bluuebunny/crossref_metadata_2025 huggingface dataset:
A Multiview Semantic Vegetation Index for Robust Estimation of Urban Vegetation Cover
Authors: Khan Asim , Asim Warda , Ulhaq Anwaar , Robinson Randall W.
Published: January 2022

Urban vegetation growth is vital for developing sustainable and liveable cities in the contemporary era since it directly helps people’s health and well-being. Estimating vegetation cover and biomass is commonly done by calculating various vegetation indices for automated urban vegetation management and monitoring. However, most of these indices fail to capture robust estimation of vegetation cover due to their inherent focus on colour attributes with limited viewpoint and ignore seasonal changes. To solve this limitation, this article proposed a novel vegetation index called the Multiview Semantic Vegetation Index (MSVI), which is robust to color, viewpoint, and seasonal variations. Moreover, it can be applied directly to RGB images. This Multiview Semantic Vegetation Index (MSVI) is based on deep semantic segmentation and multiview field coverage and can be integrated into any vegetation management platform. This index has been tested on Google Street View (GSV) imagery of Wyndham City Council, Melbourne, Australia. The experiments and training achieved an overall pixel accuracy of 89.4% and 92.4% for FCN and U-Net, respectively. Thus, the MSVI can be a helpful instrument for analysing urban forestry and vegetation biomass since it provides an accurate and reliable objective method for assessing the plant cover at street level.
Stream Boundary Detection of a Hyper-Arid, Polar Region Using a U-Net Architecture: Taylor Valley, Antarctica
Authors: Barlow Mary C. , Zhu Xinxiang , Glennie Craig L.
Published: January 2022

Convolutional neural networks (CNNs) are becoming an increasingly popular approach for classification mapping of large complex regions where manual data collection is too time consuming. Stream boundaries in hyper-arid polar regions such as the McMurdo Dry Valleys (MDVs) in Antarctica are difficult to locate because they have little hydraulic flow throughout the short summer months. This paper utilizes a U-Net CNN to map stream boundaries from lidar derived rasters in Taylor Valley located within the MDVs, covering ∼770 km2. The training dataset consists of 217 (300 × 300 m2) well-distributed tiles of manually classified stream boundaries with diverse geometries (straight, sinuous, meandering, and braided) throughout the valley. The U-Net CNN is trained on elevation, slope, lidar intensity returns, and flow accumulation rasters. These features were used for detection of stream boundaries by providing potential topographic cues such as inflection points at stream boundaries and reflective properties of streams such as linear patterns of wetted soil, water, or ice. Various combinations of these features were analyzed based on performance. The test set performance revealed that elevation and slope had the highest performance of the feature combinations. The test set performance analysis revealed that the CNN model trained with elevation independently received a precision, recall, and F1 score of 0.94±0.05, 0.95±0.04, and 0.94±0.04 respectively, while slope received 0.96±0.03, 0.93±0.04, and 0.94±0.04, respectively. The performance of the test set revealed higher stream boundary prediction accuracies along the coast, while inland performance varied. Meandering streams had the highest stream boundary prediction performance on the test set compared to the other stream geometries tested here because meandering streams are further evolved and have more distinguishable breaks in slope, indicating stream boundaries. These methods provide a novel approach for mapping stream boundaries semi-automatically in complex regions such as hyper-arid environments over larger scales than is possible for current methods.
Identifying Forest Structural Types along an Aridity Gradient in Peninsular Spain: Integrating Low-Density LiDAR, Forest Inventory, and Aridity Index
Authors: Tijerín-Triviño Julián , Moreno-Fernández Daniel , Zavala Miguel A. , Astigarraga Julen , García Mariano
Published: January 2022

