Land use land cover (LULC) change occurs under a variety of pressure and it is the result of changes or modifications in the intensity of an existing LULC type to determine the location and nature of the land use change. The changes of LULC have been perceived as worldwide environmental change drivers in the watershed that are very sensitive to LULC dynamics [1
]. A dynamic LULC offers an inclusive sympathetic of the interactions and relations that are crucial for sustainable land resource management [2
]. At global and local levels, the changes of LULC are driven by anthropogenic and natural processes at different spatiotemporal levels. The LULC changes are dynamic, non-linear human–nature interactions that are significant land surface conversions and involve complex processes. The LULC change trajectory worldwide for the past 300 years has been categorized by gains in agriculture and losses in forests [3
]. According to the authors of [5
], LULC changes are associated with the change of forest land to agricultural expansion, urbanization, and deforestation.
The LULC changes were broadly assessed in various areas of the world, for instance, Europe and USA [6
], South America [7
], Asia, and Africa [8
], Ethiopia [9
]. In Africa, the expansion of agricultural land influenced by rapid population growth has been recognized as a primary driver of LUC. Many developed countries including Europe and the United states practiced massive deforestation because of agricultural expansion and industrialization until the early 19th century [11
]. Urbanization dynamics and urban growth are usually linked to demographic factors mostly in developing countries [12
]. In Africa, the expansion of agriculture influenced by population growth has been recognized as a primary driver of land use land cover change (LULCC). The landscape has been intensely changed due to socio-economic and political changes that occurred in the first half of the 19th century in Europe [11
]. The decrease in agricultural land at the expenditure of increased urban areas and water bodies in Europe is another strong trend [13
The dynamics of LULC intensities and rates are changing because they are highly associated with the overexploitation of natural resources. The natural variability issues like climate change, soil conditions, and terrain characteristics have also accounted for land use changes [14
]. Therefore, the integration of natural and human factors in LULC dynamics have become a significant issue throughout the world in efficient land use. The assessment of LULC change and the drivers that have direct consequences on the natural environment and human societies are the focus of the current scientific examination of scientists [15
]. In order to develop sustainable strategies and to have informed planning decisions, understanding the drivers and dynamics of LULC change is crucial. The LULC change driving forces can be direct or indirect for change over time and space; this was incorporated to provide an estimate of future scenarios [16
The drivers’ assessment and predicting their future LULC status in the watershed is expected to have an essential contribution for land use planning management and sustainable water resources. The use of historical satellite imageries are used to effectively monitor and analyze LULC change [17
]. Future LULC change analysis and prediction are often complicated for the stochastic change of nature and the dynamic of natural and socioeconomic variables. The prediction of forthcoming LULC dynamics in areas where the economic condition depends upon agriculture has a profound impact. The LULC model developed in this study predicts a future LULC state based on a business-as-usual scenario. Therefore, understanding the earlier and current LULC changes and simulating for the future is vital for the management of land and water resources of the basin [18
Models of land change are useful tools for environmental and other types of research concerning LULC change [20
]. The magnitude and location are the two important issues of LULC that have been considered in the modeling. LULC change assessment models are either dynamic or static, non-spatial or spatial, deductive or inductive, pattern-based or agent-based [21
]. Modeling the LULC process is to properly calibrate and validate the model for predicting future changes [23
]. The LCM embedded in the TerrSet model has been utilized to inspect the historical and to predict the future changes of LULC of the watershed. LCM primarily uses the MLPNN-CA-MC approach. The model is strong due to its dynamic projection proficiency, suitable calibration, and capability to simulate several types of land cover [24
]. LCM evaluates changes of the land use of two different periods, determines the changes, visualizes changes, and presents the results with various maps and graphs.
