Geospatial Analysis of Wetland Land Use/Land Cover Dynamics On Lake Abaya-Chamo, Southern Rift-Valley of Ethiopia.

Background: Wetlands worldwide and in Ethiopia have long been subject to severe degradation due to anthropogenic factors. This study was aimed at analyzing the impact of land use/cover dynamics on Lake Abaya-Chamo wetland in 1990 – 2019. Data were acquired via Landsat TM of 1990, ETM+ of 2000, and OLI of 2010 and 2019 images plus using interview. Supervised classications (via ERDAS14 and ArcGIS10.5) were applied to detect land use/cover classes. Change matrix model and Kappa coecients were used for analysis of the land use/cover dynamics in the lake-wetland. Result: It was found that forest; water, shrub land, agricultural land, settlement and swamp area were the main land use/cover classes. Wetland/swamp area has continuously declined throughout 1990 – 2000, 2000 – 2010 and 2010 – 2019 where its magnitude of shrinkage in the respective periods was 11.4 % (700 ha), 16 % (867 ha) and 31.3 % (1,424 ha). While ‘settlement’ and ‘water body’ of the lake-wetland increased at progressively increasing magnitudes of changes in three periods within 1990 – 2019, ‘shrub land’ and ‘swamp’ declined at progressively increasing magnitudes of loss in the same periods Siltation, rapid population growth-led expansion of settlement and irrigation-based farming were the main drivers of the land use/cover dynamics and degradation of the lake-wetland. Conclusion: Thus, consistent mapping and integrated actions should be taken to curb the threats on the sustainability of the lake-wetland in Southern Ethiopia. To curb the impact of LULC dynamics on wetlands, the government should: formulate clear policy, institutional and legal framework on the management of wetlands.

Wetland ecosystems provide numerous services, which range from provisioning ( sh, rice production, animal fodder and fossil fuels) through supportive (e.g. habitat, breeding ground of birds and crop farming) and regulatory (carbon sink, climate control and regulate hydrological cycle) to cultural (e.g. recreation and aesthetic) services (MEA, 2005;Dise, 2009;Erwin, 2009;Davidson, 2014;Clarkson et al., 2014;CBD, 2015). Huge proportion of the wetlands (marine and inland) in the world undergoes complete and/or partial degradation or loss in response to the impact of natural and human factors (Schuyt, 2005;Zedler and Kercher, 2005). Climate change, sea-level rise, sediment load into wetlands, volcanism, earthquake and drought are among the main natural causes of degradation of wetlands ( Studies reveal that most of the riverine and lacustrine wetlands of Ethiopia have been critically threatened due to the impact of LULC changes (Feoli and Zerihun, 2000; Giweta and Worku, 2018). Numerous studies were made about various issues of wetlands in different parts of Ethiopia. Investigations about the threats and opportunities (Zinabu, 2002;Teklu and Kassahun, 2017) and hydrogeochemical and water level changes (Alemayehu et al., 2006) of Rift-Valley lake-wetlands, sustainable management (Wood and Dixon, 2002) and wetland ecosystem dynamics (Legesse, 2007) in Illubabor Zone, status of natural lakes of Ethiopia (Tenalem, 2009), water level and siltation problem of Abaya lake (Schütt et al., 2002), ecology and wetland vegetation composition (Unbushe, 2013)  , lack of regulations about use and abuse of resources, absence of service costs (i.e. the free riders/tragedy of the commons' scenario), draining wetlands, dam construction, intensi cation of farming, infrastructure expansion and diversion of water owing to wetlands (Zinabu, 2002;Wood and Dixon, 2002;Wondie, 2018), poor catchment management, sedimentation, eutrophication and pollution by chemical farm-inputs (Giweta and Worku, 2018) were among the major drivers of LULC dynamics and wetland degradation in different parts of Ethiopia. But all the causes (shown above) cannot be equally signi cant in inducing LULC changes and threatening wetlands everywhere as the drivers and their impacts vary in space and time depending on variations in circumstances across the different speci c areas of the country (Giweta and Worku, 2018).
GIS and satellite image-based studies about the dynamics and threats of wetlands in Ethiopia were limited. Dynamics of Dawa Chefa Wetland in North central part (Hussien, 2014), characterizing wetlands and their dynamics in Central Highlands (Dubeau, 2016), and the impact of LULC change on the landscape of Abaya-Chamo basin (Wolde-Yohannes et al., 2018) were among the few GIS and remote sensing-based studies made in Ethiopia in the recent past. Studies made through integrated application of different data acquisitions (GIS and satellite images, ground survey and interview) techniques for addressing the dynamics and threats of wetlands were rare in and around lake Abaya-Chamo wetland (Southern Rift-Valley of Ethiopia) where this study is conducted. Remote sensing-based mapping and analyses of the dynamics of biophysical status of wetland (upon multispectral Landsat images) is useful to develop bases for detecting and monitoring changes (degradation) of the wetland, and to respond for its restoration (Baker et al., 2007;Klemas, 2011;Ballanti et al., 2017). This is so because, GIS and remote sensing techniques, by providing synoptic coverage and repeatability of spatial information, enable to get accurate results about wetland dynamics and are more cost-effective for monitoring the changes overtime (Ballanti et al., 2017). This study was aimed to: (1) quantify the magnitude and rate of LULC dynamics of Lake Abaya-Chamo wetland within 1990-2019; (2) explain the trends of LULC changes in three decades; (3) analyze the driving forces and impacts of the LULC dynamics on the sustenance of the lake-wetland.

