bluuebunny/crossref_metadata_2025 huggingface dataset:
Pharmacological Strategies for Suicide Prevention Based on the Social Pain Model: A Scoping Review
Authors: Rajkumar Ravi Philip
Published: August 2022

Suicidal behaviour is a public health problem whose magnitude is both substantial and increasing. Since many individuals seek medical treatment following a suicide attempt, strategies aimed at reducing further attempts in this population are a valid and feasible secondary prevention approach. An evaluation of the available evidence suggests that existing treatment approaches have a limited efficacy in this setting, highlighting the need for innovative approaches to suicide prevention. Existing research on the neurobiology of social pain has highlighted the importance of this phenomenon as a risk factor for suicide, and has also yielded several attractive targets for pharmacological strategies that could reduce suicidality in patients with suicidal ideation or a recent attempt. In this paper, the evidence related to these targets is synthesized and critically evaluated. The way in which social pain is related to the “anti-suicidal” properties of recently approved treatments, such as ketamine and psilocybin, is examined. Such strategies may be effective for the short-term reduction in suicidal ideation and behaviour, particularly in cases where social pain is identified as a contributory factor. These pharmacological approaches may be effective regardless of the presence or absence of a specific psychiatric diagnosis, but they require careful evaluation.
Equal Radiation Frequencies from Different Transitions in the Non-Relativistic Quantum Mechanical Hydrogen Atom
Authors: Do Tuan K. , Phan Trung V.
Published: August 2022

Is it possible that two different transitions in the non-relativistic quantum mechanical model of the hydrogen atom give the same frequency of radiation? That is, can different energy level transitions in a hydrogen atom have the same photon radiation frequency? This question, which was asked during a Ph.D. oral exam in 1997 at the University of Colorado Boulder, is well-known among physics graduate students. We show a general solution to this question, in which all equifrequency transition pairs can be obtained from the set of solutions of a Diophantine equation. This fun puzzle is a simple yet concrete example of how number theory can be relevant to quantum systems, a curious theme that emerges in theoretical physics but is usually inaccessible to a general audience.
Unpicking the Gender Gap: Examining Socio-Demographic Factors and Repair Resources in Clothing Repair Practice
Authors: McQueen Rachel H. , McNeill Lisa S. , Huang Qinglan , Potdar Balkrushna
Published: August 2022

Increased fashion consumption spurred by fast fashion has led to excessive textile waste, giving rise to a global crisis as textile waste pollutes land and waterways, while landfill and incineration contribute to global greenhouse gas emissions. Extending a product’s life for as long as possible is a core principle of the circular economy (CE) to ensure that the maximum value of the original product is realized over its lifetime. As such, repair is an essential component of a CE because it supports the preferred waste hierarchy elements of reduce and reuse, with recycling being the last resort in a CE necessary to close resource loops. Consumers are an essential enabler of a CE; therefore, it is critical to understand consumers’ characteristics in the context of behaviors such as repair. The purpose of this study was to examine the role of gender on engagement in clothing repair practices; women have often only been the focus of clothing repair studies. An online survey was conducted to collect responses from Canadian and U.S. consumers (n = 512). Findings showed that self-repair was the most common form of clothing repair, with women being more highly engaged in self-repair practices, increasing with age. Paid repair is the type of repair that has the lowest level of engagement, and there are only negligible differences between the genders. Men utilize unpaid forms of repair more than women. However, among the youngest age group (18–24), both genders are equally likely to have clothing repaired for free. Gender gaps exist, but opportunities for increased utilization in repair can be created to encourage full participation within a CE. In particular, the findings point to the importance of increasing repair activities amongst men and younger consumers.
“Ritual and Magic” in Buddhist Visual Culture from the Bird Totem
Authors: Yan Zhilong , Zhang Aixin
Published: August 2022

Despite numerous research findings related to medieval Chinese Buddhism, the witchcraft role of bird totems in Buddhist history has not received sufficient attention. In order to fill this gap, this paper analyzes how Buddhist monks in medieval China developed a close relationship with bird-totem worship. This relationship has been documented in Buddhist scriptures, rituals, oral traditions, biographies, and mural art. Although bird-totem worship was practiced in many regions of medieval China, this paper specifically examines the visual culture of bird totems in Tibetan and Chinese Buddhism. Furthermore, some details of this culture were recorded in Buddhist texts and images. According to these works, various bird-totem patterns and symbols are believed to be effective ritual arts used by Tibetan and Chinese Buddhist monks to influence nature and the supernatural through ritual and magic.
Hyperzographia in Neglect Exposing a Spatial Dissociation between Painting and Writing—A Case Study
Authors: Camenzind Magdalena , Eberhard-Moscicka Aleksandra K. , Cazzoli Dario , Müri René M.
Published: August 2022

