A correct assessment of the landslide susceptibility component is extremely useful for the diminution of associated potential risks to local economic development, particularly in regard to land use planning and soil c...A correct assessment of the landslide susceptibility component is extremely useful for the diminution of associated potential risks to local economic development, particularly in regard to land use planning and soil conservation. The purpose of the present study was to compare the usefulness of two methods, i.e., binary logistic regression(BLR) and analytical hierarchy process(AHP), for the assessment of landslide susceptibility over a 130-km^2 area in the Moldavian Plateau(eastern Romania) region, where landslides affect large areas and render them unsuitable for agriculture. A large scale inventory mapping of all types of landslides(covering 13.7% of the total area) was performed using orthophoto images, topographical maps, and field surveys. A geographic information system database was created, comprising the nine potential factors considered as most relevant for the landsliding process. Five factors(altitude, slope angle, slope aspect, surface lithology, and land use) were further selected for analysis through the application of a tolerance test and the stepwise filtering procedure of BLR. For each predictor, a corresponding raster layer was built and a dense grid of equally spaced points was generated, with an approximately equal number of points inside and outside the landslide area, in order to extract the values of the predictors from raster layers. Approximately half of the total number of points was used for model computation, while the other half was used for validation. Analytical hierarchy process was employed to derive factor weights, with several pair-wise comparison matrices being tested for this purpose. The class weights, on a scale of 0 to 1, were taken as normalized landslide densities. A comparison of results achieved through these two approaches showed that BLR was better suited for mapping landslide susceptibility, with 82.8% of the landslide area falling into the high and very high susceptibility classes. The susceptibility class separation using standard deviation was superior to either the equal interval or the natural break method. Results from the study area suggest that the statistical model achieved by BLR could be successfully extrapolated to the entire area of the Moldavian Plateau.展开更多
Juniperus excelsa subsp.polycarpos,(Persian juniper),is found in northeast Iran.In this study,the relationship between ground cover and vegetation indices have been investigated using remote sensing data for a Persian...Juniperus excelsa subsp.polycarpos,(Persian juniper),is found in northeast Iran.In this study,the relationship between ground cover and vegetation indices have been investigated using remote sensing data for a Persian juniper forest.Multispectral data were analyzed based on the Advanced Visible and Near Infrared Radiometer type 2 and panchromatic data obtained by the Panchromatic Remote-sensing Instrument for Stereo Mapping sensors,both on board the advanced land observing satellite(ALOS).The ground cover was calculated using field survey data from 25 sub-sample plots and the vegetation indices were derived with 595 maximum filtering algorithm from ALOS data.R2 values were calculated for the normalized difference vegetation index(NDVI)and various soil-adjusted vegetation indices(SAVI)with soilbrightness-dependent correction factors equal to 1 and 0.5,a modified SAVI(MSAVI)and an optimized SAVI(OSAVI).R2 values for the NDVI,MSAVI,OSAVI,SAVI(1),and SAVI(0.5)were 0.566,0.545,0.619,0.603,and 0.607,respectively.Total ratio vegetation index for arid and semi-arid regions based on spectral wavelengths of ALOS data with an R2 value 0.633 was considered.Results of the current study will be useful for forest inventories in arid and semi-arid regions in addition to assisting decisionmaking for natural resource managers.展开更多
The physically based WEPP （Water Erosion Prediction Project） model was implemented in a small agricultural watershed located in central Belgium, called Ganspoel. The watershed, mainly agricultural and resulting in a...The physically based WEPP （Water Erosion Prediction Project） model was implemented in a small agricultural watershed located in central Belgium, called Ganspoel. The watershed, mainly agricultural and resulting in a smooth topography, covers about 115 ha in a landscape typical of large parts of central Europe. Seventeen runoff, peak flow and sediment yield events, collected during a 2-year monitoring period, were simulated by the model. Even though the runoff volume predictions were well correlated to the corresponding observations, WEPP prediction capability was generally unsatisfactory also when different set-up methods of the soil effective hydraulic conductivity were used. The poor performance achieved for runoff volume and peak flow simulations affected sediment yield predictions. The differences between observed and simulated values for runoff, peak flow and sediment yield events may depend on： i） the great number of small runoff and sediment yield events within the available database with which is associated large natural variation and which in many cases are not well reproduced by WEPP; ii） the lack of model calibration processes; iii） the scarceness of information about some important soil physical and hydrological parameters; iv） the land use heterogeneity and crop schedule complexity of the Ganspoel watershed.展开更多
