为了更加深刻地理解气候和地形条件对土地覆盖的作用机理,该研究以2001年-2009年逐年的MODIS Land Cover(MCD12Q1)为主要数据源,参考中国土地覆盖分类体系整合国际上通用的5种(IGBP、UMD、LAI-fPAR、NPP和PFT)土地覆盖分类体系,分析年...为了更加深刻地理解气候和地形条件对土地覆盖的作用机理,该研究以2001年-2009年逐年的MODIS Land Cover(MCD12Q1)为主要数据源,参考中国土地覆盖分类体系整合国际上通用的5种(IGBP、UMD、LAI-fPAR、NPP和PFT)土地覆盖分类体系,分析年际尺度上中国西北干旱半干旱地区的土地覆盖时空分异及其对气候和地形的响应。研究结果表明:IGBP较其他4种更适合年际尺度上干旱半干旱地区土地覆盖的时空分异研究。2001年-2009年,5种分类体系中农田和草地增加,水体湿地和荒漠减少,聚落保持不变,森林有增有减,并且土地覆盖类型间相互发生转换,其在空间上的转移与干旱半干旱地区的自然条件变化和人类活动相适应。6种土地覆盖类型对自然条件的响应与光、热、水在时空格局上的分配以及人类活动作用的强弱相一致。研究结果可为区域或全球土地利用和土地覆盖研究提供参考。展开更多
The dynamic transformation of land use and land cover has emerged as a crucial aspect in the effective management of natural resources and the continual monitoring of environmental shifts. This study focused on the la...The dynamic transformation of land use and land cover has emerged as a crucial aspect in the effective management of natural resources and the continual monitoring of environmental shifts. This study focused on the land use and land cover (LULC) changes within the catchment area of the Godavari River, assessing the repercussions of land and water resource exploitation. Utilizing LANDSAT satellite images from 2009, 2014, and 2019, this research employed supervised classification through the Quantum Geographic Information System (QGIS) software’s SCP plugin. Maximum likelihood classification algorithm was used for the assessment of supervised land use classification. Seven distinct LULC classes—forest, irrigated cropland, agricultural land (fallow), barren land, shrub land, water, and urban land—are delineated for classification purposes. The study revealed substantial changes in the Godavari basin’s land use patterns over the ten-year period from 2009 to 2019. Spatial and temporal dynamics of land use/cover changes (2009-2019) were quantified using three Satellite/Landsat images, a supervised classification algorithm and the post classification change detection technique in GIS. The total study area of the Godavari basin in Maharashtra encompasses 5138175.48 hectares. Notably, the built-up area increased from 0.14% in 2009 to 1.94% in 2019. The proportion of irrigated cropland, which was 62.32% in 2009, declined to 41.52% in 2019. Shrub land witnessed a noteworthy increase from 0.05% to 2.05% over the last decade. The key findings underscored significant declines in barren land, agricultural land, and irrigated cropland, juxtaposed with an expansion in forest land, shrub land, and urban land. The classification methodology achieved an overall accuracy of 80%, with a Kappa Statistic of 71.9% for the satellite images. The overall classification accuracy along with the Kappa value for 2009, 2014 and 2019 supervised land use land cover classification was good enough to detect the changing scenarios of Godavari展开更多
文摘为了更加深刻地理解气候和地形条件对土地覆盖的作用机理,该研究以2001年-2009年逐年的MODIS Land Cover(MCD12Q1)为主要数据源,参考中国土地覆盖分类体系整合国际上通用的5种(IGBP、UMD、LAI-fPAR、NPP和PFT)土地覆盖分类体系,分析年际尺度上中国西北干旱半干旱地区的土地覆盖时空分异及其对气候和地形的响应。研究结果表明:IGBP较其他4种更适合年际尺度上干旱半干旱地区土地覆盖的时空分异研究。2001年-2009年,5种分类体系中农田和草地增加,水体湿地和荒漠减少,聚落保持不变,森林有增有减,并且土地覆盖类型间相互发生转换,其在空间上的转移与干旱半干旱地区的自然条件变化和人类活动相适应。6种土地覆盖类型对自然条件的响应与光、热、水在时空格局上的分配以及人类活动作用的强弱相一致。研究结果可为区域或全球土地利用和土地覆盖研究提供参考。
文摘The dynamic transformation of land use and land cover has emerged as a crucial aspect in the effective management of natural resources and the continual monitoring of environmental shifts. This study focused on the land use and land cover (LULC) changes within the catchment area of the Godavari River, assessing the repercussions of land and water resource exploitation. Utilizing LANDSAT satellite images from 2009, 2014, and 2019, this research employed supervised classification through the Quantum Geographic Information System (QGIS) software’s SCP plugin. Maximum likelihood classification algorithm was used for the assessment of supervised land use classification. Seven distinct LULC classes—forest, irrigated cropland, agricultural land (fallow), barren land, shrub land, water, and urban land—are delineated for classification purposes. The study revealed substantial changes in the Godavari basin’s land use patterns over the ten-year period from 2009 to 2019. Spatial and temporal dynamics of land use/cover changes (2009-2019) were quantified using three Satellite/Landsat images, a supervised classification algorithm and the post classification change detection technique in GIS. The total study area of the Godavari basin in Maharashtra encompasses 5138175.48 hectares. Notably, the built-up area increased from 0.14% in 2009 to 1.94% in 2019. The proportion of irrigated cropland, which was 62.32% in 2009, declined to 41.52% in 2019. Shrub land witnessed a noteworthy increase from 0.05% to 2.05% over the last decade. The key findings underscored significant declines in barren land, agricultural land, and irrigated cropland, juxtaposed with an expansion in forest land, shrub land, and urban land. The classification methodology achieved an overall accuracy of 80%, with a Kappa Statistic of 71.9% for the satellite images. The overall classification accuracy along with the Kappa value for 2009, 2014 and 2019 supervised land use land cover classification was good enough to detect the changing scenarios of Godavari
