气候变化领域集成服务门户(CCPortal): Monitoring tropical forest degradation using spectral unmixing and Landsat time series analysis

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DOI	10.1016/j.rse.2018.11.011
	Monitoring tropical forest degradation using spectral unmixing and Landsat time series analysis
	Bullock E.L.; Woodcock C.E.; Olofsson P.
发表日期	2020
ISSN	00344257
卷号	238
英文摘要	Tropical forest loss currently contributes 5 to 15% of anthropogenic carbon emissions to the atmosphere. The large uncertainty in emissions estimates is a consequence of many factors, including differences in definitions of forests and degradation, as well as estimation methodologies. However, a primary factor driving uncertainty is an inability to properly account for forest degradation. While remote sensing offers the only practical way of monitoring forest disturbances over large areas, and despite recent improvements in data quality and quantity and processing techniques, remote sensing approaches are still limited in their ability to detect forest degradation. In this paper, a system is presented that uses time series of Landsat data and spectral mixture analysis to detect both degradation and deforestation in forested landscapes. The Landsat data are transformed into spectral endmember fractions and are used to calculate the Normalized Degradation Fraction Index (NDFI; Souza et al., 2005). The spectrally unmixed data are used for disturbance monitoring and land cover classification via time series analysis. To assess the performance of the system, maps of deforestation and degradation were used to stratify the study area for collection of sample data to which unbiased estimators were applied to produce accuracy and area estimates of degradation and deforestation from 1990 to 2013. The approach extends previous research in spectral mixture analysis for identifying forest degradation to the temporal domain. The method was applied using the Google Earth Engine and tested in the Brazilian State of Rondônia. Degradation and deforestation were mapped with 88.0% and 93.3% User's Accuracy, and 68.1% and 85.3% Producer's Accuracy. Area estimates of degradation and deforestation were produced with margins of error of 13.9% and 5.3%, respectively, over the 24 year time period. These results indicate that for Rondônia a decreasing trend in deforestation after 2004 corresponds to an increase in degradation during the same time period. © 2018 Elsevier Inc.
英文关键词	Area estimation; Change detection; Deforestation; Degradation; Landsat; REDD; Time series analysis
语种	英语
scopus关键词	Deforestation; Degradation; Harmonic analysis; Mixtures; Remote sensing; Tropics; Uncertainty analysis; Area estimation; Change detection; Estimation methodologies; Land cover classification; LANDSAT; REDD; Remote sensing approaches; Spectral mixture analysis; Time series analysis; anthropogenic effect; carbon emission; data processing; data quality; deforestation; detection method; environmental degradation; environmental monitoring; estimation method; land cover; Landsat; methodology; remote sensing; spectral analysis; time series analysis; tropical forest; uncertainty analysis; Nia
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/179490
作者单位	Department of Earth and Environment, Boston University, 685 Commonwealth Avenue, Boston, MA 02215, United States
推荐引用方式 GB/T 7714	Bullock E.L.,Woodcock C.E.,Olofsson P.. Monitoring tropical forest degradation using spectral unmixing and Landsat time series analysis[J],2020,238.
APA	Bullock E.L.,Woodcock C.E.,&Olofsson P..(2020).Monitoring tropical forest degradation using spectral unmixing and Landsat time series analysis.Remote Sensing of Environment,238.
MLA	Bullock E.L.,et al."Monitoring tropical forest degradation using spectral unmixing and Landsat time series analysis".Remote Sensing of Environment 238(2020).