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标题：Effects of contemporary land-use and land-cover change on the carbon balance of terrestrial ecosystems in the United States
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作者：Benjamin M Sleeter ; Jinxun Liu ; Colin Daniel 等
期刊名称：Environmental Research Letters
印刷版ISSN：1748-9326
电子版ISSN：1748-9326
出版年度：2018
卷号：13
期号：4
页码：045006
DOI：10.1088/1748-9326/aab540
语种：English
出版社：IOP Publishing Ltd
摘要：Changes in land use and land cover (LULC) can have profound effects on terrestrial carbon dynamics, yet their effects on the global carbon budget remain uncertain. While land change impacts on ecosystem carbon dynamics have been the focus of numerous studies, few efforts have been based on observational data incorporating multiple ecosystem types spanning large geographic areas over long time horizons. In this study we use a variety of synoptic-scale remote sensing data to estimate the effect of LULC changes associated with urbanization, agricultural expansion and contraction, forest harvest, and wildfire on the carbon balance of terrestrial ecosystems (forest, grasslands, shrublands, and agriculture) in the conterminous United States (i.e. excluding Alaska and Hawaii) between 1973 and 2010. We estimate large net declines in the area of agriculture and forest, along with relatively small increases in grasslands and shrublands. The largest net change in any class was an estimated gain of 114 865 km² of developed lands, an average rate of 3282 km² yr⁻¹. On average, US ecosystems sequestered carbon at an annual rate of 254 Tg C yr⁻¹. In forest lands, the net sink declined by 35% over the study period, largely a result of land-use legacy, increasing disturbances, and reductions in forest area due to land use conversion. Uncertainty in LULC change data contributed to a ~16% margin of error in the annual carbon sink estimate prior to 1985 (approximately ±40 Tg C yr⁻¹). Improvements in LULC and disturbance mapping starting in the mid-1980s reduced this uncertainty by ~50% after 1985. We conclude that changes in LULC are a critical component to understanding ecosystem carbon dynamics, and continued improvements in detection, quantification, and attribution of change have the potential to significantly reduce current uncertainties.