Wetland mapping and evaluating the impacts on hydrology, using geospatial techniques: a case of Geba Watershed, Southwest Ethiopia

Berhanu, Mintesnot; Suryabhagavan, K. V.; Korme, Tesfaye

doi:10.1080/24749508.2021.1953744

Cited by 7 publications

(5 citation statements)

References 38 publications

(47 reference statements)

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“…The area has large upstream water potential sites, intensive irrigable downstream lands, and high hydropower potential (Dibaba et al, 2020). Previous studies in southwestern Ethiopia have documented a decline in wetland resources due to environmental pressure and human stresses (Berhanu et al, 2021;Dibaba et al, 2020;Dixon et al, 2021;Hussien et al, 2018). The study area has different LULC classes: cultivated land, forest, grassland, shrubs land, and settlements.…”

Section: Description Of the Study Areamentioning

confidence: 99%

Analyzing Wetland Dynamics Using Geospatial Techniques: A Case of Abay Choman and Jimma Geneti Watershed, Horo Guduru Wollega Zone, Western Ethiopia

Moisa

Bulto

Werku

et al. 2023

Air, Soil and Water Research

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Wetland ecosystems are one of the most important areas that provides different ecosystems services as well as habitat for plant and animal species. In spite of multipurpose, wetland ecosystems are under threats. This study attempts to analyze wetland dynamics of Abay Choman and Jimma Geneti watershed in Horo Guduru Wollega Zone, Western Ethiopia using geospatial techniques. The land use land cover (LULC), the Normalized Difference Vegetation Index (NDVI), and Normalized Difference Water Index (NDWI) were investigated using Landsat 5 TM of 1991, Landsat 7ETM+ of 2003, and OLI/TIRS of 2021. In the present study, the LULC was classified using a supervised classification method with maximum likelihood algorithm. The red and infrared bands of Landsat imagery from three different time periods were used to calculate NDVI, while the NDWI was estimated using the green and near infrared (NIR) bands of multispectral Landsat images. Results show that wetland ecosystem in the study area decreased by about 125.2 km2 (8.8%) with the rate of 4.2 km2/year. In contrast, agricultural land increased by 223.4 km2 with the rate of 7.4 km2/year between 1991 and 2021. About 66.7 km2 wetland was converted to cultivated land whereas 29.3 and 24.7 km2 of grassland and shrubs land were converted into cultivated land. As a result, the maximum NDVI and NDWI values were decreased between 1991 and 2021. To minimize the rapid loss of wetland and water bodies in the study area, proper land use planning and environmental education should be promoted.

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Section: Description Of the Study Areamentioning

confidence: 99%

Analyzing Wetland Dynamics Using Geospatial Techniques: A Case of Abay Choman and Jimma Geneti Watershed, Horo Guduru Wollega Zone, Western Ethiopia

Moisa

Bulto

Werku

et al. 2023

Air, Soil and Water Research

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“…Previous studies have also highlighted the influence of topographic indices on wetlands mapping. Indeed, Hansen et al 74 , Rapinel et al 11 , Guasselli et al 75 ; Berhanu et al 56 utilized digital terrain models (DTMs) as explanatory variables to identify estuarine-fringe wetlands. Three topographic variables derived from DTMs were commonly used: (a) the multiscale topographic position index (TPI), (b) the vertical distance to the channel network (VDCN), and (c) the topographic wetness index (TWI), TWI, which characterizes potential soil wetness based on contributing area and local slope, typically ranges from 0 to 30, with higher values indicating a higher probability of wet soil 67 .…”

Section: Discussionmentioning

confidence: 99%

“…The TWI also helps predict soil characteristics, including horizon depth, silt percentage, organic matter content, and phosphorus. The index is used to define biological processes such as yearly net primary production, vegetation patterns, and forest site quality, as well as to explore spatial scale effects on hydrological processes and identify hydrological flow pathways for geochemical modeling 56 , 75 , 76 . However, according to Ludwig et al 73 , even though a variety of spectral indices for water and wet soils detection (hereafter referred to as wetness) is available, wetlands are still easily confused with other upland land cover types such as forests and shadows, since they share similar spectral profiles.…”

Section: Discussionmentioning

confidence: 99%

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Mapping small inland wetlands in the South-Kivu province by integrating optical and SAR data with statistical models for accurate distribution assessment

