کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
1058677 947131 2007 8 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Combining remote sensing imagery and forest age inventory for biomass mapping
موضوعات مرتبط
مهندسی و علوم پایه مهندسی انرژی انرژی های تجدید پذیر، توسعه پایدار و محیط زیست
پیش نمایش صفحه اول مقاله
Combining remote sensing imagery and forest age inventory for biomass mapping
چکیده انگلیسی

Aboveground biomass (AGB) of forests is an important component of the global carbon cycle. In this study, Landsat ETM+ images and field forest inventory data were used to estimate AGB of forests in Liping County, Guizhou Province, China. Three different vegetation indices, including simple ratio (SR), reduced simple ratio (RSR), and normalized difference vegetation index (NDVI), were calculated from atmospherically corrected ETM+ reflectance images. A leaf area index (LAI) map was produced from the RSR map using a regression model based on measured LAI and RSR. The LAI map was then used to develop an initial AGB map, from which forest stand age was deduced. Vegetation indices, LAI, and forest stand age were together used to develop AGB estimation models for different forest types through a stepwise regression analysis. Significant predictors of AGB changed with forest types. LAI and NDVI were significant predictors of AGB for Chinese fir (R2=0.93). The model using LAI and stand age as predictors explained 94% of the AGB variance for coniferous forests. Stand age captured 79% of the AGB variance for broadleaved forests (R2=0.792). AGB of mixed forests was predicted well by LAI and SR (R2=0.931). Without differentiating among forest types, the model with SR and LAI as predictors was able to explain 90% of AGB variances of all forests. In Liping County, AGB shows a strong gradient that increases from northeast to southwest. About 64% of the forests have AGB in the range from 90 to 180 t ha−1.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Environmental Management - Volume 85, Issue 3, November 2007, Pages 616–623
نویسندگان
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