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Land use land cover analysis of the Great Ethiopian Renaissance Dam (GERD) catchment using remote sensing and GIS techniques

Land use land cover analysis of the Great Ethiopian Renaissance Dam (GERD) catchment using remote... GEOLOGY, ECOLOGY, AND LANDSCAPES INWASCON https://doi.org/10.1080/24749508.2022.2138027 RESEARCH ARTICLE Land use land cover analysis of the Great Ethiopian Renaissance Dam (GERD) catchment using remote sensing and GIS techniques Tamirat Solomon and Paulos Lukas College of Agriculture, Department of Natural Resources Management and College of Social Science, Wolaita Sodo University, Wolaita Sodo, Ethiopia ABSTRACT ARTICLE HISTORY Received 25 May 2022 Evaluation of land use change is important for understanding the relationship between nature Accepted 15 October 2022 and the future policy design in sustainable natural resources management. In this study the dam-triggered land use cover changes since the time of its commencement of construction KEYWORDS was evaluated by using satellite remote sensing and GIS techniques. To detect changes Satellite image; land use/ between 2011 and 2021, the post-processing technique was applied using QGIS’s SCP tool land cover analysis; QGIS; and two raster images of 2011 and 2021 as inputs for change simulation and the output classes grand Ethiopian Renaissance were edited using reference classes. The study results revealed that the overall accuracy for the dam; remote sensing 2011 classification and 2021 classification was 0.89 (89%) and 0.94 (94%) respectively. The highest conversion was recorded between agricultural land to the mixed forest (14.61%) and mixed forest to shrub land (13.40%), while the lowest conversion was observed between the built-up area to a water body (0.02%) and to agricultural land (0.1%). It was recommended to consider the correlation between forest biodiversity conservation and the sustainability of hydroelectric dam by increasing restoration of degraded forests in the GERD catchment and growing forest cover should be viewed as an issue of national energy security as early as possible. 1. Background and justification environmental problems (Meyer & Turner, 1996); The construction of a dam and reservoir is human and play a key role in sustainable development intervention in the natural environment, which results (Schößer et al., 2010). In this regard, it is clear that changes in biodiversity (Bempah et al. 2021), that all of human life and development involves an inter- requires attention for the sustainable management of action with nature (Iyan, 1989), and the interaction we the dam itself and the environment (Karami & make either modifies, or changes the natural habitat. Karami, 2020). When the environment is properly The major agent for the habitat change includes urba- handled in man’s endeavor to attain his needs, the nization, infrastructure, agriculture development, ecosystem is maintained thus sustainable develop- manufacturing, recreation, or transportation (Alsaleh ment. On the other hand, careless handling of the et al., 2021; Cheţan et al., 2018; Ciach et al., 2019). This natural resources through human activity will abuse type of change in the natural habitat especially in the environment (Abraham, 2013); causing scarcity or terms of change from forest cover to other land loss of biodiversity (Roe et al., 2019; Singh et al., 2021); cover has been one of the important issues on global affecting the sustainability (Cengiz, 2013; Warford & change research (Assfaw & Del Hierro, 2020). Partow, 1990; Wassie, 2020). Construction of large hydroelectric dams is impor- The relationship between human activities and its tant for renewable energy production, water resources, impacts on the ecosystems has been discussed for provide numerous cultural and ecological services and decades by both natural and social scientists (Deng economic development (Fan et al., 2022; Lindsey & et al., 2013). However, the trends of damages have Yasarer, 2016). However, it results in habitat fragmen- been increasing, and the degraded land area has not tation causing loss of hotspots of biodiversity decreased significantly (Zhuge et al., 2019); due to the (Barbarossa et al., 2020); which could affect the produc- drastically increasing human population over time tion potential and sustainability of the dam (Lindsey & (Yohannes et al., 2021). As a result, land use change Yasarer, 2016; Lopes et al., 2014; Moran et al., 2018). is one of the important problems due to its impact on In order to make the sustainable benefits of large the ecosystem services (Chen et al., 2014); deteriorate dams it is necessary to work on the sustainability of the water bodies and aquatic diversities (Wubie et al., project (the large dams) and the natural resources that 2016); it’s a new addition to the concern of global could enhance the productivity and reduce the CONTACT Tamirat Solomon tasolmame@gmail.com College of Social Science and Humanities, Department of Geography and Environmental Studies, Wolaita Sodo University, Wolaita Sodo, 138, Ethiopia © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group on behalf of the International Water, Air & Soil Conservation Society(INWASCON). This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 2 T. SOLOMON AND P. LUKAS impacts on the dam. Human-induced land degrada- hydropower project on the Blue Nile in Ethiopia. tion monitoring is of crucial scientific significance in Ethiopia contributes 86% of the Nile flow with a total revealing the evolution of land degradation and guid- average annual flow of 77 billion m . For Ethiopia acces- ing its governance (Zhuge et al., 2019). sing and utilizing its water resources is not a matter of One of the main focuses should be on the integrated choice, but an imperative of continued existence because forest and land management, reduction of sedimentation the Nile River is a source of livelihood for the Ethiopian and silt through management of the catchment of the dam population of more than 120 million people. The pur- as poor management of land resources including soil and poses of the GERD project are hence lifting millions of forest vegetation causes problems such as landslides, silta- people out of poverty and providing access to electricity tion and sedimentation (Tamene et al., 2005). to more than 60 million Ethiopians and provide afford - Hydrological response was more sensitive to LULC able electricity to the service, industrial and agricultural dynamics and sediment yield of the watershed (Aghsaei economic sectors. Therefore, for the sustainability of the et al., 2020; Assfaw & Del Hierro, 2020; Saddique et al., GERD, it is very important issue considering dynamics of 2020; Welde & Gebremariam, 2017); and thus, natural