Miracle-dz/UniFAR-FTUs huggingface dataset:
A Kernel-Driven BRDF Approach to Correct Airborne Hyperspectral Imagery over Forested Areas with Rugged Topography

Airborne hyper-spectral imaging has been proven to be an efficient means to provide new insights for the retrieval of biophysical variables. However, quantitative estimates of unbiased information derived from airborne hyperspectral measurements primarily require a correction of the anisotropic scattering properties of the land surface depicted by the bidirectional reflectance distribution function (BRDF). Hitherto, angular BRDF correction methods rarely combined viewing-illumination geometry and topographic information to achieve a comprehensive understanding and quantification of the BRDF effects. This is in particular the case for forested areas, frequently underlaid by rugged topography. This paper describes a method to correct the BRDF effects of airborne hyperspectral imagery over forested areas overlying rugged topography, referred in the reminder of the paper as rugged topography-BRDF (RT-BRDF) correction. The local viewing and illumination geometry are calculated for each pixel based on the characteristics of the airborne scanner and the local topography, and these two variables are used to adapt the Ross-Thick-Maignan and Li-Transit-Reciprocal kernels in the case of rugged topography. The new BRDF model is fitted to the anisotropy of multi-line airborne hyperspectral data. The number of pixels is set at 35,000 in this study, based on a stratified random sampling method to ensure a comprehensive coverage of the viewing and illumination angles and to minimize the fitting error of the BRDF model for all bands. Based on multi-line airborne hyperspectral data acquired with the Chinese Academy of Forestry’s LiDAR, CCD, and Hyperspectral system (CAF-LiCHy) in the Pu’er region (China), the results applying the RT-BRDF correction are compared with results from current empirical (C, and sun-canopy-sensor (SCS) adds C (SCS+C)) and semi-physical (SCS) topographic correction methods. Both quantitative assessment and visual inspection indicate that RT-BRDF, C, and SCS+C correction methods all reduce the topographic effects. However, the RT-BRDF method appears more efficient in reducing the variability in reflectance of overlapping areas in multiple flight-lines, with the advantage of reducing the BRDF effects caused by the combination of wide field of view (FOV) airborne scanner, rugged topography, and varying solar illumination angle over long flight time. Specifically, the average decrease in coefficient of variation (CV) is 3% and 3.5% for coniferous forest and broadleaved forest, respectively. This improvement is particularly marked in the near infrared (NIR) region (i.e., >750 nm). This finding opens new possible applications of airborne hyperspectral surveys over large areas.
Individual Tree Position Extraction and Structural Parameter Retrieval Based on Airborne LiDAR Data: Performance Evaluation and Comparison of Four Algorithms

Information for individual trees (e.g., position, treetop, height, crown width, and crown edge) is beneficial for forest monitoring and management. Light Detection and Ranging (LiDAR) data have been widely used to retrieve these individual tree parameters from different algorithms, with varying successes. In this study, we used an iterative Triangulated Irregular Network (TIN) algorithm to separate ground and canopy points in airborne LiDAR data, and generated Digital Elevation Models (DEM) by Inverse Distance Weighted (IDW) interpolation, thin spline interpolation, and trend surface interpolation, as well as by using the Kriging algorithm. The height of the point cloud was assigned to a Digital Surface Model (DSM), and a Canopy Height Model (CHM) was acquired. Then, four algorithms (point-cloud-based local maximum algorithm, CHM-based local maximum algorithm, watershed algorithm, and template-matching algorithm) were comparatively used to extract the structural parameters of individual trees. The results indicated that the two local maximum algorithms can effectively detect the treetop; the watershed algorithm can accurately extract individual tree height and determine the tree crown edge; and the template-matching algorithm works well to extract accurate crown width. This study provides a reference for the selection of algorithms in individual tree parameter inversion based on airborne LiDAR data and is of great significance for LiDAR-based forest monitoring and management.
Confidence Levels, Sensitivity, and the Role of Bathymetry in Coral Reef Remote Sensing

