Enhancing Coffee Agroforestry Systems Suitability Using Geospatial Analysis and Sentinel Satellite Data in Gedeo Zone, Ethiopia
The Gedeo zone agroforestry systems are the main source of Ethiopia’s coffee beans. However, land-use and suitability analyses are not well documented due to complex topography, heterogeneous agroforestry, and lack of information. This research aimed to map the coffee coverage and identify land suit...
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MDPI AG
2024-09-01
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| author | Wondifraw Nigussie Husam Al-Najjar Wanchang Zhang Eshetu Yirsaw Worku Nega Zhijie Zhang Bahareh Kalantar |
| author_facet | Wondifraw Nigussie Husam Al-Najjar Wanchang Zhang Eshetu Yirsaw Worku Nega Zhijie Zhang Bahareh Kalantar |
| author_sort | Wondifraw Nigussie |
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| description | The Gedeo zone agroforestry systems are the main source of Ethiopia’s coffee beans. However, land-use and suitability analyses are not well documented due to complex topography, heterogeneous agroforestry, and lack of information. This research aimed to map the coffee coverage and identify land suitability for coffee plantations using remote sensing, Geographic Information Systems (GIS), and the Analytical Hierarchy Process (AHP) in the Gedeo zone, Southern Ethiopia. Remote sensing classifiers often confuse agroforestry and plantations like coffee cover with forest cover because of their similar spectral signatures. Mapping shaded coffee in Gedeo agroforestry using optical or multispectral remote sensing is challenging. To address this, the study identified and mapped coffee coverage from Sentinel-1 data with a decibel (dB) value matched to actual coffee coverage. The actual field data were overlaid on Sentinel-1, which was used to extract the raster value. Pre-processing, classification, standardization, and reclassification of thematic layers were performed to find potential areas for coffee plantation. Hierarchy levels of the main criteria were formed based on climatological, edaphological, physiographic, and socioeconomic factors. These criteria were divided into 14 sub-criteria, reclassified based on their impact on coffee growing, with their relative weights derived using AHP. From the total study area of 1356.2 km<sup>2</sup>, the mapped coffee coverage is 583 km<sup>2</sup>. The outcome of the final computed factor weight indicated that average annual temperature and mean annual rainfall are the primary factors, followed by annual mean maximum temperature, elevation, annual mean minimum temperature, soil pH, Land Use/Land Cover (LULC), soil texture, Cation Exchange Capacity (CEC), slope, Soil Organic Matter (SOM), aspect, distance to roads, and distance to water, respectively. The identified coffee plantation potential land suitability reveals unsuitable (413 km<sup>2</sup>), sub-suitable (596.1 km<sup>2</sup>), and suitable (347.1 km<sup>2</sup>) areas. This study provides comprehensive spatial details for Ethiopian cultivators, government officials, and agricultural extension specialists to select optimal coffee farming locations, enhancing food security and economic prosperity. |
| format | Article |
| id | doaj-art-ca09532cbdee404ca31c152af238aed3 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-09-01 |
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| spelling | doaj-art-ca09532cbdee404ca31c152af238aed32025-08-20T01:47:38ZengMDPI AGSensors1424-82202024-09-012419628710.3390/s24196287Enhancing Coffee Agroforestry Systems Suitability Using Geospatial Analysis and Sentinel Satellite Data in Gedeo Zone, EthiopiaWondifraw Nigussie0Husam Al-Najjar1Wanchang Zhang2Eshetu Yirsaw3Worku Nega4Zhijie Zhang5Bahareh Kalantar6Department of Land Administration and Surveying, Injibara University, Injibara P.O. Box 40, EthiopiaSchool of Computer Science, Faculty of Engineering and IT, University of Technology Sydney, Sydney, NSW 2007, AustraliaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaDepartment of Natural Resource Management, Dilla University, Dilla