This paper presents findings from a spaceborne Earth observation experiment utilizing a novel, ultra-compact hyperspectral imaging camera aboard a 3U CubeSat. Leveraging the Offner optical scheme, the camera's hyperspectrometer captures hyperspectral images of terrestrial regions with a 200 m spatial resolution and 12 nanometer spectral resolution across a 400 to 1000 nanometer wavelength range, covering 150 channels in the visible and near-infrared spectrums. The hyperspectrometer is specifically designed for deployment on a 3U CubeSat nanosatellite platform, featuring a robust all-metal cylindrical body of the hyperspectrometer, and a coaxial arrangement of the optical elements ensures optimal compactness and vibration stability. The performance of the imaging hyperspectrometer was rigorously evaluated through numerical simulations prior to construction. Analysis of hyperspectral data acquired over a year-long orbital operation demonstrates the 3U CubeSat's ability to produce various vegetation indices, including the normalized difference vegetation index (NDVI). A comparative study with the European Space Agency's Sentinel-2 L2A data shows a strong agreement at critical points, confirming the 3U CubeSat's suitability for hyperspectral imaging in the visible and near-infrared spectrums. Notably, the ISOI 3U CubeSat can generate unique index images beyond the reach of Sentinel-2 L2A, underscoring its potential for advancing remote sensing applications.
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http://dx.doi.org/10.3390/s24092885 | DOI Listing |
Sci Rep
January 2025
Department of Forest Engineering, Faculty of Forestry, Kastamonu University, Kastamonu, Türkiye, Turkey.
Rapid urban growth is a subject of worldwide interest due to environmental problems. Population growth, especially migration from rural to urban areas, leads to land use and land cover (LULCC) changes in urban centres. Therefore, LULCC and urban growth analyses are among the studies that will help decision-makers achieve better sustainable management and planning.
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January 2025
Computer Vision Center, Universitat Autònoma de Barcelona, Barcelona, 08193, Spain.
In this study, we explore an enhancement to the U-Net architecture by integrating SK-ResNeXt as the encoder for Land Cover Classification (LCC) tasks using Multispectral Imaging (MSI). SK-ResNeXt introduces cardinality and adaptive kernel sizes, allowing U-Net to better capture multi-scale features and adjust more effectively to variations in spatial resolution, thereby enhancing the model's ability to segment complex land cover types. We evaluate this approach using the Five-Billion-Pixels dataset, composed of 150 large-scale RGB-NIR images and over 5 billion labeled pixels across 24 categories.
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January 2025
State Key Laboratory of Pathogen and Biosecurity, Academy of Military Medical Sciences, Beijing, China.
The relative contributions of mutation rate variation, selection, and recombination in shaping genomic variation in bacterial populations remain poorly understood. Here we analyze 3318 Yersinia pestis genomes, spanning nearly a century and including 2336 newly sequenced strains, to shed light on the patterns of genetic diversity and variation distribution at the population level. We identify 45 genomic regions ("hot regions", HRs) that, although comprising a minor fraction of the genome, are hotbeds of genetic variation.
View Article and Find Full Text PDFCurr Opin Plant Biol
January 2025
Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR 97331, USA.
Plant diseases constantly threaten crops and food systems, while global connectivity further increases the risks of spreading existing and exotic pathogens. Here, we first explore how an integrative approach involving plant pathway knowledgegraphs, differential gene expression data, and biochemical data informing Raman spectroscopy could be used to detect plant pathways responding to pathogen attacks. The Plant Reactome (https://plantreactome.
View Article and Find Full Text PDFEnviron Monit Assess
January 2025
College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing, China.
Exploring the response relationship between civil war, population and land cover change is of great practical significance for social stability in Myanmar. However, the ongoing civil war in Myanmar hinders direct understanding of the situation on the ground, which in turn limits detailed study of the intricate relationship between the dynamics of the civil war and its impact on population and land. Therefore, this paper explores the response relationship between civil war conflict and population and land cover change in Myanmar from 2010 to 2020 from the perspective of remote sensing using the land cover data we produced, the open spatial demographics data, and the armed conflict location and event data project.
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