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We describe changes in the cropland distribution for physiographic and bioregions of continental Ecuador between 2000 and 2016 using Landsat satellite data and government statistics. The cloudy conditions in Ecuador are a major constraint to satellite data analysis. We developed a two-stage cloud filtering algorithm to create cloud-free multi-temporal Landsat composites that were used in a Random Forest model to identify cropland. The overall accuracy of the model was 78% for the Coast region, 86% for the Andes, and 98% for the Amazon region. Cropland density was highest in the coastal lowlands and in the Andes between 2500 and 4400 m. During this period, cropland expansion was most pronounced in the Páramo, Chocó Tropical Rainforests, and Western Montane bioregions. There was no cropland expansion detected in the Eastern Foothill forests bioregion. The satellite data analysis further showed a small contraction of cropland (4%) in the Coast physiographic region, and cropland expansion in the Andes region (15%), especially above 3500m, and in the Amazon region (57%) between 2000 and 2016. The government data showed a similar contraction for the Coast (7%) but, in contrast with the satellite data, they showed a large agricultural contraction in the Andes (39%) and Amazon (50%). While the satellite data may be better at estimating relative change (trends), the government data may provide more accurate absolute numbers in some regions, especially the Amazon because separating pasture and tree crops from forest with satellite data is challenging. These discrepancies illustrate the need for careful evaluation and comparison of data from different sources when analyzing land use change.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10508625 | PMC |
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0291753 | PLOS |
Nat Plants
September 2025
Guangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Cycle, School of Atmospheric Sciences, School of Ecology, Sun Yat-sen University, Zhuhai, China.
Increasing leaf area and extending vegetation growing seasons are two primary drivers of global greening, which has emerged as one of the most significant responses to climate change. However, it remains unclear how these two leaf acclimation strategies would vary across forests at a large spatial scale. Here, using multiple satellite-based datasets and field measurements, we analysed the temporal changes (Δ) in maximal leaf area index (LAI) and length of the growing season (LOS) from 2002 to 2021 across deciduous broadleaf forests (DBFs) in the middle to high latitudes.
View Article and Find Full Text PDFJ Ethnopharmacol
September 2025
Department of Pharmaceutics and Pharmaceutical Technology, Usmanu Danfodiyo University, Sokoto.
Ethnopharmacological Relevance: Moringa oleifera L. is widely used in Traditional Medicine across Africa and Asia for managing inflammation, infections, diabetes, and malnutrition. Although its aqueous and ethanolic extracts have been extensively studied, little is known about the safety of its non-polar (hexane) fraction, which may contain unique bioactive compounds.
View Article and Find Full Text PDFJ Hepatol
July 2025
Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany; Department of Medicine I, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, 01307 Dresden, Germany; Medical Oncology, National Center for Tumor Disease
Artificial intelligence (AI) methods in hepatology have proliferated since the mid-2010s, with numerous publications and some regulatory approvals. Yet, adoption of AI methods in real-world clinical practice and clinical research remains limited. Despite clear benefits of using AI to analyze complex data types in hepatology, such as histopathology, radiology images, multi-omics and more recently, natural language patient data, there are still substantial barriers and challenges to its integration into routine clinical workflows.
View Article and Find Full Text PDFSci Total Environ
September 2025
Department of Geological Sciences and Geological Engineering, Queen's University, 99 University Ave, K7L 3N6 Kingston, Ontario, Canada.
Hyperspectral data have been overshadowed by multispectral data for studying algal blooms for decades. However, newer hyperspectral missions, including the recent Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) Ocean Color Instrument (OCI), are opening the doors to accessible hyperspectral data, at spatial and temporal resolutions comparable to ocean color and multispectral missions. Simulation studies can help to understand the potential of these hyperspectral sensors prior to launch and without extensive field data collection.
View Article and Find Full Text PDFSci Total Environ
September 2025
European Commission, Joint Research Centre (JRC), Ispra, Italy. Electronic address:
Drought stress has profound impacts on ecosystems and societies, particularly in the context of climate change. Traditional drought indicators, which often rely on integrated water budget anomalies at various time scales, provide valuable insights but often fail to deliver clear, real-time assessments of vegetation stress. This study introduces the Cooling Efficiency Factor Index (CEFI), a novel metric purely derived from geostationary satellite observations, to detect vegetation drought stress by analyzing daytime surface warming anomalies.
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