P.O. Box 419, EthiopiaInstitute of Land Administration, Debre Markos University, Debre Markos P.O. Box 269, EthiopiaSchool of Geography, Development and Environment, University of Arizona, Tucson, AZ 85721, USARIKEN Center for Advanced Intelligence Project, Disaster Resilience Science Team, Tokyo 103-0027, JapanThe Gedeo zone agroforestry systems are the main source of Ethiopia’s coffee beans. However, land-use and suitability analyses are not well documented due to complex topography, heterogeneous agroforestry, and lack of information. This research aimed to map the coffee coverage and identify land suitability for coffee plantations using remote sensing, Geographic Information Systems (GIS), and the Analytical Hierarchy Process (AHP) in the Gedeo zone, Southern Ethiopia. Remote sensing classifiers often confuse agroforestry and plantations like coffee cover with forest cover because of their similar spectral signatures. Mapping shaded coffee in Gedeo agroforestry using optical or multispectral remote sensing is challenging. To address this, the study identified and mapped coffee coverage from Sentinel-1 data with a decibel (dB) value matched to actual coffee coverage. The actual field data were overlaid on Sentinel-1, which was used to extract the raster value. Pre-processing, classification, standardization, and reclassification of thematic layers were performed to find potential areas for coffee plantation. Hierarchy levels of the main criteria were formed based on climatological, edaphological, physiographic, and socioeconomic factors. These criteria were divided into 14 sub-criteria, reclassified based on their impact on coffee growing, with their relative weights derived using AHP. From the total study area of 1356.2 km<sup>2</sup>, the mapped coffee coverage is 583 km<sup>2</sup>. The outcome of the final computed factor weight indicated that average annual temperature and mean annual rainfall are the primary factors, followed by annual mean maximum temperature, elevation, annual mean minimum temperature, soil pH, Land Use/Land Cover (LULC), soil texture, Cation Exchange Capacity (CEC), slope, Soil Organic Matter (SOM), aspect, distance to roads, and distance to water, respectively. The identified coffee plantation potential land suitability reveals unsuitable (413 km<sup>2</sup>), sub-suitable (596.1 km<sup>2</sup>), and suitable (347.1 km<sup>2</sup>) areas. This study provides comprehensive spatial details for Ethiopian cultivators, government officials, and agricultural extension specialists to select optimal coffee farming locations, enhancing food security and economic prosperity.https://www.mdpi.com/1424-8220/24/19/6287coffee plantationsentinelland suitabilityGISAHPGedeo |
| spellingShingle | Wondifraw Nigussie Husam Al-Najjar Wanchang Zhang Eshetu Yirsaw Worku Nega Zhijie Zhang Bahareh Kalantar Enhancing Coffee Agroforestry Systems Suitability Using Geospatial Analysis and Sentinel Satellite Data in Gedeo Zone, Ethiopia Sensors coffee plantation sentinel land suitability GIS AHP Gedeo |
| title | Enhancing Coffee Agroforestry Systems Suitability Using Geospatial Analysis and Sentinel Satellite Data in Gedeo Zone, Ethiopia |
| title_full | Enhancing Coffee Agroforestry Systems Suitability Using Geospatial Analysis and Sentinel Satellite Data in Gedeo Zone, Ethiopia |
| title_fullStr | Enhancing Coffee Agroforestry Systems Suitability Using Geospatial Analysis and Sentinel Satellite Data in Gedeo Zone, Ethiopia |
| title_full_unstemmed | Enhancing Coffee Agroforestry Systems Suitability Using Geospatial Analysis and Sentinel Satellite Data in Gedeo Zone, Ethiopia |
| title_short | Enhancing Coffee Agroforestry Systems Suitability Using Geospatial Analysis and Sentinel Satellite Data in Gedeo Zone, Ethiopia |
| title_sort | enhancing coffee agroforestry systems suitability using geospatial analysis and sentinel satellite data in gedeo zone ethiopia |
| topic | coffee plantation sentinel land suitability GIS AHP Gedeo |
| url | https://www.mdpi.com/1424-8220/24/19/6287 |
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