Volume 9, Number 4 (July 2022):3703-3714, doi:10.15243/jdmlm.2022.094.3703 ISSN: 2339-076X (p); 2502-2458 (e), www.jdmlm.ub.ac.id
Open Access 3703 Research Article
Land management, dynamics and vegetation vulnerability analysis in the Guna-Tana watershed as a predictor of land degradation, using remote sensing data
Mulualem Asfaw Ejegu1*, Kinda Gebyahu Reta1, Endalkachew Sisay Yegizaw1, Debrie Mersha Mekonnen1, Tiku Melak Dirar3, Gebrie Kassa Wassie1, Belay Zeleke Biru2, Melak Abebe Tegegne1, Yosef Gebremikeal Dubale4
1 Department of Geography and Environmental Studies, Debre Tabor University, Ethiopia
2 Department of Geography and Environment Studies, Injibara University, Ethiopia
3 Department of Urban Land Administration, Kotebe Metropolitan University, Addis Ababa, Ethiopia
4 Department of Sociology, Debre Tabor University, Ethiopia
*corresponding author: [email protected]
Abstract Article history:
Received 7 March 2022 Accepted 27 May 2022 Published 1 July 2022
The vegetative coverage throughout the world is rapidly changing, which also directly affects the land degradation. Therefore, this study was intended to investigate the vegetation vulnerability analysis triggered by land use/land cover (LULC) dynamics as an indicator of land degradation conditions using Remote Sensing and GIS in Guna-Tana watershed. Trend studies and cross-sectional research design was employed, which produces data from the population at a particular point in time and to examine patterns of change with a mixed research approach to examine the degree of influence to measure the sensitivity analysis.
A multi-criteria decision evaluation was used to create a vegetation vulnerability map for the research area. The vulnerability model was run using four sets of parameters: closeness to the road, slope, settlement closeness, and land use planning. Landsat imageries of 1995 and 2020 was used to conduct a comparative study of land use pattern. The study area has experienced a sequence of land degradation from 1995 to 2020, according to the results of multitemporal data. Agricultural and built-up lands have increased throughout this time, while forest and shrub land has decreased. The vegetation vulnerability of the area also shows that 19.23% extreme vulnerable and 67.03% very strongly vulnerable which is more than 80% of the area is highly vulnerable to vegetation.
Vegetation suitability and land management evaluation is critical for determining the risk of land deterioration, that shows the adverse effects on ecological elements due to a decrease in metabolic capacity and patch disintegration processes.
Keywords:
GIS Guna-Tana RS vegetation vulnerability
To cite this article: Ejegu, M.A., Reta, K.G., Yegizaw, E.S., Mekonnen, D.M., Dirar, T.M., Wassie, G.K., Biru, B.Z., Tegegne, M.A. and Dubale, Y.G. 2022. Land management, dynamics and vegetation vulnerability analysis in the Guna-Tana watershed as a predictor of land degradation, using remote sensing data. Journal of Degraded and Mining Lands Management 9(4):3703- 3714, doi:10.15243/jdmlm.2022.094.3703.
Introduction
Vegetation Vulnerability is the tendency of vegetation coverage clearance, that is forced to change other land cover types (Raphael, 2018) by human activities, pests
and diseases, and natural hazards (Sun et al., 2019). A virtuous vegetation condition of an area is associated with a good ecosystem health condition and productivity (Babiso et al., 2017). Land exploitation must be aligned to the land's potential, the ecosystem,
Open Access 3704 and global climate systems (Kidane et al., 2012;
Minale and Gelaye, 2019). Land use/land cover (LULC) change was among the most evident consequences of mankind altering the natural ecosystem, and it has a profound impact on the local, regional, and global environment (Haregeweyn et al., 2006; Ahmed et al., 2012; Geremew, 2013),
The world’s vegetation coverage is projected to be around 31% of the entire land surface area (Ibrahim, 2017; Faridatul and Ahmed, 2020; Jia et al., 2020). The radiative and non-radiative characteristics of the surface are affected by changes in vegetation cover (Smith et al., 2014). The impact of conflicting physicochemical activities on the earth's surface reflectance varies spatially and temporally and depending on the specific vegetation change and baseline temperature can result in warming or cooling (Dale et al., 2017). Climate change has an impact on vegetation cover, which is an important component in ecological sustainability, land use management, and land suitability planning (Song et al., 2010). Hence, the devise strategies to reduce the adverse vegetation susceptibility is of utmost importance to the planners of ecosystem health condition management programs in these regions (Kang et al., 2015).
