کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6345666 | 1621227 | 2016 | 13 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Spatial distribution of forest aboveground biomass in China: Estimation through combination of spaceborne lidar, optical imagery, and forest inventory data
ترجمه فارسی عنوان
توزیع فضایی بیوماس زمین های جنگلی در چین: برآورد از طریق ترکیب لیدار فضایی، تصاویر نوری و داده های موجودی جنگل
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
علوم زمین و سیارات
کامپیوتر در علوم زمین
چکیده انگلیسی
The global forest ecosystem, which acts as a large carbon sink, plays an important role in modeling the global carbon balance. An accurate estimation of the total forest carbon stock in the aboveground biomass (AGB) is therefore necessary for improving our understanding of carbon dynamics, especially against the background of global climate change. The forest area of China is among the top five globally. However, because of limitations in forest AGB mapping methods and the availability of ground inventory data, there is still a lack in the nationwide wall-to-wall forest AGB estimation map for China. In this study, we collected over 8000 ground inventory records from published literatures, and developed an AGB mapping method using a combination of these ground inventory data, Geoscience Laser Altimeter System (GLAS)/Ice, Cloud, and Land Elevation Satellite (ICESat) data, optical imagery, climate surfaces, and topographic data. An uncertainty field model was introduced into the forest AGB mapping procedure to minimize the influence of plot location uncertainty. Our nationwide wall-to-wall forest AGB mapping results show that the forest AGB density in China is 120Â Mg/ha on average, with a standard deviation of 61Â Mg/ha. Evaluation with an independent ground inventory dataset showed that our proposed method can accurately map wall-to-wall forest AGB across a large landscape. The adjusted coefficient of determination (R2) and root-mean-square error between our predicted results and the validation dataset were 0.75 and 42.39Â Mg/ha, respectively. This new method and the resulting nationwide wall-to-wall forest AGB map will help to improve the accuracy of carbon dynamic predictions in China.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Remote Sensing of Environment - Volume 173, February 2016, Pages 187-199
Journal: Remote Sensing of Environment - Volume 173, February 2016, Pages 187-199
نویسندگان
Yanjun Su, Qinghua Guo, Baolin Xue, Tianyu Hu, Otto Alvarez, Shengli Tao, Jingyun Fang,