Forest structure is a key driver of forest functional processes. The characterization of forest structure across spatiotemporal scales is essential for forest monitoring and management. LiDAR data have proven particularly useful for cost-effectively estimating forest structural attributes. This paper evaluates the ability of combined forest inventory data and low-density discrete return airborne LiDAR data to discriminate main forest structural types in the Mediterranean-temperate transition ecotone. Firstly, we used six structural variables from the Spanish National Forest Inventory (SNFI) and an aridity index in a k-medoids algorithm to define the forest structural types. These variables were calculated for 2770 SNFI plots. We identified the main species for each structural type using the SNFI. Secondly, we developed a Random Forest model to predict the spatial distribution of structural types and create wall-to-wall maps from LiDAR data. The k-medoids clustering algorithm enabled the identification of four clusters of forest structures. A total of six out of forty-one potential LiDAR metrics were utilized in our Random Forest, after evaluating their importance in the Random Forest model. Selected metrics were, in decreasing order of importance, the percentage of all returns above 2 m, mean height of the canopy profile, the difference between the 90th and 50th height percentiles, the area under the canopy curve, and the 5th and the 95th percentile of the return heights. The model yielded an overall accuracy of 64.18%. The producer’s accuracy ranged between 36.11% and 88.93%. Our results confirm the potential of this approximation for the continuous monitoring of forest structures, which is key to guiding forest management in this region.
CO2 Injection Deformation Monitoring Based on UAV and InSAR Technology: A Case Study of Shizhuang Town, Shanxi Province, China
Authors: Zhang Tian , Zhang Wanchang , Yang Ruizhao , Cao Dan , Chen Longfei , Li Dewei , Meng Lingbin
Published: January 2022

Carbon Capture, Utilization and Storage, also referred to as Carbon Capture, Utilization and Sequestration (CCUS), is one of the novel climate mitigation technologies by which CO2 emissions are captured from sources, such as fossil power generation and industrial processes, and further either reused or stored with more attention being paid on the utilization of captured CO2. In the whole CCUS process, the dominant migration pathway of CO2 after being injected underground becomes very important information to judge the possible storage status as well as one of the essential references for evaluating possible environmental affects. Interferometric Synthetic Aperture Radar (InSAR) technology, with its advantages of extensive coverage in surface deformation monitoring and all-weather traceability of the injection processes, has become one of the promising technologies frequently adopted in worldwide CCUS projects. In this study, taking the CCUS sequestration area in Shizhuang Town, Shanxi Province, China, as an example, unmanned aerial vehicle (UAV) photography measurement technology with a 3D surface model at a resolution of 5.3 cm was applied to extract the high-resolution digital elevation model (DEM) of the study site in coordination with InSAR technology to more clearly display the results of surface deformation monitoring of the CO2 injection area. A 2 km surface heaving dynamic processes before and after injection from June 2020 to July 2021 was obtained, and a CO2 migration pathway northeastward was observed, which was rather consistent with the monitoring results by logging and micro-seismic studies. Additionally, an integrated monitoring scheme, which will be the trend of monitoring in the future, is proposed in the discussion.
Quantitative Assessment for the Spatiotemporal Changes of Ecosystem Services, Tradeoff–Synergy Relationships and Drivers in the Semi-Arid Regions of China
Authors: Li Yongge , Liu Wei , Feng Qi , Zhu Meng , Yang Linshan , Zhang Jutao
Published: January 2022

Ecosystem services in arid inland regions are significantly affected by climate change and land use/land cover change associated with agricultural activity. However, the dynamics and relationships of ecosystem services affected by natural and anthropogenic drivers in inland regions are still less understood. In this study, the spatiotemporal patterns of ecosystem services in the Hexi Region were quantified based on multiple high-resolution datasets, the InVEST model and the Revised Wind Erosion Equation (RWEQ) model. In addition, the trade-offs and synergistic relationships among multiple ecosystem services were also explored by Pearson correlation analysis and bivariate spatial autocorrelation, and redundancy analysis (RDA) was also employed to determine the environmental drivers of these services and interactions. The results showed that most ecosystem services had a similar spatial distribution pattern with an increasing trend from northwest to southeast. Over the past 40 years, ecosystem services in the Hexi Region have improved significantly, with the water retention and soil retention increasing by 87.17 × 108 m3 and 287.84 × 108 t, respectively, and the sand fixation decreasing by 369.17 × 104 t. Among these ecosystem services, strong synergistic relationships were detected, while the trade-offs were found to be weak, and showed significant spatial heterogeneity in the Hexi Region. The spatial synergies and trade-offs in the Qilian Mountains were 1.02 and 1.37 times higher than those in the Hexi Corridor, respectively. Human activities were found to exacerbate the trade-offs between ecosystem services by increasing water consumption in the Hexi Corridor, with the exception of carbon storage. In particular, there were significant tradeoffs between food production and water retention, and between soil retention and habitat quality in the oases of the Hexi Corridor, which is affected by rapid population growth and cropland expansion. Additionally, precipitation, temperature and vegetation cover in the Qilian Mountains have increased significantly over the past four decades, and these increases significantly contributed to the enhancements in water retention, carbon storage, habitat quality, soil retention and food production. Nevertheless, the amount of sand fixation significantly decreased, and this was probably associated with the reduction in wind speed over the past four decades. Our results highlighted the importance of climate wetting and water resource management in the enhancement of ecosystem services and the mitigation of food production trade-offs for arid inland regions.
Comparison of Multi-Methods for Identifying Maize Phenology Using PhenoCams
Authors: Guo Yahui , Chen Shouzhi , Fu Yongshuo H. , Xiao Yi , Wu Wenxiang , Wang Hanxi , Beurs Kirsten de
Published: January 2022

