Spatial dynamic prediction of landuse / landcover change (case study: tamalanrea sub-district, makassar city)

4th International Conference of Indonesian Society for Remote Sensing

物理学;能源学

Hakim, A.M.Y.^1 ; Baja, S.^1 ; Rampisela, D.A.^1 ; Arif, S.^2^3

Departement of Soil Science, Faculty of Agriculture, Hasanuddin University, Makassar

90245, Indonesia^1

Department of Physics, Faculty of Mathematics and Natural Science, Hasanuddin University, Makassar

90245, Indonesia^2

Center for Regional Development and Spatial Information (WITARIS), Hasanuddin University, Makassar

90245, Indonesia^3

关键词: Human activities;    Landsat satellite;    Makassar cities;    Multi-layer perceptron neural networks;    Population growth;    Region development;    Spatial dynamics;    Undeveloped areas;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/280/1/012023/pdf
DOI  :  10.1088/1755-1315/280/1/012023

来源: IOP

【 摘 要 】

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%.