Classification of tropical natural forests in Hainan Island based on multi-temporal Landsat8 remote sensing images
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摘要: 热带森林在生物多样性保护以及全球气候变化研究中起着至关重要的作用,其植被类型的复杂性和多样性给遥感热带森林精细分类工作带来挑战。该文依托Google Earth Engine(GEE)平台多时相Landsat8数据,对我国海南省尖峰岭地区热带天然林进行分类探究,在分析多时相数据数量、组合方式对分类精度的影响基础上,针对典型热带雨林、热带季雨林、常绿阔叶林等的热带天然林植被型组类型,提出了一种基于多时相Landsat8影像的分类方法。结果表明: ①随着多时相数据数量的增加,分类精度得到显著提升,海南岛天然林植被型组类型分类精度可以提高到91%; ②当多时相数据达到一定数量后,分类精度趋于稳定; 不同时相数据的组合方式都能提升热带森林分类精度,尤其是在参与分类的数据单独分类精度较低时,其多时相组合对分类精度的提升更加明显,体现了参与分类数据时相选择的宽泛性。所提方法发挥了遥感数据时相变化优势,为海南岛热带天然林类型遥感分类提供有效的参考。
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关键词:
- 热带森林分类 /
- Google Earth Engine /
- 支持向量机 /
- 时相数据 /
- 地球大数据
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. -
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[1] Aragao L, Poulter B, Barlow J, et al. Environmental change and the carbon balance of Amazonian forests[J]. Biological Reviews, 2014, 89(4):913-931. [2] Finer M, Babbitt B, Novoa S, et al. Future of oil and gas development in the western Amazon[J]. Environmental Research Letters, 2015, 10(2):024003. [3] 邓燔, 陈秋波, 刘建波. 海南省热带天然林保护和补偿现状及缺陷分析[J]. 热带农业科学, 2007, 27(2):72-76.[3] Deng F, Chen Q B, Liu J B. Analysis of the status quo and deficiencies of the protection and compensation of tropical natural forests in Hainan Province[J]. Chinese Journal of Tropical Agriculture, 2007, 27(2):72-76.[4] Lawrence D, Vandecar K. Effects of tropical deforestation on climate and agriculture[J]. Nature Climate Change, 2015, 5(1):27-36. [5] Kalamandeen M, Gloor E, Mitchard E, et al. Pervasive rise of small-scale deforestation in Amazonia[J]. Scientific Reports, 2018, 8(1):1-10.[6] 陈新云, 李利伟, 刘承芳, 等. 热带原始森林类型分类和蓄积量遥感反演研究[J]. 林业资源管理, 2019, 28(2):39.[6] Chen X Y, Li L W, Liu C F, et al. Research on classification of tropical primeval forest types and stock volume remote sensing inversion[J]. Forest Resources Management, 2019, 28(2):39.[7] 田庆久, 闵祥军. 植被指数研究进展[J]. 地球科学进展, 1998, 13(4):327-333.[7] Tian Q J, Min X J. Research progress in vegetation index[J]. Advances in Earth Science, 1998, 13(4):327-333.[8] Homer C, Dewitz J, Fry J, et al. Completion of the 2001 national land cover database for the counterminous United States[J]. Photogrammetric Engineering and Remote Sensing, 2007, 73(4):337.[9] Merchant J W. Remote sensing of the environment:An earth resource perspective[J]. Cartography and Geographic Information Science, 2000, 27(4):311. [10] Gomez C, White J C, Wulder M A. Optical remotely sensed time series data for land cover classification:A review[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 116:55-72. [11] Jamali S, Jonsson P, Eklundh L, et al. Detecting changes in vegetation trends using time series segmentation[J]. Remote Sensing of Environment, 2015, 156:182-195. [12] Senf C, Leitao P J, Pflugmacher D, et al. Mapping land cover in complex Mediterranean landscapes using Landsat:Improved classification accuracies from integrating multi-seasonal and synthetic imagery[J]. Remote Sensing of Environment, 2015, 