Accurately identifying the phenology of summer maize is crucial for both cultivar breeding and fertilizer controlling in precision agriculture. In this study, daily RGB images covering the entire growth of summer maize were collected using phenocams at sites in Shangqiu (2018, 2019 and 2020) and Nanpi (2020) in China. Four phenological dates, including six leaves, booting, heading and maturity of summer maize, were pre-defined and extracted from the phenocam-based images. The spectral indices, textural indices and integrated spectral and textural indices were calculated using the improved adaptive feature-weighting method. The double logistic function, harmonic analysis of time series, Savitzky–Golay and spline interpolation were applied to filter these indices and pre-defined phenology was identified and compared with the ground observations. The results show that the DLF achieved the highest accuracy, with the coefficient of determination (R2) and the root-mean-square error (RMSE) being 0.86 and 9.32 days, respectively. The new index performed better than the single usage of spectral and textural indices, of which the R2 and RMSE were 0.92 and 9.38 days, respectively. The phenological extraction using the new index and double logistic function based on the PhenoCam data was effective and convenient, obtaining high accuracy. Therefore, it is recommended the adoption of the new index by integrating the spectral and textural indices for extracting maize phenology using PhenoCam data.
Reconstructing High-Precision Coral Reef Geomorphology from Active Remote Sensing Datasets: A Robust Spatial Variability Modified Ordinary Kriging Method
Authors: Wang Qi , Xiao Han , Wu Wenzhou , Su Fenzhen , Zuo Xiuling , Yao Guobiao , Zheng Guoqiang
Published: January 2022

Active remote sensing technology represented by multi-beam and lidar provides an important approach for the effective acquisition of underwater coral reef geomorphological information. A spatially continuous surface model of coral reef geomorphology reconstructed from active remote sensing datasets can provide important geomorphological parameters for the research of coral reef geomorphological and ecological changes. However, the surface modeling methods commonly used in previous studies, such as ordinary kriging (OK) and natural neighborhood (NN), often represent a “smoothing effect”, which causes the strong spatial variability of coral reefs to be imprecisely reflected by the reconstructed surfaces, thus affecting the accurate calculation of subsequent geomorphological parameters. In this study, a spatial variability modified OK (OK-SVM) method is proposed to reduce the impact of the “smoothing effect” on the high-precision reconstruction of the complex geomorphology of coral reefs. The OK-SVM adopts a collaborative strategy of global parameter transformation, local residual correction, and extremum correction to modify the spatial variability of the reconstructed model, while maintaining high local accuracy. The experimental results show that the OK-SVM has strong robustness to spatial variability modification. This method was applied to the geomorphological reconstruction of the northern area of a coral atoll in the Nansha Islands, South China Sea, and the performance was compared with that of OK and NN. The results show that OK-SVM has higher numerical accuracy and attribute accuracy in detailed morphological fidelity, and is more adaptable in the geomorphological reconstruction of coral reefs with strong spatial variability. This method is relatively reliable for achieving high-precision reconstruction of complex geomorphology of coral reefs from active remote sensing datasets, and has potential to be extended to other geomorphological reconstruction applications.
Spatiotemporal Analysis of Soil Moisture Variation in the Jiangsu Water Supply Area of the South-to-North Water Diversion Using ESA CCI Data
Authors: Wang Yue , Cao Jianjun , Liu Yongjuan , Zhu Ying , Fang Xuan , Huang Qing , Chen Jian
Published: January 2022

