基于GEE平台LandTrendr算法的海南岛森林扰动快速监测方法及分析

doi:10.12082/dqxxkx.2023.220691

Abstract

Abstract:

Tropical forests have significant economic and ecological value, and timely and accurate monitoring of forest disturbance is critical to promoting their sustainable development. In this study, all Landsat 5/7/8 and Sentinel-2 optical images since 1987 and the LandTrendr algorithm were used to monitor tropical forest disturbance in Hainan Island. The dense time-series satellite images were quickly processed on the Google Earth Engine platform (GEE), and the spatiotemporal distribution characteristics of annual forest disturbances on Hainan Island over the past 30 years were identified. The main causes of forest disturbance were analyzed with the development of rubber plantations, changes in forestry policies, and several severe natural disasters. The results show that: 1) the total area of forest disturbance from 1990 to 2020 is 2.53×10³ km² (equivalent to 11.74% of the total forest area in 2020), and is mainly concentrated in the central, northern, and northwestern regions. The three regions with the largest forest disturbances are Danzhou City, Qiongzhong County, and Baisha County, respectively; 2) most forest disturbances occur at elevation below 300 m (83.40%) and slope less than 25° (94.86%), and forests at higher elevations are well preserved; 3) forest disturbance occurred more intensely between 2000 and 2010 and decreased significantly after 2010, with the largest affected area in 2005; 4) rapid development of rubber plantations (accounting for 43.48% of total forest disturbance area), changes in forestry policies (e.g., promotion of eucalyptus cultivation), and severe natural disasters (drought and hurricane) around 2005 are the main causes of forest disturbance. The rapid monitoring method of forest disturbance proposed in this study and long-term forest disturbance dataset provide a reference for forest monitoring research and forestry department decision making on Hainan Island.

Key words: Hainan Island, forest disturbance, rubber plantation, LandTrendr, Google Earth Engine, Landsat, Sentinel-2

YIN Xiong, CHEN Bangqian, GU Xiaowei, YUN Ting, WU Zhixiang, CHEN Yue, LAI Hongyan, KOU Weili. Rapid Monitoring of Tropical Forest Disturbance in Hainan Island Based on GEE Platform and LandTrendr Algorithm[J].Journal of Geo-information Science, 2023, 25(10): 2093-2106.DOI:10.12082/dqxxkx.2023.220691

