- Department of Bioresource Engineering, Macdonald Campus, McGill University, 21,111 Lakeshore, Ste-Anne-de-Bellevue, QC, Canada, H9X 3V9
Hossein Saadat
McGill University, Bioresource Engineering, Department Member
- Dr. Robert Bonnelledit
Satellite remote sensing provides a synoptic view of the land and a spatial context for measuring drought impacts, which have proved to be a valuable source of spatially continuous data with improved information for monitoring vegetation... more
Satellite remote sensing provides a synoptic view of the land and a spatial context for measuring drought impacts, which have proved to be a valuable source of spatially continuous data with improved information for monitoring vegetation dynamics. Many studies have focused on detecting drought effects over large areas, given the wide availability of low-resolution images. In this study, however, the objective was to focus on a smaller area (1085 km
Research Interests:
Research Interests:
The assessment of soil erosion hazard can identify critical regions in a watershed and prioritize management plans and soil conservation measures. This study aims to present a new approach for mapping potential soil erosion hazard using... more
The assessment of soil erosion hazard can identify critical regions in a watershed and prioritize management plans and soil conservation measures. This study aims to present a new approach for mapping potential soil erosion hazard using fuzzy logic and GIS in the Gharnave watershed, Golestan Province. Three main factors affecting soil erosion processes including slope (%), soil erodibility and rainfall erosivity were prepared as input layers of the fuzzy model. The slope factor map (%) was obtained from a Digital Elevation Model (DEM) with a spatial resolution of 30m, provided by ASTER global DEM data. The rainfall erosivity factor raster map (R) was generated using interpolated annual R values at 14 stations and utilizing the Ordinary Kriging (OK) Geostatistical method and Gaussian semi-variogram model. Then, the fuzzy maps of the three layers were made by defining appropriate membership functions. These maps are combined by applying fuzzy rules in the MATLAB software. This led to ...
The awareness of soil erosion risk in watersheds can facilitate the identification of critical areas as well as the prioritizing of soil conservation and management plans. The present research aims to develop a new fuzzy model and its... more
The awareness of soil erosion risk in watersheds can facilitate the identification of critical areas as well as the prioritizing of soil conservation and management plans. The present research aims to develop a new fuzzy model and its validation for soil erosion risk mapping in Gharnaveh watershed, located in the Golestan province, Iran. Soil erosion risk mapping was conducted in three steps: at first, the map of soil protection index (SPI) (NDVI) was created using two primary factors of Land Use-Land Cover (LU-LC) and the normalized difference vegetation index. At the second stage, a map of potential erosion index (PERI) was created based on three raster layers of slope, rainfall erosivity and soil erodibility. Finally, The output of the fuzzy modeling (the SPI and PERI maps) were combined to produce the actual erosion risk index (AERI) map. The RUSLE model was utilized to validate the fuzzy model of soil risk map. The kappa coefficient of the class of very high risk (VH) reveals t...
Research Interests:
Research Interests:
Research Interests:
Research Interests:
Research Interests:
Satellite remote sensing provides a synoptic view of the land and a spatial context for measuring drought impacts, which have proved to be a valuable source of spatially continuous data with improved information for monitoring vegetation... more
Satellite remote sensing provides a synoptic view of the land and a spatial context for measuring drought impacts, which have proved to be a valuable source of spatially continuous data with improved information for monitoring vegetation dynamics. Many studies have focused on detecting drought effects over large areas, given the wide availability of low-resolution images. In this study, however, the objective was to focus on a smaller area (1085 km2) using Landsat ETM+ images (multispectral resolution of 30 m and 15 m panchromatic), and to process very accurate Land Use Land Cover (LULC) classification to determine with great precision the effects of drought in specific classes. The study area was the Tortugas-Tepezata sub watershed (Moctezuma River), located in the state of Hidalgo in central Mexico. The LULC classification was processed using a new method based on available ancillary information plus analysis of three single date satellite images. The newly developed LULC methodology developed produced overall accuracies ranging from 87.88% to 92.42%. Spectral indices for vegetation and soil/vegetation moisture were used to detect anomalies in vegetation development caused by drought; furthermore, the area of water bodies was measured and compared to detect changes in water availability for irrigated crops. The proposed methodology has the potential to be used as a tool to identify, in detail, the effects of drought in rainfed agricultural lands in developing regions, and it can also be used as a mechanism to prevent and provide relief in the event of droughts.
