One of the down sides of crude oil exploration and exploitation in the developing nations is its impacts on the environment. A major manifestation of poor crude oil management is oil-spillages. Mitigation strategies h...One of the down sides of crude oil exploration and exploitation in the developing nations is its impacts on the environment. A major manifestation of poor crude oil management is oil-spillages. Mitigation strategies have been too expensive, but a cheaper recent way of managing crude-spills is by developing a severity risk analysis matrix ranking (SRAMR). The spatial data-sets deployed in this study were acquired from the USGS, Google Earth Pro, and NOSDRA. A buffer zone of 100 - 400 meters was created to characterize the LULC characteristics of the area. Also, this was to help develop a risk sensitivity characteristic. The study found that the vegetal cover was the environmental resource at high risk to crude-spills in the area, while other land-uses were at low risk of crude-spill. It is hoped that the finding from this study informs policy development and planning for crude oil spill incidents.展开更多
During emergency response to oil spills incident accurate information is required in order to reduce the risk associated with oil spill disasters. This study focuses on Environmental Sensitivity mapping for sustainabl...During emergency response to oil spills incident accurate information is required in order to reduce the risk associated with oil spill disasters. This study focuses on Environmental Sensitivity mapping for sustainable environmental clean-up and contingency planning along the 3.0 km of AGIP pipeline at Asemoku in Delta State, Nigeria. Geographic information systems (GIS) techniques were used to create an Environmental Sensitivity Index (ESI) map in the study area. A 2018 Google Earth Satellite imagery of the study area was downloaded, and landuse/cover classification scheme comprising of Vegetation, Farmland, Water Body, Wetland, built up area and Bare Surface was adopted. Existing categorization, ranking and classification of the inland habitat were adopted and used to create a Landuse/cover Environmental Sensitivity Index (ESI) map, while the buffer zones of 100 m, 200 m, 300 m and 400 m were adopted. In the ArcGIS 10.8 environment, the landuse/cover map was generated and buffer distances of 100 m, 200 m, 300 m and 400 m were created on the landuse/cover map to ascertain the features that are vulnerable and could be at risk in the event of oil spill. This study established that the Natural Vegetation areas are the most vulnerable and sensitive feature as a result of their size along the created buffer zones. Findings from this study thus provide insight into the most sensitive land-use/land-cover, in the event of a spill or emergency oil spill clean-up response.展开更多
With the need in the global change research project for the land -use/land-cover change information, most international and regional research organization or groups have put amounts of efforts to improve of the dynami...With the need in the global change research project for the land -use/land-cover change information, most international and regional research organization or groups have put amounts of efforts to improve of the dynamics monitoring and database updating techniques. With the pressure on nature environment from increasing population and decreasing farmland be- coming significant more and more in China, the farmland urban dynamics in historical and current times, even the change trends in the future, should be monitored and analyzed serving for regional and national social, economic and environmental sustain- able development in the long future. Based on spatial and temporal series of land -use/land-cover database resources, Chinese Academy of Sciences designed a sampling framework for monitoring farmland and urban area dynamics in regional and national level. In order to test the accuracy of the sampling schema for national and regional level, we took two provinces area into overall covered change detecting process with TM images data through being inter’Preted by digitalization on the screen. The result shows that our stratified random sampling schema is suitable for monitoring land -use/land-cover change at national and regional level with quick response, high accuracy and low expenses. The land-use/land-cover change (LUCC) information can update the LUTEA database for global change research during certain period so that the forecasting process and evaluating analysis on land resources and environment under human and natural driving force will get essential data and produce valuable conclusions.展开更多
With the classifi cation data covering American land-use/land-cover (LUCC) with 30 m resolu tion from the project of National Land Cover Data (NLCD), we normalize d them and made their resolution changed into 1 km ...With the classifi cation data covering American land-use/land-cover (LUCC) with 30 m resolu tion from the project of National Land Cover Data (NLCD), we normalize d them and made their resolution changed into 1 km ×1 km, created the data of American land-use grade and analyzed the spatial distribution and featur es of American LUCC as well as the influence of population and altit ude on the land-use grade in light of methods of sampling analysis a nd correlation study. Based on the analysis, we concluded that forestr y and grassland, accounting for 71.24% of the whole country, has taken the main part of American land cover, and besides, construction and arable land has occupied 19.22% of the total land, the rest of land cover types, including water area, wetland and underdeveloped land, is 9. 54% of the country's total. The developing potential of American land resources is enormous with less destroyed and disturbed ecological environment. Although, in some sense, the population and altitude influence the sp atial variation of American land-use grade respectively, the influence of spatial variation of altitude and population density on that of la nd-use grade is not significanct.展开更多
