The Suluh river basin is subjected to soil erosion due to land use and land cover change. Yet, the impact of land use/land cover change soil erosion has not been applied in the study area. Thus, the ...current study focused on the modeling of the impact of land use/land cover changes on soil erosion in the Suluh river basin, the northern highland part of Ethiopia. Landsat image data sources were used to achieve the objectives. Ancillary data was also used. The nearest neighbor's fuzzy way of classification and the land change modeler for modeling, and the revised universal soil loss equation model for estimating the soil erosion rate were employed. Both qualitative and quantitative data were analyzed qualitatively and quantitatively. The study's findings confirmed that cultivated land, bare land, and built-up areas significantly increased while areas occupied by natural vegetation such as forest land, shrub lands, and grazing lands decreased at a rapid rate in between 1990 to 2018. The predicted results suggest the continuation of the trend up to 2048 if business as usual is continued. The annual mean of soil lost in the study area was about 36.31, 43.32, and 47.78 in the years 1990, 2002, 2018, and will be 56.54, and 71.62 tons per hectare per year in 2028, and 2048, respectively. When we consider 15 t ha−1 year−1 as the maximum tolerable soil loss (TSL) rate for ease of presentation, the areas above the TSL have increased from 88.3% in 1990 to 88.6% in 2002 and to 89.6% in 2018, and are expected to increase to 89.9% and 99.8% in 2028 and 2048 periods, respectively. Thus, land use and land cover change information and its impact on soil erosion should be taken under consideration by land use planners to apply sustainable land management activities in the Suluh river basin.
The concept of leadership is introduced into the context of participatory modelling. Different leadership paradigms are described. The role of the modeler and the question who should be assigned the ...leadership role is analyzed. The leadership and management perspectives are compared. The leadership function assumes seeing the big picture and setting the right goals. Leadership is essential in guaranteeing responsible practices and the overall effectiveness of a project. This includes transparent, ethical and bias-aware modelling, resource planning and addressing challenges related to the changing project conditions as well as to sensitivity to social dynamics and stakeholder engagement. The paper suggests that there are clear benefits in taking the leadership perspective.
•It is important to consider the leadership function in participatory modelling.•Leadership styles can be different ranging from laissez-faire to systems leadership.•Leadership role helps to keep in mind the big picture, overall goals and efficiency.•Responsible and ethical practices are in the focus of a leader.•Stakeholder engagement and project leadership require people skills.
In order to make decisions for regional planning and achieve sustainable development, it is necessary to quantify land use and land cover changes. In this study, the land use and land cover maps of ...the Zayandehrood Dam watershed were prepared for the period of 1991 to 2021, using Landsat satellite images, and the changes that occurred in this period were revealed. Using the land change modeler (LCM), land use and land cover and their future changes for 2051 were modeled and predicted. The results showed that in the period between 1991 and 2021, the coverage of poor rangelands with 51,871 hectares of change had the largest decreasing trend, and the agricultural class had the largest increasing trend with 71,478 hectares of change. The largest decline in the period from 2021 to 2051 is related in the coverage of the fair rangelands class with 66192 hectares, and the agricultural class potentially has the largest increasing trend with 70328 hectares of change. The findings of this research will be useful for policymakers and planners. They can use the findings of this study for spatial planning in the region, managing the process of land use and land cover changes for sustainable development.
In the areas declared to be a tourism center by state planning, a rapid tourism-related development occurs depending on the investments in tourism, which causes a dramatic land use/land cover (LULC) ...change. Determining, monitoring, and modeling of LULC changes are required in order to ensure the conservation-use balance and sustainability within such vulnerable areas that are under development pressure. This study consists of four steps. In the first step, the Landsat images dated 1985, 2000, 2010, and 2021 were classified using the maximum likelihood method and the LULC of Belek Tourism Center located in Turkey were determined. The second step included the identification of areal and spatial changes between the LULC classes for the four periods. In the third step, the LULC changes in Belek Tourism Center for 2040 were modeled using the land change modeler. Last step evaluated the relationship between the modeled spatial development pattern and the current planning decisions. According to the results obtained during 36 years, the rates of built-up, forest, and water body areas have increased by 11.91%, 13.67%, and 0.82%, respectively, whereas the rates of barren land and agricultural areas have reduced by 22.25% and 4.15%, respectively. The LULC map modeled for 2040 predicts the built-up areas to expand by 8.25% and the agricultural areas to shrink by 5.42% by comparison with 2021. This study will contribute as a key measure for planners, policy-, and decision-makers to make decisions related to sustainable land use in the areas declared to be a tourism center.
