Palm trees detection from high spatial resolution satellite imagery using a new contextual classification method with constraints

Abstract

Palm groves are one of the most characteristic agroecosystems of Morocco. Therefore, conservation and monitoring have become a primary objective, not just from an environmental and landscaping point of view but also from the socio-economic. In this context, remote sensing presents an effective tool to map palm groves, to count palm trees and to detect their possible diseases. The present study attempts to map palm trees from very high resolution WorldView 2 (WV 2) imagery, using a new supervised contextual classification method based on Markov Random Fields and palm trees shadow orientation. A combined layer of pan-sharpened multispectral (MS) bands and eight mean texture measures based Gray Level Co-occurrence Matrices (GLCM) were used as input variables. Total accuracy of 83.4% palm trees detection was achieved. Using a decision criterion based on palm trees: shape, shadow orientation and the distance, the total accuracy of palm trees detection reached 88.1%.