Phenological responses of vegetation to climate, in particular to the ongoing warming trend, have received much attention. However, divergent results from the analyses of remote sensing data have been obtained for the Tibetan Plateau (TP), the world's largest high-elevation region. This study provides a perspective on vegetation phenology shifts during 1960-2014, gained using an innovative approach based on a well-validated, process-based, tree-ring growth model that is independent of temporal changes in technical properties and image quality of remote sensing products. Twenty composite site chronologies were analyzed, comprising about 3,000 trees from forested areas across the TP. We found that the start of the growing season (SOS) has advanced, on average, by 0.28 d/y over the period 1960-2014. The end of the growing season (EOS) has been delayed, by an estimated 0.33 d/y during 1982-2014. No significant changes in SOS or EOS were observed during 1960-1981. April-June and August-September minimum temperatures are the main climatic drivers for SOS and EOS, respectively. An increase of 1 °C in April-June minimum temperature shifted the dates of xylem phenology by 6 to 7 d, lengthening the period of tree-ring formation. This study extends the chronology of TP phenology farther back in time and reconciles the disparate views on SOS derived from remote sensing data. Scaling up this analysis may improve understanding of climate change effects and related phenological and plant productivity on a global scale.
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http://dx.doi.org/10.1073/pnas.1616608114 | DOI Listing |
PLoS One
January 2025
North China Institute of Aerospace Engineering, Langfang, China.
As the global economy expands, waterway transportation has become increasingly crucial to the logistics sector. This growth presents both significant challenges and opportunities for enhancing the accuracy of ship detection and tracking through the application of artificial intelligence. This article introduces a multi-object tracking system designed for unmanned aerial vehicles (UAVs), utilizing the YOLOv7 and Deep SORT algorithms for detection and tracking, respectively.
View Article and Find Full Text PDFAstrobiology
January 2025
School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia, USA.
Exploration missions to Mars rely on landers or rovers to perform multiple analyses over geographically small sampling regions, while landing site selection is done using large-scale but low-resolution remote-sensing data. Utilizing Earth analog environments to estimate small-scale spatial and temporal variation in key geochemical signatures and biosignatures will help mission designers ensure future sampling strategies meet mission science goals. Icelandic lava fields can serve as Mars analog sites due to conditions that include low nutrient availability, temperature extremes, desiccation, and isolation from anthropogenic contamination.
View Article and Find Full Text PDFEnviron Monit Assess
January 2025
Department of Landscape Architecture, Remote Sensing and GIS Laboratory, University of Cukurova, Adana, 01330, Turkey.
Recent advancements in satellite technology have greatly expanded data acquisition capabilities, making satellite imagery more accessible. Despite these strides, unlocking the full potential of satellite images necessitates efficient interpretation. Image classification, a widely adopted for extracting valuable information, has seen a surge in the application of deep learning methodologies due to their effectiveness.
View Article and Find Full Text PDFEnviron Monit Assess
January 2025
Laboratory for Ecotoxicology and Environmental Forensics, University of Benin, PMB 1154, Benin City, Nigeria.
This research was carried out to assess the concentrations of carbon monoxide (CO) and formaldehyde (HCHO) in Edo State, Southern Nigeria, using remote sensing data. A secondary data collection method was used for the assessment, and the levels of CO and HCHO were extracted annually from Google Earth Engine using information from Sentinel-5-P satellite data (COPERNISCUS/S5P/NRTI/L3_) and processed using ArcMap, Google Earth Engine, and Microsoft Excel to determine the levels of CO and HCHO in the study area from 2018 to 2023. The geometry of the study location is highlighted, saved and run, and a raster imagery file of the study area is generated after the task has been completed with a 'projection and extent' in the Geographic Tagged Image File Format (.
View Article and Find Full Text PDFEnviron Monit Assess
January 2025
Department of Geography, The University of Hong Kong, Pokfulam Road, Hong Kong, 999077, China.
Excessive total suspended matter (TSM) concentrations can exert a considerable impact on the growth of aquatic organisms in fishponds, representing a significant risk to aquaculture health. This study revised existing unified models using empirical data to develop an optimized TSM retrieval model tailored for the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) (R = 0.69, RMSE = 7.
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