Wetland mapping and evaluating the impacts on hydrology, using geospatial techniques: a case of Geba Watershed, Southwest Ethiopia

ABSTRACT Wetlands are one of the world’s most important ecosystems threatened by man. This investigation explores the use of Landsat TM and OLI imageries with SRTM DEM for mapping them. Mapping and monitoring of wetlands is done with 86.66% accuracy. As a result, a loss of 21,400 ha yr−1 could be noted. Due to this, differences were also found in the water quality and groundwater level between the degraded and un-degraded areas. As most rivers within the watershed punctuated from the wetlands, their existence was determined based on the hydrological function. Wetland degradation occurred mainly due to climate and agricultural changes over time. Thus, geospatial techniques employed in the present study have proved very useful in simplification and visualization of wetland monitoring.


Introduction
Wetlands are among the most significant, multifunctional, and productive ecosystems on the earth (Davidson et al., 2019;Mengesha, 2017).Primarily, wetlands can provide essential environmental services, including storing floodwater, reducing peak runoff, recharging groundwater, filtering impurities in water, carbon storage, and also ecologically serve as breeding grounds and critical habitat for several species of plant communities, invertebrates, fish, and wildlife (Chouari, 2021;Kaplan & Avdan, 2017;Wang & Weng, 2014;Wu, 2018).Since the early 20th century, the global wetland ecosystem has confronted significant challenges, including rapid economic development and habitat destruction.Wetlands cover approximately 8% of the world's land surface and contain 20% of the global terrestrial carbon (Dixon et al., 2021;Mitsch & Gosselink, 2007).However, despite the environmental degradation faced by wetlands, there is an increasing demand for ecosystem services they provide (Suding, 2011).By 2050, global water demand is projected to increase by 55% (Terefe, 2017).To meet this growing demand, the services of wetlands must be valued appropriately, or water security risks will rapidly increase.The need for information supporting wetland management is multi-scalar worldwide, and the challenge demands urgent and consistent wetland monitoring mechanism assessments to guide policymaking.
The wetlands are important in East Africa as they make up more than 80% of the total wetland area and cover 12 million ha in Tanzania and Kenya (Sakane et al., 2011).The majority of wetlands lost historically were drained or filled to create agricultural land.A comprehensive inventory of wetlands in Ethiopia is not yet done, but wetlands are estimated to cover about 2% of the country's surface area (Amsalu & Addisu, 2014;Mengesha, 2017;Terefe, 2017).Wetlands are a common feature of the landscapes in the highlands of southwestern Ethiopia, particularly Western Wellega and Illubabor (Abebe & Geheb, 2003;Dixon et al., 2021).Despite recognizing their many uses by people, their ecological services to humankind, and their environmental significance, Ethiopian wetlands are under severe pressure and degradation.The loss of these wetlands is devastating to several wetland-dependent endemic species (Bezabih & Mosissa, 2017).This is due to improper extraction and misconceptions forwarded to wetlands, the health of the wetlands is continuously decreasing from time to time which puts in doubt their existence soon (Abebe & Geheb, 2003;Woldu & Yeshitela, 2003).
In Ethiopia, wetland destruction and alteration saw as an advanced development mode, even at the government level (Dixon & Wood, 2003).This indicates that wetlands and their value remain little understood (Gebresllassie et al., 2014).Convention on Biological diversity of Ethiopia 4th report describe; Fogera marsh has been changed to the rice field, Sululta marsh is distributed to investors, ELFORA PLC has transformed the Chefa wetland in South Wello to farmland, and these are only a few examples of wetland degradation in Ethiopia.Lake Tana is loaded with silt and invasive water hyacinth because the wetland vegetation in the surrounding catchments was destroyed and used for agriculture.The wetlands used to stop silt and plant nutrition that is discharged to the lake have been converted to a rice paddy.The recent total drying up of Lake Alemaya and the precarious existence of Lake Abijata are clear evidence of the looming danger on the wetland ecosystem (Amsalu & Addisu, 2014).
In recent years, several studies have focused on wetland mapping in Ethiopia's southwest regions.The complete drainage and cultivation of wetlands have become common phenomena (Hailu, 1998;Dixon & Wood, 2007).For instance, approximately one-third of the total valley bottom wetlands have come under cultivation for growing food crops from 1974 to 1983 (Hailu, 1998).More severely from this, approximately 20% of the total wetlands in Illubabor have been cultivated during 1986during −1998during , and this intensity increased to 35% in 1999during (Hailu, 2005)).The loss and degradation of these critical resources (wetlands) need urgent mapping and monitoring of the resources and determining potential restoration areas (Gerjevic, 2004).To better manage and conserve wetland resources, we need to know the distribution and extent of wetlands and monitor their dynamic changes (Kaplan & Avdan, 2019;Wu, 2018).Remote sensing offers the opportunity to map and inventory wetlands rapidly and consistently, irrespective of the geographic location.Combining remote sensing and geographic information system approach integrated with in-situ measurement provides an advanced tool in detecting and identifying degraded wetland resources at regional and local scales.Wetland mapping involves most often using satellite data and aerial photos due to their remoteness and inaccessibility (Baker et al., 2006).Imageries from the Landsat, Aster, SPOT, Sentinel-2, IRS, IKONOS, QuickBird, and WorldView provide the necessary spatial and temporal resolution to implementing effective wetland monitoring.The satellite data consisted of high resolution (1−4 m) and medium resolution (10−30 m) multispectral imagery.The spatial and spectral resolution satellite data has significantly improved wetland and habitat mapping (Jensen, 2007;Suryabhagavan, 2017).Therefore, this study aims to find how degraded wetland resources have been mapped.The spatial relationship between wetland degradation and its hydrological and related impacts on the surrounding environment was assessed using geospatial tools.A recent wetland degradation map was produced for future mitigation and management purposes to sustain the development at regional and national levels.