Ethiopia is a country of geographical diversity and rich in biodiversity from arid lowlands in the east to tropical rainforests in the west; It is often known as
“the roof of Africa” due to its mountainous nature such as Ras Dashen, Bale, and Guna Mountains (Ismail, 2010). Approximately 66 percent of Ethiopia's terrain is composed of drought-prone areas, that are affiliated with tropical deciduous forests (Alemu et al., 2015).
The two dominant plants that cover huge portions of the dryland areas are Combretum-Terminalia woodlands and Acacia-Commiphora woodlands (Eshete et al., 2011). Ethiopian woodlands are now under significant stress and are reducing due to of consumption for manufacturing, including the expansion of agriculture (Sahalu, 2014).
Guna Mountain is a source of many rivers including those currently used as a source of hydroelectric power such as Tekeze, Blue Nile, and Lake Tana (Zhang et al., 2017). It is also endowed with unique and endemic species of fauna and flora associated with the Afro-alpine and Afro-montane ecosystems (Temesgen, 2014; Gella, 2018), However, the Guna-Tana watershed at present characterized by severe and widespread natural resources, mainly vegetation and soils, are being degraded, due to deforestation, overgrazing and soil erosion that led to land degradation, largely caused by humans who are encroaching the very steep slopes for farming (Jadhav et al., 2017). Changes in land-use patterns influence the environment and biotic variety of the area Guna- Tana Watershed which is covered with agriculture, grazing land, and other shrublands. As some findings were undertaken at some micro-watersheds of the Guna tana watershed (Birhanu et al., 2016) and the Upper Blue Nile watershed identical (Hurni et al.,
2005; Mellander et al., 2013; Haregeweyn et al., 2017), there is a threat on environment emanated and vegetation vulnerability. To minimize the ongoing accelerated degradation of the Guna-Tana watershed vegetation, immediate and integrated action is required. So the objective of this study is to detect and examine the vegetation vulnerability analysis triggered by land use/land cover dynamics as a metric for ecosystem health in the Guna-Tana watershed, using remotely sensed evidence data, Northcentral Highlands of Ethiopia (Fraters, 2012).
Materials and Methods Study area location
Guna-Tana watershed is absolutely located within 37°30′-38°30′ East Longitude and 11°30′-12°30′
North Latitude (Figure 1). Guna-Tana watershed is relatively located in Amhara National Regional State's south Gondar Zone, and it stretches from the top of Guna Mountain to Lake Tana. encompasses 437648.28 hectares of land with an altitude difference ranging from more than 4000 meters above mean sea level at the Guna Mountain's peaks to 1700 meters above sea level at the low-lying Fogera Plains. Kola (Tropical) to Wurch (Afro-alpine) ecologies are characterized by agro-ecological zones that may be found at the research site (Birhanu et al, 2016; Gella, 2018).
Research design
Vegetation vulnerability besides LULC correlation of the research was employed by using trend study and a cross-sectional research design produces data from the population at a particular point in time and to examine patterns of change that have already occurred with a mixed research approach to examine the degree of influence sensitivity analysis. LULC dynamics matrixes and the vegetation vulnerability was developed quantitatively, finally, the two results were correlated numerically and described in qualitatively.
Sampling techniques
For this study, stratified random sampling was chosen as the sampling method. In stratified sampling, the populations were assembled based on the similarity of spectral appearances by using satellite imagery. The stratum was more homogeneous than the entire population. Each LULC class will be painstaking as a layer or a cluster. The combination of these traits makes it possible to produce an unbiased selection of clusters for respectively LULC category for totals.
Sample size
For each LULC the number of sample plots was 30 n, where n is the number of bands. and 30 is the maximum spatial resolution of a satellite image. A cluster of 5 sample units, including the midpoint, was taken for one LULC, with a 15-meter radius (for the lowermost spatial resolution of a satellite image) with
Open Access 3705 a sample unit size of 15 meters. A sample unit covers
an area of 437648.28 hectares. Seven land use land cover class was identified, such as forest, shrub and bush land, grass land, agriculture, built-up, wetland, and water body of a total sample of 2,520 (12*30*7
(number of bands * spatial resolution * number of land class)) ground control points was collected for these seven LULC class in the Guna-Tana water shade (Mount Guna, Farta Woreda, Gumara Water shade, Rib water shade).