The model predicts future LULC images based upon MLPNN (Multi-Layer Perceptron neural networks) and CA-Markov Chain (CA-MC) [26
]. The “transition potential” of pixel to change into another class determination is through MLP drivers of change [27
]. The MC model is stochastic, which requires pairs of LULC images to derive the transition potential into the future predictions based on the amount of historical change [28
]. The blend of CA-MC can simulate the spatiotemporal dynamics of LULC change. The model predicts transitions of a cell from one LULC to another depending on “physical and socio-economic” data [30
]. The model validation process is the accuracy assessment of prediction and contrast made between the predicted and observed land cover maps. Therefore, to assess the change drivers of the past and to simulate for the future, the validated data have been used.
Remote sensing data particularly Landsat images provide suitable possibility for LULC change monitoring, particularly for developing countries where geospatial technologies are not well developed [32
]. In Africa, around 40% of the population were living in urban centers as of 2014. By 2050 the urban residents’ percentage has been projected to reach 56% [33
]. In Europe, between 1990 and 2006 the population grew up by 146% [34
] as per the study was conducted in 24 European countries. Currently in Europe around 73% of the population lives in urban areas and it is estimated to be over 80% by 2050 [33
]. The availability of resources as well as their dynamics and management varies considerably from area to area, especially in Ethiopia. Ethiopia is highly vulnerable to environmental changes induced by natural and anthropogenic activities. LULC change is a fundamental problem in the country [35
Due to changes of LULC, Ethiopia experienced serious environmental problems including soil erosion, land degradation, loss of soil fertility, and deforestation [36
]. The “Upper Blue Nile Basin (UBNB)” is the most varied and a highly important river basin in Ethiopia [37
]. Nashe watershed is the tributary of the Basin that faced LULC change driven by population growth, urbanization, agricultural land expansion, deforestation, overgrazing, expansion of industrial activities, and political dynamics. The analysis of LULC change in Nashe watershed, its drivers, and prediction of future land use change are vital for understanding the dynamics of human environment interactions and environmental management interventions. Therefore, it is very important to assess and predict the LULC changes based on the historical data using Landsat images and Land Change Modeler. The following research questions were addressed: (i) what was the trend of LULC change within the study area in the past (1990–2019) and which LULC classes were mostly affected? (ii) What growth and change patterns can be expected in the future? (iii) What are the major driving factors of LULC change in the watershed?
The above research questions are so significant and addressed in this study of watershed since a dramatic socio-economic change with expected enormous effect on the land use is undergoing and this will influence various hydrological processes. Thus, especially in the UBNB, those changes need to be predicted in time for environmental management in this area in an effective and sustainable manner. The study findings are utilized to provide empirical evidence on patterns and rates and identify major driving forces of LULC dynamics at watershed level, and improve policies in land use within the framework of sustainable land use planning in relation to future changes or development.
3. Results and Discussions
3.1. Accuracy Assessment of the Classified Images
The assessment of accuracy was performed for LULC change analysis by generating confusion/error matrix in each LULC category of 1990, 2005, and 2019 classified maps. The overall accuracy, kappa statistics, producer’s and user’s accuracy have been used for assessment. The kappa statistics and overall accuracy of classified images shows 91.43%, 87.59%, 85.71% and 0.93, 0.90, and 0.88 for the years 2019, 2005, and 1990, respectively (Table 4
). The more recent LULC map accuracy results were higher and these may be related to a higher spatial resolution of satellite images.
The accuracy assessment of LULC is required in any study using remote sensing Landsat data for the historical LULC. According to the authors of [61
], LULC map accuracy is quantified by creating an error matrix or a confusion matrix, which compares the classified map with a reference classification map. The results of the study is consistent with some other studies conducted by the authors of [41
] in Dera District, of [36
] in Borena Woreda, of [10
] in Blue Nile basin, of [9
] in Gumera, of [62
] in Portugal, of [11
] in Poland, Slovakia, and Czechia, and of [63
] in India. The accuracy of this study shows that the result is within the range of accuracies, in which Land Change Modeler and Landsat images were used [55
3.2. LULC Change Analysis
The change of the LULC analysis was through evaluation of gains, net change, and losses experienced by different categories using change analysis in LCM. The evaluation of spatial and temporal changes between various classes during the period 1990, 2005, and 2019 was analyzed (Figure 2
and Table 5
). The “from–to” transformations are summarized as loss, gain, and net change of LULC in Figure 3
and Figure 4
. The gain of LULC for each class was determined from the result of persistence and the total column value, whereas the loss is from total row and the persistence.
