AUTOMATED PREDICTION SYSTEM for VEGETATION COVER BASED on MODIS-NDVI SATELLITE DATA and NEURAL NETWORKS
Yazarlar (2)
Dr. Öğr. Üyesi Sohaib K.M. Abujayyab Karabük Üniversitesi, Türkiye
Prof. Dr. İsmail Rakıp KARAŞ Karabük Üniversitesi, Türkiye
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Tebliğ/Bildiri
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Bildiri Dili | İngilizce |
| Bildiri Alt Türü | |||
| Bildiri Niteliği | |||
| DOI Numarası | 10.5194/ISPRS-ARCHIVES-XLII-4-W19-9-2019 | ||
| Kongre Adı | International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences ISPRS Archives | ||
| Kongre Tarihi | / | ||
| Basıldığı Ülke | Basıldığı Şehir | ||
| Bildiri Linki | https://publons.com/wos-op/publon/80612579/ | ||
| UAK Araştırma Alanları |
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| Özet |
| 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. |
| Anahtar Kelimeler |
| Automated Systems | Early Warning. | MODIS | NDVI | Neural Networks | Prediction |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Scopus | 11 |