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Application of deep learning with stratified K-fold for vegetation species discrimation in a protected mountainous region using Sentinel-2 image / Efosa Gbenga Adagbasa in Geocarto international, vol 37 n° 1 ([01/01/2022])  

Public [article]  inGeocarto international > vol 37 n° 1 [01/01/2022] . - pp 142 - 162 Titre : Application of deep learning with stratified K-fold for vegetation species discrimation in a protected mountainous region using Sentinel-2 image Type de document : Article/Communication Auteurs : Efosa Gbenga Adagbasa, Auteur ; Samuel Adelabu, Auteur ; Tom W. Okello, Auteur Année de publication : 2022 Article en page(s) : pp 142 - 162 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification par Perceptron multicouche [Termes IGN] distribution spatiale [Termes IGN] espèce végétale [Termes IGN] image Sentinel-MSI [Termes IGN] indice de végétation [Termes IGN] MNS ASTER [Termes IGN] montagne [Termes IGN] Poaceae Résumé : (auteur) Understanding the spatial distribution of vegetation species is essential to gain knowledge on the recovery process of an ecosystem. Few studies have used deep learning and machine learning models for image processing focusing on forest/crop classification. This study, therefore, makes use of a multi-layer perceptron (MLP) deep neural network to discriminate grass species in a mountainous region using Sentinel-2 images. Vegetation indices, Sentinel-1 and ASTER DEM were combined with Sentinel-2 images to improve classification accuracy. Stratified K-fold was used to ensure balanced training and test data. The results, when compared with other commonly used machine learning models, outperformed them all. It produced a better discriminate of the grass species when ASTER DEM was combined with Sentinel-2 images, with overall F1 score of 92%. The results of the species discrimination show a general increase in increaser II species such as Eragrostis curvula and a decrease in decreaser species like Phragmites australis. Numéro de notice : A2022-301 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1080/10106049.2019.1704070 En ligne : https://doi.org/10.1080/10106049.2019.1704070 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100378 [article]

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Mapping vegetation species succession in a mountainous grassland ecosystem using Landsat, ASTER MI, and Sentinel-2 data / Efosa Gbenga Adagbasa in Plos one, vol 17 n° 1 (January 2022)  

Public [article]  inPlos one > vol 17 n° 1 (January 2022) . - n° e0256672 Titre : Mapping vegetation species succession in a mountainous grassland ecosystem using Landsat, ASTER MI, and Sentinel-2 data Type de document : Article/Communication Auteurs : Efosa Gbenga Adagbasa, Auteur ; Geofrey Mukwada, Auteur Année de publication : 2002 Article en page(s) : n° e0256672 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Afrique du sud (état) [Termes IGN] carte de la végétation [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] composition floristique [Termes IGN] Google Earth Engine [Termes IGN] image Landsat-ETM+ [Termes IGN] image Sentinel-MSI [Termes IGN] image Terra-ASTER [Termes IGN] montagne [Termes IGN] prairie Résumé : (auteur) Vegetation species succession and composition are significant factors determining the rate of ecosystem biodiversity recovery after being disturbed and subsequently vital for sustainable and effective natural resource management and biodiversity. The succession and composition of grasslands ecosystems worldwide have significantly been affected by accelerated environmental changes due to natural and anthropogenic activities. Therefore, understanding spatial data on the succession of grassland vegetation species and communities through mapping and monitoring is essential to gain knowledge on the ecosystem and other ecosystem services. This study used a random forest machine learning classifier on the Google Earth Engine platform to classify grass vegetation species with Landsat 7 ETM+ and ASTER multispectral imager (MI) data resampled with the current Sentinel-2 MSI data to map and estimate the changes in vegetation species succession. The results indicate that ASTER MI has the least accuracy of 72%, Landsat 7 ETM+ 84%, and Sentinel-2 had the highest of 87%. The result also shows that other species had replaced four dominant grass species totaling about 49 km2 throughout the study. Numéro de notice : A2022-310 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1371/journal.pone.0256672 Date de publication en ligne : 26/01/2022 En ligne : http://dx.doi.org/10.1371/journal.pone.0256672 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100406 [article]