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Author (up) Yin, H.; Pflugmacher, D.; Kennedy, R.E.; Sulla-Menashe, D.; Hostert, P. doi  openurl
  Title Mapping Annual Land Use and Land Cover Changes Using MODIS Time Series Type Journal Article
  Year 2014 Publication Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of Abbreviated Journal Yin, Pflugmacher et al. 2014 – Mapping Annual Land Use  
  Volume 7 Issue 8 Pages 3421–3427  
  Keywords Accuracy; annual land cover change mapping; annual land use change mapping; dynamic land systems; Earth; fast-growing plantations; geophysical image processing; geophysical techniques; image classification; image segmentation; Inner Mongolia; land cover; land use; land use and land use change; machine learning; Moderate Resolution Imaging Spectroradiometer; Moderate Resolution Imaging Spectroradiometer (MODIS); Modis; MODIS scale; MODIS time series; MODTrendr; MODTrendr algorithm; per-pixel land cover probabilities; probability time series; random forest (RF); random forest classification; remote sensing; Satellites; temporal segmentation algorithm; Time series analysis; time-series-based analysis; Vegetation mapping  
  Abstract Mapping land use and land cover change (LULCC) over large areas at regular time intervals is a key requisite to improve our understanding of dynamic land systems. In this study, we developed and tested an automated approach for mapping LULCCs at annual time intervals using data from the Moderate Resolution Imaging Spectroradiometer (MODIS). Our approach characterizes changes between land cover types based on annual time series of per-pixel land cover probabilities. We used the temporal segmentation algorithm MODTrendr to identify trends and changes in the probability time series that were associated with land cover/use conversions. Accuracy assessment revealed good performance of our approach (overall accuracy of 92.0%). The method detected conversions from forest to grassland with a user's accuracy of 94.0 ± 2.0% and a producer's accuracy of 95.6 ± 1.6%. Conversions between cropland and grassland were detected with a user's and a producer's accuracy of 65.8 ± 4.8% and 72.2 ± 9.2%, respectively. We here present for the first time an approach that combines probabilities derived from machine learning (random forest classification) with time-series-based analysis (MODTrendr) for land cover/use change analysis at MODIS scale.  
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  ISSN 1939-1404 ISBN Medium  
  Area Expedition Conference  
  Notes 28 Approved no  
  Call Number geomatics @ Yin2014 Serial 413  
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