The South-to-North Water Transfer Jiangsu Water Supply Area (JWSA) is a mega inter-basin water transfer area (water source) that provides water resources from JiangHuai, combines drainage and flooding management, and regulates nearby rivers and lakes. Analyzing the spatiotemporal soil moisture dynamics in the area will be informative regarding agricultural drought along with flood disaster assessment and will provide early warning studies. Therefore, we evaluated the quality of European Space Agency Climate Change Initiative Soil Moisture (ESA CCI_SM) data in the South-North Water Transfer JWSA. Furthermore, we utilized ensemble empirical modal decomposition, Mann-Kendall tests, and regression analysis to study the spatiotemporal variation in soil moisture for the past 29 years. The CCI _SM data displayed a high correlation with local soil measurements at nine sites. We next analyzed the CCI_SM data from three pumping stations (the Gaogang, Hongze, and Liushan stations) in the South-North Water Transfer JWSA. These stations had similar periodic characteristics of soil moisture, with significant periodic fluctuations around 3.1 d. The overall soil moisture at the three typical pumping stations demonstrated an increasing trend. We further investigated whether abrupt soil moisture changes existed at each station or not. The spatial distribution of soil moisture in the South-North Water Transfer JWSA was characterized as “dry north and wet south”, with higher soil moisture in winter, followed by autumn, and low soil moisture in spring and summer. Although the linear trend of soil moisture in the South-North Water Transfer JWSA varied in significance, the overall soil moisture in the JWSA has increased over the past 29 years. The areas with significantly enhanced soil moisture are mostly distributed in the Yangzhou and Huai’an areas in the southeastern part of the study area. The areas with significantly decreased soil moisture are small in size and mostly located in northern Xuzhou.
LS-VCE Applied to Stochastic Modeling of GNSS Observation Noise and Process Noise
Authors: Hou Pengyu , Zha Jiuping , Liu Teng , Zhang Baocheng
Published: January 2022

Stochastic models play a crucial role in global navigation satellite systems (GNSS) data processing. Many studies contribute to the stochastic modeling of GNSS observation noise, whereas few studies focus on the stochastic modeling of process noise. This paper proposes a method that is able to jointly estimate the variances of observation noise and process noise. The method is flexible since it is based on the least-squares variance component estimation (LS-VCE), enabling users to estimate the variance components that they are specifically interested in. We apply the proposed method to estimate the variances for the dual-frequency GNSS observation noise and for the process noise of the receiver code bias (RCB). We also investigate the impact of the stochastic model upon parameter estimation, ambiguity resolution, and positioning. The results show that the precision of GNSS observations differs in systems and frequencies. Among the dual-frequency GPS, Galileo, and BDS code observations, the precision of the BDS B3 observations is highest (better than 0.2 m). The precision of the BDS phase observations is better than two millimeters, which is also higher than that of the GPS and Galileo observations. For all three systems, the RCB process noise ranges from 0.5 millimeters to 1 millimeter, with a data sampling rate of 30 s. An improper stochastic model of the observation noise results in an unreliable ambiguity dilution of precision (ADOP) and position dilution of precision (PDOP), thus adversely affecting the assessment of the ambiguity resolution and positioning performance. An inappropriate stochastic model of RCB process noise disturbs the estimation of the receiver clock and the ionosphere delays and is thus harmful for timing and ionosphere retrieval applications.
A Comparison of Various Correction and Blending Techniques for Creating an Improved Satellite-Gauge Rainfall Dataset over Australia
Authors: Chua Zhi-Weng , Kuleshov Yuriy , Watkins Andrew B. , Choy Suelynn , Sun Chayn
Published: January 2022

