Assessing land degradation and identifying potential sustainable land management practices at the subnational level in Lebanon.

Assessing land degradation is essentially needed for decision-makers to monitor and mitigate degradation and implement sustainable land management practices. More specifically, monitoring land degradation on a regular basis enables decision-makers to analyze impact of degradation and effects of sustainable land management practices. This requires adopting a precise methodology for assessing land degradation. With information form assessments, decision-makers can establish achievable management goals to attain land degradation neutrality as addressed by the Sustainable Development Goal 15 (i.e., SDG 15). Accordingly, this work aimed at developing a systematic approach for assessing land degradation at the subnational level with the combined use of geo-spatial information and field data. The specific objectives were to (1) assess trends in land degradation and the impact of historical land uses on the current landscape characters, (2) investigate and characterize principal criteria of land degradation, and (3) identify sustainable land management practices. Multi-source and multi-resolution spatial data were employed for assessing the three indicators of land degradation neutrality (i.e., changes in vegetation cover, land productivity, and soil organic carbon). Prioritizing lands prone to degradation was investigated using a set of spatial attributes of different weights. Field data collection involved the use of pre-defined set of questions for assessing the principal criteria of land degradation including type, extent, rate, degree, and causes of land degradation. Sustainable land management practices were identified and categorized accordingly. This assessment provided a means for prioritization of interventions (i.e., prevention, mitigation, and/or rehabilitation) in an area characterized by a climate varying from semi-arid to moist subhumid.

Evaluating exposure to land degradation in association with repetitive armed conflicts in North Lebanon using multi-temporal satellite data.

Repetitive armed conflicts may be directly and indirectly responsible for severe biophysical modification to the environment. This, in turn, makes land more susceptible to degradation. Mapping and monitoring land degradation are essential for designing and implementing post-conflict recovery plans and informed policy decisions. The aim of this work was to evaluate the effect of repetitive armed conflicts on land degradation along the coastal zone of North Lebanon using multi-temporal satellite data. The specific objectives were to (1) identify a list of indicators for use in conjunction with satellite remote sensing, (2) monitor land cover change throughout repetitive events of armed conflicts and (3) model the effect of repetitive armed conflicts on land degradation. The methodology of work comprised the use of multi-temporal Landsat images and literature review data in GEographic Object-Based Image Analysis (GEOBIA) approach. The work resulted in the development of (1) a list of indicators to be employed, (2) land cover change detection maps with the use of multi-temporal Landsat images and, consequently, a fire risk associated with changes in vegetation cover throughout repetitive armed conflict events, and (3) an integrated approach for modelling the effect of repetitive armed conflicts on land degradation with the use of a composite land degradation index (CLDI). The final synthetic map showed four classes of exposure to land degradation associated with repetitive armed conflicts. Data collected from field visits showed that the final classification results highly reflected (average of 90 %) the effect of repetitive armed conflicts on the different classes of exposure to land degradation.

Determination of asbestos cement rooftop surface composition using regression analysis and hyper-spectral reflectance data in the visible and near-infrared ranges.

The effects of asbestos on human health have spurred numerous studies examining its risks in urban environments. Recent works have shifted towards less-invasive techniques for remote detection and classification of asbestos-cement. In this context, this study combines visible (VIS) and near-infrared (NIR) reflectance data collected in-situ with reference signals from the USGS spectral library, utilizing optimized regression analysis to determine the surface composition of corrugated asbestos-cement rooftops. An outlier filter was successfully implemented to enhance the accuracy of regression calculations, achieving a high level of agreement with actual field observations. The regression analysis revealed varying proportions of weathered cement, hazardous asbestos fibers (specifically chrysotile and cummingtonite), and biological growth (such as lichens and moss). These results are consistent with previous research on the composition of asbestos-cement rooftops, including a comparable field study and XRD analysis conducted in 2019. This underscores the importance of using regression analysis, preceded by an outlier filtering step, on VIS and NIR reflectance data to ascertain the surface composition of asbestos-cement rooftops. This methodology holds potential for application to larger hyperspectral datasets across more extensive sample surfaces and areas.

Determining the Accuracy and Reliability of Indirect Calorimeters Utilizing the Methanol Combustion Technique.

BACKGROUND: Several indirect calorimetry (IC) instruments are commercially available, but comparative validity and reliability data are lacking. Existing data are limited by inconsistencies in protocols, subject characteristics, or single-instrument validation comparisons. The aim of this study was to compare accuracy and reliability of metabolic carts using methanol combustion as the cross-laboratory criterion. METHODS: Eight 20-minute methanol burn trials were completed on 12 metabolic carts. Respiratory exchange ratio (RER) and percent O2 and CO2 recovery were calculated. RESULTS: For accuracy, 1 Omnical, Cosmed Quark CPET (Cosmed), and both Parvos (Parvo Medics trueOne 2400) measured all 3 variables within 2% of the true value; both DeltaTracs and the Vmax Encore System (Vmax) showed similar accuracy in measuring 1 or 2, but not all, variables. For reliability, 8 instruments were shown to be reliable, with the 2 Omnicals ranking best (coefficient of variation [CV] < 1.26%). Both Cosmeds, Parvos, DeltaTracs, 1 Jaeger Oxycon Pro (Oxycon), Max-II Metabolic Systems (Max-II), and Vmax were reliable for at least 1 variable (CV ≤ 3%). For multiple regression, humidity and amount of combusted methanol were significant predictors of RER (R2 = 0.33, P < .001). Temperature and amount of burned methanol were significant predictors of O2 recovery (R2 = 0.18, P < .001); only humidity was a predictor for CO2 recovery (R2 = 0.15, P < .001). CONCLUSIONS: Omnical, Parvo, Cosmed, and DeltaTrac had greater accuracy and reliability. The small number of instruments tested and expected differences in gas calibration variability limits the generalizability of conclusions. Finally, humidity and temperature could be modified in the laboratory to optimize IC conditions.