Landsat时序变化检测综述

doi:10.3724/SP.J.1047.2017.01069

摘要/Abstract

摘要：

时序变化检测已成为当前Landsat数据主流的变化检测方法。本文从检测算法对比、时序数据构建和精度评价等方面对Landsat时序变化检测进行回顾和评述,进而提出Landsat时序变化检测当前所存在的问题,及其所面临的挑战。Landsat时序变化检测算法可大致归纳为轨迹拟合法、光谱-时间轨迹法、基于模型的方法3大类,这些算法大多基于森林扰动提出;变化检测常用指标有波段型、植被指数型、线性变换型、组合型4大类,每类指标的优势不同,可综合多类指标以更全面地检测不同扰动类型。尽管Landsat时序变化检测已取得长足发展,但仍然面临诸多挑战,其中最大挑战是缺少一致性的参考数据集进行变化检测精度评价。

关键词: Landsat影像, 时序数据, 变化检测, 检测指标

Abstract:

Change detection based on Landsat time series has become one of the most popular methods of remote sensing change detection. This paper reviews the status of Landsat time series change detection, including comparison of change detection algorithms, Landsat time series construction and accuracy assessment of change detection results. Major problems and challenges of performing Landsat time series change detection are presented. Landsat time series change detection algorithms can roughly be classified into three categories, i.e., trajectory fitting methods, spectral-temporal trajectory methods, and model-based methods. These algorithms are mostly developed based on forest disturbance. Only few of them were used to detect changes in other land use/land cover types (e.g. urban expansion). Their applications in other fields need further verification. In particular, the comparative study of those different algorithms should be strengthened, which would provide better guidance for users to select optimal detection methods in specific fields. These indices commonly used for Landsat time series change detection can be divided into four groups, including spectral band, vegetation index, linear transformation and their combinations. It is often suggested to combine the advantages of various indices to detect different disturbance types. Although change detection methods based on Landsat time series have developed rapidly, many challenges remain. Upon now, the lack of consistent reference data set for accuracy assessment of Landsat time series change detection is the most serious challenge. Confronted with new challenges, new approaches are needed to calibrate the time series change detection algorithms.

Key words: Landsat images, time series data, change detection, detection indices

汤冬梅, 樊辉, 张瑶. Landsat时序变化检测综述[J]. 地球信息科学学报, 2017, 19(8): 1069-1079.

TANG Dongmei,FAN Hui,ZHANG Yao. Review on Landsat Time Series Change Detection Methods[J]. Journal of Geo-information Science, 2017, 19(8): 1069-1079.

图/表 4

表1

主要Landsat时序变化检测方法的优势与局限性"

