The phenomenon of landuse change from an undeveloped area into a built-up area is often the case, especially in big cities. Population growth, both in birth and migration rates, is one of the factors that causes the need for land for various human activities. Tendency for landuse change is expected to continue in the following years along with a region development. The city of Makassar has a tendency for landuse change. This is due to the position of Makassar as the capital of the South Sulawesi province which has A-level public service and it has become a separate magnet for people from outside the city to conduct activities and live in the city. The purpose of this research is to predict landuse/landcover (LULC) change until 2033 by classifying using Landsat satellite imagery include 2008, 2013, and 2018 into 5 landuse/landcover classes in Tamalanrea Sub-District with the Modules for Land Use Change Simulations (MOLUSCE): Multi-Layer Perceptron Neural Network and Geographic Information System method. This research shows the percentage of changes in 5 classes of landuse from 2018 to 2033, are: agriculture area with -0,30%; built-up area with 3.15%; barren area with -5.11%; vegetation with 0.98%; and water body with 1.27%.

This study used Remote Sensing and Geographic Information System (GIS) tools to produce a predictive model of land use/land cover changes in Makassar City by 2031. The model was based on the classification of SPOT satellite images from 2006, 2011, and 2016 with an assessment of 10 driving factors. The Multi-Layer Perceptron Neural Network (MLPNN) method was run with the Markov Chain model using the Business as Usual scenario. The results predict that built-up areas will cover about 80% of the total area of Makassar City by 2031. This research can inform stakeholder decision-making in Makassar City.

The spatial plan program for Makassar City and the surrounding area called Mamminasata (Makassar, Maros, Sungguminasa, and Takalar) was created by the Indonesian Government. The program regulates the proportion of land cover, but predictions about land cover changes were not considered. Therefore, in this study, we predict what the land cover may be in 2031 using the multi-layer perceptron neural network and the Markov chain methods. For this purpose, image composite, support vector machine classifier, and change detection were applied to a time series of satellite data. Visual validation showed the hot-spots of land cover changes related to population density, and statistical validation scored 0.99 and 0.78 in no information kappa and grid-cell level location kappa, respectively. The model was performed to predict land cover in 2031, and the predicted result was then compared with the spatial plan using an overlapping method. The results showed that built-up area, dryland agriculture, and wetland agriculture occupied two, twenty, and eight percent of the protected zone, respectively. Meanwhile, fifteen percent of the development zone was covered by forest, mainly in the eastern part of Mamminasata. The result can be used to help the Government decide future plans for the Mamminasata area.

Several studies have focused on present environmental carrying capacity, but predictions based on future conditions are rarely considered. In this study, land use/land cover (LU/LC) data and ecosystem services valuation (ESV) were used to quantify the future environmental carrying capacity in Makassar City by 2031. The LU/LC data were based on previous research and the ESV coefficients were based on Muta’ali (2015). The results predict that all types of ecosystem services in Makassar City will decrease every year and will be below 0.5 of current values by 2031. These results can be used to inform plans for sustainable development in Makassar City.

Environmental carrying capacity is central to the sustainable development concept that has been applied in spatial planning programs by the Government of Indonesia. These programs focus on population needs based on existing conditions and projected population growth. This study predicted the future environmental carrying capacity of the region called Mamminasata (Makassar City, Maros, Sungguminasa and Takalar Regencies) to 2031 through statistical calculation. Census data from local government agencies and land cover prediction data from previous studies were used. The respective results for demographic, residential, and protected function carrying capacities by 2031 were 0.104 (good), 6.297 (good), and 0.520 (good but requiring attention to prevent further decline). Projected agricultural carrying capacity in 2031 would leave about 45% of the agricultural land now used for food crops. The results of this research can assist sustainable development in Mamminasata.