Study Area
Abaya-Chamo lake-wetland is located in 5°43'19''N -6°38'51''N latitude and 37°21'55''E -38°15'05''E longitude ( Figure 1). In Figure 1, the large Northeastern water body is Abaya Lake and the smaller Southwestern one is Chamo lake. The area of Abaya-Chamo lake-wetland is 242,615 ha ( Figure 1). Abaya and Chamo lakes, being Rift-Valley lakes in Southern Ethiopia, lie on a graven (depression) created by faulting due to divergent movement along the boundary of the Africa plate (westward) and the Somali plate (eastward). The Western part of the lake-wetland is largely plain, where some dome-shape and conical volcanic hills, and elevated spurs are observed.
Climatically, Abaya-Chamo wetland, based on data of 1987 -2018 Mean annual temperature, was about 24 0 C; and, the mean monthly temperature of the area is the highest in march (26 0 C) and the lowest in July (23 0 C), November (23.1 0 C) and December (23.1 0 C) (NMA, 2019). The wetland receives a somewhat low rainfall amount where the mean total annual (1982 -2018) was 870.9 mm. The study area has two rainfall seasons: that is, spring (March, April and May) with total rainfall of 362.9 mm is the main rainy season. In spring, rainfall, averaged for 37 years (1982 -2018), is the highest in April (153.7 mm). Autumn (September, October and November) with total rainfall amount of 265.6 mm is the second rainy season where it peaks in October (115 mm) (NMA, 2019).
Abaya-Chamo lake-wetland provides multiple ecological and economic bene ts to people in the surrounding area. The wetland vegetation, being a vital nesting site and feeding source for hundreds of birds and hippopotamus, supports wildlife and serves as a spawning-area for crocodiles (Unbushe, 2013). Rich bird fauna, sport shing for Tilapia, Nile Perch and Tiger Fish, the 'Azo-gebeya'/'Crocodile Market' (where crocs are not exchanged rather crowds of crocs are visited), the 'Forty-Springs' (from which name of 'Arba-Minch' Town was coined) provide special attraction to tourists. Crocodile Ranching/Farming is important income source via tourism and the export of skin of crocs (Legesse, 2007). The lakes also harbor large population of common hippopotamus (Hippopotamus amphibius) and several rare bird species including migratory ones.
Lake Abaya-Chamo wetland revealed rapid change in land uses/land covers due to fast population growth-induced expansion of cultivated land and settlement at the cost forest and shrubland (Bekele, 2001  Agroforestry is the main activity in the alluvial plain of the western shores of the lakes, where it is practiced using rain-fed and irrigation. Fruits (e.g. banana, mango, avocado, papaya, tomato,), cereals (e.g. maize), vegetables (e.g. cabbage, pepper), tuber and root crops (casava, onion, carrot) and cotton are cultivated on the fertile soils adjacent to the wetland (Gelaw, 2007;Gelaw, 2019). Wetlands, forest, woodland and bush-lands have changed to settlement and cropland (Kebede, 2012). These wetlands present a rich biodiversity in western shores of the lake Abaya-Chamo wetland even if it has been extremely impacted by anthropogenic pressure.

