Effect of Spatial Resolution on Land Cover Mapping in an Agropastoral Area of Niger (Aguié and Mayahi) Using Sentinel-2 and Landsat 8 Imagery Within a Random Forest Regression Framework

Abdou Amadou, Sanoussi; Lawali, Dambo; Bastin, Jean-François; Bogaert, Jan; Michez, Adrien; Meersmans, Jeroen

doi:10.3390/rs18050750

Article (Scientific journals)

Effect of Spatial Resolution on Land Cover Mapping in an Agropastoral Area of Niger (Aguié and Mayahi) Using Sentinel-2 and Landsat 8 Imagery Within a Random Forest Regression Framework

Abdou Amadou, Sanoussi; Lawali, Dambo; Bastin, Jean-François et al.

2026 • In Remote Sensing

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10.3390/rs18050750

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Keywords :

stratified random sampling; Google Earth Engine (GEE); random forest regression; Collect Earth Online (CEO); Niger

Abstract :

[en] Monitoring environmental changes over time requires images with extensive historical depth. However, high spatial resolution images often lack such depth. This study investigates the impact of spatial resolution on image classification. Thus, Landsat 8 and Sentinel-2 images acquired between October and December 2020 were processed and classified using Random Forest regression on Google Earth Engine (GEE). This method allows for continuous land cover maps, required for robust assessment of land cover dynamics in patchy landscapes. A total of 1719 training samples were collected from the Collect Earth Online (CEO) platform to train the model. In addition to the spectral bands, vegetation indices were considered to optimize classification results. The study revealed statistical differences in land cover areas estimated by the two sensors. These differences are statistically significant at p < 0.001, although they are small. Validation results showed that the RMSE from Sentinel-2 is slightly lower than that from Landsat 8, with this difference significant at p < 0.05. Therefore, spatial resolution influences the accuracy of image classification. Nevertheless, given the observed differences between the two sensors, which ranged from 0.03% to 3.94% across land covers, Landsat imagery remains suitable for producing reliable land cover maps in heterogeneous landscapes.

Disciplines :

Earth sciences & physical geography

Author, co-author :

Abdou Amadou, Sanoussi ; TERRA Teaching and Research Center, Gembloux Agro-Bio Tech, University of Liège, Gembloux, Belgium ; Department of Geography, Faculty of Letters and Human Sciences, Abdou Moumouni University, Niamey, Niger

Lawali, Dambo; Department of Geography, Faculty of Letters and Human Sciences, Abdou Moumouni University, Niamey, Niger

Bastin, Jean-François ; Université de Liège - ULiège > TERRA Research Centre > Biodiversité, Ecosystème et Paysage (BEP)

Bogaert, Jan ; Université de Liège - ULiège > TERRA Research Centre > Biodiversité, Ecosystème et Paysage (BEP)

Michez, Adrien ; Université de Liège - ULiège > Département GxABT

Meersmans, Jeroen ; Université de Liège - ULiège > Département GxABT > Echanges Eau - Sol - Plantes

Language :

English

Title :

Effect of Spatial Resolution on Land Cover Mapping in an Agropastoral Area of Niger (Aguié and Mayahi) Using Sentinel-2 and Landsat 8 Imagery Within a Random Forest Regression Framework

Publication date :

01 March 2026

Journal title :

Remote Sensing

eISSN :

2072-4292

Publisher :

Multidisciplinary Digital Publishing Institute (MDPI), Basel, Switzerland

Peer reviewed :

Peer Reviewed verified by ORBi

Available on ORBi :

since 02 April 2026

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