文献类型： 外文期刊

作者： Peng, Dailiang ¹ ; Zhang, Helin ³ ; Liu, Liangyun ¹ ; Huang, Wenjiang ¹ ; Huete, Alfredo R. ⁴ ; Zhang, Xiaoyang ⁵ ; Wa ¹ ;

作者机构： 1.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China

2.Key Lab Earth Observat, Sanya 572029, Hainan, Peoples R China

3.Beijing Normal Univ, Inst Remote Sensing Sci & Engn, Fac Geog Sci, Beijing Engn Res Ctr Global Land Remote Sensing P, Beijing 100875, Peoples R China

4.Univ Technol Sydney, Sch Life Sci, Fac Sci, Sydney, NSW 2007, Australia

5.South Dakota State Univ, Geospatial Sci Ctr Excellence, Brookings, SD 57007 USA

6.Zhejiang Univ, Inst Agr Remote Sensing & Informat Technol, Hangzhou 310058, Zhejiang, Peoples R China

7.Tsinghua Univ, Dept Earth Syst Sci, Key Lab Earth Syst Modelling, Minist Educ, Beijing 100084, Peoples R China

8.Fujian Agr & Forestry Univ, Coll Resources & Environm, Fuzhou 350002, Fujian, Peoples R China

9.Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

关键词： Three-North Shelterbelt Forest Program (TNSFP); aboveground biomass (AGB); planted forest; stand age; time-series Landsat images

期刊名称：REMOTE SENSING （ 影响因子：4.848； 五年影响因子：5.353 ）

ISSN：

年卷期： 2019 年 11 卷 19 期

页码：

收录情况： SCI

摘要： Measuring forest aboveground biomass (AGB) at local to regional scales is critical to understanding their role in regional and global carbon cycles. The Three-North Shelterbelt Forest Program (TNSFP) is the largest ecological restoration project in the world, and has been ongoing for over 40 years. In this study, we developed models to estimate the planted forest aboveground biomass (PF_AGB) for Yulin, a typical area in the project. Surface reflectances in the study area from 1978 to 2013 were obtained from Landsat series images, and integrated forest z-scores were constructed to measure afforestation and the stand age of planted forest. Normalized difference vegetation index (NDVI) was combined with stand age to develop an initial model to estimate PF_AGB. We then developed additional models that added environment variables to our initial model, including climatic factors (average temperature, total precipitation, and total sunshine duration) and a topography factor (slope). The model which combined the total precipitation and slope greatly improved the accuracy of PF_AGB estimation compared to the initial model, indicating that the environmental variables related to water distribution indirectly affected the growth of the planted forest and the resulting AGB. Afforestation in the study area occurred mainly in the early 1980s and early 21st century, and the PF_AGB in 2003 was 2.3 times than that of 1998, since the fourth term TNSFP started in 2000. The PF_AGB in 2013 was about 3.33 times of that in 2003 because many young trees matured. The leave-one-out cross-validation (LOOCV) approach showed that our estimated PF_AGB had a significant correlation with field-measured data (correlation coefficient (r) = 0.89, p < 0.001, root mean square error (RMSE) = 6.79 t/ha). Our studies provided a method to estimate long time series PF_AGB using satellite repetitive measures, particularly for arid or semi-arid areas.