Figure 1. Study area (Guna Tana Watershed).
Data source
The ground truth data was taken to determine the location of different land uses in enable to use them as training sites during categorization and to evaluate the accuracy of the classification. Field observation will be carried out for accuracy assessment and to see the existing general situation of the Guna-Tana watershed and also to cross-check the data collected (Table 1).
Data analysis
A GIS application was employed to manage, analyze, combine, correlate, and map spatial data. The basic data preprocessing methods were applied to satellite data using ERDAS imagine 2015 and ENVI 5.2 application, commencing with picture rectification, restoration, improvement, image classification, and results analysis.
Table 1. Data source of Guna-Tana watershed.
Data Resolution Source of Data
DEM 30 m USGS (http://www.usgs.gov/)
Landsat 30 m USGS (https://earthexplorer.usgs.gov/)
Settlement EthioGIS3 Points data Ethiopian Geospatial Information Institute Road EthioGIS3 Points data Ethiopian Geospatial Information Institute
The vegetation exposure simulation is run using four parameters of the model: slope variation, closeness to the road, land use planning, and closeness to settlement. Before the design parameters were integrated with the weighted sum evaluation, all input variables were proportionally evaluated. Moreover,
depending on its impact on the vegetation's exposure, all variables have been classified into seven vulnerability classes scale from 1 to 7. The susceptibility ranges from 1 to 7, with 1 becoming the least susceptibility impact and 7 having one of most susceptibility impact.
Open Access 3706 Slope gradient
The research area's slope steepness ranges from 0 to 74.87 degrees (Figure 2a), Because the steepness of a slope can easily affect the ease with which a vegetative resource can be utilized, the slope has been considered as a parameter estimate in vegetation vulnerability analysis. The person participating in the evaluation is thought to become less susceptible in areas with hillsides, while regions with rolling hills are believed to be highly vulnerable.
Proximity to settlement
People living in the Guna-Tana watershed area, like most of Ethiopia, rely heavily on plants for vigor, construction resources, and social standards. In more than six towns (Guna Begimeder, Gasiay, Debre Tabor, Alem Ber, and Woreta town) within the research area, even those within a 9-kilometer straight- line length, the vegetation has been influenced by the area boundaries. The straight-line distance between townships was calculated using ArcGIS 10.8's spatial
analyst tool (Figure 2b), shows that the distance between settlements ranges between 0 and 30 kilometers and the locations that are remote from towns have been given lower values than the places that are close to settlements.
Proximity to roads
Solitary of the most important influences in facilitating the extraction of natural resources is the road. Because it would be difficult to overcome natural barriers, areas without accessible roadways are less likely to be damaged by human interference. The same can be said regarding the use of plants. The vegetation near access highways is more likely to be exploited than the vegetation near less accessible roadways. According to (Higginbottom and Symeonakis, 2014), roads influence the configuration of vegetable communities in environments together with asphalt and earth roads (Minale and Gelaye, 2019). The distance between the roadways has been separated into seven equal interval classes ranging from 0 to 70 kilometers (Figure 3b).
Figure 2. Slope data (a) and proximity to settlement data (b).
Land management
The research area's LULC practices were classified depending on the type and degree of involvement of the people in vegetation management on that particular land use type. In the study area, seven categories have been identified as different spatial planning units (Figure 3a and Table 2). The susceptibility of these site use management units to plant degradation was then reclassified. According to Anderson et al. (1976), the overall accuracies performed between 1995 and 2020 were 93.18 percent and 96.67 percent, respectively the true positive value computed from a confusion matrix should be greater than 85% for a trustworthy land
cover categorization. As a result, the overall accuracies for both LULC classifications were greater than 85%, indicating that it is a trustworthy land use and land cover categorization system. Overall, Kappa Statistics for 1995, and 2020 were = 0.9211 and 0.9488 respectively, this indicates that the categorization procedure was avoided 92%, and 94.88% of the errors respectively that a completely random classification would generate, and also it is closer to one. Overall classification accuracy of 93.3%, and 96.67%, and overall, Kappa statistics of 0.911 and 0.9488 irrespective of 1995 and 2020 were achieved, and it is possible to apply it in the future. The inaccuracies could be caused by a variety of factors.
