Open AccessResidential Population Estimation in Small-Area using LiDAR and Aerial Photograph Data
Author(s) -
Y F Hanif,
Hepi Hapsari Handayani,
Nurwatik Nurwatik
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/731/1/012034
Subject(s) - population , cohen's kappa , linear regression , mean squared error , statistics , confusion matrix , estimation , mathematics , land cover , coefficient of determination , random forest , regression analysis , geography , remote sensing , computer science , land use , artificial intelligence , engineering , civil engineering , demography , systems engineering , sociology
Population data has an important role in various aspects, such as policy determination, urban planning, and disaster mitigation. But, the most accurate population data in Indonesia is obtained once every 10 years. In this research, population estimation is conducted by applying the Object-Based Image Analysis (OBIA) classification method to detect the residential area. The OBIA classification utilizes aerial photogrammetry data and DSM & DTM of LiDAR. Then the population estimation is generated by the calculation of mathematical demographics and linear regression. Based on the results, OBIA classification produces a high accuracy land use / land cover map, assigned from the accuracy assessment using confusion matrix with kappa coefficient of 0.929 and overall accuracy of 95.24%. While, the habitable surface area classification achieves a high accuracy map with kappa coefficient of 0.86 and overall accuracy of 92.86%. The population estimation results, reveal that the linear regression method has a smaller error than the mathematical demographic method. The MAE, MAPE, RMSE, and RRMSE in Wisma Menanggal values are 40, 21%, 45, dan 0.25, while the ones of Gayungsari Timur are 23, 18%, 34, dan 0.278. In addition to the small error value, the MAE, MAPE, RMSE, and RRMSE values indicate that the population estimation produced by the linear regression model is most optimal.