4.5 Article

Spatial patterns and estimates of global forest litterfall

期刊

ECOSPHERE
卷 10, 期 2, 页码 -

出版社

WILEY
DOI: 10.1002/ecs2.2587

关键词

actual evapotranspiration; Cokriging; forest coverage map; litterfall; remote sensing

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资金

  1. National Key R&D Program of China [2017YFD0800200]
  2. Strategic Priority Research Program of the Chinese Academy of Sciences [XDA05050200]
  3. Interdisciplinary Program of Agriculture and Engineering of Shanghai Jiao Tong University [Agri-X2015004]

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The quantitative knowledge of global forest litterfall is very important for understanding the global biogeochemical cycle and evaluating of forest ecosystem services. Our aims are to show the spati-otemporal patterns of forest litterfall and the variation in different forest types and climate zones in the world. We compiled the global forest litterfall dataset of 2347 total litterfall and 1507 leaf litterfall measurements by a survey of literature published. The total litterfall and leaf litterfall were estimated in 2000 and 2009, respectively, through raster and vector calculation based on remote sensing-based global vegetation cover data. The total litterfall and leaf litterfall were 31.5 Pg and 22 Pg in 2000 and 26 Pg and 18 Pg in 2009, respectively. The spatial pattern of litterfall and leaf litterfall at global scale between 2000 and 2009 was generally similar. The largest fractions of forest litterfall were in evergreen broadleaved forests (37%), followed by needle-leaved forests (25%), deciduous broadleaved forests (20%), and others (18%) in 2000. The order of the fractions for forest litterfall was the tropical (50%), boreal (24%), temperate (17%), and subtropical forests (9%) in 2000. The ratios of leaf litterfall to litterfall were 70% in 2000 and 72% in 2009. The variability of global litterfall was most explained by the actual evapotranspiration. The reduction in total litterfall and leaf litterfall between 2000 and 2009 was coupled with the decrease in forest areas. The GIS-based geostatistics combining with the regression model represents a powerful approach for estimating the global spatial distribution, composition, and magnitude of litterfall.

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