Chin J Plan Ecolo ›› 2015, Vol. 39 ›› Issue (12): 1125-1135.doi: 10.17521/cjpe.2015.0109

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Classification of Pinus massoniana and secondary deciduous tree species in northern subtropical region based on high resolution and hyperspectral remotely sensed data

SHEN Xin, CAO Lin, XU Ting, SHE Guang-Hui*()   

  1. Co-innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China
  • Online:2015-12-31 Published:2015-12-01
  • Contact: Guang-Hui SHE E-mail:ghshe@njfu.com.cn
  • About author:

    # Co-first authors

Abstract:

Aims Using remote sensing data for tree species classification plays a key role in forestry resource monitoring, sustainable forest management and biodiversity research.Methods This study used integrated sensor LiCHy (LiDAR, CCD and Hyperspectral) to obtain both the high resolution imagery and the hyperspectral data at the same time for the natural secondary forest in south Jiangsu hilly region. The data were used to identify the crown and to classify tree species at multiple levels. Firstly, tree crowns were selected by segmenting high-resolution imagery at multiple scales based on edge detection; secondly, characteristic variables of hyperspectral images were extracted, then optimization variables were selected based on the theory of information entropy. Tree species and forest types were classified using either all characteristic variables or optimization variables only. Finally, tree species and forest types were reclassified along with the tree crowns information, and the accuracy of classification was discussed. Important findings Based on all available characteristic variables, the overall accuracy for four typical tree species classification was 64.6%, and the Kappa coefficient was 0.493. The overall accuracy for forest types classification was 81.1%, and the Kappa coefficient was 0.584. Based on optimization variables only, the overall accuracy for four typical tree species classification dropped to 62.9%, and the Kappa coefficient was 0.459. The overall accuracy for forest types classification was 77.7%, and the Kappa coefficient was 0.525. Obtaining both high resolution image and hyperspectral data at the same time by integrated sensor can increase overall accuracy in classifying forest types and tree species in northern subtropical forest.

Key words: northern subtropical forest, tree species classification, high resolution image, hyperspectral data, tree crowns

Fig. 1

The technical route of tree species classification using high resolution and hyperspectral data."

Table 1

Summary of forest metrics for the four main tree species"

林木参数
Forest metrics
马尾松 Pinus massoniana 麻栎 Quercus acutissima 板栗 Castanea mollissima 枫香树 Liquidambar formosana
范围
Range
平均值
Mean
标准偏差
SD
范围
Range
平均值
Mean
标准偏差
SD
范围
Range
平均值
Mean
标准偏差
SD
范围
Range
平均值
Mean
标准偏差
SD
胸径 DBH (cm) 8.6-26.7 15.5 3.6 5.7-35.4 16.1 7.0 13.0-39.2 28.3 6.9 6.6-32.0 15.6 6.6
树高 Tree height (m) 6.1-18.2 10.1 1.8 6.0-18.7 11.4 2.6 8.1-16.1 12.5 1.9 5.9-19.5 11.3 3.3
冠幅半径 Crown radius (m) 0.4-3.7 1.3 0.5 0.7-6.3 2.2 1.1 1.8-4.9 3.0 0.7 0.5-4.1 2.2 0.9

Fig. 2

The mean reflectance value of tree clusters for four species."

Table 2

The accuracy of extracted crown position"

探测率
Detection rate
准确率
Precision
总体精度
Overall accuracy
百分比
Percentage (%)
77.3 85.9 81.4

Fig. 3

The crowns extracted by object-oriented method (A) and accuracy assessment of extracted crown radius (B)."

Fig. 4

The diagram of important characteristic variables. A, The first band of MNF rotation (MNF1). B, The third band of MNF rotation (MNF3). C, The second band of principal components (PCA2). D, The third band of principal components (PCA3). E, Soil-adjusted vegetation index (SAVI). F, The characteristic of band combination VI (40, 15)."