The agricultural land is the dominant LULC type of the watershed which covered 43.97% of the study area in 1990, 50.39% in 2005, and 61.19% in 2019 (Table 5
). The changes in LULC have influenced forest distribution in the study area. The forest land area, which is also the largest part of land use class, has significantly decreased. Similarly, according to the least prevalent types of land use, the urban areas and water body increased from 1990 to 2019. The significant increase of agricultural land, water body, urban land, and the sharp decline of forest land in the watershed were the major transformations observed. Although forest land, range land, and grass land experienced reduction in coverage throughout the study periods, the greatest reduction rate was observed in forest land. To mitigate the rapid rates of LULC conversions at watershed, the application of integrated watershed management strategies, managing the rapid population growth, afforestation of degraded or deforested areas, and reducing the dependency of locals on forest products is critically important. The findings of the study are consistent with other studies conducted in Ethiopia by the authors of [64
] in Birr and Upper Didesa watersheds of the Blue Nile basin, and as outlined in [41
] for Dera district of northwestern Ethiopia, where the agricultural land increased significantly and the forest land was shrinking.
3.3. Driver Variables of LULC Change
The driver variables influencing changes are based on spatial analysis and added to the model either as static or dynamic components [47
]. The LULC prediction in the watershed was based on change in a driver’s impact. In this study, both topography and proximity factors were selected to analyze the LULC change. Before the drivers are added to the model, the selected driver variables were tested for their explanatory value using Cramer’s V and P values (Table 6
). The Cramer’s V value does not give decisive proof that a particular variable explains the change in land use. It is rather a more intuitive tool that can be utilized to understand the significance that a particular variable has in influencing change.
Evidence likelihood is used for the determination of the relative frequency of pixels of different LULC types within the areas of change. It is recommended in cases where there are low Cramer’s V values. The obtained result for evidence likelihood is considered as good. In this study, it is a quantitative measure of the frequency of change between agricultural land and all other land classes (also called disturbance).
From Table 6
, it was observed that the variable such as elevation, distance from urban areas, distance from stream, distance from road are considered as useful variables of transitions. Variables such as slope have low Cramer’s V values, and it shows that the effect of slope on LULC change in the study area is not critical. The variables with good Cramer’s V value show that they are the most explanatory variables for LULC change. All driver variables were used to model the transitions in this study. The elevation and slope are recognized as the imperative topographic factors affecting LULC change. Topography has effects on the spread and extent of urban distribution, forest and range land conversion to agricultural land. The authors of [65
] found that deforestation decreases with the increase of the slope gradient. The other driver variables such as distance from stream, distance from urban areas, and distance from roads also play an important role in land use change, as each provides convenience to residents to access resources.
3.4. Transition Probability Matrix (TPM)
Transition potential modeling is assessing the likelihood of LULC change from one class to another depending on the suitability transition of area and the presence of driving forces [66
]. The TPM records the probability of each land use class to change into the other class. The LULC changes of the future predictions are utilized through the probability of the transition matrix [67
]. The transition probability matrices produced by the model between LULC types during the periods 1990–2005 and 2005–2019 were depicted in Table 7
. The spatiotemporal LULC change assessment between the earlier and later land cover maps were cross-tabulated. The cross-tabulation is used to determine the amounts of change and conversions between different land cover maps. In the table of cross-tabulation shown in Table 7
, the bolded frequencies along the transition probability matrix of the diagonal confirm the probability of LULC class remaining unchanged (persistence) from the earlier to the later land cover map. Whereas, the off-diagonal frequencies express the possibility of a given LULC experiences changes from one to another. The change analysis is depends on the changes in LULC between time 1 and time 2 [28
The agricultural land which is the highest class has 39,662.13 ha with the probability of remaining as agricultural land in 1990–2005. The conversion of forest land, range land, and grass land was the major contributing land use for the agricultural land. The minimum loss of LULC category was observed from water body to grass land and urban land. Although in urban land there was minimum or no conversion to range land and water body. In 2005–2019 the highest class loss was the change of forest land to agricultural land by 12,051.96 ha.