The paper depicts and describes the observation of a remarkable post-stroke production of paintings made by a 54-year-old, right-handed man who suffered an acute right hemispheric stroke. The patient’s post-stroke productivity and the spatial distribution of text and drawings were assessed by means of structural analysis of the paintings, as well as neuropsychological and creativity testing. Compared to the age-matched healthy control group, the patient did not only produce more valid answers in the verbal creativity task, but he also drew more images in the figural creativity task. Most strikingly, the painted images were located on the right side in 70% of the paintings, while the text was aligned to the left side in 42% of the paintings. This dissociation between writing and painting behavior was further mirrored in the patient’s neuropsychological performance in a reading test and in a design fluency task. This observation of an increased post-stroke production of paintings may coin a new term, i.e., “hyperzographia”, in analogy to hypergraphia. Additionally, the puzzling dissociation of the writing and painting behavior highlights an important new clinical aspect concerning a differential influence of hemispatial neglect on writing and painting.
Adopting Hyperspectral Anomaly Detection for Near Real-Time Camouflage Detection in Multispectral Imagery
Authors: Hupel Tobias , Stütz Peter
Published: August 2022

Tactical reconnaissance using small unmanned aerial vehicles has become a common military scenario. However, since their sensor systems are usually limited to rudimentary visual or thermal imaging, the detection of camouflaged objects can be a particularly hard challenge. With respect to SWaP-C criteria, multispectral sensors represent a promising solution to increase the spectral information that could lead to unveiling camouflage. Therefore, this paper investigates and evaluates the applicability of four well-known hyperspectral anomaly detection methods (RX, LRX, CRD, and AED) and a method developed by the authors called local point density (LPD) for near real-time camouflage detection in multispectral imagery based on a specially created dataset. Results show that all targets in the dataset could successfully be detected with an AUC greater than 0.9 by multiple methods, with some methods even reaching an AUC relatively close to 1.0 for certain targets. Yet, great variations in detection performance over all targets and methods were observed. The dataset was additionally enhanced by multiple vegetation indices (BNDVI, GNDVI, and NDRE), which resulted in generally higher detection performances of all methods. Overall, the results demonstrated the general applicability of the hyperspectral anomaly detection methods for camouflage detection in multispectral imagery.
Standoff Infrared Measurements of Chemical Plume Dynamics in Complex Terrain Using a Combination of Active Swept-ECQCL Laser Spectroscopy with Passive Hyperspectral Imaging
Authors: Phillips Mark C. , Bernacki Bruce E. , Conry Patrick T. , Brown Michael J.
Published: August 2022

Chemical plume detection and modeling in complex terrain present numerous challenges. We present experimental results from outdoor releases of two chemical tracers (sulfur hexafluoride and Freon-152a) from different locations in mountainous terrain. Chemical plumes were detected using two standoff instruments collocated at a distance of 1.5 km from the plume releases. A passive long-wave infrared hyperspectral imaging system was used to show time- and space-resolved plume transport in regions near the source. An active infrared swept-wavelength external cavity quantum cascade laser system was used in a standoff configuration to measure quantitative chemical column densities with high time resolution and high sensitivity along a single measurement path. Both instruments provided chemical-specific detection of the plumes and provided complementary information over different temporal and spatial scales. The results show highly variable plume propagation dynamics near the release points, strongly dependent on the local topography and winds. Effects of plume stagnation, plume splitting, and plume mixing were all observed and are explained based on local topographic and wind conditions. Measured plume column densities at distances ~100 m from the release point show temporal fluctuations over ~1 s time scales and spatial variations over ~1 m length scales. The results highlight the need for high-speed and spatially resolved measurement techniques to provide validation data at the relevant spatial and temporal scales required for high-fidelity terrain-aware microscale plume propagation models.
Sensing Requirements and Vision-Aided Navigation Algorithms for Vertical Landing in Good and Low Visibility UAM Scenarios
Authors: Veneruso Paolo , Opromolla Roberto , Tiana Carlo , Gentile Giacomo , Fasano Giancarmine
Published: August 2022

To support the rapid development of the Urban Air Mobility framework, safe navigation must be ensured to Vertical Take-Off and Landing aircraft, especially in the approach and landing phases. Visual sensors have the potential of providing accurate measurements with reduced budgets, although integrity issues, as well as performance degradation in low visibility and highly dynamic environments, may pose challenges. In this context, this paper focuses on autonomous navigation during vertical approach and landing procedures and provides three main contributions. First, visual sensing requirements relevant to Urban Air Mobility scenarios are defined considering realistic landing trajectories, landing pad dimensions, and wind effects. Second, a multi-sensor-based navigation architecture based on an Extended Kalman Filter is presented which integrates visual estimates with inertial and GNSS measurements and includes different operating modes and ad hoc integrity checks. The presented processing pipeline is built to provide the required navigation performance in different conditions including day/night flight, atmospheric disturbances, low visibility, and can support the autonomous initialization of a missed approach procedure. Third, performance assessment of the proposed architecture is conducted within a highly realistic simulation environment which reproduces real world scenarios and includes variable weather and illumination conditions. Results show that the proposed architecture is robust with respect to dynamic and environmental challenges, providing cm-level positioning uncertainty in the final landing phase. Furthermore, autonomous initialization of a Missed Approach Procedure is demonstrated in case of loss of visual contact with the landing pad and consequent increase of the self-estimated navigation uncertainty.
A Novel Double Ensemble Algorithm for the Classification of Multi-Class Imbalanced Hyperspectral Data
Authors: Quan Daying , Feng Wei , Dauphin Gabriel , Wang Xiaofeng , Huang Wenjiang , Xing Mengdao
Published: August 2022