habitat fragmentation and change of the forest cover to resource degradation resulting from inappropriate land reduce the sediment yields from the dam catchment. use and subsequent hydrological change is one of the key Consequently, the finding of the study could provide problems threatening environmental welfare and sustain- information for the concerned bodies for the intervention able development (Bekele et al., 2021). As the process of in forest resource management, restoration and sustain- natural resources degradation is complex and caused by able watershed management in the catchment of the different factors, it is usually difficult to identify the degree GERD. of degradation and the critical vulnerability values in the affected systems (Tsesmelis et al., 2022;). Satellite remote sensing and geographic information systems (GIS) have 2. Materials and methods been widely applied and been recognized as a powerful and 2.1. Study area description effective tool in detecting land use and land cover change due to its multi-temporal, multi-spectral and multi-spatial The Grand Ethiopian Renaissance Dam (GERD) for- resolution potential (Junge et al., 2010; Kumar et al., 2015; merly known as the Millennium Dam is a hydropower Lefsky et al., 1998;). Also, it allows direct or indirect detec- dam that is under construction in the Benishangul- tion, extrapolation and interpretation, area calculation, Gumuz region of Ethiopia; on the Blue Nile about mapping, monitoring and identification of physiographic 40 Km away from the Ethiopia-Sudanese border. The units required (Gebeyehu, 2019; Pei et al., 2021; Van project is located 620 KM away from the country’s capital Lynden & Mantel, 2001). city, Addis Ababa (Figure 1). Previous studies have highlighted the need for GERD is the largest hydropower project in Africa research on location-specific land-use and land-cover with a total tributary catchment of 172,250 Km (LULC) dynamics for decision-making processes related (Abdelazim et al., 2020) which has promising potential to the management of forest resources (Abdelazim et al., to fulfill the hydroelectric power demand of the coun- 2020; Mariye et al., 2021) and the political stability of the try and provide multidimensional benefits for the region. Different studies in Ethiopia related to dam suit- communities in and around the GERD catchment in ability stream model and multi-criteria decision analysis particular and for Ethiopian in general through pro- in north Ethiopia (Hagos et al., 2022); impacts of dams on duction of hydroelectric power, irrigation, reduced Downstream Hydrogeomorphology Tekeze, north flood risk and drought normalization. Ethiopia (Annys et al., 2020) monitoring across a range of geopolitical scales (Coulston et al., 2014) were con- ducted. However, there has been no study examining the 2.2. Topography and elevation LULC caused by the construction of GERD by using DEM is frequently used to refer to any digital repre- remote sensing and GIS. Therefore, the current study is sentation of a topographic surface (Balasubramanian, designed to evaluate the reservoir induced LULC changes 2017). In this study, a Digital Elevation Model (DEM) since the time of beginning of construction to the recent from shuttle radar topographic mission (SRTM) with by using satellite remote sensing and GIS application, so 30 m spatial elevation was obtained from USGS Earth as to assure the sustainability of the mega hydroelectric Explorer data provision center (https://earthexplorer. dam and reduce the fear of water scarcity for the down- usgs.gov/) and used to delineate the elevation of the stream countries mainly Sudan and Egypt by managing study catchment. the watershed. The dam is situated at an elevation between 2568 According to its first phase proposal the Grand and 483 (Figure 2). Ethiopian Renaissance Dam (GERD) is a 6,450 MW GEOLOGY, ECOLOGY, AND LANDSCAPES 3 Figure 1. Map of study area. Figure 2. Topography and elevation of the area. 4 T. SOLOMON AND P. LUKAS which was suggested by (Sigurdsson et al., 2022). The 2.3. Soil and geology of the GERD catchment method was used to sharpen all Landsat 8 OLI and According to Harmonized World Soil Database of Sentinel-2 bands to a 10 m resolution in order to FAO (2012) the following are the soil groups of the produce suitable band composition and uniform catchment (Table 1, Table 2 and Figure 3). The geol- pixel contents (Sigurdsson et al., 2022). ogy of the study area is dominated by precambrian basement rocks (Figure 4). 2.2. Data analysis 2.1 Methods of data collection The general flow of the study was represented in Figure 6. 2.1.1 Data used Multispectral remote sensing data were used to ana- lyze and detect LULC change in Grand Ethiopian 2.2.1. Land use/land cover change detection and Renaissance Dam Catchment. In this study, Landsat mapping 5 Thematic Mapper (TM) of 2011 and Landsat 8 To delineate land use/land cover change for the years Operational Land Imager (OLI) of 2021 with 30 m 2011 and 2021, Semi-Automatic Classification spatial resolution have been used (Figure 5). Both data Program (SCP) in QGIS 3.16 software was used. The have been downloaded from (http://earthexplorer.usg. satellite image was first classified in QGIS using the gov). The atmospheric correction was conducted in Supervised multispectral image classification techni- QGIS using SCP (Semi-automatic classification que. The Maximum Likelihood Classifier algorithm Program). The boundary of the catchment was derived was employed to consider the probability distribution from DEM-data which was obtained from Shutle of pixel values in relation to neighboring pixels. There Radar Topographic Mission (SRTM) with 30 m pixel are various algorithms used in remote sensing image resolution. The resampling method employed in this classification based on the research objective such as study was Sentinel-2 Landsat 8 Sharpening (SLSharp) artificial neural network (ANN; Saputra & Lee, 2019), Table 1. Soil groups and their properties in GERD catchment. S.N COUNT SU_SYM90 REF_DEPTH T_GRAVEL T_SAND T_SILT T_CLAY T_REF_BULK_DENSI T_BULK_DENSITY T_PH_H2O 1 451 LPe 30 31 50 30 20 1.42 1.35 6.5 2 65 CMx 100 1 42 26 32 1.34 1.31 6.7 3 142 LPq 10 32 43 29 28 1.36 1.31 7.5 4 160 CMe 100 1 45 31 24 1.38 1.38 6.6 5 778 LPe 30 31 50 30 20 1.42 1.35 6.5 6 337 CMe 100 1 45 31 24 1.38 1.38 6.6 7 148 LPe 30 31 50 30 20 1.42 1.35 6.5 8 1 LPq 10 32 43 29 28 1.36 1.31 7.5 9 2193 LPe 30 31 50 30 20 1.42 1.35 6.5 10 240 LPq 10 32 43 29 28 1.36 1.31 7.5 11 2962 LPe 30 31 50 30 20 1.42 1.35 6.5 12 3886 LPe 30 31 50 30 20 1.42 1.35 6.5 13 46 LPq 10 32 43 29 28 1.36 1.31 7.5 14 613 ANz 100 1 61 32 7 1.6 0.97 5.8 15 729 CLh 100 1 43 32 25 1.38 1.32 8.1 16 3173 FLe 100 1 44 33 23 1.39 1.33 7 17 867 LPq 10 32 43 29 28 1.36 1.31 7.5 18 87 LPk 30 29 36 