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Determination of future land use changes using remote sensing imagery and artificial neural network algorithm: A case study of Davao City, Philippines

Land use and land cover (LULC) changes refer to alterations in land use or physical characteristics. These changes can be caused by human activities, such as urbanization, agriculture, and resource extraction, as well as natural phenomena, for example, erosion and climate change. LULC changes significantly impact ecosystem services, biodiversity, and human welfare. In this study, LULC changes in Davao City, Philippines, were simulated, predicted, and projected using a multilayer perception artificial neural network (MLP-ANN) model. The MLP-ANN model was employed to analyze the impact of elevation and proximity to road networks (i.e., exploratory maps) on changes in LULC from 2017 to 2021. The predicted 2021 LULC map shows a high correlation to the actual LULC map of 2021, with a kappa index of 0.91 and a 96.68% accuracy. The MLP-ANN model was applied to project LULC changes in the future (i.e., 2030 and 2050). The results suggest that in 2030, the built-up area and trees are increasing by 4.50% and 2.31%, respectively. Unfortunately, water will decrease by up to 0.34%, and crops is about to decrease by approximately 3.25%. In the year 2050, the built-up area will continue to increase to 6.89%, while water and crops will decrease by 0.53% and 3.32%, respectively. Overall, the results show that anthropogenic activities influence the land's alterations. Moreover, the study illustrates how machine learning models can generate a reliable future scenario of land usage changes.

Availability
297551Perpustakaan BIG (Eksternal Harddisk)Available
Detail Information
Series Title
Artificial Intelligence in Geosciences
Call Number
551
Publisher
Beijing : KeAi Communications Co. Ltd..,
Collation
8 hlm PDF, 6.696 KB
Language
Inggris
ISBN/ISSN
2666-5441
Classification
551
Content Type
text
Media Type
-
Carrier Type
-
Edition
Vol.4, December 2023
Subject(s)
Artificial neural network
Remote sensing
Multilayer perception
LULC
Land use land cover prediction
Philippines
Specific Detail Info
-
Statement of Responsibility
Other version/related

No other version available

File Attachment
  • Determination of future land use changes using remote sensing imagery and artificial neural network algorithm: A case study of Davao City, Philippines
    Land use and land cover (LULC) changes refer to alterations in land use or physical characteristics. These changes can be caused by human activities, such as urbanization, agriculture, and resource extraction, as well as natural phenomena, for example, erosion and climate change. LULC changes significantly impact ecosystem services, biodiversity, and human welfare. In this study, LULC changes in Davao City, Philippines, were simulated, predicted, and projected using a multilayer perception artificial neural network (MLP-ANN) model. The MLP-ANN model was employed to analyze the impact of elevation and proximity to road networks (i.e., exploratory maps) on changes in LULC from 2017 to 2021. The predicted 2021 LULC map shows a high correlation to the actual LULC map of 2021, with a kappa index of 0.91 and a 96.68% accuracy. The MLP-ANN model was applied to project LULC changes in the future (i.e., 2030 and 2050). The results suggest that in 2030, the built-up area and trees are increasing by 4.50% and 2.31%, respectively. Unfortunately, water will decrease by up to 0.34%, and crops is about to decrease by approximately 3.25%. In the year 2050, the built-up area will continue to increase to 6.89%, while water and crops will decrease by 0.53% and 3.32%, respectively. Overall, the results show that anthropogenic activities influence the land's alterations. Moreover, the study illustrates how machine learning models can generate a reliable future scenario of land usage changes.
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