当前位置: X-MOL 学术Appl. Water Sci. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Assessment of land-use and land-cover changes in Pangari watershed area (MS), India, based on the remote sensing and GIS techniques
Applied Water Science ( IF 5.5 ) Pub Date : 2021-05-24 , DOI: 10.1007/s13201-021-01425-1
Chaitanya B. Pande , Kanak N. Moharir , S. F. R. Khadri

In this paper, we focus on the assessment of land-use and land-cover change detection mapping to the effective planning and management policies of environment, land-use policy and hydrological system in the study area. In this study the soil and water conservation project has been applied during the five years and after five years what changes have been found in the land-use and land-cover classes and vegetation. In this view, this land-use and land-cover mapping is a more important role to decide the policy for watershed planning and management project in the semiarid region. In an emerging countries, fast industrialization and urbanization impose a significant threat to the natural atmosphere. The remote sensing and GIS techniques are crucial roles in the study of land-use and land-cover mapping during the years of 2007, 2014, and 2017. The main objective of this is to prepare the land-use and NDVI maps in the years of 2008, 2014 and 2017; these maps have prepared from satellite data using the supervised classification method. A normalized difference vegetation index map (NDVI) was done by using Landsat 8 and LISS-III satellite data. NDVI values play a major role in monitoring the vegetation and variation in land-use and land-cover classes. In these maps, four types of land are divided into four classes as agriculture, built-up, wasteland, and water body. The results of study show that agriculture land of 18.71% (158.24 Ha), built-up land of 0.62% (5.31 Ha), wasteland of 40.33% (341.02 Ha), and water body land of 17.39% (147 Ha) are increased. Land-use and land-cover maps and NDVI values show that agriculture land of 22.97% (194.29 Ha), 5.46% (14.59 Ha), and 0.08% (0.22 Ha) decreases during the years of 2008, 2014, and 2017. The results directly indicate that the supervised classification method has been the accurate identified feature in the land-use map classes. This classification method has been given the better accuracy (95%) from spatiotemporal satellite data. The accuracy was also tally with ground-truth and Google earth information. These results can be a very useful for the land-use policy, watershed planning, and management with natural resources, animals, and ecological systems.



中文翻译:

基于遥感和GIS技术的印度潘加里(Pangari)集水区(MS)的土地利用和土地覆盖变化评估

在本文中,我们着重于对土地利用和土地覆被变化检测的评估,以映射研究区域内有效的环境,土地利用政策和水文系统的规划和管理政策。在这项研究中,水土保持项目已在五年中实施,五年后,在土地利用和土地覆盖类别以及植被方面发现了什么变化。因此,这种土地利用和土地覆盖制图对于决定半干旱地区的流域规划和管理项目的政策起着更为重要的作用。在新兴国家,快速的工业化和城市化对自然环境构成了重大威胁。在2007年,2014年和2017年期间,遥感和GIS技术在土地利用和土地覆盖制图研究中至关重要。其主要目的是准备2008、2014和2017年的土地利用和NDVI图。这些地图是使用监督分类方法根据卫星数据准备的。使用Landsat 8和LISS-III卫星数据完成了归一化差异植被指数图(NDVI)。NDVI值在监测植被以及土地利用和土地覆盖类别的变化中起着重要作用。在这些地图中,四种类型的土地被分为四类:农业,建筑,荒地和水体。研究结果表明,增加的农业用地为18.71%(158.24公顷),建筑用地为0.62%(5.31公顷),荒地为40.33%(341.02公顷),水体土地为17.39%(147公顷)。 。土地利用和土地覆盖图以及NDVI值显示,农业用地分别为22.97%(194.29公顷),5.46%(14.59公顷)和0.08%(0。22 Ha)在2008年,2014年和2017年期间有所减少。结果直接表明,监督分类方法已成为土地利用地图类别中的准确识别特征。根据时空卫星数据,这种分类方法具有更高的准确度(95%)。准确性还与地面真实性和Google地球信息相吻合。这些结果对于土地使用政策,流域规划以及自然资源,动物和生态系统的管理非常有用。准确性还与地面真实性和Google地球信息相吻合。这些结果对于土地使用政策,流域规划以及自然资源,动物和生态系统的管理非常有用。准确性还与地面真实性和Google地球信息相吻合。这些结果对于土地使用政策,流域规划以及自然资源,动物和生态系统的管理非常有用。

更新日期:2021-05-25
down
wechat
bug