Landscapes are the composition of dynamic components of complex ecological, economic, and cultural elements on which human and other life forms depend directly. Landscape dynamics driven by land use land cover (LULC) changes due to anthropogenic activities are affecting ecology, biodiversity, hydrological regime, and hence people’s livelihood. There has been increasing apprehensions about environmental degradation, depletion of natural resources due to uncontrolled anthropogenic activities, and its consequences on long-term sustainability of socio-economic systems around the world. This necessitates an understanding of landscape dynamics and the visualization of likely changes for evolving appropriate strategies for prudent management of natural resources. Modeling of forest cover changes offers to incorporate human decision making on land use in a systematic and spatially explicit way through an accumulation of land use choices, social interaction, and adaptation at various levels. Several models developed by the research community so far has largely been utilized to evaluate the empirical studies, explore theoretical aspects of particular systems rather than forecasting their effectiveness across the various landscapes representing bio-physical dissimilarities. Hence, there is a need to demonstrate an appropriate modeling technique, that captures the current degradation in an effective way as compared with the traditional agent-based or non-agent based land use change modeling techniques. In this regard, the objectives of current research are to understand and model the spatiotemporal patterns of landscape dynamics in the Uttara Kannada district of Central Western Ghats. This involves, (i) developing an appropriate modeling framework incorporating the spatiotemporal changes in the landscape at the regional level; (ii) implementing a hybrid model to capture the changes at the landscape level by integrating bio-ecological aspects with socio-economic growth; (iii) evaluating the environmental conditions in response to scenarios of drivers of change like developmental policies and their potential impacts; (iv) assessing the likely scenario of the landscape dynamics based on conservation policies of ecologically sensitive regions (ESR) and other recommendations. The vegetation dynamics quantified using spatial data acquired through spaceborne sensors along with collateral data shows a decline in vegetation cover from 92.87% (1973) to 80.42% (2016). Land use analyses through supervised classifiers based on the Gaussian maximum likelihood algorithm reveals a deforestation trend as evident from the decline of evergreensemi evergreen forest cover to 29.5% (2016) from 67.73% (1973). In addition, agricultural spatial extent (7.00 to 14.3 %) and the area under human habitations (0.38% to 4.97%) have also shown a steep increase. This has also led to forest fragmentation (interior forest cover lost by 64.42 to 22.25 %) in the district. In order to visualize the likely changes, the current work proposes a modified Hybrid Fuzzy-Analytical Hierarchical Process-Markov Cellular Automata model by accounting for the land use changes and to evaluate the role of policy decisions. The impacts are noticed at the microscale (landscape level) with policies that are framed at a macro level and how they propagate under various scenarios. To understand the landscape dynamics in the region, modeling has been carried out under four scenarios to account for potential changes driven by economic growth and climatic aspects at a landscape level. Modeling and visualization further confirm the loss of forest cover in near future with an increase in monoculture plantations from 14.8 to 17.97% and an increase in built-up area from 4.81 to 9.30 % by 2022 under business as usual (BAU) scenario. The proposed hybrid modeling approach with the constraints in the cellular automata technique has been used to simulate various scenarios (i) managed growth rate (2022), (ii) IPCC climate change rapid growth (2031, 2046), (iii) policy-induced constrained Ecological Sensitive Regions. The rapid growth rate scenario highlights a likely loss of forest cover by 11.1%, with an increase in plantations covering 20.9% and built-up as 10.2% of the region by 2046. Land use changes assessed through considering constraints of Ecological Sensitive Regions (ESR1) and the protection of intact or contiguous (interior) forest patches, highlights the role of policy decisions in land use changes. ESR-1 protection scenario shows forest cover is likely to remain at 48% (2021) and 45% (2031) though there is an increase in built-up area from 5.8 to 7% (2031) and agriculture area. The comparison of policy scenario-1 (ESR-1) and scenario-2 (protection of interior forest) depicts scenario-1 focuses more on conservation and limits the growth to the ESR- 2, 3 and 4 regions, whereas scenario-2 shows growth can occur throughout the district excluding regions covered with interior forests, which is likely to induce further fragmentation of forests. This research shows that the insights from the changes to the forest cover and its dynamics through modeling will aid decision making processes for formulating appropriate land use policies. It is important that such policies mitigate changes in the ecologically sensitive regions iv | P a g e and maintain sustenance of natural resources to ensure water and food security while supporting the livelihood of local people.

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