Table 3

The confusion matrix of four species classification"

马尾松 Pinus massoniana 麻栎 Quercus acutissima 枫香树 Liquidambar formosana 板栗 Castanea mollissima
全部特征变量 All metrics (n = 47)
马尾松 Pinus massoniana 61.2 19.0 15.5 4.3
麻栎 Quercus acutissima 15.0 65.8 7.5 11.7
枫香树 Liquidambar formosana 10.0 16.7 60.0 13.3
板栗 Castanea mollissima 13.9 5.6 5.6 75.0
总体精度 Over accuracy: 64.6% Kappa系数 Kappa coefficient: 0.493
重要特征变量 The most important metrics (n = 12)
马尾松 Pinus massoniana 58.6 25.9 11.2 4.3
麻栎 Quercus acutissima 21.7 65.8 3.3 9.2
枫香树 Liquidambar formosana 13.3 19.0 51.0 16.7
板栗 Castanea mollissima 8.3 8.6 5.3 77.8
总体精度 Over accuracy: 62.9% Kappa系数 Kappa coefficient: 0.459

Table 4

The confusion matrix of the forest type classification"

针叶树种
Coniferous trees
阔叶树种
Broadleaf trees
全部特征变量 All metrics (n = 47)
针叶树种
Coniferous trees
64.7 35.3
阔叶树种
Broadleaf trees
8.6 91.4
总体精度
Over accuracy: 81.1%
Kappa系数
Kappa coefficient: 0.584
重要特征变量 The most important metrics (n = 12)
针叶树种
Coniferous trees
68.1 31.9
阔叶树种
Broadleaf trees
16.2 83.8
总体精度
Over accuracy: 77.7%
Kappa系数
Kappa coefficient: 0.525

"

特征变量 Metrics 变量描述 Description
原始单个波段 Initial bands
B38-39, B41-44, B48-53 高光谱原始第38-39、41-44、48-53波段
The 38-39, 41-44, 48-53 bands from hyperspectral data
波段组合 Band combination
VI (39, 52, 53) (B39 + B52 + B53) / 3
VI (42, 38, 53) (B42 + B38 + B53) / 3
VI (43, 38, 53) (B43 + B38 + B53) / 3
VI (44, 38, 53) (B44 + B38 + B53) / 3
VI (51, 38, 39) (B51 + B38 + B39) / 3
VI (41, 38, 31) (B41 - B38) / B31
VI (40, 15) (B40 - B15) / (B40 + B15)
VI (45, 31) B45 - B31
植被指数 Vegetation index
简单比值植被指数 Simple ratio vegetation index (SR) B44 / B31
修正型简单比值植被指数
Modified simple ratio vegetation index (MSR)
(B39 - B6) / (B34 - B6)
归一化植被指数679
Normalized difference vegetation index 679 (NDVI-679 nm)
(B44 - B31) / (B44 + B31)
归一化植被指数705
Normalized difference vegetation index 705 (NDVI-705 nm)
(B39 - B34) / (B39 + B34)
修正型归一化植被指数705
Modified normalized difference vegetation index 705 (NDVI-705 nm)
(B39 - B34) / (B39 + B34 - 2B6)
土壤调整植被指数
Soil-adjusted vegetation index (SAVI)
(B44 - B31) / (B44 + B31 + 0.5)
红边植被胁迫指数
Red-edge vegetation stress index (RVSI)
(B36 + B39) / 2 - B37
植被衰减指数
Plant senescence reflectance index (PSRI)
(B31 - B12) / B39
植被水含量指数
Water band index (WBI)
B54 / B62
数理统计特征 Statistical metrics
第一主成分
First principal component (PC1)
提取的主成分分析第一波段
The first band from principal component analysis
第二主成分
Second principal component (PC2)
提取的主成分分析第二波段
The second band from principal component analysis
第三主成分
Third principal component (PC3)
提取的主成分分析第三波段
The third band from principal component analysis
独立成分分析第一波段
First band of independent component analysis (ICA1)
提取的独立成分分析第一波段
The first band from independent component analysis
独立成分分析第二波段
Second band of independent component analysis (ICA2)
提取的独立成分分析第二波段
The second band from independent component analysis
独立成分分析第三波段
Third band of independent component analysis (ICA3)
提取的独立成分分析第三波段
The third band from independent component analysis
最小噪声分离变换第一波段
First band of minimum noise fraction rotation (MNF1)
提取的MNF变换第一波段
The first band from minimum noise fraction rotation
最小噪声分离变换第二波段
Second band of minimum noise fraction rotation (MNF2)
提取的MNF变换第二波段
The second band from minimum noise fraction rotation
最小噪声分离变换第三波段
Third band of minimum noise fraction rotation (MNF3)
提取的MNF变换第三波段
The third band from minimum noise fraction rotation
纹理特征 Texture metrics
相关度
Correlation (CR)
附录1 (续) Appendix 1 (continued)
特征变量 Metrics 变量描述 Description
对比度
Contrast (CO)
相异性
Dissimilarity (DI)
信息熵
Entropy (EN)
均匀度
Homogeneity (HO)
平均值
Mean (ME)
二阶矩
Second moment (SM)
偏斜度
Skewness (SK)
方差
Variance (VA)
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