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- W2995201670 endingPage "111592" @default.
- W2995201670 startingPage "111592" @default.
- W2995201670 abstract "Abstract 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”." @default.
- W2995201670 created "2019-12-26" @default.
- W2995201670 creator A5040677388 @default.
- W2995201670 creator A5064576647 @default.
- W2995201670 creator A5073589082 @default.
- W2995201670 date "2020-02-01" @default.
- W2995201670 modified "2023-09-29" @default.
- W2995201670 title "A reporting framework for Sustainable Development Goal 15: Multi-scale monitoring of forest degradation using MODIS, Landsat and Sentinel data" @default.
- W2995201670 cites W1459728376 @default.
- W2995201670 cites W1575088315 @default.
- W2995201670 cites W1626806761 @default.
- W2995201670 cites W1966715855 @default.
- W2995201670 cites W1976376898 @default.
- W2995201670 cites W1977009091 @default.
- W2995201670 cites W1977474833 @default.
- W2995201670 cites W1987124634 @default.
- W2995201670 cites W1989242738 @default.
- W2995201670 cites W1990653740 @default.
- W2995201670 cites W1994433187 @default.
- W2995201670 cites W1995121449 @default.
- W2995201670 cites W1995622703 @default.
- W2995201670 cites W2007421948 @default.
- W2995201670 cites W2007468519 @default.
- W2995201670 cites W2010527224 @default.
- W2995201670 cites W2013232829 @default.
- W2995201670 cites W2015839640 @default.
- W2995201670 cites W2017630579 @default.
- W2995201670 cites W2022204818 @default.
- W2995201670 cites W2026820898 @default.
- W2995201670 cites W2029058476 @default.
- W2995201670 cites W2031063649 @default.
- W2995201670 cites W2039677390 @default.
- W2995201670 cites W2042809130 @default.
- W2995201670 cites W2045332448 @default.
- W2995201670 cites W2046206266 @default.
- W2995201670 cites W2048382588 @default.
- W2995201670 cites W2051447204 @default.
- W2995201670 cites W2053154970 @default.
- W2995201670 cites W2053707902 @default.
- W2995201670 cites W2056845648 @default.
- W2995201670 cites W2065311929 @default.
- W2995201670 cites W2068474991 @default.
- W2995201670 cites W2075575598 @default.
- W2995201670 cites W2079613925 @default.
- W2995201670 cites W2080378488 @default.
- W2995201670 cites W2081403294 @default.
- W2995201670 cites W2088351080 @default.
- W2995201670 cites W2095472131 @default.
- W2995201670 cites W2097467169 @default.
- W2995201670 cites W2101999464 @default.
- W2995201670 cites W2113637050 @default.
- W2995201670 cites W2117309700 @default.
- W2995201670 cites W2117706739 @default.
- W2995201670 cites W2119451057 @default.
- W2995201670 cites W2121025662 @default.
- W2995201670 cites W2122266551 @default.
- W2995201670 cites W2127392229 @default.
- W2995201670 cites W2129521039 @default.
- W2995201670 cites W2131615718 @default.
- W2995201670 cites W2132424470 @default.
- W2995201670 cites W2134993770 @default.
- W2995201670 cites W2138408852 @default.
- W2995201670 cites W2142843930 @default.
- W2995201670 cites W2145270887 @default.
- W2995201670 cites W2145327161 @default.
- W2995201670 cites W2146302196 @default.
- W2995201670 cites W2160425600 @default.
- W2995201670 cites W2161783224 @default.
- W2995201670 cites W2162414982 @default.
- W2995201670 cites W2165037481 @default.
- W2995201670 cites W2170882512 @default.
- W2995201670 cites W2186884787 @default.
- W2995201670 cites W2191416030 @default.
- W2995201670 cites W2244457783 @default.
- W2995201670 cites W2261645655 @default.
- W2995201670 cites W2262955851 @default.
- W2995201670 cites W2301599595 @default.
- W2995201670 cites W2306718339 @default.
- W2995201670 cites W2336426693 @default.
- W2995201670 cites W2468878309 @default.
- W2995201670 cites W2517111176 @default.
- W2995201670 cites W2606034233 @default.
- W2995201670 cites W2606986252 @default.
- W2995201670 cites W2611080000 @default.
- W2995201670 cites W2615130348 @default.
- W2995201670 cites W2620166670 @default.
- W2995201670 cites W2625901340 @default.
- W2995201670 cites W2747398809 @default.
- W2995201670 cites W2757806320 @default.
- W2995201670 cites W2762436944 @default.
- W2995201670 cites W2788145508 @default.
- W2995201670 cites W2830368892 @default.
- W2995201670 cites W2838446377 @default.
- W2995201670 cites W2896250947 @default.
- W2995201670 cites W2911964244 @default.
- W2995201670 cites W2912077313 @default.
- W2995201670 cites W4212883601 @default.
- W2995201670 doi "https://doi.org/10.1016/j.rse.2019.111592" @default.