156:527-536. [13] Wang J, Zhao Y, Li C, et al. Mapping global land cover in 2001 and 2010 with spatial-temporal consistency at 250 m resolution[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 103:38-47. [14] Shimabukuro Y E, Beuchle R, Grecchi R C, et al. Assessment of forest degradation in Brazilian Amazon due to selective logging and fires using time series of fraction images derived from Landsat ETM+ images[J]. Remote Sensing Letters, 2014, 5(9):773-782. [15] Pouliot D, Latifovic R, Zabcic N, et al. Development and assessment of a 250 m spatial resolution MODIS annual land cover time series (2000—2011) for the forest region of Canada derived from change-based updating[J]. Remote Sensing of Environment, 2014, 140:731-743. [16] 陈焕镛. 海南植物志[M]. 北京: 科学出版社, 1964:338.[16] Chen H Y. Flora of Hainan[M]. Beijing: Science Press, 1964:338.[17] 李意德. 海南岛尖峰岭热带山地雨林的群落结构特征[J]. 热带亚热带植物学报, 1997, 5(1):18-26.[17] Li Y D. Community structure characteristics of tropical mountain rainforest in Jianfengling Hainan Island[J]. Journal of Tropical and Subtropical Botany, 1997, 5(1):18-26.[18] 盛大勇, 庄雪影, 许涵, 等. 尖峰岭热带山地雨林海南特有木本植物群落结构[J]. 植物生态学报, 2012, 36(9):935.[18] Sheng D Y, Zhuang X Y, Xu H, et al. Community structure of endemic woody plants in the tropical mountain rain forest of Jianfengling in Hainan[J]. Chinese Journal of Plant Ecology, 2012, 36(9):935. [19] Chambers J Q, Tribuzy E S, Toledo L C, et al. Respiration from a tropical forest ecosystem:Partitioning of sources and low carbon use efficiency[J]. Ecological Applications, 2004, 14(s4):72-88. [20] 王伯荪, 彭少麟, 郭泺, 等. 海南岛热带森林景观类型多样性[J]. 生态学报, 2007, 27(5):1690-1695.[20] Wang B S, Peng S L, Guo L, et al. Diversity of tropical forest landscape types in Hainan Island[J]. Acta Ecologica Sinica, 2007, 27(5):1690-1695. [21] Chander G, Markham B L, Helder D L. Summary of current radiometric calibration coefficients for Landsat MSS,TM,ETM+,and EO-1 ALI sensors[J]. Remote Sensing of Environment, 2009, 113(5):893-903. [22] Oreopoulos L, Wilson M J, Varnai T. Implementation on Landsat data of a simple cloud-mask algorithm developed for MODIS land bands[J]. IEEE Geoscience and Remote Sensing Letters, 2011, 8(4):597-601. [23] 张滔, 唐宏. 基于Google Earth Engine的京津冀2001—2015年植被覆盖变化与城镇扩张研究[J]. 遥感技术与应用, 2018, 33(4):593-599.[23] Zhang T, Tang H. Beijing-Tianjin-Hebei 2001—2015 based on Google Earth Engine vegetation cover change and urban expansion[J]. Remote Sensing Technology and Application, 2018, 33(4):593-599.[24] 宋永昌. 对中国植被分类系统的认知和建议[J]. 植物生态学报, 2011, 35(8):882. [24] Song Y C. Cognition and suggestions on China’s vegetation classification system[J]. Chinese Journal of Plant Ecology, 2011, 35(8):882. [25] Zhang L, Wan X, Sun B. Tropical natural forest classification using time-series Sentinel-1 and Landsat-8 images in Hainan Island[C]// IEEE International Geoscience and Remote Sensing Symposium, 2019:6732-6735.[26] Cherkassky V, Ma Y. Practical selection of SVM parameters and noise estimation for SVM regression[J]. Neural Networks, 2004, 17(1):113-126.
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