Changes using Landsat Images

Chapter Contents (Back)
Change Detection. Land Cover. Temporal Analysis. Remote Sensing. Landsat.

Eghbali, H.J.,
K-S Test for Detecting Changes from Landsat Imagery Data,
SMC(9), No. 1, 1979, pp. 17-23. BibRef 7900

Song, C.H.[Cong-He], Woodcock, C.E.[Curtis E.], Seto, K.C.[Karen C.], Lenney, M.P.[Mary Pax], Macomber, S.A.[Scott A.],
Classification and Change Detection Using Landsat TM Data. When and How to Correct Atmospheric Effects?,
RSE(75), No. 2, 2001, pp. 230-244. 0102

Rogan, J.[John], Miller, J.[Jennifer], Stow, D.[Doug], Franklin, J.[Janet], Levien, L.[Lisa], Fischer, C.[Chris],
Land-Cover Change Monitoring with Classification Trees Using Landsat TM and Ancillary Data,
PhEngRS(69), No. 7, July 2003, pp. 793-804.
WWW Link. 0307
Overall accuracies of the land-cover change maps ranged between 72 percent and 92 percent, with ancillary variables playing an important discriminatory role in the most detailed level of land-cover change. BibRef

Matejicek, L., Kopackova, V.,
Changes in Croplands as a Result of Large Scale Mining and the Associated Impact on Food Security Studied Using Time-Series Landsat Images,
RS(2), No. 6, June 2010, pp. 1463-1480.
DOI Link 1203

Mitchell, J.J.[Jessica J.], Shrestha, R.[Rupesh], Moore-Ellison, C.A.[Carol A.], Glenn, N.F.[Nancy F.],
Single and Multi-Date Landsat Classifications of Basalt to Support Soil Survey Efforts,
RS(5), No. 10, 2013, pp. 4857-4876.
DOI Link 1311

Brooks, E.B., Wynne, R.H., Thomas, V.A., Blinn, C.E., Coulston, J.W.,
On-the-Fly Massively Multitemporal Change Detection Using Statistical Quality Control Charts and Landsat Data,
GeoRS(52), No. 6, June 2014, pp. 3316-3332.
Control charts BibRef

Li, Q.T.[Qing-Ting], Wang, C.Z.[Cui-Zhen], Zhang, B.[Bing], Lu, L.L.[Lin-Lin],
Object-Based Crop Classification with Landsat-MODIS Enhanced Time-Series Data,
RS(7), No. 12, 2015, pp. 15820.
DOI Link 1601

Mandanici, E.[Emanuele], Bitelli, G.[Gabriele],
Multi-Image and Multi-Sensor Change Detection for Long-Term Monitoring of Arid Environments With Landsat Series,
RS(7), No. 10, 2015, pp. 14019.
DOI Link 1511

Wang, C.Z.[Cui-Zhen], Fan, Q.[Qian], Li, Q.T.[Qing-Ting], Soo Hoo, W.M.[William M.], Lu, L.L.[Lin-Lin],
Energy crop mapping with enhanced TM/MODIS time series in the BCAP agricultural lands,
PandRS(124), No. 1, 2017, pp. 133-143.
Elsevier DOI 1702

Schmidt, M.[Michael], Pringle, M.[Matthew], Devadas, R.[Rakhesh], Denham, R.[Robert], Tindall, D.[Dan],
A Framework for Large-Area Mapping of Past and Present Cropping Activity Using Seasonal Landsat Images and Time Series Metrics,
RS(8), No. 4, 2016, pp. 312.
DOI Link 1604

Dannenberg, M.P.[Matthew P.], Hakkenberg, C.R.[Christopher R.], Song, C.H.[Cong-He],
Consistent Classification of Landsat Time Series with an Improved Automatic Adaptive Signature Generalization Algorithm,
RS(8), No. 8, 2016, pp. 691.
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Classification at frequent intervals. BibRef

