AUTOMATED PREDICTION SYSTEM FOR VEGETATION COVER BASED ON MODIS-NDVI SATELLITE DATA AND NEURAL NETWORKS
| Autor: | Ismail Rakip Karas, Sohaib K. M. Abujayyab |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
lcsh:Applied optics. Photonics
010504 meteorology & atmospheric sciences Artificial neural network Warning system lcsh:T 0211 other engineering and technologies lcsh:TA1501-1820 Coverage data 02 engineering and technology Prediction system lcsh:Technology 01 natural sciences Normalized Difference Vegetation Index Vegetation cover lcsh:TA1-2040 Environmental science Moderate-resolution imaging spectroradiometer lcsh:Engineering (General). Civil engineering (General) Image resolution 021101 geological & geomatics engineering 0105 earth and related environmental sciences Remote sensing |
| Zdroj: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLII-4-W19, Pp 9-15 (2019) |
| ISSN: | 2194-9034 |
| DOI: | 10.5194/isprs-archives-xlii-4-w19-9-2019 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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