“…2 ) Fig. 2 The flowchart for the entire procedure used for the LULC change assessment Adopted from [ 41 ]. …”
Section: Focus Group Discussion and A Household Surveymentioning
confidence: 99%
“…The study area's general land usage is arranged in circular patterns surrounding homesteads, with Ensete occupying the innermost areas that encircle the farmhouse. However, south-central Ethiopia is renowned for its favorable agroecological environments for human habitation, the natural forest cover of this research region has long been degraded [ 41 ].…”
Section: Materials and Investigation Approachesmentioning
“…2 ) Fig. 2 The flowchart for the entire procedure used for the LULC change assessment Adopted from [ 41 ]. …”
Section: Focus Group Discussion and A Household Surveymentioning
confidence: 99%
“…The study area's general land usage is arranged in circular patterns surrounding homesteads, with Ensete occupying the innermost areas that encircle the farmhouse. However, south-central Ethiopia is renowned for its favorable agroecological environments for human habitation, the natural forest cover of this research region has long been degraded [ 41 ].…”
Section: Materials and Investigation Approachesmentioning
“…We gathered data from KIIs and focus groups about the various factors that in uence changes as well as local residents' opinions of the LULC changes adopted by references (Mariye et al, 2022b). According to the summary results of the KIIs, eld observations, preference ratings, and FGDs, LULC changes in the Satame and Legabora watersheds were found to be primarily caused by proximate and underlying drivers (Munthali et al, 2019).…”
Section: Lulc Change and Their Drivers In Satame And Legabora Watershedmentioning
Identifying the causes and drivers of land change is one of the main objectives of the land use and cover change investigation. Land use and cover change data are crucial for managing water resources and land preservation. This study examined the trends, driving factors, and implications of land use and land cover dynamics in contrasting agro-ecologies to provide empirical evidence about increases and losses in land use classes. Landsat images of MSS (1976), TM (1991 and 2001), ETM+ (2011), and OLI (2022) were used to study LULC dynamics. To identify the potential driving forces and impacts, field observations, discussions with key informants, analysis of land use policy, and focus group discussions (FGDs) were undertaken. The analysis of remote sensing data revealed that there was an increasing tendency for croplands, settlements, and forest lands at the expense of grasslands and shrublands. In Satame, shrub land and grassland decreased annually by –128.7 and –139.9 ha per year, while grassland and shrub cover reduced annually by –248.2 and –144.5 ha per year in Legabora, respectively. The Local communities perceived agricultural expansion, population growth, settlement expansion, and poverty as the main drivers of LULC changes, while deforestation, land degradation, low production and loss of biodiversity were the major consequences. From this study, it can be inferred that LULC has undergone significant changes over the past 45 years, with both positive and negative impacts. Hence, this study provides a foundation for land management policies and strategies in this watershed and other watersheds facing similar problems.
“…The primary objective of the change detection technique is to identify alterations in two or more images of identical sites captured at different periods [50]. Consequently, a second image was required to assess and detect changes.…”
Land use/land cover (LULC) mapping and change detection are fundamental aspects of remote sensing data application. Therefore, selecting an appropriate classifier approach is crucial for accurate classification and change assessment. In the first part of this study, the performance of machine learning classification algorithms was compared using Landsat 9 image (2023) of the Manouba government (Tunisia). Three different classification methods were applied: Maximum Likelihood Classification (MLC), Support Vector Machine (SVM), and Random Trees (RT). The classification aimed to identify five land use classes: urban area, vegetation, bare area, water and forest. A qualitative assessment was conducted using Overall Accuracy (OA) and the Kappa coefficient (K), derived from a confusion matrix. The results of the land cover classification demonstrated a high level of accuracy. The SVM method exhibited the best performance, with an overall accuracy of 93% and a kappa accuracy of 0.9. The ML method is the second-best classifier with an overall accuracy of 92% and a kappa accuracy of 0.88. The Random Trees method yielded the lowest accuracy among the three approaches, with an overall accuracy of 91% and a kappa accuracy of 0.87. The second part of the study focused on analyzing LULC changes in the study area. Based on the classification results, the SVM method was chosen to classify the Landsat 7 image acquired in 2000. LULC changes from 2000 to 2023 were investigated using change detection comparison. The findings indicate that over the last 23 years, vegetation land and urban areas in the study area have experienced significant increases of 31.94% and 5.47%, respectively.
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