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Remote Sensing for Natural Resources    2024, Vol. 36 Issue (1) : 118-127     DOI: 10.6046/zrzyyg.2022383
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Identifying predominant tree species based on airborne hyperspectral images using machine learning algorithms
YU Hang1,2(), TAN Bingxiang1,2(), SHEN Mingtan1,2, HE Chenrui1,2, HUANG Yifei1,2
1. Research Institute of Forest Resources Information Techniques, Chinese Academy of Forestry, Beijing 100091, China
2. Key Laboratory of Forestry Remote Sensing and Information System, NFGA, Beijing 100091, China
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Abstract  

Identifying forest tree species can provide a valuable scientific reference for ascertaining forest resources. However, it is difficult to achieve accurate tree species classification even using hyperspectral data with high spatial resolution. Hence, there is an urgent need to meet this challenge. This study investigated the Genhe Forest Reserve in the Great Xing’an Range within Inner Mongolia. At spatial resolutions of 1 m and 3 m, two sample value scales were employed: sample points (i.e., the spectral values of pixels corresponding to sample plots) and sample planes (i.e., the average spectral values of pixels in a 3×3 window corresponding to sample plots). Then, this study explored the identification effects of predominant tree species using airborne hyperspectral images based on three machine learning algorithms: neural network (NN), three-dimensional convolution neural network (3DCNN), and support vector machine (SVM). Key findings include: ① Regardless of spatial resolution and sample value scales, the 3DCNN exhibited the highest classification accuracy, yielding the highest overall accuracy and Kappa coefficient of 95.42% and 0.94, respectively; ② Compared to a low spatial resolution (3 m), a high spatial resolution was more favorable to the identification of predominant tree species, with overall accuracy and Kappa coefficient increased by 30.97% and 54.24% at most, respectively; ③ In the case of NN/SVM-based classification, sample points outperformed sample planes in improving the accuracy of tree species identification. In contrast, sample planes outperformed sample points for 3DCNN-based classification at a spatial resolution of 3 m. Overall, spatial resolution, sample value scales, and classification algorithms manifested varying degrees of effects on the identification accuracy of predominant tree species. High-spatial-resolution images, small-sample data, and deep-learning algorithms can be combined to enhance the accuracy of predominant tree species identification using airborne hyperspectral images.
Keywords hyperspectral data      identification of predominant tree species      spatial resolution      multiscale sample     
ZTFLH:  TP79  
  S725.2  
Issue Date: 13 March 2024
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Hang YU
Bingxiang TAN
Mingtan SHEN
Chenrui HE
Yifei HUANG
Cite this article:   
Hang YU,Bingxiang TAN,Mingtan SHEN, et al. Identifying predominant tree species based on airborne hyperspectral images using machine learning algorithms[J]. Remote Sensing for Natural Resources, 2024, 36(1): 118-127.
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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2022383     OR     https://www.gtzyyg.com/EN/Y2024/V36/I1/118
Fig.1  Geographical location of the study area
Fig.2  Signal to noise ratio of hyperspectral image and spectral curve of typical ground features
Ⅰ级类型 Ⅱ级类型 Ⅲ级类型 Ⅳ级类型 Ⅴ级类型
林地 有林地 乔木林 纯林 白桦林
落叶松林
混交林 针阔混交林
灌木林地
草地
湿地
非植被 建设用地
水体
Tab.1  Classification system of study area
地类编号 地物类型 样本数(像元)/个
1 白桦林 3 946
2 落叶松林 2 444
3 针阔混交林 2 292
4 灌木林地 3 721
5 草地 1 318
6 湿地 3 924
7 非植被 5 450
Tab.2  Number of samples of each category
Fig.3  Network structure diagram of 3DCNN
Fig.4  Three classification results of plan 1
Fig.5  Three classification results of plan 2
Fig.6  Three classification results of plan 3
Fig.7  Three classification results of plan 4
Fig.8  Overall accuracy and Kappa coefficient of all schemes
分类模型 空间分
辨率/m		 采样方式 白桦林 落叶松林 针阔混交林 灌木林
生产者
精度		 使用者
精度		 生产者
精度		 使用者
精度		 生产者
精度		 使用者
精度		 生产者
精度		 使用者
精度
NN 1 样本点 0.98 1 0.96 0.99 0.98 0.91 0.95 0.93
样本面 0.69 0.58 0.81 0.91 0.64 0.37 0.74 0.91
3 样本点 0.64 0.70 0.81 0.93 0.58 0.23 0.82 0.81
样本面 0.69 0.50 0.78 0.93 0.45 0.07 0.67 0.87
3D
CNN		 1 样本点 0.98 0.99 0.96 0.91 0.95 0.95 0.97 0.87
样本面 0.94 0.86 0.97 0.91 0.62 0.95 0.91 0.94
3 样本点 0.91 0.82 0.93 0.96 0.65 0.71 0.86 0.95
样本面 0.91 0.92 0.96 0.97 0.81 0.71 0.89 0.92
SVM 1 样本点 0.97 1 0.86 1 0.98 0.73 0.97 0.87
样本面 0.69 0.42 0.71 0.96 0 0 0.71 0.87
3 样本点 0.72 0.48 0.71 0.97 0 0 0.69 0.84
样本面 0.64 0.33 0.70 0.95 0 0 0.59 0.85
Tab.3  Accuracy of producers and users of dominant tree species
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