气候变化领域集成服务门户(CCPortal): A generalized regression-based unmixing model for mapping forest cover fractions throughout three decades of Landsat data

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DOI	10.1016/j.rse.2020.111691
	A generalized regression-based unmixing model for mapping forest cover fractions throughout three decades of Landsat data
	Senf C.; Laštovička J.; Okujeni A.; Heurich M.; van der Linden S.
发表日期	2020
ISSN	00344257
卷号	240
英文摘要	The Landsat archive offers great potential for monitoring forest cover change, and new approaches moving from categorical towards continuous change products emerge rapidly. Most approaches, however, require vast amounts of high-quality reference data, limiting their applicability across space and time. We here propose the use of a generalized regression-based unmixing approach to overcome this limitation. The unmixing approach relies on temporally generalized machine learning regression models (random forest regression [RFR] and support vector regression [SVR]), which are trained on synthetically mixed data from a multi-year library of pure and hence easy to identify image spectra. We apply the model to three decades of Landsat data, mapping both overall forest cover and broadleaved/coniferous forest cover fractions across space and time. The resulting maps well represented the spatial-temporal patterns of forest (change) in our study region. The SVR model outperformed the RFR model, yielding accuracies of r2 = 0.74/RMSE = 0.18 for the forest cover fraction maps, r2 = 0.50/RMSE = 0.24 for the broadleaved forest cover fraction maps, and r2 = 0.59/RMSE = 0.23 for coniferous forest cover fraction maps, respectively. Highest map errors were found in mature stands, residential areas, and recently disturbed forests. We also found some variability in forest cover fractions for stable forest pixels over time, which were explained by variation in Landsat image acquisition dates. We conclude that regression-based unmixing using synthetically mixed training data from a multi-year spectral library offers an innovative strategy for mapping forest cover fractions and forest types throughout the Landsat archive that likely can be extended to large areas. © 2020 Elsevier Inc.
英文关键词	Forest cover; Forest type; Random forest regression; Spectral unmixing; Support vector regression
语种	英语
scopus关键词	Decision trees; Forestry; Photomapping; Forest cover; Forest type; Random forests; Spectral unmixing; Support vector regression (SVR); Regression analysis; coniferous forest; forest cover; innovation; Landsat; machine learning; regression analysis; spatiotemporal analysis; support vector machine
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/179419
作者单位	Institute of Silviculture, Department of Forest and Soil Sciences, University of Natural Resources and Life Sciences (BOKU) Vienna, Peter-Jordan-Str. 82, Vienna, 1190, Austria; Department of Applied Geoinformatics and Cartography, Charles University, Albertov 6, Prague, 128 43, Czech Republic; Geography Department, Humboldt-Universität zu Berlin, Unter den Linden 6, Berlin, 10099, Germany; Bavarian Forest National Park, Freyunger Str. 2, Grafenau, 94481, Germany; Wildlife Ecology and Wildlife Management, University of Freiburg, Tennenbacher Straße 4, Freiburg, Germany
推荐引用方式 GB/T 7714	Senf C.,Laštovička J.,Okujeni A.,et al. A generalized regression-based unmixing model for mapping forest cover fractions throughout three decades of Landsat data[J],2020,240.
APA	Senf C.,Laštovička J.,Okujeni A.,Heurich M.,&van der Linden S..(2020).A generalized regression-based unmixing model for mapping forest cover fractions throughout three decades of Landsat data.Remote Sensing of Environment,240.
MLA	Senf C.,et al."A generalized regression-based unmixing model for mapping forest cover fractions throughout three decades of Landsat data".Remote Sensing of Environment 240(2020).