Figures/Tables 9

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References 58

[1]	De Marzo T, Pflugmacher D, Baumann M, et al. Characterizing forest disturbances across the Argentine Dry Chaco based on Landsat time series[J]. International Journal of Applied Earth Observation and Geoinformation, 2021, 98:102310. DOI:10.1016/j.jag.2021.102310
[2]	郁达威, 沈润平, 杨辰, 等. 武宁县森林扰动及驱动因子分析[J]. 生态与农村环境学报, 2013, 29(5):581-586.
	[Yu D W, Shen R P, Yang C, et al. Analysis of forest disturbance and its driving factors in Wuning County[J]. Journal of Ecology and Rural Environment, 2013, 29(5):581-586.] DOI:10.3969/j.issn.1673-4831.2013.05.007
[3]	Cohen W B, Yang Z Q, Stehman S V, et al. Forest disturbance across the conterminous United States from 1985-2012: The emerging dominance of forest decline[J]. Forest Ecology and Management, 2016, 360:242-252. DOI:10.1016/j.foreco.2015.10.042
[4]	朱教君, 刘足根. 森林干扰生态研究[J]. 应用生态学报, 2004, 15(10):1703-1710.
	[Zhu J J, Liu Z G. A review on disturbance ecology of forest[J]. Chinese Journal of Applied Ecology, 2004, 15(10):1703-1710.]
[5]	Fao. Global Forest Resources Assessment 2020—Key findings[Z]. 2020.
[6]	沈文娟, 李明诗, 黄成全. 长时间序列多源遥感数据的森林干扰监测算法研究进展[J]. 遥感学报, 2018, 22(6):1005-1022.
	[Shen W J, Li M S, Huang C Q. Review of remote sensing algorithms for monitoring forest disturbance from time series and multi-source data fusion[J]. Journal of Remote Sensing, 2018, 22(6):1005-1022.] DOI:10.11834/jrs.20187089
[7]	Pflugmacher D, Cohen W B, Kennedy R E, et al. Using Landsat-derived disturbance and recovery history and lidar to map forest biomass dynamics[J]. Remote Sensing of Environment, 2014, 151:124-137. DOI:10.1016/j.rse.2013.05.033
[8]	Kennedy R E, Yang Z Q, Cohen W B. Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr-Temporal segmentation algorithms[J]. Remote Sensing of Environment, 2010, 114(12):2897-2910. DOI:10.1016/j.rse.2010.07.008
[9]	Huang C Q, Goward S N, Masek J G, et al. An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks[J]. Remote Sensing of Environment, 2010, 114(1):183-198. DOI:10.1016/j.rse.2009.08.017
[10]	Verbesselt J, Zeileis A, Herold M. Near real-time disturbance detection using satellite image time series[J]. Remote Sensing of Environment, 2012, 123:98-108. DOI:10.1016/j.rse.2012.02.022
[11]	Zhu Z, Woodcock C E. Continuous change detection and classification of land cover using all available Landsat data[J]. Remote Sensing of Environment, 2014, 144:152-171. DOI:10.1016/j.rse.2014.01.011
[12]	Liu S S, Wei X L, Li D Q, et al. Examining forest disturbance and recovery in the subtropical forest region of Zhejiang Province using landsat time-series data[J]. Remote Sensing, 2017, 9(5):479. DOI:10.3390/rs9050479
[13]	黄金城. 中国海南岛热带森林可持续经营研究[D]. 北京: 中国林业科学研究院, 2006.
	[Huang J C. Study on sustainable management of tropical forests in Hainan Island,China[D]. Beijing: Chinese Academy of Forestry, 2006.]
[14]	Li Y, Wu Z, Xu X, et al. Forest disturbances and the attribution derived from yearly Landsat time series over 1990—2020 in the Hengduan Mountains Region of Southwest China[J]. Forest Ecosystems, 2021, 8(1):73. DOI:10.1186/s40663-021-00352-6