变化检测技术	方法	适用范围	优势	局限性	例子
轨迹拟合法	TBCD	捕捉变化趋势和事件;检测森林扰动的时间和地点,扰动强度和恢复速率;主要检测以年为步长的变化	自动化;不需要选取非森林样本;不需要特定的阈值;可以评估不连续的（森林扰动时间和强度）和连续的现象（扰动后的森林恢复）;充分利用已有数据来设定假设轨迹和统计阈值,避免手工解译和人为干预	主要误差来源为时序数据中的配准误差;依赖Landsat影像的时间长度;效率低;只有当观测的曲线符合假设曲线时才起作用	自动分析森林扰动轨迹,检测总体精度为84%,Kappa系数为0.77。其中,皆伐检测总体精度为91%,Kappa系数为0.87;择伐检测总体精度为74%,Kappa系数为0.60^[24] 森林扰动时间检测的总体精度为83%^[25-26];城市扩张检测的总体精度为83%,Kappa系数为0.80^[27]
光谱-时间轨迹法	VCT	检测大多数突变的森林扰动事件（火灾和城市扩张）、非突变的森林扰动事件（择伐）	高度自动化;除非不同森林系统并存,很少或不需要微调;部分质量差的观测点对检测结果影响很小或没有影响;检测结果对相对大气纠正不甚敏感;效率高,分析12幅或更多影像的时序数据只需2-3小时	太多质量差的观测点连续出现会导致伪变化;不能检测出所有的森林扰动类型;现有参考数据集无法满足变化检测评价要求;应用于高异质化景观区域会出现问题	重建森林干扰的历史轨迹,扰动时间检测总体精度为77%-86%,Kappa为0.67-0.76^{[14, 48]} 森林变化历史（植树造林、砍伐）,检测结果总体精度为89%,Kappa系数为0.86^{[31, 49]} 评估人工林生物量^[32]、绘制森林扰动图,估计标准误差为0.8%时,整个研究区检测总体精度为91%^[30]
光谱-时间轨迹法	LandTrendr	捕获森林扰动和恢复过程中的渐变和突变事件,主要检测以年为步长的变化	可以同时捕获变化趋势、渐变和突变事件;通过一系列的控制参数可减少时间分割过程中的过拟合问题	需要设计一系列的控制参数和滤波过程来降低时间分割过程中的过拟合现象,且捕捉理想的轨迹特征过程很复杂	检测森林扰动和恢复趋势,可以捕捉到大范围的扰动和恢复现象^{[33, 39]}、检测虫害的影响^[35]、原始森林的变化^[40]、预测地上生物量的动态趋势^[41],突变扰动检测总体精度为80%^[34];森林扰动检测总体精度为86%^[38]
基于模型的方法	BFAST	可以检测季节变化;可以处理不同的遥感时序数据;可以应用于其它季节性或非季节性变化检测	不受噪声和季节性趋势的影响	不能检测扰动后的恢复和重复扰动过程	检测热带森林砍伐和退化,其总体精度为87%^[43]
	CMFDA	检测年内和年际间的森林变化;检测自然扰动和人为扰动	全自动化;只要有新的观测数据就可连续检测森林扰动;当连续有3个清晰的观测点时,该算法很稳健;该方法可以降低坏条带带来的问题;不仅可以检测人为的森林扰动,也可以检测自然因素导致的森林扰动;可以在30 m的空间分辨率和几周的时间分辨率上提供扰动发生的时间和位置图	该算法的效果取决于足够的观测数据,其效率与建立预测模型有关;该算法是基于检测时段只发生一次变化的假设,当检测时段发生多次变化时,该方法就不成立;检测变化的耗时比传统方法少	连续检测由人类干扰引起的森林扰动,其总体精度可达99%^[16]
	CCDC	可模拟趋势、季节性变化、突变等,可检测多种土地覆被变化类型（物候变化、缓慢的年际变化、突变）;可在任何给定的时间绘制出土地利用图	完全自动;可以检测多种类型的土地覆被变化;不需要经验性或全局性的阈值;该算法的运算速度取决于可用观测点的频率;不需要对每幅影像进行标准化处理;不受噪声的影响;不仅可以诊断出年内的趋势也可以诊断出年际趋势;可以应用于高异质化景观区域	该算法需要大量储存数据;计算成本高;需要很多高时间分辨率的清晰数据;该算法可能不适用于农业地区;不适合应用于年际变化较大的区域;无法检测到模型初始化期间的变化	连续检测土地覆被变化和分类^[19],其总体精度为90% 分析绿度变化的趋势,其总体精度为87%^[44] 连续检测缓慢变化包括年际和年内的变化^[20]

表1

图1

表2

Landsat时序变化检测的主要指标"

