Miracle-dz/UniFAR-FTUs huggingface dataset:
Impacts of Heat and Drought on Gross Primary Productivity in China

Heat and drought stress, which often occur together, are the main environmental factors limiting the survival and growth of vegetation. Studies on the response of gross primary production (GPP) to extreme climate events such as heat and drought are highly significant for the identification of ecologically vulnerable regions, ecological risk assessments, and ecological environmental protection. We got 1982–2017 climatic data from the University of East Anglia Climatic Research Unit, Norwich, England, and GPP data from National Earth System Science Data Sharing Service Platform, Beijing, China. Using Theil–Sen median trend analysis and the Mann–Kendall test, we analyzed trends in temperature and the standardized precipitation/standardized precipitation evapotranspiration indices in the eight vegetation regions of China. Additionally, the response of GPP to the single and combined impacts of heat and drought were analyzed using multidimensional copula functions, and GPP reduction probabilities were estimated under different drought levels and heat intensities. The results showed that the probability of a drastic GPP reduction increases with increasing drought levels and heat intensities. The combined impacts of heat and drought on vegetation productivity is greater than the impacts of either drought or heat alone and presents a nonlinear superposition of the two extremes. The impact of heat on GPP is not evident when the drought level is high. The temperate grassland and warm temperate deciduous broad-leaved forest regions are the most sensitive regions to drought and heat in China. This study provides a scientific basis for the comprehensive evaluation of the risk of GPP reduction under the single and combined impacts of heat stress and drought stress.
DR-Net: An Improved Network for Building Extraction from High Resolution Remote Sensing Image

At present, convolutional neural networks (CNN) have been widely used in building extraction from remote sensing imagery (RSI), but there are still some bottlenecks. On the one hand, there are so many parameters in the previous network with complex structure, which will occupy lots of memories and consume much time during training process. On the other hand, low-level features extracted by shallow layers and abstract features extracted by deep layers of artificial neural network cannot be fully fused, which leads to an inaccurate building extraction from RSI. To alleviate these disadvantages, a dense residual neural network (DR-Net) was proposed in this paper. DR-Net uses a deeplabv3+Net encoder/decoder backbone, in combination with densely connected convolution neural network (DCNN) and residual network (ResNet) structure. Compared with deeplabv3+net (containing about 41 million parameters) and BRRNet (containing about 17 million parameters), DR-Net contains about 9 million parameters; So, the number of parameters reduced a lot. The experimental results for both the WHU Building Dataset and Massachusetts Building Dataset, DR-Net show better performance in building extraction than other two state-of-the-art methods. Experiments on WHU building data set showed that Intersection over Union (IoU) increased by 2.4% and F1 score increased by 1.4%; in terms of Massachusetts Building Dataset, IoU increased by 3.8% and F1 score increased by 2.9%.
Road Extraction from Remote Sensing Images Using the Inner Convolution Integrated Encoder-Decoder Network and Directional Conditional Random Fields

Road extraction from remote sensing images is of great significance to urban planning, navigation, disaster assessment, and other applications. Although deep neural networks have shown a strong ability in road extraction, it remains a challenging task due to complex circumstances and factors such as occlusion. To improve the accuracy and connectivity of road extraction, we propose an inner convolution integrated encoder-decoder network with the post-processing of directional conditional random fields. Firstly, we design an inner convolutional network which can propagate information slice-by-slice within feature maps, thus enhancing the learning of road topology and linear features. Additionally, we present the directional conditional random fields to improve the quality of the extracted road by adding the direction of roads to the energy function of the conditional random fields. The experimental results on the Massachusetts road dataset show that the proposed approach achieves high-quality segmentation results, with the F1-score of 84.6%, which outperforms other comparable “state-of-the-art” approaches. The visualization results prove that the proposed approach is able to effectively extract roads from remote sensing images and can solve the road connectivity problem produced by occlusions to some extent.
Thermal Summer Diurnal Hot-Spot Analysis: The Role of Local Urban Features Layers

