Changes using Landsat Images

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

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

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

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.
DOI Link 1609
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

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.[Jiayi], 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

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, Cartography, Aerial Images, Buildings, Roads, Terrain, ATR continues in
NDVI, Normalized Difference Vegetation Index, Changes .

Last update:Apr 19, 2021 at 11:17:57