Landsat时序变化检测综述
Review on Landsat Time Series Change Detection Methods
查看参考文献82篇
|
文摘
|
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。 |
|
其他语种文摘
|
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. |
|
来源
|
地球信息科学学报
,2017,19(8):1069-1079 【核心库】
|
|
DOI
|
10.3724/SP.J.1047.2017.01069
|
|
关键词
|
Landsat影像
;
时序数据
;
变化检测
;
检测指标
|
|
地址
|
云南大学国际河流与生态安全研究院, 云南省国际河流与跨境生态安全重点实验室, 昆明, 650091
|
|
语种
|
中文 |
|
文献类型
|
综述型 |
|
ISSN
|
1560-8999 |
|
学科
|
测绘学 |
|
基金
|
国家自然科学基金项目
;
国家重点研发计划课题
;
云南省中青年学术技术带头人后备人才培育计划
;
云南大学青年英才培育计划
|
|
文献收藏号
|
CSCD:6057257
|
参考文献 共
82
共5页
|
|
1.
Vitousek P M. Human domination of Earth's ecosystems.
Science,1997,277(5325):494-499
|
CSCD被引
435
次
|
|
|
|
|
2.
Mooney H. Confronting the human dilemma.
Nature,2005,434(7033):561-562
|
CSCD被引
10
次
|
|
|
|
|
3.
Turner B L. The emergence of land change science for global environmental change and sustainability.
Proceedings of the National Academy of Sciences,2007,104(52):20666-20671
|
CSCD被引
193
次
|
|
|
|
|
4.
Reid W V. Earth system science for global sustainability:grand challenges.
Science,2010,330(6006):916-917
|
CSCD被引
44
次
|
|
|
|
|
5.
傅伯杰. 我国生态系统研究的发展趋势与优先领域.
地理研究,2010,29(3):383-396
|
CSCD被引
71
次
|
|
|
|
|
6.
徐冠华. 全球变化和人类可持续发展:挑战与对策.
科学通报,2013,58(21):2100-2106
|
CSCD被引
63
次
|
|
|
|
|
7.
Singh A. Digital change detection techniques using remotely-sensed data.
International Journal of Remote Sensing,1989,10(6):989-1003
|
CSCD被引
200
次
|
|
|
|
|
8.
Coppin P. Digital change detection methods in natural ecosystem monitoring:A review.
International Journal of Remote Sensing,2004,25(9):1565-1596
|
CSCD被引
67
次
|
|
|
|
|
9.
Lu D. Change detection techniques.
International Journal of Remote Sensing,2004,25(12):2365-2407
|
CSCD被引
129
次
|
|
|
|
|
10.
Woodcock C E. Free access to Landsat imagery.
Science,2008,320(5879):1011-1011
|
CSCD被引
32
次
|
|
|
|
|
11.
Hilker T. A new data fusion model for high spatial-and temporal-resolution mapping of forest disturbance based on Landsat and MODIS.
Remote Sensing of Environment,2009,113(8):1613-1627
|
CSCD被引
86
次
|
|
|
|
|
12.
Vogelmann J E. Monitoring forest changes in the southwestern United States using multitemporal Landsat data.
Remote Sensing of Environment,2009,113(8):1739-1748
|
CSCD被引
4
次
|
|
|
|
|
13.
Huang C Q. An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks.
Remote Sensing of Environment,2010,114(1):183-198
|
CSCD被引
60
次
|
|
|
|
|
14.
Vogelmann J E. Monitoring gradual ecosystem change using Landsat time series analyses:Case studies in selected forest and rangeland ecosystems.
Remote Sensing of Environment,2012,122(SI):92-105
|
CSCD被引
13
次
|
|
|
|
|
15.
Zhu Z. Continuous monitoring of forest disturbance using all available Landsat imagery.
Remote Sensing of Environment,2012,122(SI):75-91
|
CSCD被引
23
次
|
|
|
|
|
16.
Zhu Z. Object-based cloud and cloud shadow detection in Landsat imagery.
Remote Sensing of Environment,2012,118:83-94
|
CSCD被引
106
次
|
|
|
|
|
17.
Banskota A. Forest monitoring using Landsat time series data:A review.
Canadian Journal of Remote Sensing,2014,40(5):362-384
|
CSCD被引
7
次
|
|
|
|
|
18.
Zhu Z. Continuous change detection and classification of land cover using all available Landsat data.
Remote Sensing of Environment,2014,144:152-171
|
CSCD被引
68
次
|
|
|
|
|
19.
Vogelmann J E. Perspectives on monitoring gradual change across the continuity of Landsat sensors using time-series data.
Remote Sensing of Environment,2016,185(SI):258-270
|
CSCD被引
7
次
|
|
|
|
|
20.
Townshend J R. Global characterization and monitoring of forest cover using Landsat data:opportunities and challenges.
International Journal of Digital Earth,2012,5(5):373-397
|
CSCD被引
18
次
|
|
|
|
|
了解气象卫星更多信息，请访问