@inproceedings{88ea315640b74370ad445a764e04babb,
title = "The application of narrow-band NDVI as a tool for mapping mangrove environment in Abu Dhabi",
abstract = "The overall utility of a visible/near infrared images and spectral profiles has several advantages and applications in environmental and ecosystem investigations. In the present paper, a close-range remote-sensing technique is used for assessing algal communities within the sabkha environment. The area is partially covered by mangrove trees and has salt at areas without visible algal mat. Other locations have dark brown and green patches of algae especially in the supratidal area. Those considered as the most important organisms living in the sabkha habitat. Visible and infrared electromagnetic bands were used to characterize productivity, biomass, and distribution of the algal communities in the sabkha environment. To accomplish this, the study tested the use of the well-documented ratio of near-infrared (NIR; 800nm) to red (670nm) reflectance in order to characterize the living component in the Sabkha area of Abu Dhabi. Multispectral visible/Near Infrared camera was used to collect images from the area of interest. The reported analysis characterized two types of algae; namely active and non-active algae. The active algea has high near-infrared (NIR) band reflectance and lower red band reflectance. However, inactive algea has low NIR band reflectance and higher Red band reflectance. The study also reported field observations, computer vision approach and remote sensing indicators that are useful in order to study the relation between spectral reflectivity and algal characteristics.",
keywords = "Algae, Computer vision, Multi-spectral imaging, NDVI",
author = "Hassan Hajjdiab and Haitham Farok and Howari, \{Fares M.\}",
note = "Publisher Copyright: {\textcopyright} 2017 ACM.; 9th International Conference on Machine Learning and Computing, ICMLC 2017 ; Conference date: 24-02-2017 Through 26-02-2017",
year = "2017",
month = feb,
day = "24",
doi = "10.1145/3055635.3056605",
language = "English",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
pages = "472--475",
booktitle = "Proceedings of 2017 9th International Conference on Machine Learning and Computing, ICMLC 2017",
address = "United States",
}