GEO-SPATIAL MODELING OF CARBON SEQUESTRATION ASSESSMENT IN DATE PALM, ABU DHABI: AN INTEGRATED APPROACH OF FIELDWORK, REMOTE SENSING, AND GIS

The United Arab Emirates (UAE) has undertaken huge efforts to green the desert and afforestation projects (planted mainly with date palms) hence, reducing its carbon footprint, which have never been accounted for, because of lack of implemented mechanisms and tools to assess the amount of biomass and carbon stock (CS) sequestered by plants in the country. The purpose of this dissertation is to implement a new approach towards assessing the carbon sequestered by date palm (DP) plantations in Abu Dhabi, in both their biomass compartment as well as the soils under beneath, using geospatial technologies (RS and GIS) assessed by field measurements. The methodology proposed in this dissertation relied on both fieldwork and labwork, besides the intensive use of geospatial technology including, digital image processing of multi-scale, multi-resolution satellite imagery as well as Geographical Information Systems (GIS) modelling. For detecting and mapping the DP, the research proposes a framework based on using multi-source/ multi-sensor data in a hierarchical integrated approach (HIA) to map DP plantations at different age stages: young, medium, and mature. The outcomes of the implemented approach were the creation of detailed and accurate maps of DP at three age stages. The overall accuracies for mixed-ages DP the value reached up to 94.5%, with an overall Kappa statistic estimated at 0.888 with total area of DP equal to 7,588.04 ha and the total number of DP planted in the study area counted an estimated number of 8,966,826 palms.The study showed that the correlation of mature DP class alone (\u3e10 years) with single bands was significant with shorwave infrared 1 (SWIR1) and shortwave infrared 2 (SWIR2), while the correlation was significant with all tested vegetation indices (VI) except for tasseled cap transformation index for brightness (TCB) and for greenness (TCG). By using different types of regression equations, tasseled cap transformation index for wetness (TCW) showed the strongest correlation using a second-order polynomial equation to estimate the biomass of mature DP with R² equal to 0.7643 and P value equal to 0.007. The exponential regression equation that uses renormalized difference vegetation index (RDVI) as RS predictor was the best single VI and had the strongest correlation among all RS variables of Landsat 8 OLI for AGB of non-mature DP, with an R2 value of 0.4987 and P value equal 0.00002. The findings of the dissertation work are promising and can be used to estimate the amount of biomass and carbon stock in DP plantations in the country as well as in arid land in general. Therefore, it can be applied to enhance the decision-making process on sustainable monitoring and management of carbon sequestration by date palms in other similar ecosystems. The research’s approach has never been developed elsewhere for date palms in arid areas