Open Access 3708 Table 2. Description of major land use land covers classes.
LULC Type Description
Forest Natural forest and plantation trees cover a large portion of the country.
Shrubland The woody plant is smaller than a tree and has several main stems arising at or near the ground.
Agriculture land The land area that is cultivated and used for agricultural Built-up Buildings and other man-made structures cover the land.
Grassland The ground is mostly planted in grasses, and it is utilized for grazing.
Wetland The area that the waters cover is the soil, swampy or marshy land.
Waterbody Water has been covering the land for a long time.
Figure 3. LULC class for land management (a) and Proximity of Road (b)
Multi criteria decision making
Multi-Criteria Analysis Method (MCDA) is used in GIS environmental to aggregate layers of spatial information reflecting criteria and to specify how the layers are combined. Each criterion is represented by a map layer. PCMs (Pairwise Comparison Matrices)
compare all conceivable pairings of criteria to determine which is more important. According to Saaty (1980), a range of 1 to 9 is adequate, with 1 representing the equivalent significance of each factor and 9 indicating that the criterion under consideration is extremely important in contrast to the other factors (Table 3).
Table 3. A scale value of pairwise comparison matrices.
Intensity of
Importance Definition Explanation
1 Equal importance in a pair Two criteria contribute equally to the objective
3 Moderate importance Judgment and Experience slightly favor one criterion over another
5 Strong importance Judgment and Experience strongly favor one criterion over another
7 Very strong importance Judgment and Experience very strongly favor one criterion over another
9 Extreme importance The evidence favoring one criterion over another is of the highest possible validity
2,4,6,8 Intermediate values When compromise is needed Reciprocals Values for inverse
comparison
If criterion I had one of the above numbers assigned to it when compared with criterion j, then j has the reciprocal value when compared with i
Open Access 3708 Following the definition of the criteria, a structured
interview with professionals and regional specialists was done to determine a scale of 1 to 9 on which the experts could rate the relative importance of each
criterion's specific criteria. The specialists were picked for their expertise in the subject area. A literature review was also carried out to assess the relative value of each criterion (Table 4).
Table 4. The scale of relative importance.
Criteria More importance Equal
importance
Less importance Criteria
9 7 5 3 1 3 5 7 9
Land Management Land Management
Slope Slope
Settlement Settlement
Road Road
The value of all the criteria was triangulated based on the magnitude of impact for vegetation vulnerability, that was generated from structural interview for forestry, natural resource exporters of South Gonder zone agricultural office. The interview result was supported by different reviews to make a decision about the variables that weight value determination
and eigenvector identification. Based on the result (Table 5) shows that to identify vegetation vulnerability site and land degradation 51% is determined by land management practice. The remaining 27%, 12% and 10% was determined by slope gradient, settlement condition and road, respectively.
Table 5. The pairwise comparison matrix of the expert’s opinions.
Standards Land Management Slope Settlement Road Eigenvector (Priority vector)
Land Management 0.54 0.64 0.38 0.50 0.51
Slope 0.18 0.21 0.38 0.30 0.27
Settlement 0.18 0.07 0.13 0.10 0.12
Road 0.11 0.07 0.13 0.10 0.10
Results and Discussion LULC of 1995 and 2020
According to 1995 LULC (Figure 4a) shows that the area was dominated by forest, shrub and bush, grass, agriculture, built-up, wetland, and waterbodies which covers 95072.20 hectares, 70781.54 hectares, 66037.71 hectares, 176751.33 hectares, 18189.95 hectares, 10495.48 hectares, 320.08 hectares of the study area respectively. The LULC result of 2020, (Figure 4b and Table 6) agriculture land, which occupies the most acreage, has a significant proportion of the region 254499.86 hectares (58.15%), followed by grass land which accounts for 51274.74 hectares (11.72%), built-up/urban covers 49850.86 hectares (11.39 %), shrub land which covered by 48177.59 hectares (11.01%), forest which accounts for 18537.48 hectares (4.24%), wetland covers 15022.22 hectares (3.43 %), and water body covers the minimum area coverage than any other land cover classes which accounts 285.53 hectares (0.07%) of the total area coverage 437648.28 hectares of the study area.
