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.DOI:10.3724/SP.J.1047.2017.01069

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.DOI:10.3724/SP.J.1047.2017.01069

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

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