In 2005–2019, especially since 2012, a dam on Nashe River was built for irrigation and hydropower purposes. The water body has increased. The displacement of communities occurred from their land during the expansion of hydropower projects and the displacement caused land scarcity. The lowland areas of the watershed was covered with forests, range lands, and grass lands before 1990 even before 2005. Currently, however, a great decline of forest cover is occurring because of urbanization and agricultural land expansion. In recent times, the expansion of urban areas has been continuously increasing at the highest rate.
The findings of the study show that urban area increase was consistent with other research findings in Africa [8
] and Ethiopia [9
]. The studies in some parts of Europe, for example, [13
] in Slovakia, [62
] in Portugal, and [11
] in Poland, Slovakia, and Czechia, reported an increase in urban areas at the expense of agricultural land. Similarly, a study in China reported that the urban built-up land expansion from conversion of cultivated land [68
]. The authors of [12
] revealed that effective urban planning is needed to address the multiple challenges and competing interests of urban environments for the rapid increase in urban built-up area with scarce land and water resources on the urban edge.
3.5. Validation of the Model
The agreement of the two categorical maps was measured by using Validation Module. In order to assess the accuracy, validation of the model is necessary. Validation is significant as it allows to determine the quality of the predicted land cover map with actual map. A comparison was made between the actual and simulated LULC map of 2019 so as to validate the predicted map. The less effective simulated LULC class is water body as the projected map was from the maps before construction of the dam and the actual LULC map is from after the construction. The validation results between the simulated and actual LULC test summary of the model are presented in Table 8
The achieved k-indices are compiled in Table 9
. The model is regarded to be validated if the Kstandard (overall kappa) score exceeds 70% [22
]. The values of k-index greater than 80% show good agreement between the projected and actual LULC map that exceeds the minimum acceptable standard [47
]. Here, all indices are greater than 80%, showing a good overall agreement and projection ability of the model.
The values of AgreementChance, AgreementQuantity, AgreementGridCell, Disagreement Grid Cell, and Disagreement Quantity in Table 10
provide statistical agreement information between the simulated map and the reference map. Namely, DisagreementGridCell and DisagreementQuantity constituents are crucial to recognize the model simulated outputs [57
The disagreement between the two maps is generally low and this is mainly due to quantity errors (0.0513) rather than allocation errors (0.0269). The agreement measures show overall good agreement between the actual and simulated map (92.81%). The result shows that in the study area the model has higher ability to predict the LULC changes in location than in quantity. This indicates the good capacity of the model in simulating future LULC states and an accurate specification of location.