Shahtahmassebi, A.R.[Amir Reza], Lin, Y.[Yue], Lin, L.[Lin], Atkinson, P.M.[Peter M.], Moore, N.[Nathan], Wang, K.[Ke], He, S.[Shan], Huang, L.Y.[Ling-Yan], Wu, J.[Jiexia], Shen, Z.Q.[Zhang-Quan], Gan, M.[Muye], Zheng, X.Y.[Xin-Yu], Su, Y.[Yue], Teng, H.F.[Hong-Fen], Li, X.Y.[Xiao-Yan], Deng, J.S.[Jin-Song], Sun, Y.Y.[Yuan-Yuan], Zhao, M.Z.[Meng-Zhu],
Reconstructing Historical Land Cover Type and Complexity by Synergistic Use of Landsat Multispectral Scanner and CORONA,
RS(9), No. 7, 2017, pp. xx-yy.
DOI Link 1708

Zhu, Z.[Zhe],
Change Detection Using Landsat Time Series: A review of frequencies, preprocessing, algorithms, and applications,
PandRS(130), No. 1, 2017, pp. 370-384.
Elsevier DOI 1708
Review BibRef

Diek, S.[Sanne], Fornallaz, F.[Fabio], Schaepman, M.E.[Michael E.], de Jong, R.[Rogier],
Barest Pixel Composite for Agricultural Areas Using Landsat Time Series,
RS(9), No. 12, 2017, pp. xx-yy.
DOI Link 1802

Gupta, N.[Neha], Pillai, G.V.[Gargi V.], Ari, S.[Samit],
Change detection in Landsat images based on local neighbourhood information,
IET-IPR(12), No. 11, November 2018, pp. 2051-2058.
DOI Link 1810

Karakizi, C.[Christina], Karantzalos, K.[Konstantinos], Vakalopoulou, M.[Maria], Antoniou, G.[Georgia],
Detailed Land Cover Mapping from Multitemporal Landsat-8 Data of Different Cloud Cover,
RS(10), No. 8, 2018, pp. xx-yy.
DOI Link 1809

Song, M.[Mi], Zhong, Y.F.[Yan-Fei], Ma, A.L.[Ai-Long],
Change Detection Based on Multi-Feature Clustering Using Differential Evolution for Landsat Imagery,
RS(10), No. 10, 2018, pp. xx-yy.
DOI Link 1811

Xie, S.[Shuai], Liu, L.Y.[Liang-Yun], Zhang, X.[Xiao], Yang, J.N.[Jiang-Ning], Chen, X.D.[Xi-Dong], Gao, Y.[Yuan],
Automatic Land-Cover Mapping using Landsat Time-Series Data Based on Google Earth Engine,
RS(11), No. 24, 2019, pp. xx-yy.
DOI Link 1912

Martín-Ortega, P.[Pablo], García-Montero, L.G.[Luis Gonzaga], Sibelet, N.[Nicole],
Temporal Patterns in Illumination Conditions and Its Effect on Vegetation Indices Using Landsat on Google Earth Engine,
RS(12), No. 2, 2020, pp. xx-yy.
DOI Link 2001

Li, J.Y.[Jia-Yi], Huang, X.[Xin], Chang, X.Y.[Xiao-Yu],
A label-noise robust active learning sample collection method for multi-temporal urban land-cover classification and change analysis,
PandRS(163), 2020, pp. 1-17.
Elsevier DOI 2005
Machine learning, Multi-temporal change detection, Landsat satellite imagery, Automatic sample collection BibRef

Bright, B.C.[Benjamin C.], Hudak, A.T.[Andrew T.], Meddens, A.J.H.[Arjan J.H.], Egan, J.M.[Joel M.], Jorgensen, C.L.[Carl L.],
Mapping Multiple Insect Outbreaks across Large Regions Annually Using Landsat Time Series Data,
RS(12), No. 10, 2020, pp. xx-yy.
DOI Link 2006

Meroni, M., Schucknecht, A., Fasbender, D., Rembold, F., Fava, F., Mauclaire, M., Goffner, D., di Lucchio, L.M., Leonardi, U.,
Remote sensing monitoring of land restoration interventions in semi-arid environments using a before-after control-impact statistical design,
statistical analysis, vegetation mapping, Great Green Wall, Landsat mission, Moderate Resolution Imaging Spectroradiometer, restoration interventions BibRef

