Abstract
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.
Highlights
As an agricultural country, the Philippines had been reliant on rice, its most important agricultural commodity, for its economic growth and food security as it constitutes for about 15% of the gross value added in the agriculture industry while serving as the main source of food (David & Balisacan, 1995; Abdullah, Ito, & Adhana, 2006)
This paper presents the following: (1) generation of Land Use and Land Cover (LULC) change maps of the study area using multi-sensor and single-sensor image analysis; (2) comparison of the approaches and results based on the accuracies of the generated LULC change maps; and (3) assessment of the spatio-temporal dynamics of agricultural land conversion in the study area
4.1.1 Principal Component Analysis (PCA): The first principal component (PC1) of the VH, VV, Mean Backscatter, and Backscatter Ratio groups represented more than 90% of the total variation of all data
Summary
The Philippines had been reliant on rice, its most important agricultural commodity, for its economic growth and food security as it constitutes for about 15% of the gross value added in the agriculture industry while serving as the main source of food (David & Balisacan, 1995; Abdullah, Ito, & Adhana, 2006). Various studies have provided substantial findings on the use of remote sensing in agricultural monitoring applications such as that of Torbick et al (2017) which utilized optical imagery to monitor rice crop lands. A research of the Philippine Rice Research Institute in 2019, on the other hand, developed a model utilizing radar-based imagery to detect and monitor rice crops. For agricultural RS applications, it was proposed to integrate both imageries as they could produce better results (Torbick et al, 2017; Boschetti et al, 2015; Joshi et al, 2016, Dusseux et al, 2014)
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