According to the results of the 2020 land use land cover categorization, agriculture covers the majority of the research area, with shrubs and woodland
accounting for 15.25 percent of the study area. This indicates that the study area would be dominated by agriculture for 25 years of the year interval from 1995 up to 2020 has a highly degraded and does not have a balanced ecosystem.
LULC change matrix
After all the LULC identification was achieved by evaluating the change pattern and matrix of the research region using the categorized LULC transition in ENVI 5.2 to see the land cover and land-use change from one land use land cover to another. As shown in Table 7, only 18537.48 hectares of forest land remained in 2020, with the remainder cleared and converted to other land use/cover types, including 28992.31 hectares of shrubland, 19274.52 hectares of grass land, 25155.77 hectares of agriculture, 6285.18 hectares of built-up, and 3646.19 hectares of wetland.
Furthermore, only 15.25 percent of the area was covered with vegetation in 2020, with the remainder being converted to other LULC categories. In general, when comparing the 1995 land use land cover class to the 2020 land use land cover class within a 25-year gap, agriculture and built-up/urban show a substantially accelerated pace, whereas forest,
Open Access 3709 shrubland, and grass land show a significantly
dropping rate.
Vegetation vulnerability site
Vegetation vulnerability is a phenomenon in which many push factors act at varying scales to cause vegetation decline. Slope gradient is a term used to describe how steep a slope is. The majority of the northeastern and central parts of the study area are generally less steep than the southwestern part (Figure 5a). This indicates that because the extent has a moderate gradient, the watershed is easily accessible, and the soil is productive. This, in turn, leads to impenetrable residents’ settlement in directive to exploit the productive soil for farming purposes. As a result, the settler's zeal and determination to force the terrestrial as far as the vegetation for additional farmland makes the vegetation area more vulnerable.
The gradient of the research area varies from 0 to 74.87 degrees in this investigation. Since steep terrain areas are much more difficult to cultivate for farming and to settle people than gently sloping places, locations with steep slopes are generally less vulnerable, whereas those with gentle slopes are very exposed.
The settlement's proximity, human-vegetation integration, and socioeconomic activity are both high.
The vegetation has changed dramatically in areas with only minor intensifications in social action. Social influences related to an imaginable shift from trees to grasses are linked to increases in human activity.
People living in the Guna-Tana watershed area are profoundly reliant on vegetation, as are people living in most parts of Ethiopia. As shown in Figure 5b, mainly as a result of the settlement, the southwestern border, middle, and certain areas of the research region are much more prone to vegetative deterioration. As a result, rising settlement or dense settlement in the designated sections of the territory indicates that all of the issues listed above occurred at the same time.
Proximity to roads, aside from the distance from the road verge, plant density may be affected by the size, sound, automobile stream of traffic, and thoroughfare mass. Human interference was much less probable in areas where there is no accessibility to roadways because natural barriers are difficult to overcome. The same can be said for the use of vegetation. Connect routes' vegetation resources are much more likely to be utilized than fewer communicators.
Figure 4. LULC of 1995 (a), and LULC of 2020 (b).
Table 6. The LULC change of 1995 and 2020.
LULC Type 1995 2020
Area (hectare) Area (%) Area (hectare) Area (%)
Forest 95072.20 21.72 18537.48 4.24
Shrub and Bush Land 70781.54 16.17 48177.59 11.01
Grass Land 66037.71 15.09 51274.74 11.72
Agriculture 176751.33 40.39 254499.86 58.15
Built-up 18189.95 4.16 49850.86 11.39
Wetland 10495.48 2.40 15022.22 3.43
Water Body 320.08 0.07 285.53 0.07
Total 437648.28 100.00 437648.28 100.00
Open Access 3710 Table 7. LULC Matrix of 1995 up to 2020.
Matrix’s LULC Class in 2020 (hectare)
Forest Shrub Land Grass Agriculture Built-up Wetland Water Body Grand Total
LULC Class of 1995 (hectare) Forest 11718.22 28992.31 19274.52 25155.77 6285.18 3646.19 0.00 95072.20 Shrub and Bush Land 2347.52 11565.33 18130.90 23233.54 12669.74 2834.50 0.01 70781.54
Grass Land 2336.95 5563.47 12075.69 27910.52 12542.01 5608.96 0.11 66037.71
Agriculture 76.66 396.98 287.16 171354.18 4599.18 37.12 0.05 176751.33
Built-up 1272.28 1244.43 584.99 944.04 13451.76 692.44 0.00 18189.95
Wetland 785.84 415.07 920.28 5900.88 302.98 2170.23 0.19 10495.48
Water Body 0.00 0.00 1.20 0.93 0.00 32.78 285.18 320.08
Grand Total 18537.48 48177.59 51274.74 254499.86 49850.86 15022.22 285.53 437648.28
Open Access 3711 Figure 5. Slope gradient vulnerability classes (a) and proximity to settlement (b).