This study was focused on the metropolitan area of Florence in Tuscany (Italy) with the aim of mapping and evaluating thermal summer diurnal hot- and cool-spots in relation to the features of greening, urban surfaces, and city morphology. The work was driven by Landsat 8 land surface temperature (LST) data related to 2015–2019 summer daytime periods. Hot-spot analysis was performed adopting Getis-Ord Gi* spatial statistics applied on mean summer LST datasets to obtain location and boundaries of hot- and cool-spot areas. Each hot- and cool-spot was classified by using three significance threshold levels: 90% (LEVEL-1), 95% (LEVEL-2), and 99% (LEVEL-3). A set of open data urban elements directly or indirectly related to LST at local scale were calculated for each hot- and cool-spot area: (1) Normalized Difference Vegetation Index (NDVI), (2) tree cover (TC), (3) water bodies (WB), (4) impervious areas (IA), (5) mean spatial albedo (ALB), (6) surface areas (SA), (7) Shape index (SI), (8) Sky View Factor (SVF), (9) theoretical solar radiation (RJ), and (10) mean population density (PD). A General Dominance Analysis (GDA) framework was adopted to investigate the relative importance of urban factors affecting thermal hot- and cool-spot areas. The results showed that 11.5% of the studied area is affected by cool-spots and 6.5% by hot-spots. The average LST variation between hot- and cold-spot areas was about 10 °C and it was 15 °C among the extreme hot- and cool-spot levels (LEVEL-3). Hot-spot detection was magnified by the role of vegetation (NDVI and TC) combined with the significant contribution of other urban elements. In particular, TC, NDVI and ALB were identified as the most significant predictors (p-values < 0.001) of the most extreme cool-spot level (LEVEL-3). NDVI, PD, ALB, and SVF were selected as the most significant predictors (p-values < 0.05 for PD and SVF; p-values < 0.001 for NDVI and ALB) of the hot-spot LEVEL-3. In this study, a reproducible methodology was developed applicable to any urban context by using available open data sources.
A Dual-Model Architecture with Grouping-Attention-Fusion for Remote Sensing Scene Classification

Remote sensing images contain complex backgrounds and multi-scale objects, which pose a challenging task for scene classification. The performance is highly dependent on the capacity of the scene representation as well as the discriminability of the classifier. Although multiple models possess better properties than a single model on these aspects, the fusion strategy for these models is a key component to maximize the final accuracy. In this paper, we construct a novel dual-model architecture with a grouping-attention-fusion strategy to improve the performance of scene classification. Specifically, the model employs two different convolutional neural networks (CNNs) for feature extraction, where the grouping-attention-fusion strategy is used to fuse the features of the CNNs in a fine and multi-scale manner. In this way, the resultant feature representation of the scene is enhanced. Moreover, to address the issue of similar appearances between different scenes, we develop a loss function which encourages small intra-class diversities and large inter-class distances. Extensive experiments are conducted on four scene classification datasets include the UCM land-use dataset, the WHU-RS19 dataset, the AID dataset, and the OPTIMAL-31 dataset. The experimental results demonstrate the superiority of the proposed method in comparison with the state-of-the-arts.
Explanation and Probabilistic Prediction of Hydrological Signatures with Statistical Boosting Algorithms

Hydrological signatures, i.e., statistical features of streamflow time series, are used to characterize the hydrology of a region. A relevant problem is the prediction of hydrological signatures in ungauged regions using the attributes obtained from remote sensing measurements at ungauged and gauged regions together with estimated hydrological signatures from gauged regions. The relevant framework is formulated as a regression problem, where the attributes are the predictor variables and the hydrological signatures are the dependent variables. Here we aim to provide probabilistic predictions of hydrological signatures using statistical boosting in a regression setting. We predict 12 hydrological signatures using 28 attributes in 667 basins in the contiguous US. We provide formal assessment of probabilistic predictions using quantile scores. We also exploit the statistical boosting properties with respect to the interpretability of derived models. It is shown that probabilistic predictions at quantile levels 2.5% and 97.5% using linear models as base learners exhibit better performance compared to more flexible boosting models that use both linear models and stumps (i.e., one-level decision trees). On the contrary, boosting models that use both linear models and stumps perform better than boosting with linear models when used for point predictions. Moreover, it is shown that climatic indices and topographic characteristics are the most important attributes for predicting hydrological signatures.
An Efficient CNN with Tunable Input-Size for Bearing Fault Diagnosis