类别	指标	公式	例子
波段型	短波红外波段	$ρ SWIR 1 、 ρ SWIR 2$	森林扰动及恢复轨迹检测^[24]、野火及伐木引起的森林扰动检测^[50]
波段型	所有波段	$ρ B 、 ρ G 、 ρ R 、 ρ NIR 、 ρ SWIR 1 、 ρ SWIR 2 、 TIR$	多种土地覆被变化检测和分类^[19]、土地覆被变化检测及土地覆被分类^[44]
植被指数型	归一化差异植被指数（NDVI）	$NDVI = ρ NIR - ρ R ρ NIR + ρ R$	连续森林扰动检测^[16]、植被缓慢变化（植被恢复、病虫害）检测^[20]、森林扰动和恢复趋势检测^[33]、量化干旱导致的森林扰动^[37]、沿边地区森林扰动检测^[38]、热带森林扰动检测^[43]、绿度变化趋势检测^[44]
	增强型植被指数（EVI）	$EVI = G ρ NIR - ρ R ρ NIR + C 1 ρ R - C 2 ρ B + L$ 其中G为调节因子一般取G=2.5;C₁和C₂为抗大气调节系数,C₁=6和C₂=7.5;L为土壤调节因子,取值一般为L=1	绿度变化趋势检测^[44]
	归一化差异湿度指数（NDMI）	$NDMI = ρ NIR - ρ SWIR 1 ρ NIR + ρ SWIR 1$	记录热带雨林扰动-恢复动态^[79]
	归一化燃烧率（NBR）	$NBR = ρ NIR - ρ SWIR 2 ρ NIR + ρ SWIR 2$	连续森林扰动检测^[16]、森林扰动和恢复趋势检测^[33]、突变扰动（城市化、森林管理、大火灾）检测^[34]、沿边地区森林扰动检测^[38]
线性变换型	穗帽变换湿度指数（TCW)）	Landsat4-5中： $TCW = 0.0315 × ρ B + 0.2021 × ρ G + 0.3102 × ρ R + 0.1594 × ρ NIR - 0.6806 × ρ SWIR 1 - 0.6109 × ρ SWIR 2$ Landsat7中： $TCW = 0.2626 × ρ TOA, B + 0.2141 × ρ TOA, G + 0.0926 × ρ TOA, R + 0.0656 × ρ TOA, NIR - 0.7629 × ρ TOA, SWIR 1 - 0.5388 × ρ TOA, SWIR 2$ Landsat8中： $TCW = 0.1511 × ρ TOA, B + 0.1973 × ρ TOA, G + 0.3283 × ρ TOA, R + 0.3407 × ρ TOA, NIR - 0.7117 × ρ TOA, SWIR 1 × - 0.4559 ρ TOA, SWIR 2$	连续森林扰动检测^[16]、森林扰动和恢复趋势检测^[33]、不同阶段森林扰动检测^[52],森林扰动检测^[80]
组合型	穗帽变换角（TCA）	$TCA = arctan (TCG TCB)$	森林扰动检测（主要检测采伐）^[25]、森林扰动历史重建^[26]、森林扰动和恢复历史检测^[41]、野火及伐木引起的森林扰动检测^[50]、量化景观变化（土地利用替换扰动速度）^[51]
	穗帽变换距离（TCD）	$TCD = TC G 2 + TC B 2$	森林扰动历史重建^[26]、森林扰动和恢复历史检测^[41]
	扰动指数（DI）	$DI = TCB - TCG + TCW$	森林扰动检测^[12]、连续森林扰动检测^[16]
	森林综合得分（IFZ）	$IFZ = 1 NB ∑ i = 1 NB b pi - bi ˉ S D i 2$ 其中NB代表使用的波段数量;b_pi代表某像元在第i波段的光谱值; $b i ˉ$ 和 $S D i$ 分别代表第i波段森林训练样本的平均值和标准差。最常用的波段是近红外（ $ρ NIR$ ）和短波红外波段（ $ρ SWIR 1 、 ρ SWIR 2$ ）	森林扰动历史重建^[14]、野火及伐木引起的森林扰动检测^[50]

表2

表3

Landsat时序变化检测精度评价方法和指标"

精度评价方法	采用数据	评价策略	评价指标	例子
TimeSync	Landsat影像、Google Earth上的高空间分辨率影像	TimeSync解译703个样本点	总体精度、Kappa系数、制图精度、用户精度	文献[33]、[39]
TimeSync	Landsat影像、Google Earth上的高空间分辨率影像	TimeSync解译1016个栅格块	制图精度、用户精度	文献[34]
以高空间分辨率影像辅助目视解译原始Landsat影像获取验证样本	Landsat影像、Google Earth上的高空间分辨率影像	目视解译	制图精度、用户精度和时间精度	文献[16]
	Landsat时序数据、Google Earth上的高空间分辨率影像	随机分层采样,每类（变化和未变化）250个像元样本	总体精度、制图精度、用户精度	文献[19]
	Landsat时序数据、Google Earth上的高空间分辨率影像	等分随机采样,每类（变化和未变化）50个3×3样本单元	总体精度	文献[82]
	Landsat影像、Google Earth上的高空间分辨率影像	分层随机采样,每层（扰动和未扰动）各500个3×3样本单元	总体精度、制图精度、用户精度	文献[38]
	Landsat影像	分层随机采样,目视解译	总体精度、用户精度	文献[28]
	Landsa影像、高空间分辨率影像、Digtal Ortho Quarter Quad（DOQQ）	实地验证、视觉验证、目视解译	总体精度、制图精度、用户精度、Kappa系数	文献[14]
	Landsat时序数据,SPOT5（2007-2011年）、QuickBird影像（2012-2013年）	只评价2009年森林扰动和未扰动两类精度,随机分层采样,112个变化像元样本,109个未变化像元样本,类似TimeSync评价方法	总体精度、制图精度、用户精度	文献[43]
其它方法（从地真数据或历史存档数据获取验证样本）	国家高空计划（NHAP）、国家航空摄影计划（NAPP）、国家农业影像计划（NAIP）解译的影像集	目视解译	总体精度	文献[30]
其它方法（从地真数据或历史存档数据获取验证样本）	地真数据和Landsat影像	目视解译和地真数据	制图精度、用户精度	文献[49]