Study area
The study area is located within the Geba River basin that constitutes the Baro River basin in Ethiopia at around 600 km from Addis Ababa.The area is bounded by latitude 7°45ʹ00"−8°36ʹ00"N and longitude 35°20ʹ00"−36°11ʹ00"E covering a total area of 7,125.35km 2 (Figure 1).The watershed geology comprises Precambrian, metamorphosed volcanics, intrusives and sediments in the north and southwest, Paleozoic and Mesozoic sediments in the center, and localized quaternary formation along the valleys of the major rivers.The southwestern highland valley bottom wetlands are developing through time to change external geological, geomorphological, and climatic conditions.The formation of ancient impermeable quaternary and tertiary bedrock in the study area could play a significant role in forming wetlands.The elevation of the region ranges from 780 to 2661 m above mean sea level at the mountains areas.Climatic conditions in the study area are quite diverse due to considerable differences in altitude and relief.About 80% of the annual rainfall occurs in the Kirmet (rainy) season from June to September, and 63% of the annual rainfall is the peak recorded in July and August.The temperature varies from a minimum average of 6.5°C to 32°C.

Data and methodology
The study used remote sensing data to map changing wetland degradation trends in the Geba Watershed from 1985 to 2018.Landsat multi-temporal imageries of 1985, 2000, 2018, and Digital Elevation Model (DEM) were used for the study area (Table 1).Using the TauDEM tool, the watershed area was generated automatically from the SRTM DEM, and two Landsat scenes of 170/054 and 171/054 path and rows filled the study area.After radiometric and atmospheric normalization, the ArcGIS mosaicking tool has been used to generate a mosaic of two scenes covering the study area.The generated map was projected to World Geodetic System (WGS) 1984 Universal Transverse Mercator (UTM) zone 37 N.The images were acquired through the USGS Earth explorer www.usgs.gov.Different dated satellite images were of varying pixel size, and resampling was done to obtain the same pixel size in all the satellite imagery used.Meteorological data such as monthly temperature and average monthly rainfall were acquired from Ethiopia's National Meteorological Agency (NMA).
Based on the wetlands' potential resources, three kebeles of the study area were visited in February and March.A field survey was conducted for groundtruth data, land-cover, water samples were collected using handheld GPS (Global Positioning System) to enhance satellite image and wetland mapping classification.The flow chart of the methodology used is given in Figure 2.This study's method holds pixelbased supervised classification, index-based classification (NDVI and NDWI), SRTM wetness index, and image enhancement methods algorithm was used to identify and delineate wetland extent.