39 25 1.36 1.33 7.7 19 938 LVx 100 1 51 22 27 1.38 1.45 6.4 20 431 VRe 100 1 21 25 54 1.22 1.51 6.9 21 115 LVv 100 1 32 26 42 1.28 1.31 6.9 22 2 ACh 100 1 57 19 24 1.41 1.4 5.1 Table 2. Meteorological stations in and around GERD catchment. SN Station Name Longitude Latitude Altitude Region Zone Woreda 1 Kemashe 35.45 10.0167 1250 B/Gumuz Kamashe Kamashe 2 Soge 34.7833 10.7333 1680 B/Gumuz Assosa Belogiganfoye 3 Abadi 34.75 10.6167 1410 B/Gumuz Assosa Kurmuk 4 Kurmuk 34.6333 10.5 800 B/Gumuz Assosa Kurmuk 5 Odda 34.95 10.0667 726 B/Gumuz Assosa Odda Godere Bildigulu 6 Sherekole 35.0833 10.8 692 B/Gumuz Kamashe Kamashe 7 Gizen 34.8 10 690 B/Gumuz Assosa Gizen Sherkole 8 Menge 34.7333 10.3333 1200 B/Gumuz Assosa Menge 9 Bullen 36.0817 10.5959 1659 B/Gumuz Metekel Bullen 10 Mankush 35.2914 11.2725 860 B/Gumuz Metekel Guba 11 Baruda 35.867 10.5351 1443 B/Gumuz Metekel Bullen 12 Debere Ziet 35.6629 10.625 2519 B/Gumuz Metekel Wombera Source: National Meteorological Agency of Ethiopia (2021). GEOLOGY, ECOLOGY, AND LANDSCAPES 5 Figure 3. Soil map of GERD Catchment. Figure 4. Geology map of the GERD catchment. random forest classifier (RF; Piao et al., 2021), support LULC change simulation was completed and the out- vector machine (SVM; Shi et al., 2015), maximum put classes were edited using reference classes. likelihood classifier (MLC; Alam et al., 2020) and others. When compared to others maximum likeli- 2.2.2. Accuracy assessment hood classifier is suitable for this study because it To check whether the classification fulfills the mini- needs relatively less number of training samples, its mum acceptable standard of satellite image classifica - suitability to complex landscape environment tion, an accuracy assessment was employed. Classified (Dagnachew et al., 2020), its assistance in the classifi - data and reference data were inputs for computation cation of overlapping signatures by assigning pixels to of confusion matrix in segmentation and classification the class of highest probability. toolset of spatial analyst tools in ArcGIS 10.5. To detect change between 2011 and 2021, the post- Classified data was validated with validation data col- processing technique was applied using QGIS’s SCP lected through high resolution Google Earth images tool. Two raster images (LULC of 2011 and LULC of and Toposheets of the study area. In this study ran- 2021) were inputs for change simulation. After the dom points generated using segmentation and 6 T. SOLOMON AND P. LUKAS Figure 5. Multispectral remote sensing satellite images: a) Landsat 5 TM (2011) image b) Landsat 8 OLI (2021) image. Landsat 5 (TM) image of 2011 Sentinel-2 Image (2021) Landsat 8 (OLI) image of 2021 Resampling Supervised Image Classification using Maximum Likelihood Classifier Land use/Land cover classification Reference Data Accuracy Assessment map of 2011 and (GPS points) Is Kappa Coefficient NO >80% YES Land use/land cover change analysis using SCP in QGIS Land use/Land cover change Map For 2011 – 2021 Figure 6. Flowcharts for land use/land cover change analysis and mapping. GEOLOGY, ECOLOGY, AND LANDSCAPES 7 Table 3. Description of LULC classes. Land use/land cover classes Description Mixed Forest The area comprises natural and manmade forests and vegetation of different types Agricultural land The land covered by farmlands, crop lands and range lands. Water body The area covered by rivers, ponds and reservoirs Built up area The land area used for settlement, urban areas, transportation, industries and construction services Shrub land Land covered by short trees, grasses and bush lands Open land The land characterized as non-built-up land with no, or with insignificant, vegetation cover Table 4. Confusion matrix for LULC classification of 2011. REFERENCE DATA C1 C2 C3 C4 C5 C6 RT U_Accu Kappa CLASSIFIED DATA C1 23 0 0 0 2 0 25 0.92 C2 2 21 0 0 2 0 25 0.84 C3 6 0 18 0 0 0 24 0.75 C4 1 0 0 24 0 0 25 0.96 C5 0 0 1 0 24 0 25 0.96 C6 2 0 0 0 0 23 25 0.92 CoT 34 21 19 24 28 149 0 P_Accu 0.68 1 0.95 1 0.86 1 0 0.89 Kappa 0 0.87 Keys: C1 = Mixed Forest, C2 = Agricultural Land, C3 = Water Body, C4 = Built up area, C5 = Shrubland, C6 = Open Land, CoT = Column Total, RT = Row Total, P_Accu = producer’s Accuracy, U_Accu = User’s Accuracy. Table 5. Confusion matrix for LULC classification of 2021. REFERENCE DATA C1 C2 C3 C4 C5 C6 RT U_Accu Kappa CLASSIFIED DATA C1 29 0 0 0 0 0 29 1 0 C2 1 27 0 1 0 0 29 0.93 0 C3 0 0 29 0 0 0 29 1 0 C4 2 0 0 27 0 0 29 0.93 0 C5 2 1 0 0 26 0 29 0.90 0 C6 0 0 2 0 0 27 29 0.93 0 CoT 34 28 31 28 26 27 174 0 0 P_Accu 0.85 0.96 0.94 0.96 1 1 0 0.95 0 Kappa 0 0 0 0 0 0 0 0 0.94 Key: C1 = Mixed Forest, C2 = Agricultural Land, C3 = Water Body, C4 = Built up area, C5 = Shrubland, C6 = Open Land, CoT = Column Total, RT = Row Total, P_Accu = producer’s Accuracy, U_Accu = User’s Accuracy. classification toolset of the spatial analyst tools in 3.2. Land Use Land Cover Classification ArcGIS 149 and 174 points (Coordinate Points) were In this study, a supervised image classification method used for 2011 and 2021 land use/land cover classifica - was employed to classify multispectral satellite images. tion validation respectively. As a result, the accuracy Training samples were collected for each LULC class assessment technique used in this study was based on in QGIS for 2011 and 2021 images separately and confusion matrix or error matrix computation in GIS a signature file was created. Using the Maximum environment (Both QGIS and ArcGIS). Likelihood Classifier algorithm the classification was simulated for the two time (Figure 7: a and b), images in order to get the real-time interaction of scenario 3. Results and discussions and spatial environmental data (Chen et al., 2014) and also the Maximum Likelihood Classifier are the most 3.1 Land use/land cover classification and recommended methods for the change detection analysis (Chughtai et al., 2021). Based on the existing situation of the study catch- ment, visual observation of the high resolution 3.2.1. Accuracy assessment Google Earth image and knowledge from different Accuracy assessment is an important step in proces- reports related to the study catchment a total num- sing land use change analysis (Islami, 2022). It estab- ber of six dominant land use/land cover classes were lishes the information value of the resulting data to identified. These include mixed forest, agricultural a user (Rwanga & n.d.ambuki, 2017). As the detection land, water body, built up areas, shrub land and of LULC is important for understanding the open land (Table 3). 8 T. SOLOMON AND P. LUKAS Figure 7. A) Land use/land cover of 2011 and b) Land use/land cover of 2021. Table 6. Land use/land cover change between 2011 and 2021. 