Since the last assessment of ecological zones of Sudan conducted in 1958 by Harrison and Jackson, the vegetation types, cover, and its distribution have undergone remarkable changes;this change occurs in most of the p...Since the last assessment of ecological zones of Sudan conducted in 1958 by Harrison and Jackson, the vegetation types, cover, and its distribution have undergone remarkable changes;this change occurs in most of the predominant ecological zones, particularly those which are currently affected by environmental degradation and desertification due to climate change impact on vegetation cover and rainfall amounts and its distribution. In addition, during the last five decades, Sudan has suffered from environmental devastating changes that have undermined food security, which is strongly linked to human displacement and related conflicts. In this study, SPOTVEG NDVI data acquired between 2000 and 2010 were used to produce a vegetation cover map, which was integrated with rainfall map to produce updated ecological zones map for the study area in 2010. This map was compared to that produced in 1958 to analyze the change during the last five decades. The obtained results showed drastic change concerning different ecological zones. The desert class increased by 11%. The Semi-Desert class decreased by approximately 13.2%. The woodland savannah class increased by 6.8%. While, the Mountain vegetation areas decreased approximately by 3%. Finally, the wetland areas completely disappeared in 2010. These results show that during the period 1958-2010, climate change impact increased desertification process, destroyed ecological zones, especially wetland, as well the ecosystems diversity in Central Sudan.展开更多
This research deals with the characterization of areas associated with flash floods and erosion caused by severe rainfall storm and sediment transport and accumulation using topographic attributes and profiles, spectr...This research deals with the characterization of areas associated with flash floods and erosion caused by severe rainfall storm and sediment transport and accumulation using topographic attributes and profiles, spectral indices (SI), and principal component analysis (PCA). To achieve our objectives, topographic attributes and profiles were retrieved from ASTER-V2 DEM. PCA and nine SI were derived from two Landsat-OLI images acquired before and after the flood-storm. The images data were atmospherically corrected, sensor radiometric drift calibrated, and geometric and topographic distortions rectified. For validation purposes, the acquired photos during the flood-storm, lithological and geological maps were used. The analysis of approximately 100 colour composite combinations in the RGB system permitted the selection of two combinations due to their potential for characterizing soil erosion classes and sediment accumulation. The first considers the “Intensity, NDWI and NMDI”, while the second associates form index (FI), brightness index (BI) and NDWI. These two combinations provide very good separating power between different levels of soil erosion and degradation. Moreover, the derived erosion risk and sediment accumulation map based on the selected spectral indices segmentation and topographic attributes and profiles illustrated the tendency of water accumulation in the landscape, and highlighted areas prone to both fast moving and pooling water. In addition, it demonstrated that the rainfall, the topographic morphology and the lithology are the major contributing factors for flash flooding, catastrophic inundation, and erosion risk in the study area. The runoff-water power delivers vulnerable topsoil and contributes strongly to the erosion process, and then transports soil material and sediment to the plain areas through waterpower and gravity. The originality of this research resides in its simplicity and rapidity to provide a solid basis strategy for regional policies to address the real causes of problems and risks in developing countries. Certainly, it can help in the improvement of the management of water regulation structures to develop a methodology to maximize the water storage capacity and to reduce the risks caused by floods in the Moroccan Atlas Mountain (Guelmim region).展开更多
Salt-affected soils, caused by natural or human activities, are a common environmental hazard in semi-arid and arid landscapes. Excess salts in soils affect plant growth and production, soil and water quality and, the...Salt-affected soils, caused by natural or human activities, are a common environmental hazard in semi-arid and arid landscapes. Excess salts in soils affect plant growth and production, soil and water quality and, therefore, increase soil erosion and land degradation. This research investigates the performance of five different semi-empirical predictive models for soil salinity spatial distribution mapping in arid environment using OLI sensor image data. This is the first attempt to test remote sensing based semi-empirical salinity predictive models in this area: the Kingdom of Bahrain. To achieve our objectives, OLI data were standardized from the atmosphere interferences, the sensor radiometric drift, and the topographic and geometric distortions. Then, the five semi-empirical predictive models based on the Normalized Difference Salinity Index (NDSI), the Salinity Index-ASTER (SI-ASTER), the Salinity Index-1 (SI-1), the Soil Salinity and Sodicity Index-1 and Index-2 (SSSI-1 and SSSI-2), developed for slight and moderate salinity in agricultural land, were implemented and applied to OLI image data. For validation purposes, a fieldwork was organized and different important spots-locations representing different salinity levels were visited, photographed, and localized using an accurate GPS (σ ≤ ±30 cm). Based on this a priori knowledge of the soil salinity, six validation sites were selected to reflect non-saline, low, moderate, high and extreme salinity classes, descriptive statistics extracted from polygons and/or transects over these sites were used. The obtained results showed that the models based on NDSI, SI-1 and SI-ASTER all failed to detect salinity bounds for both extreme salinity (Sabkhah) and non-saline conditions. In Fact, NDSI and SI-ASTER gave respectively only 35% dS/m and 25% dS/m in extreme salinity validation site, while SI-1 and SI-ASTER indicated 38% dS/m and 39% dS/m in non-saline validation site. Therefore, these three models were deemed inadequate for the study site. However, both SSSI-1 and SSSI-2 allowed a detection of the previous salinity bounds and furthermore described similarly and correctly the urban-vegetation areas and the open-land areas. Their predicted EC is around 10% dS/m for non-saline urban soil, about 25% dS/m for low salinity urban-vegetation soil, approximately 30% to 75% dS/m, respectively, for moderate to high salinity soils. SSSI-2 based semi-empirical salinity models was able to differentiate the high salinity versus extreme salinity in areas where both exist and was very accurate to highlight the pure salt where SSSI-1 has reach saturation for both salinity classes. In conclusion, reliable salinity map was produced using the model based on SSSI-2 and OLI sensor data that allows a better characterization of the soil salinity problem in an Arid Environment.展开更多
文摘Since the last assessment of ecological zones of Sudan conducted in 1958 by Harrison and Jackson, the vegetation types, cover, and its distribution have undergone remarkable changes;this change occurs in most of the predominant ecological zones, particularly those which are currently affected by environmental degradation and desertification due to climate change impact on vegetation cover and rainfall amounts and its distribution. In addition, during the last five decades, Sudan has suffered from environmental devastating changes that have undermined food security, which is strongly linked to human displacement and related conflicts. In this study, SPOTVEG NDVI data acquired between 2000 and 2010 were used to produce a vegetation cover map, which was integrated with rainfall map to produce updated ecological zones map for the study area in 2010. This map was compared to that produced in 1958 to analyze the change during the last five decades. The obtained results showed drastic change concerning different ecological zones. The desert class increased by 11%. The Semi-Desert class decreased by approximately 13.2%. The woodland savannah class increased by 6.8%. While, the Mountain vegetation areas decreased approximately by 3%. Finally, the wetland areas completely disappeared in 2010. These results show that during the period 1958-2010, climate change impact increased desertification process, destroyed ecological zones, especially wetland, as well the ecosystems diversity in Central Sudan.
