(Likely Implications without ESZ status)

The various systems existing such as forest/urban are very dynamic due to existing socio-economic and environmental factors acting at a variety of scales. Modeling and visualization helps in understanding and managing dynamic entities, so that system integrity is maintained. Dynamic modelling framework encompasses the decision support capability by integrating socio-economic and environmental factors representing spatial dynamics with a high level of geographical detail. Rapid advances in availability of temporal data and geospatial models have made it increasingly possible to design and simulate spatial patterns for land use change (Ramachandra et al., 2014b). CA (cellular automata), Markov chains, multivariate statistics, optimization, system dynamics,multi criteria evaluation (MCE), CLUE (Conversion of Land Use and its Effects model) and various other modeling approaches are existing to visualize complex pattern and prediction. The Markov model is based on a stochastic theory based on random process systems for the prediction and optimal control theory method (Jiang et al., 2009). The Markov explains the quantification of conversion states betweenthe land use types through the transfer rate among different land use types.Though, Markov model for land use changes has been widely used, butwith the traditional Markov model alone it is difficult to predict the spatial pattern of land use changes.CA model has a strong space conception, which is a strong capability of space-time dynamic evolution with complex space systems. So, Markov with CA model together can be used for to simulate the spatial variation of the system effectively.

The integrated Markov-cellular automata (CA_MARKOV) is a robust and effective modeling approach in analyzing spatio temporal land use land cover changes with integration of geographic information system (GIS) and temporal remote sensing data (Kamusoko et al., 2009; Bharath et al., 2016). CA-Markov based simulation provides vital information for understanding forest structure dynamics. CA is a discrete, spatially explicit extended dynamic system characterized by a simple structure of individual elements, called cells (Yang and Li, 2007), which evolve in discrete time steps according to a various transition rules (Bharath et al., 2014). CA-Markov analysis incorporates rules based on consideration of biophysical and socioeconomic data to define initial conditions, parameterise the Markov-cellular automata model, calculate transition probabilities, and the neighbourhood rules with transition potential maps. CA models consist of a simulation environment with a set of transition rules determine the attribute of each given cell consists of a finite number of k possible states, updated synchronously in discrete time steps according to a local, identical interaction rule. The state of a cell is determined by the previous states of a surrounding neighborhood of cells. The transition rules for neighborhood conditions are determined from empirical analyses of land use change map and other critical landscape features. The transition rules of CA models well patronage in quantifying temporal and spatial complexities of forest systems.
The present study aims in visualizing and quantifying changing forest cover by 2027 in BNP and 5 km buffer. The CA-Markov analysis has incorporated various land use decisions in transition rules from 1999-2009; 2009-2015, which are further aided for predicting 2021 and 2017. The land use maps of 1999-2015 were reclassed as five categories for effective visualization as explained in Table 1. The base land use map of 1999 and 2009 data for generating simulated land use map of 2015, which has validated with actual 2015 land use map (Figure 2). The transition areas and probabilities were computed from 1999-2009 land use maps (Table 2).The net neighbourhood influences were determined by 5×5 contiguity filter explains past land use changes and used to simulate future changes.The validation results showed in Table 3 provides a very good agreement between the actual and predicted maps of 2009, 2015 land use with kappa of 0.86. The Kappa-standard index of optimum point of the diagram as well as Kappa-location index were computed proves a significant correlation between the simulated and the actual maps (Table 3). The simulated land use map of 2015 has good accuracy as compared with actual land use of 2015. Based on the evaluation the land use map of 2021 has generated by using base land use maps of 2009-2015. The time stamp of 6 years was maintained based on existing land use datasets. The land use projections were made for 2021 and further projected land use map of 2027 has been generated based on 2015 land use and projected 2021 map. The projected land use of 2027 depicts the loss of forest cover from 41.38% to 35.59% and shows an increase in urban area from 4.49 to 9.62 % due to various layouts in the periphery. The barren land has reduced from 4.17 to 3.36 % by conversion of open spaces to urban. The forest cover present in southern parts represents minimal disturbances (connected to Tali reserve forest and Cauvery wild life sanctuary), whereas northern portion shows more transitions. The uncontrolled and unplanned growth of greater Bangalore would have irreversible impact on forests of BNP. The regions of Kolifram gate, BatterayanaDoddi village, Weavers colony, Kalkere region and Bannerghatta village, Anekal road represents higher loss in forest cover due to new paved surfaces created in the form of townships. The barren lands and fallow lands near to state highways and major roads were converted as layouts resulted in creating new agriculture areas in gomla and other fragmented forests. Bilwaradahalli village, Bhoothanahalli, Gulakamale, UttarahalliManavartha Kaval (Gullahatti Kaval) regions located in the periphery of BNP boundary are losing their forests due to new developments, quarriesand mining. Figure 5 highlights the regions which is likely to undergo major transition in 2027 in BNP and buffer area. The built-up area expansion in Anekal industrial area, Kalkere, Basavanapura and Weavers colony,UttarahalliManavartha Kaval are the major regions represents urban growth. The proposed layouts in the buffer region of BNP are overlaid on visualized growth by 2027 in Figure 6. The proposed layouts would pose serious threat on BNP’s forest cover and agriculture area in the periphery of BNP region, which are evident from the predicted land use of 2021 and 2027.

S.NO.	Land use categories	Description
1.	Forest	Moist deciduous forest, Dry deciduous forest, Scrub/grass lands and forest plantations
2.	Agriculture	Agriculture fields, permanent sown areas, Coconut and other commercial  plantations
3.	Open fields	Rocks, Quarry pits, Barren land
4.	Water	Rivers, Tanks, Lakes, Reservoirs, Drainages
5.	Built-up	Residential Area, Industrial Area, Paved surfaces

 

Given	Probability of changing to
	Forest	Agriculture	Barren land	Water	Built-up
Forest	0.783	0.1649	0.0252	0.0022	0.0246
Agriculture	0.025	0.9	0.025	0.025	0.025
Barren land	0.1573	0.259	0.4493	0.0026	0.1318
Water	0.1055	0.0803	0.0165	0.7011	0.0967
Built-up	0.025	0.025	0.025	0.025	0.9

 

Index	Simulated 2015
Kno	0.93
Klocation	0.86
Kstandard	0.87

 

 

Year	Simulated 2015		Projected 2021		Projected 2027
Categories	Ha	%	Ha	%	Ha	%
Forest	49444.88	40.62	45895.57	37.67	43315.05	35.59
Agriculture	58208.18	48.64	60411.02	49.67	60207.03	49.46
Barren land	6668.57	4.66	5030.71	4.13	4460.76	3.66
Water	2011.68	1.65	2033.13	1.67	2031.11	1.67
Urban	5387.75	4.43	8350.63	6.85	11707.15	9.62
Total area	121721.06

 

Figure 2: Method used for the analysis.

Figure 3: Land use maps of BNP from 1999 to 2015.

Figure 4: Simulate and projected land use of BNP 2015-2027.


Figure 5: The regions likely to experience higher land use changes


Figure 6: Proposed layouts overlaid on predicted land use of 2027.

Acknowledgement: We thank Dr. Sunil Panwar, Deputy Conservator of Forest (DCF), Bannerghatta National Park, Karnataka Forest Department, Bangalore-560 083 for entrusting the study and making all necessary arrangements (vehicle, staff, etc.) during field research.  We are grateful to Mr.VishnuNarainand R K Mission, Shivanhalli for arranging local logistics (accommodation, food, etc.). We acknowledge the support from field staff of forest department for accompanying the research team and sharing their knowledge.