Authors :
Indra Kumar Subedi; Netra Bahadur Katuwal
Volume/Issue :
Volume 8 - 2023, Issue 12 - December
Google Scholar :
https://tinyurl.com/47sy492y
Scribd :
https://tinyurl.com/ys5dmjvy
DOI :
https://doi.org/10.5281/zenodo.10781688
Abstract :
This study employs a Cellular Automation-Artificial Neural Network (CA-ANN) model to predict Land Use and Land Cover (LULC) changes in Chitwan District, Nepal, using spatial variables. The model achieves high accuracy (Kappa value of 0.81), projecting LULC maps for 2020 and 2030. Results reveal notable changes. Agriculture shows a modest increase of 5.01% (2000-2010) but experiences successive declines of 4.55% (2010-2020) and 3.89% (2020-2030). Barren land undergoes a drastic reduction of -78.81% (2000-2010), followed by a slower decline of 39.00% (2010-2020) and a subsequent increase of 5.37% (2020-2030). Built-up areas consistently grow, with a significant rise of 41.02% (2000-2010), a remarkable surge of 209.09% (2010-2020), and a further increase of 38.45% (2020-2030). Forest cover sees a positive change of 6.99% (2000- 2010), a reduction of 1.78% (2010-2020), and a subsequent positive change of 1.73% (2020-2030). Water bodies exhibit fluctuations, with a decrease of 4.42% (2000-2010), an increase of 37.73% (2010-2020), and a notable decrease of 24.35% (2020-2030). Land Surface Temperature (LST) trends indicate warming summers and cooling winters, aligning with global warming expectations. The study emphasizes the impact of urban development on rising temperatures and underscores the importance of vegetation in temperature regulation. Additionally, the increase in other wooded land from 2010 to 2020 is noted, followed by a decrease in 2030. Analysis of LST maps highlights higher temperatures in settlement areas, suggesting potential urban heat island effects. The study emphasizes the significance of considering LULC changes for effective environmental management in Chitwan District, Nepal.
Keywords :
LULC Changes, CA-ANN Model, Land Surface Temperature (LST) Trends, Environmental Management.
This study employs a Cellular Automation-Artificial Neural Network (CA-ANN) model to predict Land Use and Land Cover (LULC) changes in Chitwan District, Nepal, using spatial variables. The model achieves high accuracy (Kappa value of 0.81), projecting LULC maps for 2020 and 2030. Results reveal notable changes. Agriculture shows a modest increase of 5.01% (2000-2010) but experiences successive declines of 4.55% (2010-2020) and 3.89% (2020-2030). Barren land undergoes a drastic reduction of -78.81% (2000-2010), followed by a slower decline of 39.00% (2010-2020) and a subsequent increase of 5.37% (2020-2030). Built-up areas consistently grow, with a significant rise of 41.02% (2000-2010), a remarkable surge of 209.09% (2010-2020), and a further increase of 38.45% (2020-2030). Forest cover sees a positive change of 6.99% (2000- 2010), a reduction of 1.78% (2010-2020), and a subsequent positive change of 1.73% (2020-2030). Water bodies exhibit fluctuations, with a decrease of 4.42% (2000-2010), an increase of 37.73% (2010-2020), and a notable decrease of 24.35% (2020-2030). Land Surface Temperature (LST) trends indicate warming summers and cooling winters, aligning with global warming expectations. The study emphasizes the impact of urban development on rising temperatures and underscores the importance of vegetation in temperature regulation. Additionally, the increase in other wooded land from 2010 to 2020 is noted, followed by a decrease in 2030. Analysis of LST maps highlights higher temperatures in settlement areas, suggesting potential urban heat island effects. The study emphasizes the significance of considering LULC changes for effective environmental management in Chitwan District, Nepal.
Keywords :
LULC Changes, CA-ANN Model, Land Surface Temperature (LST) Trends, Environmental Management.