表3

参考文献 83

[1]	Committee on Strategic Directions for the Geographical Sciences in the Next Decade, National Research Council. Understanding the changing planet: Strategic directions for the geographical sciences[M]. Washington: National Academies Press, 2010.
[2]	Vitousek P M, Mooney H A, Lubchenco J, et al.Human domination of Earth's ecosystems[J]. Science, 1997,277(5325):494-499. doi: 10.1007/978-0-387-73412-5_1
[3]	Mooney H, Cropper A, Reid W.Confronting the human dilemma[J]. Nature, 2005,434(7033):561-562. doi: 10.1038/434561a
[4]	Turner B L, Lambin E F, Reenberg A.The emergence of land change science for global environmental change and sustainability[J]. Proceedings of the National Academy of Sciences, 2007,104(52):20666-20671. doi: 10.1073/pnas.0704119104
[5]	Reid W V, Chen D, Goldfarb L, et al.Earth system science for global sustainability: grand challenges[J]. Science, 2010,330(6006):916-917. doi: 10.1126/science.1196263
[6]	傅伯杰. 我国生态系统研究的发展趋势与优先领域[J].地理研究,2010,29(3):383-396. doi: 10.11821/yj2010030001
	[ Fu B J.Trends and priority areas in ecosystem research of China[J]. Geographical Research, 2010,29(3):383-396. ] doi: 10.11821/yj2010030001
[7]	徐冠华,葛全胜,宫鹏,等.全球变化和人类可持续发展:挑战与对策[J].科学通报,2013,58(21):2100-2106. doi: 10.1007/s11434-013-5947-3
	[ Xu G H, Ge Q S, Gong P, et al.Societal response to challenges of global change and human sustainable development[J]. Chinese Science Bulletin, 2013,58(21):2100-2106. ] doi: 10.1007/s11434-013-5947-3
[8]	Singh A.Digital change detection techniques using remotely-sensed data[J]. International Journal of Remote Sensing, 1989,10(6):989-1003. doi: 10.1080/01431168908903939
[9]	Coppin P, Jonckheere I, Nackaerts K, et al.Digital change detection methods in natural ecosystem monitoring: A review[J]. International Journal of Remote Sensing, 2004,25(9):1565-1596. doi: 10.1080/0143116031000101675
[10]	Lu D, Mausel P, Brondízio E, et al.Change detection techniques[J]. International Journal of Remote Sensing, 2004,25(12):2365-2407. doi: 10.1080/0143116031000139863
[11]	Woodcock C E, Allen R, Anderson M, et al.Free access to Landsat imagery[J]. Science, 2008,320(5879):1011-1011. doi: 10.1126/science.320.5879.1011a pmid: 18497274
[12]	Hilker T, Wulder M A, Coops N C, et al.A new data fusion model for high spatial- and temporal-resolution mapping of forest disturbance based on Landsat and MODIS[J]. Remote Sensing of Environment, 2009,113(8):1613-1627. doi: 10.1016/j.rse.2009.03.007
[13]	Vogelmann J E, Tolk B, Zhu Z L.Monitoring forest changes in the southwestern United States using multitemporal Landsat data[J]. Remote Sensing of Environment, 2009,113(8):1739-1748. doi: 10.1016/j.rse.2009.04.014
[14]	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
[15]	Vogelmann J E, Xian G, Homer C, et al.Monitoring gradual ecosystem change using Landsat time series analyses: Case studies in selected forest and rangeland ecosystems[J]. Remote Sensing of Environment, 2012,122(SI):92-105. doi: 10.1016/j.rse.2011.06.027
[16]	Zhu Z, Woodcock C E, Olofsson P.Continuous monitoring of forest disturbance using all available Landsat imagery[J]. Remote Sensing of Environment, 2012,122(SI):75-91. doi: 10.1016/j.rse.2011.10.030
[17]	Zhu Z, Woodcock C E.Object-based cloud and cloud shadow detection in Landsat imagery[J]. Remote Sensing of Environment, 2012,118:83-94. doi: 10.1016/j.rse.2011.10.028
[18]	Banskota A, Kayastha N, Falkowski M J, et al.Forest monitoring using Landsat time series data: A review[J]. Canadian Journal of Remote Sensing, 2014,40(5):362-384. doi: 10.1080/07038992.2014.987376