Research Design
This study, being viewed via the pragmatic lens, was conducted based on the mixed-methods approach.
That is, data acquisition and analyses were carried out using a mixture of methods from both the quantitative and qualitative approaches (Creswell, 2009), were used for statistical based inferences about the LULC dynamics and the degradation of Lake Abaya-Chamo wetland. Methods of the qualitative approaches such as interview and observation were used to check, con rm and strengthen the ndings of the quantitative approach. The concurrent embedded model was used to mix the quantitative and qualitative approaches (Creswell, 2009). Cross-sectional survey design was used to acquire and analyze data using both the methods of quantitative and qualitative approaches simultaneously (in parallel).

Acquisition and Processing of Satellite Imageries
Satellite data of Landsat TM of 1990, ETM of 2000, and OLI of 2010 and 2019 of Lake Abaya-Chamo wetland, having spatial resolution of 30 m were downloaded from the website (https://earthexplorer.usgs.gov/) of the US Geological Survey (USGS) ( Table 1). Satellite data is the basic source of information which can be used for mapping and change detection in different land use/land cover categories of an area over the period of time. Landsat images captured during January and February were preferred since these dates enable to acquire satellite images free of the impact of cloud cover and to avoid the effect of seasonal variation on the classi cation of LULC classes. Ancillary data were also utilized during analysis. All data (images) were projected to the Universal Transverse Mercator (UTM) projection system, zone 37N and datum of World Geodetic System-84 (WGS84) to ensure consistency between datasets during analyses. The imageries were checked against any defects such as striping. All image scenes were subjected to image processing using ENVI software (version 5.3), and each was clipped using the base-map of lake Abaya-Chamo wetland. Geometric and radiometric corrections were made for the images of the four periods ( Table 1). The two scenes (i.e. the one that fall within path 169 and row 56, and the other that fall in path 169 and row 55) of each data set were mosaicked using linear contrast stretching and histogram equalization technique to create a single image covering the whole study area for each period.

Image Classi cation
Landsat TM of 1990, ETM+ of 2000, and OLI of 2010 and 2019 were also classi ed using supervised classi cation (maximum likelihood technique) separately to identify LULC classes of the study area. This method assumes the normal distribution of DN values, allowing the function to determine the probability of a pixel belonging to a speci c feature class and assign each pixel to the highest probability class (Lillesand et al., 2004). The classi cations were repeated numerous times by adding more training sites so as to come up with satisfactory results. Supervised classi cation was chosen to compare the outputs with results of the unsupervised classi cation; this was particularly vital for this study because it identi es and locates LULC types, which are known priori through a combination of interpretation of aerial photography, survey analysis and eldwork.
In the accuracy assessment, confusion matrices and Kappa coe cient of agreement were calculated for each classi cation map. Estimation of Kappa coe cients yields statistics, which are measures of agreement or accuracy between the remote sensing-derived classi cation map and reference data (as shown by the major diagonal) and the chance agreement, which is indicated by the row and column totals (referred to as marginal) (Jensen, 2009). The classi cation results were compared with the ground truth (data) to con rm accuracy of the classi cation process. It is a way of assuring how many ground truth pixels were classi ed correctly, and how much errors were propagated during data acquisition, analysis and conversion (Edwards et al., 1998).

Accuracy Assessment
The accuracy of LULC maps produced was evaluated using overall accuracy (OA), producer's accuracy (PA), user's accuracy (UA) and Kappa statistics. PA quanti es the error of omission, while UA quanti es error of commission. Kappa is another method of expressing classi cation accuracy as it measures the chance agreement. Accuracy assessment was run in order to measure (statistically) the level of accuracy and degree of acceptance of analysis results of the GIS and remote sensing-based LULC classi cation and change detection of Abaya-Chamo lake-wetland, Southern Ethiopia (Table 3). In this study, reference data were collected during eld work using Global Positioning System (GPS) and the reference points were independent of the ground truths that are used in the classi cation scheme. About 596 GCPs were collected from the eld for accuracy assessment. Besides, Google Earth was also used to aid the validation process. Accordingly, the overall accuracy, Kappa coe cient, producer's accuracy and user's accuracy were computed from the confusion matrix. Kappa is expressing classi cation accuracy as it measures the chance agreement. It has been found to be stronger than the overall accuracy of images (Jensen 2005;Lillesand et al. 2014). The Ǩ ("KHAT') statistic is a measure of the difference between the actual agreement between reference data and an automated classi er and the chance agreement between the reference data and a random classi er (Jensen, 1996). Conceptually, Ḱ can be de ned as:

