ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume V-3-2021
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2021, 117–124, 2021
https://doi.org/10.5194/isprs-annals-V-3-2021-117-2021
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2021, 117–124, 2021
https://doi.org/10.5194/isprs-annals-V-3-2021-117-2021

  17 Jun 2021

17 Jun 2021

MONITORING AND ASSESSMENT OF AGRI-URBAN LAND CONVERSION USING MULTI-SENSOR REMOTE SENSING AND GIS TECHNIQUES

D. C. Fargas Jr., G. A. M. Narciso, and A. C. Blanco D. C. Fargas Jr. et al.
  • Department of Geodetic Engineering, College of Engineering, University of the Philippines Diliman, Quezon City 1101, Philippines

Keywords: Data fusion, LULC, Change Detection, Random Forest, Google Earth Engine

Abstract. Continuous agricultural land conversion poses threat to food security but this has not been monitored due to ineffectual policies. One of the Philippine provinces with a high rate of conversion is the rice-producing province of Cavite. To assess the spatiotemporal dynamics of agricultural land conversion in Cavite, this study aims to develop an operational methodology to produce Land Use and Land Cover (LULC) change maps using a multi-sensor remote sensing approach for decision making and planning. LULC maps were generated using Random Forest Classification of Landsat 8 and Sentinel-1 image collections. Spectral indices, combinations of radar polarizations (VV, VH), and their principal components were included to improve its accuracy. Conversion maps were generated by taking the bi-annual difference of LULC maps from 2016 to 2019. Accuracy was assessed using visual inspection with Google Earth Pro. Classification was carried out using single-sensor (optical or radar) and multi-sensor (optical and radar) approach in combination with three feature selection algorithms, namely, Sandri and Zuccolotto (2006), Liaw and Wiener (2015), Kursa and Rudnicki (2010). Multi-sensor and single sensor yielded similarly high overall accuracies (OA = 96%) with the exception of single-sensor radar approach (OA = 53%). Multi-sensor approaches exhibit high accuracies (Cumulative Accuracy = 91%) in detecting agricultural to built-up LULC change up to 5,000 square meters unlike single-sensor optical approach (Cumulative Accuracy = 76%). Among the multi-sensor approaches, the method of Liaw and Wiener (2015) remains to be superior as it only uses eight (8) variables.