Miracle-dz/UniFAR-FTUs huggingface dataset:
Photogrammetric Digital Surface Model Reconstruction in Extreme Low-Light Environments

Digital surface models (DSM) have become one of the main sources of geometrical information for a broad range of applications. Image-based systems typically rely on passive sensors which can represent a strong limitation in several survey activities (e.g., night-time monitoring, underground survey and night surveillance). However, recent progresses in sensor technology allow very high sensitivity which drastically improves low-light image quality by applying innovative noise reduction techniques. This work focuses on the performances of night-time photogrammetric systems devoted to the monitoring of rock slopes. The study investigates the application of different camera settings and their reliability to produce accurate DSM. A total of 672 stereo-pairs acquired with high-sensitivity cameras (Nikon D800 and D810) at three different testing sites were considered. The dataset includes different camera configurations (ISO speed, shutter speed, aperture and image under-/over-exposure). The use of image quality assessment (IQA) methods to evaluate the quality of the images prior to the 3D reconstruction is investigated. The results show that modern high-sensitivity cameras allow the reconstruction of accurate DSM in an extreme low-light environment and, exploiting the correct camera setup, achieving comparable results to daylight acquisitions. This makes imaging sensors extremely versatile for monitoring applications at generally low costs.
Determination of Structural Characteristics of Old-Growth Forest in Ukraine Using Spaceborne LiDAR

A forest’s structure changes as it progresses through developmental stages from establishment to old-growth forest. Therefore, the vertical structure of old-growth forests will differ from that of younger, managed forests. Free, publicly available spaceborne Laser Range and Detection (LiDAR) data designed for the determination of forest structure has recently become available through NASA’s General Ecosystem and Development Investigation (GEDI). We use this data to investigate the structure of some of the largest remaining old-growth forests in Europe in the Ukrainian Carpathian Mountains. We downloaded 18489 cloud-free shots in the old-growth forest (OGF) and 20398 shots in adjacent non-OGF areas during leaf-on, snow-free conditions. We found significant differences between OGF and non-OGF over a wide range of structural metrics. OGF was significantly more open, with a more complex vertical structure and thicker ground-layer vegetation. We used Random Forest classification on a range of GEDI-derived metrics to classify OGF shapefiles with an accuracy of 73%. Our work demonstrates the use of spaceborne LiDAR for the identification of old-growth forests.
Comparative Evaluation of Microwave L-Band VOD and Optical NDVI for Agriculture Drought Detection over Central Europe

Agricultural droughts impose many economic and social losses on various communities. Most of the effective tools developed for agricultural drought assessment are based on vegetation indices (VIs). The aim of this study is to compare the response of two commonly used VIs to meteorological droughts—Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) and Soil Moisture and Ocean Salinity (SMOS) vegetation optical depth (VOD). For this purpose, meteorological droughts are calculated by using a standardized precipitation index over more than 24,000 pixels at 0.25° × 0.25° spatial resolution located in central Europe. Then, to evaluate the capability of VIs in the detection of agricultural droughts, the average values of VIs anomalies during dry and wet periods obtained from meteorological droughts are statistically compared to each other. Additionally, to assess the response time of VIs to meteorological droughts, a time lag of one to six months is applied to the anomaly time series of VIs during their comparison. Results show that over 35% of the considered pixels NDVI, over 22% of VOD, and over 8% of both VIs anomalies have a significant response to drought events, while the significance level of these differences and the response time of VIs vary with different land use and climate conditions.
Ocean-Surface Heterogeneity Mapping (OHMA) to Identify Regions of Change

