Orieschnig C. A., Belaud G., Venot Jean-Philippe, Massuel Sylvain, Ogilvie Andrew. (2021). Input imagery, classifiers, and cloud computing : insights from multi-temporal LULC mapping in the Cambodian Mekong Delta. European Journal of Remote Sensing, 54 (1), 398-416.
Titre du document
Input imagery, classifiers, and cloud computing : insights from multi-temporal LULC mapping in the Cambodian Mekong Delta
Année de publication
2021
Auteurs
Orieschnig C. A., Belaud G., Venot Jean-Philippe, Massuel Sylvain, Ogilvie Andrew
Source
European Journal of Remote Sensing, 2021,
54 (1), 398-416
The increased open-access availability of radar and optical satellite imagery has engendered numerous land use and land cover (LULC) analyses combining these data sources. In parallel, cloud computing platforms have enabled a wider community to perform LULC classifications over long periods and large areas. However, an assessment of how the performance of classifiers available on these cloud platforms can be optimized for the use of multi-imagery data has been lacking for multi-temporal LULC approaches. This study provides such an assessment for the supervised classifiers available on the open-access Google Earth Engine platform: Naive Bayes (NB), Classification and Regression Trees (CART), Random Forest (RF), Gradient Tree Boosting (GTB), and Support Vector Machines (SVM). A multi-temporal LULC analysis using Sentinel-1 and 2 is implemented for a study area in the Mekong Delta. Classifier performance is compared for different combinations of input imagery, band sets, and training datasets. The results show that GTB and RF yield the highest overall accuracies, at 94% and 93%. Combining optical and radar imagery boosts classification accuracy for CART, RF, GTB, and SVM by 10-15 percentage points. Furthermore, it reduces the impact of limited training dataset quality for RF, GTB, and SVM.
Plan de classement
Sciences du monde végétal [076]
;
Etudes, transformation, conservation du milieu naturel [082]
;
Télédétection [126]
Description Géographique
CAMBODGE
Localisation
Fonds IRD [F B010082634]
Identifiant IRD
fdi:010082634
