Significance: The southern Amazon is one of the fastest changing places on Earth. Deforestation is giving place to a dynamic and diverse landscape, comprising small-scale farmers and large-scale commercial agriculture with differing land uses. Understanding how these different land uses affect ecosystems and local climates is essential for promoting strategies to mitigate environmental changes. Here, we show that large-scale commercial agriculture leads to a higher increase in surface temperature, in comparison with small-scale farms. We also found evidence that changes in land surface attributes over large commercial farms lead to a more prominent reduction in rainfall volumes. Our results provide compelling arguments indicating that changes in farming practices are needed to guaranty a sustainable future in the Amazon region.

Temperature is a fundamental driver of species distribution and ecosystem functioning. Yet, our knowledge of the microclimatic conditions experienced by organisms inside tropical forests remains limited. This is because ecological studies often rely on coarse-gridded temperature estimates representing the conditions at 2 m height in an open-air environment (i.e., macroclimate). In this study, we present a high-resolution pantropical estimate of near-ground (15 cm above the surface) temperatures inside forests. We quantify diurnal and seasonal variability, thus revealing both spatial and temporal microclimate patterns. We find that on average, understory near-ground temperatures are 1.6 °C cooler than the open-air temperatures. The diurnal temperature range is on average 1.7 °C lower inside the forests, in comparison to open-air conditions. More importantly, we demonstrate a substantial spatial variability in the microclimate characteristics of tropical forests. This variability is regulated by a combination of large-scale climate conditions, vegetation structure and topography, and hence could not be captured by existing macroclimate grids. Our results thus contribute to quantifying the actual thermal ranges experienced by organisms inside tropical forests and provide new insights into how these limits may be affected by climate change and ecosystem disturbances.

Taita Taveta County (TTC) is one of the world’s biodiversity hotspots in the highlands with some of the world’s megafaunas in the lowlands. Detailed mapping of the terrestrial ecosystem of the whole county is of global significance for biodiversity conservation. Here, we present a land cover map for 2020 based on satellite observations, a machine learning algorithm, and a reference database for accuracy assessment. For the land cover map production processing chain, temporal metrics from Sentinel-1 and Sentinel-2 (such as median, quantiles, and interquartile range), vegetation indices from Sentinel-2 (normalized difference vegetation index, tasseled cap greenness, and tasseled cap wetness), topographic metrics (elevation, slope, and aspect), and mean annual rainfall were used as predictors in the gradient tree boost classification model. Reference sample points which were collected in the field were used to guide the collection of additional reference sample points based on high spatial resolution imagery for training and validation of the model. The accuracy of the land cover map and uncertainty of area estimates at 95% confidence interval were assessed using sample-based statistical inference. The land cover map has an overall accuracy of 81 ± 2.3% and it is freely accessible for land use planners, conservation managers, and researchers.