文摘A correct assessment of the landslide susceptibility component is extremely useful for the diminution of associated potential risks to local economic development, particularly in regard to land use planning and soil conservation. The purpose of the present study was to compare the usefulness of two methods, i.e., binary logistic regression(BLR) and analytical hierarchy process(AHP), for the assessment of landslide susceptibility over a 130-km^2 area in the Moldavian Plateau(eastern Romania) region, where landslides affect large areas and render them unsuitable for agriculture. A large scale inventory mapping of all types of landslides(covering 13.7% of the total area) was performed using orthophoto images, topographical maps, and field surveys. A geographic information system database was created, comprising the nine potential factors considered as most relevant for the landsliding process. Five factors(altitude, slope angle, slope aspect, surface lithology, and land use) were further selected for analysis through the application of a tolerance test and the stepwise filtering procedure of BLR. For each predictor, a corresponding raster layer was built and a dense grid of equally spaced points was generated, with an approximately equal number of points inside and outside the landslide area, in order to extract the values of the predictors from raster layers. Approximately half of the total number of points was used for model computation, while the other half was used for validation. Analytical hierarchy process was employed to derive factor weights, with several pair-wise comparison matrices being tested for this purpose. The class weights, on a scale of 0 to 1, were taken as normalized landslide densities. A comparison of results achieved through these two approaches showed that BLR was better suited for mapping landslide susceptibility, with 82.8% of the landslide area falling into the high and very high susceptibility classes. The susceptibility class separation using standard deviation was superior to either the equal interval or the natural break method. Results from the study area suggest that the statistical model achieved by BLR could be successfully extrapolated to the entire area of the Moldavian Plateau.
文摘Juniperus excelsa subsp.polycarpos,(Persian juniper),is found in northeast Iran.In this study,the relationship between ground cover and vegetation indices have been investigated using remote sensing data for a Persian juniper forest.Multispectral data were analyzed based on the Advanced Visible and Near Infrared Radiometer type 2 and panchromatic data obtained by the Panchromatic Remote-sensing Instrument for Stereo Mapping sensors,both on board the advanced land observing satellite(ALOS).The ground cover was calculated using field survey data from 25 sub-sample plots and the vegetation indices were derived with 595 maximum filtering algorithm from ALOS data.R2 values were calculated for the normalized difference vegetation index(NDVI)and various soil-adjusted vegetation indices(SAVI)with soilbrightness-dependent correction factors equal to 1 and 0.5,a modified SAVI(MSAVI)and an optimized SAVI(OSAVI).R2 values for the NDVI,MSAVI,OSAVI,SAVI(1),and SAVI(0.5)were 0.566,0.545,0.619,0.603,and 0.607,respectively.Total ratio vegetation index for arid and semi-arid regions based on spectral wavelengths of ALOS data with an R2 value 0.633 was considered.Results of the current study will be useful for forest inventories in arid and semi-arid regions in addition to assisting decisionmaking for natural resource managers.
文摘The physically based WEPP （Water Erosion Prediction Project） model was implemented in a small agricultural watershed located in central Belgium, called Ganspoel. The watershed, mainly agricultural and resulting in a smooth topography, covers about 115 ha in a landscape typical of large parts of central Europe. Seventeen runoff, peak flow and sediment yield events, collected during a 2-year monitoring period, were simulated by the model. Even though the runoff volume predictions were well correlated to the corresponding observations, WEPP prediction capability was generally unsatisfactory also when different set-up methods of the soil effective hydraulic conductivity were used. The poor performance achieved for runoff volume and peak flow simulations affected sediment yield predictions. The differences between observed and simulated values for runoff, peak flow and sediment yield events may depend on： i） the great number of small runoff and sediment yield events within the available database with which is associated large natural variation and which in many cases are not well reproduced by WEPP; ii） the lack of model calibration processes; iii） the scarceness of information about some important soil physical and hydrological parameters; iv） the land use heterogeneity and crop schedule complexity of the Ganspoel watershed.