2011 2021 Changes LULC_Class Area in Sq. Km Area in % Area in Sq. Km Area in % 2011–2021 (%) Mixed Forest 5912.838 38.12 6475.2435 41.75 3.63 Agricultural Land 3209.1066 20.69 2662.5024 17.17 −3.52 Water Body 136.7316 0.88 423.4779 2.73 1.85 Built-up area 25.2225 0.16 239.1606 1.54 1.38 Shrub land 5750.892 37.08 5586.6528 36.02 −1.06 Open land 474.4872 3.60 122.4513 0.79 −2.81 Total 100 15,509.4885 100 Source: USGS-Earth Explorer data center (2021) relationship between humans and nature and the As it is shown in confusion matrix of 2011and 2021, future policy design in the sustainable natural producer’s accuracy and user’s accuracy are computed resources management (Chughtai et al., 2021; using correctly classified cells on the diagonal and Rwanga & n.d.ambuki, 2017), the following accuracy column and row total. The overall accuracy for 2011 assessment was computed. Based on the classified and classification and 2021classification are 0.89 (89%) and reference data accuracy assessment table or Confusion 0.94 (94%) respectively. According to the confusion Matrix was constructed for both (2011 and 2021) matrix of 2011 and 2021 the Kappa coefficient for 2011 classifications (Table 4 and 5). and 2021 become 0.87 (87%) and 0.94 (94%) GEOLOGY, ECOLOGY, AND LANDSCAPES 9 Figure 8. Map of land use/land cover change between 2011 and 2021. Land use/Land cover change Figure 9. Land use/land cover change between 2011 and 2021. respectively. This indicates that in the last 10 years 3.3 Land use/land cover change detection and period the construction of GERD has made changes mapping on the LULC. The amount of forest vegetation has The hydrological cycle and river basin processes are shown a significant variation requiring attention for under significant stress as natural habitats are changed the sustainability of the dam through watershed man- into other land use systems (Guerra et al., 1998). As agement and understanding the changes in hydrolo- a result, land use and land cover change research have gical processes in the catchment (Tewabe et al., 2020). been applied to different land use types including Area in % 10 T. SOLOMON AND P. LUKAS Table 7. LULC change (2011–2021). Land use/Land cover change (2011–2021) Area in Sq. Km Area in % Mixed Forest (No Change) 2,702,448.9 17.42 Agricultural Land to Mixed Forest 2,265,592.5 14.61 Water Body to Mixed Forest 343,544.4 2.22 Built up area to Mixed Forest 42,177.6 0.27 Shrub land to Mixed Forest 838,795.5 5.41 Open Land to Mixed Forest 282,684.6 1.82 Mixed Forest to Agricultural Land 848,397.6 5.47 Agricultural Land (No Change) 1,060,877.7 6.84 Water Body to Agricultural Land 11,945.7 0.08 Built up area to Agricultural Land 14,934.6 0.10 Shrub land to Agricultural Land 536,304.6 3.46 Open Land to Agricultural Land 190,042.2 1.23 Mixed Forest to Water Body 68,099.4 0.44 Agricultural Land to Water Body 157,508.1 1.02 Water Body (No Change) 41,316.3 0.27 Built up area to Water Body 360.9 0.02 Shrub land to Water Body 82,830.6 0.53 Open Land to Water Body 73,362.6 0.47 Mixed Forest to Built-up area 47,166.3 0.30 Agricultural Land to Built-up area 65,894.4 0.30 Water Body to Built-up area 7477.2 0.05 Built up area (No Change) 1688.4 0.02 Shrub land to Built-up area 45,384.3 0.29 Open Land to Built-up area 71,550 0.46 Mixed Forest to Shrub land 2,078,830.8 13.40 Agricultural Land to Shrub land 2,051,443.8 13.23 Water Body to Shrub land 107,383.5 0.69 Built up area to Shrub land 39,337.2 0.25 Shrub land (No Change) 803,452.5 5.18 Open Land to Shrub land 506,205 3.26 Mixed Forest to Open Land 41,876.1 0.27 Agricultural Land to Open Land 24,816.6 0.16 Water Body to Open Land 35,924.4 0.23 Built up area to Open Land 144.9 0.01 Shrub land to Open Land 10,265.4 0.07 Open Land (No Change) 9423.9 0.06 Total 15,509,488.5 99.88 deforestation, landslides, erosion, land planning, any the natural regeneration of the watershed but the type of modification or disturbance, and global change artificial method of restoration is more required to (Baig et al., 2022; Liping et al., 2018). In the current increase the vegetation coverage. study, it was identified that shrubland was reduced by The amount of change in mixed forest converted to 36.02% but the coverage of mixed forest showed other land use showed an increment in the catchment a 41.75% increment (Table 6). As hydropower relies indicating that the rate of deforestation has been on the protection of watersheds to regulate water and increasing (Table 7). As the GERD is in its way to sediment yields (Mohit, 2018), the trends of the forest starting the production of electric power, it is neces- should get more focus from the upper catchment to sary to consider the effects of future deforestation in the dam. It is necessary to consider the correlation line with the sustainability of the dam. Moreover, the between forest biodiversity conservation and the sus- potential of the dam in changing the dynamics of tainability of electric dam (Dorber et al., 2020). natural ecosystems should be the center of focus for Increment in built up area is one of the causes of the management (Velastegui-Montoya et al., 2020); as destruction and deforestation of forest resources, the the mixed forest to shrubland, water body and built up cost of protecting and restoring watersheds should be areas showed increment. The more forest cover in the considered as an annual investment towards sustain- catchment of the river and the dam the more water able reservoir management and hydropower genera- we’ll have in the rivers, dam and the more electricity tion (Mohit, 2018). It is necessary to consider the we’ll be able to get from these projects (Stickler et al., change in vegetation-related variables resulting since 2013). Thus, increasing the restoration of degraded the flooding of lower elevations areas is expected to forests and growing forest cover should be viewed as negatively affect the mega projects of hydroelectric an issue of national energy security as early as possible. dams (GERD) (Bobrowiec et al., 2017). In the last It was identified that the amount of forest land ten years, the amount of open area converted to forest converted to agricultural land in the Nile river catch- land or shrubland is only 3% in the catchment ment in the last ten years accounted for about 5.47% (Figure 10 and Table 7) indicating the trend of restor- (Figure 8 and 9). This implies that the trends of issues ing degraded lands by vegetation is significantly low. of degradation of the environment together with cli- This amount of change might be related more of with mate change can negatively impact hydropower GEOLOGY, ECOLOGY, AND LANDSCAPES 11 generation and its sustainability (Kaunda et al., 2012). 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Land use land cover analysis of the Great Ethiopian Renaissance Dam (GERD) catchment using remote sensing and GIS techniques

