the upwelling and downwelling (ir)radiance are needed to determine the reflectance of the water column as well, as to intercalibrate with satellite sensors. Intercalibrations are hindered by the application of different instruments with varying spectal bands. It is shown that when an optical data bank (ODB) of high resolution spectra (400-720 nm) of a specific sea area is available, it is possible to reconstruct new and old reflectance spectra, accurate to within 1 per cent almost over the full spectral range, out of the reflectance measured in five bands. The ODB could contain subsurface- or airborne-collected data. It appears to be possible to use simple instruments with five specific bands to compare with satellite data, even if these differ in central wavelengths. In this way high resolution spectral data could also be stored by means of only five bands. The reconstruction technique used is based upon a multiple regression analysis (MRA) or the OBD. To validate this full reflectance spectrum reconstruction method, spectral data collected with different radiometers in different locations were successfully regenerated from five key bands (412, 492, 556, 620, and 672nm). It is proposed that airborne spectral reflectance measurements could remain limited to only five specific spectral bands.     

Pre-harvest wheat yield and production estimation for the Punjab,India

Two different approaches to relate wheat yield with spectral indices derived from remotely-sensed data have been explored for the state of Punjab, India. In the study based on site-level approach yield obtained from crop-cutting sites was found to be linearly related to NIR/Red ratio derived from Landsat MSS data of corresponding sites in Ludhiana and Patiala districts of Punjab. Incorporation of agrometeorological data was also tried. Certain inherent limitations of the site-level approach led to the district-level studies which focused on the relation of district yields with corresponding average spectral indices derived from satellite sensors like Landsat MSS and lRS-LISS-i. Significant correlations were observed in all cases and the relation based on Landsat MSS/IRS LISS-I data was used for trial forecast of wheat yields for 1989–90 season. A comparison of remote-sensing based production forecast showed good agreement with the conventional estimate of Bureau of Economics and Statistics at state level although at district level, deviations were larger.     

Mapping vegetation in a heterogeneous mountain rangeland using landsat data: an alternative method to define and classify land-cover units

Three major problems are faced when mapping natural vegetation with mid-resolution satellite images using conventional supervised classification techniques: defining the adequate hierarchical level for mapping; defining discrete land cover units discernible by the satellite; and selecting representative training sites. In order to solve these problems, we developed an approach based on the: (1) definition of ecologically meaningful units as mosaics or repetitive combinations of structural types, (2) utilization of spectral information (indirectly) to define the units, (3) exploration of two alternative methods to classify the units once they are defined: the traditional, Maximum Likelihood method, which was enhanced by analyzing objective ways of selecting the best training sites, and an alternative method using Discriminant Functions directly obtained from the statistical analysis of signatures. The study was carried out in a heterogeneous mountain rangeland in central Argentina using Landsat data and 251 field sampling sites. On the basis of our analysis combining terrain information (a matrix of 251 stands×14 land cover attributes) and satellite data (a matrix of 251 stands×8 bands), we defined 8 land cover units (mosaics of structural types) for mapping, emphasizing the structural types which had stronger effects on reflectance. The comparison through field validation of both methods for mapping units showed that classification based on Discriminant Functions produced better results than the traditional Maximum Likelihood method (accuracy of 86% vs. 78%).     

