Evaluating the Applicability of Global LULC Products and an Author-Generated Phenology-Based Map for Regional Analysis: A Case Study in Ecuador’s Ecoregions

Gladys Maria Villegas Rugel (), Daniel Ochoa, Jose Miguel Menendez and Frieke Van Coillie
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Gladys Maria Villegas Rugel: Remote Sensing, Spatial Analysis Lab (REMOSA), Department of Environment, Ghent University, 9000 Ghent, Belgium
Daniel Ochoa: Facultad de Ingeniería en Electricidad y Computación, Campus Gustavo Galindo, ESPOL Polytechnic University, Km. 30.5 Vía Perimetral, Guayaquil 090101, Ecuador
Jose Miguel Menendez: Facultad de Ingeniería en Electricidad y Computación, Campus Gustavo Galindo, ESPOL Polytechnic University, Km. 30.5 Vía Perimetral, Guayaquil 090101, Ecuador
Frieke Van Coillie: Remote Sensing, Spatial Analysis Lab (REMOSA), Department of Environment, Ghent University, 9000 Ghent, Belgium

Land, 2023, vol. 12, issue 5, 1-32

Abstract: An accurate and detailed understanding of land-use change affected by anthropogenic actions is key to environmental policy decision-making and implementation. Although global land cover products have been widely used to monitor and analyse land use/land cover (LULC) change, the feasibility of using these products at the regional level needs to be assessed due to the limitation and biases of generalised models from around the world. The main objective of the present study was to generate regional LULC maps of three target areas located in the main ecoregions of Ecuador at a resolution of 10 m using Google Earth Engine (GEE) cloud-based computing. Our approach is based on (1) Single Date Classification (SDC) that processes Sentinel-2 data into fuzzy rule-driven thematic classes, (2) rule refinement using Visible Infrared Imaging Radiometer Suite (VIIRS) data, and (3) phenology-based synthesis (PBS) classification that combines SDC into LULC based on the occurrence rule. Our results show that the three target areas were classified with an overall accuracy of over 80%. In addition, cross-comparison between the global land cover products and our LULC product was performed and we found discrepancies and inaccuracies in the global products due to the characteristics of the target areas that included a dynamic landscape. Our LULC product supplements existing official statistics and showcases the effectiveness of phenology-based mapping in managing land use by providing precise and timely data to support agricultural policies and ensure food security.

Keywords: LULC; phenology-based algorithm; image classification; GEE; Sentinel-2 (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2023
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