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Figure 1. Location map of the study area: terrain slope, geographical setting of major river basins, and third-order and second-order network of streams.

Figure 1 Location map of the study area: terrain slope, geographical setting of major river basins, and third-order and second-order network of streams.

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Figure 2. A flow diagram for LULC mapping and change detection analysis modified from Ayele et al.” Image data were first classified by aggregating images into natural groupings or clusters present in the scene based on their inherent spectral properties. The false colour composite unsu- pervised land cover c assification maps (Figure 3) were pre- pared using the ISODATA (Iterative Self-Organizing Data Analysis) clustering algorithm.** During RS image classifica- tion and interpretations, field and local area knowledge of land cover types and patterns were used as training site development for the supervised classification. Following the supervised image classification, change detection analysis was made to quantify the rate, magnitude, and pattern of land cover change.*” The most common satellite image classification algo- rithms applied in this study is the ML classifier that touches a pro which a specific pixel belongs to a specific c bability density function and guesses the probability with ass. Larger devia- tions from the centre point will be allowed where a pixel is not int he area of a contesting category.°’ The M L-supervised clas- sification techniques work with Bayes theorem and uses a dis- criminant function to assign the pixel to the class with the hig hest probability.48 Each image pixel be ongs to the land cover class for which they have the highest membership likeli- hood following the Gaussian normal distribution function. Although the ML classifier is slow in computation and some- times unsafe in assuming Gaussian-distributed input data classes, yet, it is a more accurate statistical decision criterion in classifying overlapping signatures. The ML-supervised classi- fication derived from the Bayes theorem is given as follows: times unsafe in assuming Gaussian-distributed input data
Figure 2. A flow diagram for LULC mapping and change detection analysis modified from Ayele et al.” Image data were first classified by aggregating images into natural groupings or clusters present in the scene based on their inherent spectral properties. The false colour composite unsu- pervised land cover c assification maps (Figure 3) were pre- pared using the ISODATA (Iterative Self-Organizing Data Analysis) clustering algorithm.** During RS image classifica- tion and interpretations, field and local area knowledge of land cover types and patterns were used as training site development for the supervised classification. Following the supervised image classification, change detection analysis was made to quantify the rate, magnitude, and pattern of land cover change.*” The most common satellite image classification algo- rithms applied in this study is the ML classifier that touches a pro which a specific pixel belongs to a specific c bability density function and guesses the probability with ass. Larger devia- tions from the centre point will be allowed where a pixel is not int he area of a contesting category.°’ The M L-supervised clas- sification techniques work with Bayes theorem and uses a dis- criminant function to assign the pixel to the class with the hig hest probability.48 Each image pixel be ongs to the land cover class for which they have the highest membership likeli- hood following the Gaussian normal distribution function. Although the ML classifier is slow in computation and some- times unsafe in assuming Gaussian-distributed input data classes, yet, it is a more accurate statistical decision criterion in classifying overlapping signatures. The ML-supervised classi- fication derived from the Bayes theorem is given as follows: times unsafe in assuming Gaussian-distributed input data
Figure 3. Comparative 10-class ISODATA unsupervised classification map for Landsat images: (A) 2005 and (B) 2009.
Figure 3. Comparative 10-class ISODATA unsupervised classification map for Landsat images: (A) 2005 and (B) 2009.
Trend of land cover change for the last 20 years Figure 4. Trend and magnitude of land cover change for the past 20 years: a call-out map for the 2014 land cover distribution
Trend of land cover change for the last 20 years Figure 4. Trend and magnitude of land cover change for the past 20 years: a call-out map for the 2014 land cover distribution
Y is the percent coverage of land cover unit after time t (years). These results are based on 5 multi-temporal cloud-free Landsat Thematic Mapper images to represent the trend of land cover change for the past 2 decades. However, the fit equation and the corresponding trend may vary with image spatiotemporal resolution, required level of spatial detailing, human activity, and unpredicted climate change. Table 1. Best fit models to analyse the trend of land use land cover change dynamics.
Y is the percent coverage of land cover unit after time t (years). These results are based on 5 multi-temporal cloud-free Landsat Thematic Mapper images to represent the trend of land cover change for the past 2 decades. However, the fit equation and the corresponding trend may vary with image spatiotemporal resolution, required level of spatial detailing, human activity, and unpredicted climate change. Table 1. Best fit models to analyse the trend of land use land cover change dynamics.