[19]	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
[20]	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(SI):258-270. doi: 10.1016/j.rse.2016.02.060
[21]	Townshend J R, Masek J G, Huang C Q, et al.Global characterization and monitoring of forest cover using Landsat data: opportunities and challenges[J]. International Journal of Digital Earth, 2012,5(5):373-397. doi: 10.1080/17538947.2012.713190
[22]	Wulder M A, White J C, Loveland T R, et al.The global Landsat archive: Status, consolidation, and direction[J]. Remote Sensing of Environment, 2016,185(SI):271-283. doi: 10.1016/j.rse.2015.11.032
[23]	Loveland T R, Irons J R.Landsat 8: The plans, the reality, and the legacy[J]. Remote Sensing of Environment, 2016,185(SI):1-6. doi: 10.1016/j.rse.2016.07.033
[24]	Kennedy R E, Cohen W B, Schroeder T A.Trajectory-based change detection for automated characterization of forest disturbance dynamics[J]. Remote Sensing of Environment, 2007,110(3):370-386. doi: 10.1016/j.rse.2007.03.010
[25]	Ahmed O S, Franklin S E, Wulder M A.Interpretation of forest disturbance using a time series of Landsat imagery and canopy structure from airborne LiDAR[J]. Canadian Journal of Remote Sensing, 2014,39(6):521-542. doi: 10.5589/m14-004
[26]	Ahmed O S, Franklin S E, Wulder M A, et al.Characterizing stand-level forest canopy cover and height using Landsat time series, samples of airborne LiDAR, and the Random Forest algorithm[J]. Isprs Journal of Photogrammetry and Remote Sensing, 2015,101:89-101. doi: 10.1016/j.isprsjprs.2014.11.007
[27]	Xue X J, Liu H P, Mu X D, et al.Trajectory-based detection of urban expansion using Landsat time series[J]. International Journal of Remote Sensing, 2014,35(4):1450-1465. doi: 10.1080/01431161.2013.878058
[28]	Huang C Q, Goward S N, Schleeweis K, et al.Dynamics of national forests assessed using the Landsat record: Case studies in eastern United States[J]. Remote Sensing of Environment, 2009,113(7):1430-1442. doi: 10.1016/j.rse.2008.06.016
[29]	Stueve K M, Housman I W, Zimmerman P L, et al.Snow-covered Landsat time series stacks improve automated disturbance mapping accuracy in forested landscapes[J]. Remote Sensing of Environment, 2011,115(12):3203-3219. doi: 10.1016/j.rse.2011.07.005
[30]	Zimmerman P L, Housman I W, Perry C H, et al.An accuracy assessment of forest disturbance mapping in the western Great Lakes[J]. Remote Sensing of Environment, 2013,128:176-185. doi: 10.1016/j.rse.2012.09.017
[31]	Liu L Y, Peng D L, Wang Z H, et al.Mapping afforestation and forest biomass using time-series Landsat stacks[C]// SPIE Asia-Pacific Remote Sensing. International Society for Optics and Photonics, 2014,92601V:1-7.
[32]	Liu L Y, Peng D L, Wang Z H, et al.Improving artificial forest biomass estimates using afforestation age information from time series Landsat stacks[J]. Environmental Monitoring and Assessment, 2014,186(11):7293-7306. doi: 10.1007/s10661-014-3927-y pmid: 25034235
[33]	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
[34]	Kennedy R E, Yang Z Q, Braaten J, et al.Attribution of disturbance change agent from Landsat time-series in support of habitat monitoring in the Puget Sound region, USA[J]. Remote Sensing of Environment, 2015,166:271-285. doi: 10.1016/j.rse.2015.05.005
[35]	Meigs G W, Kennedy R E, Cohen W B.A Landsat time series approach to characterize bark beetle and defoliator impacts on tree mortality and surface fuels in conifer forests[J]. Remote Sensing of Environment, 2011,115(12):3707-3718. doi: 10.1016/j.rse.2011.09.009
