Analysis of the Current and Future Land Use/Land Cover Changes in Peri-Urban Areas of Dar es Salaam City, Tanzania using Remote Sensing and GIS Techniques

  • Erick T Mnyali Department of Geography and Environmental Studies, College of Natural and Applied Sciences, Sokoine University of Agriculture, PO Box 3038 Morogoro, Tanzania
  • Silvia F Materu Department of Biosciences, College of Natural and Applied Sciences, Sokoine University of Agriculture, PO Box 3038 Morogoro, Tanzania
Keywords: Artificial Neural Network, Land use/cover changes, Maximum likelihood, Peri-urban, Remote sensing

Abstract

A rapid increase in population and urban development has direct impacts on the natural environment of peri-urban areas of big cities, especially in the developing countries. This study examined land use and land cover changes (LULCC) of peri-urban areas of Kinondoni Municipality in Dar es Salaam city for the period between 1999 and 2019 and predicted future changes by 2039. The specific objectives were to; (i) map the spatial-temporal land use and cover changes (ii) analyze settlements (built-up areas) patterns and factors influencing their changes (iii) predict changes that might happen for the next two decades. LULCC for 1999 to 2019 were detected through Maximum Likelihood (ML) supervised classification techniques while Cellular Automata based on Artificial Neural Network (CA-ANN) model was used to predict future changes. Over the past two decades, 33.7% of vegetation has been lost due to increased settlements, as shown by linear development pattern of built-up areas, which has gained about 30% of other land covers. This study predicts that by 2039, built-up areas will increase drastically to occupy 51.6%, while vegetation and water cover will decrease to occupy 40.4% and 1.4%, respectively. Without good planning strategies and land use policies, the control and management of the magnitude and patterns of peri-urban growth and sustainable management of natural and built environments in Tanzania will remain a challenge. This paper seeks to contribute to the body of knowledge required for evidence-based policy formulation and decision making to attain sustainable urbanization through setting suitable management plans for peri-urban areas of Tanzanian cities.

Keywords: Artificial Neural Network, Land use/cover changes, Maximum likelihood, Peri-urban, Remote sensing

Published
2021-12-01
Section
Articles

Journal Identifiers


eISSN: 2507-7961
print ISSN: 0856-1761