Assessment of Desert Locust Infestation by Using GIS and Remote Sensing Technology: A Case Study in Dire Dawa and the Northern Part of Somali Region, Ethiopia
| dc.contributor.advisor | Tibebu, Kassawmar (PhD) | |
| dc.contributor.author | Fentahun, Abebaw | |
| dc.date.accessioned | 2022-06-28T06:27:18Z | |
| dc.date.accessioned | 2023-11-28T13:46:18Z | |
| dc.date.available | 2022-06-28T06:27:18Z | |
| dc.date.available | 2023-11-28T13:46:18Z | |
| dc.date.issued | 2022-06 | |
| dc.description.abstract | Desert locust (Schistocerca gregaria, Forskal) is the most serious insect pest devastating and damaging agricultural products of cropland areas and pastureland during the invasion of locust. The main cause of desert locust outbreaks is the trigger of rainfall occurring and the growth of green vegetation expands the area and the population of locust density leads to an upsurge and possibly develop plagues. The aim of the study is to assess desert locust infestation using GIS and remote sensing technology. The open-source satellite data of EVI from MODIS with 250 m, sentinel-1 SAR data from Copernicus 10 m, DEM from SRTM 1 arc seconds, precipitation data from GPM 0.1 degree spatial resolution, and ground survey data were applied to analyze the effect of desert locust environmental variables of rainfall, vegetation, and digital elevation model (DEM), damaged vegetation assessment of desert locust and determine whether kriging interpolation correctly predict the unobserved area using the surveyed site. The methodology of the study was Preprocessing, reclassification, zonal attribute analysis, and geostatistical analysis of kriging, and (IDW) method of interpolation was performed. The distribution of locusts in September and October 2019 and September 2020 occurred in the Northern part of the study with low vegetation levels and low rainfall amount. However, in November 2019, October, and November 2020 desert locust infestations occupied and migrated to the southern part of the study area with high vegetation and rainfall. The lower mean pixel reflectance value of EVI data produced in September 2019 is 0.11 and a higher mean pixel value reflectance of 0.23 was produced in October 2020 damaged vegetation of desert locust infestation. Whereas the sentinel-1 SAR data value of lower mean pixel backscatter value of damaged vegetation (vertical-horizontal (VH -20.86 dB) and (vertical-vertical (VV -14.25 dB) produced October 2019) and higher mean pixel backscatter value (VH -17.78 dB and (VV -11.48 dB produced in September 2020). the kriging interpolation was applied to predict un-surveyed areas by the survey team using a surveyed site of spherical modeling. the regression value between measured and predicted of 2019 square r = 0.24 with p-value = 0.0003 and 2020 square r = 0.0084 and p-value = 0.26. The result indicates kriging interpolation need randomly distributed and accurately measured data. | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/12345678/32162 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Desert locust | en_US |
| dc.subject | Damage assessment | en_US |
| dc.subject | Geo-statistical | en_US |
| dc.subject | GIS | en_US |
| dc.subject | MODIS EVI | en_US |
| dc.subject | sentinel-1 SAR data | en_US |
| dc.subject | RS | en_US |
| dc.title | Assessment of Desert Locust Infestation by Using GIS and Remote Sensing Technology: A Case Study in Dire Dawa and the Northern Part of Somali Region, Ethiopia | en_US |
| dc.type | Thesis | en_US |