The class imbalance problem has been reported to exist in remote sensing and hinders the classification performance of many machine learning algorithms. Several technologies, such as data sampling methods, feature selection-based methods, and ensemble-based methods, have been proposed to solve the class imbalance problem. However, these methods suffer from the loss of useful information or from artificial noise, or result in overfitting. A novel double ensemble algorithm is proposed to deal with the multi-class imbalance problem of the hyperspectral image in this paper. This method first computes the feature importance values of the hyperspectral data via an ensemble model, then produces several balanced data sets based on oversampling and builds a number of classifiers. Finally, the classification results of these diversity classifiers are combined according to a specific ensemble rule. In the experiment, different data-handling methods and classification methods including random undersampling (RUS), random oversampling (ROS), Adaboost, Bagging, and random forest are compared with the proposed double random forest method. The experimental results on three imbalanced hyperspectral data sets demonstrate the effectiveness of the proposed algorithm.
Laboratory Evaluation of the (355, 532) nm Particle Depolarization Ratio of Pure Pollen at 180.0° Lidar Backscattering Angle
Authors: Cholleton Danaël , Rairoux Patrick , Miffre Alain
Published: August 2022

While pollen is expected to impact public human health and the Earth’s climate more and more in the coming decades, lidar remote sensing of pollen has become an important developing research field. To differentiate among the pollen taxa, a polarization lidar is an interesting tool since pollen exhibit non-spherical complex shapes. A key attribute is thus the lidar particle depolarization ratio (PDR) of pollen, which is however difficult to quantify as pollen are large and complex-shaped particles, far beyond the reach of light scattering numerical simulations. In this paper, a laboratory π-polarimeter is used to accurately evaluate the PDR of pure pollen, for the first time at the lidar exact backscattering angle of 180.0°. We hence reveal the lidar PDR of pure ragweed, ash, birch, pine, cypress and spruce pollens at 355 and 532 nm lidar wavelengths, as presented at the ELC 2021 conference. A striking result is the spectral dependence of the lidar PDR, highlighting the importance of dual-wavelength (or more) polarization lidars to identify pollen taxa. These spectral and polarimetric fingerprints of pure pollen, as they are accurate, can be used by the lidar community to invert multi-wavelength lidar polarization measurements involving pollen.
CO2 in Beijing and Xianghe Observed by Ground-Based FTIR Column Measurements and Validation to OCO-2/3 Satellite Observations
Authors: Zhou Minqiang , Ni Qichen , Cai Zhaonan , Langerock Bavo , Nan Weidong , Yang Yang , Che Ke , Yang Dongxu , Wang Ting , Liu Yi , Wang Pucai
Published: August 2022

Monitoring the atmospheric CO2 columns inside and around a city is of great importance to understand the temporal–spatial variation of XCO2 near strong anthropogenic emissions. In this study, we use two FTIR CO2 column measurements in Beijing (Bruker EM27/SUN) and Xianghe (Bruker IFS 125HR) between 2019 and 2021 to investigate the differences of XCO2 between Beijing (urban) and Xianghe (suburb) in North China and to validate the OCO-2 and OCO-3 satellite XCO2 retrievals. The mean and standard deviation (std) of the ΔXCO2 between Beijing and Xianghe (Beijing–Xianghe) observed by two FTIR instruments are 0.206 ± 1.736 ppm, which has a seasonal variation and varies with meteorological conditions (wind speed and wind direction). The mean and std of the XCO2 differences between co-located satellite and FTIR measurements are −0.216 ± 1.578 ppm in Beijing and −0.343 ± 1.438 ppm in Xianghe for OCO-2 and 0.637 ± 1.594 ppm in Beijing and 1.206 ± 1.420 ppm in Xianghe for OCO-3. It is found that the OCO-3 snapshot area mode (SAM) measurements can capture the spatial gradient of XCO2 between urban and suburbs well. However, the FTIR measurements indicate that the OCO-3 SAM measurements are about 0.9–1.4 ppm overestimated in Beijing and Xianghe.
MIMO Radar Sparse Recovery Imaging with Wideband Interference Prediction
Authors: Pu Tao , Tong Ningning , Feng Weike , Wan Pengcheng , Hu Xiaowei
Published: August 2022

Multiple-input multiple-output (MIMO) radar three-dimensional (3D) imaging is widely applied in military and civil fields. However, MIMO is easily affected by wideband interference (WBI). To solve this problem, in this study, we propose a sparse recovery imaging method with WBI prediction based on the predictive recurrent neural network (PredRNN) and the tensor-based smooth L0 (TSL0) algorithm. Firstly, we extract the time-frequency (TF) feature of the historical measured WBI via the short-time Fourier transform (STFT) operation. In this way, we can use PredRNN to exploit the spatiotemporal correlation of the WBI in the TF domain to predict the TF feature of the WBI in the future. Then, we adaptively design the random sparse stepped frequency waveform by selecting non-overlapped frequencies with the WBI according to the predicted WBI TF feature. Finally, we apply the TSL0 algorithm to reconstruct the 3D high-resolution target image from the sparse signal cube. Simulation results show the high performance and robustness of the proposed imaging method in the presence of different WBIs.
Uncertainties of Global Historical Land Use Datasets in Pasture Reconstruction for the Tibetan Plateau
Authors: Hua Lei , Li Shicheng , Gao Deng , Li Wangjun
Published: August 2022