Figure 6a divides the horizontal length of the highway into seven high similarity groups scaling from 0 to 63 kilometers. It also can be determined from (Figure 6a) that the northcentral and northern portions of the study region have a free road frequency, whilst the Western boundary of the research region has a lower population density. People come to settle along the road to benefit from the activity. As a result, people are using more wood for construction and firewood. As an outcome, logging and vegetation deterioration is a problem in some areas. As just a reason, regions with a much more developed road network are far more sensitive than regions with a less developed road system. Land management has been characterized based on human intervention in vegetation suitability. Agriculture,
built-up, shrubland, grassland, wetland, water body, and woodland parts of the study area are classified into seven classes (Figure 6b). These terrestrial practice managing units were before resected created on their susceptibility to vegetation degradation. Condensed woodland is the smallest exposed land-use type.
Slightly vegetation coverage like shrubland is regarded as the subsequent smallest exposed kind of land use management because it preserves vegetation in conjunction with other practices. Because of the increased demand for settlement and agricultural land, grassland management is thought to contribute the most to vegetation degradation. Based on the foregoing, it is concluded that southerly regions are deplorable (Figure 6b).
Figure 6. Land management (a) and proximity of road (b).
Vegetation vulnerability findings show that areas with gentle slopes, close to the much more sensitive places to plant destruction are streets and populations, as well as regions having improper land use planning methods,
including desertification. The lowest sensitive to plant destruction have been recognized as regions on hillsides, far from highways and populations, also with adequate land uses. The grass is much more prone to
Open Access 3712 plant susceptibility than some other species, according
to the study. This is due to the studied area's rising population necessitating the creation of new farming
and settlement properties. The Guna mountain's pike point has the least vegetation vulnerability. while forest land is in a low vulnerable zone (Figure 7).
Figure 7. Level of a vegetation vulnerability map.
The vegetation vulnerability of the area also shows that 84143.85 hectares or 19.23% are extremely vulnerable, 293366.85 hectares or 67.03% are very strongly vulnerable, and 29264.21 hectares or 6.69%
are strongly vulnerable, which is more than 80% of the area is highly vulnerable for vegetation. Spatially around the towns and public facility areas are extremely vulnerable to vegetation (Table 8).
Table 8. Level of vegetation vulnerability with area coverage.
Level of
Vulnerability Area
(hectare) Area (%) Extreme Vulnerable 84143.85 19.23 Very Strongly
Vulnerable 293366.85 67.03
Strongly Vulnerable 29264.21 6.69 Moderately
Vulnerable 28230.05 6.45
Less Vulnerable 1882.54 0.43
Very Less Vulnerable 760.18 0.17
Total 437647.68 100
Land degradation condition
In the 21st century, land degradation has become a severe environmental challenge as a consequence of climate change influence and global warming. The aggregate of conditions in populations, streams, landforms, and coastlines determines land degradation and ecosystem health. Vegetation suitability evaluation is critical for determining the risk of land deterioration, especially in regions that are still productive. Pressured vegetation can have adverse effects on other ecological elements due to a decrease in metabolic capacity and/or patch disintegration processes. The vegetation vulnerability result shows that the area is highly vulnerable, which leads to a great risk of land degradation. Environmental health refers to a state of habitats and related functioning in their natural form. Evolutionary fluctuations in plant situations have an impact on these markers. However, the vegetation vulnerability associated with human activities that cause disturbance is increasingly affecting land, resulting in a wide range of alterations in the surroundings changes in climate help certain organisms, populations, and natural systems while others lose significantly.
Open Access 3713 Conclusion
Construction, farmland, and moist land reflect the growing land use/land cover shift exposure pattern over 25 years from 1995 to 2020, correspondingly, by 7%, 17%, and 1% of the overall land use land cover.