Collection of Field Data
Reference data were collected for training and validation of each LULC type of Abaya-Chamo lake wetland for each satellite image in each period. Geographic locations of ground truth LULC classes, used to calibrate the classification procedure, were identified using high spatial resolution imagery made freely available through Google Earth Pro. About 596 reference samples were derived from the LULC of the lake- Change matrix model (the raster calculator) was used to compute the area change from one LULC class to another type between the periods accounted in the study. The magnitude of change for each period was statistically tested using the Wilcoxon Signed-ranks test. The Wilcoxon Signed-ranks test is a nonparametric statistical test used to assess the difference between two conditions where the samples, in this case change of LULC class, are correlated. The data sets can be compared repeatedly over consistent periods (between initial and recent years).

Data Analysis
LULC changes of Lake Abaya-Chamo wetland were analyzed using GIS and remote sensing techniques. Different spectral signatures of similar pixel samples were selected from satellite imageries using the maximum likelihood method, which served as a separability measure for different land use/land cover classes which were later on grouped with spectrally identical signatures. Determination of appropriate classes was done based on level 'I' of the LULC classi cation and six classes were identi ed ( Table  2). Computer aided interpretation of images was conducted using environmental resources data analysis system (ERDAS) Imagine 2014, ArcGIS 10.5, GPS (Garmin 5.1)-based data and environment for visualizing images (ENVI) 5.0 software, which were used for satellite image processing, classi cation of LULC, accuracy assessment and analysis of the wetland dynamics. Microsoft excel was also used for analysis   (Table  3) Table 4 and 5). Therefore, based on the GIS-based image analysis six land use/land cover types were identi ed. The detail land use/land cover status of the area is presented as follows; Forest, water-body, settlement, shrub-land, agricultural land and wetland are the main LULC classes of the lake-wetland.

Forest
A continuous decline of forest cover was observed over the study period. Of the total area of the area in 1990, forest constituted about 4.005%. In 2000 it accounted for 2.597% and 2010and 2019 showed increase 5.156% and 4.36% respectively, of the total area of the study site (Table 4). During the study period, forest showed reduction in coverage by 35.2% at an average rate of 3418. ha/decade. The depletion of forest cover occurred due to the destruction of natural forests for farm plots, settlement expansion and construction materials.
In the second period of the study 2010-2019 the forest cover was increased by 8.9%. as one moves from the period 1990 -2000 to 2000 -2010 and then to 2010 -2019, forest cover and agricultural land of Abaya-Chamo lake-wetland revealed no clear trends in their patterns of change across the three decades.

Agriculture
This land cover includes areas, which are continuously and seasonally cultivated with rain fed and using irrigation schemes. Agricultural land continued to decline by 0.8 % (242 ha) in the next/second period (2000 -2010) under study (Table 4). In the last period (2010 -2019) accounted in by the study, the trend of forest cover of Lake Abaya-Chamo wetland was reversed to decline by 15.4 % (1,926 ha); whereas, the agricultural land of the study site increased by 12.2 % (3,723 ha) ( Table 4). This LULC showed drastic expansion as compared to other cover classes during the study period. There was few cultivated land prior to 1970s and more frequently after the 1991 government change, which resulted in a fast increase of settlement and agricultural areas in the lake shores. This also further share with migration played a significant role in the reduction of shrub-land and forest areas surrounding Lake Abaya-Chamo wetlands, whereas the resettlement programs played a rivers owing into lakes. The use of rivers that feed the lakes for irrigation decreased the lakes depth and consequently resulted in drastic effects on the wetlands and aquatic communities.

Settlement
As is illustrated in Table 4, settlement area in the Western coast of Abaya-Chamo lake-wetland revealed a continuously increasing trend in the three periods accounted in by the study, that is, where it has  Source: Own Analysis (Note: Settle = Settlement. Agri = Agriculture land)

Shrub-Land
This land cover contains short; grazing lands, tress, grass and bushes, which have an opened cover.  Source: Own Analysis (Note: Settle = Settlement. Agri = Agriculture land)

Shrub
In the last two columns of Table 4 year in the three decades' period (Table 4).

