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Remote Sensing Technology and Application  2019, Vol. 34 Issue (5): 925-938    DOI: 10.11873/j.issn.1004-0323.2019.5.0925
    
Estimation of Large-scale Forest Above-ground Biomass based on Fast Optimizing Remotely Sensed Features from Pptical Multi-spectral and SAR Data
Shaowei Zhang1,2(),Gangying Hui1(),Zongtao Han3,Shanshan Sun3,Xin Tian3
1.Institute of Forestry,Chinese Academy of Forestry,Beijing 100091,China
2.College of Horticulture and Landscape,Henan Vocational College of Agriculture,Zhengzhou 451450,China
3.Institute of Forest Resource Information Techniques,Chinese Academy of Forestry,Beijing 100091,China
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Abstract  

Aiming at the problem of low efficiency for estimating large-area forest Above-Ground Biomass(AGB) using multi-mode remote sensing, this study fully integrated multi-dimensional observation characteristics of forest AGB from active and passive remotely sensed features, in order to improve the regional estimation result. Based on an analysis on two temporal estimation results, this study disclosed the spatial patterns of the regional forest AGB changes. It could provide data supports for the scientific assessments on the regional eco-environmental protection projects (i.e., the Natural Forest Protection Project) and for improving the ability of continuous dynamic monitoring and early warning the national eco-environment by use of remote sensing. The study area is located at the Great Khingan, the Inner Monolia. Based on the active and passive multi-mode remotely sensed features extracted from the Landsat-TM5(TM) and ALOS-1 PALSAR mainly acquired in 2009,and the Gaofen-1(GF-1)and ALOS-2 PALSAR data mainly acquired in 2014, respectively, the k- Nearest Neighbor with Fast Iterative Features Selection (KNN-FIFS) method was applied to fast select the features composition to establish the optimal estimating model. The 7th and 8th National Forest resource Inventory (NFI) data were applied to training and validating (by Leave One Out method, LOO) the optimal KNN-FIFS for estimating two-temporal forest(arbor forest) AGB over study area. Based on the comparison between the two-temporal AGB results, the local forest changes from 2009 to 2014 at pixel and regional scales were quantitatively analyzed. At pixel scale, the validation based on NFI and LOO method showed that, estimates obtained a R2=0.56 and Root-Mean-Square Error (RMSE) = 25.95 t/ha, and a R2=0.64; RMSE=24.55 t/ha for 2009 and 2014, respectively. Meanwhile, as compared with NFI measurements, the average of 2009 results was over-estimated (predictions: 81.59 t/ha VS NFI measurements:78.64 t/ha), but the average of 2014 was under-estimated (predictions: 79.63 t/ha VS NFI measurements:82.48 t/ha). At regional scale, the overall averages of 2009 and 2014 were 88.33 t/ha, 94.61 t/ha respectively, with a increment of 6.28 t/ha,which were closed to those from previous studies using the Biomass Expansion Factor method, 87.14 t/ha for 2008, and 92.20 t/ha for 2013, respectively. The KNN-FIFS method used in this study, could largely improve the efficiency for selecting the optimal composition from high-dimensional multi-mode remotely sensed features. Full integration of the multi-dimensional observation characteristics from active and passive remotely sensed information, could improve the estimating accuracy and saturation level of forest AGB. Validation based the LOO method at pixel scale made the KNN-FIFS more robust with avoiding the random errors brought form the selection of training and validation data set. From 2009 to 2014, the local vegetation fractional coverage got to increase obviously, as well as the local forest AGB. Thanks to the implement of National Forest Protection Project, the situation of the local forest resource was effectively improved, although some forest fire were occasionally witnessed by the study years.

Key words:  Active and passive remote sensing      Multi-mode remotely sensed features      Fast iterative features selection      Regional forest above-ground biomass      Change pattern     
Received:  28 October 2018      Published:  05 December 2019
ZTFLH:  TP79  
Corresponding Authors:  Gangying Hui     E-mail:  hncazsw@126.com;hui@caf.ac.cn
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Shaowei Zhang
Gangying Hui
Zongtao Han
Shanshan Sun
Xin Tian

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Shaowei Zhang,Gangying Hui,Zongtao Han,Shanshan Sun,Xin Tian. Estimation of Large-scale Forest Above-ground Biomass based on Fast Optimizing Remotely Sensed Features from Pptical Multi-spectral and SAR Data. Remote Sensing Technology and Application, 2019, 34(5): 925-938.

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http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2019.5.0925     OR     http://www.rsta.ac.cn/EN/Y2019/V34/I5/925

Fig.1  Land use types of the study area
Fig.2  The KNN-FIFS algorithm flowchart
Fig. 3  The comparison between the traversal of feature combinations and the feature selection using KNN-FIFS
Fig.4  Fractional vegetation coverage of the Great Khingan within the Inner Monolia
Fig. 5  Change of fractional vegetation coverage of the Great Khingan within the Inner Monolia from 2009 to 2014
Fig.6  Validations and scatter density patterns of two-temporal forest AGB estimates based on the NFI data and LOO
(the gray dash line is 1∶1 line and the black dot dash is the fitting line )
Fig.7  Two temporal forest AGB estimates over the Great Khingan within the Inner Monolia
Fig.8  Change pattern of two temporal forest AGB estimates over the Great Khingan within the Inner Monolia from 2009 to 2014
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