Satellites offer a way of estimating rainfall away from rain gauges which can be utilised to overcome the limitations imposed by gauge density on traditional rain gauge analyses. In this study, Australian station data along with the Japan Aerospace Exploration Agency’s (JAXA) Global Satellite Mapping of Precipitation (GSMaP) and the Bureau of Meteorology’s (BOM) Australian Gridded Climate Dataset (AGCD) rainfall analysis are combined to develop an improved satellite-gauge rainfall analysis over Australia that uses the strengths of the respective data sources. We investigated a variety of correction and blending methods with the aim of identifying the optimal blended dataset. The correction methods investigated were linear corrections to totals and anomalies, in addition to quantile-to-quantile matching. The blending methods tested used weights based on the error variance to MSWEP (Multi-Source Weighted Ensemble Product), distance to the closest gauge, and the error from a triple collocation analysis to ERA5 and Soil Moisture to Rain. A trade-off between away-from- and at-station performances was found, meaning there was a complementary nature between specific correction and blending methods. The most high-performance dataset was one corrected linearly to totals and subsequently blended to AGCD using an inverse error variance technique. This dataset demonstrated improved accuracy over its previous version, largely rectifying erroneous patches of excessive rainfall. Its modular use of individual datasets leads to potential applicability in other regions of the world.
Assessing Snow Phenology and Its Environmental Driving Factors in Northeast China
Authors: Guo Hui , Wang Xiaoyan , Guo Zecheng , Chen Siyong
Published: January 2022

Snow cover is an important water source and even an Essential Climate Variable (ECV) as defined by the World Meteorological Organization (WMO). Assessing snow phenology and its driving factors in Northeast China will help with comprehensively understanding the role of snow cover in regional water cycle and climate change. This study presents spatiotemporal variations in snow phenology and the relative importance of potential drivers, including climate, geography, and the normalized difference vegetation index (NDVI), based on the MODIS snow products across Northeast China from 2001 to 2018. The results indicated that the snow cover days (SCD), snow cover onset dates (SCOD) and snow cover end dates (SCED) all showed obvious latitudinal distribution characteristics. As the latitude gradually increases, SCD becomes longer, SCOD advances and SCED delays. Overall, there is a growing tendency in SCD and a delayed trend in SCED across time. The variations in snow phenology were driven by mean temperature, followed by latitude, while precipitation, aspect and slope all had little effect on the SCD, SCOD and SCED. With decreasing temperature, the SCD and SCED showed upward trends. The mean temperature has negatively correlation with SCD and SCED and positively correlation with SCOD. With increasing latitude, the change rate of the SCD, SCOD and SCED in the whole Northeast China were 10.20 d/degree, −3.82 d/degree and 5.41 d/degree, respectively, and the change rate of snow phenology in forested areas was lower than that in nonforested areas. At the same latitude, the snow phenology for different underlying surfaces varied greatly. The correlations between the snow phenology and NDVI were mainly positive, but weak correlations accounted for a large proportion.
A Natural Images Pre-Trained Deep Learning Method for Seismic Random Noise Attenuation
Authors: Zhao Haixia , Bai Tingting , Wang Zhiqiang
Published: January 2022

Seismic field data are usually contaminated by random or complex noise, which seriously affect the quality of seismic data contaminating seismic imaging and seismic interpretation. Improving the signal-to-noise ratio (SNR) of seismic data has always been a key step in seismic data processing. Deep learning approaches have been successfully applied to suppress seismic random noise. The training examples are essential in deep learning methods, especially for the geophysical problems, where the complete training data are not easy to be acquired due to high cost of acquisition. In this work, we propose a natural images pre-trained deep learning method to suppress seismic random noise through insight of the transfer learning. Our network contains pre-trained and post-trained networks: the former is trained by natural images to obtain the preliminary denoising results, while the latter is trained by a small amount of seismic images to fine-tune the denoising effects by semi-supervised learning to enhance the continuity of geological structures. The results of four types of synthetic seismic data and six field data demonstrate that our network has great performance in seismic random noise suppression in terms of both quantitative metrics and intuitive effects.
BO-DRNet: An Improved Deep Learning Model for Oil Spill Detection by Polarimetric Features from SAR Images
Authors: Wang Dawei , Wan Jianhua , Liu Shanwei , Chen Yanlong , Yasir Muhammad , Xu Mingming , Ren Peng
Published: January 2022

