Spatiotemporal Mapping and Monitoring of Mangrove Forests Changes From 1990 to 2019 in the Northern Emirates, UAE Using Random Forest, Kernel Logistic Regression and Naive Bayes Tree Models

© Copyright © 2020 Elmahdy, Ali, Mohamed, Howari, Abouleish and Simonet. Mangrove forests are acting as a green lung for the coastal cities of the United Arab Emirates, providing a habitat for wildlife, storing blue carbon in sediment and protecting shoreline. Thus, the first step toward conservation and a better understanding of the ecological setting of mangroves is mapping and monitoring mangrove extent over multiple spatial scales. This study aims to develop a novel low-cost remote sensing approach for spatiotemporal mapping and monitoring mangrove forest extent in the northern part of the United Arab Emirates. The approach was developed based on random forest (RF), Kernel logistic regression (KLR), and Naive Bayes Tree machine learning algorithms which use multitemporal Landsat images. Our results of accuracy metrics include accuracy, precision, and recall, F1 score revealed that RF outperformed the KLR and NB with an F1 score of more than 0.90. Each pair of produced mangrove maps (1990–2000, 2000–2010, 2010–2019, and 1990–2019) was used to image difference algorithm to monitor mangrove extent by applying a threshold ranges from +1 to −1. Our results are of great importance to the ecological and research community. The new maps presented in this study will be a good reference and a useful source for the coastal management organization

The anesthetizing sites supervised to anesthesiologist ratio is an invalid surrogate for group productivity in academic anesthesia departments when used without consideration of the corresponding managerial decisions

When the anesthesiologist does not individually perform the anesthesia care, then to make valid comparisons among US anesthesia departments, one must consider the staffing ratio (i.e., how many cases each anesthesiologist supervises when working with Certified Registered Nurse Anesthetists [CRNAs] or Certified Anesthesiologist Assistants [CAA]). The staffing ratio also must be considered when accurately measuring group productivity. In this narrative review, we consider anesthesia departments with non-physician anesthesia providers and anesthesiology residents. We investigate the validity of such departments assessing the overall ratio of anesthetizing sites supervised per anesthesiologist as a surrogate for group clinical productivity. The sites/anesthesiologist ratio can be estimated accurately using the arithmetic mean calculated by anesthesiologist, the harmonic mean calculated by case, or the harmonic mean calculated by CRNA or CAA, but not by the arithmetic mean ratio by case. However, there is lack of validity to benchmarking the percentage time that anesthesiologists are supervising the maximum possible number of CRNAs or CAAs when some of the anesthesiologists also are supervising resident physicians. Assignments can differ in the total number anesthesiologists needed while every anesthesiologist is supervising as many sites as possible. Similarly, there is lack of validity to limiting assessment to the anesthesiologists supervising only CRNAs or CAAs. There also is lack of validity to limiting assessment only to cases performed by supervised CRNAs or CAAs. When cases can be assigned to anesthesiology residents or CRNAs or CAAs, increasing sites/anesthesiologist while limiting consideration to the CRNAs or CAAs creates incentive for the CRNAs or CAAs to be assigned cases, even when lesser productivity is the outcome. Decisions also can increase sites/anesthesiologist without increasing productivity (e.g., when one anesthesiologist relieves another before the end of the regular workday). A suitable alternative approach to fallaciously treating the sites/anesthesiologist ratio as a surrogate for productivity is that, when a teaching hospital supplies financial support, a responsibility of the anesthesia department is to explain annually the principal factors affecting productivity at each facility it manages and to show annually that decisions were made that maximized productivity, subject to the facilities' constraints

Land subsidence and sinkholes susceptibility mapping and analysis using random forest and frequency ratio models in Al Ain, UAE

This paper presents an approach to susceptibility mapping land subsidenceand sinkholes in the Al Ain area, UAE. A frequency ratio model was utilized to spatially analyse the relationship between locations of land subsidence and sinkhole and conditioning factors (CFs) to land subsidence susceptibility map. The values of eight essential CFs were employed as inputs to a random forest (RF) model. The produced map was compared with land subsidence and sinkhole locations and verified using the receiver operating characteristics (ROC). The results indicated a positive relationship and showed that the area under the curve was 88.4%, for the RF model. Thus, application of the approach using different algorithms could improve the performance of the modelling and the accuracy of the produced maps. The results of this study not only permit a better understanding of the impact of human activity and excessive groundwater extraction on ground surface stability, but also assist in enhancing geohazard mitigation strategies

Lipophilic Metabolites and Anatomical Acclimatization of <i>Cleome amblyocarpa</i> in the Drought and Extra-Water Areas of the Arid Desert of UAE

Plants adapt to different environmental conditions by developing structural and metabolic mechanisms. In this study, anatomical features and lipophilic metabolites were investigated in Cleome amblyocarpa Barr. &amp; Murb., Cleomaceae plants growing in the arid desert of United Arab Emirates (UAE) in either low-water or extra-water areas, which were caused by the surrounding road run-off. The plant showed the presence of shaggy-like trichomes. The plant also developed special mechanisms to ensure its survival via release of lipophilic metabolites. The lipophilic metabolites, stained red with Sudan III, were apparently released by glandular trichomes and idioblasts of the shoot and roots, respectively. The identified lipophilic metabolites included those required for drought tolerance, protection against pathogens invasion, and detoxification. Plants growing in the low-water area caused an increase in the production of lipophilic metabolites&#8212;in particular, hydrocarbons and terpenoids. The lipophilic metabolites are known to provide the plant with unique waxy surfaces that reduce water loss and avoid penetration by pathogens. The release of lipid metabolites and the presence of shaggy-like trichomes represented unique features of the species that have never been reported. The provided chemical ecology information can be extended for several plant-related applications, particularly including drought tolerance