The Shaheen cyclone triggered coastal areas of Al-Batinah Governorate of the Sultanate of Oman and caused devastating impacts on vegetation areas, infrastructure and properties that resulted in severe damages and human casualties. A comprehensive evaluation of the cyclone is essential to identify the most impacted areas in the Governorate especially in its four regions Al-Musanaah, Al-Suwaiq, Al-Khaboura and Saham. An advanced techniques and very high resolution datasets have been used to study, analyze and mapping the effects caused by the shaheen Cyclone. The systematic approach included investigating changes before and after the cyclone of various parameters such as vegetation coverage, detection of buildup damages in agriculture lands, detailed study on coastline changes and inundations in agriculture areas & urban community. Both pre-classification and post classification change detection techniques were used to assess the impact of the cyclone. Using very high resolution datasets and application of latest techniques of Geographical information system and remote sensing like vegetation indices, deep learning models, spatial analysis and advanced object based detection methods were used to analyze the damages caused by the cyclone. Agricultural land change detection and its coverage calculation was studied and mapped. All individual vegetation parcels within the study area were analyzed and delineated. Date palm trees classification and counting was conducted and mapped. Inundations in agriculture lands and urban buildings in the agriculture areas were identified and mapped. The changes in the coastline and marine features were studied and mapped using latest object based classification. The outcome of this study was helpful in identifying the most affected areas and providing tempo-geospatially damage assessment that assist the humanitarian aid as well as paving the road for future hazard mitigation and new protection strategies.
Soils in arid countries predominantly suffer from salinity and drought related to environmental problems that can lead to crop stress and low productivity. In this study, true-color aerial images from an unmanned aerial vehicle were used to assess the effect of soil and water salinity on date palm growth. Random soil samples (n = 75) and irrigation water samples were collected from five sites and chemically analyzed. The custom algorithms were developed in ENVI® and MATLAB.® Green leaf index (GLI) was implemented to determine crop canopy attributes. Two segmentation methods namely between-class variance and foreground pixels were used to recognize the vegetation cover from other image pixels. The image analysis demonstrated that the mean value of GLI increased as the salinity levels decreased, R = 0.96 and 0.92 for soil electrical conductivity (EC) and water EC, respectively. The percentage of area covered with vegetation was correlated to soil EC and water EC with about 70% accuracy. On the other hand, the percentage of area covered with palm trees only was used accurately to evaluate the soil EC by R2 = 0.89 and the water EC by R2 = 0.86. The findings of this research can set foundations for the development of aerial color imaging on salinity stressed date palm monitoring, providing useful information for decision makers on salinity management.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.