[36]	Bright B C, Hudak A T, Kennedy R E, et al.Landsat time series and LiDAR as predictors of live and dead basal area across five bark beetle-affected forests[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014,7(8):3440-3452. doi: 10.1109/JSTARS.2014.2346955
[37]	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
[38]	Grogan K, Pflugmacher D, Hostert P, et al.Cross-border forest disturbance and the role of natural rubber in mainland Southeast Asia using annual Landsat time series[J]. Remote Sensing of Environment, 2015,169:438-453. doi: 10.1016/j.rse.2015.03.001
[39]	Cohen W B, Yang Z Q, Kennedy R.Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync - Tools for calibration and validation[J]. Remote Sensing of Environment, 2010,114(12):2911-2924. doi: 10.1016/j.rse.2010.07.010
[40]	Ohmann J L, Gregory M J, Roberts H M, et al.Mapping change of older forest with nearest-neighbor imputation and Landsat time-series[J]. Fuel and Energy Abstracts, 2012,272(SI):13-25. doi: 10.1016/j.foreco.2011.09.021
[41]	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(SI):124-137. doi: 10.1016/j.rse.2013.05.033
[42]	Verbesselt J, Hyndman R, Newnham G, et al.Detecting trend and seasonal changes in satellite image time series[J]. Remote Sensing of Environment, 2010,114(1):106-115. doi: 10.1016/j.rse.2009.08.014
[43]	Devries B, Verbesselt J, Kooistra L, et al.Robust monitoring of small-scale forest disturbances in a tropical montane forest using Landsat time series[J]. Remote Sensing of Environment, 2015,161:107-121. doi: 10.1016/j.rse.2015.02.012
[44]	Zhu Z, Fu Y C, Woodcock C E, et al.Including land cover change in analysis of greenness trends using all available Landsat 5, 7, and 8 images: A case study from Guangzhou, China (2000-2014)[J]. Remote Sensing of Environment, 2016,185(SI):243-257. doi: 10.1016/j.rse.2016.03.036
[45]	Healey S P, Cohen W B, Yang Z Q, et al.Comparison of Tasseled Cap-based Landsat data structures for use in forest disturbance detection[J]. Remote Sensing of Environment, 2005,97(3):301-310. doi: 10.1016/j.rse.2005.05.009
[46]	Healey S P, Yang Z Q, Cohen W B, et al.Application of two regression-based methods to estimate the effects of partial harvest on forest structure using Landsat data[J]. Remote Sensing of Environment, 2006,101(1):115-126. doi: 10.1016/j.rse.2005.12.006
[47]	Huang C Q, Goward S N, Masek J G, et al.Development of time series stacks of Landsat images for reconstructing forest disturbance history[J]. International Journal of Digital Earth, 2009,2(3):195-218. doi: 10.1080/17538940902801614
[48]	Liu L Y, Tang H, Caccetta P, et al.Mapping afforestation and deforestation from 1974 to 2012 using Landsat time-series stacks in Yulin District, a key region of the Three-North Shelter region, China[J]. Environmental Monitoring and Assessment, 2013,185(12):9949-9965. doi: 10.1007/s10661-013-3304-2 pmid: 23813096
[49]	Schroeder T A, Wulder M A, Healey S P, et al.Mapping wildfire and clearcut harvest disturbances in boreal forests with Landsat time series data[J]. Remote Sensing of Environment, 2011,115(6):1421-1433. doi: 10.1016/j.rse.2011.01.022
[50]	White J C, Wulder M A, Gómez C, et al.A history of habitat dynamics: Characterizing 35 years of stand replacing disturbance[J]. Canadian Journal of Remote Sensing, 2011,37(2):234-251. doi: 10.5589/m11-034
