Miracle-dz/UniFAR-FTUs huggingface dataset:
Selective Feeding by a Predatory Sea Star Across a Depth Gradient in Northern Patagonia, Chile

Sea stars often function as keystone predators in food webs of intertidal and subtidal communities, especially in temperate and sub-polar regions. In South America the sea star Cosmasterias lurida is distributed along both the Atlantic and Pacific coasts of Patagonia and is one of the most conspicuous and abundant benthic predators in the shallow subtidal zone (<25 m). Its feeding strategy and prey selection are, however, still poorly known. This study describes the feeding behavior of C. lurida at a site in the Seno del Reloncaví (Chile), assessing its abundance, size and prey selection in the field relative to observed prey abundance and size along a bathymetric gradient. We hypothesized that C. lurida is a generalist predator, feeding on suitable prey according to their availability. However, we found that this predator only consumed a limited number (7 of 48) of potential prey species, primarily the slipper limpets Crepipatella spp. and the mussels Aulacomya ater and Mytilus chilensis. Electivity analysis revealed a clear preference for one mussel (A. ater) but not the other (M. chilensis) as well as depth-dependent selectivity for the slipper limpets, which changed from avoidance to preference with increasing depth. Sea star densities varied with depth, peaking between depths of 5 and 10 m, but the size of sea stars and the size of their prey did not vary significantly along a depth gradient. No significant correlations were found with the most commonly selected prey. These results would indicate that while this predator may be a generalist–opportunist, its feeding behavior is context-dependent and its high selectivity for certain species suggests that this sea star plays a key role structuring subtidal benthic communities in Patagonia.
Synthesizing a Regional Territorial Evapotranspiration Dataset for Northern China

As a vital role in the processes of the energy balance and hydrological cycles, actual evapotranspiration (ET) is relevant to many agricultural, ecological and water resource management studies. The available global or regional ET products provide ET estimations with various temporal ranges, spatial resolutions and calculation methods (algorithms, inputs and parameterization, etc.), leading to varying degrees of introduced uncertainty. Northern China is the main agriculturally productive region supporting the whole country; thus, understanding the spatial and temporal changes in ET is essential to ensure water resource and food security. We developed a synthesis ET dataset for Northern China at a 1000 m spatial resolution, with a monthly temporal resolution covering a period ranging from 1982 to 2017, using an in-depth assessment of several ET products. Specifically, assessments were performed using in situ measured ET from eddy covariance (EC) observation towers at the site-pixel scale over interannual months under the conditions of different land cover types, climatic zones and elevation levels to select the most optimally performing ET products to be used in the synthesized ET dataset. Eight indicators under 21 conditions were involved in the assessment sheet, while the statistics of the different ET product occurrences and corresponding ratios were analyzed to select the best-performing ET products to build the synthesis ET dataset using the weighted mean method. The weights were determined by the Taylor skill score (TSS), calculated with ET products and EC ET observation data. Based on the assessment results, the Penman–Monteith–Leuning (PML_v2), ETWatch and Operational Simplified Surface Energy Balance (SSEBop) datasets were selected for implementation in the synthesis ET dataset from 2003 to 2017, while Global Land Evaporation Amsterdam Model (GLEAM) v3.3a, complementary relationship (CR) ET, and Numerical Terradynamic Simulation Group (NTSG) datasets were chosen for the synthesis ET dataset from 1982 to 2002. The weighted mean synthesized results from 2003 to 2017 performed well when compared to the in situ measured EC ET values produced under all of the above conditions, while the synthesized results from 1982 to 2002 performed well through the water balance method in Heihe River Basin. These results can provide more stable ET estimations for Northern China, which can contribute to relevant agricultural, ecological and hydrological studies.
Spatiotemporal Patterns of Ecosystem Restoration Activities and Their Effects on Changes in Terrestrial Gross Primary Production in Southwest China

