Proximate Causes of Land-Use and Land-Cover Change in Bannerghatta National Park: A Spatial Statistical Model

Abstract

Abstract:Land change modeling has become increasingly important in evaluating the unique driving factors and proximate causes that underlie a particular geographical location. In this article, a binary logistic regression analysis was employed to identify the factors influencing deforestation and simultaneous plantation driven reforestation in Bannerghatta National Park, located at the periphery of one of the fastest growing cities in India, i.e., Bangalore. Methodologically, this study explores the inclusion of different sub-regions and statistical population to address spatial autocorrelation in land change modeling. The results show negative relationship between deforestation and protected area status and edge of previous forest clearing. In addition, the deforestation models found differences in the processes that are affecting forest clearing in our two sub-periods of 1973-1992 and 1992-2007. The plantation driven reforestation in the region were attributed to distance to major towns, Bangalore city, rural centers and major and minor roads suggesting the importance of accessibility to market for heavy cash crops such as coconut palm and eucalyptus. Finally, the inclusion of different sub-regions and statistical population facilitated a better understanding of varying driving factors in different zones within the overall landscape.

Comprehensive review on land use/land cover change classification in remote sensing

“…[7][8][9]12,15,[18][19][20]23,28,29,33,35,37,40,41,45,49,[61][62][63]66,67,70,74,75,79 Geometric corrections include Orthorectification, Geo-referencing, Image Registration, ASCII Coordinate Conversion and Resampling. 1,7,13,16,[22][23][24][25][26]29,30,[32][33][34][35][36][37]40,[47][48][49]53,55,69,70,…”

Exploring the Factors Driving Changes in Farmland within the Tumen/Tuman River Basin

“…All independent variables were standardized using the Z-score standardization method to reduce the disparities in scale of measurement and variance, because of different units of the variables. For these standardized variables, a multicollinearity test was conducted to avoid multicollinearity between independent variables by using the variance inflation factor (VIF) analysis [59]; variables with significant collinearity (VIF ≥ 10) must be removed from the LR model. These Z-score methods and multicollinearity analysis were performed in SPSS, and results shown that all independent variables were required to enter into LR model.…”

“…Amongst these, the Wald statistic values are useful for judging the relative weights of each explanatory variable in a LR model, and they can be used to evaluate the contribution of each explanatory variable in event-prediction [17,25]. This means that higher Wald statistic values for an independent variable are the most important LUCC variables; these values have been widely used to assess the relative importance of each driving factor in such studies [10,17,59]. Variables in each LR model were therefore selected if they were statistically significant at a p-value less than 0.05, while Nagelkerke's R 2 values (between 0 and 1) in each logistic model were used to test the goodness of fit.…”

“…Variables in each LR model were therefore selected if they were statistically significant at a p-value less than 0.05, while Nagelkerke's R 2 values (between 0 and 1) in each logistic model were used to test the goodness of fit. A good logistic model fit was defined for each value of 0.2 and above [59], while relative operating characteristics curves have commonly been used to validate LR model performance; the area under the curve (AUC) was connected via plotted points [44], including values between 0.5 and 1 [51]. A value higher than 0.9 was considered as a high value, while one between 0.7 and 0.9 was credible, and a value that was less than 0.7 was considered relatively low [25,60].…”

Land-Cover Change Analysis and Simulation in Conakry (Guinea), Using Hybrid Cellular-Automata and Markov Model

“…Secondly, the model can be easily implemented into GIS and RS, as it operates on lattice, raster-format geographic data, and consequently, it can work at high spatial resolution with computational efficiency [45]. In addition to its operational simplicity, it has the potential to simulate land-cover change with minimum data requirements [46]. This model has been widely used in many land-cover change simulations, for example, a case study in the Ashanti region (Ghana) based on the CA-Markov model showed an upsurge in built up area, and a decline in agricultural and forest land-cover [47].…”