Most countries’ land use and land cover (LULC) are changing dramatically today. Most of these changes are related to the way humans and the environment interact. Various methodologies and data sources have been used ...Most countries’ land use and land cover (LULC) are changing dramatically today. Most of these changes are related to the way humans and the environment interact. Various methodologies and data sources have been used in conjunction with remote sensing (RS) to categorize and map changes in LULC. This study used RS and Geographic Information System (GIS) tools to analyze LULC change and transitions from 1984 to 2022 in a tropical forested landscape in southwest Mauritania. Using a suitable and high-quality collection of Landsat satellite images. For the classification and creation of LULC maps for the selected periods, the supervised technique using a maximum likelihood classifier was used. The results indicated that there was a remarkable change in all classes of LULC, with an increase in all classes, except barren land, which had a tremendous decrease of −68.58% for the total study area. Therefore, for the total study area, an increase in agricultural land (221%), water bodies (118.46%), vegetation (57.50%), and built-up areas (14.65%) was observed. We believe that by informing policymakers, environmental managers, and the general public about the current changes, our study will help the region to establish appropriate land use rules that may lead to policy document development.展开更多
A major threat to biodiversity in North Dakota is the conversion of forested land to cultivable land, especially those that act as riparian buffers. To reverse this trend of transformation, a validation and prediction...A major threat to biodiversity in North Dakota is the conversion of forested land to cultivable land, especially those that act as riparian buffers. To reverse this trend of transformation, a validation and prediction model is necessary to assess the change. Spatial prediction within a Geographic Information System (GIS) using Kriging is a popular stochastic method. The objective of this study was to predict spatial and temporal transformation of a small agricultural watershed—Pipestem Creek in North Dakota;USA using satellite imagery from 1976 to 2015. To enhance the difference between forested land and non-forested land, a spectral transformation method—Tasseled-Cap’s Greenness Index (TCGI) was used. To study the spatial structure present in the imagery within the study period, semivariograms were generated. The Kriging prediction maps were post-classified using Remote Sensing techniques of change detection to obtain the direction and intensity of forest to non-forest change. TCGI generated higher values from 1976 to 2000 and it gradually reduced from 2000 to 2011 indicating loss of forested land.展开更多
文摘One of the down sides of crude oil exploration and exploitation in the developing nations is its impacts on the environment. A major manifestation of poor crude oil management is oil-spillages. Mitigation strategies have been too expensive, but a cheaper recent way of managing crude-spills is by developing a severity risk analysis matrix ranking (SRAMR). The spatial data-sets deployed in this study were acquired from the USGS, Google Earth Pro, and NOSDRA. A buffer zone of 100 - 400 meters was created to characterize the LULC characteristics of the area. Also, this was to help develop a risk sensitivity characteristic. The study found that the vegetal cover was the environmental resource at high risk to crude-spills in the area, while other land-uses were at low risk of crude-spill. It is hoped that the finding from this study informs policy development and planning for crude oil spill incidents.
文摘During emergency response to oil spills incident accurate information is required in order to reduce the risk associated with oil spill disasters. This study focuses on Environmental Sensitivity mapping for sustainable environmental clean-up and contingency planning along the 3.0 km of AGIP pipeline at Asemoku in Delta State, Nigeria. Geographic information systems (GIS) techniques were used to create an Environmental Sensitivity Index (ESI) map in the study area. A 2018 Google Earth Satellite imagery of the study area was downloaded, and landuse/cover classification scheme comprising of Vegetation, Farmland, Water Body, Wetland, built up area and Bare Surface was adopted. Existing categorization, ranking and classification of the inland habitat were adopted and used to create a Landuse/cover Environmental Sensitivity Index (ESI) map, while the buffer zones of 100 m, 200 m, 300 m and 400 m were adopted. In the ArcGIS 10.8 environment, the landuse/cover map was generated and buffer distances of 100 m, 200 m, 300 m and 400 m were created on the landuse/cover map to ascertain the features that are vulnerable and could be at risk in the event of oil spill. This study established that the Natural Vegetation areas are the most vulnerable and sensitive feature as a result of their size along the created buffer zones. Findings from this study thus provide insight into the most sensitive land-use/land-cover, in the event of a spill or emergency oil spill clean-up response.