Solomon, Tamirat; Lukas, Paulos
Geology Ecology and Landscapes , Volume OnlineFirst: 13 – Oct 30, 2022
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Abstract

GEOLOGY, ECOLOGY, AND LANDSCAPES INWASCON https://doi.org/10.1080/24749508.2022.2138027 RESEARCH ARTICLE Land use land cover analysis of the Great Ethiopian Renaissance Dam (GERD) catchment using remote sensing and GIS techniques Tamirat Solomon and Paulos Lukas College of Agriculture, Department of Natural Resources Management and College of Social Science, Wolaita Sodo University, Wolaita Sodo, Ethiopia ABSTRACT ARTICLE HISTORY Received 25 May 2022 Evaluation of land use change is important for understanding the relationship between nature Accepted 15 October 2022 and the future policy design in sustainable natural resources management. In this study the dam-triggered land use cover changes since the time of its commencement of construction KEYWORDS was evaluated by using satellite remote sensing and GIS techniques. To detect changes Satellite image; land use/ between 2011 and 2021, the post-processing technique was applied using QGIS’s SCP tool land cover analysis; QGIS; and two raster images of 2011 and 2021 as inputs for change simulation and the output classes grand Ethiopian Renaissance were edited using reference classes. The study results revealed that the overall accuracy for the dam; remote sensing 2011 classification and 2021 classification was 0.89 (89%) and 0.94 (94%) respectively. The highest conversion was recorded between agricultural land to the mixed forest (14.61%) and mixed forest to shrub land (13.40%), while the lowest conversion was observed between the built-up area to a water body (0.02%) and to agricultural land (0.1%). It was recommended to consider the correlation between forest biodiversity conservation and the sustainability of hydroelectric dam by increasing restoration of degraded forests in the GERD catchment and growing forest cover should be viewed as an issue of national energy security as early as possible. 1. Background and justification environmental problems (Meyer & Turner, 1996); The construction of a dam and reservoir is human and play a key role in sustainable development intervention in the natural environment, which results (Schößer et al., 2010). In this regard, it is clear that changes in biodiversity (Bempah et al. 2021), that all of human life and development involves an inter- requires attention for the sustainable management of action with nature (Iyan, 1989), and the interaction we the dam itself and the environment (Karami & make either modifies, or changes the natural habitat. Karami, 2020). When the environment is properly The major agent for the habitat change includes urba- handled in man’s endeavor to attain his needs, the nization, infrastructure, agriculture development, ecosystem is maintained thus sustainable develop- manufacturing, recreation, or transportation (Alsaleh ment. On the other hand, careless handling of the et al., 2021; Cheţan et al., 2018; Ciach et al., 2019). This natural resources through human activity will abuse type of change in the natural habitat especially in the environment (Abraham, 2013); causing scarcity or terms of change from forest cover to other land loss of biodiversity (Roe et al., 2019; Singh et al., 2021); cover has been one of the important issues on global affecting the sustainability (Cengiz, 2013; Warford & change research (Assfaw & Del Hierro, 2020). Partow, 1990; Wassie, 2020). Construction of large hydroelectric dams is impor- The relationship between human activities and its tant for renewable energy production, water resources, impacts on the ecosystems has been discussed for provide numerous cultural and ecological services and decades by both natural and social scientists (Deng economic development (Fan et al., 2022; Lindsey & et al., 2013). However, the trends of damages have Yasarer, 2016). However, it results in habitat fragmen- been increasing, and the degraded land area has not tation causing loss of hotspots of biodiversity decreased significantly (Zhuge et al., 2019); due to the (Barbarossa et al., 2020); which could affect the produc- drastically increasing human population over time tion potential and sustainability of the dam (Lindsey & (Yohannes et al., 2021). As a result, land use change Yasarer, 2016; Lopes et al., 2014; Moran et al., 2018). is one of the important problems due to its impact on In order to make the sustainable benefits of large the ecosystem services (Chen et al., 2014); deteriorate dams it is necessary to work on the sustainability of the water bodies and aquatic diversities (Wubie et al., project (the large dams) and the natural resources that 2016); it’s a new addition to the concern of global could enhance the productivity and reduce the CONTACT Tamirat Solomon tasolmame@gmail.com College of Social Science and Humanities, Department of Geography and Environmental Studies, Wolaita Sodo University, Wolaita Sodo, 138, Ethiopia © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group on behalf of the International Water, Air & Soil Conservation Society(INWASCON). This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 2 T. SOLOMON AND P. LUKAS impacts on the dam. Human-induced land degrada- hydropower project on the Blue Nile in Ethiopia. tion monitoring is of crucial scientific significance in Ethiopia contributes 86% of the Nile flow with a total revealing the evolution of land degradation and guid- average annual flow of 77 billion m . For Ethiopia acces- ing its governance (Zhuge et al., 2019). sing and utilizing its water resources is not a matter of One of the main focuses should be on the integrated choice, but an imperative of continued existence because forest and land management, reduction of sedimentation the Nile River is a source of livelihood for the Ethiopian and silt through management of the catchment of the dam population of more than 120 million people. The pur- as poor management of land resources including soil and poses of the GERD project are hence lifting millions of forest vegetation causes problems such as landslides, silta- people out of poverty and providing access to electricity tion and sedimentation (Tamene et al., 2005). to more than 60 million Ethiopians and provide afford - Hydrological response was more sensitive to LULC able electricity to the service, industrial and agricultural dynamics and sediment yield of the watershed (Aghsaei economic sectors. Therefore, for the sustainability of the et al., 2020; Assfaw & Del Hierro, 2020; Saddique et al., GERD, it is very important issue considering dynamics of 2020; Welde & Gebremariam, 2017); and thus, natural habitat fragmentation and change of the forest cover to resource degradation resulting from inappropriate land reduce the