Your new experience awaits. Try the new design now and help us make it even better

ORIGINAL RESEARCH article

Front. Remote Sens.

Sec. Land Cover and Land Use Change

Volume 6 - 2025 | doi: 10.3389/frsen.2025.1536105

This article is part of the Research TopicOne Forest Vision Initiative (OFVi) for Monitoring Tropical Forests: The Remote Sensing PilarView all 7 articles

Comparison of remote sensing-derived LULC products to analyse long term landscape evolution on the Pacific Coast of Ecuador (1960-2019)

Provisionally accepted
  • 1INRAE ​​Nouvelle-Aquitaine Bordeaux, Bordeaux, France
  • 2Inria Bordeaux - Sud-Ouest Research Centre, Talence, Aquitaine, France
  • 3UMR5563 Géosciences Environnement Toulouse (GET), Toulouse, Midi-Pyrénées, France
  • 4IRD UMR232 Diversité, adaptation, développement des plantes (DIADE), Montpellier, Languedoc-Roussillon, France

The final, formatted version of the article will be published soon.

Ecosystems services provided by forests are increasingly threatened by anthropogenic and climatic disturbances. International initiatives to reduce greenhouse gas emissions from forest disturbances, such as Reducing Emissions from Deforestation and Degradation + (REDD+), require robust quantifications of the dynamics and extent of Land Use/Land Cover (LULC). However, no study present yet a comparative synthesis of existing LULC products and long-term landscape evolution on the Pacific Slope and Coast of Ecuador (EPSC). In addition, previous studies on the evolution of the forest cover in the EPSC were achieved on small regions and short time-scales, never analyzing before the 1990s. In this context, we conducted a long-term study of landscape dynamics at the scale of the EPSC on the last six decades (1960-2019). In addition, we propose a comparative synthesis of the main land use databases from remote sensing. To do this, we compared six LULC databases (HILDA +, ESA-CCI, MODIS, GLCLUC, TMF, GFC) derived from remote sensing using the Ecuadorian Ministry of Environment and Water (MAATE) LULC dataset as a reference. This comparison was performed with confusion matrices. Three metrics are calculated from the confusion matrices: Accuracy, F1-score and MCC. HILDA+ and TMF products showed the best agreement with the MAATE map (F1-score of 0.63 and 0.65, respectively). HILDA+ captured net forest cover losses better than TMF (65% vs. 27% of the net losses recorded by MAATE). Thus, HILDA+ was identified as the best LULC product to analyse deforestation since 1960 in the EPSC. The major limitation encountered using HILDA+ is the coarse spatial resolution of 1 km. Yet, four deforestation phases were identified in the EPSC over 1960-2019. They reflect the historical, social, political, and climatical context of each ecosystem. Over the entire period (1960-2019), forest cover decreased by 43.9%. Since the 1960s, tropical rainforest areas declined by a third. Dry and transitional tropical forests lost more than half their area.

Keywords: deforestation1, landscape2, tropical forests3, spatio-temporal dynamics4, long term5, remote sensing6, anthropogenic and climatic disturbances7, Pacific slope

Received: 28 Nov 2024; Accepted: 22 Jul 2025.

Copyright: © 2025 Sollier, Frappart, Bourrel, Couvreur, Peaucelle, Renaudineau and Wigneron. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Valentine Sollier, INRAE ​​Nouvelle-Aquitaine Bordeaux, Bordeaux, France
Frédéric Frappart, INRAE ​​Nouvelle-Aquitaine Bordeaux, Bordeaux, France

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.