基金the auspices of the national key project(96-802-01).
文摘With the need in the global change research project for the land -use/land-cover change information, most international and regional research organization or groups have put amounts of efforts to improve of the dynamics monitoring and database updating techniques. With the pressure on nature environment from increasing population and decreasing farmland be- coming significant more and more in China, the farmland urban dynamics in historical and current times, even the change trends in the future, should be monitored and analyzed serving for regional and national social, economic and environmental sustain- able development in the long future. Based on spatial and temporal series of land -use/land-cover database resources, Chinese Academy of Sciences designed a sampling framework for monitoring farmland and urban area dynamics in regional and national level. In order to test the accuracy of the sampling schema for national and regional level, we took two provinces area into overall covered change detecting process with TM images data through being inter’Preted by digitalization on the screen. The result shows that our stratified random sampling schema is suitable for monitoring land -use/land-cover change at national and regional level with quick response, high accuracy and low expenses. The land-use/land-cover change (LUCC) information can update the LUTEA database for global change research during certain period so that the forecasting process and evaluating analysis on land resources and environment under human and natural driving force will get essential data and produce valuable conclusions.
基金National Natural Science Foundation of China, No. 90202002.
文摘With the classifi cation data covering American land-use/land-cover (LUCC) with 30 m resolu tion from the project of National Land Cover Data (NLCD), we normalize d them and made their resolution changed into 1 km ×1 km, created the data of American land-use grade and analyzed the spatial distribution and featur es of American LUCC as well as the influence of population and altit ude on the land-use grade in light of methods of sampling analysis a nd correlation study. Based on the analysis, we concluded that forestr y and grassland, accounting for 71.24% of the whole country, has taken the main part of American land cover, and besides, construction and arable land has occupied 19.22% of the total land, the rest of land cover types, including water area, wetland and underdeveloped land, is 9. 54% of the country's total. The developing potential of American land resources is enormous with less destroyed and disturbed ecological environment. Although, in some sense, the population and altitude influence the sp atial variation of American land-use grade respectively, the influence of spatial variation of altitude and population density on that of la nd-use grade is not significanct.
文摘Most countries’ land use and land cover (LULC) are changing dramatically today. Most of these changes are related to the way humans and the environment interact. Various methodologies and data sources have been used in conjunction with remote sensing (RS) to categorize and map changes in LULC. This study used RS and Geographic Information System (GIS) tools to analyze LULC change and transitions from 1984 to 2022 in a tropical forested landscape in southwest Mauritania. Using a suitable and high-quality collection of Landsat satellite images. For the classification and creation of LULC maps for the selected periods, the supervised technique using a maximum likelihood classifier was used. The results indicated that there was a remarkable change in all classes of LULC, with an increase in all classes, except barren land, which had a tremendous decrease of −68.58% for the total study area. Therefore, for the total study area, an increase in agricultural land (221%), water bodies (118.46%), vegetation (57.50%), and built-up areas (14.65%) was observed. We believe that by informing policymakers, environmental managers, and the general public about the current changes, our study will help the region to establish appropriate land use rules that may lead to policy document development.
文摘A major threat to biodiversity in North Dakota is the conversion of forested land to cultivable land, especially those that act as riparian buffers. To reverse this trend of transformation, a validation and prediction model is necessary to assess the change. Spatial prediction within a Geographic Information System (GIS) using Kriging is a popular stochastic method. The objective of this study was to predict spatial and temporal transformation of a small agricultural watershed—Pipestem Creek in North Dakota;USA using satellite imagery from 1976 to 2015. To enhance the difference between forested land and non-forested land, a spectral transformation method—Tasseled-Cap’s Greenness Index (TCGI) was used. To study the spatial structure present in the imagery within the study period, semivariograms were generated. The Kriging prediction maps were post-classified using Remote Sensing techniques of change detection to obtain the direction and intensity of forest to non-forest change. TCGI generated higher values from 1976 to 2000 and it gradually reduced from 2000 to 2011 indicating loss of forested land.