Driving Forces and Consequences of LULC Changes in the Lake Abaya-Chamo Wetland
The net increase in forest cover of lake Abaya-Chamo wetland (by 8.9 % or 866 ha) in the period 1990 -2019, most likely, was a result of the expansion of agroforestry (e.g. banana, mango, avocado and papaya) practice at the expense of shrubland in the Western cost of the lake-wetland. In other words, the categorization of, especially, mango-forest during image classi cation, to a little extent, is thought to have contributed to the increase in forest cover in 1990 -2019; that is why the accuracy level of forest cover (upon the producer's index) was the lowest for 1990 (79%), 2010 (79%) and 2019 (78.8%) ( Table 3). The expansion of agroforestry practice is also assumed to have been among the reasons for the low magnitude of increase (by 10 % or 3,065 ha only) of agricultural land in the three decades studied; this is so because, smallholder farmers in the Western coast of the lake-wetland were indicated to have been replacing the maize-dominated cereal croplands with banana and mango-dominated agroforestry (Gelaw, 2007); that is why (despite the increasing human population in Abaya-Chamo depression/basin) agricultural land of the study site had exhibited decreasing trend by 1.4 % (416 ha) in 1990 -2000 and by 0.8 % (242 ha) in 2000 -2010 (Table 4). In fact, the lateral expansion of Abaya and Chamo lakes' water was also the other driving force for the decline of agricultural land; the siltation-led expansion of the lake water, according to a priest (age 58) having farmland in Lante Kebele (administrative unit) (Western cost of lake Abaya), has invaded signi cant share of his farmland and the landholdings of other smallholder farmers who have farm-plots proximate to the lake.
Population growth-induced expansion of settlement (by 190 % or 20,818 ha) in the study area (in 1990 -2019) has also contributed for the signi cant decline in shrubland cover (Table 3) 'Swamp' area of the lake-wetland, like shrubland cover, has been decreasing at progressively increasing magnitudes of changes in the four decades' period (1990 -2019); that is, where the magnitude of decline of the swamp cover was smaller (11.4 % or 700 ha) in the initial period (1990 -2000), moderate (16 % or in by the study (Table 3). This is mainly a result of the progressively increasing sediment load into Abaya  (Table 3). That is, the progressively increasing siltation-led expansion of 'water' body was the main cause of decline of the 'swamp' area of Abaya-Chamo lake-wetland; this is so because, the progressively increasing magnitude of increasing trend of 'water' body had been accompanied by progressively increasing magnitude of decline trend of 'swamp' area in the three decades' period as these LULC classes are con gurated inherently adjacent to each other (Table 3 and Figure 2). Analysis results of the NDVI and NDWI also con rm the contradicting trends of 'wet/swamp' area and 'water' cover of the lake-wetland (Table 5 and

Conclusion
Lake Abaya-Chamo wetland is getting threatened overtime due to largely anthropogenic factors-induced LULC dynamics in the period 1990-2019. Settlement, agriculture, water body and forest cover of the lakewetland showed net increases in three decades. Whereas, shrubland and 'swamp' area experienced signi cant net decline (by almost half of each) in 30 years. While settlement and water body increased at progressively increasing magnitudes of changes in three decades, shrubland and swamp cover declined at progressively increasing magnitudes of loss in the same periods. Increasing agroforestry practice by smallholder farmers and small-scale investors overtime contributed to the net increase in forest cover, and for the huge magnitude of shrinkage of shrubland (by 48.9 %) in lake Abaya-Chamo wetland. Decline of shrubland and natural forest was also driven by settlement and farm expansion. Generally, the LULC dynamics led to depletion of natural forest and shrubland in the coasts and uplands of the lake-basin, increasing runoff erosion and sediment load into plus pollution of the lake-wetland, invasion of the lakes by a strange plant water hyacinth('emboch'), siltation-led displacement of the lakes' water, area shrinkage and loss of biodiversity of the swamp, and to the overall degradation of lake Abaya-Chamo -wetland and its ecological services in the Southern Rift-Valley of Ethiopia.
To curb the impact of LULC dynamics on wetlands, the government should: (i) formulate clear policy, institutional and legal framework on the management of wetlands; (ii) revise the investment policy and enforcement of impact assessment; (iii) enforce 'user tax' (on users of land, water) and/or 'pollution charge' (on polluters of land, water) on investors in agriculture and other sectors in risky areas, adjacent to wetlands; (iv) reforest and afforest the uplands surrounding wetlands; and (v) revise the policy on the allocation of land uses. Analysis results of the NDVI and NDWI also con rm the contradicting trends of 'wet/swamp' area and 'water' cover of the lake-wetland