Tropical rain forests are suffering the highest deforestation and reforestation ever recorded. Interactions between direct (proximate or direct causes) and indirect (underling or indirect causes) drivers could cluster these forest cover changes forming hotspots (areas that exhibit significant spatial correlation of deforestation or reforestation transitions). Using land use–land cover maps and global (I) and local (I_i) Moran's tests, we identified these hotspots in the Chocó‐Darien Global Ecoregion (CGE) of South America, a natural region that was declared one of the top 25 hotspots for conservation priorities in the world. Subsequently, we tested and studied the effects and interactions between deforestation and reforestation hotspots and their direct and indirect drivers using Bayesian Structural Equation Modeling (Bayesian SEM). We found that deforestation and reforestation were spatially auto‐correlated forming hotspots (I = 0.49,P = 0.001 for deforestation transitions andI = 0.48,P = 0.001 for reforestation transitions). Also, hotspots of deforestation and reforestation were auto‐correlated within municipality borders (I = 0.5,P = 0.001 for deforestation transitions;I = 0.49,P = 0.001 for reforestation transitions). Eighteen municipalities located on the border between Colombia and Ecuador showed significant aggregations of deforestation hotspots, while thirty‐four municipalities in three areas of Colombia and the area between the Colombian and Ecuadorian border showed significant clustering of reforestation hotspots. Eleven of these municipalities presented significant clustering of both reforestation and deforestation hotspots. The Bayesian SEM for deforestation showed that population growth and road density were indirect drivers of deforestation hotspots (0.191 and 0.127 standard deviation units). The Bayesian SEM for reforestation found that armed conflicts, Gross Domestic Product, and average annual rain were indirect drivers related to reforestation hotspots (0.228, 0.076, and 0.081 standard deviation units, respectively). Our assessment shows a novel methodology to study interactions among direct and indirect drivers of forest change and their potential dissimilar effects on forest transitions.

Climate change is a global challenge that impacts rangeland and pastureland landscapes by inducing shifts in temperature variability, precipitation patterns, and extreme weather events. These changes alter soil and plant conditions, reducing forage availability and chemical composition and leading to nutritional stress in cattle. This stress occurs when animals lack adequate water and feed sources or when these resources are insufficient in quantity, composition, or nutrient balance. Several strategies are essential to address these impacts. Genetic selection, epigenetic biomarkers, and exploration of epigenetic memories present promising avenues for enhancing the resilience of cattle populations and improving adaptation to environmental stresses. Remote sensing and GIS technologies assist in locating wet spots to establish islands of plant diversity and high forage quality for grazing amid ongoing climate change challenges. Establishing islands of functional plant diversity improves forage quality, reduces carbon and nitrogen footprints, and provides essential nutrients and bioactives, thus enhancing cattle health, welfare, and productivity. Real-time GPS collars coupled with accelerometers provide detailed data on cattle movement and activity, aiding livestock nutrition management while mitigating heat stress. Integrating these strategies may offer significant advantages to animals facing a changing world while securing the future of livestock production and the global food system.

The purpose of this study was to identify the ecological effects of drought on the vegetation resources of subsistence agropastoral communities on the Bolivian Altiplano. The objectives of the study were to (1) characterize vegetation cover response during a typical year, and (2) identify vegetation cover type's response to drought using an image differencing change detection technique. A geographical information system (GIS), which included multi-temporal (from 1972-1987) Landsat satellite imagery, was used as the assessment tool. Vegetation index difference images showed that (1) all vegetation types were impacted by drought, but the wet meadow vegetation cover type had the least response, and (2) approximately 90 per cent of vegetation cover had not changed between 1972 and 1987. Crop and animal production in agropastoral systems are dependant on the availability of vegetation resources. The wet meadow vegetation type was the most resistant to drought, which suggests that during drought periods it is a key resource upon which the livestock of the community would depend. Little change in vegetation cover over the 15-year span of the study, and the rapid recovery of this parameter following the drought of 1983-84, suggest that agricultural practices (especially livestock grazing) are not contributing to resource degradation when measured only in terms of change in vegetation cover.

The water balance in a watershed can be disrupted by forest disturbances such as harvests and fires. Techniques to accurately and efficiently map forest cover changes due to disturbance are evolving quickly, and it is of interest to ask how useful maps of different types of disturbances over time can be in the prediction of water yield. We assessed the benefits of using land cover maps produced at annual vs. five-year intervals in the prediction of monthly streamflows across 10 watersheds contained entirely within the US National Forest System. We found that annually updating land cover maps with forest disturbance data significantly improved water yield predictions using the Soil and Water Assessment Tool (SWAT; p < 0.01 improvement for both the Nash–Sutcliffe efficiency measure and the ratio of the root mean square error to the standard deviation of the measured data). Improvement related to using annually updated land cover maps was directly related to the amount of disturbance observed in a watershed. Our results lay a foundation to apply new high-resolution disturbance datasets in the field of hydrologic modeling to monitor ungauged watersheds and to explore potential water yield changes in watersheds if climate conditions or management practices were to change forest disturbance processes.

The daily images produced by the MODIS (Moderate Resolution Imaging Spectroradiometer) sensor aboard the Terra and Aqua satellites have been widely used to monitor global vegetation. Using these data, the Earth Observing System operated by US National Aeronautics and Space Administration (NASA) has developed a variety of MODIS products focused on the monitoring and evaluation of vegetation condition. These products have three possible sources of variation that can affect the sensitivity of vegetation detection: 1) orbital and mechanical differences between MODIS sensors aboard Aqua or Terra, 2) the preprocessing algorithms used to generate multitemporal cloud-free mosaics (MAIAC or original MODIS algorithm), and/or 3) post-processing algorithms applied by users to optimize vegetation index values derived from temporal sequences of imagery. We evaluated these sources of variation by comparing the results of a vegetation classification for two different ecoregions. The accuracies of vegetation classifications utilizing either the Aqua or Terra MODIS sensors, the MAIAC or original MODIS preprocessing algorithms, and two common post-processing techniques (Asymmetric Gaussian or Savitzky and Golay function) were compared to determine which set of techniques or sensors yielded the best results. The ecoregions we chose to use were the Great Basin of North America and Chocó-Darien of South America. We compared four different MODIS data products (MOD13Q1, MYD13Q1, MOD09Q1, and MYD09Q1) as predictor variables using Random Forest as the classification algorithm to generate a land cover map. We found that the accuracy of the vegetation classifications (using Kappa as measure of accuracy) changed significantly depending on the MODIS platform (Terra or Aqua), the preprocessing algorithm (MAIAC or MODIS), and the two postprocessing algorithms for both ecoregions. Our result suggests that comparative analyses are needed to optimize the results when equivalent MODIS products are used in vegetation detection and classification.