Geo-spatial technologies (i.e., remote sensing (RS) and Geographic Information Systems (GIS)) offer the means to enable a rapid assessment of terrestrial carbon stock (CS) over large areas. The utilization of an integrated RS-GIS approach for above ground biomass (AGB) estimation and precision carbon management is a timely and cost-effective solution for implementing appropriate management strategies at a localized and regional scale. The current study reviews various RS-related techniques used in the CS assessment, with emphasis on arid lands, and provides insight into the associated challenges, opportunities and future trends. The study examines the traditional methods and highlights their limitations. It explores recent and developing techniques, and identifies the most significant RS variables in depicting biophysical predictors. It further demonstrates the usefulness of geo-spatial technologies for assessing terrestrial CS, especially in arid lands. RS of vegetation in these ecosystems is constrained by unique challenges specific to their environmental conditions, leading to high inaccuracies when applying biomass estimation techniques developed for other ecosystems. This study reviews and highlights advantages and limitations of the various techniques and sensors, including optical, RADAR and LiDAR, that have been extensively used to estimate AGB and assess CS with RS data. Other new methods are introduced and discussed as well. Finally, the study highpoints the need for further work to fill the gaps and overcome limitations in using these emerging techniques for precision carbon management. Geo-spatial technologies are shown to be a valuable tool for estimating carbon sequestered especially in difficult and remote areas such as arid land.

The abundance and recurrence of particulate matter in Abu Dhabi Emirate (ADE), are often derived from different emission sources such as the combustion of hydrocarbon, producing much of the PM2.5 found in outdoor air, as well as a significant proportion of PM10. Wind-blown dust from open desert areas and construction sites, landfills and agriculture, brush/waste burning, and industrial sources, has contributed markedly to the problem of the spread of haze and the long-range movement of pollutants in the country. In this study, the spatio-temporal characterization of PM10 concentration across the Emirate was analyzed utilizing geospatial interpolation, spanning the period between 2013 and 2017. The results suggest that the fluctuations of the PM10 concentration can be decomposed into three dominant types, each characterizing different spatial and temporal variations. First, the western region with PM10 showing a peak concentration during the summer season i.e., when the winds are predominantly northerlies or northwesterly, and a minimal concentration during the winter season. Second, the central region with the PM10 exhibiting a concentration surge in July-August, as a result of a mix of strong winds and high temperatures. Third, the eastern region with a low concentration of PM10. Seasonally, this component exhibits two concentration maxima during quarters 2 and 3 (summer), and two minima during quarters 1 and 4 (winter). Indeed, the seasonal variability of PM10 concentration in desertic countries like the UAE is closely linked to the seasonal variation of heat waves and dust storms, which are characteristic of the dryland climate. During the summer months, the UAE experiences high temperatures and arid conditions, creating favorable conditions for the formation of heat waves. Furthermore, it was noticed that the PM10 concentration also fluctuated markedly throughout the study period with anomalies detected in open desert areas and regions characterized by extensive industrial operations.

This study aims at developing a remote sensing based model to estimate carbon stock (CS) in date palm plantations in the United Arab Emirates (UAE). Data from Landsat 8 OLI were used to assess the correlation between spectral reflectance and different vegetation indices on one side, and aboveground biomass (AGB) derived from ground measurements on the other. AGB and CS (ton ha⁻¹) were estimated using traditional destructive methods of standard sampling techniques and allometric equations developed in a previous study for date palms in the area. The relationships between the estimated AGB and parameters derived from remote sensing (RS) data were tested using single and multiple linear regression analysis. The results indicated a significant correlation with certain RS parameters. For mature palms class alone (>10 years), the correlation with single bands was only significant with SWIR1 and SWIR2 while the correlation was significant with all tested VI’s except for TCB vegetation index. A combination of bands R, SWIR1, and SWIR2 improved the determination of this class to a coefficient of determination (R²) value of 0.961. However, for the medium and young age classes (10–5 and less than 5 years), the correlation was not significant (with the exception of SWIR1 and TCB index for medium class), where the use of higher spatial resolution can be a good alternative. On the other hand, for mixed ages (young, medium and mature palms), the strongest correlations were found using SWIR2 single band and the SR vegetation index; having R² values of 0.753 and 0.871, respectively. The R² was improved to 0.952 by using stepwise multiple regression combining DVI, NDI, and RVI vegetation indices. Finally, results obtained showed that CS represented 53.87% of the total AGB in date palms. Subsequently, the average amount of carbon stock (CS) for both mature and mixed classes was calculated at 15.81 and 10.3 ton ha⁻¹ respectively.

The United Arab Emirates (UAE) government has declared the increased use of alternative energy a strategic goal and has invested in identifying and developing various sources of such energy. This study aimed at assessing the viability of establishing wind farms offshore the Emirate of Abu Dhabi, UAE and to identify favourable sites for such farms using Geographic Information Systems (GIS) procedures and algorithms. Based on previous studies and on local requirements, a set of suitability criteria was developed including ocean currents, reserved areas, seabed topography, and wind speed. GIS layers were created and a weighted overlay GIS model based on the above mentioned criteria was built to identify suitable sites for hosting a new offshore wind energy farm. Results showed that most of Abu Dhabi offshore areas were unsuitable, largely due to the presence of restricted zones (marine protected areas, oil extraction platforms and oil pipelines in particular). However, some suitable sites could be identified, especially around Delma Island and North of Jabal Barakah in the Western Region. The environmental impact of potential wind farm locations and associated cables on the marine ecology was examined to ensure minimal disturbance to marine life. Further research is needed to specify wind mills characteristics that suit the study area especially with the presence of heavy traffic due to many oil production and shipping activities in the Arabian Gulf most of the year.

Landfill sites receive 92% of total annual solid waste produced by municipalities in the emirate of Abu Dhabi. In this study, candidate sites for an appropriate landfill location for the Abu Dhabi municipal area are determined by integrating geographic information systems (GIS) and multi-criteria evaluation (MCE) analysis. To identify appropriate landfill sites, eight input map layers including proximity to urban areas, proximity to wells and water table depth, geology and topography, proximity to touristic and archeological sites, distance from roads network, distance from drainage networks, and land slope are used in constraint mapping. A final map was generated which identified potential areas showing suitability for the location of the landfill site. Results revealed that 30% of the study area was identified as highly suitable, 25% as suitable, and 45% as unsuitable. The selection of the final landfill site, however, requires further field research.