Nevertheless, even as purpose-built, farmland and wetland expand, the growth of forest land and shrub property coverage patterns shrink by 17 percent and 5%, including both, from of the total land use land cover covers. As a consequence, the proposed area vegetation coverage is declining rapidly in scope and rowdiness. The vegetation vulnerability of the area also shows that 84143.85 hectares or 19.23% are extremely vulnerable, 293366.85 hectares or 67.03%
are very strongly vulnerable, and 29264.21 hectares or 6.69% are strongly vulnerable, which is more than 80% of the area is highly vulnerable for vegetation.
Spatially around the towns and public facility areas are extremely vulnerable to vegetation.
Acknowledgement
Special thanks to Debre Tabor University for their financial fund and covering all the necessary costs, next to that Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and the United States Geological Survey for freely availing 10m spatial resolution DEM and satellite imagery.
References
Ahmed, B. and Ahmed, R. 2012. Modeling urban land cover growth dynamics using multi-temporal satellite images:
a case study of Dhaka, Bangladesh. ISPRS International Journal of Geo-Information 1(1):3-31, doi:10.3390/ijgi1010003.
Alemu, B., Garedew, E., Eshetu, Z. and Kassa, H. 2015.
Land Use and Land cover changes and associated driving forces in north-western lowlands of Ethiopia.
International Research Journal of Agricultural Science
and Soil Science 5(1):28-44,
doi:10.14303/irjas.2014.063.
Anderson, J.R. 1976. A land use and land cover classification system for use with remote sensor data (Vol. 964). US Government Printing Office.
Babiso, B., Toma, S. and Bajigo, A. 2017. Land use / land cover dynamics and its implication on sustainable land management in Wallecha watershed, Southern Ethiopia.
Global Journal of Science Frontier Research: H Environment & Earth Science 16(4) Version 1.0.
Birhanu, A., Frankl, A., Jacob, M., Lanckriet, S., Hendrickx, H. and Nyssen, J. 2016. Afro-alpine Forest Cover Change on Mt. Guna (Ethiopia). Conference: European Geosciences Union, 18(EGU2016-1992).
Dale, A., Fant, C., Strzepek, K., Lickley, M. and Solomon, S. 2017. Climate model uncertainty in impact assessments for agriculture: A multi-ensemble case study on maize in sub-Saharan Africa. Earth’s Future 5(3):337-353, doi:10.1002/2017EF000539.
Eshete, A., Sterck, F. and Bongers F. 2011. Diversity and production of Ethiopian dry woodlands explained by
climate and soil stress gradients. Forest Ecology and Management 261:1499-1509.
Faridatul, M.I. and Ahmed, B. 2020. Assessing agricultural vulnerability to drought in a heterogeneous environment:
A remote sensing-based approach. Remote Sensing 12(20):1-17, doi:10.3390/rs12203363.
Fraters, B. 2012. Monitoring of the soil quality and vegetation using data of remote sensing in Ukraine.
Netherland Environmental Agency, 50.
Gella, G.W. 2018. Impacts of integrated soil and water conservation programs on vegetation regeneration and productivity as an indicator of ecosystem health in Guna- Tana watershed: evidences from satellite imagery.
Environmental System Research 7, Article number: 2 (2018).
Geremew, A.A. 2013. Assessing the impacts of land use and land cover change on hydrology of watershed: a case study on Gligel-Abbay Watershed, Lake Tana Basin, Ethiopia. Bahir Dar, Ethiopia.
Haregeweyn, N., Poesen, J., Nyssen, J., De Wit, J., Haile, M., Govers, G. and Deckers, S. 2006. Reservoirs in Tigray (Northern Ethiopia): characteristics and sediment deposition problems. Land Degradation and Development 17(2):211-230.
Haregeweyn, N., Tsunekawa, A., Poesen, J., Tsubo, M., Meshesha, D.T., Fenta, A.A., Nyssen, J. and Adgo, E.
2017. Comprehensive assessment of soil erosion risk for better land use planning in river basins: case study of the Upper Blue Nile River. Science of the Total Environment 574:95-108, doi:10.1016/j.scitotenv.2016.09.019.
Higginbottom, T.P. and Symeonakis, E. 2014. Assessing land degradation and desertification using vegetation index data: current frameworks and future directions.
Remote Sensing 6(10):9552-9575,
doi:10.3390/rs6109552.
