长时间序列多源遥感数据的森林干扰提取

doi:10.13287/j.1001-9332.202506.005

摘要/Abstract

摘要： 本研究针对黑龙江省2001—2023年森林生态系统干扰特征,融合多源遥感数据构建了森林干扰识别方法体系,对黑龙江整体森林干扰强度进行评估,并进行干扰提取和干扰类型识别。结果表明: 2001—2023年间,2003年森林干扰强度达到峰值, 主要源于大规模森林火灾。基于LandTrendr方法使用多种光谱指数融合的干扰检测结果与Global Forest Change数据空间的一致性在90%以上。将黑龙江的森林干扰分为3类,分别是火灾干扰、病虫害干扰和采伐干扰,干扰类型分类总体精度为87.8%(Kappa系数为0.81)。不同光谱指数对干扰类型具有差异性响应,归一化燃烧指数对火灾干扰敏感性最强,归一化植被指数对植被整体变化比较敏感,归一化差分湿度指数在病虫害识别中贡献显著,而全球干扰指数可对人为采伐活动进行辅助判别。综上,多光谱指数协同分析和时序特征融合可有效提升森林干扰类型的识别精度,为东北亚寒温带森林生态系统管理提供科学依据。

关键词: 森林干扰, 遥感监测, LandTrendr算法, 时间序列分析, 机器学习

Abstract: We developed a method of comprehensive forest disturbance identification system based on the distur-bance characteristics of forest ecosystem and the integrated multi-source remote sensing data to evaluate the overall forest disturbance intensity of Heilongjiang Province from 2001 to 2023. We further conducted disturbance extraction and types identification. The results showed that forest disturbance intensity peaked in 2003, primarily due to large-scale forest fires. The spatial consistency between disturbance detection using the LandTrendr method and the Global Forest Change dataset exceeded 90%. Forest disturbances could be categorized into three types, including fire disturbance, pest disturbance, and logging disturbance. The overall classification accuracy for disturbance types was 87.8% (Kappa coefficient=0.81). Different spectral indices had different responses to disturbance types. Specifically, the normalized burn ratio was the most sensitive to fire disturbance. The normalized difference vegetation index was more responsive to overall vegetation change. The normalized difference moisture index made a more significant contribution to the identification of pest disease, while the modified greenness difference index could assist in detecting logging activities. In conclusion, the integrative analysis of multi-spectral indices and the fusion of temporal features could effectively improve the accuracy of identifying forest disturbance types, which would provide a scientific basis for forest ecosystem management in cold temperate zone of Northeast Asia.

Key words: forest disturbance, remote sensing monitoring, LandTrendr, time series analysis, machine learning

侯卓涵, 于颖, 杨曦光. 长时间序列多源遥感数据的森林干扰提取[J]. 应用生态学报, 2025, 36(6): 1722-1730.

HOU Zhuohan, YU Ying, YANG Xiguang. Extraction of forest disturbance information from multi-source and long-term series of remote sensing data[J]. Chinese Journal of Applied Ecology, 2025, 36(6): 1722-1730.