[15]	胡小婵, 高宏华. 海南岛热带天然林概况及其保护[J]. 现代农业科技, 2008(22):76-77.
	[Hu X C, Gao H H. General situation and protection of tropical natural forests in Hainan Island[J]. Modern Agricultural Science and Technology, 2008(22):76-77.] DOI:10.3969/j.issn.1007-5739.2008.22.053
[16]	Chen B Q, Xiao X M, Ye H C, et al. Mapping forest and their spatial-temporal changes from 2007 to 2015 in tropical Hainan Island by integrating ALOS/ALOS-2 L-band SAR and landsat optical images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(3):852-867. DOI:10.1109/jstars.2018.2795595
[17]	古晓威, 陈帮乾, 云挺, 等. 基于多源遥感数据的海南岛2007—2018年森林时空变化研究[J]. 热带作物学报, 2022, 43(2):418-429.
	[Gu X W, Chen B Q, Yun T, et al. Spatio-temporal changes of forest in Hainan Island from 2007 to 2018 based on multi-source remote sensing data[J]. Chinese Journal of Tropical Crops, 2022, 43(2):418-429.] DOI:10.3969/j.issn.1000-2561.2022.02.023
[18]	张戈, 王树东, 夏建新. 海南岛近30年天然林、橡胶林、浆纸林生态调节功能价值演变研究[J]. 中央民族大学学报(自然科学版), 2020, 29(3):28-35.
	[Zhang G, Wang S D, Xia J X. Research on evolution characteristics of pattern and service function for natural, rubber and wood pulp forests in Hainan, China[J]. Journal of Minzu University of China (Natural Sciences Edition), 2020, 29(3):28-35.]DOI:10.3969/j.issn.1005-8036.2020.03.005
[19]	王树东, 欧阳志云, 张翠萍, 等. 海南岛主要森林类型时空动态及关键驱动因子[J]. 生态学报, 2012, 32(23):7364-7374.
	[Wang S D, Ouyang Z Y, Zhang C P, et al. The dynamics of spatial and temporal changes to forested land and key factors driving change on Hainan Island[J]. Acta Ecologica Sinica, 2012, 32(23):7364-7374.] DOI:10.5846/stxb201112231964
[20]	Bai J K, Meng Y C, Gou R K, et al. Mangrove diversity enhances plant biomass production and carbon storage in Hainan Island, China[J]. Functional Ecology, 2021, 35(3):774-786. DOI:10.1111/1365-2435.13753
[21]	Ren H, Chen H, Li L J, et al. Spatial and temporal patterns of carbon storage from 1992 to 2002 in forest ecosystems in Guangdong, Southern China[J]. Plant and Soil, 2013, 363(1):123-138. DOI:10.1007/s11104-012-1302-8
[22]	Meng Y C, Gou R K, Bai J K, et al. Spatial patterns and driving factors of carbon stocks in mangrove forests on Hainan Island, China[J]. Global Ecology and Biogeography, 2022, 31(9):1692-1706. DOI:10.1111/geb.13549
[23]	Chen B Q, Yun T, Ma J, et al. Correction: Chen et al. high-precision stand age data facilitate the estimation of rubber plantation biomass: A case study of Hainan Island, China. remote Sens. 2020, 12, 3853[J]. Remote Sensing, 2022, 14(19):5044. DOI:10.3390/rs12233853
[24]	Lin M Z, Ling Q P, Pei H Q, et al. Remote sensing of tropical rainforest biomass changes in Hainan Island, China from 2003 to 2018[J]. Remote Sensing, 2021, 13(9):1696. DOI:10.3390/rs13091696
[25]	王晓亮, 李光范, 胡伟, 等. 浅析吊罗山滑坡诱发因素及影响机理[J]. 自然灾害学报, 2014, 23(1):144-148.
	[Wang X L, Li G F, Hu W, et al. Preliminary analysis of inductive factors and the influence mechanism of Diaoluo Mountain landslide[J]. Journal of Natural Disasters, 2014, 23(1):144-148.] DOI:10.13577/j.jnd.2014.0120
