Introduction
Landscapes are the cluster of heterogeneous landforms arranged in a nested pattern with diverse land use elements. Biodiversity in a landscape depends on the structure and interactions among various landscape elements. During the post-industrialization and globalization period, unplanned developmental activities have contributed to large scale land cover transitions, which impacted ecology, hydrologic regime, sustenance of natural resources, etc. Landscapes alterations have become intensive with human-induced activities such as transport corridors/networks, burgeoning built-up areas, increasing demand for food and other resources, etc. Analyses of landscape dynamics provide vital information for prudent decision making towards the sustainable management of natural resources. Modeling and prediction of land-use dynamics with the proposed projects would help to evolve appropriate mitigation strategies.
Linear infrastructure projects (such as roads, railways, high tension electrical lines) are being implemented to cater to the growing demand, improving the regional economy, and societal wellbeing (Biasotto and Kindel 2018). These projects would fragment ecosystems, hamper the hydrologic regime, and disrupt the rivers' ecological processes and functions (Blanton and Marcus 2009). The linear projects are closely related to land-use changes, and improvements in the transportation network provide impetus to urbanisation (and vice versa) and associated land-use transformations (Kasraian et al. 2016; Ramachandra et al. 2019a) and result in social, economic, and environmental impacts (Litman 1995).
Unplanned developmental projects have accentuated the degradation process leading to deforestation (Asher et al. 2017), affecting the wildlife habitat, animal movement path, foraging grounds, diversity, ecosystems (Bennett et al. 2011; CCIBIS 2018), frequent occurrence of landslides and mudslides, etc. Table 1 lists ecological and environmental consequences with the fragmentation of landscapes due to the implementation of transportation corridors. Railway network implemented in ecologically fragile regions would have disastrous effects, despite railway projects having the edge over roads in terms of safety, economy, pollution, etc. (Borda-de-Água et al. 2017).
Table 1. The ecological implication of transportation corridors (Forman and Alexander 1998; Nyhus 2016; CCIBIS 2018; Shaffer et al. 2019).
Category | Description |
Habitat loss | Land cover changes, enhancing disturbance and barrier effects on the wildlife that contribute to the overall habitat fragmentation. |
Habitat modification | The native species tend to disappear, leading to change in species composition, population leading to loss of biodiversity (flora and fauna). Ecosystem changes would alter habitat, nutrient cycling, crop pollination, nutrient recycling, seed dispersal, aesthetics, etc. |
Disturbance | Vehicular traffic in the ecologically fragile regions would disturb and pollute the physical, chemical, and biological environment, hampering sustenance of the native fauna and flora diversity for a much wider zone beyond the transportation network. |
Mortality | Death of animals due to accidents while crossing the road or using it as habitat. In the recent past, globally, the mortality rates show an increasing trend. |
Barrier | Restriction of animal movement through fencing/elevation etc., making it inaccessible for animal movement. Due to this, the habitats are disconnected, isolating the fauna population. |
Conflict | Competition for food and other resources between man and wild has led to mortality of animals, people, damage to the crops, etc. |
Land-use change | Expansion of human settlements, agriculture fields, and monoculture plantations has led to loss of forest, habitat, and foraging grounds, which has forced the animals to contact people, resulting in higher frequency and severity of conflicts. |
Climate Change | Enhanced emissions and loss of carbon sequestration with deforestation increase temperatures due to the land-use change, variability in rainfall-runoff, and silt accumulation patterns. Increase in extreme conditions (floods or drought) |
Pollution | pollutants due to vehicular movements would degrade water, soil, and air in the vicinity. |
Corridor | The transportation network (road/railway) act as a refuge to animal or serve as movement path. These movement corridors would enhance the mortality of animals. |
Implementation of the transportation network would change the land cover with environmental impacts such as loss of forests, water bodies, fragmentation of animal habitats, a decline of biodiversity (Ramachandra et al. 2014; McGrane 2016; Miller and Hutchins 2017), which necessitates foreseeing the likely impacts of linear projects (Xiong et al. 2018). Simulation of land use dynamics provides insights into spatial patterns of land use changes and aids in planning site-specific mitigation measures, adopting environmentally sound alternatives, prudent management of natural resources, etc.
Advanced approaches of geoinformatics (GIS: Geographic Information Systems) using temporal-spatial data (remote sensing data) provide information about the landscape patterns, change dynamics, and causal factors (Lillesand et al. 2004; Setturu et al. 2014; Joshi et al. 2016) to understanding the landscape dynamics. Modeling and simulation of land-use changes help understand the influence of policies, decisions, and interventions (Bharath et al. 2014, 2017; Das Chatterjee et al. 2015). Numerous predictive models (Agent-Based Models (ABM) and Non Agent-Based models/Rule-based models) such as CA, CA-Markov, Geomod, SLEUTH, SLUCE, Dynamica, Dyna-CLUES, ANN, Regression, AHP, etc. have been widely applied for geo-visualization of landscape dynamics (Brown et al. 2008; Rafiee et al. 2009; Jokar Arsanjani et al. 2012; Johnston et al. 2013; Mishra et al. 2014; Jain et al. 2017; Chandan et al. 2019). The hybrid approach of integrating rule-based CA with Markov models has been extensively used worldwide to simulate the land-use changes. The hybrid CA-Markov fails to fit in the landscape elements precisely, and this challenge has been effectively overcome by the Agent-based models that combine the hybrid CA-Markov with various agents causing changes in land use or constricting the changes in land use through advanced spatio-numerical methods such as fuzzy logic, boolean algebra, neural networks, genetic algorithms, multi-criteria evaluation, regression, etc. ABM simulates landscape change trends and the direction of change, considering influencing factors and constraints, which can be used for appropriate decision-making (Ramachandra et al. 2019b). The current study focuses on understanding the landscape dynamics with the implementation of railway networks between i) Mysore and Kushalnagar and ii) Mysore and Thalassery, using remote sensing data, GIS and visualizing future changes using ABM.