A Systematic Review of Landsat Data for Change Detection Applications: 50 Years of Monitoring the Earth

被引:131
作者
Hemati, MohammadAli [1 ]
Hasanlou, Mahdi [1 ]
Mahdianpari, Masoud [2 ,3 ]
Mohammadimanesh, Fariba [2 ]
机构
[1] Univ Tehran, Sch Surveying & Geospatial Engn, Coll Engn, Tehran 1417466191, Iran
[2] C CORE, 1 Morrissey Rd, St John, NF A1B 3X5, Canada
[3] Mem Univ Newfoundland, Dept Elect & Comp Engn, St John, NF A1C 5S7, Canada
关键词
Landsat; change detection; land cover change; land use; meta-analysis; systematic review; SENSING CHANGE DETECTION; TIME-SERIES; SURFACE REFLECTANCE; FOREST DISTURBANCE; COVER; CLASSIFICATION; RESOLUTION; RECOVERY; SCIENCE; DEFORESTATION;
D O I
10.3390/rs13152869
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
With uninterrupted space-based data collection since 1972, Landsat plays a key role in systematic monitoring of the Earth's surface, enabled by an extensive and free, radiometrically consistent, global archive of imagery. Governments and international organizations rely on Landsat time series for monitoring and deriving a systematic understanding of the dynamics of the Earth's surface at a spatial scale relevant to management, scientific inquiry, and policy development. In this study, we identify trends in Landsat-informed change detection studies by surveying 50 years of published applications, processing, and change detection methods. Specifically, a representative database was created resulting in 490 relevant journal articles derived from the Web of Science and Scopus. From these articles, we provide a review of recent developments, opportunities, and trends in Landsat change detection studies. The impact of the Landsat free and open data policy in 2008 is evident in the literature as a turning point in the number and nature of change detection studies. Based upon the search terms used and articles included, average number of Landsat images used in studies increased from 10 images before 2008 to 100,000 images in 2020. The 2008 opening of the Landsat archive resulted in a marked increase in the number of images used per study, typically providing the basis for the other trends in evidence. These key trends include an increase in automated processing, use of analysis-ready data (especially those with atmospheric correction), and use of cloud computing platforms, all over increasing large areas. The nature of change methods has evolved from representative bi-temporal pairs to time series of images capturing dynamics and trends, capable of revealing both gradual and abrupt changes. The result also revealed a greater use of nonparametric classifiers for Landsat change detection analysis. Landsat-9, to be launched in September 2021, in combination with the continued operation of Landsat-8 and integration with Sentinel-2, enhances opportunities for improved monitoring of change over increasingly larger areas with greater intra- and interannual frequency.
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页数:33
相关论文
共 105 条
[1]   Improving the Consistency of Multitemporal Land Cover Maps Using a Hidden Markov Model [J].
Abercrombie, S. Parker ;
Friedl, Mark A. .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2016, 54 (02) :703-713
[2]   Mapping abandoned agriculture with multi-temporal MODIS satellite data [J].
Alcantara, Camilo ;
Kuemmerle, Tobias ;
Prishchepov, Alexander V. ;
Radeloff, Volker C. .
REMOTE SENSING OF ENVIRONMENT, 2012, 124 :334-347
[3]  
[Anonymous], GCOS GLOBAL OBSERVIN
[4]  
[Anonymous], 2010, Cloud Computing Technology and Science (CloudCom)
[5]   Who launched what, when and why; trends in global land-cover observation capacity from civilian earth observation satellites [J].
Belward, Alan S. ;
Skoien, Jon O. .
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2015, 103 :115-128
[6]   Continental-scale land surface phenology from harmonized Landsat 8 and Sentinel-2 imagery [J].
Bolton, Douglas K. ;
Gray, Josh M. ;
Melaas, Eli K. ;
Moon, Minkyu ;
Eklundh, Lars ;
Friedl, Mark A. .
REMOTE SENSING OF ENVIRONMENT, 2020, 240
[7]   Climate, ecosystems, and planetary futures: The challenge to predict life in Earth system models [J].
Bonan, Gordon B. ;
Doney, Scott C. .
SCIENCE, 2018, 359 (6375) :533-+
[8]  
Boriah S., 2010, THESIS U MINNESOTA M
[9]   Integrating LiDAR-derived tree height and Landsat satellite reflectance to estimate forest regrowth in a tropical agricultural landscape [J].
Caughlin, T. Trevor ;
Rifai, Sami W. ;
Graves, Sarah J. ;
Asner, Gregory P. ;
Bohlman, Stephanie A. .
REMOTE SENSING IN ECOLOGY AND CONSERVATION, 2016, 2 (04) :190-203
[10]   A bootstrap method for assessing classification accuracy and confidence for agricultural land use mapping in Canada [J].
Champagne, Catherine ;
McNairn, Heather ;
Daneshfar, Bahram ;
Shang, Jiali .
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2014, 29 :44-52