[26]	蒋必凤, 凌毅, 开潘栋. 海南台风强降雨对边坡稳定性的影响及防治[J]. 山西建筑. 2022, 48(19):76-79.
	[Jiang B F, Ling Y, Kai P D. Influence of typhoon heavy rainfall on slope stability and prevention in Hainan[J]. Shanxi Architecture. 2022, 48(19):76-79.] DOI:10.13719/j.cnki.1009-6825.2022.19.019
[27]	罗红霞, 戴声佩, 李茂芬, 等. 海南岛1959—2015年气候变化特征分析[J]. 江苏农业科学, 2018, 46(15):261-268.
	[Luo H X, Dai S P, Li M F, et al. Analysis of climatic change characteristics on Hainan Island from 1959 to 2015[J]. Jiangsu Agricultural Sciences, 2018, 46(15):261-268.] DOI:10.15889/j.issn.1002-1302.2018.15.066
[28]	胡盈盈, 戴声佩, 罗红霞, 等. 2001—2015年海南岛橡胶林物候时空变化特征分析[J]. 自然资源遥感, 2022, 34(1):210-217.
	[Hu Y Y, Dai S P, Luo H X, et al. Spatio-temporal change characteristics of rubber forest phenology in Hainan Island during 2001—2015[J]. Remote Sensing for Natural Resources, 2022, 34(1):210-217.]
[29]	薛杨, 宿少锋, 林之盼, 等. 海南岛东北部5种森林土壤有机碳分布特征[J]. 热带作物学报, 2019, 40(11):2294-2299.
	[Xue Y, Su S F, Lin Z P, et al. Soil organic carbon distribution in five forest types in the northeast of Hainan Island[J]. Chinese Journal of Tropical Crops, 2019, 40(11):2294-2299.] DOI:10.3969/j.issn.1000-2561.2019.11.027
[30]	张明洁, 张京红, 刘少军, 等. 基于FY-3A的海南岛橡胶林台风灾害遥感监测——以“纳沙”台风为例[J]. 自然灾害学报, 2014, 23(3):86-92.
	[Zhang M J, Zhang J H, Liu S J, et al. TY-3A-based remote sensing monitoring of “Nesat” typhoon damage to rubber plantations in Hainan Island[J]. Journal of Natural Disasters, 2014, 23(3):86-92.] DOI:10.13577/j.jnd.2014.0311
[31]	张亚杰, 陈升孛, 吴胜安, 等. 基于综合气象干旱指数的海南岛干旱特征分析[J]. 海南大学学报(自然科学版), 2019, 37(1):41-50.
	[Zhang Y J, Chen S B, Wu S G, et al. Characteristics of droughts in Hainan Island based on meteorological drought composite index[J]. Natural Science Journal of Hainan University, 2019, 37(1):41-50.] DOI:10.15886/j.cnki.hdxbzkb.2019.0007
[32]	Wu Q S. Geemap: A Python package for interactive mapping with Google Earth Engine[J]. Journal of Open Source Software, 2020, 5(51):2305. DOI:10.21105/joss.02305.
[33]	Wu Q S, Lane C R, Li X C, et al. Integrating LiDAR data and multi-temporal aerial imagery to map wetland inundation dynamics using Google Earth Engine[J]. Remote Sensing of Environment, 2019, 228:1-13. DOI:10.1016/j.rse.2019.04.015
[34]	李广洋, 寇卫利, 吴志祥, 等. 近30年海南岛橡胶林时空变化分析[J]. 南京林业大学学报(自然科学版), 2023: 47(1):189-198.
	[Li G Y, Kou W L, Wu Z X, et al. Spatio-temporal changes of rubber plantations in Hainan Island over the past 30 years[J]. Journal of Nanjing Forestry University(Natural Sciences Edition). 2023: 47(1):189-198.] DOI:10.12302/j.issn.1000-2006.202109041
[35]	Hermosilla T, Wulder M A, White J C, et al. Prevalence of multiple forest disturbances and impact on vegetation regrowth from interannual Landsat time series (1985-2015)[J]. Remote Sensing of Environment, 2019, 233:111403. DOI:10.1016/j.rse.2019.111403
[36]	Vogelmann J E, Gallant A L, Shi H, et al. Perspectives on monitoring gradual change across the continuity of Landsat sensors using time-series data[J]. Remote Sensing of Environment, 2016, 185:258-270. DOI:10.1016/j.rse.2016.02.060