Remote sensing is playing an increasingly important role in the monitoring and management of coastal regions, coral reefs, inland lakes, waterways, and other shallow aquatic environments. Ongoing advances in algorithm development, sensor technology, computing capabilities, and data availability are continuing to improve our ability to accurately derive information on water properties, water depth, benthic habitat composition, and ecosystem health. However, given the physical complexity and inherent variability of the aquatic environment, most of the remote sensing models used to address these challenges require localized input parameters to be effective and are thereby limited in geographic scope. Additionally, since the parameters in these models are interconnected, particularly with respect to bathymetry, errors in deriving one parameter can significantly impact the accuracy of other derived parameters and products. This study utilizes hyperspectral data acquired in Hawaii in 2000–2001 and 2017–2018 using NASA’s Classic Airborne Visible/Infrared Imaging Spectrometer to evaluate performance and sensitivity of a well-established semi-analytical inversion model used in the assessment of coral reefs. Analysis is performed at several modeled spatial resolutions to emulate characteristics of different feasible moderate resolution hyperspectral satellites, and data processing is approached with the objective of developing a generalized, scalable, automated workflow. Accuracy of derived water depth is evaluated using bathymetric lidar data, which serves to both validate model performance and underscore the importance of image quality on achieving optimal model output. Data are then used to perform a sensitivity analysis and develop confidence levels for model validity and accuracy. Analysis indicates that derived benthic reflectance is most sensitive to errors in bathymetry at shallower depths, yet remains significant at all depths. The confidence levels provide a first-order method for internal quality assessment to determine the physical extent of where and to what degree model output is considered valid. Consistent results were found across different study sites and different spatial resolutions, confirming the suitability of the model for deriving water depth in complex coral reef environments, and expanding our ability to achieve automated widespread mapping and monitoring of global coral reefs.
Development of an Image De-Noising Method in Preparation for the Surface Water and Ocean Topography Satellite Mission

In the near future, the Surface Water Ocean Topography (SWOT) mission will provide images of altimetric data at kilometric resolution. This unprecedented 2-dimensional data structure will allow the estimation of geostrophy-related quantities that are essential for studying the ocean surface dynamics and for data assimilation uses. To estimate these quantities, i.e., to compute spatial derivatives of the Sea Surface Height (SSH) measurements, the uncorrelated, small-scale noise and errors expected to affect the SWOT data must be smoothed out while minimizing the loss of relevant, physical SSH information. This paper introduces a new technique for de-noising the future SWOT SSH images. The de-noising model is formulated as a regularized least-square problem with a Tikhonov regularization based on the first-, second-, and third-order derivatives of SSH. The method is implemented and compared to other, convolution-based filtering methods with boxcar and Gaussian kernels. This is performed using a large set of pseudo-SWOT data generated in the western Mediterranean Sea from a 1/60 ∘ simulation and the SWOT simulator. Based on root mean square error and spectral diagnostics, our de-noising method shows a better performance than the convolution-based methods. We find the optimal parametrization to be when only the second-order SSH derivative is penalized. This de-noising reduces the spatial scale resolved by SWOT by a factor of 2, and at 10 km wavelengths, the noise level is reduced by factors of 10 4 and 10 3 for summer and winter, respectively. This is encouraging for the processing of the future SWOT data.
Regionalization of Coarse Scale Soil Moisture Products Using Fine-Scale Vegetation Indices - Prospects and Case Study

