The main objective of this research is to determine the capacity of land cover classification combining spec- tral and textural features of Landsat TM imagery with ancillary geographical data in wetlands of the Sanjia...The main objective of this research is to determine the capacity of land cover classification combining spec- tral and textural features of Landsat TM imagery with ancillary geographical data in wetlands of the Sanjiang Plain, Heilongjiang Province, China. Semi-variograms and Z-test value were calculated to assess the separability of grey-level co-occurrence texture measures to maximize the difference between land cover types. The degree of spatial autocorrelation showed that window sizes of 3×3 pixels and 11×11 pixels were most appropriate for Landsat TM im- age texture calculations. The texture analysis showed that co-occurrence entropy, dissimilarity, and variance texture measures, derived from the Landsat TM spectrum bands and vegetation indices provided the most significant statistical differentiation between land cover types. Subsequently, a Classification and Regression Tree (CART) algorithm was applied to three different combinations of predictors: 1) TM imagery alone (TM-only); 2) TM imagery plus image texture (TM+TXT model); and 3) all predictors including TM imagery, image texture and additional ancillary GIS in- formation (TM+TXT+GIS model). Compared with traditional Maximum Likelihood Classification (MLC) supervised classification, three classification trees predictive models reduced the overall error rate significantly. Image texture measures and ancillary geographical variables depressed the speckle noise effectively and reduced classification error rate of marsh obviously. For classification trees model making use of all available predictors, omission error rate was 12.90% and commission error rate was 10.99% for marsh. The developed method is portable, relatively easy to im- plement and should be applicable in other settings and over larger extents.展开更多
Taking an area of about 2.3×10~4 km~2 of southeastern Iran, this study aims to detect and predict regional-scale salt-affected lands. Three sets of Landsat images, each set containing 4 images for 1986, 2000, and...Taking an area of about 2.3×10~4 km~2 of southeastern Iran, this study aims to detect and predict regional-scale salt-affected lands. Three sets of Landsat images, each set containing 4 images for 1986, 2000, and 2015 were acquired as the main source of data. Radiometric, atmospheric and cutline blending methods were used to improve the quality of images and help better classify salinized land areas under the support vector machine method. A set of landscape metrics was also employed to detect the spatial pattern of salinized land expansion from 1986 to 2015. Four factors including distance to sea, distance to sea water channels, slope, and elevation were identified as the main contributing factors to land salinization. These factors were then integrated using the multi-criteria evaluation (MCE) procedure to generate land sensitivity map to salinization and also to calibrate the cellular-automata (CA) Markov chain (CA-Markov) model for simulation of salt-affected lands up to 2030, 2040 and 2050. The results of this study showed a dramatic dispersive expansion of salinized land from 7.7 % to 12.7% of the total study area from 1986 to 2015. The majority of areas prone to salinization and the highest sensitivity of land to salinization was found to be in the southeastern parts of the region. The result of the MCE-informed CA-Markov model revealed that 20.3% of the study area is likely to be converted to salinized lands by 2050. The findings of this research provided a view of the magnitude and direction of salinized land expansion in a past-to-future time period which should be considered in future land development strategies.展开更多
In Senegal, the agricultural sector remains a major component of the economy and national growth. As the main subsistence activity for 60% of the population, agricultural activity is essential to reduce poverty and en...In Senegal, the agricultural sector remains a major component of the economy and national growth. As the main subsistence activity for 60% of the population, agricultural activity is essential to reduce poverty and ensure food security for the population. In this context, land degradation is a major constraint. In the Fatick region, in the commune of Fimela, land salinization is a worrying environmental problem. The purpose of this study is to understand the dynamics of soil salinization in Fimela in the context of climate change that tends to modify the evolution of landscapes. It required direct observations in the field, socio-economic surveys based on a questionnaire administered in six (6) villages. The processing of the data from these surveys was carried out with Sphinx software for the extraction of data in numerical form and SPSS to carry out the correlations between the collected variables. Excel was also used to perform calculations and make tables and graphs. In addition, the acquisition and processing of Landsat multi-spectral satellite images from 1973, 1988 and 2020 allowed us to observe the evolution of landscape units according to determining climatic events such as drought. The areas of tans have experienced a positive evolution during the period 1973-2020 with an increase of 163.11 hectares or an evolution rate of 7.47%. The localities most affected are Ndangane, Fimela, Djilor, Simal and the villages of the Mar Islands. The overall dynamics of cultivable land are marked by a decline with a rate of change of −18%. Despite the multiple reforestation campaigns, the mangrove has recorded a continuous decrease of 54.58% or a loss estimated at 5335.59 ha during the period 1973-2020. Finally, the analysis of the results of our study shows that land salinization is a determining element of the dynamics of land use and deteriorates the already precarious living conditions of rural populations and compromises the future of the agropastoral production system.展开更多