Large-scale ecosystem restoration projects (ERPs) have been implemented since the beginning of the new millennium to restore vegetation and improve the ecosystem in Southwest China. However, quantifying the effects of specific restoration activities, such as afforestation and grass planting, on vegetation recovery is difficult due to their incommensurable spatiotemporal distribution. Long-term and successive ERP-driven land use/cover changes (LUCCs) were used to recognise the spatiotemporal patterns of major restoration activities, and a contribution index was defined to assess the effects of these activities on gross primary production (GPP) dynamics in Southwest China during the period of 2001–2015. The results were as follows. (1) Afforestation and grass planting were major restoration activities that accounted for more than 54% of all LUCCs in Southwest China. Approximately 96% of restoration activities involved afforestation, and these activities were mostly distributed around Yunnan Province. (2) The Breathing Earth System Simulator (BESS) GPP performed better than the Moderate Resolution Imaging Spectroradiometer (MODIS) GPP validated by field observation data. Nevertheless, their annual GPP trends were similar and increased by 12,581 g C m−2 d−1 and 13,406 g C m−2 d−1 for MODIS and BESS GPPs, respectively. (3) Although the afforestation and grass planting areas accounted for less than 1% of the total area of Southwest China, they contributed to more than 1% of the annual GPP increase in the entire study area. Afforestation directly contributed 14.94% (BESS GPP) or 24.64% (MODIS GPP) to the annual GPP increase. Meanwhile, grass planting directly contributed only 0.41% (BESS GPP) or 0.03% (MODIS GPP) to the annual GPP increase.
Injury epidemiology in professional ballet: a five-season prospective study of 1596 medical attention injuries and 543 time-loss injuries

Objectives To describe the incidence rate, severity, burden and aetiology of medical attention and time-loss injuries across five consecutive seasons at a professional ballet company. Methods Medical attention injuries, time-loss injuries and dance exposure hours of 123 professional ballet dancers (women: n=66, age: 28.0±8.3 years; men: n=57, age: 27.9±8.5 years) were prospectively recorded between the 2015/2016 and 2019/2020 seasons. Results The incidence rate (per 1000 hours) of medical attention injury was 3.9 (95% CI 3.3 to 4.4) for women and 3.1 (95% CI 2.6 to 3.5) for men. The incidence rate (per 1000 hours) of time-loss injury was 1.2 (95% CI 1.0 to 1.5) for women and 1.1 (95% CI 0.9 to 1.3) for men. First Soloists and Principals experienced between 2.0–2.2 additional medical attention injuries per 1000 hours and 0.9–1.1 additional time-loss injuries per 1000 hours compared with Apprentices (p≤0.025). Further, intraseason differences were observed in medical attention, but not time-loss, injury incidence rates with the highest incidence rates in early (August and September) and late (June) season months. Thirty-five per cent of time-loss injuries resulted in over 28 days of modified dance training. A greater percentage of time-loss injuries were classified as overuse (women: 50%; men: 51%) compared with traumatic (women: 40%; men: 41%). Conclusion This is the first study to report the incidence rate of medical attention and time-loss injuries in professional ballet dancers. Incidence rates differed across company ranks and months, which may inform targeted injury prevention strategies.
Comparison of Absorbed and Intercepted Fractions of PAR for Individual Trees Based on Radiative Transfer Model Simulations

The fraction of absorbed photosynthetically active radiation (fAPAR) is a key parameter for estimating the gross primary production (GPP) of trees. For continuous, dense forest canopies, fAPAR, is often equated with the intercepted fraction, fIPAR. This assumption is not valid for individual trees in urban environments or parkland settings where the canopy is sparse and there are well-defined tree crown boundaries. Here, the distinction between fAPAR and fIPAR can be strongly influenced by the background and large illumination variations due to multi-scattering and shadows of buildings. This study investigates the radiative budget of PAR bands using a coupled leaf-canopy radiative transfer model (PROSPECT-DART), considering a suite of tropical tree species over a wide range of assumed leaf chlorophyll contents. The analyses simulate hyperspectral images (5 nm bandwidth) of individual tree crowns for the selected background (concrete vs. grass) and illumination conditions. We then use an artificial neural network-based method to partition sunlit vs. shaded leaves within each crown, as the latter have lower fAPAR and fIPAR values. Our results show fAPAR of sunlit leaves decreases with the ratio of diffuse to direct scene irradiance (SKYL), while SKYL has minimal influence for shaded leaves. Both fAPAR and fIPAR decrease at more oblique solar zenith angles (SZA). Higher values of fAPAR and fIPAR occur with concrete backgrounds and the influence of the background is larger at higher diffuse ratio and solar zenith angles. The results show that fIPAR is typically 6–9% higher than fAPAR, and up to 14% higher for sunlit leaves with a concrete background at SKYL = 0. The differences between the fIPAR and fAPAR also depend on the health condition of the leaves, such as chlorophyll content. This study can improve the understanding of urban individual trees fAPAR/fIPAR and facilitate the development of protocols for fAPAR field measurements.
Risk Factor Detection and Landslide Susceptibility Mapping Using Geo-Detector and Random Forest Models: The 2018 Hokkaido Eastern Iburi Earthquake

