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Proceedings Paper

Palm trees detecting and counting from high-resolution WorldView-3 satellite images in United Arab Emirates
Author(s): A. AlMaazmi
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Paper Abstract

The United Arab Emirates (UAE) is one of the fastest agriculture economical growing country in the world. One aspect of this agriculture growth is the development of the date palm trees sector in the UAE. The date palm tree is considered one of the oldest and most widely cultivated tree, which is commercially the most important tree in the life of its people and their heritage. Moreover, the date palm tree was believed to be a part of the UAE strategy to control desertification. With this huge investment and interest in palm trees in the UAE, there is limited knowledge of the actual tree counts and their exact spatial locations, which is a requirement for any agricultural census. WorldView-3 satellite images were used to develop an algorithm to detect and count palm trees in the UAE. The processing was done in two steps: the first step is to detect palm trees which involved supervised classification using maximum likelihood with four feature classes: Red, Blue, Green and Near infrared (NIR) bands associated with palm trees objects taken by the labeling. The second step is to count palm trees which involved extracting local spatial maxima of Laplacian blob from Normalized Difference Vegetation Index (NDVI) masking. The algorithm was tested in different regions of interest in AlAin city, part of the capital Emirate Abu Dhabi. The algorithm and final results are compared with ground truth images for accuracy assessment. The results were satisfactory with an accuracy of 89% and higher and very minimum negligible misclassification.

Paper Details

Date Published: 10 October 2018
PDF: 11 pages
Proc. SPIE 10783, Remote Sensing for Agriculture, Ecosystems, and Hydrology XX, 107831M (10 October 2018); doi: 10.1117/12.2325733
Show Author Affiliations
A. AlMaazmi, Mohammed Bin Rashid Space Ctr. (United Arab Emirates)

Published in SPIE Proceedings Vol. 10783:
Remote Sensing for Agriculture, Ecosystems, and Hydrology XX
Christopher M. U. Neale; Antonino Maltese, Editor(s)

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