According to the authors of [55
], the model was validated by comparing the map of observed LULC of 2019 with the predicted LULC map of 2019 using the statistics of kappa index. For accuracy assessment measurement in a number of studies, kappa coefficient is still considered as a vital tool [61
]. The LULC change model performance is different for different study areas because of varied environmental features and situations of the individual study area [62
3.6. Future LULC Prediction
The LULC change of the future has been predicted for the years 2035 and 2050. The future probable percentages of changes in LULC for the periods of 2019–2035 and 2035–2050 were analyzed by transition probabilities matrix. The quantity of change and the spatial distribution are the two aspects of LULC prediction in LCM that are provided by Markov chain and MLP neural network, respectively [69
]. The simulated future LULC images of the watershed obtained from the LCM are shown in Figure 5
. Similarly, the area coverage, percentage, and rate of change are provided in Table 11
. Generally, the LULC change increase or decrease was provided in Figure 6
Significant change was observed from the change analysis result in LULC change between 1990 and 2050. Agricultural land will be the predominant LULC type. It was seen from the result as the area of agricultural land increment from 61.19% in 2019 to 72.98% in 2035 and 73.24% in 2050. This was mainly caused by converting forest, range, and some parts of grass land. Agricultural land increased significantly from 1990 to 2035 and then slowly from 2035 to 2050 (Figure 7
and Table 11
). Similarly, a continuous increment was also observed in urban areas and water bodies from the 2019 to 2050 periods. The development of infrastructure, industry, and housing have taken place and are expected to take place around the watershed, therefore, the urban land will increase. The urban coverage around watershed totaled 1.15% in 2019, which is predicted to reach 2.00% and 3.82% by 2035 and 2050, respectively. The graphical demonstration of the area covered by six LULC classes for past years (1990, 2005, and 2019) and for the prediction years (2035 and 2050) are shown in Figure 8
The forest land and range land show a decreasing trend from 2019 to 2035. Unfortunately, the grass land, range land, and agricultural land will slightly increase from 2035 to 2050. This might be due to the limited area of land for different purposes. The major contributing factors to LULC change were the expansion of hydropower and irrigation projects, mostly at the downstream, for expansion of a sugar factory. The scarcity of jobs and urban expansions in the catchment amplified the socio-economic activities for LULC changes. In the watershed, the downstream and partly at the upstream areas, which were previously covered by forests and range lands, have been converted to agricultural land and commercial crop farms.
Forest reduction also occurred as a result of using charcoal and firewood as the energy source for most of the people living around the watershed, who depend on fuel wood. Additionally, most of the evacuated population during the construction of Nashe and Amerti projects were involved in converting the forest land to agriculture and settlement. Illegal and unplanned settlements by the local people to expand agriculture and settlement also contributed to destruction of forest land, range land, and grass land. Thus, the forest conversion needs to be controlled and well-managed, and a reasonable land use plan should be developed in an organized way. The expansion of one LULC type occurs at the detriment of other LULC classes [70
]. Currently, the government has given more emphasis to the plantation trees program. Hence, in the country, many of the areas that were deforested might become covered by plants again. In the future prediction of LULC scenarios, the change of the area in the transition matrix was determined (Table 12
The study shows that agricultural land experienced the largest increase from the historical analysis to the projections. The conversion of other land uses to agricultural land might be mainly associated with the land demand for crop production to satisfy the food demand of the increasing human population, deforestation for household energy consumption, construction of materials, and loss of land productivity. This may cause serious environmental impacts unless proper environmental management strategies are planned and implemented. As per the analysis in the entire watershed, the urban area was increased. This increase was assumed to be closely associated with the rise of infrastructure to accommodate the increasing population. To minimize land degradation, it is necessary to apply management measures such as soil and water conservation technologies, family planning, and promotion of agricultural land use intensification in the study watershed. The future land use and land cover change plan for the study area should be made in advance and needs to be incorporated at policy level.
A Multilayer Perceptron neural network and CA-Markov modeling in LCM-based analysis was combined with GIS, and remote sensing technologies was used to perform the analysis of the LULC change. The performance of the MLPNN-CA-MC in LCM for the LULC pattern was not assessed previously over this study watershed as far as the authors are aware. Additionally, analyses regarding the dynamics of LULC changes and their drivers are not conducted in the study area. Therefore, in this study, the LULC dynamics of the historical and future LULC were assessed using Landsat images and LCM by using the drivers of LULC dynamics. Consequently, this study will also help to assess the performance of the MLPNN-CA MC approach over the watershed area. The future LULC is somewhat known in some parts of Ethiopia but not in the study area. The LCM embedded in the TGMMS model was successfully used by different researchers in other areas and it confirmed that LCM, based on MLPNN-CA-MC, is a capable model for the assessment and prediction of LULC change, urban growth, and the validation of results [9
Generally, the patterns of LULC change in the past almost three decades shows forest land decreased at an average rate of 48.38%. The results showed that the agricultural land gained the most area compared to the other LULC types. However, at the expense of forest LULC categories, agricultural land is expanding at an average rate of 39.15% (1990–2019). From the temporal patterns of the changes between 1990 and 2019, forest land decreased at a higher rate. The other affected LULC types were range land and grass land. The urban land and water body LULC classes gained trends in the study.
