Transitioning from change detection to monitoring with remote sensing: A paradigm shift

被引:133
|
作者
Woodcock, Curtis E. [1 ]
Loveland, Thomas R. [2 ]
Herold, Martin [3 ]
Bauer, Marvin E. [4 ]
机构
[1] Boston Univ, Dept Earth & Environm, Boston, MA 02215 USA
[2] US Geol Survey, Earth Resources Observat & Sci EROS Ctr, Sioux Falls, SD 57198 USA
[3] Wageningen Univ & Res, Dept Environm Sci, Wageningen, Netherlands
[4] Univ Minnesota, Dept Forest Resources, St Paul, MN 55108 USA
关键词
Change detection and monitoring; Time series analysis; Future trends; Paradigm shift; LAND; MODIS; AREA;
D O I
10.1016/j.rse.2019.111558
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The use of time series analysis with moderate resolution satellite imagery is increasingly common, particularly since the advent of freely available Landsat data. Dense time series analysis is providing new information on the timing of landscape changes, as well as improving the quality and accuracy of information being derived from remote sensing. Perhaps most importantly, time series analysis is expanding the kinds of land surface change that can be monitored using remote sensing. In particular, more subtle changes in ecosystem health and condition and related to land use dynamics are being monitored. The result is a paradigm shift away from change detection, typically using two points in time, to monitoring, or an attempt to track change continuously in time. This trend holds many benefits, including the promise of near real-time monitoring. Anticipated future trends include more use of multiple sensors in monitoring activities, increased focus on the temporal accuracy of results, applications over larger areas and operational usage of time series analysis.
引用
收藏
页数:5
相关论文
共 50 条
  • [41] Change detection of multisource remote sensing images: a review
    Jiang, Wandong
    Sun, Yuli
    Lei, Lin
    Kuang, Gangyao
    Ji, Kefeng
    [J]. INTERNATIONAL JOURNAL OF DIGITAL EARTH, 2024, 17 (01)
  • [42] Kernel Anomalous Change Detection for Remote Sensing Imagery
    Padron-Hidalgo, Jose A.
    Laparra, Valero
    Longbotham, Nathan
    Camps-Valls, Gustau
    [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2019, 57 (10): : 7743 - 7755
  • [43] Deep learning for change detection in remote sensing: a review
    Bai, Ting
    Wang, Le
    Yin, Dameng
    Sun, Kaimin
    Chen, Yepei
    Li, Wenzhuo
    Li, Deren
    [J]. GEO-SPATIAL INFORMATION SCIENCE, 2023, 26 (03) : 262 - 288
  • [44] Subpixel anomalous change detection in remote sensing imagery
    Theiler, James
    [J]. 2008 IEEE SOUTHWEST SYMPOSIUM ON IMAGE ANALYSIS & INTERPRETATION, 2008, : 165 - 168
  • [45] Research on CA Differencing for Remote Sensing Change Detection
    He, Fenqin
    Yin, Jianzhong
    [J]. PROCEEDINGS OF THE 2009 2ND INTERNATIONAL CONGRESS ON IMAGE AND SIGNAL PROCESSING, VOLS 1-9, 2009, : 2984 - 2987
  • [46] Multistage Interaction Network for Remote Sensing Change Detection
    Zhou, Meng
    Qian, Weixian
    Ren, Kan
    [J]. REMOTE SENSING, 2024, 16 (06)
  • [47] Change detection techniques for remote sensing applications: a survey
    Asokan, Anju
    Anitha, J.
    [J]. EARTH SCIENCE INFORMATICS, 2019, 12 (02) : 143 - 160
  • [48] Unsupervised change detection methods for remote sensing images
    Melgani, F
    Moser, G
    Serpico, SB
    [J]. IMAGE AND SIGNAL PROCESSING FOR REMOTE SENSING VII, 2002, 4541 : 211 - 222
  • [49] Forest Change Detection Using Remote Sensing Data
    Denisova, Anna
    Egorova, Anna
    Sergeyev, Vladislav
    [J]. 2020 VI INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND NANOTECHNOLOGY (IEEE ITNT-2020), 2020,
  • [50] Remote Sensing Image Change Saliency Detection Technology
    Yang, Shuai
    Zhao, Xi'an
    [J]. 3RD ANNUAL INTERNATIONAL CONFERENCE ON INFORMATION SYSTEM AND ARTIFICIAL INTELLIGENCE (ISAI2018), 2018, 1069