[51]	Griffiths P, Kuemmerle T, Kennedy R E, et al.Using annual time-series of Landsat images to assess the effects of forest restitution in post-socialist Romania[J]. Remote Sensing of Environment, 2012,118:199-214. doi: 10.1016/j.rse.2011.11.006
[52]	Neigh C S R, Bolton D K, Diabate M, et al. An automated approach to map the history of forest disturbance from insect mortality and harvest with Landsat time-series data[J]. Remote Sensing, 2014,6(4):2782-2808. doi: 10.3390/rs6042782
[53]	Liang L, Hawbaker T J, Zhu Z L, et al.Forest disturbance interactions and successional pathways in the Southern Rocky Mountains[J]. Forest Ecology and Management, 2016,375:35-45. doi: 10.1016/j.foreco.2016.05.010
[54]	Lehmann E A, Wallace J F, Caccetta P A, et al.Forest cover trends from time series Landsat data for the Australian continent[J]. International Journal of Applied Earth Observation and Geoinformation, 2013,21:453-462. doi: 10.1016/j.jag.2012.06.005
[55]	Vicente-Serrano S M, Pérez-Cabello F, Lasanta T. Assessment of radiometric correction techniques in analyzing vegetation variability and change using time series of Landsat images[J]. Remote Sensing of Environment, 2008,112(10):3916-3934. doi: 10.1016/j.rse.2008.06.011
[56]	USGS. Product Guide:Landsat 4-7 Climate Data Record(CDR).Surface Reflectance [DB/OL]. 2016.
[57]	USGS. Product Guide:Landsat 8 Surface Reflectance Code(LaSRC) Product [DB/OL]. 2016.
[58]	USGS. [DB/OL]. 2017.
[59]	Bannari A, Morin D, Bonn F, et al.A review of vegetation indices[J]. Remote Sensing Reviews, 1995,13:95-120. doi: 10.1080/02757259509532298
[60]	Chen J M.Evaluation of vegetation indices and a modified simple ratio for boreal applications[J]. Canadian Journal of Remote Sensing, 1996,22(3):229-242. doi: 10.1080/07038992.1996.10855178
[61]	姚晨,黄微,李先华.地形复杂区域的典型植被指数评估[J].遥感技术与应用,2009,24(4):496-501.
	[ Yao C, Huang W, Li X H.2009. Evaluation of topographical influence on vegetation indices of rugged terrain[J]. Remote Sensing Techonology and Application, 2009,24(4):496-501. ]
[62]	朱高龙,柳艺博,居为民,等. 4种常用植被指数的地形效应评估[J].遥感学报,2013,17(1):210-234. doi: 10.11834/jrs.20131380
	[ Zhu G L, Liu Y B, Ju W M, et al.Evaluation of topographic effects on four commonly used vegetation indices[J]. Journal of Remote Sensing, 2013,17(1):210-234. ] doi: 10.11834/jrs.20131380
[63]	Galvão L S, Breunig F M, Teles T S, et al.Investigation of terrain illumination effects on vegetation indices and VI-derived phenological metrics in subtropical deciduous forests[J]. GIScience and Remote Sensing, 2016,53(3):360-381. doi: 10.1080/15481603.2015.1134140
[64]	穆悦,曹晓阳,冯益明,等.地形复杂山区常用植被指数的地形校正对比[J].地球信息科学学报,2016,18(7):951-961. doi: 10.3724/SP.J.1047.2016.00951
	[ Mu Y, Cao X Y, Feng Y M, et al.Comparison of topographic correction on commonly used vegetation indices in rugged terrain area[J]. Journal of Geo-Information Science, 2016,18(7):951-961. ] doi: 10.3724/SP.J.1047.2016.00951
[65]	Brown L, Chen J M, Leblanc S G, et al.A shortwave infrared modification to the simple ratio for LAI retrieval in boreal forests : An image and model analysis[J]. Remote Sensing of Environment, 2000,71(1):16-25. doi: 10.1016/S0034-4257(99)00035-8
[66]	Chuvieco E, Cocero D, Aguado I, et al.Improving burning efficiency estimates through satellite assessment of fuel moisture content[J]. Journal of Geophysical Research Atmospheres, 2004,109(D14):251-256. doi: 10.1029/2003JD003467
[67]	Wagtendonk J W V, Root R R, Key C H. Comparison of AVIRIS and Landsat ETM+ detection capabilities for burn severity[J]. Remote Sensing of Environment, 2004,92(3):397-408. doi: 10.1016/j.rse.2003.12.015
