Volume XLII-2/W7
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 693-697, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W7-693-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 693-697, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W7-693-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.

  13 Sep 2017

13 Sep 2017

A LANDSAT TIME-SERIES STACKS MODEL FOR DETECTION OF CROPLAND CHANGE

J. Chen1,2, J. Chen1, and J. Zhang1 J. Chen et al.
  • 1NationalGeomatics Centre of China, Beijing 100830, China
  • 2School of Geography, Beijing Normal University, Beijing 100875, China

Keywords: LTSM change detection LTS CVA

Abstract. Global, timely, accurate and cost-effective cropland monitoring with a fine spatial resolution will dramatically improve our understanding of the effects of agriculture on greenhouse gases emissions, food safety, and human health. Time-series remote sensing imagery have been shown particularly potential to describe land cover dynamics. The traditional change detection techniques are often not capable of detecting land cover changes within time series that are severely influenced by seasonal difference, which are more likely to generate pseuso changes. Here,we introduced and tested LTSM ( Landsat time-series stacks model), an improved Continuous Change Detection and Classification (CCDC) proposed previously approach to extract spectral trajectories of land surface change using a dense Landsat time-series stacks (LTS). The method is expected to eliminate pseudo changes caused by phenology driven by seasonal patterns. The main idea of the method is that using all available Landsat 8 images within a year, LTSM consisting of two term harmonic function are estimated iteratively for each pixel in each spectral band .LTSM can defines change area by differencing the predicted and observed Landsat images. The LTSM approach was compared with change vector analysis (CVA) method. The results indicated that the LTSM method correctly detected the “true change” without overestimating the “false” one, while CVA pointed out “true change” pixels with a large number of “false changes”. The detection of change areas achieved an overall accuracy of 92.37 %, with a kappa coefficient of 0.676.