Pixel-based classification of wetland
Thematic mapping from satellite data can be defined as grouping together cases (pixels) by their relative spectral similarity (unsupervised component) to allocate instances based on their similarity to a set of predefined classes that have been characterized spectrally (supervised component) (Foody, 2002).In this study, the Level 1 land-use and land-cover classification system was used and adapted from United States Geological Survey (USGS) (James & Randolph, 2011).Most pixel-based classifications tend to utilize spectral information at individual pixels and potentially textural information extracted from neighboring pixels.Pixel-based classification can highlight noise, salt-and-pepper effects and ignore important topological and semantic information in the images (Blaschke, 2010).Based on the maximum likelihood classification algorithm, six different classes were identified; agricultural land, forest, shrubland, wetland, Gumro tea plantation, and settlement.

Vegetation indices NDVI and NDWI
The Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) are selected from this study's satellite data image processing.Several researchers have used both NDVI and NDWI for wetlands change detection (Das, 2017;Dehm et al., 2019;Li et al., 2019;Nsubuga et al., 2017;Xu, 2006); and they all found that there are significant changes of both indices values during their study periods.Several developed spectral vegetation and water indices can highlight wetlands and extract water bodies while efficiently suppressing (Xu, 2006).They can be calculated using the following   Equations ( 1) and ( 2), respectively (Jones, 2015;Lane et al., 2014).
where NIR represents the Near Infrared Band and Red represents the Red band.

Topography Wetness Index
Wetlands are easily confused with other upland habitat cover types, such as forests because these classes show overlapping spectral signatures (Ozesmi & Bauer, 2002).Thematic maps of both primary and secondary key hydrological parameters attribute of slope, drainage, flow direction, flow accumulation, stream order, Topographic Wetness Index (TWI) were derived from Shuttle Radar Topography Mission (SRTM) 90 m DEM using d-infinity algorithm in TauDEM hydrological tools as described by (Islam et al., 2008;Tarboton, 1997).TWI were used to reduce the error of commission with upland land covers since the wetlands' occurrence strongly depends on the topographic conditions of a region.
where a is the local up-slope contributing area draining a point per unit contour length, b is the local slope in radians.

Accuracy assessment
An accuracy assessment for the supervised wetland classification was done for the 1985 image using ERDAS Imagine @ 15. From the classifier, 80, 75, and 95 points were generated randomly for 1985, 2000, and 2018 supervised images, respectively.Each point had a specific color tone and pixel value recognized by the software itself when the data sets were trained during supervised wetland classification.These values were considered as reference values.All the randomly generated points were then identified by the user and assigned to different classes.This process was done for the three supervised classification images (i.e., 1985, 2000, and 2018).The correctly identified points were considered as classified values.An Error matrix and Kappa statistics were also generated from this reference and classified data from the report section of ERDAS Imagine @ 15 software.Accuracy assessment was conducted by collecting 250 in situ ground truth points (GTP), which were systematically distributed throughout the study areas in the accessible parts.Overall accuracy was calculated from the error matrix by dividing the sum of the entries that make major diagonal by the total number of examined pixels.Kappa coefficient of the agreement was also calculated by using the following Equations ( 4), ( 5), and (6), respectively (Kulawardhana et al., 2007).
where observed accuracy is determined by diagonal in error matrix, and chance agreement incorporates offdiagonal (sum of the product of row and column totals for each class).

Wetland classification
Initially, the NDVI and NDWI were considered for water detection, as these indices have already been proven suitable for this purpose in previous studies (Das, 2017;Dehm et al., 2019).Supervised classifications were performed for separating the valley wetlands from the other land-cover types.The pixel-based classification was conducted on the study images' multiple segmentation levels using the maximum likelihood classification.The results were compared with corresponding pixel-based classifications to delineate the wetlands' extent and boundaries in the study area (Figure 3).However, wetland spectral mixing with other land-cover regions was observed.

