利用遥感和机器学习监测印度植被退化--一种多传感器、多时相和多尺度方法

K. Sur, V. Verma, Pankaj Panwar, G. Shukla, S. Chakravarty, Arun Jyoti Nath
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引用次数: 0

摘要

植被退化通常是一个复杂的现象,与气候变异和人类活动密切相关。保护生物多样性非常重要,因为数百万人直接或间接依赖植被(森林和作物)及其相关副产品。联合国可持续发展目标(SDGs)建议量化植被占各国土地总面积的比例。卫星图像是捕捉细微季节变化的主要准确信息来源之一,因此可以准确评估长期植被退化情况。本研究采用了多传感器、多时空和多尺度(MMM)方法来估算植被退化的脆弱性。开源云计算系统谷歌地球引擎(GEE)被用来系统监测植被退化情况,并评估不同空间分辨率的多种卫星数据的潜力。利用机器学习技术划定了热点地区,使用粗分辨率的 MODIS 归一化差异植被指数(NDVI)来识别植被的绿化和褐化效应。此外,还使用了 2000-2022 年期间气候灾害组红外降水与站点数据(CHIRPS)的降雨数据集,以发现该地区的降雨异常。此外,根据长期的植被和降雨分析,利用高分辨率数据集确定了主要植被退化地区的热点区域,以了解和验证人为或气候性质的变化原因。这项研究对于一些邦/中央政府用户部门、大学和非政府组织制定保护印度植被/森林的管理计划非常重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Monitoring vegetation degradation using remote sensing and machine learning over India – a multi-sensor, multi-temporal and multi-scale approach
Vegetation cover degradation is often a complex phenomenon, exhibiting strong correlation with climatic variation and anthropogenic actions. Conservation of biodiversity is important because millions of people are directly and indirectly dependent on vegetation (forest and crop) and its associated secondary products. United Nations Sustainable Development Goals (SDGs) propose to quantify the proportion of vegetation as a proportion of total land area of all countries. Satellite images form as one of the main sources of accurate information to capture the fine seasonal changes so that long-term vegetation degradation can be assessed accurately. In the present study, Multi-Sensor, Multi-Temporal and Multi-Scale (MMM) approach was used to estimate vulnerability of vegetation degradation. Open source Cloud computing system Google Earth Engine (GEE) was used to systematically monitor vegetation degradation and evaluate the potential of multiple satellite data with variable spatial resolutions. Hotspots were demarcated using machine learning techniques to identify the greening and the browning effect of vegetation using coarse resolution Normalized Difference Vegetation Index (NDVI) of MODIS. Rainfall datasets of Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) for the period 2000–2022 were also used to find rainfall anomaly in the region. Furthermore, hotspot areas were identified using high-resolution datasets in major vegetation degradation areas based on long-term vegetation and rainfall analysis to understand and verify the cause of change whether anthropogenic or climatic in nature. This study is important for several State/Central Government user departments, Universities, and NGOs to lay out managerial plans for the protection of vegetation/forests in India.
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