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Remote Sensing for Natural Resources    2022, Vol. 34 Issue (2) : 215-223     DOI: 10.6046/zrzyyg.2021156
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Classification of tropical natural forests in Hainan Island based on multi-temporal Landsat8 remote sensing images
ZHU Qi1,2(), GUO Huadong1,2, ZHANG Lu1,2,3(), LIANG Dong1,2, LIU Xuting1,2, WAN Xiangxing4
1. Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
3. Sanya Institute of Remote Sensing, Sanya 572029, China
4. Institute of Forest Resources Information Technique, Chinese Academy of Forestry, Beijing 100091, China
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Abstract  

Tropical forests play a vital role in biodiversity conservation and research on global climate change. However, the complexity and diversity of vegetation types pose challenges to the fine remote sensing-based classification of tropical forests. The classification of tropical forests in the Jianfengling area, Hainan Province was analyzed using the multi-temporal Landsat8 data of the Google Earth Engine (GEE) platform. Based on the analysis of the impacts of the size and combination of multi-temporal data on the classification accuracy, this study proposed a classification method based on multi-temporal Landsat8 images for the vegetation type groups of tropical natural forests, such as typical tropical rain forest, tropical monsoon forest, and evergreen broad-leaved forests. The results are as follows. ① The classification accuracy of tropical natural forests was significantly improved as the size of multi-temporal data increased. The classification accuracy of the vegetation type groups of natural forests in Hainan Island reached 91%. ② When the multi-temporal data reached a certain size, the classification accuracy tended to be stable. Different combinations of multi-temporal data can improve the classification accuracy of tropical forests, especially when the classification accuracy of individual data involved was low. This finding reflects the broadness of the selection of temporal data. The proposed method, taking advantage of the temporal changes in remote sensing data, provides an effective reference for the remote sensing-based classification of tropical natural forests in Hainan Island.
Keywords classification of tropical forests      Google Earth Engine      support vector machine      temporal data      big earth data     
ZTFLH:  TP79  
Corresponding Authors: ZHANG Lu     E-mail: zhuqi20@mails.ucas.ac.cn;zhanglu@aircas.ac.cn
Issue Date: 20 June 2022
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Qi ZHU
Huadong GUO
Lu ZHANG
Dong LIANG
Xuting LIU
Xiangxing WAN
Cite this article:   
Qi ZHU,Huadong GUO,Lu ZHANG, et al. Classification of tropical natural forests in Hainan Island based on multi-temporal Landsat8 remote sensing images[J]. Remote Sensing for Natural Resources, 2022, 34(2): 215-223.
URL:  
https://www.gtzyyg.com/EN/10.6046/zrzyyg.2021156     OR     https://www.gtzyyg.com/EN/Y2022/V34/I2/215
Fig.1  Location of the Jianfengling study area
Fig.2  Technical route of remote sensing classification of natural forests in Hainan Island based on temporal changes
Fig.3  Statistics of each band of Landsat8 OLI data
Fig.4  Statistics of vegetation index information
Fig.5  Time-phase spectral characteristic curve of typical features in 2018
Fig.6  Multi-band Landsat8 image classification result
Fig.7  Changes in the accuracy of monthly time series classification in 2018
Fig.8  Increase in OA and Kappa coefficients
植被指数 NDVI NDTI LSWI EVI SAVI 绝对值
求和
NDVI 1.000 0 -0.731 9 -0.954 4 0.688 1 0.862 5 4.236 9
NDTI -0.731 9 1.000 0 0.696 7 -0.552 5 -0.659 0 3.640 0
LSWI -0.954 4 0.696 7 1.000 0 -0.494 5 -0.904 5 4.050 1
EVI 0.688 1 -0.552 5 -0.494 5 1.000 0 0.478 2 3.213 2
SAVI 0.862 5 -0.659 0 -0.904 5 0.478 2 1.000 0 3.904 3
Tab.1  Correlation of 5 vegetation indexes
Fig.9  Comparison of OA before and after the feature band selection
Fig.10  Accuracy of time series classification results
分组 原始平均分类结果 时序分类结果 增幅/% 分组 原始平均分类结果 时序分类结果 增幅/%
OA Kappa OA Kappa OA Kappa OA Kappa OA Kappa OA Kappa
a 0.705 9 0.548 4 0.789 1 0.685 6 11.78 25.02 c 0.658 1 0.471 7 0.801 4 0.695 5 21.77 47.45
b 0.567 4 0.328 2 0.721 5 0.547 7 27.15 66.88 d 0.655 6 0.469 4 0.844 6 0.739 7 28.83 57.58
Tab.2  Accuracy change of time series image classification
Fig.11  Classification results of natural forests in Jianfengling, Hainan Island
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