Version 1
: Received: 29 December 2020 / Approved: 31 December 2020 / Online: 31 December 2020 (09:51:50 CET)
How to cite:
Evandi, S. Apply Remote Sensing to Monitoring Dynamics of Built Environmental in The Core Area and The Border of Sokok City. Preprints2020, 2020120785. https://doi.org/10.20944/preprints202012.0785.v1
Evandi, S. Apply Remote Sensing to Monitoring Dynamics of Built Environmental in The Core Area and The Border of Sokok City. Preprints 2020, 2020120785. https://doi.org/10.20944/preprints202012.0785.v1
Evandi, S. Apply Remote Sensing to Monitoring Dynamics of Built Environmental in The Core Area and The Border of Sokok City. Preprints2020, 2020120785. https://doi.org/10.20944/preprints202012.0785.v1
APA Style
Evandi, S. (2020). Apply Remote Sensing to Monitoring Dynamics of Built Environmental in The Core Area and The Border of Sokok City. Preprints. https://doi.org/10.20944/preprints202012.0785.v1
Chicago/Turabian Style
Evandi, S. 2020 "Apply Remote Sensing to Monitoring Dynamics of Built Environmental in The Core Area and The Border of Sokok City" Preprints. https://doi.org/10.20944/preprints202012.0785.v1
Abstract
The development of unmanned satellite space technology is increasingly willing, the emergence of medium resolution satellites with sensitivity and spectral variants such as Landsat is very effective in observing environmental changes, while the purpose of this study is to monitor the development of built-in land using image transformation techniques, estimating built-in land changes. The research method uses the NDVI image transformation technique, NDBI and Built Up Index, with Landsat satellite image data obtained from USGS. Accuracy sampling is done by purposive sampling with confusion matrix accuracy test technique. The research results were found. developed land for the period 2004 - 2010 with a percentage of 19.25%, for stages 2010 - 2018 with a percentage of 30.25%. The land development was built based on the area of the highest sub-district in the Kubung area in the early period with a percentage of 7.20% then in the second period with a percentage of 32.23%. The quality of the accuracy of the results of image analysis using confusion matrix technique with an image accuracy level in a field sample of 185 with an image accuracy of 86.04%.
Keywords
built environment; image analysis; remote sensing
Subject
Environmental and Earth Sciences, Atmospheric Science and Meteorology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
