Age information retrieval of Larix gmelinii forest using Sentinel-2 data

被引：0
|
作者：
Tang S. [1 ]
Tian Q. [1 ]
Xu K. [1 ]
Xu N. [1 ]
Yue J. [1 ]
机构：
[1] International Institute for Earth System Science, Nanjing University, Nanjing
来源：
Yaogan Xuebao/Journal of Remote Sensing | 2020年 / 24卷 / 12期
基金：
中国国家自然科学基金;
关键词：
Biophysical parameters; Larix gmelinii; Random Forest; Remote sensing; Sentinel-2; Stand age retrieval;
D O I：
10.11834/jrs.20208500
中图分类号：
学科分类号：
摘要：
The information of forest age structure can effectively reflect the carbon sequestration capacity of regional forest communities at different growth stages. This way is important for assessing the health status of forest ecosystems. In this study, the typical dominant tree species Larix gmelinii forest in temperate zone of China is selected as the object, and Sentinel-2 images of its bud germination period, elongating period of leaf, and defoliation period are selected. The retrieval model of Larix gmelinii stand age is constructed using Multiple Linear Regression (MLR), Random Forest (RF), support vector regression, feedforward back propagation neural network, and multiple adaptive regression spline. The optimal phenophase of remote sensing retrieval is first determined through correlation analysis. On this basis, five optimal characteristic variables, namely, Canopy Water Content (CWC), normalized difference water index, leaf area index, fraction of absorbed photosynthetically active radiatio, and fractional vegetation cover, are selected for model retrieval according to the difference in correlation. Results show that the elongating period of leaf is the optimal remote sensing retrieval phenophase. Except for the plant senescence reflectance index and NDVI and RVI in defoliation period, a negative correlation exists between the stand age of Larix gmelinii and each index, among which the correlation between the stand age and (CWC is the closest, and the correlation coefficient of Pearson reaches -0.74 (p<0.01). The results of different model retrievals indicate that RF model is the best model for estimating the age of Larix gmelinii, and its average coefficient of determination (R2) and mean Root Mean Square Error (RMSE) are 0.89 and 2.91 a, respectively. MLR is the worst for estimating Larix gmelinii forest age, and its average R2 and RMSE are 0.57 and 5.69 a, respectively. Nonlinear models can better explain the relationship between stand age and modeling variables. © 2020, Science Press. All right reserved.
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页码：1511 / 1524
页数：13
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