Analyzing the Effect of High Resolution Satellite Images for Drought Prediction
| dc.contributor.advisor | Atnafu, Solomon(PhD) | |
| dc.contributor.author | Getachew, Yonatan | |
| dc.date.accessioned | 2018-06-26T12:51:00Z | |
| dc.date.accessioned | 2023-11-29T04:05:53Z | |
| dc.date.available | 2018-06-26T12:51:00Z | |
| dc.date.available | 2023-11-29T04:05:53Z | |
| dc.date.issued | 2013-04-16 | |
| dc.description.abstract | Drought is a condition of moisture deficit sufficient to have an adverse effect on vegetation. When such event occurs it leaves a devastating effect on the community. This is mainly due to the lack of prediction and mitigation efforts. Thus predicting this event should be carefully considered. Although, there are many attempts to predict drought using satellite images, none have come across in using a higher resolution satellite images. These type of images have a significant role in developing a better model and a more reliable drought prediction system. This study aims in analyzing the effect of using higher spatial resolution satellite images in drought prediction systems. The first step we followed towards this objective is to consider the for drought indicating attributes that were identified in earlier research for the study area (Ethiopia). Out of this attributes, the satellite image (showing the actual condition on the ground) shows a higher coloration to drought incidents. The second step is to process the acquired data and produce the appropriate model. In this study, producing the model is typically generating models. The models, if satisfied, will help in classifying a condition as dry, wet or normal. Finally, we have produced a system that uses the model developed to predict drought. The system produces a map showing the actual conditions on the ground. After going through all the steps specified, we showed that higher resolution satellite images help in producing a more accurate model as compared to the previous studies on the same study area. Key words: Drought Prediction, Satellite Image for Drought Prediction, Normalized Difference Vegetation Index (NDVI), Standard Seasonal Greenness (SSG). | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/123456789/3724 | |
| dc.language.iso | en | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Drought Prediction | en_US |
| dc.subject | Satellite Image for Drought Prediction | en_US |
| dc.subject | Normalized Difference Vegetation Index (NDVI) | en_US |
| dc.subject | Standard Seasonal Greenness (SSG) | en_US |
| dc.title | Analyzing the Effect of High Resolution Satellite Images for Drought Prediction | en_US |
| dc.type | Thesis | en_US |