Urban tourism expansion monitoring by remote sensing and random forest

Abstract

Abstract Tourism and urban areas experienced rapid development at the beginning of the 21st century. This condition is caused by natural, cultural, and artificial tourist destinations and adequate infrastructure support. Tourist destinations in urban areas add to urbanization because apart from being the center of government, trade, and industry, it is also a tourist destination that can attract tourists. Monitoring the development of urban tourism is carried out in the cities of Denpasar and Bali, as well-known destinations at the world level. The development of the urban area can be detected through multi-temporal and multispectral remote sensing imagery in combination with machine learning technology. This study aims to determine the spatial distribution of urban tourism development from 2013 to 2021. This study uses remote sensing and machine learning methods with the Random Forest (RF) algorithm on Google Earth Engine (GEE) cloud computing. The RF algorithm is one of the non-parametric classification algorithms which is widely applied in remote sensing data classification because of its insensitivity to excessive noise and training data and its good performance. The material used is Landsat 8, especially on the Operational Land Imager (OLI) sensor. The result showed that integrating remote sensing, GEE cloud computing, and machine learning, especially the RF algorithm, effectively monitors urban tourism expansion. The overall accuracy of the RF model with simple training data is above 90%. We found that within nine years, vegetated land was changed into an urban area of 20.23 km2. For this reason, special attention is needed from the government to make regulations on spatial planning and control over land conversion so that there will still be green open spaces in the future.