[37]	Chastain R, Housman I, Goldstein J, et al. Empirical cross sensor comparison of Sentinel-2A and 2B MSI, Landsat-8 OLI, and Landsat-7 ETM+ top of atmosphere spectral characteristics over the conterminous United States[J]. Remote Sensing of Environment, 2019, 221:274-285. DOI:10.1016/j.rse.2018.11.012
[38]	Schneibel A, Stellmes M, Röder A, et al. Assessment of spatio-temporal changes of smallholder cultivation patterns in the Angolan Miombo belt using segmentation of Landsat time series[J]. Remote Sensing of Environment, 2017, 195:118-129. DOI:10.1016/j.rse.2017.04.012
[39]	国家林业局调查规划设计院. 森林资源规划设计调查技术规程(GB/T 26424-2010)[S]. 北京: 中国标准出版社, 2011.
	[Survey & Planning Institute of State Forestry Administration. Technical regulations for inventory for forest management planning and design(GB/T 26424-2010)[S]. Beijing: Standards Press of China, 2011.]
[40]	殷崎栋, 柳彩霞, 田野. 基于Landsat时序影像和LandTrendr算法的森林保护区植被扰动研究——以陕西柴松和太白山保护区为例[J]. 生态学报, 2020, 40(20):7343-7352.
	[Yin Q D, Liu C X, Tian Y. Detecting dynamics of vegetation disturbance in forest natural reserve using Landsat imagery and LandTrendr algorithm: The case of Chaisong and Taibaishan Natural Reserves in Shaanxi, China[J]. Acta Ecologica Sinica, 2020, 40(20):7343-7352.] DOI:10.5846/stxb201910112115
[41]	钟莉, 陈芸芝, 汪小钦. 基于Landsat时序数据的森林干扰监测[J]. 林业科学, 2020, 56(5):80-88.
	[Zhong L, Chen Y Z, Wang X Q. Forest disturbance monitoring based on time series of landsat data[J]. Scientia Silvae Sinicae, 2020, 56(5):80-88.]
[42]	鲁宁, 寇卫利, 徐伟恒, 等. 西双版纳12 a间森林扰动监测研究[J]. 森林与环境学报, 2017, 37(4):446-452.
	[Lu N, Kou W L, Xu W H, et al. Forest disturbance monitoring of Xishuangbanna in the recent 12 years[J]. Journal of Forest and Environment, 2017, 37(4):446-452.] DOI:10.13324/j.cnki.jfcf.2017.04.012
[43]	Chen B Q, Xiao X M, Wu Z X, et al. Identifying establishment year and pre-conversion land cover of rubber plantations on Hainan Island, China using landsat data during 1987-2015[J]. Remote Sensing, 2018, 10(8):1240. DOI:10.3390/rs10081240
[44]	余伟, 张木兰, 麦全法, 等. 台风“达维”对海南农垦橡胶产业的损害及所引发的对今后产业发展的思考[J]. 热带农业科学, 2006, 26(4):41-43.
	[Yu W, Zhang M L, Mai Q F, et al. Damage of typhoon damrey to the rubber industry in Hainan state farm bureau and its countermeasures for future development[J]. Chinese Journal of Tropical Agriculture, 2006, 26(4):41-43.]
[45]	黄海静, 杨再强, 王春乙, 等. 海南岛天然橡胶林台风灾害风险评价[J]. 气象与环境学报, 2019, 35(5):130-136.
	[Huang H J, Yang Z Q, Wang C Y, et al. Evaluation of typhoon disaster risk for Hevea brasiliensis in Hainan Island[J]. Journal of Meteorology and Environment, 2019, 35(5):130-136.] DOI:10.3969/j.issn.1673-503X.2019.05.017
[46]	莫业勇. 天然橡胶供需形势和风险分析[J]. 中国热带农业, 2019(2):4-6,10.
	[Mo Y Y. Supply and demand situation and risk analysis of natural rubber[J]. China Tropical Agriculture, 2019(2):4-6,10.] DOI:10.3969/j.issn.1673-0658.2019.02.002
[47]	陈朝辉. 海南省三亚市的生态环境建设[J]. 热带地理, 2001, 21(3):202-206,222.
	[Chen C H. The construction of ecological environment in Sanya City of Hainan Province[J]. Tropical Geography, 2001, 21(3):202-206,222.] DOI:10.3969/j.issn.1001-5221.2001.03.003
[48]	任传帅, 叶回春, 崔贝, 等. 基于面向对象分类的芒果林遥感提取方法研究[J]. 资源科学, 2017, 39(8):1584-1591. doi: 10.18402/resci.2017.08.14
