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Rapid land cover classification using a 36-year time series of multi-source remote sensing data
Get PDFLong-time series land cover classification information is the basis for scientific research on urban sprawl, vegetation change, and the carbon cycle. The rapid development of cloud computing platforms such as the Google Earth Engine (GEE) and access to multi-source satellite imagery from Landsat and Sentinel-2 enables the application of machine learning algorithms for image classification. Here, we used the Random Forest algorithm to quickly achieve a time series land cover classification at different scales based on the fixed land classification sample points selected from images acquired in 2022, and the year-by-year spectral differences of sample points. The classification accuracy was enhanced by using multi-source remote sensing data, such as synthetic aperture radar (SAR) and digital elevation model (DEM) data. The results showed that: (i) the maximum difference (threshold) of sample points without land class change determined by counting the sample points of each band of landsat time series from 1986 to 2022 was 0.25; (ii) the kappa coefficient and observed accuracy of the same sensor from Landsat 8 are higher than the results of TM and ETM+ sensor data from 2013 to 2022; (iii) the addition of a mining land cover type increase the kappa coefficient and overall accuracy mean values of the Sentinel 2 image classification for a complex mining and -forest area. Among the land classifications by multi-source remote sensing, the combined variables spectral band + index + topography + SAR result in the highest accuracy, but the overall improvement is limited. The method proposed is applicable to remotely sensed images at different scales and using sensors under complex terrain conditions. The use of GEE cloud computing platform enabled rapid analysis of remotely sensed data to produce land cover maps with high-accuracy and a long time series
Heterogeneous microgeographic genetic structure of the common cockle (Cerastoderma edule) in the Northeast Atlantic Ocean: biogeographic barriers and environmental factors
Get PDFKnowledge of genetic structure at the finest level is essential for the conservation of genetic resources. Despite no visible barriers limiting gene flow, significant genetic structure has been shown in marine species. The common cockle (Cerastoderma edule) is a bivalve of great commercial and ecological value inhabiting the Northeast Atlantic Ocean. Previous population genomics studies demonstrated significant structure both across the Northeast Atlantic, but also within small geographic areas, highlighting the need to investigate fine-scale structuring. Here, we analysed two geographic areas that could represent opposite models of structure for the species: (1) the SW British Isles region, highly fragmented due to biogeographic barriers, and (2) Galicia (NW Spain), a putative homogeneous region. A total of 9250 SNPs genotyped by 2b-RAD on 599 individuals from 22 natural beds were used for the analysis. The entire SNP dataset mostly confirmed previous observations related to genetic diversity and differentiation; however, neutral and divergent SNP outlier datasets enabled disentangling physical barriers from abiotic environmental factors structuring both regions. While Galicia showed a homogeneous structure, the SW British Isles region was split into four reliable genetic regions related to oceanographic features and abiotic factors, such as sea surface salinity and temperature. The information gathered supports specific management policies of cockle resources in SW British and Galician regions also considering their particular socio-economic characteristics; further, these new data will be added to those recently reported in the Northeast Atlantic to define sustainable management actions across the whole distribution range of the species
Thermal sensitivity across forest vertical profiles: patterns, mechanisms, and ecological implications
Get PDFRising temperatures are influencing forests on many scales, with potentially strong variation vertically across forest strata. Using published research and new analyses, we evaluate how microclimate and leaf temperatures, traits, and gas exchange vary vertically in forests, shaping tree, and ecosystem ecology. In closed-canopy forests, upper canopy leaves are exposed to the highest solar radiation and evaporative demand, which can elevate leaf temperature (Tleaf), particularly when transpirational cooling is curtailed by limited stomatal conductance. However, foliar traits also vary across height or light gradients, partially mitigating and protecting against the elevation of upper canopy Tleaf. Leaf metabolism generally increases with height across the vertical gradient, yet differences in thermal sensitivity across the gradient appear modest. Scaling from leaves to trees, canopy trees have higher absolute metabolic capacity and growth, yet are more vulnerable to drought and damaging Tleaf than their smaller counterparts, particularly under climate change. By contrast, understory trees experience fewer extreme high Tleaf's but have fewer cooling mechanisms and thus may be strongly impacted by warming under some conditions, particularly when exposed to a harsher microenvironment through canopy disturbance. As the climate changes, integrating the patterns and mechanisms reviewed here into models will be critical to forecasting forest–climate feedback