Géant,

Gustave,

Schmitz

2023

Sci Rep

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There are several techniques for mapping wetlands. In this study, we examined four statistical models to assess the potential distribution of wetlands in the South-Kivu province by combining optical and SAR images. The approach involved integrating topographic, hydrological, and vegetation indices into the four most used classifiers, namely Artificial Neural Network (ANN), Random Forest (RF), Boosted Regression Tree (BRT), and Maximum Entropy (MaxEnt). A wetland distribution map was generated and classified into 'wetland' and 'non-wetland.' The results showed variations in predictions among the different models. RF exhibited the most accurate predictions, achieving an overall classification accuracy of 95.67% and AUC and TSS values of 82.4%. Integrating SAR data improved accuracy and precision, particularly for mapping small inland wetlands. Our estimations indicate that wetlands cover approximately 13.5% (898,690 ha) of the entire province. BRT estimated wetland areas to be ~ 16% (1,106,080 ha), while ANN estimated ~ 14% (967,820 ha), MaxEnt ~ 15% (1,036,950 ha), and RF approximately ~ 10% (691,300 ha). The distribution of these areas varied across different territories, with higher values observed in Mwenga, Shabunda, and Fizi. Many of these areas are permanently flooded, while others experience seasonal inundation. Through digitization, the delineation process revealed variations in wetland areas, ranging from tens to thousands of hectares. The geographical distribution of wetlands generated in this study will serve as an essential reference for future investigations and pave the way for further research on characterizing and categorizing these areas.

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“…Fifth, an accuracy assessment was performed for land cover maps by following an approach suggested by Congalton and Green (2019). User's Accuracy (UA), Producer's Accuracies (PA), Overall Accuracy (OA), and Kappa coefficients were determined, and then the error matrix of the land cover classification was produced by applying an approach used by (Berhanu et al, 2021).…”

Section: Data Collection Methodsmentioning

confidence: 99%

Assessment of Taungya agroforestry system in dryland forests rehabilitation in Sudan

Hemida

Yasin²,

Kheiry³

et al. 2023

J. Degrade. Min. Land Manage.

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The decline of the forest cover due to deforestation and agricultural expansion in Sudan has reached a critical situation. Taungya agroforestry systems have been used as a strategy to safeguard against this process, allowing farmers to cultivate their subsistence crops at the early stage of tree plantations. The current study research tackled Nabag Forest Reserve (NFR) as a case study to assess and explore Taungya agroforestry system as a practice used for forest rehabilitation in the study area. Satellite images of Landsat 5 Thematic Mapper, and Landsat 8 Operational Land Imager/Thermal Infrared Sensor of 1991, 2001, 2011, and 2021 were used to generate forest cover maps. Both unsupervised and supervised classification, as well as ground truth points, were applied to classify the vegetation cover in NFR as dense forest (DF), light forest (LF), agricultural field (AF), and bare land (BL). The results showed that two trends of forest cover changes occurred in NFR between 1991-2001 and 2011-2021. It was well explored that through Taungya agroforestry, there was a considerable increase in forest cover over this period. This could be clearly detected by increasing dense forest and light forest cover by 1041.73 ha (24.95%) and 2.95 ha (0.07%), respectively and decreasing of bare land and agricultural field by 409.79 ha (9.81%) and 634.52 ha (15.15%) during the addressed period. The findings of the study indicated that Taungya agroforestry system could be a feasible land-use alternative for forest recovery in the dry land of Sudan.

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Wetland mapping and evaluating the impacts on hydrology, using geospatial techniques: a case of Geba Watershed, Southwest Ethiopia

Cited by 7 publications

References 38 publications

Analyzing Wetland Dynamics Using Geospatial Techniques: A Case of Abay Choman and Jimma Geneti Watershed, Horo Guduru Wollega Zone, Western Ethiopia

Analyzing Wetland Dynamics Using Geospatial Techniques: A Case of Abay Choman and Jimma Geneti Watershed, Horo Guduru Wollega Zone, Western Ethiopia

Mapping small inland wetlands in the South-Kivu province by integrating optical and SAR data with statistical models for accurate distribution assessment

Assessment of Taungya agroforestry system in dryland forests rehabilitation in Sudan

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