Vegetation indices NDVI and NDWI
The < NDVI > 0.10 and −0.15 < NDWI > 0 are presented in Figure 4. Findings of the study by Kulawardhana et al. (2007) showed the best of these NDVI indices provided the only accuracy of less than 30% with high levels of omissions and commissions.A primary cause for this is rugged topography, except few areas in different parts of the watershed.Some studies showed that topography significantly affects VIs in a rugged mountainous area (Veraverbeke et al., 2010;Verbyla et al., 2008;Wang et al., 2012).Deng et al. (2007) observed that the NDVI and the Normalized Difference Infrared Index (NDII) showed a significant correlation (r 2 ) (p = 0.001) with topography variables such as slope and the cosine of the aspect.The highest amount of rainfall in the study area (mono-modal rainfall pattern) exerts seasonal control on vegetation greenness, leading to similar spectral reflectance of wetlands with other land-cover types.

Wetlands derived from slope and topographic wetness index
The results of delineation of wetlands using SRTM DEM.Different threshold values were used to identify wetlands within various studies based on topography, data type, and landscape nature.The overwhelming proportions of the wetlands in any landscape are along with the drainage system, with drainage forming its centre.For example (Ozesmi & Bauer, 2002;Zhang et al., 2016), a threshold value of <5 degrees was considered as a wetland (Islam et al., 2008).The SRTM DEM slope of less than 1% also helped delineate higher-order wetlands rapidly and accurately.Based on the higher accuracy of threshold values, a 5% slope threshold was used for the study area and produce potential maps for higher-order wetland boundaries (Figure 5).
The TWI was used as an additional method to map wetlands in the study area.TWI was calculated for the whole watershed (Figure 6).The result showed that the TWI performs better than NDVI and NDWI to detect wetlands in the study area.A larger wetland area was identified with better accuracy, while smaller wetlands were invisible.TWI maps with their respective wetland threshold were considered as a detected wetland.Thus, the percentage of pixels that were well predicted was compared to the total of pixels of the potential wetlands mapped using supervised classification.Bisrat and Berhanu (2018) stated TWI was used to represent the spatial distribution of water flow and water stagnating across the study area.According to Wu (2018), high-resolution LiDAR-based DEMs have been used to derive TWI and facilitate forested wetland mapping.The enhanced FD8 TWI provided a good prediction of wetland location but could not predict the periodicity of inundation.

Wetland mapping
The combination of automated and pixel-based classification methods consisted of slope and TWI separate wetlands from other upland land areas.TWI was overlaid with a Wetland map to identify topographically wet areas.The main challenge for detecting wetland within densely vegetated areas is to differentiate dry upland forests from forested wetlands.However, using TWI, upland forests were eliminated, and detection of the forested wetland was enhanced.But TWI did not eliminate upland areas from the forested wetland.As a result, digitization was done by enhancing wetlands using false color Infrared color combinations.TWI, slope, and classification maps were then combined according to the rule that pixels with a slope <5%, wet in classification map, and pixel that falls under TWI threshold value were considered wetlands, and the other pixels were coded to upland.The land cover classification map was used to generate the wetland boundaries.For example, pixels classified as forest in the classification map were recoded to forest wetland if they overlapped with a wetland threshold with TWI and slope.Those pixels coinciding with upland were coded as nonwetland.The final wetland classification map was validated based on field data and Google Earth data.Combining the information from Landsat TM/OLI, TWI, and slope, effectively map wetlands in the study area (Figure 7).Most of the valley wetland land-covers vegetation, locally known as cheffe (Cyperus latifolious), grasses, and forested wetlands were the dominant types of wetland vegetation types.A potential hotspot location of the wetland was identified, and some of them are Enago, chebere, Tulube wetland, Hamuma, and Wangegnye wetlands were some of the wetlands in the watershed.Most of the study area, wetlands are called by the name of standing kebele names of the wetlands.Enago and Wangegnye wetlands were commonly dominated by grasses cyperus latiolious wetland vegetation type.Vegetation types of Enago and Wangenye wetlands are shown in Figure 8.