Sun, J.[Jing], Ongsomwang, S.[Suwit],
Multitemporal Land Use and Land Cover Classification from Time-Series Landsat Datasets Using Harmonic Analysis with a Minimum Spectral Distance Algorithm,
IJGI(9), No. 2, 2020, pp. xx-yy.
DOI Link 2003

Hemati, M.[Mohammad_Ali], Hasanlou, M.[Mahdi], Mahdianpari, M.[Masoud], Mohammadimanesh, F.[Fariba],
A Systematic Review of Landsat Data for Change Detection Applications: 50 Years of Monitoring the Earth,
RS(13), No. 15, 2021, pp. xx-yy.
DOI Link 2108
Survey, Landsat Change Detection. BibRef

Aghababaei, M.[Masoumeh], Ebrahimi, A.[Ataollah], Naghipour, A.A.[Ali Asghar], Asadi, E.[Esmaeil], Verrelst, J.[Jochem],
Vegetation Types Mapping Using Multi-Temporal Landsat Images in the Google Earth Engine Platform,
RS(13), No. 22, 2021, pp. xx-yy.
DOI Link 2112

Zhao, F.[Fen], Xia, L.[Lang], Kylling, A.[Arve], Shang, H.[Hua], Yang, P.[Peng],
Mapping global flying aircraft activities using Landsat 8 and cloud computing,
PandRS(184), 2022, pp. 19-30.
Elsevier DOI 2202
Flying aircraft detection, Landsat 8, 1.38 µm, Cloud computing, Global aviation, COVID-19 BibRef

Guo, Y.T.[Yan-Tao], Long, T.F.[Teng-Fei], Jiao, W.[Weili], Zhang, X.M.[Xiao-Mei], He, G.J.[Guo-Jin], Wang, W.[Wei], Peng, Y.[Yan], Xiao, H.[Han],
Siamese Detail Difference and Self-Inverse Network for Forest Cover Change Extraction Based on Landsat 8 OLI Satellite Images,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202

Zhang, Y.Z.[Yu-Zhen], Liu, J.D.[Jin-Dong], Liang, S.L.[Shun-Lin], Li, M.[Manyao],
A New Spatial-Temporal Depthwise Separable Convolutional Fusion Network for Generating Landsat 8-Day Surface Reflectance Time Series over Forest Regions,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205

Graesser, J.[Jordan], Stanimirova, R.[Radost], Tarrio, K.[Katelyn], Copati, E.J.[Esteban J.], Volante, J.N.[José N.], Verón, S.R.[Santiago R.], Banchero, S.[Santiago], Elena, H.[Hernan], de Abelleyra, D.[Diego], Friedl, M.A.[Mark A.],
Temporally-Consistent Annual Land Cover from Landsat Time Series in the Southern Cone of South America,
RS(14), No. 16, 2022, pp. xx-yy.
DOI Link 2208

Li, J.Z.[Jian-Zhou], Ma, J.J.[Jin-Ji], Ye, X.J.[Xiao-Jiao],
A Batch Pixel-Based Algorithm to Composite Landsat Time Series Images,
RS(14), No. 17, 2022, pp. xx-yy.
DOI Link 2209

Sun, X.Y.[Xiao-Yu], Li, G.Y.[Gui-Ying], Wu, Q.Q.[Qin-Quan], Li, D.Q.[Deng-Qiu], Lu, D.S.[Deng-Sheng],
Examining the Effects of Soil and Water Conservation Measures on Patterns and Magnitudes of Vegetation Cover Change in a Subtropical Region Using Time Series Landsat Imagery,
RS(16), No. 4, 2024, pp. 714.
DOI Link 2402

Devadas, R., Denham, R.J., Pringle, M.,
Support Vector Machine Classification Of Object-based Data For Crop Mapping, Using Multi-temporal Landsat Imagery,
DOI Link 1209

Rasi, R.[Rastislav], Kissiyar, O.[Ouns], Vollmar, M.[Michael],
Land cover change detection thresholds for Landsat data samples,

Chapter on Remote Sensing General Issue, Land Use, Land Cover continues in
NDVI, Normalized Difference Vegetation Index, Changes .

Last update:Jul 13, 2024 at 15:27:21