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Remote Sensing Technology and Application  2019, Vol. 34 Issue (5): 959-969    DOI: 10.11873/j.issn.1004-0323.2019.5.0959
    
Estimation of Forest Canopy Closure by the KNN-FIFS Method in the Genhe of Inner Mongolia
Shanshan Sun1(),Xin Tian1(),Chengyan Gu2,Zongtao Han1,Chongyang Wang1,Zhaopeng Zhang3
1.Institute of Forest Resource Information Techniques,Chinese Academy of Forestry,Beijing 100091,China
2.Planning and Design Institute of Forestry Product Industry,National Forestry and Grassland Administrition,Beijing 100010,China
3.The First Geodetic Surveying Brigade of MNR,Xi'an 710054,China
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Abstract  

Aiming at exploring the potentials of Gaofen-1 (GF-1) WFV data and Gaogen-6 (GF-6) satellite data in quantitative inversion of Forest Canopy Closure (FCC), based on GF-1data, the simulated GF-6 data by adding two Sentinel-2A red-edge bands (RE) into GF-1 WFV multispectral data and the extracted relevant Texture Information (TI) ,Vegetation Index (VI) and Red edge Index (RI) , a k- Nearest Neighbor with Fast Iterative Features Selection( KNN-FIFS) method, was used to estimate the Forest Canopy Closure (FCC) in the Genhe of the Great Khingan, Inner Mongolia. Besides that, the impact of terrain was further explored by adding Topographic Factors (TF) into the feature compositions. The verification using 44 field samples and the Leave-One-Out (LOO) method showed that: FCC estimation based on GF-1 WFV is in good agreement with measured data, with R2 = 0.52, RMSE = 0.08; the GF-1 WFV+VI+TI’s has R2 = 0.56, RMSE = 0.08; the GF-1 WFV+RE+RI+TI’s has been significantly improved with R2=0.63 and RMSE=0.07; and highest accuracy from the GF-1 WFV+RE+RI+TI+TF composition with R2=0.68 and RMSE=0.07 was superior to the results from both stepwise multiple linear regressions (SMLR) (R2=0.39, RMSE=0.10) and support vector machine (SVM) (R2=0.49, RMSE=0.10) methods. It indicated that the KNN-FIFS method is more reliable for FCC estimation than both SMLR and SVM methods, and the simulated GF-6 data with red-edge information can effectively improve the estimation accuracy of FCC, especially after topographic correction.

Key words:  Forest canopy closure      GF-1 WFV      GF-6      Red-edge      KNN-FIFS      Forest of Genhe     
Received:  21 February 2019      Published:  05 December 2019
ZTFLH:  TP79  
Corresponding Authors:  Xin Tian     E-mail:  shanss_caf@163.com;tianxin@ifrit.ac.cn
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Articles by authors
Shanshan Sun
Xin Tian
Chengyan Gu
Zongtao Han
Chongyang Wang
Zhaopeng Zhang

Cite this article: 

Shanshan Sun,Xin Tian,Chengyan Gu,Zongtao Han,Chongyang Wang,Zhaopeng Zhang. Estimation of Forest Canopy Closure by the KNN-FIFS Method in the Genhe of Inner Mongolia. Remote Sensing Technology and Application, 2019, 34(5): 959-969.

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http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2019.5.0959     OR     http://www.rsta.ac.cn/EN/Y2019/V34/I5/959

Fig.1  Location of the study area and the field plots
Fig.2  ASTER GDEM of the study area
Fig.3  The cosi value of the study area
名称简称说明
均值(Mean)Mi

i=1,2,…6,分别对应

GF-1WFV数据中的

4个多光谱波段和哨兵

2A数据的B5和B6红

边波段

方差(Variance)Vi
均一性(Homogeneity)Hi
对比度(Contrast)Coi
相异性(Dissimilarity)Di
熵(Entropy)Ei
二阶矩(Second moment)Si
相关性(Correlation)Cri
Table1  Textures of remote sensing data
序号波段组合
1GF-1 WFV
2GF-1 WFV、VI、TI
3GF-1 WFV、RE、RI、TI
4GF-1 WFV、RE、RI、TI、TF
Table 2  Different bands combination in KNN-FIFS experiment
Fig. 4  The KNN-FIFS algorithm flowchart
Fig.5  The cross-validation of forest canopy closure using different features combination
Fig.6  The cross-validation of forest canopy closure using
Fig.7  Distribution of forest canopy closure of the study area
Fig.8  Classification diagram of forest canopy closure of the study area
Fig.9  The spectral response curve
排序k值优选特征RMSE
13M2、M4、S3、DVIr、cosi0.067 260
23M2、M4、S3、DVIr、Cr20.067 262
33M2、M4、S3、DVIr0.069 961
45E2、NDRE、S3、V1、cosi0.075 287
55E2、NDRE、S3、V10.076 796
65M5、B、M4、S3、V10.077 759
74E4、G、S2、NDRE、SRr、Cr20.078 086
Table 3  Experimental results in KNN-FIFS experiment
Fig.10  The slope values of the field samples in the study area
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