Mapping and quantification of wetland degradation
Wetlands within the study area have been experiencing huge losses over the last 33 years.Detailed wetland losses have been consecutively captured between 1985, 2000, and 2018.Spatial changes in surface area and shape over a long period were considered wetland degradation.Wetland disturbance through cultivation and vegetation clearance alters wetlands functionality leading to their degradation.The total wetland area in the watershed accounted for 21,400 ha, 16,000 ha, and 12,700 ha for 1985, 2000, and 2018, respectively.The detailed degradation map of wetland classes within the study area shows a similar pattern (Figure 9).Wetland degradation mapping from multi-temporal image analysis revealed a significant loss of wetlands area during 1985−2018 in the area.Figure 10 shows that 21,400 ha of the area was covered by wetland in the year 1985.Though, this coverage was reduced to 16,000 ha by the year 2000.The total wetland cover degraded during 2000 and 2018 amounts to 3,300 ha.The wetland area was decreased by 59.34% between Table 2 shows the overall classification accuracy assessment and Kappa statistics of the results for the study years 1985, 2000, and 2018.An accuracy assessment for all of the used methods has been made by comparing the results with high-resolution images from Google Earth and field-collected data.The ground sample points (Figure 11) were overlaid on wetland maps to determine the classification accuracies and errors of each class.The overall accuracy of the three aggregated wetlands (1985, 2000, and 2018) in the study area was 86.66%, with reasonable errors of omissions (7.54%) and low errors of commissions (13.33%).This showed that all the maps meet the recommended minimum 87% accuracy and there is a strong agreement between the reference data and the classified habitat classes.

Impact of wetland degradation in hydrology
Major river and lake systems, together with their associated wetlands, are fundamental parts of life interwoven into the structure and welfare of societies and natural ecosystems (Leykun, 2003).Major rivers within the watershed, which are Birbir, Geba, Dabena, Sor, and Keber rivers, are tributary rivers of the Baro riverine basin.Most of these rivers are punctuated by numerous valley bottom wetlands that occur in their upper and lower courses.The primary sources of all rivers in the watershed start from the wetland ecosystem (Figure 12).The hydrological function of wetlands determines the existence of rivers within the watershed and the surrounding area.As the wetland within a watershed degrades, it will significantly impact the hydrology of the watershed.Dixon (2002) had monitored the water table wetlands of Illubabor.Monitoring was conducted on 10-12 deep wells within each wetland every week.The hydrologic analysis of the well data showed that lowering the wetland water table was observed in the degraded and cultivated wetlands and reducing the rate of water movement through the wetlands.Analysis of degraded or cultivated wetlands versus undrained wetland observed temporal variability and height change in the weekly wetland water table (Figures 13,14).Dixon (2002) indicated environmental degradation on wetlands unable to provide their full range of function, which has implications for food security in the study    There is a significant difference between wetland water levels of cultivated/degraded wetlands and undrained wetlands.Hence, over-cultivation and draining of wetlands within the watershed directly impact the water level of the wetlands.Additionally, alterations of the hydrological regime of wetlands have significant physical, chemical, and biological effects that can have significant ecological and socioeconomic implications at a broader scale (Bezabih & Mosissa, 2017).The complete drainage of wetlands in Illubabor Zones, southwest Ethiopia has led to several ecological and economic problems.Some of these are immediate and linked to drainage, such as the scarcity of thatching reeds, vegetation change, lowered water tables, reduced accessibility, and provides unsafe water (Wood & Dixon, 2000).