Analysis of a small agricultural watershed using remote sensing techniques

Salinization of land and sweet water is an increasing problem worldwide. In the Carpathian Basin, particularly in arid and semi‐arid regions, irrigation is a contributing factor to the secondary salinization problems, one of the major problems affecting soils in Hungary. Conventional broadband sensors such as SPOT, Landsat MSS, and Landsat ETM+ are not suitable for mapping soil properties, because their bandwidth of 100–200 mm cannot resolve diagnostic spectral features of terrestrial materials. Analytical techniques, developed for analysis of broadband spectral data, are incapable of taking advantage of the full range of information present in hyperspectral remote sensing imagery. In our pilot project in Tedej farm in the Great Plain Region, Hungary, the DAIS sensor was used to assess salinity risk, covering the spectral range from the visible to the thermal infrared wavelengths at 5 m spatial resolution, and other major indicators of soil salinization (NDVI, SAVI, canopy cover) were quantified with advanced remote sensing techniques using the TETRACAM ADC agricultural multispectral camera which offers red/green and NIR imaging at megapixel resolution. As a result, prominent absorption bands around 1450 nm and 1950 nm wavelength in most soil spectra are attributed to water and hydroxyl ions. Occasional weaker absorption bands caused by water also occur at 970, 1200, and 1700 nm. Absorption features near the 400 nm wavelength for all samples are also noticeable. Absorption bands at 1800 and 2300 nm are attributed to gypsum, while strong absorption features near 2350 nm are assigned to calcite (CaCo₃). Saline soils exhibited significantly higher reflectance values all throughout the 325–2500 nm wavelengths of the spectrum. Soils with a high amount of soluble salts gave a higher average reflectance than soils with a low salt content. In the project, an ADC camera‐based real‐time integrated system was developed to take advantage of more specialized spectral information and to provide even more accurate and useful data directly from the field. The results revealed that the NDVI and SAVI index and the canopy cover mapping taken with multispectral cameras can be useful as an indirect marker and help for detecting salinization. However, we did not find a strong correlation between NDVI and soil salinity. This is probably because the detection and assessment of lower levels of salinity are difficult, mainly owing to the nature of the remotely sensed images; with such images, it is not possible to obtain information on the third dimension of the 3‐D soil body. Also, the impact of salinity on electromagnetic properties needs to be explored further to understand how it can be derived indirectly from remotely sensed information. With the rapid validation of remotely sensed hyperspectral data, the decision in the future, with the best trade‐off between irrigation and sustainable land use made by agricultural specialists in this region, can be more environmentally sound and more accurate using the results from the pilot.     

Modelling land‐cover types using multiple endmember spectral mixture analysis in a desert city

Spectral mixture analysis is probably the most commonly used approach among sub‐pixel analysis techniques. This method models pixel spectra as a linear combination of spectral signatures from two or more ground components. However, spectral mixture analysis does not account for the absence of one of the surface features or spectral variation within pure materials since it utilizes an invariable set of surface features. Multiple endmember spectral mixture analysis (MESMA), which addresses these issues by allowing endmembers to vary on a per pixel basis, was employed in this study to model Landsat ETM+ reflectance in the Phoenix metropolitan area. Image endmember spectra of vegetation, soils, and impervious surfaces were collected with the use of a fine resolution Quickbird image and the pixel purity index. This study employed 204 (3×17×4) total four‐endmember models for the urban subset and 96 (6×6×2×4) total five‐endmember models for the non‐urban subset to identify fractions of soil, impervious surface, vegetation and shade. The Pearson correlation between the fraction outputs from MESMA and reference data from Quickbird 60 cm resolution data for soil, impervious, and vegetation were 0.8030, 0.8632, and 0.8496 respectively. Results from this study suggest that the MESMA approach is effective in mapping urban land covers in desert cities at sub‐pixel level.     

Comparative use of processed satellite images in remote sensing of mass movements: Lebanon as a case study

Mass movements (MM) represent a serious threat to human life and activities in most mountainous areas. However, due to the rugged nature of such terrain, it is often difficult to detect such phenomena in remote areas. Hence, satellite imagery offers many attractions for the examination of MM in such environments, especially in less developed nations in which resources are stretched and levels of environmental information limited. There is a need to ensure that the techniques and images used are effective, reliable, and cheap in terms of the amount and accuracy of data that can be extracted. Taking Lebanon as a case study, this paper compares the applicability of different satellite data sensors (Landsat TM (Thematic Mapper), IRS (Indian Remote Sensing Satellite), SPOT4 (Système Probatoire pour l’Observation de la Terre)) and preferred image‐processing techniques (False Colour Composite ‘FCC’, pan‐sharpen, principal‐component analysis ‘PCA’, Anaglyph) for the mapping of MM recognized as landslides, rock and debris falls, and earth flows. Results from the imagery have been validated by field surveys and analysis of IKONOS imagery acquired in some locations witnessing major MM during long periods. Then, levels of accuracies of detected MM from satellite imageries were plotted. This study has demonstrated that the anaglyph produced from the two panchromatic stereo‐pairs SPOT4 images remains the most effective tool setting the needed 3D properties for visual interpretation and showing a maximum accuracy level of 67%. The PCA pan‐sharpened Landsat TM‐IRS image gave better results in detecting MM, among other processing techniques, with a maximum accuracy level of 62%.

     

Probabilistic landslide hazard mapping using GIS and remote sensing data at Boun,Korea

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.