International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume XLII-4/W19
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W19, 9–15, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W19-9-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W19, 9–15, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W19-9-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  23 Dec 2019

23 Dec 2019

AUTOMATED PREDICTION SYSTEM FOR VEGETATION COVER BASED ON MODIS-NDVI SATELLITE DATA AND NEURAL NETWORKS

S. K. M. Abujayyab and İ. R. Karaş S. K. M. Abujayyab and İ. R. Karaş
  • Dept. of Computer Engineering, Karabuk University, Demir Celik Campus, 78050 Karabuk, Turkey

Keywords: Automated Systems, Prediction, NDVI, MODIS, Neural Networks, Early Warning

Abstract. Around the world, vegetation cover functioning as shelter to wildlife, clean water, food security as well as treat large part of air pollution problem. Accurate predictive data early warn and provide knowledge for decision makers to reduce the effects of changes in vegetation cover. In this paper, an automated prediction system was developed to forecast vegetation cover. Prediction system based on moderate satellite data spatial resolution and global coverage data. The tools of system automate processing Moderate Resolution Imaging Spectroradiometer (MODIS) images and training neural networks (NN) model based on 60,000 observations to forecast future density of Normalized Difference Vegetation Index (NDVI). Zonguldak data, located in north of Turkey as dense vegetation cover area utilized as case study for system application. This system significantly facilitates predictive process for users than previous long and complex models.