Driving forces and implications
As the classification of Landsat 8 of 2018, which has 30 m spatial resolution images, showed that in the study period of 2018, Forest and agricultural land   were the dominant land-use and land-cover types.
Forest and agricultural LU/LC types together accounted for 616,700 ha (87.07%) of the study area's total area in 2018 (Table 3).Land-use and land-cover maps quantified land-cover area is shown in Figure 15.
Wetland land-cover was cover only 12,700 ha in 2018.
According to the wetland change matrix obtained from the land-use and land-cover map of 2018, the majority of wetland area 3,111.5 ha (14.53%) converted typically to agricultural land from 1985 to 2000.Similarly, in the same fashion, most wetlands 17.515 ha (10.9%) converted typically to agricultural  land (Table 4).Accuracy assessment of land-cover classification of 2018 is shown in Table 5. Wetland cultivation and degradation practiced in the watershed dominated by the cultivation of different vegetables, maize, teff, and sugarcane were more common among rural ranchers.
The cultivation of wetlands is still going on in the study area.The highlands of southwest Ethiopia (Illubabur) and swamps of Awash valley are good examples of where the farmers are engaged in producing more than seeing sustainable use of the resources (Dixon, 2002;Dixon & Wood, 2007).Field photo capture of wetland cultivation and draining (Figure 16).Food insecurity due to pests and crop storage problems, land shortages for cultivation and grazing due to coffee planting on uplands giving more people access to wetlands, and encouraging use listed as the main drivers of wetland degradation in the Illubabor zone.A previous study report of the wetlands policy briefing   workshop done by Wood and Dixon (2000) described that wetland cultivation had been practiced in the Illubabor zone for about eight decades and about 70% of the study farmers area have cultivated wetlands at least once a year.Also, a significant correlation between the rate of rainfall and the change in wetland area were analyzed, a linear regression model was set, change in rainfall and wetland degradation positively correlated with r 2 = 0.81 for .The rate of change in rainfall and the change rate of the wetland was positively correlated (Figure 17).

Wetlands as protected areas
Some wetlands were found inside protected areas in the study area.Some organizations within the watershed saved and improved wetland management by including these areas within protected areas.For example, Bishari Park in Mettu town results from the Rehabilitation of wetlands by Mettu town Beshari prison center (Figure 18).Hence, the park provides endless popular recreational activities, such as boating, birdwatching, and many fascinating life forms, making the wetlands especially enjoyable.Bishari Park not only contains or provides recreational value but also substantial economic value.Because of the magnitude and ubiquity of Geba watershed wetland cover change effects, we are compelled to reflect on past policy, management, and other decision-making processes and improve them for the future.The policy will need to consider actions that help the wetland ecosystem accommodate changes adaptively to improve the wetland ecosystem's capacity to return to desired states after disturbance and measures that reduce anthropogenic influences on wetland components.

Hydro-Chemistry of Wetland water quality assessment
Wetland water contains many chemical species in the dissolved state.Hamuma degraded and cultivated wetlands have lots of organic matter.
Based on the hydro-chemistry analysis results, the effluent from agricultural land goes through a wetland.A moderate difference in qualities of water compared to undrained/or uncultivated wetlands was observed (Tables 6,7 and 8).The surface wetland average values of pH for the Enago, Hamuma, and bake Chora was ranged from 7.45 to 7. 55, 7.15 to 8.35, and 7.15 to 8.01, respectively.As a result of cultivation on wetlands, the rise of PH values to 7.98 of degraded Hamuma wetland was received at a maximum limit of WHO water quality standard.However, partially undrained Enago wetland results in an average pH value of 7.
3. An excess of agricultural fertilizers/ pesticides near a wetland and cultivated wetland increases the value of leached chemical pollutants to Hamuma Degraded wetland represented by the nitrogenous pollutant chemicals.The average values of Enago and Bake Chora partially undrained wetland was ranged from 0.22 and 0.12 mg/N, respectively.However, the average values of drained Hamuma wetlands were over ranged values of nitrate nitrogen and nitrite nitrogen was verified.Additionally, in terms of color and turbidity, a significant difference was recorded between degraded/or cultivated wetlands and undrained wetlands within the watershed.The average watercolor values for the undrained Hamuma and Bake Chora were 19 and 38.33 mg/l pt was verified, which is acceptable water quality based on WHO standards.But, a very significant average value of color was observed in cultivated degraded wetland of 228.6 mg/l pt.The turbidity values for uncultivated Enago were recorded, and 4NTU and Bake Chora Surface wetland water samples were obtained for four values that are acceptable based on WHO water quality guideline standard.But, again, a massive difference in turbidity between drained and undrained wetlands was observed.Based on the result, average turbidity values of 18NTU were verified in cultivated and drained Hamuma wetlands.Generally, wetland degradation has significant impact on the surface wetland water qualities.Wetland distraction resulting from draining, cultivating, and grazing activities by the local community leads to the hydrologic and water quality functions of wetlands in question.There is a significant difference in the water quality of degraded and un-degraded wetlands.Many of the key issues for wetlands are related to water use.Demand for water in the study area will likely increase due to the growing human population (CSA, 2007), agricultural expansion, and climatic changes.Climate change will also reduce water availability.Geba watershed wetlands or watersheds cross national or regional borders, presenting both challenges and opportunities for management.Make the sciencepolicy arena more interactive, with scientists and politicians working closely together to mutual benefit (Toomey et al., 2017).Our horizon scan highlighted perceived deficiencies in the governance of wetlands, at both national scales, that are likely to continue into the future.Unified wetland ecosystem evaluation indicators are helpful for providing a guide for integrating wetland ecosystem evaluation data in different geographical regions to improve the reliability of horizontal studies (Janse et al., 2019).Studies of similar geographical regions in which the same wetland ecosystem are examined could be evaluated to develop standardized indexes for wetland ecosystem across different geographical regions.A long-term monitoring program on wetland with field observations and remote sensing will assist in gathering big data for evaluation, analysis, and management of wetland ecosystem.Exploring trade-offs among ecosystem service and linking them with stakeholders can help to determine the potential losers and winners of wetland management (Guida et al., 2016).This analysis will support research, policy and practice related to environmental conservation and sustainable development in the Geba watershed, and provides a model for similar analyses elsewhere in the world.

