The majority of the population and economic activity of the northern half of Vietnam is clustered in the Red River Delta and about half of the country’s rice production takes place here. There are significant problem...The majority of the population and economic activity of the northern half of Vietnam is clustered in the Red River Delta and about half of the country’s rice production takes place here. There are significant problems associated with its geographical position and the intensive exploitation of resources by an overabundant population (population density of 962 inhabitants/km2). Some thirty years after the economic liberalization and the opening of the country to international markets, agricultural land use patterns in the Red River Delta, particularly in the coastal area, have undergone many changes. Remote sensing is a particularly powerful tool in processing and providing spatial information for monitoring land use changes. The main methodological objective is to find a solution to process the many heterogeneous coastal land use parameters, so as to describe it in all its complexity, specifically by making use of the latest European satellite data (Sentinel-2). This complexity is due to local variations in ecological conditions, but also to anthropogenic factors that directly and indirectly influence land use dynamics. The methodological objective was to develop a new Geographic Object-based Image Analysis (GEOBIA) approach for mapping coastal areas using Sentinel-2 data and Landsat 8. By developing a new segmentation, accuracy measure, in this study was determined that segmentation accuracies decrease with increasing segmentation scales and that the negative impact of under-segmentation errors significantly increases at a large scale. An Estimation of Scale Parameter (ESP) tool was then used to determine the optimal segmentation parameter values. A popular machine learning algorithms (Random Forests-RFs) is used. For all classifications algorithm, an increase in overall accuracy was observed with the full synergistic combination of available data sets.展开更多
This study seeks a routine to quantify spatial pattern of land cover changes in semiarid environment of China based on post-classification comparison method. The method consists of three major steps: (1) the image cla...This study seeks a routine to quantify spatial pattern of land cover changes in semiarid environment of China based on post-classification comparison method. The method consists of three major steps: (1) the image classification and unification of classified results based on two-level land cover classification themes, (2) the establishment of land cover change classes based on an unification land cover classification theme, (3) the reclassification and mapping of land cover change classes with three overall classes including no-change, gain and loss based on the unification land cover class. This method was applied to detect the spatial pattern of land cover changes in Yinchuan Plain, one of famous irrigation agricultural zones of the Yellow River, China. The results showed the land cover had undergone a remarkable change from 1991 to 2002 in the study area (the changed area was over 30%). Rapid increase of cropland (12.5%), built-up area (131.4%) and rapid decrease of bare ground (51.7%) were alarming. The spatial pattern of land cover changes showed clear regional difference in the study area and was clearly related to human activities or natural factors. Thus, it obtained a better understanding of the human impact on the fragile ecosystem of China’s semiarid environment.展开更多
In this paper, we attempted to determine the most stable or unstable regions of vegetation cover in Mongolia and their spatio-temporal dynamics using Terra/MODIS Normalized Difference Vegetation Index （NDVI） dataset...In this paper, we attempted to determine the most stable or unstable regions of vegetation cover in Mongolia and their spatio-temporal dynamics using Terra/MODIS Normalized Difference Vegetation Index （NDVI） dataset, which had a 250-m spatial resolution and comprised 6 periods of 16-day composited temporal resolution data （from 10 June to 13 September） for summer seasons from 2000 to 2012. We also used precipitation data as well as biomass data from 12 meteorological stations located in 4 largest natural zones of Mongolia. Our study showed that taiga and forest steppe zones had relatively stable vegetation cover because of forest characteristics and relatively high precipitation. The highest coefficient of variation （CV） of vegetation cover occurred frequently in the steppe and desert steppe zones, mainly depending on variation of precipitation. Our results showed that spatial and temporal variability in vegetation cover （NDVI or plant biomass） of Mongolia was highly dependent on the amount, distribution and CV of precipitation. This suggests that the lowest inter-annual CV of NDVI can occur dur- ing wet periods of growing season or in high precipitation regions, while the highest inter-annual CV of NDVI can occur during dry periods and in low precipitation regions. Although the desert zone received less precipitation than other natural zones of the country, it had relatively low variation compared to the steppe and desert steppe, which could be attributed to the very sparse vegetation in the desert.展开更多
We used spatial analysis to assess the Land Use Land Cover (LULC) changes, and studied the impacts of LC changes on conservation of buffer zone of the Selous Game Reserve (SGR) and their implication on community’s li...We used spatial analysis to assess the Land Use Land Cover (LULC) changes, and studied the impacts of LC changes on conservation of buffer zone of the Selous Game Reserve (SGR) and their implication on community’s livelihood in Vikumbulu Ward of Kisarawe District, Tanzania. Socio-economic data from Kisarawe District and TNBS were linked to spatial data to offer an integrated perspetive of LULC change in the Ward. Three cloud free image dates of 1998, 2011 and 2015 were analysed using System for Automated Geoscientific Analyses (SAGA) GIS for three categories of land cover, i.e. forest, wooded grassland and bare land/settlements/cultivation. Vikumbulu demographic and socio-economic data were linked to spatial data applying distance as a function of LULC change. Spatial analysis has shown a decreasing trend of forest and woodland cover in Vikumbulu Ward between 1998 and 2015. The sharp decline indicates increasing social economic activities such as shifting agriculture and charcoal burning as an outcome of population growth and poverty. Rapid conversion of forest cover to wooded grassland occurred between 1998 and 2015 in Vikumbulu Ward. However, loss of forest cover was associated with a decreasing trend in wooded land in the ward between 2011 and 2015. As there was only 0.15% area under crop cultivation in Vikumbulu until 2015, it is highly likely that LC change is caused by charcoal burning and shifting cultivation. This study suggests developing integrated strategies that target LULC change, conservation and people’s livelihoods to effectively improve the current situation in rural areas of Tanzania.展开更多
文摘The majority of the population and economic activity of the northern half of Vietnam is clustered in the Red River Delta and about half of the country’s rice production takes place here. There are significant problems associated with its geographical position and the intensive exploitation of resources by an overabundant population (population density of 962 inhabitants/km2). Some thirty years after the economic liberalization and the opening of the country to international markets, agricultural land use patterns in the Red River Delta, particularly in the coastal area, have undergone many changes. Remote sensing is a particularly powerful tool in processing and providing spatial information for monitoring land use changes. The main methodological objective is to find a solution to process the many heterogeneous coastal land use parameters, so as to describe it in all its complexity, specifically by making use of the latest European satellite data (Sentinel-2). This complexity is due to local variations in ecological conditions, but also to anthropogenic factors that directly and indirectly influence land use dynamics. The methodological objective was to develop a new Geographic Object-based Image Analysis (GEOBIA) approach for mapping coastal areas using Sentinel-2 data and Landsat 8. By developing a new segmentation, accuracy measure, in this study was determined that segmentation accuracies decrease with increasing segmentation scales and that the negative impact of under-segmentation errors significantly increases at a large scale. An Estimation of Scale Parameter (ESP) tool was then used to determine the optimal segmentation parameter values. A popular machine learning algorithms (Random Forests-RFs) is used. For all classifications algorithm, an increase in overall accuracy was observed with the full synergistic combination of available data sets.