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Abstract: Soil erosion is a complex, natural process that often is accelerated by such human activities as land clearance, agriculture, construction, and surface mining. Accurate soil erosion type/intensity maps can be effective tools... more
Abstract:
Soil erosion is a complex, natural process that often is accelerated by such human activities as land clearance, agriculture, construction, and surface mining. Accurate soil erosion type/intensity maps can be effective tools in aid of soil erosion control efforts.
The principal objective of this research was to use geographic information system (GIS) and remotely sensed data to extract and define erosion types/intensities over a large area (4,511.8 km2 ) in Iran. The study proceeded in three major steps: (i) a 10-m resolution digital elevation model (DEM), land slope, elevation range, and stream network pattern were created. These basic identifying parameters plus Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images were used to differentiate various landforms, (ii) a land use and land cover map was created based on analysis of three Landsat Enhanced Thematic Mapper (ETM+) images from the growing season plus use of a landform map and climatic zones as ancillary information, and (iii) in order to extract and identify various erosion types/intensities, the difference in brightness combination over two growing season intervals derived from the Landsat ETM+ images were used. Further, land slope, landform, land use, and land cover layers were used to assist in the classification of the erosion types (interrill and rill).
The approach presented produced soil erosion type/intensity maps with an overall accuracy of 93.4%. Considering only rangeland and forest a unique relationship exists between seasonal brightness combinations and erosion intensity. It was found that for the lower erosion levels it is the later season or second brightness combination (BJS ) which indicates degree of erosion intensity, but for the areas of severe and very severe erosion it is the early season or first brightness combination (BMJ ) that differentiates degree of erosion intensity. Further, this study illustrated that land use, land cover, landform, and land slope layers can be used for differentiating erosion types.
The approach presented has been shown to be an effective tool for the creation of soil erosion maps over a large area of Iran and is expected to be useful for aiding in the development of soil conservation and watershed management plans in other areas. The main advantages of this approach are accuracy, lower demands on time and funds for field work and ready availability of required data for many regions of the world.
Soil erosion is a complex, natural process that often is accelerated by such human activities as land clearance, agriculture, construction, and surface mining. Accurate soil erosion type/intensity maps can be effective tools in aid of soil erosion control efforts.
The principal objective of this research was to use geographic information system (GIS) and remotely sensed data to extract and define erosion types/intensities over a large area (4,511.8 km2 ) in Iran. The study proceeded in three major steps: (i) a 10-m resolution digital elevation model (DEM), land slope, elevation range, and stream network pattern were created. These basic identifying parameters plus Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images were used to differentiate various landforms, (ii) a land use and land cover map was created based on analysis of three Landsat Enhanced Thematic Mapper (ETM+) images from the growing season plus use of a landform map and climatic zones as ancillary information, and (iii) in order to extract and identify various erosion types/intensities, the difference in brightness combination over two growing season intervals derived from the Landsat ETM+ images were used. Further, land slope, landform, land use, and land cover layers were used to assist in the classification of the erosion types (interrill and rill).
The approach presented produced soil erosion type/intensity maps with an overall accuracy of 93.4%. Considering only rangeland and forest a unique relationship exists between seasonal brightness combinations and erosion intensity. It was found that for the lower erosion levels it is the later season or second brightness combination (BJS ) which indicates degree of erosion intensity, but for the areas of severe and very severe erosion it is the early season or first brightness combination (BMJ ) that differentiates degree of erosion intensity. Further, this study illustrated that land use, land cover, landform, and land slope layers can be used for differentiating erosion types.
The approach presented has been shown to be an effective tool for the creation of soil erosion maps over a large area of Iran and is expected to be useful for aiding in the development of soil conservation and watershed management plans in other areas. The main advantages of this approach are accuracy, lower demands on time and funds for field work and ready availability of required data for many regions of the world.