Deep learning can automatically learn the complex features of input data and is recognized as an effective method for bearing fault diagnosis. Convolution neuron network (CNN) has been successfully used in image classification, and images of vibration signal or time-frequency information from short-time Fourier transform (STFT), wavelet transform (WT), and empirical mode decomposition (EMD) can be fed into CNN to achieve promising results. However, the CNN structure is complex and not efficientenoughfordifferentdatasets.Furthermore,itislessefficienttoprocesstheinputdatabyWTandEMDthanbySTFT.In thiswork,thelowboundforinputsizeof2DdataisanalyzedbyconsideringtherelationshipbetweenthecharacteristicvibrationfrequenciesandthewindowsizeofSTFTtoguidethedeterminationoftheminimuminputsize.ThenageneraladaptiveCNN structurefordifferentdatasetsisdesigned.Accordingtotheexperimentalresultsforfourdatasets,theproposedmethodis universalandtheparametersettingscanbeguidedbythelowboundofinputsize.Surprisingly,allclassificationaccuraciesfor thefourdatasetscanachieve100%intentimesofindependentrunwithoutredesigningtheCNNstructure
Assessing the Potential of the DART Model to Discrete Return LiDAR Simulation - Application to Fuel Type Mapping

Fuel type is one of the key factors for analyzing the potential of fire ignition and propagation in agricultural and forest environments. The increase of three-dimensional datasets provided by active sensors, such as LiDAR (Light Detection and Ranging), has improved the classification of fuel types through empirical modelling. Empirical methods are site and sensor specific while Radiative Transfer Models (RTM) approaches provide broader universality. The aim of this work is to analyze the suitability of Discrete Anisotropic Radiative Transfer (DART) model to replicate low density small-footprint Airborne Laser Scanning (ALS) measurements and subsequent fuel type classification. Field data measured in 104 plots are used as ground truth to simulate LiDAR response based on the sensor and flight characteristics of low-density ALS data captured by the Spanish National Plan for Aerial Orthophotography (PNOA) in two different dates (2011 and 2016). The accuracy assessment of the DART simulations is performed using Spearman rank correlation coefficients between the simulated metrics and the ALS-PNOA ones. The results show that 32% of the computed metrics overpassed a correlation value of 0.80 between simulated and ALS-PNOA metrics in 2011 and 28% in 2016. The highest correlations were related to high height percentiles, canopy variability metrics as for example standard deviation and Rumple diversity index, reaching correlation values over 0.94. Two metric selection approaches and Support Vector Machine classification method with variants were compared to classify fuel types. The best-fitted classification model, trained with the DART simulated sample and validated with ALS-PNOA data, was obtained using Support Vector Machine method with radial kernel. The overall accuracy of the classification after validation was 88% and 91% for the 2011 and 2016 years, respectively. The use of DART demonstrates its value for simulating generalizable 3D data for fuel type classification providing relevant information for forest managers in fire prevention and extinction.
How Can Microwave Observations at 23.8 GHz Help in Acquiring Water Vapor in the Atmosphere over Land?

