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Remote Sensing for Land & Resources    2019, Vol. 31 Issue (2) : 118-124     DOI: 10.6046/gtzyyg.2019.02.17
Tree species and age groups classification based on GF-2 image
Feng FU1, Xinjie WANG1(), Jin WANG1, Na WANG2, Jihong TONG1
1.College of Forestry, Beijing Forestry University, Beijing 100083, China
2.College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
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With the Jiangle state-owned forest farm of Fujian Province as the study area, the potential of classification in tree species and age groups through GF-2 image were explored. First, the canopy spectral curve of main tree species were measured and the reflectance differences between them were analyzed. After image preprocessing and in combination with normalized difference vegetation index (NDVI) and topographic factors, multi band remote sensing images were constructed. Object-oriented multi-scale segmentation technology was applied to extracting the spectral and texture attributes, followed by attributes filter. On the basis of 7 kinds of schemes, Cunninghamia lanceolata (3 age groups),Pinus massoniana and Phyllostachys edulis were classified by random forest classifier. The role of spectrum, texture and auxiliary data in classification was quantitatively analyzed. The results show that the scheme of spectra combined with 4 directions of texture attributes has overall accuracy of 87.4% with Kappa coefficient being 0.85, and age groups in Cunninghamia lanceolate were effectively classified. Random forest classifier can achieve better classification results based on the optimal attribute set. GF-2 has great potential in tree species and age group classification and provides reliable data source for forest resources investigation and management.

Keywords GF-2      tree species classification      object-oriented      random forest     
:  TP701  
Corresponding Authors: Xinjie WANG     E-mail:
Issue Date: 23 May 2019
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Feng FU
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Feng FU,Xinjie WANG,Jin WANG, et al. Tree species and age groups classification based on GF-2 image[J]. Remote Sensing for Land & Resources, 2019, 31(2): 118-124.
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类型 属性 参与
光谱 最小像元值(minimum pixel value,MIN) Blue,Green,Red,NIR,NDVI 25
最大像元值(maximum pixel value)
像元标准差(standard deviation,SD)
纹理 同质性(homogeneity,HOM) Blue,Green,Red,NIR 全方向 32
4方向 128
纹理标准差(standard deviation,STD)
角二阶矩(angular second moment,ASM)
辅助数据 最小像元值(minimum pixel value,MIN) DEM,坡向(Aspect),坡度(Slope) 15
最大像元值(maximum pixel value)
像元标准差(standard deviation,SD)
Tab.1  Object-oriented image attributes extraction
分类方案 属性类型 属性数量
方案1 光谱+4方向纹理+辅助数据 25+128+15=168
方案2 光谱+4方向纹理 25+128=153
方案3 光谱+全方向纹理 25+32=57
方案4 光谱+辅助数据 25+15=40
方案5 光谱 5×5=25
方案6 4方向纹理 8×4×4=128
方案7 全方向纹理 8×4=32
Tab.2  Attributes number of different classification schemes
Fig.1  Canopy spectral curves of five tree species in Jiangle forest farm
分类方案 最优属性子集包含的属性 最优属性数/
1 mean_NDVI; mean_NIR; SD_NDVI; SD_Blue; skewness_Green; HOM_0_NIR; COR_45_NIR; COR_45_Green; DIS_135_Green; mean_Aspect 10/168 5 1 200
2 mean_NDVI; mean_NIR; SD_NDVI; SD_Blue; skewness_Green; HOM_0_NIR; COR_45_NIR; COR_45_Green; DIS_135_Green 9/153 4 100
3 mean_NDVI; mean_NIR; SD_NDVI; SD_Blue; skewness_Blue; CON_All_Red; CON_All_Blue; DIS_All_NIR; DIS_All_Blue; COR_All_NIR; COR_All_Red 11/57 3 1 100
4 mean_NDVI; mean_NIR; SD_NDVI; SD_Blue; skewness_Green; skewness_Blue; mean_Aspect; SD_DEM 8/40 3 1 600
5 MIN_Green; mean_NDVI; mean_NIR; SD_NDVI; SD_Blue; skewness_Green; skewness_Blue 7/25 3 3 000
6 HOM_0_NIR; COR_45_NIR; COR_45_Green; DIS_135_Green; COR_135_Red; COR_135_NIR; COR_135_Blue 7/128 3 3 000
7 CON_All_Red; CON_All_Blue; DIS_All_NIR; DIS_All_Green; DIS_All_Blue; COR_All_NIR; COR_All_Red 7/32 3 100
Tab.3  Attributes screening and parameter optimization under classification schemes
Fig.2  F accuracy of classification under 7 schemes
树种 马尾松 毛竹 杉木
马尾松 92 4 0 0 3
毛竹 2 54 9 4 0
杉木幼龄林 0 4 86 5 0
杉木中龄林 0 2 4 49 7
杉木成熟林 3 0 0 5 52
制图精度/% 94.8 84.4 86.9 77.8 83.9
用户精度/% 92.8 77.0 90.8 79.2 86.8
Tab.4  Confusion matrix of GF-2 image in tree species classification
Fig.3  Results of object-oriented random forest classification
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