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

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  • Gladys Maria Villegas Rugel

    (Remote Sensing, Spatial Analysis Lab (REMOSA), Department of Environment, Ghent University, 9000 Ghent, Belgium
    Facultad de Ingeniería en Electricidad y Computación, Campus Gustavo Galindo, ESPOL Polytechnic University, Km. 30.5 Vía Perimetral, Guayaquil 090101, Ecuador)

  • 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
    Department of Telecommunications and Information Processing, Interuniversity Microelectronics Centre, Ghent University/IMEC, 9000 Ghent, Belgium)

  • Frieke Van Coillie

    (Remote Sensing, Spatial Analysis Lab (REMOSA), Department of Environment, Ghent University, 9000 Ghent, Belgium)

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.

Suggested Citation

  • Gladys Maria Villegas Rugel & Daniel Ochoa & Jose Miguel Menendez & Frieke Van Coillie, 2023. "Evaluating the Applicability of Global LULC Products and an Author-Generated Phenology-Based Map for Regional Analysis: A Case Study in Ecuador’s Ecoregions," Land, MDPI, vol. 12(5), pages 1-32, May.
    • Handle: RePEc:gam:jlands:v:12:y:2023:i:5:p:1112-:d:1152846
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    References listed on IDEAS

    as
    1. Junmei Kang & Xiaomei Yang & Zhihua Wang & Hongbin Cheng & Jun Wang & Hongtao Tang & Yan Li & Zongpan Bian & Zhuoli Bai, 2022. "Comparison of Three Ten Meter Land Cover Products in a Drought Region: A Case Study in Northwestern China," Land, MDPI, vol. 11(3), pages 1-20, March.
    2. Yonaba, R. & Koïta, M. & Mounirou, L.A. & Tazen, F. & Queloz, P. & Biaou, A.C. & Niang, D. & Zouré, C. & Karambiri, H. & Yacouba, H., 2021. "Spatial and transient modelling of land use/land cover (LULC) dynamics in a Sahelian landscape under semi-arid climate in northern Burkina Faso," Land Use Policy, Elsevier, vol. 103(C).
    3. Kotapati Narayana Loukika & Venkata Reddy Keesara & Venkataramana Sridhar, 2021. "Analysis of Land Use and Land Cover Using Machine Learning Algorithms on Google Earth Engine for Munneru River Basin, India," Sustainability, MDPI, vol. 13(24), pages 1-15, December.
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