Mapping heterogeneity of the ocean’s surface waters is important for understanding biogeographical distributions, ocean surface habitat mapping, and ocean surface stability. This article describes the Ocean-surface Heterogeneity MApping (OHMA) algorithm—an objective, replicable approach that uses hypertemporal, satellite-derived datasets to map the spatio-temporal heterogeneity of ocean surface waters. The OHMA produces a suite of complementary datasets—a surface spatio-temporal heterogeneity dataset, and an optimised spatio-temporal classification of the ocean surface. It was demonstrated here using a hypertemporal Sea Surface Temperature image dataset of the North Atlantic. Validation with Underway-derived temperature data showed higher heterogeneity areas were associated with stronger surface temperature gradients, or an increased presence of locally extreme temperature values. Using four exploratory case studies, spatio-temporal heterogeneity values were related to a range of region-specific surface and sub-surface characteristics including fronts, currents and bathymetry. The values conveyed the interactions between these parameters as a single metric. Such over-arching heterogeneity information is virtually impossible to map from in-situ instruments, or less temporally dense satellite datasets. This study demonstrated the OHMA approach is a useful and robust tool to explore, examine, and describe the ocean’s surface. It advances our capability to map biologically relevant measures of ocean surface heterogeneity. It can support ongoing efforts in Ocean Surface Partitioning, and attempts to understand marine species distributions. The study highlighted the need to establish dedicated spatio-temporal ocean validation sites, specifically measured using surface transits, to support advances in hypertemporal ocean data use, and exploitation. A number of future research avenues are also highlighted.
Velocity Analysis Using Separated Diffractions for Lunar Penetrating Radar Obtained by Yutu-2 Rover

The high-frequency channel of lunar penetrating radar (LPR) onboard Yutu-2 rover successfully collected high quality data on the far side of the Moon, which provide a chance for us to detect the shallow subsurface structures and thickness of lunar regolith. However, traditional methods cannot obtain reliable dielectric permittivity model, especially in the presence of high mix between diffractions and reflections, which is essential for understanding and interpreting the composition of lunar subsurface materials. In this paper, we introduce an effective method to construct a reliable velocity model by separating diffractions from reflections and perform focusing analysis using separated diffractions. We first used the plane-wave destruction method to extract weak-energy diffractions interfered by strong reflections, and the LPR data are separated into two parts: diffractions and reflections. Then, we construct a macro-velocity model of lunar subsurface by focusing analysis on separated diffractions. Both the synthetic ground penetrating radar (GPR) and LPR data shows that the migration results of separated reflections have much clearer subsurface structures, compared with the migration results of un-separated data. Our results produce accurate velocity estimation, which is vital for high-precision migration; additionally, the accurate velocity estimation directly provides solid constraints on the dielectric permittivity at different depth.
Vegetation Change and Its Response to Climate Extremes in the Arid Region of Northwest China

Changes in climate extremes have a profound impact on vegetation growth. In this study, we employed the Moderate Resolution Imaging Spectroradiometer (MODIS) and a recently published climate extremes dataset (HadEX3) to study the temporal and spatial evolution of vegetation cover, and its responses to climate extremes in the arid region of northwest China (ARNC). Mann-Kendall test, Anomaly analysis, Pearson correlation analysis, Time lag cross-correlation method, and Least absolute shrinkage and selection operator logistic regression (Lasso) were conducted to quantitatively analyze the response characteristics between Normalized Difference Vegetation Index (NDVI) and climate extremes from 2000 to 2018. The results showed that: (1) The vegetation in the ARNC had a fluctuating upward trend, with vegetation significantly increasing in Xinjiang Tianshan, Altai Mountain, and Tarim Basin, and decreasing in the central inland desert. (2) Temperature extremes showed an increasing trend, with extremely high-temperature events increasing and extremely low-temperature events decreasing. Precipitation extremes events also exhibited a slightly increasing trend. (3) NDVI was overall positively correlated with the climate extremes indices (CEIs), although both positive and negative correlations spatially coexisted. (4) The responses of NDVI and climate extremes showed time lag effects and spatial differences in the growing period. (5) Precipitation extremes were closely related to NDVI than temperature extremes according to Lasso modeling results. This study provides a reference for understanding vegetation variations and their response to climate extremes in arid regions.
Photogrammetry Using UAV-Mounted GNSS RTK: Georeferencing Strategies without GCPs