Global historical land use datasets have been widely used in global or regional environmental change studies. Historical pasture data are essential components of these spatially explicit global datasets, and their uncertainties have not been well evaluated. Using the livestock-based historical pasture dataset for the Tibetan Plateau (TP), we evaluated the uncertainties of these representative global historical land use datasets in pasture reconstruction for the TP over the past 300 years in terms of pasture area estimation and spatial pattern mapping. We found that only the Sustainability and the Global Environment (SAGE) dataset can roughly reflect the temporal and spatial characteristics of historical pasture changes on the TP. The History Database of the Global Environment (HYDE) version 3.2 and the Pongratz Julia (PJ) datasets overestimated pasture area for the TP dramatically, with a maximum area ratio of about 221% and 291%, respectively, and the Kaplan and Krumhardt 2010 (KK10) dataset underestimated pasture area for the TP dramatically, with a minimum area ratio of only 9%. As for the spatial pattern, all these global datasets overestimated the spatial scope of grazing activities obviously. The KK10 dataset unreasonably allocated pasture to forest areas in southeastern Tibet because only climate and soil factors were considered in assessing land suitability for grazing. Using population to estimate pasture area and only using natural factors to allocate pasture area into grids is unsuitable for the TP historical pasture reconstruction. In the future, more information directly related to grazing activities, e.g., the number of livestock and its spatial distribution, and social-cultural factors, including technology and diet, should be used for area estimation and spatial pattern mapping to improve the accuracy of pasture data in these global datasets.
Forty Years of Wetland Status and Trends Analyses in the Great Lakes Using Landsat Archive Imagery and Google Earth Engine
Authors: Amani Meisam , Kakooei Mohammad , Ghorbanian Arsalan , Warren Rebecca , Mahdavi Sahel , Brisco Brian , Moghimi Armin , Bourgeau-Chavez Laura , Toure Souleymane , Paudel Ambika , Sulaiman Ablajan , Post Richard
Published: August 2022

Wetlands provide many benefits, such as water storage, flood control, transformation and retention of chemicals, and habitat for many species of plants and animals. The ongoing degradation of wetlands in the Great Lakes basin has been caused by a number of factors, including climate change, urbanization, and agriculture. Mapping and monitoring wetlands across such large spatial and temporal scales have proved challenging; however, recent advancements in the accessibility and processing efficiency of remotely sensed imagery have facilitated these applications. In this study, the historical Landsat archive was first employed in Google Earth Engine (GEE) to classify wetlands (i.e., Bog, Fen, Swamp, Marsh) and non-wetlands (i.e., Open Water, Barren, Forest, Grassland/Shrubland, Cropland) throughout the entire Great Lakes basin over the past four decades. To this end, an object-based supervised Random Forest (RF) model was developed. All of the produced wetland maps had overall accuracies exceeding 84%, indicating the high capability of the developed classification model for wetland mapping. Changes in wetlands were subsequently assessed for 17 time intervals. It was observed that approximately 16% of the study area has changed since 1984, with the highest increase occurring in the Cropland class and the highest decrease occurring in the Forest and Marsh classes. Forest mostly transitioned to Fen, but was also observed to transition to Cropland, Marsh, and Swamp. A considerable amount of the Marsh class was also converted into Cropland.
Trends in Lesser Prairie-Chicken Habitat Extent and Distribution on the Southern High Plains
Authors: Portillo-Quintero Carlos , Grisham Blake , Haukos David , Boal Clint W. , Hagen Christian , Wan Zhanming , Subedi Mukti , Menkiti Nwasinachi
Published: August 2022

The lesser prairie-chicken (Tympanuchus pallidicinctus) is a species of prairie grouse that occupies grassland ecosystems in the Southern and Central High Plains of the Great Plains. Reduced abundance and occupied ranges have led to increased conservation efforts throughout the species’ range. Habitat loss is considered the predominant cause of these declines. In the Southern High Plains of Texas and New Mexico, lesser prairie-chicken habitat corresponds to the Sand Shinnery Oak Prairie Ecoregion, which is comprised of a mixture of sand shinnery oak (Quercus havardii)-dominated grasslands, sand sagebrush (Artemisia filifolia)-dominated grasslands, and mixed grasslands. In sand shinnery oak–grassland communities, conversion to row-crop agriculture, continuous unmanaged livestock grazing, restriction of natural fire, invasive plant species (e.g., mesquite (Prosopis spp.)), extensive use of herbicides, energy development, and a variety of other factors have also negatively affected ecosystem extent and function. We integrated historical maps and remote sensing-derived information to measure trends in the extent and geographical distribution of sand shinnery oak prairies in eastern New Mexico and northwest Texas. Potential lesser prairie-chicken habitat was reduced by 56% from a potential of 43,258 km2 to 18,908 km2 in ~115 years (since pre-settlement). Our assessment indicated both mixed grasslands and sand shinnery oak-dominated grasslands were transformed from large parcels of existing vegetation communities to urban settlements, row crops, roads, and industrial land uses by the 1970s. Currently, potential habitat is highly fragmented and restricted to isolated locations in Texas and New Mexico, with an increasing dominance in mixed grasslands, especially in the southeastern portion of the lesser prairie-chicken range. Sand shinnery oak-dominated grasslands have been declining rapidly, from 69% of its potential extent in 1985, 65% in 1995, 54% in 2005, to 42% in 2015. Mixed grasslands drastically declined to 50% of its potential distribution by 1985. Since then, it has been stable until the 2005–2015 period when it declined to 45% of its potential extent. Based on the 2015 assessment, the current potential habitat for lesser prairie-chicken is estimated at 18,908 km2 (1,890,800 ha or 4.6 million acres), where 13,126 km2 corresponds to mixed grasslands and 5782 km2 corresponds to sand shinnery oak-dominated grasslands.
Influence of Block Geometry Configuration on Multi-Image Dense Matching
Authors: Bruno Nazarena , Roncella Riccardo , Diotri Fabrizio , Thoeni Klaus , Giacomini Anna
Published: August 2022