Haftad-Gholleh Area has recently encountered many changes. In this study, by using landsat images of the years 1996 and 2016, landuse maps were classified into four classes including: agriculture, ...rangeland, residential areas, and rocks. Land Change Modeler (LCM) and Habitat and Biodiversity Modeler (HBM) modules in the Idrisi GIS software were used to analyze the land use changes and habitat evaluation for the prediction of the land uses status in 2016, based on the Artificial Neural Network (ANN), Markov Chain analysis and logistic regression. The results showed that most of the changes in the landscape of the region between 1996 and 2016 were related, respectively, to attrition, aggregation and creation indicators; between the years 2016 to 2041, they can be related, respectively, to the creation and dissection indicators. The habitat evaluation showed that 4.5% of the habitat was decreased in 2016, as compared to 1996. With the continuation of this trend, 6.5% of the habitat will fall in 2041, as compared to 2016. Receiver Operating Characteridtic (ROC) of the model also specified that the desirability model validity was equal to 0.9558, showing the excellent performance of logistic regression method. In general, this can be an important principle approach preventing from changes in the habitat of wild sheep to other land uses.
Land use/cover (LULC) change is a major concern in Africa’s river basins and policy makers, environmentalists and other stakeholders tackling biodiversity and sustainable development issues in these ...watersheds require accurate information on past, present and future LULC projections to develop management strategies for the concerned watersheds. This study assessed the historical, current and future LULC changes in Mpologoma catchment. Remote sensing and supervised classification were used to analyze 33-year multitemporal LULC changes in Mpologoma catchment while future patterns for the next two decades were predicted using the Cellular Automata-Markov modelling technique in TerrSet’s Land Change Modeler. Initially, in 1986, the catchment was dominated by grassland (32.08%). However, most grassland (92.77%) was gradually converted to subsistence farming (75%) and built-up (15.7%). Grassland, woodland and wetland annually declined at a rate of 5.52%, 2.47% and 0.63% respectively while farmland and built-up expanded at 9.32% and 6.22% respectively and by 2019 subsistence farming was the dominant class (53.16%). Prediction results showed that by 2039, woodland, grassland, wetland and open water will decrease while there will be major increases in built-up and commercial farming from 11.61% to 27.91% and 0.18% to 0.34% respectively. Subsistence farming will continue to be the dominant land use by 2039 attributed to gains from woodland (4.7%), grassland (3.7%) and wetland (4.9%). These LULC changes indicate an intensifying land use pressure in Mpologoma catchment and provide useful information for land use planners, environmentalists and policymakers in this catchment to consider when planning for sustainable management of the watershed.
Abstract An implicit ambiguity in the field of prediction-based decision-making concerns the relation between the concepts of prediction and decision. Much of the literature in the field tends to ...blur the boundaries between the two concepts and often simply refers to ‘fair prediction’. In this paper, we point out that a differentiation of these concepts is helpful when trying to implement algorithmic fairness. Even if fairness properties are related to the features of the used prediction model, what is more properly called ‘fair’ or ‘unfair’ is a decision system, not a prediction model. This is because fairness is about the consequences on human lives, created by a decision, not by a prediction. In this paper, we clarify the distinction between the concepts of prediction and decision and show the different ways in which these two elements influence the final fairness properties of a prediction-based decision system. As well as discussing this relationship both from a conceptual and a practical point of view, we propose a framework that enables a better understanding and reasoning of the conceptual logic of creating fairness in prediction-based decision-making. In our framework, we specify different roles, namely the ‘prediction-modeler’ and the ‘decision-maker,’ and the information required from each of them for being able to implement fairness of the system. Our framework allows for deriving distinct responsibilities for both roles and discussing some insights related to ethical and legal requirements. Our contribution is twofold. First, we offer a new perspective shifting the focus from an abstract concept of algorithmic fairness to the concrete context-dependent nature of algorithmic decision-making, where different actors exist, can have different goals, and may act independently. In addition, we provide a conceptual framework that can help structure prediction-based decision problems with respect to fairness issues, identify responsibilities, and implement fairness governance mechanisms in real-world scenarios.
The changing pattern of land cover is increasingly becoming of global concern in the sustainable management of environmental resources. Different facets of the natural ecosystem continue witnessing ...devastation orchestrated by rapid population growth and urban expansion in the face of climate change. This study examined the contribution of human's to the global environmental change by assessing the dynamics of land cover between 1984 and 2017 while predicting the future extent of land cover pattern for 2047 at the Epe and Igbokoda areas on the coast of southwestern Nigeria. Landsat Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI) imageries of 1984, 2002, and 2017 respectively were acquired from the USGS to analyse the land cover changes. Supervised classification was done using the maximum likelihood classifier of Terrset version 18.31. The Change Demand Modelling of Land Change Modeller (LCM) in Terrset integrates the Markov chain for future predictions for 2047. The Epe area which typifies a rapidly urbanizing coastal environment recorded an 84.6% increase in built-up area extent between 1984 and 2017, while the built-up area of the Igbokoda area increased by 103.8% for the same period. This increment corresponds to a decrease in the spatial extent of the forested wetlands with an increase in water bodies. Expansion of water body extents indicates the interaction between the elements of climate change such as incessant flooding and anthropogenic activities like deforestation, urban expansion through sand mining and dredging. Future prediction into 2047 connotes further worsening of the situation. Therefore, solution-based sustainable coastal management practices are recommended to salvage the impoverishing coastal ecosystems from further impairment.
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