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This paper introduces a novel framework for estimating the spectral power distribution of daylight illuminants in uncalibrated hyperspectral images, particularly beneficial for drone-based applications in agriculture and forestry. The proposed method uniquely combines image-dependent plausible spectra with a database of physically possible spectra, utilizing an image-independent principal component space (PCS) for estimations. This approach effectively narrows the search space in the spectral domain and employs a random walk methodology to generate spectral candidates, which are then intersected with a pre-trained PCS to predict the illuminant. We demonstrate superior performance compared to existing statistics-based methods across various metrics, validating the framework's efficacy in accurately estimating illuminants and recovering reflectance values from radiance data. The method is validated within the spectral range of 382-1002 nm and shows potential for extension to broader spectral ranges.
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Animal movement behaviours are shaped by diverse factors, including resource availability and human impacts on the landscape. We generated home range estimates and daily movement rate estimates for 149 giraffe (Giraffa spp.) from all four species across Africa to evaluate the effects of environmental productivity and anthropogenic disturbance on space use. Using the continuous time movement modelling framework and a novel application of mixed effects meta-regression, we summarized overall giraffe space use and tested for the effects of resource availability and human impact on 95% autocorrelated kernel density estimate (AKDE) size and daily movement. The mean 95% AKDE was 359.9 km2 and the mean daily movement was 14.2 km, both with marginally significant differences across species. We found significant negative effects of resource availability, and significant positive effects of resource heterogeneity and protected area overlap on 95% AKDE size. There were significant negative effects of overall anthropogenic disturbance and positive effects of the heterogeneity of anthropogenic disturbance on daily movements and 95% AKDE size. Our results provide unique insights into the interactive effects of resource availability and anthropogenic development on the movements of a large-bodied browser and highlight the potential impacts of rapidly changing landscapes on animal space-use patterns.
Assuntos
Ecossistema , Girafas , Humanos , Animais , Efeitos Antropogênicos , Movimento , ÁfricaRESUMO
Drought in Australia has widespread impacts on agriculture and ecosystems. Satellite-based Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) has great potential to monitor and assess drought impacts on vegetation greenness and health. Various FAPAR products based on satellite observations have been generated and made available to the public. However, differences remain among these datasets due to different retrieval methodologies and assumptions. The Quality Assurance for Essential Climate Variables (QA4ECV) project recently developed a quality assurance framework to provide understandable and traceable quality information for Essential Climate Variables (ECVs). The QA4ECV FAPAR is one of these ECVs. The aim of this study is to investigate the capability of QA4ECV FAPAR for drought monitoring in Australia. Through spatial and temporal comparison and correlation analysis with widely used Moderate Resolution Imaging Spectroradiometer (MODIS), Satellite Pour l'Observation de la Terre (SPOT)/PROBA-V FAPAR generated by Copernicus Global Land Service (CGLS), and the Standardized Precipitation Evapotranspiration Index (SPEI) drought index, as well as the European Space Agency's Climate Change Initiative (ESA CCI) soil moisture, the study shows that the QA4ECV FAPAR can support agricultural drought monitoring and assessment in Australia. The traceable and reliable uncertainties associated with the QA4ECV FAPAR provide valuable information for applications that use the QA4ECV FAPAR dataset in the future.
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OBJECTIVE: To perform a systematic review and meta-analysis comparing overall prostate cancer detection rate and clinically-significant prostate cancer detection rate between MRI-ultrasound image guided fusion biopsy (MRI-US FB) and cognitive biopsy (CB). METHODS: A systematic review of Pubmed, EMBASE, MEDLINE, and Cochrane library databases was performed. Identified studies were assessed for clinical relevance and excluded based on a set of predefined criteria. Final articles included in the analysis comprised only prospective trials that compared CB vs. MRI-US FB in men with MRI-identifiable lesions (Prostate Imaging Reporting and Data System score 2+). Articles were reviewed for patient demographics, MRI protocol, and rates of overall and clinically significant prostate cancer detection by both modalities. RESULTS: Nine studies were analyzed. A composite 1,714 men with mean age 64.6 years and mean PSA 8.2 ng/dL were reviewed. When comparing FB to CB, the odds ratio for overall and for clinically significant prostate cancer detection was 1.11 (95%CI 0.91-1.36, Pâ¯=â¯0.30) and 1.13 (95%CI 0.89-1.44, Pâ¯=â¯0.32), respectively. Heterogeneity among the studies was moderate but not significant for either overall (X2â¯=â¯14.67; I2â¯=â¯45%; Pâ¯=â¯0.07) or clinically significant prostate cancer detection (X2â¯=â¯11.81; I2â¯=â¯49%; Pâ¯=â¯0.07). CONCLUSION: MRI-US FB demonstrates a trend toward improved rates of prostate cancer detection compared to CB, although this is not statistically significant. Further comparative studies may help to further elucidate whether one of these modalities is superior over the other.