Digital photogrammetry is a widespread surveying technique in different fields of application due to its flexibility, versatility and cost-effectiveness. Despite its increasing automation and simplicity, a proper image block design is crucial to ensure high standards of performance and accuracy. Studies on camera network design have been largely dealt with in the scientific literature with reference to image orientation process, while they are still poor on dense matching. This paper investigates the influence of different block geometry configurations on multi-image dense matching. Starting from the same orientation solution, dense matching was performed considering different combinations of number of images and base length distance between the first and the last image within a strip. The raster Digital Elevation Models (DEM) resulting from each sequence of images were compared with a reference DEM to assess accuracy and completeness. The tests were conducted using different cameras and at various test sites to assess different survey conditions and generalize the findings. The presented results provide some operational guidance on block geometry optimization to maximize the accuracy and completeness.
Changes in Glaciers and Glacial Lakes in the Bosula Mountain Range, Southeast Tibet, over the past Two Decades
Authors: Li Jia , Gu Yunyang , Wu Lixin , Guo Lei , Xu Haodong , Miao Zelang
Published: August 2022

Glaciers and glacial lakes in the Bosula Mountain Range need special attention, because their instability may cause disastrous consequences to the downstream settlements and the Sichuan-Tibet Road. The latter is a pivotal traffic line in the Southeast Tibetan Plateau. In order to investigate the state of glaciers and glacial lakes in the Bosula Mountain Range, we estimated the changes in glacier/glacial lake boundaries, glacier surface elevation, and glacier flow velocity between 2000 and 2021 based on multisource remote sensing data. Our results showed that, from the period 2000–2013 to the period 2013–2021, the average shrinking rate of glacier area increased from 0.99 km2/a to 1.74 km2/a, and the average expanding rate of glacial lake area increased from 0.04 km2/a to 0.06 km2/a. From the period 1990–2011 to the period 2015–2019, the average thinning rate of glaciers increased from 0.83 m/a to 1.58 m/a. These results indicate the Bosula Mountain Range is one of the fastest melting glacierized regions in the High Mountain Asia, and the factors that account for this may include quick temperature rise, abundant summer rainfall, and thin debris cover. In spite of strong ice melting, the observed changes in glacier boundaries, surface elevation, and flow velocity show no sign of surge activity, and the frequency of glacier lake outburst has not increased since 1989. Currently, three proglacial lakes that expanded quickly during 2000–2021 are now prominent hazards. They are directly threatened by accidental ice calving and ice avalanche, and their outburst could cause considerable damage to the downstream settlements and the Sichuan-Tibet Road.
Coherence of Eddy Kinetic Energy Variation during Eddy Life Span to Low-Frequency Ageostrophic Energy
Authors: Zhang Zhisheng , Xie Lingling , Zheng Quanan , Li Mingming , Li Junyi , Li Min
Published: August 2022

The evolution of mesoscale eddies is crucial for understanding the ocean energy cascade. In this study, using global reanalysis sea surface velocity data and a mesoscale eddy trajectory product tracked by satellite altimeters, we aimed to reveal the coherence of eddy kinetic energy (EKE) variation to low-frequency ageostrophic energy during the eddy life span. The variation in EKE throughout the eddy life span was highly coherent to that of the seven-day low-passed ageostrophic kinetic energy, with a correlation coefficient of −0.94. The low-frequency ageostrophic motions supplied 38% of the EKE variation in the growing stage of mesoscale eddies and absorbed 42% in the decaying stage. The evolution rate of the EKE during the eddy life span was consistent with the barotropic conversion rate of the low-frequency ageostrophic motions, further confirming the dominant role of low-frequency ageostrophic motions in eddy growth and decay.
Soil Texture and Its Relationship with Environmental Factors on the Qinghai–Tibet Plateau
Authors: Liu Yadong , Wu Xiaodong , Wu Tonghua , Zhao Lin , Li Ren , Li Wangping , Hu Guojie , Zou Defu , Ni Jie , Du Yizhen , Wang Mengjuan , Li Zhihong , Wei Xianhua , Yan Xuchun
Published: August 2022