基金Under the auspices of National Natural Science Foundation of China (No. 40871188) National Key Technologies R&D Program of China (No. 2006BAD23B03)
文摘The main objective of this research is to determine the capacity of land cover classification combining spec- tral and textural features of Landsat TM imagery with ancillary geographical data in wetlands of the Sanjiang Plain, Heilongjiang Province, China. Semi-variograms and Z-test value were calculated to assess the separability of grey-level co-occurrence texture measures to maximize the difference between land cover types. The degree of spatial autocorrelation showed that window sizes of 3×3 pixels and 11×11 pixels were most appropriate for Landsat TM im- age texture calculations. The texture analysis showed that co-occurrence entropy, dissimilarity, and variance texture measures, derived from the Landsat TM spectrum bands and vegetation indices provided the most significant statistical differentiation between land cover types. Subsequently, a Classification and Regression Tree (CART) algorithm was applied to three different combinations of predictors: 1) TM imagery alone (TM-only); 2) TM imagery plus image texture (TM+TXT model); and 3) all predictors including TM imagery, image texture and additional ancillary GIS in- formation (TM+TXT+GIS model). Compared with traditional Maximum Likelihood Classification (MLC) supervised classification, three classification trees predictive models reduced the overall error rate significantly. Image texture measures and ancillary geographical variables depressed the speckle noise effectively and reduced classification error rate of marsh obviously. For classification trees model making use of all available predictors, omission error rate was 12.90% and commission error rate was 10.99% for marsh. The developed method is portable, relatively easy to im- plement and should be applicable in other settings and over larger extents.
文摘Taking an area of about 2.3×10~4 km~2 of southeastern Iran, this study aims to detect and predict regional-scale salt-affected lands. Three sets of Landsat images, each set containing 4 images for 1986, 2000, and 2015 were acquired as the main source of data. Radiometric, atmospheric and cutline blending methods were used to improve the quality of images and help better classify salinized land areas under the support vector machine method. A set of landscape metrics was also employed to detect the spatial pattern of salinized land expansion from 1986 to 2015. Four factors including distance to sea, distance to sea water channels, slope, and elevation were identified as the main contributing factors to land salinization. These factors were then integrated using the multi-criteria evaluation (MCE) procedure to generate land sensitivity map to salinization and also to calibrate the cellular-automata (CA) Markov chain (CA-Markov) model for simulation of salt-affected lands up to 2030, 2040 and 2050. The results of this study showed a dramatic dispersive expansion of salinized land from 7.7 % to 12.7% of the total study area from 1986 to 2015. The majority of areas prone to salinization and the highest sensitivity of land to salinization was found to be in the southeastern parts of the region. The result of the MCE-informed CA-Markov model revealed that 20.3% of the study area is likely to be converted to salinized lands by 2050. The findings of this research provided a view of the magnitude and direction of salinized land expansion in a past-to-future time period which should be considered in future land development strategies.
文摘In Senegal, the agricultural sector remains a major component of the economy and national growth. As the main subsistence activity for 60% of the population, agricultural activity is essential to reduce poverty and ensure food security for the population. In this context, land degradation is a major constraint. In the Fatick region, in the commune of Fimela, land salinization is a worrying environmental problem. The purpose of this study is to understand the dynamics of soil salinization in Fimela in the context of climate change that tends to modify the evolution of landscapes. It required direct observations in the field, socio-economic surveys based on a questionnaire administered in six (6) villages. The processing of the data from these surveys was carried out with Sphinx software for the extraction of data in numerical form and SPSS to carry out the correlations between the collected variables. Excel was also used to perform calculations and make tables and graphs. In addition, the acquisition and processing of Landsat multi-spectral satellite images from 1973, 1988 and 2020 allowed us to observe the evolution of landscape units according to determining climatic events such as drought. The areas of tans have experienced a positive evolution during the period 1973-2020 with an increase of 163.11 hectares or an evolution rate of 7.47%. The localities most affected are Ndangane, Fimela, Djilor, Simal and the villages of the Mar Islands. The overall dynamics of cultivable land are marked by a decline with a rate of change of −18%. Despite the multiple reforestation campaigns, the mangrove has recorded a continuous decrease of 54.58% or a loss estimated at 5335.59 ha during the period 1973-2020. Finally, the analysis of the results of our study shows that land salinization is a determining element of the dynamics of land use and deteriorates the already precarious living conditions of rural populations and compromises the future of the agropastoral production system.
