The Puuc Biocultural State Reserve (PBSR is an unique model for tropical dry forest conservation in Mexico. Preserving forest biodiversity and carbon within the PBSR depends on the mainte-nance of low impact productive activities coordinated by multiple communal and private land-owners. In this study, we used state-of-the-art remote sensing data to investigate past spatial patterns in forest clearing dynamics and its relation to forest carbon density and forest plant species richness and diversity in the context of the forest conservation goals of the PBSR. We used a Landsat-based continuous change detection product for the 2000-2021 period and compared it to carbon density and tree species richness models generated from ALOS-2 PALSAR 2 imagery and national scale forest inventory data. The estimated error-adjusted area of detected annual forest clearings from the year 2000 until the year 2021 was 230,511 ha in total (+19,979 ha). The analysis of annual forest clearing frequency and area suggests that although forest clearing was significantly more intensive outside of the PBSR than within the PBSR during the entire 2000-2021 period, there is no evidence suggesting that the frequency and magnitude of forest clearing has changed over the years after the creation of the PBSR in 2011. An emergent hotspot analysis shows, however, that high spatiotemporal clustering of forest clearing events (hotspots) during the 2012-2021 pe-riod was less common than prior to 2011 and these more recent hotspots have been confined to areas outside the PBSR. After comparing forest clearing events to carbon density and tree species richness models, results show that land owners outside the PBSR often clear forests with lower carbon density and species diversity than land owners inside the PBSR. This suggests that, compared to land owners outside the PBSR, land owners within the PBSR might be practicing longer fallow periods allowing forests to attain higher carbon density and tree species richness and hence better soil nutrient recovery after land abandonment. In conclusion, our results show that the PBSR effectively acted as a stabilizing forest management scheme during the 2012-2021 period minimizing the impact of productive activities by lowering the frequency of forest clearing events and preserving late secondary forests within the PBSR. We recommend the continuation of efforts for providing alternative optimal field data collection strategies and modeling techniques to spatially predict key tropical forest attributes. The combination of these models with contin-uous change detection datasets will allow to reveal underlying ecological processes and generate information better adapted to forest governance scales.

In the United States, several land use and land cover (LULC) data sets are available based on Landsat. These data sets often fail to accurately represent the features on the ground despite having several advantages over other data. Detailed mapping of complex heterogeneous landscapes for informed decision-making is possible using high spatial resolution orthoimagery from the National Agricultural Imagery Program (NAIP). However, large-area mapping at this resolution remains challenging due to radiometric differences between scenes, low spectral depth of imagery, landscape heterogeneity, and computational limitations. Machine learning (ML) techniques have shown promise in improving LULC maps. The primary purposes of this study are to examine and evaluate bagging (Random Forest; RF), Boosting (Gradient Boosting Machines [GBM] and Extreme Gradient Boosting [XGB]), and stacking ensemble models on sentinel 2A fusion data on NAIP imagery. We also compared the accuracy based on random cross-validation (80% / 20% split) without accounting for spatial autocorrelation and target-oriented validation (10 clusters) accounting for spatial structures of the training data set. To make the LULC map of a portion of Tom Green and Irion counties in central Texas, we used a time series of sentinel data and NAIP orthoimagery. We created several spectral indices, structural variables, and geometry-based variables, reducing the dimensionality of features generated on Sentinel and NAIP data. The random and target-oriented validation results show that autocorrelation in the training data offers over-optimistic results. In our case, the overestimation ranged from 2 % to 3.5 %. The XGB-boosted stacking ensemble on-base learners (RF, XGB, and GBM) improved model performance over individual base-learner. The main contribution of this research is that meta-learners are just as sensitive to overfitting as base models, as these algorithms are not designed to account for spatial information. Finally, we show that the fusion of Sentinel 2A data with NAIP data improves land use land cover classification using Geographic Object-Based Image Analysis (GEOBIA).

Excess energy from photosynthesis can be collected through biophotovoltaic platforms to generate green electrical power. This research evaluated the in-situ performance of different CAM (Crassulaceae acid metabolism) plants from Calama city located in the Atacama Desert for energy recovery as biophotovoltaic cell (BPV) using AISI 316L and Cu as electrodes. The species evaluated were Aloe perfoliata, Cereus jamacaru, Austrocylindropuntia subulata, Agave potatorum, Malephora crocea, and Kalanchoe daigremontiana. The results indicate that K. daigremontiana can be used as in-situ BFV because it has a maximum cell potential of 0.248 V and a minimum of 0.139 V with a recurrence close to 89% in the ranges [0.2 - 0.25] V. This is one of the few investigations that evaluate the potential of native CAM plants as BFV energy sources, providing new knowledge for the development of sustainable alternatives for horticultural crop production systems.

Hydrogen has emerged as a promising energy carrier, offering a viable solution to address our current global energy demands. Solar energy stands out as a primary source of renewable power and has the potential to produce hydrogen through the utilization of solar cells. The quest for efficient, durable, and cost-effective photocatalysts is imperative for the advancement of solar-driven hydrogen generation. Copper slag, a by-product stemming from copper smelting and refining processes, primarily consists of metal oxides like hematite, silica, and alumina. This composition renders it a compelling secondary resource for deployment as a photocatalyst, thereby diverting copper slag from landfills. This review aims to comprehensively examine copper slag as a photocatalytic material, exploring its chemical, physical, photocatalytic, and electrochemical properties. Furthermore, it assesses its suitability for water treatment and its potential as an emerging material for large-scale solar hydrogen production.

Solar saline water splitting is a promising approach for sustainable hydrogen production, harnessing abundant solar energy and the availability of brine resources, especially in the Atacama Desert. Bischofite (MgCl2·6H2O), a naturally occurring salt, has garnered significant attention due to its wide range of industrial applications and the challenges associated with efficient hydrogen production in arid or hyper arid locations. This work discloses the electrochemical performance of Mn2O3 electrodes using a superposition model based on mixed potential theory and evaluates the superficial performance of this electrode in contact with a 0.5 M nature Bischofite salt solution focusing on hydrogen evolution reaction (HER), and oxygen reduction reaction (ORR), that occurs during saline water splitting. The application of the non-linear superposition model provides valuable electrochemical kinetic parameters that complement the understanding of Mn2O3, this being one of the novelties of the manuscript.

The presence of clays in copper minerals has a significant negative impact during their processing, leading to low recoveries during the flotation process. In saline environments such as seawater, the presence of clays contribute to an increase in operational problems caused by salinity, a decrease in the grade of the copper concentrate, alteration of the rheology of the mineral pulp, a decrease in the selectivity of copper during the flotation process, a reduction in the quality of clarified water, and excessive corrosion of metallic components. This study explores the electroflotation of kaolinite and montmorillonite clays in NaCl solutions using a modified Hallimond tube coupled with Ti Gr. 2 electrodes for bubble generation via water electrolysis, and the corrosion analysis of these electrodes applying the superposition model. The electroflotation results show recovery of clays close to 72.68% for kaolinite, 88.44% for montmorillonite, and 67.36% for a mixtures of both clays. The presence of clays helps reduce the corrosive effects of Ti Gr. 2 from 0.069 A/m2 in NaCl to 0.0073 A/m2 in NaCl with montmorillonite clay.