Nigussie Wondifraw, Al-Najjar Husam, Zhang Wanchang, Yirsaw Eshetu, Nega Worku, Zhang Zhijie, Kalantar Bahareh
Department of Land Administration and Surveying, Injibara University, Injibara P.O. Box 40, Ethiopia.
Department of Land Administration and Surveying, Dilla University, Dilla P.O. Box 419, Ethiopia.
Sensors (Basel). 2024 Sep 28;24(19):6287. doi: 10.3390/s24196287.
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, the mapped coffee coverage is 583 km. 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), sub-suitable (596.1 km), and suitable (347.1 km) 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.
格代奥地区的农林业系统是埃塞俄比亚咖啡豆的主要来源。然而，由于地形复杂、农林业异质性以及信息匮乏，土地利用和适宜性分析的记录并不完善。本研究旨在利用遥感、地理信息系统（GIS）和层次分析法（AHP）绘制埃塞俄比亚南部格代奥地区的咖啡种植面积图，并确定咖啡种植园的土地适宜性。由于农林业和咖啡种植园与森林覆盖具有相似的光谱特征，遥感分类器常常将它们混淆。利用光学或多光谱遥感绘制格代奥农林业中的遮荫咖啡具有挑战性。为解决这一问题，本研究从哨兵-1数据中识别并绘制了与实际咖啡种植面积相匹配的分贝（dB）值的咖啡种植面积。将实际田间数据叠加在哨兵-1数据上，用于提取栅格值。对专题图层进行预处理、分类、标准化和重新分类，以找到咖啡种植的潜在区域。根据气候、土壤、地形和社会经济因素形成主要标准的层次级别。这些标准分为14个子标准，根据它们对咖啡种植的影响进行重新分类，并使用层次分析法得出其相对权重。在1356.2平方公里的总研究区域中，绘制的咖啡种植面积为583平方公里。最终计算得出的因子权重结果表明，年均温度和年平均降雨量是主要因素，其次分别是年平均最高温度、海拔、年平均最低温度、土壤pH值、土地利用/土地覆盖（LULC）、土壤质地、阳离子交换容量（CEC）、坡度、土壤有机质（SOM）、坡向、距道路距离和距水源距离。确定的咖啡种植园潜在土地适宜性显示出不适宜（413平方公里）、次适宜（596.1平方公里）和适宜（347.1平方公里）的区域。本研究为埃塞俄比亚的种植者、政府官员和农业推广专家提供了全面的空间细节，以便他们选择最佳的咖啡种植地点，加强粮食安全和经济繁荣。