Surface soil moisture (SSM) plays a critical role in many hydrological, biological and biogeochemical processes. It is relevant to farmers, scientists, and policymakers for making effective land management decisions. However, coarse spatial resolution and complex interactions of microwave radiation with surface roughness and vegetation structure present limitations within active remote sensing products to directly monitor soil moisture variations with sufficient detail. This paper discusses a strategy to use vegetation indices (VI) such as greenness, water stress, coverage, vigor, and growth dynamics, derived from Earth Observation (EO) data for an indirect characterization of SSM conditions. In this regional-scale study of a wetland environment, correlations between the coarse Advanced SCATterometer-Soil Water Index (ASCAT-SWI or SWI) product and statistical measurements of four vegetation indices from higher resolution Sentinel-2 data were analyzed. The results indicate that the mean value of Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) correlates most strongly to the SWI and that the wet season vegetation traits show stronger linear relation to the SWI than during the dry season. The correlation between VIs and SWI was found to be independent of the underlying dominant vegetation classes which are not derived in real-time. Therefore, fine-scale vegetation information from optical satellite data convey the spatial heterogeneity missed by coarse synthetic aperture radar (SAR)-derived SSM products and is linked to the SSM condition underneath for regionalization purposes.
Extracting Impervious Surface from Aerial Imagery Using Semi-Automatic Sampling and Spectral Stability

The quantification of impervious surface through remote sensing provides critical information for urban planning and environmental management. The acquisition of quality reference data and the selection of effective predictor variables are two factors that contribute to the low accuracies of impervious surface in urban remote sensing. A hybrid method was developed to improve the extraction of impervious surface from high-resolution aerial imagery. This method integrates ancillary datasets from OpenStreetMap, National Wetland Inventory, and National Cropland Data to generate training and validation samples in a semi-automatic manner, significantly reducing the effort of visual interpretation and manual labeling. Satellite-derived surface reflectance stability is incorporated to improve the separation of impervious surface from other land cover classes. This method was applied to 1-m National Agriculture Imagery Program (NAIP) imagery of three sites with different levels of land development and data availability. Results indicate improved extractions of impervious surface with user’s accuracies ranging from 69% to 90% and producer’s accuracies from 88% to 95%. The results were compared to the 30-m percent impervious surface data of the National Land Cover Database, demonstrating the potential of this method to validate and complement satellite-derived medium-resolution datasets of urban land cover and land use.
Skeletal frame-size variations in adolescent female Nigerian school children in Lagos

Introduction: Limited information is available about the skeletal frame size of urban-dwelling adolescent female West African children. Aim and Objective: This study assessed variations in skeletal frame-size as estimated from the truncal breadths of adolescent female Nigerian children from two different socioeconomic backgrounds in Lagos. Materials and Methods: Anthropometric measurements of biacromial, bi-iliocristal, bitrochanteric, and chest breadths were taken from 320 adolescent female school children aged 10-17 years selected from public and private schools in Lagos using a systematic random method. The socioeconomic status (SES) of the subjects was classified on the basis of attendance at either fee-paying private or non-fee-paying public schools. Results: The range of skeletal breadth measurements were as follows: Biacromial breadth 32.24 ± 2.01 cm to 34.23 ± 1.35 cm; bi-iliocristal breadth 22.46 ± 1.07 cm to 23.75 ± 1.47 cm; bitrochanteric breadth 24.47 ± 2.22 cm to 28.29 ± 2.10 cm; and chest breadth 18.43 ± 2.14 cm to 19.71 ± 0.75 cm. Statistically significant differences were observed in the mean biacromial, bi-iliocristal, bitrochanteric, and chest breadths between age-matched private school girls (PRG) and public school girls (PUG). Conclusion: The observed variations in skeletal frame size among female adolescents may reflect a complex interaction between lifestyle and biological variables existing in this population, suggesting a morphologic uniqueness among the adolescent females of Lagos that calls for further systematic investigation.
Droughts Amplify Differences Between the Energy Balance Components of Amazon Forests and Croplands

