INTRODUCTION

The global forest loss of 2.3 million sq.km (2000-2012) was reported to cater the growing demands of burgeoning population coupled with the unplanned developmental activities, while the afforestation due to global forest protection initiatives is only 0.8 million sq.km [1]. Forests transitions leading to deforestation in the landscape due to widespread land use land cover (LULC) changes have been acknowledged as prime agents towards contributing global warming with the enhanced emissions as well as loss of carbon sequestration potential. LULC changes information provide vital details on natural resources availability, its utilization, etc. for evolving appropriate management strategies to ensure sustainability of natural resources. Thus, LULC changes in a region are the outcome of the complex interaction of policy, economics, management, culture, and environmental factors.  Drastic changes in LULC will have significant impact on biodiversity [2], climate [3, 4], hydrological cycles [5, 6], biogeochemical dynamics [7, 8], sustainability of natural resources [9]. This will have intensive effects on land surface climate by altering the exchange of heat, moisture and albedo at regional and global scales [4, 10, 11, 12]. Clearing of large scale forests will contribute to releasing the carbon stored in vegetation and soils by altering their physical as well as chemical properties, which affects the global climate by increasing levels of greenhouse gases in the atmosphere, decreasing evapotranspiration and hydrological cycle [13]. Land use changes in forested landscapes has gained momentum in the middle of the last century [14] due to industrialization, urbanization and globalization. Mitigation of these impacts entails actions at local/micro scale by preservation of ecosystems [15]. Knowledge of agents in LULC transition with the quantification of landscape dynamics would help in framing conservation strategies towards sustainable land uses.

LULC changes due to unplanned developmental activities have been posing serious challenges on the carrying capacity of landscape. Integrating environmental dimensions into land use planning and management process can greatly contribute to the sustenance of natural resources. Sustainable landscape management requires the advance information of likely land use changes, which would help in evolving mitigation strategies to sustain the livelihood of dependent communities. This entails modeling and visualization of landscape transitions with temporal spatial data. The change detection analyses have gained prominence with the availability of spatial data since 1970’s. Multi resolution spatial data with advances in modeling techniques would aid in the sustainable resources management [16]. This would help to examine and statistically define the spatial patterns of LULC changes at a precise interval. Modeling helps in identifying the most appropriate spatial pattern of future land uses and this information helps in assessing resource availability and serves as a decision support system (DSS) for managers, planner and decisions makers [17, 18]. Modeling and visualization will satisfy numerous conditions for sustainable development path such as conservation of biological and cultural diversities through ecosystem protection [19]. Apart from inventorying, mapping, monitoring and change analyses, modeling and visualization would aid in empirically interpreting the consequences of spatial changes. Predictive statistical and geospatial models used for modelling landscape dynamics are logistic regression models [20], spatial dynamic model [21, 22], spatial Markov chains (MC) [23, 24], Cellular automata (CA) [25] and multi criteria decision making (MCDM) techniques [26]. CA model the local interaction of non-linear spatial process reflecting the dynamic system evolution [27, 28, 29] with consideration of discrete grid (lattice) units and its patterns.

MC-CA based simulation has advantages than other traditional procedures, but fails to link additional drivers of land use transitions [30]. While, techniques integrating agents and distance based relationship of driving forces include Multi criteria evaluation (MCE), Analytical Hierarchical Process (AHP) [31], Fuzzy based estimations. Fuzzy based modeling with geographic information system (GIS) helps in integrating the expert knowledge on spatial data (to determine the weight of each factor), which influences land suitability criteria [32]. However, standalone fuzzy system is insufficient for modeling complex natural resource systems, where relations between indicator variables are difficult to model [33, 34]. Integrated approach of fuzzy system with AHP has a potential to enhance the effectiveness of factor evaluation and accuracy by ranking alternatives for land suitability assessment [35, 36]. Analytical Hierarchy Process (AHP) is a well-known weight evaluation method has steps as specifying the hierarchical structure, determining relative important weights of the criteria and sub criteria, assigning preferred weights of each alternative and determining the final score [37]. AHP decision hierarchy helps in identifying ‘n’ criteria and ‘m’ alternatives in interactive decision making by comparing the relative importance of two elements (criteria or alternatives) ‘i’ and ‘j’ for a given pairwise comparison matrix of the likelihood of events for all possible alternative ranking outcomes [38, 39]. Fuzzy-AHP suitability maps are created by systematic, multi factor analysis for evaluating influence of land suitability [40, 41]. Model inputs include a variety of physical, cultural, economic and environmental factors [42]. CA-Markov models combined with MCE and AHP provides spatial land use transitions for distinct time steps [30, 43]. Fuzzy based estimations accounts for the influence of factors on land use based on distance relationship which aid in spatial allocation process of the simulation and model future changes. Hybrid approach of Markov Chain cellular automata coupling with fuzzy logic algorithms [44] helps to overcome the limitations of standalone CA model’s neighborhood effect and thereby improves relative probability [45]. The objectives of this communication are:

1. Quantification of land use changes during 2007-2013 across three agro climatic regions,
2. visualizing the changes in forest cover during 2013 to 2022 by Fuzzy-AHP-CA analysis considering the growth agents and constraints,
3. evaluating the influence of protection measures in reserve forests towards sustainable management of forest resources.