Prognosis of ecosystem services richness based on land use land cover changes

Abstract

The major cause of unstructured and undefined land use patterns is urbanisation, which has a direct and indirect influence on the environment as well as natural resources that supply high-quality ecosystem services for well-being components. The tremendous spread of cities (urban sprawls), especially in emerging countries, has resulted in more poverty and environmental devastation than ever before. At the turn of the twenty-first century more than fifty percent resides in the urban area. This number is expected to increase by more than 60%, with Asia and Africa accounting for the bulk of the growth. Around the globe, Urbanization trends are growing exponentially, as per the World Urbanization Prospects 2018 more people resides in urban area, than that of rural around the globe, which accounts for 55% of the global population as per 2018. In 1950, only 30% population was urbanized which is projected to 68% for 2050 (WUP-2018). The procuring of services derived by the ecosystems are under increasing pressure as a consequence of these major changes and continuous population expansion with economic progress. As a result, their ability to continue delivering these services is jeopardised. Because they are inextricably interconnected and related to land use changes; studies that link these concerns are crucial in the context of improved land management and the long-term supply of ecosystem services. Urbanization is the most significant human influence affecting urban environments. Because this changes the interplay between the system's atmosphere, hydrosphere, and biosphere Ecosystem services are the tangible advantages that humans get from natural phenomena that are non-human in nature. As the loss of ecosystem services increases there are many direct losses. First, it limits the amount of available habitat, which may have a significant effect on creatures that need large areas of land. Second, habitats become increasingly separated. Habitats are swallowed in a sea of farmland or civilization. Animal species are further isolated by roads and fences, making movement across environments difficult, if not impossible. Third, environmental changes and habitat modification cause habitat changes. As a consequence, habitat quality deteriorates, and biodiversity suffers. The ecosystem could no longer sustain native species richness (number of species) or composition before landscape changes (types of species). The major gap that is addressed, there are much significant work remained in developing the current ecosystem services data resourcing, model configuration, and integrating it in decision making for the future planning processes, also simulating the model based on evident ecosystem services for a potential land use and land cover configuration, which can be utilized for developing land use planning and representing spatially the identified land cover projection, with respect to the impacted services of ecosystem which is based on cellular automata, that implies a real life scenario, and thus the integration of Artificial Neural Network and Markov model. Department of Environmental Planning, School of Planning and Architecture, Bhopal (MP)-462 030 This integrated model is entirely based on physical presumption that, the future state is directly dependent on the current state of urban expansion, by taking human activities and natural deriving factors of ecology into consideration with determining the extensive linkages between the land use pattern and fabricated or natural deriving factors that alter the changes. The study is caried from understanding the impact of Urbanisation (Urban Expansion) on ecosystem services variables and environment which include the delineation of the ecosystem services of first level and secondary level services and mapping the ecosystem services and its changes with respect to the base years of 2001, 2011 and 2021 to evaluation of the delineated variables and their thresholds for Land use land cover distribution. Further, model calibration is done of the basis of ANN (Artificial Neural Network) and Markov chain model which include the deriving factors for projecting the Land use Land cover for simulating and developing a potential land use land cover configuration having minimum impact on Ecosystem Services and quantifying the services on the basis of highly vulnerable area by standardizing the ecosystem services values. Further, by taking an ideal Eco System Services Richness map which will be based on the previous year services maps using weighted evidence methodology to configure it on the basis of changing land use and land cover. Deriving factor data rasters contains the learnings as these are independent variables, while initials datasets have information about the current land use land cover categories which are dependent variables on factors affecting the growth. The modelling initiates as a predictor that predict and calculate the potential based on transition based on the current state of independent variables and dependent variable i.e., LULC. In result, model totally relies on transition potentials generated using the model, omitting implicit transition. If the model uses its neighbourhood for training neighbourhood effect is obtained, for instance the logistic regression develops corelation with each neighbourhood and thus, it impacts each of its neighbour for transition potential modelling, the simulation will be so generalized if the training of data does not use its neighbourhood. Output derived after the processing of model is projected LULC for 2051, which has used the base year of 2001-2011 and 2011-2021 simulated maps having the accuracy of correctness about 72.05%. Major growth has observed in the build-up, of about 51.28% for 2051 from 36.50% for 2021 with in the planning area boundary. The barren or open patches of land has decreased from 20.69% to 14.08% from 2021 to 2051, majorly changes to build-up, also same with agricultural or cultivable land, vegetation cover and water bodies has observed to be decreased of about 27.0% to 21.43%, 15.01% to 12.78% and 0.82% to 0.42% overall respectively from 2021 to 2051. We have simulated ecosystem services maps with land use land cover classification for deriving the vulnerable patches and further proceeding with these patches possible configuration has been done to map the amount of simultaneous changes the services have with changing land cover pattern. As a conclusion, we have created a potential Department of Environmental Planning, School of Planning and Architecture, Bhopal (MP)-462 030 land use and land cover configuration that has a lower adverse impact on ecosystem services and the environment. The derived outcome of simulated patches for 2051 has shown affirmative results for the delineated ecosystem services, under the quantification of highly vulnerable degraded services it can be conserve using such model.