Hurni, H., Tato, K. and Zeleke, G. 2005. The implications of changes in population, land use, and land management for surface runoff in the upper Nile basin area of Ethiopia. Mountain Research and Development 25(2):147-154.
Ibrahim, Y.Z. 2017. Vegetation and Land Cover Change in the Context of Land Degradation in Sub-Saharan West Africa. Thesis submitted for the degree of Doctor of Philosophy at the University of Leicester, UK.
Ismail, M.H. 2010. Determining and mapping of vegetation using GIS and phytosociological approach in Mount Tahan, Malaysia. Journal of Agricultural Science 2(2):80-89, doi:10.5539/jas.v2n2p80.
Jadhav, A., Anderson, S., Dyer, M.J.B. and Sutton, P.C.
2017. Revisiting ecosystem services: assessment and valuation as starting points for environmental politics.
Sustainability 2017, 9(10):1755, doi:10.3390/su9101755.
Jia, H., Chen, F., Zhang, J. and Du, E. 2020. Vulnerability analysis to drought based on remote sensing indexes.
International Journal of Environmental Research and
Public Health 17(20):1-20,
doi:10.3390/ijerph17207660.
Kang, H., Xuxiang, L. and Jing, Z. 2015. GIS analysis of changes in ecological vulnerability using a SPCA model in the Loess Plateau of Northern Shaanxi, China.
International Journal of Environmental Research and
Public Health 12(4):4292-4305,
doi:10.3390/ijerph120404292.
Kidane, Y., Stahlmann, R. and Beierkuhnlein, C. 2012.
Vegetation dynamics and land use and land cover change
Open Access 3714 in the Bale Mountains, Ethiopia. Environmental
Monitoring and Assessment 184(12):7473-7489, doi:10.1007/s10661-011-2514-8.
Mellander, P-E., Gebrehiwot, S.G., Gardenas, A.I., Bewket, W. and Bishop, K. 2013. Summer rains and dry seasons in the upper Blue Nile basin: the predictability of half a century of past and future spatiotemporal patterns. PLoS ONE 8(7):e68461, doi:10.1371/journal.pone.0068461.
Minale, A.S. and Gelaye, S. 2019. Vegetation vulnerability analysis by using GIS and remote sensing techniques: a case study of Sensawuha Watershed, Ethiopia.
GeoJournal 6, doi:10.1007/s10708-019-10082-6.
Raphael, L.J. 2018. Vegetation land cover/use dynamics and their effects in Mbulu and Karatu Districts in the north- eastern highlands of Tanzania. International Journal of Geospatial and Environmental Research 5(1):1-25.
Sahalu, A.G. 2014. Analysis of Urban Land Use and Land Cover Changes: A Case Study in Bahir Dar, Ethiopia.
Castellon, Spain. Master Thesis, Universidade Nova de Lisboa, Lisbon, Portugal.
Smith, A.M.S., Kolden, C.A., Tinkham, W.T., Talhelm, A.F., Marshall, J.D., Hudak, A.T., Boschetti, L., Falkowski, M.J., Greenberg, J.A., Anderson, J.W., Kliskey, A., Alessa, L., Keefe, R.F. and Gosz, J.R. 2014.
Remote sensing the vulnerability of vegetation in natural terrestrial ecosystems. Remote Sensing of Environment 154:322-337, doi:10.1016/j.rse.2014.03.038.
Song, G., Chen, Y., Tian, M., Lv, S., Zhang, S. and Liu, S.
2010. The ecological vulnerability evaluation in southwestern mountain region of China based on GIS and AHP method. Procedia Environmental Sciences 2(5):465-475, doi:10.1016/j.proenv.2010.10.051.
Sun, C., Hurley, J., Amati, M., Arundel, J., Saunders, A., Boruff, B., and Caccetta, P. 2019. Urban Vegetation, Urban Heat Islands and Heat Vulnerability Assessment in Melbourne, 2018. Clean Air and Urban Landscapes Hub.https://www.planning.vic.gov.au/__data/assets/pdf _file/0018/440181/UHI-and-HVI2018_Report_v1.pdf.
Zhang, H., Wu, J., Gao, Y. and Chen, Z. 2017. Spatial assessment of ecological vulnerability in fuzhou district in China using remote sensing and GIS. Nature Environment and Pollution Technology 16(4):1303- 1312.
