Applications of Remote Sensing for Land Use Planning Scenarios With Suitability Analysis

Abstract

In regions undergoing rapid urbanization, such as West Africa, land use planning (LUP) is vital to accommodate a growing population and manage natural resources. Suitability analysis modeling is a widely-used tool in LUP to determine the extent to which a land area is suitable for a designated purpose, but there is a gap in the integration of remote sensing time series data into land use decisions. The goal of this study was to incorporate remote sensing time series information with suitability analyses to inform LUP decisions in urban areas. In the study area of Kumasi, Ghana, land cover trends, and land surface temperature from 2000 to 2019 were used to understand climate change trends. Suitability analyses determined the fitness of land areas for predetermined uses. These background processes informed a genetic algorithm to project plausible futures for three land use scenarios. One scenario represented current LUP practices for addressing population growth, another scenario prioritized minimizing climate change impacts while also accommodating population growth, and the final scenario focused on both of these climate and population goals in addition to high density urban development. Each of these scenarios was successful in achieving population accommodation and respective climate change mitigation goals. The results for these scenarios provide insight into plausible land use distributions in 2050 based on different planning approaches. The genetic algorithm was able to effectively develop results for each scenario through the integration of remotely sensed trends and suitability models, providing a novel approach to land use decision-making.

Keywords

• Genetic algorithm (GA)
• land surface temperature (LST)
• land use planning (LUP)
• remote sensing (RS) time series
• suitability analysis