气候变化领域集成服务门户(CCPortal): A reporting framework for Sustainable Development Goal 15: Multi-scale monitoring of forest degradation using MODIS, Landsat and Sentinel data

CCPortal

DOI	10.1016/j.rse.2019.111592
	A reporting framework for Sustainable Development Goal 15: Multi-scale monitoring of forest degradation using MODIS, Landsat and Sentinel data
	Mondal P.; McDermid S.S.; Qadir A.
发表日期	2020
ISSN	00344257
卷号	237
英文摘要	Sustainable Development Goal (SDG) indicator 15.1.1 proposes to quantify “Forest area as a proportion of total land area” in order to achieve SDG target 15.1. While area under forest cover can provide useful information regarding discrete changes in forest cover, it does not provide any insight on subtle changes within the broad vegetation class, e.g. forest degradation. Continental or national-level studies, mostly utilizing coarse-scale satellite data, are likely to fail in capturing these changes due to the fine spatial and long temporal characteristics of forest degradation. Yet, these long-term changes affect forest structure, composition and function, thus ultimately limiting successful implementation of SDG targets. Using a multi-scale, satellite-based monitoring approach, our goal is to provide an easy-to-implement reporting framework for South Asian forest ecosystems. We systematically analyze freely available remote sensing assets on Google Earth Engine for monitoring degradation and evaluate the potential of multiple satellite data with different spatial resolutions for reporting forest degradation. Taking a broad-brush approach in step 1, we calculate vegetation trends in six south Asian countries (Bangladesh, Bhutan, India, Nepal, Pakistan, and Sri Lanka) using the Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) during 2000–2016. We also calculate rainfall trends in these countries using the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) rainfall data, and further calculate Rain-Use Efficiency (RUE) that shows vegetation trends in the context of rainfall variability. In step 2, we focus on two protected area test cases from India and Sri Lanka for evaluating the potential of finer-resolution satellite data compared to MODIS, i.e. Landsat 8, and Sentinel-2 data, for capturing forest degradation signals, which will ultimately contribute towards SDG indicators 15.1.1 and 15.1.2. We find that most countries show a fluctuating trend in vegetation condition over the years, along with localized greening and browning. The Random Forest (RF) classifier utilized in step 2 was able to generate accurate maps (87% and 91% overall accuracy for Indian and Sri Lankan test cases, respectively) of non-intact forest within the protected areas. We find that almost one-third of the Indian test case is degraded forest, even though it shows overall greening as per the broad-brush approach. This finding corroborates our argument that utilizing higher-resolution satellite data (e.g. 10-m) than those normally used for national-level studies will be crucial for reporting SDG indicator 15.2.1: “progress towards sustainable forest management”. © 2019 Elsevier Inc.
英文关键词	Asia; CHIRPS; Climate; Forest degradation; Google Earth Engine; Landsat; MODIS; Protected area; Rain-Use Efficiency; SDG indicator; Sentinel
语种	英语
scopus关键词	Chirp modulation; Conservation; Decision trees; Earth (planet); Ecosystems; Efficiency; Engines; Environmental protection; Planning; Radiometers; Rain; Remote sensing; Satellites; Sustainable development; Vegetation; Asia; CHIRPS; Climate; Forest degradation; Google earths; LANDSAT; MODIS; Protected areas; Sentinel; Use efficiency; Forestry; forest cover; forest ecosystem; Landsat; MODIS; NDVI; protected area; rainfall; remote sensing; satellite data; Sentinel; sustainability; Bangladesh; Bhutan; India; Nepal; Pakistan; Sri Lanka
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179512
作者单位	Department of Geography and Spatial Sciences, University of Delaware, Newark, DE 19716, United States; Department of Plant and Soil Sciences, University of Delaware, Newark, DE 19716, United States; Department of Environmental Studies, New York University, New York, NY 10003, United States
推荐引用方式 GB/T 7714	Mondal P.,McDermid S.S.,Qadir A.. A reporting framework for Sustainable Development Goal 15: Multi-scale monitoring of forest degradation using MODIS, Landsat and Sentinel data[J],2020,237.
APA	Mondal P.,McDermid S.S.,&Qadir A..(2020).A reporting framework for Sustainable Development Goal 15: Multi-scale monitoring of forest degradation using MODIS, Landsat and Sentinel data.Remote Sensing of Environment,237.
MLA	Mondal P.,et al."A reporting framework for Sustainable Development Goal 15: Multi-scale monitoring of forest degradation using MODIS, Landsat and Sentinel data".Remote Sensing of Environment 237(2020).