Georeferencing using ground control points (GCPs) is the most common strategy in photogrammetry modeling using unmanned aerial vehicle (UAV)-acquired imagery. With the increased availability of UAVs with onboard global navigation satellite system–real-time kinematic (GNSS RTK), georeferencing without GCPs is becoming a promising alternative. However, systematic elevation error remains a problem with this technique. We aimed to analyze the reasons for this systematic error and propose strategies for its elimination. Multiple flights differing in the flight altitude and image acquisition axis were performed at two real-world sites. A flight height of 100 m with a vertical (nadiral) image acquisition axis was considered primary, supplemented with flight altitudes of 75 m and 125 m with a vertical image acquisition axis and two flights at 100 m with oblique image acquisition axes (30° and 15°). Each of these flights was performed twice to produce a full double grid. Models were reconstructed from individual flights and their combinations. The elevation error from individual flights or even combinations yielded systematic elevation errors of up to several decimeters. This error was linearly dependent on the deviation of the focal length from the reference value. A combination of two flights at the same altitude (with nadiral and oblique image acquisition) was capable of reducing the systematic elevation error to less than 0.03 m. This study is the first to demonstrate the linear dependence between the systematic elevation error of the models based only on the onboard GNSS RTK data and the deviation in the determined internal orientation parameters (focal length). In addition, we have shown that a combination of two flights with different image acquisition axes can eliminate this systematic error even in real-world conditions and that georeferencing without GCPs is, therefore, a feasible alternative to the use of GCPs.
Mapping Surficial Soil Particle Size Fractions in Alpine Permafrost Regions of the Qinghai-Tibet Plateau

Spatial information of particle size fractions (PSFs) is primary for understanding the thermal state of permafrost in the Qinghai-Tibet Plateau (QTP) in response to climate change. However, the limitation of field observations and the tremendous spatial heterogeneity hamper the digital mapping of PSF. This study integrated log-ratio transformation approaches, variable searching methods, and machine learning techniques to map the surficial soil PSF distribution of two typical permafrost regions. Results showed that the Boruta technique identified different covariates but retained those covariates of vegetation and land surface temperature in both regions. Variable selection techniques effectively decreased the data redundancy and improved model performance. In addition, the spatial distribution of soil PSFs generated by four log-ratio models presented similar patterns. Isometric log-ratio random forest (ILR-RF) outperformed the other models in both regions (i.e., R2 ranged between 0.36 to 0.56, RMSE ranged between 0.02 and 0.10). Compared with three legacy datasets, our prediction better captured the spatial pattern of PSFs with higher accuracy. Although this study largely improved the accuracy of spatial distribution of soil PSFs, further endeavors should also be made to improve model accuracy and interpretability for a better understanding of the interaction and processes between environmental predictors and soil PSFs at permafrost regions.
Retrieving Photometric Properties and Soil Moisture Content of Tidal Flats Using Bidirectional Spectral Reflectance

Moisture content in tidal flats changes frequently and spatially on account of tidal fluctuations, which greatly influence the reflectance of the tidal flat surface. Precise prediction of the spatial-temporal variation of tidal flats’ moisture content is an important foundation of surface bio-geophysical information research by remote sensing. In this paper, we first measured the multi-angle reflectance of soil samples obtained from tidal flats in the northeastern Dongtai, Jiangsu Province, China, in the laboratory. Then, based on the particle swarm optimization (PSO) algorithm, we retrieved the photometric characteristics of the soil surface by employing the SOILSPECT bidirectional reflectance model. Finally, the soil moisture content was retrieved by introducing the equivalent water thickness of the soil. The results showed that: (i) A significant correlation existed between the retrieved equivalent water thickness and the measured soil moisture content. The SOILSPECT model is capable of estimating soil moisture with high precision by using multi-angle reflectance. (ii) Retrieved values of single scattering albedo (ω) were consistent with the variation of soil moisture content. The roughness parameter (h) and the asymmetry factor (Θ) were consistent with the structure and particle composition of the soil surface in dry soil samples. (iii) When the soil samples were soaked with water, the roughness parameter (h) and the type of scattering on the soil surface both showed irregular changes. These results support the importance of using the measured soil particle size as one of the parameters for the retrieval of soil moisture content, which is a method that should be used cautiously, especially in tidal flats.
An Intimate Analysis of Cuckoo Hashing with a Stash