[68]	Myneni R B, Hall F G.The interpretation of spectral vegetation indexes[J]. IEEE Transactions on Geoscience and Remote Sensing, 1995,33(2):481-486. doi: 10.1109/36.377948
[69]	Carlson T N, Ripley D A.On the relation between NDVI, fractional vegetation cover, and leaf area index[J]. Remote Sensing of Environment, 1997,62(3):241-252. doi: 10.1016/S0034-4257(97)00104-1
[70]	Huete A, Didan K, Miura T, et al.Overview of the radiometric and biophysical performance of the MODIS vegetation indices[J]. Remote Sensing of Environment, 2002,83(1):195-213. doi: 10.1016/S0034-4257(02)00096-2
[71]	Wilson E H, Sader S A.Detection of forest harvest type using multiple dates of Landsat TM imagery[J]. Remote Sensing of Environment, 2002,80(3):385-396. doi: 10.1016/S0034-4257(01)00318-2
[72]	Howard S M, Ohlen D O, Mckinley R A, et al.Historical fire severity mapping from Landsat data[C]// Integrating Remote Sensing at the Global, Regional and Local Scale. Pecora 15/Land Satellite Information IV Conference, 2002.
[73]	Cohen W B, Goward S N.Landsat's role in ecological applications of remote sensing[J]. Bioscience, 2004,54(6): 535-545. doi: 10.1641/0006-3568(2004)054[0535:LRIEAO]2.0.CO;2
[74]	Cohen W B, Spies T A.Estimating structural attributes of Douglas-fir/western hemlock forest stands from Landsat and SPOT imagery[J]. Remote Sensing of Environment, 1992,41(1):1-17. doi: 10.1016/0034-4257(92)90056-P
[75]	Powell S L, Cohen W B, Healey S P, et al.Quantification of live aboveground forest biomass dynamics with Landsat time-series and field inventory data: A comparison of empirical modeling approaches[J]. Remote Sensing of Environment, 2010,114(5):1053-1068. doi: 10.1016/j.rse.2009.12.018
[76]	Duane M V, Cohen W B, Campbell J L, et al.Implications of alternative field-sampling designs on Landsat-based mapping of stand age and carbon stocks in Oregon forests[J]. Forest Science, 2010,56(4):405-416. doi: 10.1529/biophysj.104.054130
[77]	Powell S L, Cohen W B, Yang Z Q, et al.Quantification of impervious surface in the Snohomish Water Resources Inventory Area of Western Washington from 1972-2006[J]. Remote Sensing of Environment, 2008,112(4):1895-1908. doi: 10.1016/j.rse.2007.09.010
[78]	Masek J G, Huang C Q, Wolfe R, et al.North American forest disturbance mapped from a decadal Landsat record[J]. Remote Sensing of Environment, 2008,112(6):2914-2926. doi: 10.1016/j.rse.2008.02.010
[79]	Devries B, Decuyper M, Verbesselt J, et al.Tracking disturbance-regrowth dynamics in tropical forests using structural change detection and Landsat time series[J]. Remote Sensing of Environment, 2015,169:320-334. doi: 10.1016/j.rse.2015.08.020
[80]	Jin S M, Sader S A.Comparison of time series tasseled cap wetness and the normalized difference moisture index in detecting forest disturbances[J]. Remote Sensing of Environment, 2005,94(3):364-372. doi: 10.1016/j.rse.2004.10.012
[81]	Thomas N E, Huang C Q, Goward S N, et al.Validation of North American forest disturbance dynamics derived from Landsat time series stacks[J]. Remote Sensing of Environment, 2011,115(1):19-32. doi: 10.1016/j.rse.2010.07.009
[82]	Jin S M, Yang L M, Danielson P, et al.A comprehensive change detection method for updating the national land cover database to circa 2011[J]. Remote Sensing of Environment, 2013,132:159-175. doi: 10.1016/j.rse.2013.01.012
[83]	Cohen W B, Healey S P, Yang Z Q, et al.How similar are forest disturbance maps derived from different Landsat time series algorithms?[J]. Forests, 2017,8(4):98. doi: 10.3390/f8040098