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The study concluded that clearing of forest and large scale agriculture has destroyed vegetation cover threatening the existence of wildlife which to a great extent requires immediate measures to counterbalance this effect. Fingerprint GIS. Trends Journal of Sciences Research , 3 1 , Mayunga, Selassie. In: Trends Journal of Sciences Research. The TWI of the study area varied from 0.
The values were reclassified into five categories such as 0.
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The roughness index expresses the amount of elevation difference between adjacent cells of a digital elevation model DEM Undulated topography is characteristic of a mountainous region where weathering and erosion processes continuously modify the landscape of a rugged into a smooth and plane surface in long run The high weights are assigned for low roughness value and vice versa. Many physical processes such as hilltop, valley bottom, exposed ridges, flat plain, upper and lower slope actions on landscape are correlated with topographic position index Equation 4 given below was used for the estimation of TPI.
TPI ranges varied from TPI values zero indicate the flat ground surface. The high weights assigned for low TPI value and vice versa. Curvature is quantitative expression of the nature of surface profile and it can be concave upward or convex upward profiles Water tends to decelerate and tends to accumulate in convex and concave profile respectively. Curvature ranges of the study area varied from 4.
High weight is assigned for high curvature value and vice versa. Groundwater is a replenishable resource, but due to various kinds of anthropogenic activities and skewed developments, recharge of this precious life sustaining resource has been reduced significantly in the past 4—5 decades. Such information is essential for the design and implementation of structures for corrective measures to improve the groundwater recharge processes.
The hydrological settings of the Vamanapuram river basin reveals that groundwater occurs in the basin in unconfined aquifer, especially in the alluvium, laterite, weathered and fractured crystalline rocks, and also in semi-confined to confined aquifer in the deep seated fractured aquifers in the crystalline rocks Generally, alluvium is composed of sand, silt and clay which generally occur in the coastal plains and valleys of the basin. Laterite forms another potential aquifer in the basin, which is blanketed over both crystallines in the highlands and midlands and, the Tertiary and Quaternary sediments in the lowlands.
The groundwater availability is not uniform in space and time and therefore, detailed and accurate assessment of the groundwater resource is required. The weighted overlay method has been applied to generate the groundwater potential zones in the Vamanapuram river basin. The resulted map is divided into very high, high, moderate, low and very low groundwater potential zones and the aerial spread of these categories are 1.
As seen from the figure, very high and high groundwater potential zones occur predominantly in midland and lowland regions. The low and very low groundwater potential zones spread mainly in highlands and lowlands but comparatively less in the midlands. The low and very low groundwater potential zones occur in the migmatite complex, steep slope, high drainage density and reserved forests.
The study agrees well with the flow computational investigations of Sajikumar It is revealed that the flow direction of groundwater in the Vamanapuram river basin is towards southeast in the northwestern part, and towards south in the northeastern part. In the western side, flow is generally towards the coast.
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The groundwater potential zones delineated in the present study are further cross - validated with the results of the observation well data of the Central Ground Water Board CGWB A total of 34 observation wells are located in the area and all these wells were analyzed for the purpose. It is found that, the wells located in the very low and low groundwater potential zones have water yielding capacity in the range of 10—50 liter per minute LPM.
However, the wells located in moderate groundwater potential zones have water yielding capacity in the range of 50— LPM and the wells located in high and very high groundwater potential zones have water yielding capacity of — LPM. The rest of the wells 5 nos are not matched fully due to various reasons. Out of these five wells, four of them well no 4, 20, 23 and 28; see Fig. The remaining well well no — 18 is low yielding because of proximity to low - moderate groundwater potential zone.
The remaining wells are characterized by water yield of 10—50 LPM. From the study, it can be concluded that the GIS and AHP - based techniques of delineation of groundwater potential zones adopted herein is a useful method that can be applied while going for river basin - based planning and developments of tropical and sub-tropical regions having varied geo — environmental setting.
According to the final output map, the study area could be classified into five distinct ground water potential zones such as very high, high, moderate, low and poor. Low and very low groundwater potential zones are situated in the migmatite complex formation of the river basin. The delineated groundwater potential zones map was validated using the groundwater flow and groundwater prospects information of the study area. Since most part of the study area are covered by agriculture land, this study will help to improve the irrigation facility and develop the agriculture productivity of the area.
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Russo, T. Assessment of managed aquifer recharge site suitability using a GIS and modeling. Groundwater 53 , — Ozdemir, A. GIS-based groundwater spring potential mapping in the Sultan Mountains Konya, Turkey using frequency ratio, weights of evidence and logistic regression methods and their comparison. Razandi, Y. Application of analytical hierarchy process, frequency ratio, and certainty factor models for groundwater potential mapping using GIS. Earth Sci.
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Mogaji, K. Regional prediction of groundwater potential mapping in a multifaceted geology terrain using GIS-based Dempster—Shafer model. Chenini, I. Groundwater recharge study in arid region: an approach using GIS techniques and numerical modeling. Maps showing the physical SWC structure detected and mapped in the Ethiopian Highlands using the developed model". Maps showing verified red lines and automatically mapped green lines physical SWC structures". Map showing mapped the physical SWC structures after masking non-cultivated land. The green colour indicates areas classified as non-cultivated land".
RichHTML 0. PDF PC Knowledge 0. Cited 4. Figure 1 Linear and non-linear features detected after edge detection technique and vectorization". Figure 2 Location map of the study area". Figure 3 Map showing an input image obtained from Google Earth". Figure 4 Map showing different linear features after applying High-pass spatial filtering a , Sobel edge detection b , and Laplacian edge detection c ".
Figure 5 Map showing physical SWC structures and other linear features after skeletonization". Figure 6 Map showing the extracted linear features". Figure 7 Maps showing SWC structure obtained in different land units: 30 m by 30 m a , 50 m by 50 m b , m by m c and verified SWC structure d ". Figure 8 Map showing the verified left and automatically mapped right physical SWC structures in one site". Figure 10 Map showing the strips where the model was validated".
Figure 11 Maps showing the physical SWC structure detected and mapped in the Ethiopian Highlands using the developed model". Figure 12 Maps showing verified red lines and automatically mapped green lines physical SWC structures". Figure 13 Map showing mapped the physical SWC structures after masking non-cultivated land. References A mirror of our world: Google Earth and the history of cartography.
Automatic detection and segmentation of orchards using very high-resolution imagery. Agrarian landscapes linear features detection from LiDAR: Application to artificial drainage networks. International Journal of Remote Sensing , 29 12 : Automatic road extraction based on multi-scale, grouping, and context. A new automatic road extraction technique using gradient operation and skeletal ray formation.
International Journal of Computer Applications , 29 1 : Estimation of subpixel target size for remotely sensed imagery. Optimising the application of the Hough transform for automatic feature extraction from geoscientific images. PII: S 98 Digital Analysis of Remotely Sensed Imagery.