Predicting the spatial expansion of an animal population with presence-only data
Get PDFPredictive models can improve the efficiency of wildlife management by guiding actions at the local, landscape and regional scales. In recent decades, a vast range of modelling techniques have been developed to predict species distributions and patterns of population spread. However, data limitations often constrain the precision and biological realism of models, which make them less useful for supporting decision-making. Complex models can also be challenging to evaluate, and the results are often difficult to interpret for wildlife management practitioners. There is therefore a need to develop techniques that are appropriately robust, but also accessible to a range of end users. We developed a hybrid species distribution model that utilises commonly available presence-only distribution data and minimal demographic information to predict the spread of roe deer ( Capreolus caprelous) in Great Britain. We take a novel approach to representing the environment in the model by constraining the size of habitat patches to the home-range area of an individual. Population dynamics are then simplified to a set of generic rules describing patch occupancy. The model is constructed and evaluated using data from a populated region (England and Scotland) and applied to predict regional-scale patterns of spread in a novel region (Wales). It is used to forecast the relative timing of colonisation events and identify important areas for targeted surveillance and management. The study demonstrates the utility of presence-only data for predicting the spread of animal species and describes a method of reducing model complexity while retaining important environmental detail and biological realism. Our modelling approach provides a much-needed opportunity for users without specialist expertise in computer coding to leverage limited data and make robust, easily interpretable predictions of spread to inform proactive population management. </p
Utilisation and transformation of organic and inorganic nitrogen by soil microorganisms and its regulation by excessive carbon and nitrogen availability
Get PDFThe process of nitrogen (N) transformation after microbial utilisation of organic and inorganic N is unclear. 15N-glycine (Gly), 15NH4+ and 15NO3− were used to investigate the uptake, release and reutilisation of N by microorganisms over 9 days. In addition, high amounts of unlabelled carbon (C) or N were added to explore how C or N availability affects the cycling of inorganic and organic N by microorganisms. Within 15 min, 67% of the added 15N-Gly was taken up by soil microorganisms; within 1 h, 8% was released as NH4+. The released 15NH4+ was reutilised by the microorganisms within hours. Microorganisms took up 50% of the added 15NH4+ (15 min) and released 13% of the taken up NH4+ (1 h). Microorganisms prefer to take up Gly rather than NH4+ because they can directly acquire C from Gly for maintaining its growth and synthesising more complex compounds. NO3− was taken up by microorganisms within minutes but not released into the soil. NO3− was likely stored in the cytoplasm, to be used as an N source to face future N-deficient environments. When high concentrations of C or N were added, the assimilation of Gly and NH4+ increased, whereas N mineralisation and nitrification rates decreased, and the uptake of NO3− remained stable. Overall, Gly and NH4+ were taken up, released and re-taken up by microorganisms and were preferentially utilised under excess C or N sources, while NO3− was stored in the microbiome. These findings provide new insights into N uptake by microorganisms in short-term
gVirtualXray (gVXR): Simulating X-ray radiographs and CT volumes of anthropomorphic phantoms
Get PDFgVirtualXray (gVXR) is an open-source framework that relies on the Beer-Lambert law to simulate X-ray images in realtime on a graphics processor unit (GPU) using triangular meshes. We produced four Jupyter Notebooks to compare images simulated with gVXR and ground truth image of an anthropomorphic phantom: (i) an X-ray projection generated using a Monte Carlo simulation code, (ii) real digitally reconstructed radiographs (DRRs), (iii) computed tomography (CT) slices, and (iv) a real radiograph acquired with a clinical X-ray imaging system. Image registration was deployed in two Notebooks to align the simulated image on the corresponding ground truth image. We demonstrated that accurate images can be generated in milliseconds with gVirtualXray when scattering can be ignored.<br/