OBJECT BASED “DAYAS” CLASSIFICATION USING SENTINEL A-2 SATELLITE IMAGERY CASE STUDY CITY OF BENSLIMANE

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

Benchelha, M.^1¹ 

[1] Geosciences Laboratory, Faculty of Science Ain Chock, Km 8 El Jadida Road, B.P 5366 Maarif Casablanca 20100, Morocco^1

关键词: Dayas;    Wetlands;    Remote sensing;    Object-oriented classification;    Pixel-based classification;    NDWI index;    Kappa index;    Sentinel-2;   

DOI  :  10.5194/isprs-archives-XLII-4-W12-33-2019

学科分类：地球科学(综合)

来源: Copernicus Publications

【 摘 要 】

The management of “DAYAS” is a major issue in the preservation and maintain of biodiversity and environmental balance, especially in a context where this fragile ecosystems face many degradation factors. The extraction of Dayas is a key component in the management process of this type of wetlands, and has been the subject of many researches related to remote sensing. The methods and instrumentation for optical remote sensing are used to improve the mapping of Dayas, based on the radiometric characteristics of local hydrosystems. The present paper studies the inputs of different methods for the delimitation and extraction of Dayas in the realm of Benslimane city, using Sentinel A-2 imagery for the mapping. The methodology for the application of the pixel-based and the object-oriented approaches requires many steps, starting from an image pre-processing with Sentinel-2 calibration, the calculation of NDWI index, to proceed to the extraction of Dayas from the very high resolution image segmentation, then the application of the object-oriented classification to validate the results. The cartographic results demonstrate the input of the applied methodology in the Dayas extraction in different situations and timing (winter/summer), and allow to measure the cartographic accuracy for each approach, finding 65% of accuracy for the pixel-based approach with Kappa index = 0.40 versus 75% of accuracy for the object-oriented approach with Kappa index = 0.72. The results achieved inform and orient about optimisation measures and regulations of the Kappa index to improve the Dayas extraction and mapping.

【 授权许可】

CC BY