sediment yields from the dam catchment. use and subsequent hydrological change is one of the key Consequently, the finding of the study could provide problems threatening environmental welfare and sustain- information for the concerned bodies for the intervention able development (Bekele et al., 2021). As the process of in forest resource management, restoration and sustain- natural resources degradation is complex and caused by able watershed management in the catchment of the different factors, it is usually difficult to identify the degree GERD. of degradation and the critical vulnerability values in the affected systems (Tsesmelis et al., 2022;). Satellite remote sensing and geographic information systems (GIS) have 2. Materials and methods been widely applied and been recognized as a powerful and 2.1. Study area description effective tool in detecting land use and land cover change due to its multi-temporal, multi-spectral and multi-spatial The Grand Ethiopian Renaissance Dam (GERD) for- resolution potential (Junge et al., 2010; Kumar et al., 2015; merly known as the Millennium Dam is a hydropower Lefsky et al., 1998;). Also, it allows direct or indirect detec- dam that is under construction in the Benishangul- tion, extrapolation and interpretation, area calculation, Gumuz region of Ethiopia; on the Blue Nile about mapping, monitoring and identification of physiographic 40 Km away from the Ethiopia-Sudanese border. The units required (Gebeyehu, 2019; Pei et al., 2021; Van project is located 620 KM away from the country’s capital Lynden & Mantel, 2001). city, Addis Ababa (Figure 1). Previous studies have highlighted the need for GERD is the largest hydropower project in Africa research on location-specific land-use and land-cover with a total tributary catchment of 172,250 Km (LULC) dynamics for decision-making processes related (Abdelazim et al., 2020) which has promising potential to the management of forest resources (Abdelazim et al., to fulfill the hydroelectric power demand of the coun- 2020; Mariye et al., 2021) and the political stability of the try and provide multidimensional benefits for the region. Different studies in Ethiopia related to dam suit- communities in and around the GERD catchment in ability stream model and multi-criteria decision analysis particular and for Ethiopian in general through pro- in north Ethiopia (Hagos et al., 2022); impacts of dams on duction of hydroelectric power, irrigation, reduced Downstream Hydrogeomorphology Tekeze, north flood risk and drought normalization. Ethiopia (Annys et al., 2020) monitoring across a range of geopolitical scales (Coulston et al., 2014) were con- ducted. However, there has been no study examining the 2.2. Topography and elevation LULC caused by the construction of GERD by using DEM is frequently used to refer to any digital repre- remote sensing and GIS. Therefore, the current study is sentation of a topographic surface (Balasubramanian, designed to evaluate the reservoir induced LULC changes 2017). In this study, a Digital Elevation Model (DEM) since the time of beginning of construction to the recent from shuttle radar topographic mission (SRTM) with by using satellite remote sensing and GIS application, so 30 m spatial elevation was obtained from USGS Earth as to assure the sustainability of the mega hydroelectric Explorer data provision center (https://earthexplorer. dam and reduce the fear of water scarcity for the down- usgs.gov/) and used to delineate the elevation of the stream countries mainly Sudan and Egypt by managing study catchment. the watershed. The dam is situated at an elevation between 2568 According to its first phase proposal the Grand and 483 (Figure 2). Ethiopian Renaissance Dam (GERD) is a 6,450 MW GEOLOGY, ECOLOGY, AND LANDSCAPES 3 Figure 1. Map of study area. Figure 2. Topography and elevation of the area. 4 T. SOLOMON AND P. LUKAS which was suggested by (Sigurdsson et al., 2022). The 2.3. Soil and geology of the GERD catchment method was used to sharpen all Landsat 8 OLI and According to Harmonized World Soil Database of Sentinel-2 bands to a 10 m resolution in order to FAO (2012) the following are the soil groups of the produce suitable band composition and uniform catchment (Table 1, Table 2 and Figure 3). The geol- pixel contents (Sigurdsson et al., 2022). ogy of the study area is dominated by precambrian basement rocks (Figure 4). 2.2. Data analysis 2.1 Methods of data collection The general flow of the study was represented in Figure 6. 2.1.1 Data used Multispectral remote sensing data were used to ana- lyze and detect LULC change in Grand Ethiopian 2.2.1. Land use/land cover change detection and Renaissance Dam Catchment. In this study, Landsat mapping 5 Thematic Mapper (TM) of 2011 and Landsat 8 To delineate land use/land cover change for the years Operational Land Imager (OLI) of 2021 with 30 m 2011 and 2021, Semi-Automatic Classification spatial resolution have been used (Figure 5). Both data Program (SCP) in QGIS 3.16 software was used. The have been downloaded from (http://earthexplorer.usg. satellite image was first classified in QGIS using the gov). The atmospheric correction was conducted in Supervised multispectral image classification techni- QGIS using SCP (Semi-automatic classification que. The Maximum Likelihood Classifier algorithm Program). The boundary of the catchment was derived was employed to consider the probability distribution from DEM-data which was obtained from Shutle of pixel values in relation to neighboring pixels. There Radar Topographic Mission (SRTM) with 30 m pixel are various algorithms used in remote sensing image resolution. The resampling method employed in this classification based on the research objective such as study was Sentinel-2 Landsat 8 Sharpening (SLSharp) artificial neural network (ANN; Saputra & Lee, 2019), Table 1. Soil groups and their properties in GERD catchment. S.N COUNT SU_SYM90 REF_DEPTH T_GRAVEL T_SAND T_SILT T_CLAY T_REF_BULK_DENSI T_BULK_DENSITY T_PH_H2O 1 451 LPe 30 31 50 30 20 1.42 1.35 6.5 2 65 CMx 100 1 42 26 32 1.34 1.31 6.7 3 142 LPq 10 32 43 29 28 1.36 1.31 7.5 4 160 CMe 100 1 45 31 24 1.38 1.38 6.6 5 778 LPe 30 31 50 30 20 1.42 1.35 6.5 6 337 CMe 100 1 45 31 24 1.38 1.38 6.6 7 148 LPe 30 31 50 30 20 1.42 1.35 6.5 8 1 LPq 10 32 43 29 28 1.36 1.31 7.5 9 2193 LPe 30 31 50 30 20 1.42 1.35 6.5 10 240 LPq 10 32 43 29 28 1.36 1.31 7.5 11 2962 LPe 30 31 50 30 20 1.42 1.35 6.5 12 3886 LPe 30 31 50 30 20 1.42 1.35 6.5 13 46 LPq 10 32 43 29 28 1.36 1.31 7.5 14 613 ANz 100 1 61 32 7 1.6 0.97 5.8 15 729 CLh 100 1 43 32 25 1.38 1.32 8.1 16 3173 FLe 100 1 44 33 23 1.39 1.33 7 17 867 LPq 10 32 43 29 28 1.36 1.31 7.5 18 87 LPk 30 29 36 39 25 1.36 1.33 7.7 19 938 LVx 100 1 51 22 27 1.38 1.45 6.4 20 431 VRe 100 1 21 25 54 1.22 1.51 6.9 21 115 LVv 100 1 