Identification of sub-compartment forest type based on multi-source data and three-tier models

HUANG Jian, WU Dasheng, FANG Luming

JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2022, Vol. 46 ›› Issue (1) : 69-80.

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南京林业大学学报（自然科学版）
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JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2022, Vol. 46 ›› Issue (1) : 69-80. DOI: 10.12302/j.issn.1000-2006.202109037

Identification of sub-compartment forest type based on multi-source data and three-tier models

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Abstract

【Objective】 Currently, forest type identification research is mainly focused on small forest areas and forest farms. To explore the forest type identification method across a large range, this study used Sentinel-2 optical remote sensing imagery, forest resource survey data, a digital elevation model (DEM), and Sentinel-1 radar remote sensing image data to establish tree species identification model.【Method】 The research area was defined as Chun’an County, where seven forest types were modeled separately, including Phyllosstachys edulis forest, Camellia sinensis forest, Carya cathayensis forest, Cunninghamia lanceolata forest, Pinus massoniana forest, broad-leaved mixed forest, and other hard broad-leaved species. Models were divided into three layers. In the first layer, the RF algorithm was used to establish the identification model of forested land and non-forested land. In the second layer, forest structure was identified from forested land. The RF, XGBoost and LightGBM methods were used to build various models and analyze the experimental results. In the third layer, the forest structures were further divided into forest type.【Result】 The overall accuracy of the first-layer model based on the RF algorithm dividing samples into forested and non-forested land samples was 98.08%. In the second layer (i.e., forest structure recognition model), the performance of the three models under various feature combinations were compared. It was found that the LightGBM model had the highest overall accuracy of 81.43%. In the third layer, the performance indicators for seven forest type models were compared; based on the combination of all features and radar factors, the overall accuracy of the LightGBM model was 84.51%, after feature selection by the recursive feature elimination algorithm, the optimal accuracy was 83.21%.【Conclusion】 The green, red, near-infrared and red-edge bands from optical remote sensing imagery, and terrain factors from a DEM are effective in identifying the forest type. However, independent variables extracted from Sentinel-1 radar do not provide significant help to identify forest type.

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forest type identification / optical remote sensing / radar remote sensing / digital elevation model / multi-layer models

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HUANG Jian , WU Dasheng , FANG Luming. Identification of sub-compartment forest type based on multi-source data and three-tier models[J]. JOURNAL OF NANJING FORESTRY UNIVERSITY. 2022, 46(1): 69-80 https://doi.org/10.12302/j.issn.1000-2006.202109037

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Abstract
高分六号卫星具有覆盖广、多种分辨率、波段多的优势,能为遥感解译提供更丰富的信息。为探究高分六号卫星新增波段在森林树种识别上的应用,本文以覆盖根河市阿龙山林业局的一期高分六号宽幅影像为数据源,基于特征优化空间算法（Feature Space Optimization,FSO）和最大似然分类法,分别利用高分六号的前4个波段和所有波段（8波段）的光谱、纹理等特征进行了森林树种分类,并逐一添加新增波段特征确定了各波段的贡献率排名。结果表明：在加入了优选出的均匀性纹理、均值纹理和角二阶矩纹理3种纹理特征后,前4波段和8波段的分类精度比只基于光谱特征时的精度分别高出13.23%和24.63%;利用8波段信息比只利用前4波段在基于光谱特征上的精度高11.88%,在基于光谱+纹理特征上则高23.24%;基于8波段光谱+纹理特征的树种分类精度最高,达到68.74%,新增4波段的贡献率排名为B6>B5>B8>B7,说明新增红边波段对于本次树种分类试验的贡献率最高,能为北方树种识别提供有效帮助。
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