文摘This research deals with the characterization of areas associated with flash floods and erosion caused by severe rainfall storm and sediment transport and accumulation using topographic attributes and profiles, spectral indices (SI), and principal component analysis (PCA). To achieve our objectives, topographic attributes and profiles were retrieved from ASTER-V2 DEM. PCA and nine SI were derived from two Landsat-OLI images acquired before and after the flood-storm. The images data were atmospherically corrected, sensor radiometric drift calibrated, and geometric and topographic distortions rectified. For validation purposes, the acquired photos during the flood-storm, lithological and geological maps were used. The analysis of approximately 100 colour composite combinations in the RGB system permitted the selection of two combinations due to their potential for characterizing soil erosion classes and sediment accumulation. The first considers the “Intensity, NDWI and NMDI”, while the second associates form index (FI), brightness index (BI) and NDWI. These two combinations provide very good separating power between different levels of soil erosion and degradation. Moreover, the derived erosion risk and sediment accumulation map based on the selected spectral indices segmentation and topographic attributes and profiles illustrated the tendency of water accumulation in the landscape, and highlighted areas prone to both fast moving and pooling water. In addition, it demonstrated that the rainfall, the topographic morphology and the lithology are the major contributing factors for flash flooding, catastrophic inundation, and erosion risk in the study area. The runoff-water power delivers vulnerable topsoil and contributes strongly to the erosion process, and then transports soil material and sediment to the plain areas through waterpower and gravity. The originality of this research resides in its simplicity and rapidity to provide a solid basis strategy for regional policies to address the real causes of problems and risks in developing countries. Certainly, it can help in the improvement of the management of water regulation structures to develop a methodology to maximize the water storage capacity and to reduce the risks caused by floods in the Moroccan Atlas Mountain (Guelmim region).
文摘Salt-affected soils, caused by natural or human activities, are a common environmental hazard in semi-arid and arid landscapes. Excess salts in soils affect plant growth and production, soil and water quality and, therefore, increase soil erosion and land degradation. This research investigates the performance of five different semi-empirical predictive models for soil salinity spatial distribution mapping in arid environment using OLI sensor image data. This is the first attempt to test remote sensing based semi-empirical salinity predictive models in this area: the Kingdom of Bahrain. To achieve our objectives, OLI data were standardized from the atmosphere interferences, the sensor radiometric drift, and the topographic and geometric distortions. Then, the five semi-empirical predictive models based on the Normalized Difference Salinity Index (NDSI), the Salinity Index-ASTER (SI-ASTER), the Salinity Index-1 (SI-1), the Soil Salinity and Sodicity Index-1 and Index-2 (SSSI-1 and SSSI-2), developed for slight and moderate salinity in agricultural land, were implemented and applied to OLI image data. For validation purposes, a fieldwork was organized and different important spots-locations representing different salinity levels were visited, photographed, and localized using an accurate GPS (σ ≤ ±30 cm). Based on this a priori knowledge of the soil salinity, six validation sites were selected to reflect non-saline, low, moderate, high and extreme salinity classes, descriptive statistics extracted from polygons and/or transects over these sites were used. The obtained results showed that the models based on NDSI, SI-1 and SI-ASTER all failed to detect salinity bounds for both extreme salinity (Sabkhah) and non-saline conditions. In Fact, NDSI and SI-ASTER gave respectively only 35% dS/m and 25% dS/m in extreme salinity validation site, while SI-1 and SI-ASTER indicated 38% dS/m and 39% dS/m in non-saline validation site. Therefore, these three models were deemed inadequate for the study site. However, both SSSI-1 and SSSI-2 allowed a detection of the previous salinity bounds and furthermore described similarly and correctly the urban-vegetation areas and the open-land areas. Their predicted EC is around 10% dS/m for non-saline urban soil, about 25% dS/m for low salinity urban-vegetation soil, approximately 30% to 75% dS/m, respectively, for moderate to high salinity soils. SSSI-2 based semi-empirical salinity models was able to differentiate the high salinity versus extreme salinity in areas where both exist and was very accurate to highlight the pure salt where SSSI-1 has reach saturation for both salinity classes. In conclusion, reliable salinity map was produced using the model based on SSSI-2 and OLI sensor data that allows a better characterization of the soil salinity problem in an Arid Environment.