32 26 42 1.28 1.31 6.9 22 2 ACh 100 1 57 19 24 1.41 1.4 5.1 Table 2. Meteorological stations in and around GERD catchment. SN Station Name Longitude Latitude Altitude Region Zone Woreda 1 Kemashe 35.45 10.0167 1250 B/Gumuz Kamashe Kamashe 2 Soge 34.7833 10.7333 1680 B/Gumuz Assosa Belogiganfoye 3 Abadi 34.75 10.6167 1410 B/Gumuz Assosa Kurmuk 4 Kurmuk 34.6333 10.5 800 B/Gumuz Assosa Kurmuk 5 Odda 34.95 10.0667 726 B/Gumuz Assosa Odda Godere Bildigulu 6 Sherekole 35.0833 10.8 692 B/Gumuz Kamashe Kamashe 7 Gizen 34.8 10 690 B/Gumuz Assosa Gizen Sherkole 8 Menge 34.7333 10.3333 1200 B/Gumuz Assosa Menge 9 Bullen 36.0817 10.5959 1659 B/Gumuz Metekel Bullen 10 Mankush 35.2914 11.2725 860 B/Gumuz Metekel Guba 11 Baruda 35.867 10.5351 1443 B/Gumuz Metekel Bullen 12 Debere Ziet 35.6629 10.625 2519 B/Gumuz Metekel Wombera Source: National Meteorological Agency of Ethiopia (2021). GEOLOGY, ECOLOGY, AND LANDSCAPES 5 Figure 3. Soil map of GERD Catchment. Figure 4. Geology map of the GERD catchment. random forest classifier (RF; Piao et al., 2021), support LULC change simulation was completed and the out- vector machine (SVM; Shi et al., 2015), maximum put classes were edited using reference classes. likelihood classifier (MLC; Alam et al., 2020) and others. When compared to others maximum likeli- 2.2.2. Accuracy assessment hood classifier is suitable for this study because it To check whether the classification fulfills the mini- needs relatively less number of training samples, its mum acceptable standard of satellite image classifica - suitability to complex landscape environment tion, an accuracy assessment was employed. Classified (Dagnachew et al., 2020), its assistance in the classifi - data and reference data were inputs for computation cation of overlapping signatures by assigning pixels to of confusion matrix in segmentation and classification the class of highest probability. toolset of spatial analyst tools in ArcGIS 10.5. To detect change between 2011 and 2021, the post- Classified data was validated with validation data col- processing technique was applied using QGIS’s SCP lected through high resolution Google Earth images tool. Two raster images (LULC of 2011 and LULC of and Toposheets of the study area. In this study ran- 2021) were inputs for change simulation. After the dom points generated using segmentation and 6 T. SOLOMON AND P. LUKAS Figure 5. Multispectral remote sensing satellite images: a) Landsat 5 TM (2011) image b) Landsat 8 OLI (2021) image. Landsat 5 (TM) image of 2011 Sentinel-2 Image (2021) Landsat 8 (OLI) image of 2021 Resampling Supervised Image Classification using Maximum Likelihood Classifier Land use/Land cover classification Reference Data Accuracy Assessment map of 2011 and (GPS points) Is Kappa Coefficient NO >80% YES Land use/land cover change analysis using SCP in QGIS Land use/Land cover change Map For 2011 – 2021 Figure 6. Flowcharts for land use/land cover change analysis and mapping. GEOLOGY, ECOLOGY, AND LANDSCAPES 7 Table 3. Description of LULC classes. Land use/land cover classes Description Mixed Forest The area comprises natural and manmade forests and vegetation of different types Agricultural land The land covered by farmlands, crop lands and range lands. Water body The area covered by rivers, ponds and reservoirs Built up area The land area used for settlement, urban areas, transportation, industries and construction services Shrub land Land covered by short trees, grasses and bush lands Open land The land characterized as non-built-up land with no, or with insignificant, vegetation cover Table 4. Confusion matrix for LULC classification of 2011. REFERENCE DATA C1 C2 C3 C4 C5 C6 RT U_Accu Kappa CLASSIFIED DATA C1 23 0 0 0 2 0 25 0.92 C2 2 21 0 0 2 0 25 0.84 C3 6 0 18 0 0 0 24 0.75 C4 1 0 0 24 0 0 25 0.96 C5 0 0 1 0 24 0 25 0.96 C6 2 0 0 0 0 23 25 0.92 CoT 34 21 19 24 28 149 0 P_Accu 0.68 1 0.95 1 0.86 1 0 0.89 Kappa 0 0.87 Keys: C1 = Mixed Forest, C2 = Agricultural Land, C3 = Water Body, C4 = Built up area, C5 = Shrubland, C6 = Open Land, CoT = Column Total, RT = Row Total, P_Accu = producer’s Accuracy, U_Accu = User’s Accuracy. Table 5. Confusion matrix for LULC classification of 2021. REFERENCE DATA C1 C2 C3 C4 C5 C6 RT U_Accu Kappa CLASSIFIED DATA C1 29 0 0 0 0 0 29 1 0 C2 1 27 0 1 0 0 29 0.93 0 C3 0 0 29 0 0 0 29 1 0 C4 2 0 0 27 0 0 29 0.93 0 C5 2 1 0 0 26 0 29 0.90 0 C6 0 0 2 0 0 27 29 0.93 0 CoT 34 28 31 28 26 27 174 0 0 P_Accu 0.85 0.96 0.94 0.96 1 1 0 0.95 0 Kappa 0 0 0 0 0 0 0 0 0.94 Key: C1 = Mixed Forest, C2 = Agricultural Land, C3 = Water Body, C4 = Built up area, C5 = Shrubland, C6 = Open Land, CoT = Column Total, RT = Row Total, P_Accu = producer’s Accuracy, U_Accu = User’s Accuracy. classification toolset of the spatial analyst tools in 3.2. Land Use Land Cover Classification ArcGIS 149 and 174 points (Coordinate Points) were In this study, a supervised image classification method used for 2011 and 2021 land use/land cover classifica - was employed to classify multispectral satellite images. tion validation respectively. As a result, the accuracy Training samples were collected for each LULC class assessment technique used in this study was based on in QGIS for 2011 and 2021 images separately and confusion matrix or error matrix computation in GIS a signature file was created. Using the Maximum environment (Both QGIS and ArcGIS). Likelihood Classifier algorithm the classification was simulated for the two time (Figure 7: a and b), images in order to get the real-time interaction of scenario 3. Results and discussions and spatial environmental data (Chen et al., 2014) and also the Maximum Likelihood Classifier are the most 3.1 Land use/land cover classification and recommended methods for the change detection analysis (Chughtai et al., 2021). Based on the existing situation of the study catch- ment, visual observation of the high resolution 3.2.1. Accuracy assessment Google Earth image and knowledge from different Accuracy assessment is an important step in proces- reports related to the study catchment a total num- sing land use change analysis (Islami, 2022). It estab- ber of six dominant land use/land cover classes were lishes the information value of the resulting data to identified. These include mixed forest, agricultural a user (Rwanga & n.d.ambuki, 2017). As the detection land, water body, built up areas, shrub land and of LULC is important for understanding the open land (Table 3). 8 T. SOLOMON AND P. LUKAS Figure 7. A) Land use/land cover of 2011 and b) Land use/land cover of 2021. Table 6. Land use/land cover change between 2011 and 2021. 