Cuckoo Hashing is a dictionary data structure in which a data item is stored in a small constant number of possible locations. It has the appealing property that the data structure size is a small constant times larger than the combined size of all inserted data elements. However, many applications, especially cryptographic applications and Oblivious RAM, require insertions, builds and accesses to have a negligible failure probability, which standard Cuckoo Hashing cannot simultaneously achieve. An alternative proposal introduced by Kirsch et al. is to store elements which cannot be placed in the main table in a “stash”, reducing the failure probability to O ( n − s ) where n is the table size and s any constant stash size. This failure probability is still not negligible. Goodrich and Mitzenmacher showed that the failure probability can be made negligible in some parameter N when n = Ω(log 7 ( N )) and s = Θ(log( N )). This paper presents a tighter analysis which shows failure probability negligible in N for all n = ω (log( N )) (which is asymptotically optimal) and presents explicit constants for the failure probability upper bound.
A Novel Strategy to Reconstruct NDVI Time-Series with High Temporal Resolution from MODIS Multi-Temporal Composite Products

Vegetation indices (VIs) data derived from satellite imageries play a vital role in land surface vegetation and dynamic monitoring. Due to the excessive noises (e.g., cloud cover, atmospheric contamination) in daily VI data, temporal compositing methods are commonly used to produce composite data to minimize the negative influence of noise over a given compositing time interval. However, VI time series with high temporal resolution were preferred by many applications such as vegetation phenology and land change detections. This study presents a novel strategy named DAVIR-MUTCOP (DAily Vegetation Index Reconstruction based on MUlti-Temporal COmposite Products) method for normalized difference vegetation index (NDVI) time-series reconstruction with high temporal resolution. The core of the DAVIR-MUTCOP method is a combination of the advantages of both original daily and temporally composite products, and selecting more daily observations with high quality through the temporal variation of temporally corrected composite data. The DAVIR-MUTCOP method was applied to reconstruct high-quality NDVI time-series using MODIS multi-temporal products in two study areas in the continental United States (CONUS), i.e., three field experimental sites near Mead, Nebraska from 2001 to 2012 and forty-six AmeriFlux sites evenly distributed across CONUS from 2006 to 2010. In these two study areas, the DAVIR-MUTCOP method was also compared to several commonly used methods, i.e., the Harmonic Analysis of Time-Series (HANTS) method using original daily observations, Savitzky–Golay (SG) filtering using daily observations with cloud mask products as auxiliary data, and SG filtering using temporally corrected composite data. The results showed that the DAVIR-MUTCOP method significantly improved the temporal resolution of the reconstructed NDVI time series. It performed the best in reconstructing NDVI time-series across time and space (coefficient of determination (R2 = 0.93 ~ 0.94) between reconstructed NDVI and ground-observed LAI). DAVIR-MUTCOP method presented the highest robustness and accuracy with the change of the filtering parameter (R2 = 0.99 ~ 1.00, bias = 0.001, root mean square error (RMSE) = 0.020). Only MODIS data were used in this study; nevertheless, the DAVIR-MUTCOP method proposed a universal and potential way to reconstruct daily time series of other VIs or from other operational sensors, e.g., AVHRR and VIIRS.
Deep Neural Networks for Quantitative Damage Evaluation of Building Losses Using Aerial Oblique Images: Case Study on the Great Wall (China)