基金supported by National Key Basic Research and Development Program Grant (2006CB701305)Hong Kong Research Grants Council Competitive Earmarked Research Grant (HKBU 2029/07P)+1 种基金Hong Kong Baptist University Faculty Research Grant (FRG/06-07/II-76)China National Natural Science Foundation Grant (40101028)
文摘This study seeks a routine to quantify spatial pattern of land cover changes in semiarid environment of China based on post-classification comparison method. The method consists of three major steps: (1) the image classification and unification of classified results based on two-level land cover classification themes, (2) the establishment of land cover change classes based on an unification land cover classification theme, (3) the reclassification and mapping of land cover change classes with three overall classes including no-change, gain and loss based on the unification land cover class. This method was applied to detect the spatial pattern of land cover changes in Yinchuan Plain, one of famous irrigation agricultural zones of the Yellow River, China. The results showed the land cover had undergone a remarkable change from 1991 to 2002 in the study area (the changed area was over 30%). Rapid increase of cropland (12.5%), built-up area (131.4%) and rapid decrease of bare ground (51.7%) were alarming. The spatial pattern of land cover changes showed clear regional difference in the study area and was clearly related to human activities or natural factors. Thus, it obtained a better understanding of the human impact on the fragile ecosystem of China’s semiarid environment.
基金funded by the Green Gold Phase IV Project of the Swiss Development Cooperation AgencyA partial support for this study has also been provided by the Asia Research Center,Mongolia
文摘In this paper, we attempted to determine the most stable or unstable regions of vegetation cover in Mongolia and their spatio-temporal dynamics using Terra/MODIS Normalized Difference Vegetation Index （NDVI） dataset, which had a 250-m spatial resolution and comprised 6 periods of 16-day composited temporal resolution data （from 10 June to 13 September） for summer seasons from 2000 to 2012. We also used precipitation data as well as biomass data from 12 meteorological stations located in 4 largest natural zones of Mongolia. Our study showed that taiga and forest steppe zones had relatively stable vegetation cover because of forest characteristics and relatively high precipitation. The highest coefficient of variation （CV） of vegetation cover occurred frequently in the steppe and desert steppe zones, mainly depending on variation of precipitation. Our results showed that spatial and temporal variability in vegetation cover （NDVI or plant biomass） of Mongolia was highly dependent on the amount, distribution and CV of precipitation. This suggests that the lowest inter-annual CV of NDVI can occur dur- ing wet periods of growing season or in high precipitation regions, while the highest inter-annual CV of NDVI can occur during dry periods and in low precipitation regions. Although the desert zone received less precipitation than other natural zones of the country, it had relatively low variation compared to the steppe and desert steppe, which could be attributed to the very sparse vegetation in the desert.
文摘We used spatial analysis to assess the Land Use Land Cover (LULC) changes, and studied the impacts of LC changes on conservation of buffer zone of the Selous Game Reserve (SGR) and their implication on community’s livelihood in Vikumbulu Ward of Kisarawe District, Tanzania. Socio-economic data from Kisarawe District and TNBS were linked to spatial data to offer an integrated perspetive of LULC change in the Ward. Three cloud free image dates of 1998, 2011 and 2015 were analysed using System for Automated Geoscientific Analyses (SAGA) GIS for three categories of land cover, i.e. forest, wooded grassland and bare land/settlements/cultivation. Vikumbulu demographic and socio-economic data were linked to spatial data applying distance as a function of LULC change. Spatial analysis has shown a decreasing trend of forest and woodland cover in Vikumbulu Ward between 1998 and 2015. The sharp decline indicates increasing social economic activities such as shifting agriculture and charcoal burning as an outcome of population growth and poverty. Rapid conversion of forest cover to wooded grassland occurred between 1998 and 2015 in Vikumbulu Ward. However, loss of forest cover was associated with a decreasing trend in wooded land in the ward between 2011 and 2015. As there was only 0.15% area under crop cultivation in Vikumbulu until 2015, it is highly likely that LC change is caused by charcoal burning and shifting cultivation. This study suggests developing integrated strategies that target LULC change, conservation and people’s livelihoods to effectively improve the current situation in rural areas of Tanzania.