2011 2021 Changes LULC_Class Area in Sq. Km Area in % Area in Sq. Km Area in % 2011–2021 (%) Mixed Forest 5912.838 38.12 6475.2435 41.75 3.63 Agricultural Land 3209.1066 20.69 2662.5024 17.17 −3.52 Water Body 136.7316 0.88 423.4779 2.73 1.85 Built-up area 25.2225 0.16 239.1606 1.54 1.38 Shrub land 5750.892 37.08 5586.6528 36.02 −1.06 Open land 474.4872 3.60 122.4513 0.79 −2.81 Total 100 15,509.4885 100 Source: USGS-Earth Explorer data center (2021) relationship between humans and nature and the As it is shown in confusion matrix of 2011and 2021, future policy design in the sustainable natural producer’s accuracy and user’s accuracy are computed resources management (Chughtai et al., 2021; using correctly classified cells on the diagonal and Rwanga & n.d.ambuki, 2017), the following accuracy column and row total. The overall accuracy for 2011 assessment was computed. Based on the classified and classification and 2021classification are 0.89 (89%) and reference data accuracy assessment table or Confusion 0.94 (94%) respectively. According to the confusion Matrix was constructed for both (2011 and 2021) matrix of 2011 and 2021 the Kappa coefficient for 2011 classifications (Table 4 and 5). and 2021 become 0.87 (87%) and 0.94 (94%) GEOLOGY, ECOLOGY, AND LANDSCAPES 9 Figure 8. Map of land use/land cover change between 2011 and 2021. Land use/Land cover change Figure 9. Land use/land cover change between 2011 and 2021. respectively. This indicates that in the last 10 years 3.3 Land use/land cover change detection and period the construction of GERD has made changes mapping on the LULC. The amount of forest vegetation has The hydrological cycle and river basin processes are shown a significant variation requiring attention for under significant stress as natural habitats are changed the sustainability of the dam through watershed man- into other land use systems (Guerra et al., 1998). As agement and understanding the changes in hydrolo- a result, land use and land cover change research have gical processes in the catchment (Tewabe et al., 2020). been applied to different land use types including Area in % 10 T. SOLOMON AND P. LUKAS Table 7. LULC change (2011–2021). Land use/Land cover change (2011–2021) Area in Sq. Km Area in % Mixed Forest (No Change) 2,702,448.9 17.42 Agricultural Land to Mixed Forest 2,265,592.5 14.61 Water Body to Mixed Forest 343,544.4 2.22 Built up area to Mixed Forest 42,177.6 0.27 Shrub land to Mixed Forest 838,795.5 5.41 Open Land to Mixed Forest 282,684.6 1.82 Mixed Forest to Agricultural Land 848,397.6 5.47 Agricultural Land (No Change) 1,060,877.7 6.84 Water Body to Agricultural Land 11,945.7 0.08 Built up area to Agricultural Land 14,934.6 0.10 Shrub land to Agricultural Land 536,304.6 3.46 Open Land to Agricultural Land 190,042.2 1.23 Mixed Forest to Water Body 68,099.4 0.44 Agricultural Land to Water Body 157,508.1 1.02 Water Body (No Change) 41,316.3 0.27 Built up area to Water Body 360.9 0.02 Shrub land to Water Body 82,830.6 0.53 Open Land to Water Body 73,362.6 0.47 Mixed Forest to Built-up area 47,166.3 0.30 Agricultural Land to Built-up area 65,894.4 0.30 Water Body to Built-up area 7477.2 0.05 Built up area (No Change) 1688.4 0.02 Shrub land to Built-up area 45,384.3 0.29 Open Land to Built-up area 71,550 0.46 Mixed Forest to Shrub land 2,078,830.8 13.40 Agricultural Land to Shrub land 2,051,443.8 13.23 Water Body to Shrub land 107,383.5 0.69 Built up area to Shrub land 39,337.2 0.25 Shrub land (No Change) 803,452.5 5.18 Open Land to Shrub land 506,205 3.26 Mixed Forest to Open Land 41,876.1 0.27 Agricultural Land to Open Land 24,816.6 0.16 Water Body to Open Land 35,924.4 0.23 Built up area to Open Land 144.9 0.01 Shrub land to Open Land 10,265.4 0.07 Open Land (No Change) 9423.9 0.06 Total 15,509,488.5 99.88 deforestation, landslides, erosion, land planning, any the natural regeneration of the watershed but the type of modification or disturbance, and global change artificial method of restoration is more required to (Baig et al., 2022; Liping et al., 2018). In the current increase the vegetation coverage. study, it was identified that shrubland was reduced by The amount of change in mixed forest converted to 36.02% but the coverage of mixed forest showed other land use showed an increment in the catchment a 41.75% increment (Table 6). As hydropower relies indicating that the rate of deforestation has been on the protection of watersheds to regulate water and increasing (Table 7). As the GERD is in its way to sediment yields (Mohit, 2018), the trends of the forest starting the production of electric power, it is neces- should get more focus from the upper catchment to sary to consider the effects of future deforestation in the dam. It is necessary to consider the correlation line with the sustainability of the dam. Moreover, the between forest biodiversity conservation and the sus- potential of the dam in changing the dynamics of tainability of electric dam (Dorber et al., 2020). natural ecosystems should be the center of focus for Increment in built up area is one of the causes of the management (Velastegui-Montoya et al., 2020); as destruction and deforestation of forest resources, the the mixed forest to shrubland, water body and built up cost of protecting and restoring watersheds should be areas showed increment. The more forest cover in the considered as an annual investment towards sustain- catchment of the river and the dam the more water able reservoir management and hydropower genera- we’ll have in the rivers, dam and the more electricity tion (Mohit, 2018). It is necessary to consider the we’ll be able to get from these projects (Stickler et al., change in vegetation-related variables resulting since 2013). Thus, increasing the restoration of degraded the flooding of lower elevations areas is expected to forests and growing forest cover should be viewed as negatively affect the mega projects of hydroelectric an issue of national energy security as early as possible. dams (GERD) (Bobrowiec et al., 2017). In the last It was identified that the amount of forest land ten years, the amount of open area converted to forest converted to agricultural land in the Nile river catch- land or shrubland is only 3% in the catchment ment in the last ten years accounted for about 5.47% (Figure 10 and Table 7) indicating the trend of restor- (Figure 8 and 9). This implies that the trends of issues ing degraded lands by vegetation is significantly low. of degradation of the environment together with cli- This amount of change might be related more of with mate change can negatively impact hydropower GEOLOGY, ECOLOGY, AND LANDSCAPES 11 generation and its sustainability (Kaunda et al., 2012). 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Journal

Geology Ecology and Landscapes – Taylor & Francis

Published: Oct 30, 2022

Keywords: Satellite image; land use/land cover analysis; QGIS; grand Ethiopian Renaissance dam; remote sensing

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