参考文献

[1] Pickett STA, White PS. The Ecology of Natural Distur-bance and Patch Dynamics. New York: Academic Press, 1985: 472
[2] White PS. Pattern, process, and natural disturbance in vegetation. Botanical Review, 1979, 45: 229-299
[3] Zhu JJ, Liu ZG, Matsuzaki T, et al. Review: Effects of wind on trees. Journal of Forestry Research, 2004, 15: 153-160
[4] Masek JG, Goward SN, Kennedy RE, et al. United states forest disturbance trends observed using LandsatTime series. Ecosystems, 2013, 16: 1087-1104
[5] Nolan C, Overpeck JT, Allen JRM, et al. Past and future global transformation of terrestrial ecosystems under climate change. Science, 2018, 361: 920-923
[6] Glasby MJ, Russell MB, Domke GM. Analyzing the impacts of forest disturbance on individual tree diameter increment across the US Lake States. Environmental Monitoring & Assessment, 2019, 191: 1-11
[7] Thom D, Rammer W, Dirnböck T, et al. The impacts of climate change and disturbance on spatio-temporal tra-jectories of biodiversity in a temperate forest landscape. Journal of Applied Ecology, 2017, 54: 28-38
[8] 袁菲, 张星耀, 梁军. 基于有害干扰的森林生态系统健康评价指标体系的构建. 生态学报, 2012, 32(3): 964-973
[9] Dale VH, Joyce LA, Mcnulty S, et al. Climate change and forest disturbances. BioScience, 2001, 51: 723-734
[10] Sulla-Menashe D, Kennedy RE, Yang Z, et al. Detecting forest disturbance in the Pacific Northwest from MODIS time series using temporal segmentation. Remote Sensing of Environment, 2014, 151: 114-123
[11] Willem JD. Monitoring the effects of forest restoration treatments on post-fire vegetation recovery with MODIS multitemporal data. Sensors, 2008, 8: 2017-2042
[12] Spruce JP, Sader S, Ryan RE, et al. Assessment of MODIS NDVI time series data products for detecting forest defoliation by gypsy moth outbreaks. Remote Sensing of Environment, 2011, 115: 427-437
[13] Chen J, Sun L. Using MODIS EVI to detect vegetation damage caused by the 2008 ice and snow storms in south China. Journal of Geophysical Research: Biogeosciences, 2010, 115, DOI: 10.1029/ 2009JG001246
[14] 吕莹莹, 庄义琳, 任芯雨, 等. 南京城市森林干扰及恢复自动制图. 应用生态学报, 2016, 27(2): 429-435
[15] 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. 生态学报, 2020, 40(20): 7343-7352
[16] Kennedy RE, Yang ZQ, Noel G, et al. Implementation of the LandTrendr algorithm on Google Earth Engine. Remote Sensing, 2018, 10: 691
[17] 邵亚奎. GEE云平台支持下的西天山森林遥感监测与时空变化分析. 测绘通报, 2020(8): 13-17
[18] Hislop S, Jones S, Soto-Berelov M, et al. High fire disturbance in forests leads to longer recovery, but varies by forest type. Remote Sensing in Ecology and Conservation, 2019, 5: 376-388
[19] 饶月明, 王川, 黄华国. 联合多源遥感数据监测四川木里县森林火灾. 遥感学报, 2020, 24(5): 559-570
[20] Xu HR, Ying YB, Fu XP, et al. Near-infrared spectroscopy in detecting leaf Miner damage on tomato leaf. Biosystems Engineering, 2007, 96: 447-454
[21] Luo J, Huang W, Zhao J, et al. Detecting aphid density of winter wheat leaf using hyperspectral measurements. IEEE Journal of Selected Topics in Applied Earth Observations& Remote Sensing, 2013, 6: 690-698
[22] Prabhakar M, Prasad YG, Thiruathi M, et al. Use of ground based hyperspectral remote sensing for detection of stress in cotton caused by leaf hopper (Hemiptera: Cicadellidae). Computers and Electronics in Agriculture, 2011, 79: 189-198
[23] Bucha T, Stibig HJ. Analysis of MODIS imagery for detection of clear cuts in the boreal forest in north-west Russia. Remote Sensing of Environment, 2008, 112: 2416-2429
[24] Wilson EH, Sader SA. Detection of forest harvest type using multiple dates of Landsat TM imagery. Remote Sensing of Environment, 2002, 80: 385-396
[25] Schroeder TA, Wulder MA, Healey SP, et al. Mapping wildfire and clearcut harvest disturbances in boreal forests with Landsat time series data. Remote Sensing of Environment, 2011, 115: 1421-1433
[26] Mildrexler DJ, Zhao M, Running SW. Testing a MODIS global disturbance index across North America. Remote Sensing of Environment, 2009, 113: 2103-2117
[27] Schmugge TJ, Kustas WP, Ritchie JC, et al. Remote sensing in hydrology. Advances in Water Resources, 2002, 25: 1367-1385
[28] Huete A, Didan K, Miura T, et al. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 2002, 83: 195-213
[29] 吴伟, 武瑞东. 利用卫星遥感数据提取生态干扰信息的方法研究. 科研信息化技术与应用, 2017, 8(3): 37-43
[30] Goetz SJ. Multi-sensor analysis of NDVI, surface temperature and biophysical variables at a mixed grassland site. International Journal of Remote Sensing, 1997, 18: 71-94
[31] Liu SL, Liu LM, Wu X, et al. Quantitative evaluation of human activity intensity on the regional ecological impact studies. Acta Ecological Sinica, 2018, 38: 6797-6809
[32] Tao J, Zhang Y, Yuan X, et al. Analysis of forest fires in Northeast China from 2003 to 2011. International Journal of Remote Sensing, 2013, 34: 8235-8251
[33] Mildrexler DJ, Zhao M, Heinsch FA, et al. A new satellite-based methodology for continental-scale disturbance detection. Ecological Applications, 2007, 17: 235-250
[34] Coops NC, Wulder MA, Iwanicka D. Large area monitoring with a MODIS-based disturbance index (DI) sensitive to annual and seasonal variations. Remote Sensing of Environment, 2009, 113: 1250-1261
[35] Kennedy RE, Yang ZQ, Cohen WB. Detecting trends in forest disturbance and recovery using yearly Landsat time series:1. LandTrendr-Temporal segmentation algorithms. Remote Sensing of Environment, 2010, 114: 2897-2910
[36] Miller JD, Thode AE. Quantifying burn severity in a heterogeneous landscape with a relative version of the Delta Normalized Burn Ratio (dNBR). Remote Sensing of Environment, 2007, 109: 66-80
[37] 李春晖, 杨志峰. 黄河流域NDVI时空变化及其与降水/径流关系. 地理研究, 2004, 23(6): 753-759
[38] Hardisky MA, Klemas V, Smart RM. The influence of soil salinity, growth form, and leaf moisture on the spectral radiance of Spartina alterniflora canopies. Photogrammetric Engineering & Remote Sensing, 1983, 49: 77-84
[39] Wilson EH, Sader SA. Detection of forest harvest type using multiple dates of Landsat TM imagery. Remote Sensing of Environment, 2002, 80: 385-396
[40] Breiman L. Random forests. Machine Learning, 2001, 45: 5-32
[41] 刘毅, 杜培军, 郑辉, 等. 基于随机森林的国产小卫星遥感影像分类研究. 测绘科学, 2012, 37(4): 194-196
[42] Giglio L, Randerson JT, Vander Werf GR. Analysis of daily, monthly, and annual burned area using the fourth-generation global fire emissions database (GFED4). Journal of Geophysical Research: Biogeosciences, 2003, 118: 317-328
[43] 钟莉, 陈芸芝, 汪小钦. 基于Landsat时序数据的森林干扰监测. 林业科学, 2020, 56(5): 80-88