Abstract The Iranian Soil and Water Research Institute has been involved in mapping the soils of Iran and classifying landforms for the last 60 years. However, the accuracy of traditional landform maps is very low (about 55%). To date,... more
Abstract
The Iranian Soil and Water Research Institute has been involved in mapping the soils of Iran and classifying landforms for the last 60 years. However, the accuracy of traditional landform maps is very low (about 55%). To date, aerial photographs and topographic maps have been used for landform classification studies. The principal objective of this research is to propose a quantitative approach for landform classification based on a 10-m resolution digital elevation model (DEM) and some use of an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) image. In order to extract and identify the various landforms, slope, elevation range, and stream network pattern were used as basic identifying parameters. These are extractable from a DEM. Further, ASTER images were required to identify the general outline shape of a landform type and the presence or absence of gravel. This study encompassed a relatively large watershed of 451 183 ha with a total elevation difference of 2445 m and a variety of landforms from flat River Alluvial Plains to steep mountains. Classification accuracy ranged from 91.8 to 99.6% with an average of 96.7% based upon extensive ground-truthing. Since similar digital and ASTER image information is available for Iran, an accurate landform map can now be produced for the whole country. The main advantages of this approach are accuracy, lower demands on time and funds for field work and ready availability of required data for many regions of the world.
Keywords: Landform identification; Digital elevation model (DEM); Elevation range; Stream network; Golestan Dam watershed; Geographic information systems (GIS)
Article Outline
1. Introduction
2. Materials and methods
2.1. The study area
2.2. Preprocessing of existing data and DEM generation
2.3. Defining parameters required for the process of creating landform maps
2.3.1. Extracting stream network pattern, watershed points and watershed polygons
2.3.2. Extracting the slope map
2.3.3. Determining elevation range
2.4. Extracting and identifying landform types
2.4.1. River Alluvial Plains (RP)
2.4.2. Piedmont Plains (PD) and Gravelly Talus Fans (GFc) and Gravelly River Fans (GFr)
2.4.3. Plateaux and Upper Terraces (TR)
2.4.4. Hills (H)
2.4.5. Mountains (M)
3. Evaluating accuracy of the landform map
4. Results and discussion
5. Conclusions and recommendations
Acknowledgements
References
The Iranian Soil and Water Research Institute has been involved in mapping the soils of Iran and classifying landforms for the last 60 years. However, the accuracy of traditional landform maps is very low (about 55%). To date, aerial photographs and topographic maps have been used for landform classification studies. The principal objective of this research is to propose a quantitative approach for landform classification based on a 10-m resolution digital elevation model (DEM) and some use of an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) image. In order to extract and identify the various landforms, slope, elevation range, and stream network pattern were used as basic identifying parameters. These are extractable from a DEM. Further, ASTER images were required to identify the general outline shape of a landform type and the presence or absence of gravel. This study encompassed a relatively large watershed of 451 183 ha with a total elevation difference of 2445 m and a variety of landforms from flat River Alluvial Plains to steep mountains. Classification accuracy ranged from 91.8 to 99.6% with an average of 96.7% based upon extensive ground-truthing. Since similar digital and ASTER image information is available for Iran, an accurate landform map can now be produced for the whole country. The main advantages of this approach are accuracy, lower demands on time and funds for field work and ready availability of required data for many regions of the world.
Keywords: Landform identification; Digital elevation model (DEM); Elevation range; Stream network; Golestan Dam watershed; Geographic information systems (GIS)
Article Outline
1. Introduction
2. Materials and methods
2.1. The study area
2.2. Preprocessing of existing data and DEM generation
2.3. Defining parameters required for the process of creating landform maps
2.3.1. Extracting stream network pattern, watershed points and watershed polygons
2.3.2. Extracting the slope map
2.3.3. Determining elevation range
2.4. Extracting and identifying landform types
2.4.1. River Alluvial Plains (RP)
2.4.2. Piedmont Plains (PD) and Gravelly Talus Fans (GFc) and Gravelly River Fans (GFr)
2.4.3. Plateaux and Upper Terraces (TR)
2.4.4. Hills (H)
2.4.5. Mountains (M)
3. Evaluating accuracy of the landform map
4. Results and discussion
5. Conclusions and recommendations
Acknowledgements
References
Research Interests:
Soil erosion is a complex, natural process that often is accelerated by such human activities as land clearance, agriculture, construction, and surface mining. Accurate soil erosion type/intensity maps can be effective tools in aid of... more
Soil erosion is a complex, natural process that often is accelerated by such human activities as land clearance, agriculture, construction, and surface mining. Accurate soil erosion type/intensity maps can be effective tools in aid of soil erosion control efforts.. The principal objective of ...