Due to concerns about radio frequency interference from emerging telecommunications technology, there have been intensive discussions on the changes to the international Radio Regulations around 24 GHz at the recent World Radiocommunication Conference in 2019. Although the sensitivity to total precipitable water (TPW) at 23.8 GHz over land is small, and in some cases close to zero, state-of-the-art retrieval systems with no dependence on real-time ancillary data (e.g., numerical weather prediction (NWP) model forecasts), such as the National Oceanic and Atmospheric Administration (NOAA) operational Microwave Integrated Retrieval System (MiRS), have been producing reliable TPW products over both ocean and land, which implies that the microwave channel at 23.8 GHz is providing valuable information on TPW over land as well as over ocean. The contradiction between the zero or near-zero sensitivity and practical performance over land raises questions for the remote sensing community and public users of such data. In this study, we examine the underlying physics and include mathematical explanations, which address and clarify the apparent contradiction. The channel at 23.8 GHz is a direct measurement and indispensable for its combined use with microwave temperature and moisture sounding channels.
Geodetic and UAV Monitoring in the Sustainable Management of Shallow Landslides and Erosion of a Susceptible Urban Environment

Landslides are a worldwide occurring hazard that can produce economic impact and even fatalities. The collection and monitoring of data regarding active landslides are important for predicting future landslides in that region, and is critical to minimize the losses caused. In the expanding metropolitan area of Cluj-Napoca, Romania, drastic changes of land use and increasement of construction zones represent a current evolution issue. The urban sprawl phenomenon imposed the expansion of the city limits and outside the old built-up area, and due to the hilly terrain and geomorphology, natural hazards such as landslides and erosion processes are susceptible to appearance or reactivation. The study incorporates interdisciplinary research composed of evaluation of a landslide susceptible hotspot located in an area of interest to the municipality by means of geodetic and topographic precise measurements, combined with the use of unmanned aerial vehicles (UAV) monitoring of surface movement and GIS spatial analysis. The data obtained in a span of over two years reveal that the investigated slope is subjected to a shallow active landslide of a few centimeters per year, and based on the 64 individual placed landmarks the highest displacement value was 67 mm. Through geomatic tools the exchange rate of the slope surface was evaluated with comprehensive volume calculations, such as displacement, erosion, and accumulation that illustrate a volume of material displaced of 107.2 m3 and the accumulated one of 55.7 m3. The results provide valuable insight into the complex landslide and erosion dynamics that are crucial when predicting future movements and prevention measures.
Characterizing Forest Dynamics with Landsat-Derived Phenology Curves

Landsat is among the most popular satellites used for forest change assessments. Traditionally, Landsat data users relied on annual or biennial images to measure forest recovery after disturbance, a process that is difficult to monitor at broad scales. With the availability of free Landsat data, intra-annual change analyses are now possible. Phenology, the timing of cyclical vegetation events, can be estimated using indices derived from intra-annual remote sensing data and used to classify different vegetation types after a disturbance. We used a smoothed harmonic modelling approach to estimate NDVI and NBR phenology patterns in pre- and post-fire Landsat sample pixels for two forest groups in South Carolina, using nearby unburned samples as an approximate control group. These methods take advantage of all available images collected by Landsat 5, 7, and 8 for the study area. We found that within burned samples, there were differences in phenology for the two forest groups, while the unburned samples showed no forest group differences. Phenology patterns also differed based on fire severity. These methods take advantage of the freely available Landsat archive and can be used to characterize intra-annual fluctuations in vegetation following a variety of disturbances in the southeastern U.S. and other regions. Our approach builds on other harmonic approaches that use the Landsat archive to detect forest change, such as the Continuous Change Detection and Classification (CCDC) algorithm, and provides a tool to describe post-disturbance forest change.
A Photogrammetry-Based Workflow for the Accurate 3D Construction and Visualization of Museums Assets

Nowadays digital replicas of artefacts belonging to the Cultural Heritage (CH) are one of the most promising innovations for museums exhibitions, since they foster new forms of interaction with collections, at different scales. However, practical digitization is still a complex task dedicated to specialized operators. Due to these premises, this paper introduces a novel approach to support non-experts working in museums with robust, easy-to-use workflows based on low-cost widespread devices, aimed at the study, classification, preservation, communication and restoration of CH artefacts. The proposed methodology introduces an automated combination of acquisition, based on mobile equipment and visualization, based on Real-Time Rendering. After the description of devices used along the workflow, the paper focuses on image pre-processing and geometry processing techniques adopted to generate accurate 3D models from photographs. Assessment criteria for the developed process evaluation are illustrated. Tests of the methodology on some effective museum case studies are presented and discussed.
Assessment of BRDF Impact on VIIRS DNB from Observed Top-of-Atmosphere Reflectance over Dome C in Nighttime