Table 2. Land cover distribution in hectares and percent coverage of 12 major land cover units from 1995 to 2014. Pre-classification change detection. The NDVIis a function of 2 bands: the red and near-infrared spectral band. The NDVI is calculated on a per-pixel basis as the normalized difference between near-infrared and red bands given as follows:
Table 2. Land cover distribution in hectares and percent coverage of 12 major land cover units from 1995 to 2014. Pre-classification change detection. The NDVIis a function of 2 bands: the red and near-infrared spectral band. The NDVI is calculated on a per-pixel basis as the normalized difference between near-infrared and red bands given as follows:
Healthy green vegetation, however, has stronger near-infra- red reflectance thereby providing NDVI values close to +1.°°
Healthy green vegetation, however, has stronger near-infra- red reflectance thereby providing NDVI values close to +1.°°
Figure 5. NDVI maps for (A) 2005 (left) and (B) 2009 (right): grey-scale representation (top). Colour representation (bottom) of bidecadal NDVI values fo! (A) 2005 and (B) 2009.
Figure 5. NDVI maps for (A) 2005 (left) and (B) 2009 (right): grey-scale representation (top). Colour representation (bottom) of bidecadal NDVI values fo! (A) 2005 and (B) 2009.
Figure 6. DNDVI-based land cover change detection for the year (A) 2005 and (B) 2009.
Figure 6. DNDVI-based land cover change detection for the year (A) 2005 and (B) 2009.
Figure 7. Trend, rate, and magnitude of land cover change for the major land cover units.
Figure 7. Trend, rate, and magnitude of land cover change for the major land cover units.
Table 3. Post-classification change detection matrix for the past 20 years from 5 cloud-free Landsat Thematic Mapper images, 1995 to 2014. cultivable land, land degradation on steep and hilltop slopes, poor land management, rising demand for cash crops, partial implementation of policy strategies, and loose land and resources administration. The post-classification matrix (Table 3) shows the indi- vidual class percentage and change statistics for 5 cloud-free multi-temporal Landsat TM images. The percent land cover distribution, percent change in the areal extent of each land
Table 3. Post-classification change detection matrix for the past 20 years from 5 cloud-free Landsat Thematic Mapper images, 1995 to 2014. cultivable land, land degradation on steep and hilltop slopes, poor land management, rising demand for cash crops, partial implementation of policy strategies, and loose land and resources administration. The post-classification matrix (Table 3) shows the indi- vidual class percentage and change statistics for 5 cloud-free multi-temporal Landsat TM images. The percent land cover distribution, percent change in the areal extent of each land
cover unit between initial and final state images, and rate of change expressed as the slope of the line, and the time rate of change of final and initial state images are clearly pre- sented in the table. In addition, the total land use unit areas, percentage coverage, and corresponding area ratio are indi- cated in Table 2. The tabular figure is self-explanatory to analyse the change statistics for the 12 major land cover units considered. The area of water bodies remained nearly constant with a changing slope significantly less than 0.01%. The only highest outliers were observed between 2005 (0.27%) and 2009 (0.27%), change slope of 0%, which covered 506 865 hectares of the total area. Similarly, in 2005, there was a substantial increase in the afro-alpine vegetation covering nearly 2% of t The forest coverage that showed a rapid decline he entire area. (1%) between 2000 and 2005 later showed a drastic increase (1.1%) between 2005 and 2009. The area remained nearly constant for the year 2014. Comparing the change statistics from 2000 to 2005 and 2005 to 2009, it can be seen that the rate of change of forest coverage was about -0.2% and 0.22%, respectively (Table 3). Results revealed that there was a general increase for cultivated and built-up areas and a decrease for riparian vegetation.
cover unit between initial and final state images, and rate of change expressed as the slope of the line, and the time rate of change of final and initial state images are clearly pre- sented in the table. In addition, the total land use unit areas, percentage coverage, and corresponding area ratio are indi- cated in Table 2. The tabular figure is self-explanatory to analyse the change statistics for the 12 major land cover units considered. The area of water bodies remained nearly constant with a changing slope significantly less than 0.01%. The only highest outliers were observed between 2005 (0.27%) and 2009 (0.27%), change slope of 0%, which covered 506 865 hectares of the total area. Similarly, in 2005, there was a substantial increase in the afro-alpine vegetation covering nearly 2% of t The forest coverage that showed a rapid decline he entire area. (1%) between 2000 and 2005 later showed a drastic increase (1.1%) between 2005 and 2009. The area remained nearly constant for the year 2014. Comparing the change statistics from 2000 to 2005 and 2005 to 2009, it can be seen that the rate of change of forest coverage was about -0.2% and 0.22%, respectively (Table 3). Results revealed that there was a general increase for cultivated and built-up areas and a decrease for riparian vegetation.
