Improving Land Use/Cover Classification Accuracy from Random Forest Feature Importance Selection Based on Synergistic Use of Sentinel Data and Digital Elevation Model in Agriculturally Dominated Landscape
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Khouloud Abida & Meriem Barbouchi & Khaoula Boudabbous & Wael Toukabri & Karem Saad & Habib Bousnina & Thouraya Sahli Chahed, 2022. "Sentinel-2 Data for Land Use Mapping: Comparing Different Supervised Classifications in Semi-Arid Areas," Agriculture, MDPI, vol. 12(9), pages 1-13, September.
- Jose Manuel Monsalve-Tellez & Jorge Luis Torres-León & Yeison Alberto Garcés-Gómez, 2022. "Evaluation of SAR and Optical Image Fusion Methods in Oil Palm Crop Cover Classification Using the Random Forest Algorithm," Agriculture, MDPI, vol. 12(7), pages 1-19, July.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Manel Khlif & Maria José Escorihuela & Aicha Chahbi Bellakanji & Giovanni Paolini & Zeineb Kassouk & Zohra Lili Chabaane, 2023. "Multi-Year Cereal Crop Classification Model in a Semi-Arid Region Using Sentinel-2 and Landsat 7–8 Data," Agriculture, MDPI, vol. 13(8), pages 1-21, August.
- Wenfei Luan & Ge Li & Bo Zhong & Jianwei Geng & Xin Li & Hui Li & Shi He, 2023. "Improving Dryland Urban Land Cover Classification Accuracy Using a Classical Convolution Neural Network," Land, MDPI, vol. 12(8), pages 1-20, August.
More about this item
Keywords
land use; land cover; classification; random forest; Sentinel data; SRTM; random forest; feature selection; accuracy; validation;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:13:y:2022:i:1:p:98-:d:1019306. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.