Automated damage evaluation is of great importance in the maintenance and preservation of heritage structures. Damage investigation of large cultural buildings is time-consuming and labor-intensive, meaning that many buildings are not repaired in a timely manner. Additionally, some buildings in harsh environments are impossible to reach, increasing the difficulty of damage investigation. Oblique images facilitate damage detection in large buildings, yet quantitative damage information, such as area or volume, is difficult to generate. In this paper, we propose a method for quantitative damage evaluation of large heritage buildings in wild areas with repetitive structures based on drone images. Unlike existing methods that focus on building surfaces, we study the damage of building components and extract hidden linear symmetry information, which is useful for localizing missing parts in architectural restoration. First, we reconstruct a 3D mesh model based on the photogrammetric method using high-resolution oblique images captured by drone. Second, we extract 3D objects by applying advanced deep learning methods to the images and projecting the 2D object segmentation results to 3D mesh models. For accurate 2D object extraction, we propose an edge-enhanced method to improve the segmentation accuracy of object edges. 3D object fragments from multiple views are integrated to build complete individual objects according to the geometric features. Third, the damage condition of objects is estimated in 3D space by calculating the volume reduction. To obtain the damage condition of an entire building, we define the damage degree in three levels: no or slight damage, moderate damage and severe damage, and then collect statistics on the number of damaged objects at each level. Finally, through an analysis of the building structure, we extract the linear symmetry surface from the remaining damaged objects and use the symmetry surface to localize the positions of missing objects. This procedure was tested and validated in a case study (the Jiankou Great Wall in China). The experimental results show that in terms of segmentation accuracy, our method obtains results of 93.23% mAP and 84.21% mIoU on oblique images and 72.45% mIoU on the 3D mesh model. Moreover, the proposed method shows effectiveness in performing damage assessment of objects and missing part localization.
Copper Oxide Nanoparticles Prepared by Solid State Thermal Decomposition: Synthesis and Characterization

In this paper, we have focused on the preparation and characterization of copper oxide nanoparticles by solid state thermal decomposition of copper(I) iodide in the presence of thiosemicarbazone ligands without the need for a catalyst, employing toxic solvent, template or surfactant and complicated equipment, which makes it efficient, one-step, simple and environment-friendly. CuO nanoparticles were achieved at 600 ˚C for 3 h as black products and characterized by Fourier transform infrared spectroscopy (FT-IR), X-ray powder diffraction (XRD) and transmission electron microscopy (TEM). The FT-IR spectra of black powders prepared show absorption maxima at ≈ 525 cm-1 which are due to Cu-O stretching mode. Also, all the X-ray diffraction peaks could be readily assigned to those of crystalline CuO. The absence of any residual ligand traces or other phases in the FT-IR spectra and XRD patterns confirmed the preparation of high purity and single phase copper oxide nanoparticles. The TEM images show that the synthesized copper oxide nanoparticles are of plate like shape with average diameters of 10 – 20 nm. On the basis of the above results, the use of thiosemicarbazone ligands at the presence of suitable transition metal ions is potentially capable of forming other transition metal oxide nanoparticles by solid state thermal decomposition.
Patch-Based Change Detection Method for SAR Images with Label Updating Strategy

Convolutional neural networks (CNNs) have been widely used in change detection of synthetic aperture radar (SAR) images and have been proven to have better precision than traditional methods. A two-stage patch-based deep learning method with a label updating strategy is proposed in this paper. The initial label and mask are generated at the pre-classification stage. Then a two-stage updating strategy is applied to gradually recover changed areas. At the first stage, diversity of training data is gradually restored. The output of the designed CNN network is further processed to generate a new label and a new mask for the following learning iteration. As the diversity of data is ensured after the first stage, pixels within uncertain areas can be easily classified at the second stage. Experiment results on several representative datasets show the effectiveness of our proposed method compared with several existing competitive methods.