Reference : The performance of random forest classification based on phenological metrics derived...
Scientific journals : Article
Life sciences : Agriculture & agronomy
http://hdl.handle.net/2268/241732
The performance of random forest classification based on phenological metrics derived from Sentinel-2 and landsat 8 to map crop cover in an irrigated semi-arid region
English
Htitiou, Abdelaziz [> >]
Boudhar, Abdelghani [> >]
Lebrini, Youssef [> >]
Hadria, Rachid [> >]
Lionboui, Hayat [> >]
Elmansouri, Loubna [> >]
Tychon, Bernard mailto [Université de Liège - ULiège > DER Sc. et gest. de l'environnement (Arlon Campus Environ.) > Eau, Environnement, Développement >]
Benabdelouahab, Tarik [> >]
29-Oct-2019
Remote Sensing in Earth Systems Sciences
Springer
Yes (verified by ORBi)
International
2520-8195
2520-8209
[en] phenological metrics ; random forest ; crop mapping ; classification ; Landsat 8 ; Sentinel-2
[en] The use of remote sensing data provides valuable information to ensure sustainable land cover management. In this paper, the potential of phenological metrics data, derived from Sentinel-2A (S2) and Landsat 8 (L8) NDVI time series, was evaluated using Random Forest (RF) classification to identify and map various crop classes over two irrigated perimeters in Morocco. The smoothed NDVI time series obtained by the TIMESAT software was used to extract profiles and phenological metrics, which constitute potential explanatory variables for cropland classification. The method of classification applied involves the use of a supervised Random Forest (RF) classifier. The results demonstrated the capability of moderate-to-high spatial resolution (10–30 m) satellite imagery to capture the phenological stages of different cropping systems over the study area. Furthermore, the classification based on S2 data presents a higher overall accuracy of 93% and a kappa coefficient of 0.91 than those produced by L8 data, which are 90% and 0.88, respectively. In other words, phenological metrics obtained from S2 time series data showed high potential for agricultural crop-types classification in semi-arid regions and thus can constitute a valuable tool for decision makers to use in managing and monitoring a complex landscape such as an irrigated perimeter.
Researchers ; Professionals ; Students
http://hdl.handle.net/2268/241732
10.1007/s41976-019-00023-9
https://link.springer.com/content/pdf/10.1007%2Fs41976-019-00023-9.pdf

File(s) associated to this reference

Fulltext file(s):

FileCommentaryVersionSizeAccess
Open access
Htitiou2019_Article_ThePerformanceOfRandomForestCl.pdfPublisher postprint9.76 MBView/Open

Bookmark and Share SFX Query

All documents in ORBi are protected by a user license.