Landslide susceptibility mapping is an effective approach for landslide risk prevention and assessments. The occurrence of slope instability is highly correlated with intrinsic variables that contribute to the occurrence of landslides, such as geology, geomorphology, climate, hydrology, etc. However, feature selection of those conditioning factors to constitute datasets with optimal predictive capability effectively and accurately is still an open question. The present study aims to examine further the integration of the selected landslide conditioning factors with Q-statistic in Geo-detector for determining stratification and selection of landslide conditioning factors in landslide risk analysis as to ultimately optimize landslide susceptibility model prediction. The location chosen for the study was Atsuma Town, which suffered from landslides following the Eastern Iburi Earthquake in 2018 in Hokkaido, Japan. A total of 13 conditioning factors were obtained from different sources belonging to six categories: geology, geomorphology, seismology, hydrology, land cover/use and human activity; these were selected to generate the datasets for landslide susceptibility mapping. The original datasets of landslide conditioning factors were analyzed with Q-statistic in Geo-detector to examine their explanatory powers regarding the occurrence of landslides. A Random Forest (RF) model was adopted for landslide susceptibility mapping. Subsequently, four subsets, including the Manually delineated landslide Points with 9 features Dataset (MPD9), the Randomly delineated landslide Points with 9 features Dataset (RPD9), the Manually delineated landslide Points with 13 features Dataset (MPD13), and the Randomly delineated landslide Points with 13 features Dataset (RPD13), were selected by an analysis of Q-statistic for training and validating the Geo-detector-RF- integrated model. Overall, using dataset MPD9, the Geo-detector-RF-integrated model yielded the highest prediction accuracy (89.90%), followed by using dataset MPD13 (89.53%), dataset RPD13 (88.63%) and dataset RPD9 (87.07%), which implied that optimized conditioning factors can effectively improve the prediction accuracy of landslide susceptibility mapping.
Field Scale Assessment of the TsHARP Technique for Thermal Sharpening of MODIS Satellite Images Using VENµS and Sentinel-2-Derived NDVI

Remotely sensed-based surface temperature is an important tool for crop monitoring and has great potential for improving irrigation management. However, current thermal satellite platforms do not display the fine spatial resolution required for identifying crop water status patterns at the field scale. The thermal sharpening (TsHARP) utility provides a technique for downscaling coarse thermal images to match the finer resolution of images acquired in the visible and near infrared bandwidths. This sharpening method is based on the inverse linear relationship between vegetation fraction calculated from the normalized difference vegetation index (NDVI) and land surface temperature (LST). The current study used the TsHARP method to sharpen low-resolution thermal data from the Moderate Resolution Imaging Spectrometer MODIS (1 km) to the finer resolution of Sentinel-2 (10 m) and Vegetation and Environment New micro-Spacecraft (VENµS) (5 m) visible-near infrared images. The sharpening methodology was evaluated at scene and field scales in southern Georgia and northern Mississippi, USA. A comparison of sharpened temperature was made with reference temperatures from Landsat-8 Operational Land Imager (OLI) in four different spatial resolutions (30, 60, 120, and 240 m) for method validation. Coarse resolution comparison on the dates in which imagery from both sensors were acquired on the same day resulted in average observed mean absolute error (MAE) of 1.63 °C, and R2 variation from 0.34 to 0.74. Temperature errors at the field scale ranged from 0.25 to 3.11 °C using both Sentinel-2 and VENµS. Sharpened maps at 120 and 60 m resolution showed the highest consistency for all fields and dates. Maps sharpened using VENµS images showed comparable or higher accuracy than maps sharpened using Sentinel-2. The superior performance coupled with the better revisit time indicates that the VENµS platform has high potential for frequent in-season crop monitoring. Further research with ground data collection is needed to explore field use limitations of this methodology, but these results give useful insights of potential benefits of implementing the TsHARP technique as a tool for crop stress monitoring.
Exploring VIIRS Continuity with MODIS in an Expedited Capability for Monitoring Drought-Related Vegetation Conditions