Soil texture data are the basic input parameters for many Earth System Models. As the largest middle–low altitude permafrost regions on the planet, the land surface processes on the Qinghai–Tibet Plateau can affect regional and even global water and energy cycles. However, the spatial distribution of soil texture data on the plateau is largely unavailable due to the difficulty of obtaining field data. Based on collection data from field surveys and environmental factors, we predicted the spatial distribution of clay, silt, and sand contents at a 1 km resolution, from 0–5, 5–15, 15–30, 30–60, 60–100, and 100–200 cm soil depth layers. The random forest models were constructed to predict the soil texture according to the relationships between environmental factors and soil texture data. The results showed that the soil particles of the QTP are dominated by sand, which accounts for more than 70% of the total particles. As for the spatial distribution, silt and clay contents are high in the southeast plateau, and low values of silt and clay mainly appeared in the northwest plateau. Climate and NDVI values are the most important factors that affect the spatial distribution of soil texture on the QTP. The results of this study provide the soil texture data at different depths for the whole plateau at a spatial resolution of 1 km, and the dataset can be used as an input parameter for many Earth System Models.
Soil Moisture Mapping with Moisture-Related Indices, OPTRAM, and an Integrated Random Forest-OPTRAM Algorithm from Landsat 8 Images
Authors: Acharya Umesh , Daigh Aaron L. M. , Oduor Peter G.
Published: August 2022

Remote sensing tools have been extensively used for large-scale soil moisture (SM) mapping in recent years, using Landsat satellite images. Rainfall, soil clay percentage, and the standardized precipitation index play key roles in determining the moisture content of crop fields. The objective of this study was to (i) calculate and determine the effectiveness of moisture-related indices in predicting surface SM, (ii) predict surface SM from satellite images using the Optical Trapezoid Model (OPTRAM), and (iii) evaluate if the OPTRAM predictions can be improved by incorporating weather station, soil, and crop data with a random forest algorithm. The ENVI® platform was used to create moisture-related indices maps, and the Google Earth Engine (GEE) was used to prepare OPTRAM maps. The results showed a very weak relationship between the moisture-related indices and surface SM content where r2 and slopes were ˂0.10 and ˂0.20, respectively. OPTRAM SM, when compared with in situ surface moisture, showed weak relationship with regression values ˂0.2. Surface SM was then predicted using random forest regression using OPTRAM moisture values, rainfall, and the standardized precipitation index (SPI), and percent clay showed high goodness of fit (r2 = 0.69) and low root mean square error (RMSE = 0.053 m3 m−3).
Spatiotemporal Variations in Drought and Vegetation Response in Inner Mongolia from 1982 to 2019
Authors: Wei Yujiao , Zhu Lin , Chen Yun , Cao Xinyu , Yu Huilin
Published: August 2022

Drought events cause ecological problems, including reduced water resources and degraded vegetation. Quantifying vegetation responses to drought is essential for ecological management. However, in existing research, the response relationships (correlations and lags) were typically determined based on Pearson correlation coefficient and the resultant lag times were constrained by the spatial and temporal resolutions of the analyzed data. Inner Mongolia is an important ecological barrier in northern China. Ecological security is one of the most concerned issues of the region’s sustainable development. Herein, we combined Global Inventory Modeling and Mapping Studies (GIMMS) normalized difference vegetation index (NDVI3g) with Systeme Probatoire d’Observation de la Terra-vegetation (SPOT-VGT) NDVI data through spatial downscaling. The obtained 1 km-resolution NDVI dataset spanning Inner Mongolia from 1982 to 2019 was used to represent the refined vegetation distribution. The standardized precipitation evapotranspiration index (SPEI) derived from gridded meteorological data was used to measure drought over the same period. We investigated the spatiotemporal characteristics of vegetation and drought in the region in the past 38 years. We then discussed changes in different vegetation responses to drought across eastern Inner Mongolia using cross wavelet transform (XWT) and wavelet coherence (WTC). The results reveal that in 82.4% of the study area, NDVI exhibited rising trends, and the SPEI values exhibited declining trends in 78.5% of the area. In eastern Inner Mongolia, the grassland NDVI was positively correlated with SPEI and significantly affected by drought events, while NDVI in forestlands, including shrubs, broad-leaved forests, and coniferous forests, was negatively correlated with SPEI in the short term and weakly affected by drought. The NDVI lag times behind SPEI in grasslands, coniferous forests, and broad-leaved forests were 1–1.5, 4.5, and 7–7.5 months, respectively. These findings provide a scientific foundation for environmental preservation in the region.
Editorial on Special Issue “Techniques and Applications of UAV-Based Photogrammetric 3D Mapping”
Authors: Jiang Wanshou , Jiang San , Xiao Xiongwu
Published: August 2022

Recently, 3D mapping has begun to play an increasingly important role in photogrammetric applications [...]
Comparison of UAV RGB Imagery and Hyperspectral Remote-Sensing Data for Monitoring Winter Wheat Growth
Authors: Feng Haikuan , Tao Huilin , Li Zhenhai , Yang Guijun , Zhao Chunjiang
Published: August 2022