	[Ren C S, Ye H C, Cui B, et al. Acreage estimation of mango orchards using object-oriented classification and remote sensing[J]. Resources Science, 2017, 39(8):1584-1591.] DOI:10.18402/resci.2017.08.14.
[49]	Luo H X, Dai S P, Li M F, et al. Comparison of machine learning algorithms for mapping mango plantations based on Gaofen-1 imagery[J]. Journal of Integrative Agriculture, 2020, 19(11):2815-2828. doi: 10.1016/S2095-3119(20)63208-7
[50]	三亚统计局. 三亚统计年鉴[M]. 北京: 中国统计出版社, 2020.
	[Sanya Statistical Burear. Sanya statistical yearbook[M]. Beijing: China Statistics Press, 2020.]
[51]	张晓晖, 余雪标, 黄金城. 海南省桉树人工林生态系统服务功能价值评估[J]. 热带林业, 2006(3):25-27.
	[Zhang X H, Yu X B, Huang J C. Assessment of Eucalyptus plantation forest ecosystem service function value in Hainan Province[J]. Tropical Forestry, 2006(3):25-27.]
[52]	朱美玲, 王旭, 王帅, 等. 海南儋州橡胶树、桉树人工林碳储量及其动态变化[J]. 生态科学, 2016, 35(3):43-51.
	[Zhu M L, Wang X, Wang S, et al. Carbon storage and distribution of rubber and eucalyptus plantations in Danzhou, Hainan Island[J]. Ecological Science, 2016, 35(3):43-51.] DOI:10.14108/j.cnki.1008-8873.2016.03.007.
[53]	林伟新. 基于生态理念的桉树种植现状与可持续发展对策[J]. 林业科技情报, 2020, 52(1):23-25.
	[Lin W X. Eucalyptus robusta planting status and sustainable development countermeasures based on ecological concept[J]. Forestry Science and Technology Information, 2020, 52(1):23-25.]
[54]	韦育凡. 桉树人工林现状及可持续发展[J]. 农村实用技术, 2021(6):122-123.
	[Wei Y F. Current status and sustainable development of eucalyptus plantation forests[J]. Applicable Technologies for Rural Areas, 2021(6):122-123.]
[55]	张春花, 吴胜安. 高温对海南岛西南部干旱影响研究[J]. 气象科技进展, 2020, 10(4):96-101.
	[Zhang C H, Wu S A. Analysis on the effect of high temperature on drought in southwestern Hainan Island[J]. Advances in Meteorological Science and Technology, 2020, 10(4):96-101.] DOI:10.3969/j.issn.2095-1973.2020.04.017
[56]	Schwantes A M, Swenson J J, Jackson R B. Quantifying drought-induced tree mortality in the open canopy woodlands of central Texas[J]. Remote Sensing of Environment, 2016, 181:54-64. DOI:10.1016/j.rse.2016.03.027
[57]	Kennedy R, Yang Z Q, Gorelick N, et al. Implementation of the LandTrendr algorithm on google earth engine[J]. Remote Sensing, 2018, 10(5):691. DOI:10.3390/rs10050691
[58]	Zhu Z, Wang S X, Woodcock C E. Improvement and expansion of the Fmask algorithm: Cloud, cloud shadow, and snow detection for Landsats 4-7, 8, and Sentinel 2 images[J]. Remote Sensing of Environment, 2015, 159:269277. DOI:10.1016/j.rse.2014.12.014

参数	参数描述	数值
maxSegments	分割最大单元数目	8
spikeThreshold	如果相邻时间点NBR值的差异百分比小于该值，那个该值会被认为是异常值，须剔除	0.90
vertexCountOvershoot	在初始阶段的潜在节点回归中可以超过的节点数	0
preventOneYearRecovery	是否阻止一年后恢复的情况	True
recoveryThreshold	如果某个分割段的恢复率大于该值的倒数，那么这个分割段将会被移除	0.50
pvalThreshold	回归分析中F检验的p值，超过该值的话，则认为该像元没有发生变化	0.05
bestModelProportion	简单模型的选择规则，如果超过该值，则被选中	0.75
minObservationsNeeded	拟合中需要的最少观测数	5
Magnitude	发生扰动时植被指数掉落的振幅	≥0.17

Rapid Monitoring of Tropical Forest Disturbance in Hainan Island Based on GEE Platform and LandTrendr Algorithm

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