Droughts can exert a strong influence on the regional energy balance of the Amazon and Cerrado, as can the replacement of native vegetation by croplands. What remains unclear is how these two forcing factors interact and whether land cover changes fundamentally alter the sensitivity of the energy balance components to drought events. To fill this gap, we used remote sensing data to evaluate the impacts of drought on evapotranspiration (ET), land surface temperature (LST), and albedo on cultivated areas, savannas, and forests. Our results (for seasonal drought) indicate that increases in monthly dryness across Mato Grosso state (southern Amazonia and northern Cerrado) drive greater increases in LST and albedo in croplands than in forests. Furthermore, during the 2007 and 2010 droughts, croplands became hotter (0.1–0.8 °C) than savannas (0.3–0.6 °C) and forests (0.2–0.3 °C). However, forest ET was consistently higher than ET in all other land uses. This finding likely indicates that forests can access deeper soil water during droughts. Overall, our findings suggest that forest remnants can play a fundamental role in the mitigation of the negative impacts of extreme drought events, contributing to a higher ET and lower LST.
Assessment of knowledge, attitude and practices of dental practitioners regarding temporomandibular joint disorders in India

Background: Temporomandibular joint disorders (TMDs) are one of the major causes of chronic orofacial pain affecting 28-86% of the population. The diagnosis of TMDs is challenging to a considerable number of practitioners and is influenced by their knowledge, attitude and experience. Aim: This study aimed at assessing the knowledge, attitude and practices regarding TMDs among TMD experts and general dental practitioners (GDPs) in India. Materials and Methods: A total of 200 dental practitioners (32 TMD experts and 168 GDPs) across India, registered under the Dental Council of India, were included in the survey. A questionnaire consisting of 21 issues with regard to TMDs was designed from relevant standard textbooks. The questionnaire was pretested for validation and distributed personally or through the web designed program. The knowledge and attitude scores of both the groups were assessed and compared. The therapeutic modalities practiced by the groups were also noted. Results: A significant difference was found in the knowledge scores and attitude between TMD experts and GDPs. A significant correlation was found between attitude score and years of experience in both the groups. Both TMD experts and GDPs expressed little confidence in the management of TMDs. About 75% of GDPs expressed concern over an inadequate number of TMD experts. Conclusion: Need for continued updating of knowledge, panel discussions, and revisions of the curriculum in graduate schools was realized.
Coastal Turbidity Derived From PROBA-V Global Vegetation Satellite

PROBA-V (Project for On-Board Autonomy-Vegetation) is a global vegetation monitoring satellite. The spectral quality of the data and the coverage of PROBA-V over coastal waters provide opportunities to expand its use to other applications. This study tests PROBA-V data for the retrieval of turbidity in the North Sea region. In the first step, clouds were masked and an atmospheric correction, using an adapted version of iCOR, was performed. The resulted water leaving radiance reflectance was validated against AERONET-OC stations, yielding a coefficient of determination of 0.884 in the RED band. Next, turbidity values were retrieved using the RED band. The PROBA-V retrieved turbidity data was compared with turbidity data from CEFAS Smartbuoys and ad-hoc measurement campaigns. This resulted in a coefficient of determination of 0.69. Finally, a time series of 1.5 year of PROBA-V derived turbidity data was plotted over MODIS data to check consistencies in both datasets. Seasonal dynamics were noted with high turbidity in autumn and winter and low values in spring and summer. For low values, PROBA-V and MODIS yielded similar results, but while MODIS seems to saturate around 50 FNU, PROBA-V can reach values up till almost 80 FNU.
Geometry Aware Evaluation of Handcrafted Superpixel-Based Features and Convolutional Neural Networks for Land Cover Mapping Using Satellite Imagery