Although crop-growth monitoring is important for agricultural managers, it has always been a difficult research topic. However, unmanned aerial vehicles (UAVs) equipped with RGB and hyperspectral cameras can now acquire high-resolution remote-sensing images, which facilitates and accelerates such monitoring. To explore the effect of monitoring a single crop-growth indicator and multiple indicators, this study combines six growth indicators (plant nitrogen content, above-ground biomass, plant water content, chlorophyll, leaf area index, and plant height) into the new comprehensive growth index (CGI). We investigate the performance of RGB imagery and hyperspectral data for monitoring crop growth based on multi-time estimation of the CGI. The CGI is estimated from the vegetation indices based on UAV hyperspectral data treated by linear, nonlinear, and multiple linear regression (MLR), partial least squares (PLSR), and random forest (RF). The results are as follows: (1) The RGB-imagery indices red reflectance (r), the excess-red index (EXR), the vegetation atmospherically resistant index (VARI), and the modified green-red vegetation index (MGRVI), as well as the spectral indices consisting of the linear combination index (LCI), the modified simple ratio index (MSR), the simple ratio vegetation index (SR), and the normalized difference vegetation index (NDVI), are more strongly correlated with the CGI than a single growth-monitoring indicator. (2) The CGI estimation model is constructed by comparing a single RGB-imagery index and a spectral index, and the optimal RGB-imagery index corresponding to each of the four growth stages in order is r, r, r, EXR; the optimal spectral index is LCI for all four growth stages. (3) The MLR, PLSR, and RF methods are used to estimate the CGI. The MLR method produces the best estimates. (4) Finally, the CGI is more accurately estimated using the UAV hyperspectral indices than using the RGB-image indices.
Local Scale (3-m) Soil Moisture Mapping Using SMAP and Planet SuperDove
Authors: Du Jinyang , Kimball John S. , Bindlish Rajat , Walker Jeffrey P. , Watts Jennifer D.
Published: August 2022

A capability for mapping meter-level resolution soil moisture with frequent temporal sampling over large regions is essential for quantifying local-scale environmental heterogeneity and eco-hydrologic behavior. However, available surface soil moisture (SSM) products generally involve much coarser grain sizes ranging from 30 m to several 10 s of kilometers. Hence, a new method is proposed to estimate 3-m resolution SSM using a combination of multi-sensor fusion, machine-learning (ML), and Cumulative Distribution Function (CDF) matching approaches. This method established favorable SSM correspondence between 3-m pixels and overlying 9-km grid cells from overlapping Planet SuperDove (PSD) observations and NASA Soil Moisture Active-Passive (SMAP) mission products. The resulting 3-m SSM predictions showed improved accuracy by reducing absolute bias and RMSE by ~0.01 cm3/cm3 over the original SMAP data in relation to in situ soil moisture measurements for the Australian Yanco region while preserving the high sampling frequency (1–3 day global revisit) and sensitivity to surface wetness (R 0.865) from SMAP. Heterogeneous soil moisture distributions varying with vegetation biomass gradients and irrigation regimes were generally captured within a selected study area. Further algorithm refinement and implementation for regional applications will allow for improvement in water resources management, precision agriculture, and disaster forecasts and responses.
Data-Free Area Detection and Evaluation for Marine Satellite Data Products
Authors: Zhang Shengjia , Zhu Hongchun , Li Jie , Yang Yanrui , Liu Haiying
Published: August 2022

The uncertainty verification of satellite ocean color products and the bias analysis of multiple data are both indispensable in the evaluation of ocean color products. Incidentally, ocean color products often have missing information that causes the methods mentioned above to be difficult to evaluate these data effectively. We propose an analysis and evaluation method based on data-free area. The objective of this study is to evaluate the quality of ocean color products with respect to information integrity and continuity. First, we use an improved Spectral Angle Mapper, also called ISAM. It can automatically obtain the optimal threshold value for each class of objects. Then, based on ISAM, we perform spectral information mining on first-level Yellow Sea and Bohai Sea data obtained from the Geostationary Ocean Color Imager (GOCI), Moderate Resolution Imaging Spectroradiometer (MODIS) and Ocean and Land Color Instrument (OLCI). In this manner, quantitative results of information related to data-free areas of ocean data products are obtained. The findings indicate that the product data of OLCI are optimal with respect to both completeness and continuity. GOCI and MODIS have striking similarities in their quantitative or visualization results for both evaluation metrics. Moreover, a concomitant phenomenon of ocean-covered objects is apparent in the data-free area with temporal and spatial distribution characteristics. The two characteristics are subsequently explored for further analysis. The evaluation method adopted in this study can help to enrich the content of ocean color product evaluation, facilitate the research of cloud detection algorithms and further understand the composition of the data-free regional information of marine data products. The method proposed in this study has a wide application value.
Classification of Precipitation Types Based on Machine Learning Using Dual-Polarization Radar Measurements and Thermodynamic Fields
Authors: Shin Kyuhee , Kim Kwonil , Song Joon Jin , Lee GyuWon
Published: August 2022