The Day–Night Band (DNB) imaging sensor of the Visible Infrared Imaging Radiometer Suite (VIIRS) adds nighttime monitoring capability to the Suomi National Polar-Orbiting Partnership and National Oceanic and Atmospheric Administration 20 weather satellite launched in 2011 and 2017, respectively. Nighttime visible imagery has already found diverse applications, but image quality is often unsatisfactory. In this study, variations in observed top-of-atmosphere (TOA) reflectance were examined in terms of nighttime bidirectional effects. The Antarctica Dome C ground site was selected due to high uniformity. First, variation of reflectance was characterized in terms of viewing zenith angle, lunar zenith angle, and relative lunar azimuth angle, using DNB data from 2012 to 2020 and Miller–Turner 2009 simulations. Variations in reflectance were observed to be strongly anisotropic, suggesting the presence of bidirectional effects. Then, based on this finding, three popular bidirectional reflectance distribution function (BRDF) models were evaluated for effectiveness in correcting for these effects on the nighttime images. The observed radiance of VIIRS DNB was compared with the simulated radiance respectively based on the three BRDF models under the same geometry. Compared with the RossThick-LiSparseReciprocal (RossLi) BRDF model and Hudson model, the Warren model has a higher correlation coefficient (0.9899–0.9945) and a lower root-mean-square-error (0.0383–0.0487). Moreover, the RossLi BRDF model and Hudson model may have similar effects in the description of the nighttime TOA over Dome C. These findings are potentially useful to evaluate the radiometric calibration stability and consistency of nighttime satellite sensors.
Wildland Fire Tree Mortality Mapping from Hyperspatial Imagery Using Machine Learning

The use of imagery from small unmanned aircraft systems (sUAS) has enabled the production of more accurate data about the effects of wildland fire, enabling land managers to make more informed decisions. The ability to detect trees in hyperspatial imagery enables the calculation of canopy cover. A comparison of hyperspatial post-fire canopy cover and pre-fire canopy cover from sources such as the LANDFIRE project enables the calculation of tree mortality, which is a major indicator of burn severity. A mask region-based convolutional neural network was trained to classify trees as groups of pixels from a hyperspatial orthomosaic acquired with a small unmanned aircraft system. The tree classification is summarized at 30 m, resulting in a canopy cover raster. A post-fire canopy cover is then compared to LANDFIRE canopy cover preceding the fire, calculating how much the canopy was reduced due to the fire. Canopy reduction allows the mapping of burn severity while also identifying where surface, passive crown, and active crown fire occurred within the burn perimeter. Canopy cover mapped through this effort was lower than the LANDFIRE Canopy Cover product, which literature indicated is typically over reported. Assessment of canopy reduction mapping on a wildland fire reflects observations made both from ground truthing efforts as well as observations made of the associated hyperspatial sUAS orthomosaic.
Biodegradable porous beads and their potential applications in regenerative medicine

This article briefly reviews recent developments in the fabrication of biodegradable porous beads and their potential applications as cell carriers in regenerative medicine. The porous beads can be fabricated from natural (e.g., chitosan, alginate, and collagen) and synthetic (e.g., PLGA and PCL) polymers, as well as inorganic materials (e.g., calcium phosphates). The highly porous structure facilitates not only cell seeding and migration but also transportation of nutrients/oxygen and metabolic wastes, leading to high levels of cell density and viability. The ability to directly inject a suspension of cell-loaded beads using a needle allows for delivery of target-specific cells to the site of defect or disease without surgical incision for tissue regeneration. Comparative studies of porous and non-porous beads clearly show the superiority of porous beads in terms of viability, proliferation, and differentiation of cells, as well as injectability of cell/bead constructs.