Percent of building density (PBD) of urban environment:  a multi-index approach based study in DKI Jakarta Province

Ardiansyah Ardiansyah; Revi Hernina; Weling Suseno; Faris Zulkarnain; Ramadhani Yanidar; Rokhmatuloh Rokhmatuloh

doi:10.22146/ijg.36113

Percent of building density (PBD) of urban environment: a multi-index approach based study in DKI Jakarta Province

https://doi.org/10.22146/ijg.36113

Ardiansyah Ardiansyah^(1*), Revi Hernina⁽²⁾, Weling Suseno⁽³⁾, Faris Zulkarnain⁽⁴⁾, Ramadhani Yanidar⁽⁵⁾, Rokhmatuloh Rokhmatuloh⁽⁶⁾

(1) Faculty of Mathematics and Natural Science, Universitas Indonesia
(2) Faculty of Mathematics and Natural Science, Universitas Indonesia
(3) PT Infimap Geospasial Sistem
(4) Center for Applied Geography Research, Universitas Indonesia
(5) Faculty of Landscape Architecture and Environmental Technology, Trisakti University
(6) Faculty of Mathematics and Natural Science, Universitas Indonesia
(*) Corresponding Author

Abstract

This study developed a model to identify the percent of building density (PBD) of DKI Jakarta Province in each pixel of Landsat 8 imageries through a multi-index approach. DKI Jakarta province was selected as the location of the study because of its urban environment characteristics. The model was constructed using several predictor variables i.e. Normalized Difference Built-up Index (NDBI), Soil-adjusted Vegetation Index (SAVI), Normalized Difference Water Index (NDWI), and surface temperature from thermal infrared sensor (TIRS). The calculation of training sample data was generated from high-resolution imagery and was correlated to the predictor variables using multiple linear regression (MLR) analysis. The R values of predictor variables are significantly correlated. The result of MLR analysis shows that the predictor variables simultaneously have correlation and similar pattern to the PBD based on high-resolution imageries. The Adjusted R Square value is 0,734, indicates that all four variables influences predicting the PBD by 73%.

Keywords

Percent of building density; multi-index approach; urban environment; Landsat 8; DKI Jakarta Province

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References

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DOI: https://doi.org/10.22146/ijg.36113

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