Vegetation has been effectively monitored using remote sensing time-series vegetation index (VI) data for several decades. Drought monitoring has been a common application with algorithms tuned to capturing anomalous temporal and spatial vegetation patterns. Drought stress models, such as the Vegetation Drought Response Index (VegDRI), often use VIs like the Normalized Difference Vegetation Index (NDVI). The EROS expedited Moderate Resolution Imaging Spectroradiometer (eMODIS)-based, 7-day NDVI composites are integral to the VegDRI. As MODIS satellite platforms (Terra and Aqua) approach mission end, the Visible Infrared Imaging Radiometer Suite (VIIRS) presents an alternate NDVI source, with daily collection, similar band passes, and moderate spatial resolution. This study provides a statistical comparison between EROS expedited VIIRS (eVIIRS) 375-m and eMODIS 250-m and tests the suitability of replacing MODIS NDVI with VIIRS NDVI for drought monitoring and vegetation anomaly detection. For continuity with MODIS NDVI, we calculated a geometric mean regression adjustment algorithm using 375-m resolution for an eMODIS-like NDVI (eVIIRS’) eVIIRS’ = 0.9887 × eVIIRS − 0.0398. The resulting statistical comparisons (eVIIRS’ vs. eMODIS NDVI) showed correlations consistently greater than 0.84 throughout the three years studied. The eVIIRS’ VegDRI results characterized similar drought patterns and hotspots to the eMODIS-based VegDRI, with near zero bias.
A Model to Estimate Leaf Area Index in Loblolly Pine Plantations Using Landsat 5 and 7 Images

Vegetation indices calculated from remotely sensed satellite imagery are commonly used within empirically derived models to estimate leaf area index in loblolly pine plantations in the southeastern United States. The data used to parameterize the models typically come with observation errors, resulting in biased parameters. The objective of this study was to quantify and reduce the effects of observation errors on a leaf area index (LAI) estimation model using imagery from Landsat 5 TM and 7 ETM+ and over 1500 multitemporal measurements from a Li-Cor 2000 Plant Canopy Analyzer. Study data comes from a 16 quarter 1 ha plot with 1667 trees per hectare (2 m × 3 m spacing) fertilization and irrigation research site with re-measurements taken between 1992 and 2004. Using error-in-variable methods, we evaluated multiple vegetation indices, calculated errors associated with their observations, and corrected for them in the modeling process. We found that the normalized difference moisture index provided the best correlation with below canopy LAI measurements (76.4%). A nonlinear model that accounts for the nutritional status of the stand was found to provide the best estimates of LAI, with a root mean square error of 0.418. The analysis in this research provides a more extensive evaluation of common vegetation indices used to estimate LAI in loblolly pine plantations and a modeling framework that extends beyond the typical linear model. The proposed model provides a simple to use form allowing forest practitioners to evaluate LAI development and its uncertainty in historic pine plantations in a spatial and temporal context.
Analysis of the Spatiotemporal Changes in Watershed Landscape Pattern and Its Influencing Factors in Rapidly Urbanizing Areas Using Satellite Data