Conclusion
The geospatial techniques provide an accurate, fast, and economical way for wetland degradation in the Geba watershed, southwest of Ethiopia.It has solved the problems faced using old traditional techniques that were difficult to undertake and consume a lot of time.Global climate change and anthropogenic impact degrade wetlands, which creates a severe problem in identifying and quantifying wetland areas.The main objective was to map the degradation of wetlands    which have been under several pressure from both anthropogenic and natural driving factors for three consequent periods of years.Wetland change produces a significant impact on the surrounding environment, mainly influenced hydrological variation.

Figure 1 .
Figure 1.Location map of the study area.

Figure 2 .
Figure 2. Methodological flow of the process involved in wetland mapping.

Figure 4 .
Figure 4. Map of NDVI and NDWI for the period of 1985, 2000, and 2018.
area and water availability to local communities, both around wetlands themselves and downstream.

Figure 14 .
Figure 14.Mean weekly water table elevation in the drained and degraded wetlands (August 1997−July 1998).

Figure 15 .
Figure 15.Land-use/land-cover map of the year 2018.

Figure 16 .
Figure 16.Wetland cultivation and draining in the watershed.

Figure 17 .
Figure 17.Spatial distribution of tendency variation of precipitation from January to December.

Table 1 .
Specifications of satellite data used.

Table 2 .
Accuracy assessment for the Wetland system.

Table 3 .
Land-use and land-cover area for the year 2018.

Table 4 .
Wetland change matrix vs other land-use and land-cover areas.

Table 5 .
Accuracy assessment of land-use and land-cover classification.

Table 6 .
The changes in the groundwater table of the study area are attributed to wetland area change.Based on the groundwater table's weekly measurement inside the watershed, a significant difference in groundwater table level was recorded on degraded wetlands.The degraded wetland maps that have been generated in the present study advance our understanding of current use, transformation dynamics in wetlands and may provide the quantitative basis needed to guide and predict future wetland uses and their impacts on surrounding natural resources.A balanced regulation or law needs to be put forward for agricultural development and wetland ecosystem sustainability.Therefore, the study suggests urgent attention of decision-makers on conservation of the wetland's remaining resources, taking necessary measures to reduce environmental risk and new techniques and different data fusion for exploring the potential of geospatial data in wetland monitoring supported with field measurements.Water quality for Enago wetland.
ReferencesAbebe, Y. D., & Geheb, K. (2003).Wetlands of Ethiopia.Proceedings of a seminar on the resources and status of

Table 7 .
Water quality for Hamuma wetland.

Table 8 .
Water quality for Bake Chora wetland.