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West Belessa District is one of 13 Central Gondar Zone Districts and 198 km away from Amhara Regional State Bahir Dar town in the North East direction. The district is also found 37038'17.8''-37057'29.1'' East in Longitude and 12013'37.8''-12039'23.6'' North in Latitude. It covers an area of about 98381.85 ha located southeast of Gondar town. The area is characterized by gently sloping with 94.51% of the area ranging 0-3% slope. The woreda drains into the Tekeze basin and is a distance 85 km from Gondar Town (Zone of the capital city). Particularly the study area is found Gund Tekelhimanot is located at an altitude of 2,361 m, 12°24? 65? N latitude and 37°41? 67? E longitude (Moges, Masersha, et al.,2018.).
Topography is related to relief characteristics and position on the landscape. West BelesaWoreda is almost gentle. Based on the output of slope reclassification from the Amhara region digital elevation model resolution (20m*20m) DEM 70.99% of the Woreda coverage area has less than 8% slope class, 23.96% area of the Woreda has 8-30% slope class and the remaining 5.05% area has >30% of the slope class according to FAO slope classification (WBWAO, 2018, unpublished data).
The district has Dega 25818.69 ha (26.25%), Dry Woyna Dega 72550.12 ha (73.75%) in Agro Climatic Zone Based on the out of Amhara DEM Traditional agro ecology reclassification, and the specific study area has Woyna Dega. It has an altitude of a maximum of 3167 meters at the highest area and a minimum of 1521 meters above sea level at the lowest land area. Based on ten years of rainfall data (2004-2013) the mean annual rainfall of the district is 824.4 mm ranging from 640.9 mm to 1005.1 mm. The average annual min temperature and max temperature are 16°C and 30°C respectively (Amhara Design and Supervision, unpublished data).
The most common agricultural economic system in the study areas is mixed agriculture. That is crop production and animal rearing. Sorghum, Check pea and Teff are the dominant crops with area coverage and sorghum is the first in its production for food and sale. Cattle, goats, and donkeys are the dominant livestock species. The major income sources of farmers in the area are selling goats, forest products, and crop production (WBWAO, 2018, unpublished data).
The population is a part of socioeconomic information dealing with people of a certain locality about environmental trends. The total population of Woreda is 181,974 among those WBWAO, 2018, unpublished data 91,897 are men and 90,077 are women and it has 44,519 household heads. All of this population was rural and urban dwellers (WBWAO, 2018, unpublished data).
The District has different climatic zones and topography due to this the vegetation of the district falls in various habitats but the dominant vegetation type occurs around Gund Teklehayimanot monastery and the surrounding area forest which is categorized under Dry evergreen Afro-mountains vegetation types and there are also other vegetation types which include riverside vegetation, seasonal wetland vegetation, open wooded grassland vegetation, and hilly area woodland vegetation. In the study area, there are different types of plant species including herbs, shrubs, and tree plant species. Some vegetation that is more common and available in the study areas are Oleaeuropaeasubspcuspidata (Weyra), Carissa edulis (Agam), Calpurnia aurea(Zigita), Acacia abyssinica ( Girar), Euphorbia tirucalli (kinship) and eucalyptus(bahirzaf) tree.
In this study, both spatial and temporal data gathered from both primary and secondary sources will be used. Primary data will be generated from the analysis of satellite images, field visits, and interviews with concerned bodies. Secondary data will be obtained from published and unpublished materials including books, journals, research articles, and census reports. Satellite imageries and ancillary data will be collected to identify the historical and recent land-use land cover of the forest. The image used for this study will be Landsat and Topographic maps and use a Digital elevation model (DEM) of the study area. This data will used to observe the relationship between topography, mainly altitude and slop for forest cover change by using 3DEM and ArcGIS software.
The researcher will use the satellite images of Landsat7ETM andlandsat8 OLI in the high resolution of 30m by 30m and multi-spectral bands in the study area.
The Landsat satellites have carried different sensor types these are Multispectral Scanner (MSS), Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM ), and the Operational Land Imager (OLI). For this study, the researcher will use the latest and recent sensors of Landsat 7 ETM for 2001 satellite data acquisition and Landsat 8 OLI for 2020 satellite data acquisition. Landsat 7 was launched in 1999 to continue the Landsat mission of providing up-to-date global satellite images. The main instrument onboard Landsat 7 is the Enhanced Thematic Mapper Plus (ETM ). Landsat 8 is the most recent satellite in the Landsat program and was launched in 2013. Originally called the Landsat Data Continuity Mission (LDCM), it is a collaboration between NASA and the United States Geological Survey (USGS). Landsat 8 carries two sensors, the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS)
The researcher will use different software and instruments i.e. ArcGIS 10.2.2, ENVI 5.0, ERDAS 2015, Trimble eCognition developer 64, and use hand handheld GPS instrument.
The applications of GIS are Effective for forest managers to monitor changing conditions and make intelligent decisions for sustainable care. GIS can be used to assess conditions through historical analysis and land-use practices. Modeling enables users to test and consider options in both temporal and spatial contexts. Geospatial records provide forest managers with a baseline for evaluating plans. Some specific image processing operations will be done using the ArcGIS software version 10.2.2. ERDAS 2015 software will used to process the satellite imagery data and ENVI 5.0 software will used to classify, analyze, preprocess, and process these land use and forest cover data. In addition, Trimble eCognition software will be used to design improve, accelerate, and automate the interpretation of geospatial data and geospatial data analysts, giving full flexibility and power to solve even the most challenging remote sensing project data.
Ground truth points will be collected using GPS with the help of a local guide and draft classified maps derived from satellite images with reference years. Besides, interviews will held with the KIIs during the field observation
To obtain tangible and practical information about the forest cover changes, key informant interviews will be conducted with the elders. The researcher will interview 21 key informants (KIIs) will conduct in the study area. Out of them10 monks who live 25 years and above in the Gund Teklehaymanot monasteries, 10 aged local people will select a good awareness knowledge about the natural resources and the rest one is will interview the forest resource officer from West Belesa agricultural office (WBAO). The participants will selected purposely based on their age (Who lived more than 25 years and above in the study area) except the officer, have the knowledge and good information on the study area
To investigate the forest cover change and rate in the study years (2001-2020) cloud-free Landsat 7ETM 2001 to 2013 and Landsat8 OLITIRS 2014 to 2020 were downloaded from freely available United States Geological Survey (USGS) Center for Earth Resources Observation and Science (EROS) via (https:earthexplorer.usgs.gov). To reduce the effect of seasonal variability images will be downloaded between February and April, two months is less in cloud cover appearance in the study area and also the availability of time-series images. In addition to this, the researcher will collect data by using field observation and KII interviews. The key informants will be selected purposively, based on their age and knowledges of the study area. The researcher follows the following flowchart steps. It shows the steps followed beginning from the acquisition and classification of multi-temporal satellite images of the study area to the extraction of the required information both secondary and primary data to answer the research questions.
The researcher will analyze the data based on the above flow charts. So the researcher will use different software for analyzing the data. For the image processing and enhancementusingse ERDAS Imagine 2015 software, to use Trimble eCognition developer 64 for Supervised Classification, the Object-Based Image Segmentation Classification, for change detection analysis, will use ENVI 5.0 software and finally will use ArcGIS tool for different purposes of analysis and map compositions. For socio-economic data, SPSS Software and Microsoft Office Excel will applied to analyze the qualitative, data The result will be presented in maps and narratives, and summarized by descriptive statistics such as frequency tables, figures, and graphs.
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