In land cover mapping at a high spatial resolution, pixel values alone are not always sufficient to recognize the more complex classes. Contextual features (computed with a sliding kernel or other kind of spatial support) can be discriminating for certain land cover classes, for example, different levels of urban density, or classes containing heterogeneous pixels, such as orchards and vineyards. However, the reference data used for training the supervised classifier are almost always sparsely labeled, in other words, not every pixel of the training area is labeled. This makes the selection of an appropriate contextual classification method for land cover mapping problematic. Indeed, the current state-of-the art contextual classification model, the Deep Convolutional Neural Network (D-CNN), encounters issues when the geometry of the desired output is absent from the training set. Data-driven methods like D-CNN rely heavily on the availability of extensive training labels to learn both the feature extraction and classification steps. With a sparse training set, sharp corners are rounded, and thin elongated elements may be either thickened, or entirely lost. Alternatively, there are several methods based on the manual selection of contextual features in a chosen neighborhood, guided by the knowledge of the data and past experience from similar problems. Such approaches should not be as sensitive to sparsely labeled data, as they do not rely on any training data for feature extraction. This paper presents a new process for including contextual information in an image classification scheme: the Histogram Of Auto Context Classes in Superpixels (HACCS), which involves classifying an image using the local class histograms as contextual features. These histograms are calculated within superpixels of different sizes in order to provide a multi-scale characterization of the neighborhood, while preserving the geometry of the image objects. This method is evaluated on two data sets presenting different spatial, temporal, and spectral resolutions, and each case is compared with a D-CNN in terms of class accuracy, but also of the quality of the geometry in the produced map. Experiments on the Sentinel-2 time series show that HACCS provides equivalent thematic accuracy compared to the D-CNN, while exhibiting a higher degree of geometric accuracy. On very high spatial resolution imagery (SPOT-7), the D-CNN provides significantly stronger thematic accuracy, but this comes at the cost of a lower level of geometric accuracy.
Mapping Glacier Forelands Based on UAV BVLOS Operation in Antarctica

The aim of this article is to show geomorphological mapping of remote Antarctic locations using images taken by a fixed-wing unmanned aerial vehicle (UAV) during the Beyond Visual Line of Sight (BVLOS) operations. We mapped landform assemblages developed in forelands of Ecology Glacier (EGF), Sphinx Glacier (SGF) and Baranowski Glacier (BGF) in Antarctic Specially Protected Area No. 128 (ASPA 128) on King George Island (South Shetland Islands) and inferred about glacial dynamics. The orthophoto and digital elevation model allowed for geomorphological mapping of glacial forelands, including (i) glacial depositional landforms, (ii) fluvial and fluvioglacial landforms, (iii) littoral and lacustrine landforms, (iv) bodies of water, and (v) other. The largest area is occupied by ground moraine and glacial lagoons on EGF and BGF. The most profound features of EGF are the large latero-frontal moraine ridges from Little Ice Age and the first half of the 20th century. Large areas of ground moraine, frequently fluted and marked with large recessional moraine ridges, dominate on SGF. A significant percentage of bedrock outcrops and end moraine complexes characterize BGF. The landform assemblages are typical for discontinuous fast ice flow of tidewater glaciers over a deformable bed. It is inferred that ice flow velocity decreased as a result of recession from the sea coast, resulting in a significant decrease in the length of ice cliffs and decrease in calving rate. Image acquisition during the fixed-wing UAV BVLOS operation proved to be a very robust technique in harsh polar conditions of King George Island.
Deep Quadruplet Network for Hyperspectral Image Classification with a Small Number of Samples

This study proposes a deep quadruplet network (DQN) for hyperspectral image classification given the limitation of having a small number of samples. A quadruplet network is designed, which makes use of a new quadruplet loss function in order to learn a feature space where the distances between samples from the same class are shortened, while those from a different class are enlarged. A deep 3-D convolutional neural network (CNN) with characteristics of both dense convolution and dilated convolution is then employed and embedded in the quadruplet network to extract spatial-spectral features. Finally, the nearest neighbor (NN) classifier is used to accomplish the classification in the learned feature space. The results show that the proposed network can learn a feature space and is able to undertake hyperspectral image classification using only a limited number of samples. The main highlights of the study include: (1) The proposed approach was found to have high overall accuracy and can be classified as state-of-the-art; (2) Results of the ablation study suggest that all the modules of the proposed approach are effective in improving accuracy and that the proposed quadruplet loss contributes the most; (3) Time-analysis shows the proposed methodology has a similar level of time consumption as compared with existing methods.