Simulation analysis was conducted for the years of 2035 and 2050 based on historical LULC change data from 1990–2005, 2005–2019, and 1990–2019, which were used as a baseline. Similar to the historical analysis of LULC change, the predicted results of forest, grass, and range land classes were registered net loss in the area from 1990 to 2035. Whereas, the range land and grass land smoothly gained from 2035–2050. The predicted results of the year 2035 and 2050 show an increase in agriculture, water body and urban land. Therefore, future land use activities ought to be based on proper land use development and land regulation to reduce the enduring adverse impact of LULC changes. Ref. [12
] confirmed that effective urban planning is needed to address the multiple challenges and competing interests of urban environments for rapid increase in urban built up area on scarce land.
] indicated that agricultural land expansion, both for commercial and crop production is the main driver of LULC change. The rate of crop land expansion is increasing whereas forests, grass land, shrub land and other lands are decreasing in the world. This study finding is in agreement with results from previous studies that confirm the major driver of LULC change [9
]. Ref. [73
] reported that weak law enforcement and growth of population are fundamental drivers of deforestation. The other driving force for agricultural land expansion is probably government policy [72
]. On the other hand, the major underlying driving forces are Demographic, Economic, Technological, Institution, policy and biophysical factors were identified by the key informant and FGDs of the study. Agricultural expansion, firewood extraction, settlement expansion, land tenure policy and infrastructure development were the top LULC change drivers.
The present study was carried out to understand the changes in the historical and predicted land use land cover patterns from the year 1990 to 2050. The integrated approach including remote sensing, GIS, and a MLPNN-based CA-MC model was used to understand the spatiotemporal dynamics of LULC and prediction of future LULC change in Nashe watershed, Ethiopia. The conclusions drawn from the research findings were the following.
The multitemporal satellite imagery data are used for informed decision-making in LULC change, providing the potential information required for monitoring and evaluating of LULC changes. The precision of the data from the remotely sensed imagery classified based on the maximum likelihood classification method with high resolution image of Landsat was checked through an error matrix and it yielded an acceptable result that was further processed for analysis.
To validate the model, the projected 2019 LULC map was compared with 2019 actual LULC map. After successful model validation, the LULC map for the years 2035 and 2050 were simulated by considering the business-as-usual scenario. In this procedure, we used 1990–2005 and 2005–2019 LULC data as a baseline and current scenario for comparison. Its validation showed a strong correlation between the simulated LULC map and satellite-derived map, which proved the simulation model’s reliability.
The rapid and massive changes of LULC in the watershed may have serious environmental impacts. The analysis of LULC change shows that forest cover has been decreasing, as well as the high increasing rate of urban area and agricultural land. The predicted LULC situation show that this cover would continue in the future. This will increase vulnerability of the watershed to landslides, soil loss, gully erosion, worsened air pollution, and impact the hydrology of the studied watershed in particular and the Blue Nile Basin in general.
Therefore, suitable and timely management measures must be taken by policy decision makers to enable sustainable development and to protect the watershed in order to reduce the severity of the changes.
Settlement expansion, agricultural expansion, firewood extraction, land tenure policy, and infrastructure development were the top LULC change drivers. Moreover, to ensure a better environmental condition, this kind of study revealed a significant prospective to contribute towards the sustainable environmental planning and management system of an area at the local and global levels.
Finally, it can be concluded that the projected conditions may be reversed, which is very important to reduce the enduring adverse impact of LULC changes on the watershed hydrological components through the announced nationwide tree planting, implementing the strategy of climate resilient green economy and formulating the local- and regional-scale policies required for sustainable development. Future studies incorporating the assessment of land use and land cover change impacts on the hydrological parameters of the watershed would be helpful for better management of the watershed.