Analyzing the spatiotemporal characteristics and causes of landscape pattern changes in watersheds around big cities is essential for understanding the ecological consequence of urbanization and provides a basic reference for the watershed management. This study used a land-use transition matrix and landscape indices to explore the spatiotemporal change of land use and landscape pattern over Liuxihe River basin of Guangzhou in the southeast of China from 1980 to 2015 with multitemporal Landsat satellite data in response to the rapid urbanization process. Primary temporal and spatial influencing factors were first quantitatively identified through grey relation analysis (calculating correlation degree between land use changes and influencing factors) and Geodetector (detecting landscape spatial heterogeneity and its driving factors), respectively. Considerable spatial and temporal differences in land use and landscape pattern changes were observed herein, thus determining the influencing factors of these differences in the Liuxihe River basin. These changes were characterized by a large increase in construction land converted from cropland, particularly in the middle and lower reaches of the basin from 2000 to 2010, causing dramatic fragmentation and homogenization of the landscape pattern there. Meanwhile, the landscape pattern gradually transitioned from an agricultural land use dominant landscape to a construction land use dominant landscape in these regions. Furthermore, the rapid growth of a nonagricultural population and the transformation of industry primarily caused the temporal changes of landscape pattern, and the landscape spatial heterogeneity was mainly caused by the interaction of complicated geomorphology and anthropogenic activities in different spatial locations, particularly after 2000. This study not only provides an improved approach to quantifying the main spatiotemporal influencing factors of landscape pattern changes during different time periods, but also offers a reference for decision-makers to formulate optimal strategies on ecological protection and urban sustainable development of different regions in this study area.
Combined Rule-Based and Hypothesis-Based Method for Building Model Reconstruction from Photogrammetric Point Clouds

Three-dimensional (3D) building models play an important role in digital cities and have numerous potential applications in environmental studies. In recent years, the photogrammetric point clouds obtained by aerial oblique images have become a major source of data for 3D building reconstruction. Aiming at reconstructing a 3D building model at Level of Detail (LoD) 2 and even LoD3 with preferred geometry accuracy and affordable computation expense, in this paper, we propose a novel method for the efficient reconstruction of building models from the photogrammetric point clouds which combines the rule-based and the hypothesis-based method using a two-stage topological recovery process. Given the point clouds of a single building, planar primitives and their corresponding boundaries are extracted and regularized to obtain abstracted building counters. In the first stage, we take advantage of the regularity and adjacency of the building counters to recover parts of the topological relationships between different primitives. Three constraints, namely pairwise constraint, triplet constraint, and nearby constraint, are utilized to form an initial reconstruction with candidate faces in ambiguous areas. In the second stage, the topologies in ambiguous areas are removed and reconstructed by solving an integer linear optimization problem based on the initial constraints while considering data fitting degree. Experiments using real datasets reveal that compared with state-of-the-art methods, the proposed method can efficiently reconstruct 3D building models in seconds with the geometry accuracy in decimeter level.
High-Resolution Gridded Population Datasets: Exploring the Capabilities of the World Settlement Footprint 2019 Imperviousness Layer for the African Continent

The field of human population mapping is constantly evolving, leveraging the increasing availability of high-resolution satellite imagery and the advancements in the field of machine learning. In recent years, the emergence of global built-area datasets that accurately describe the extent, location, and characteristics of human settlements has facilitated the production of new population grids, with improved quality, accuracy, and spatial resolution. In this research, we explore the capabilities of the novel World Settlement Footprint 2019 Imperviousness layer (WSF2019-Imp), as a single proxy in the production of a new high-resolution population distribution dataset for all of Africa—the WSF2019-Population dataset (WSF2019-Pop). Results of a comprehensive qualitative and quantitative assessment indicate that the WSF2019-Imp layer has the potential to overcome the complexities and limitations of top-down binary and multi-layer approaches of large-scale population mapping, by delivering a weighting framework which is spatially consistent and free of applicability restrictions. The increased thematic detail and spatial resolution (~10 m at the Equator) of the WSF2019-Imp layer improve the spatial distribution of populations at local scales, where fully built-up settlement pixels are clearly differentiated from settlement pixels that share a proportion of their area with green spaces, such as parks or gardens. Overall, eighty percent of the African countries reported estimation accuracies with percentage mean absolute errors between ~15% and ~32%, and 50% of the validation units in more than half of the countries reported relative errors below 20%. Here, the remaining lack of information on the vertical dimension and the functional characterisation of the built-up environment are still remaining limitations affecting the quality and accuracy of the final population datasets.