these land cover maps. We used the ML classifier to create the signature class of significant land cover category and PCC anal- ysis to assess the change dynamics. Image differencing and sta- tistical change detection techniques were used to quantify the spatial coverage of the different land use units for the year 2014 (Figure 8). respectively, at the expense of the other land use units labelled by negative values.
these land cover maps. We used the ML classifier to create the signature class of significant land cover category and PCC anal- ysis to assess the change dynamics. Image differencing and sta- tistical change detection techniques were used to quantify the spatial coverage of the different land use units for the year 2014 (Figure 8). respectively, at the expense of the other land use units labelled by negative values.
15% to 30% and 30% to 60%, respectively. However, the per- cent coverage of cultivated land dropped to 1.6% of the total (4.1%) for agriculturally inaccessible slopes (slope >60%). For terrain slopes between 0% to 2% and 5% to 8%, nearly half the total slope area, 0.5% of 1.1% and 2.1% of 2.9%, respectively, was covered by cultivated land. The remaining half percentage was represented by the other 11 land cover units. deforestation of higher slope areas. Cultivated land cover in the study area expanded substantially to the higher slopes when- ever agriculture was possible. Table 4 shows the spatial distri- bution of land cover with terrain slope. The highest percentage of land coverage, 674 826 hectares (35.9%) of land, was found in the slope ranging from 30% to 60% and the next higher value, covering 489021 hectares (26%) lied under the 15% to 30% slope range. The cultivated land unit covered nearly 18% and 22% of the total land cover for terrain slopes ranging from
15% to 30% and 30% to 60%, respectively. However, the per- cent coverage of cultivated land dropped to 1.6% of the total (4.1%) for agriculturally inaccessible slopes (slope >60%). For terrain slopes between 0% to 2% and 5% to 8%, nearly half the total slope area, 0.5% of 1.1% and 2.1% of 2.9%, respectively, was covered by cultivated land. The remaining half percentage was represented by the other 11 land cover units. deforestation of higher slope areas. Cultivated land cover in the study area expanded substantially to the higher slopes when- ever agriculture was possible. Table 4 shows the spatial distri- bution of land cover with terrain slope. The highest percentage of land coverage, 674 826 hectares (35.9%) of land, was found in the slope ranging from 30% to 60% and the next higher value, covering 489021 hectares (26%) lied under the 15% to 30% slope range. The cultivated land unit covered nearly 18% and 22% of the total land cover for terrain slopes ranging from
In general, the major proportion of the area for each slope range was covered by cultivated land. From the row total column, rounded to the nearest 10th, it can be seen that following the cultivated land (62.8%), wood (12.4%), bush (8.4%), shrub (6.1%), and grass (4.1%) land units accounted 31% of the total area. The remaining 7 land cover units covered nearly 6.2% of the entire area. The significant change in cultivated land areas across the slope was mainly due to accelerated human impact with popula- tion increase and subsequent agricultural land expansion. and 0.1% of the total area for increasing order of slopes indi- cated in the table. The distribution of the other land cover units can also be statisticized from the tabular data. The relation between terrain slope and land cover units for the year 2014 is detailed in Table 4. The slope map of the area (Figure 12, left) and the land use, soil, and slope overlay map for the country at large (Figure 12, right) were presented to spatially cross-refer- ence the distribution of different land cover units with soil and terrain slope for the study area.
In general, the major proportion of the area for each slope range was covered by cultivated land. From the row total column, rounded to the nearest 10th, it can be seen that following the cultivated land (62.8%), wood (12.4%), bush (8.4%), shrub (6.1%), and grass (4.1%) land units accounted 31% of the total area. The remaining 7 land cover units covered nearly 6.2% of the entire area. The significant change in cultivated land areas across the slope was mainly due to accelerated human impact with popula- tion increase and subsequent agricultural land expansion. and 0.1% of the total area for increasing order of slopes indi- cated in the table. The distribution of the other land cover units can also be statisticized from the tabular data. The relation between terrain slope and land cover units for the year 2014 is detailed in Table 4. The slope map of the area (Figure 12, left) and the land use, soil, and slope overlay map for the country at large (Figure 12, right) were presented to spatially cross-refer- ence the distribution of different land cover units with soil and terrain slope for the study area.
Table 4. Percent terrain slope and distribution of land cover units for the 2014 land cover map.
Table 4. Percent terrain slope and distribution of land cover units for the 2014 land cover map.
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Hydrology and water resources
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