An accurate classification of the precipitation type is important for forecasters, particularly in the winter season. We explored the capability of three supervised machine learning (ML) methods (decision tree, random forest, and support vector machine) to determine ground precipitation types (no precipitation, rain, mixed, and snow) for winter precipitation. We provided information on the particle characteristics within a radar sampling volume and the environmental condition to the ML model with the simultaneous use of polarimetric radar variables and thermodynamic variables. The ML algorithms were optimized using predictor selection and hyperparameter tuning in order to maximize the computational efficiency and accuracy. The random forest (RF) had the highest skill scores in all precipitation types and outperformed the operational scheme. The spatial distribution of the precipitation type from the RF model showed a good agreement with the surface observation. As a result, RF is recommended for the real-time precipitation type classification due to its easy implementation, computational efficiency, and satisfactory accuracy. In addition to the validation, this study confirmed the strong dependence of precipitation type on wet-bulb temperature and a 1000–850 hPa layer thickness. The results also suggested that the base heights of the radar echo are useful in discriminating non-precipitating area.
DGPolarNet: Dynamic Graph Convolution Network for LiDAR Point Cloud Semantic Segmentation on Polar BEV
Authors: Song Wei , Liu Zhen , Guo Ying , Sun Su , Zu Guidong , Li Maozhen
Published: August 2022

Semantic segmentation in LiDAR point clouds has become an important research topic for autonomous driving systems. This paper proposes a dynamic graph convolution neural network for LiDAR point cloud semantic segmentation using a polar bird’s-eye view, referred to as DGPolarNet. LiDAR point clouds are converted to polar coordinates, which are rasterized into regular grids. The points mapped onto each grid distribute evenly to solve the problem of the sparse distribution and uneven density of LiDAR point clouds. In DGPolarNet, a dynamic feature extraction module is designed to generate edge features of perceptual points of interest sampled by the farthest point sampling and K-nearest neighbor methods. By embedding edge features with the original point cloud, local features are obtained and input into PointNet to quantize the points and predict semantic segmentation results. The system was tested on the Semantic KITTI dataset, and the segmentation accuracy reached 56.5%
Forest Damage by Super Typhoon Rammasun and Post-Disturbance Recovery Using Landsat Imagery and the Machine-Learning Method
Authors: Zhang Xu , Jiao Hongbo , Chen Guangsheng , Shen Jianing , Huang Zihao , Luo Haiyan
Published: August 2022

Typhoon Rammasun landed on the southern coastal region of Guangdong and Hainan Provinces on 18 July 2014, and is the strongest recorded typhoon since the 1970s in China. It caused enormous losses in human lives, property, and crop yields in two provinces; however, its impact on forests and subsequent recovery has not yet been assessed. Here we detected forest damage area and severity from Typhoon Rammasun using Landsat 8 OLI imagery, the Random Forest (RF) machine-learning algorithm, and univariate image differencing (UID) methods, and the controlling factors on damage severity and canopy greenness recovery were further analyzed. The accuracy evaluations against sample plot data indicated that the RF approach can more accurately detect the affected forest area and damage severity than the UID-based methods, with higher overall accuracy (94%), Kappa coefficient (0.92), and regression coefficient (R2 = 0.81; p < 0.01). The affected forest area in Guangdong and Hainan was 13,556 km2 and 3914 km2, accounting for 13.8% and 18.5% total forest area, respectively. The highest affected forest fractions reached 70% in some cities or counties. The proportions of severe damage category accounted for 20.85% and 21.31% of all affected forests in Guangdong and Hainan, respectively. Our study suggests that increasing tree density and choosing less sensitive tree species would reduce damage from typhoons in vulnerable areas such as fringe, scattered, and high-slope forests. The canopy greenness of damaged forests recovered rapidly within three months for both provinces; however, management strategies should still be applied in the severely damaged areas to sustain forest functions since the persistent forest canopy structure and biomass may require a longer time to recover.
Long-Term Investigation of Aerosols in the Urmia Lake Region in the Middle East by Ground-Based and Satellite Data in 2000–2021
Authors: Abadi Abbas Ranjbar Saadat , Hamzeh Nasim Hossein , Shukurov Karim , Opp Christian , Dumka Umesh Chandra
Published: August 2022

Dried lake beds are some of the largest sources of dust in the world and have caused environmental problems in the surrounding areas in recent decades. In the present work, we studied the monthly and annual occurrence of dust storms at selected weather stations around Urmia Lake in northwestern (NW) Iran. Furthermore, we investigated the variations in the daily aerosol optical depth (AOD at 550 nm) and the Ångström exponent (at 412/470 nm), as well as the vertical profile of the total aerosol extinction coefficient and AOD at 532 nm, using space-borne MODIS (Moderate Resolution Imaging Spectroradiometer) Aqua and CALIPSO Satellite LiDAR data over the Urmia Lake region (36–39°N, 44–47°E). The monthly variations of AOD550 and AOD532 for the regions 37–39°N and 46–59°E were compared, and it was found that the CALIPSO AOD532 and MODIS AOD532 (reconstructed using the Ångström exponent) were in good agreement. In general, the dust storms during 2000–2021 increased the AOD550 above average around the Urmia Lake. The vertical profile of aerosols showed that the largest contribution to total aerosol loading over the Urmia Lake was from 1.5–3 km, 1.5–4 km, 1.5–5 km, and 1.5–3 km during winter, spring, summer, and autumn seasons, respectively.