Assessing an Optical Tool for Identifying Tidal and Associated Mangrove Swamp Rice Fields in Guinea-Bissau, West Africa

Jesus Céspedes, Jaime Garbanzo-León, Marina Temudo () and Gabriel Garbanzo
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Jesus Céspedes: School of Agriculture, University of Lisbon, Tapada da Ajuda, 1349-017 Lisboa, Portugal
Jaime Garbanzo-León: School of Surveying Engineering, University of Costa Rica, San José 11501, Costa Rica
Marina Temudo: Forest Research Centre (CEF), Associate Laboratory TERRA, School of Agriculture, University of Lisbon, Tapada da Ajuda, 1349-017 Lisboa, Portugal
Gabriel Garbanzo: Soil and Foliar Laboratory, Agronomic Research Center, School of Agriculture, University of Costa Rica, San José 11501, Costa Rica

Land, 2025, vol. 14, issue 11, 1-23

Abstract: An optical remote sensing approach was developed to identify areas with high and low salinity within the mangrove swamp rice system in West Africa. Conducted between 2019 and 2024 in Guinea-Bissau, this study examined two contrasting rice-growing environments, tidal mangrove (TM) and associated mangrove (AM), to assess changes in vegetation dynamics, soil salinity concentration, and soil chemical properties. Field sampling was conducted during the dry season to avoid waterlogging, and soil analyses included texture, cation exchange capacity, micronutrients, and electrical conductivity (EC e ). Meteorological stations recorded rainfall and environmental conditions over the period. Moreover, orthorectified and atmospherically corrected surface reflectance satellite imagery from PlanetScope and Sentinel-2 was selected due to their high spatial resolution and revisit frequency. From this data, vegetation dynamics were monitored using the Normalized Difference Vegetation Index (NDVI), with change detection calculated as the difference in NDVI between sequential images (ΔNDVI). Thresholds of 0.15 ≤ NDVI ≤ 0.5 and ΔNDVI > 0.1 were tested to identify significant vegetation growth, with smaller polygons (<1000 m 2 ) removed to reduce noise. In this process, at least three temporal images per season were analyzed, and multi-year intersections were done to enhance accuracy. Our parameter optimization tests found that a locally calibrated NDVI threshold of 0.26 improved site classification. Thus, this integrated field–remote sensing approach proved to be a reproducible and cost-effective tool for detecting AM and TM environments and assessing vegetation responses to seasonal changes, contributing to improved land and water management in the salinity-affected mangrove swamp rice system.

Keywords: normalized difference vegetation index; early appearance of vegetation; micronutrients; electrical conductivity; soil salinity; PlanetScope; Sentinel-2 (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:14:y:2025:i:11:p:2144-:d:1781519

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