Oil spill pollution at sea causes significant damage to marine ecosystems. Quad-polarimetric Synthetic Aperture Radar (SAR) has become an essential technology since it can provide polarization features for marine oil spill detection. Using deep learning models based on polarimetric features, oil spill detection can be achieved. However, there is insufficient feature extraction due to model depth, small reception field lend due to loss of target information, and fixed hyperparameter for models. The effect of oil spill detection is still incomplete or misclassified. To solve the above problems, we propose an improved deep learning model named BO-DRNet. The model can obtain a more sufficiently and fuller feature by ResNet-18 as the backbone in encoder of DeepLabv3+, and Bayesian Optimization (BO) was used to optimize the model’s hyperparameters. Experiments were conducted based on ten prominent polarimetric features were extracted from three quad-polarimetric SAR images obtained by RADARSAT-2. Experimental results show that compared with other deep learning models, BO-DRNet performs best with a mean accuracy of 74.69% and a mean dice of 0.8551. This paper provides a valuable tool to manage upcoming disasters effectively.
Climate, CO2, and Anthropogenic Drivers of Accelerated Vegetation Greening in the Haihe River Basin
Authors: Yang Wenjing , Zhao Yong , Wang Qingming , Guan Buliao
Published: January 2022

Vegetation regulates the exchange of terrestrial carbon and water fluxes and connects the biosphere, hydrosphere, and atmosphere. Over the last four decades, vegetation greening has been observed worldwide using satellite technology. China has also experienced a notably widespread greening trend. However, the responsiveness of vegetation dynamics to elevated CO2 concentration, climate change, and human activities remains unclear. In this study, we attempted to explore the impact of natural (precipitation, air temperature), biogeochemical (CO2), and anthropogenic drivers (nighttime light, afforestation area) on changes in vegetation greenness in the Haihe River Basin (HRB) during 2002–2018 at the county-level. We further determined the major factors affecting the variation in satellite-derived normalized difference vegetation index (NDVI) from moderate resolution imaging spectroradiometer (MODIS) for each county. The results indicated that over 85% of the counties had a significantly increased NDVI trend, and the average linear trend of annual NDVI across the study region was 0.0037 per year. The largest contributor to the NDVI trend was CO2 (mean contribution 45%), followed by human activities (mean contribution of 27%). Additionally, afforestation was a pronounced driving force for NDVI changes in mountainous areas, resulting from ecosystem restoration efforts. Our findings emphasize the crucial role of CO2 fertilization in vegetation cover change, while considering CO2 concentration, climate change, and human activities, and shed light on the significant influences of afforestation programs on water resources, especially in mountainous areas.
Estimation of Individual Tree Biomass in Natural Secondary Forests Based on ALS Data and WorldView-3 Imagery
Authors: Zhao Yinghui , Ma Ye , Quackenbush Lindi J. , Zhen Zhen
Published: January 2022

Individual-tree aboveground biomass (AGB) estimation can highlight the spatial distribution of AGB and is vital for precision forestry. Accurately estimating individual tree AGB is a requisite for accurate forest carbon stock assessment of natural secondary forests (NSFs). In this study, we investigated the performance of three machine learning and three ensemble learning algorithms in tree species classification based on airborne laser scanning (ALS) and WorldView-3 imagery, inversed the diameter at breast height (DBH) using an optimal tree height curve model, and mapped individual tree AGB for a site in northeast China using additive biomass equations, tree species, and inversed DBH. The results showed that the combination of ALS and WorldView-3 performed better than either single data source in tree species classification, and ensemble learning algorithms outperformed machine learning algorithms (except CNN). Seven tree species had satisfactory accuracy of individual tree AGB estimation, with R2 values ranging from 0.68 to 0.85 and RMSE ranging from 7.47 kg to 36.83 kg. The average individual tree AGB was 125.32 kg and the forest AGB was 113.58 Mg/ha in the Maoershan study site in Heilongjiang Province, China. This study provides a way to classify tree species and estimate individual tree AGB of NSFs based on ALS data and WorldView-3 imagery.
Geodetic Mass Balance of Haxilegen Glacier No. 51, Eastern Tien Shan, from 1964 to 2018
Authors: Xu Chunhai , Li Zhongqin , Wang Feiteng , Mu Jianxin , Zhang Xin
Published: January 2022

The eastern Tien Shan hosts substantial mid-latitude glaciers, but in situ glacier mass balance records are extremely sparse. Haxilegen Glacier No. 51 (eastern Tien Shan, China) is one of the very few well-measured glaciers, and comprehensive glaciological measurements were implemented from 1999 to 2011 and re-established in 2017. Mass balance of Haxilegen Glacier No. 51 (1999–2015) has recently been reported, but the mass balance record has not extended to the period before 1999. Here, we used a 1:50,000-scale topographic map and long-range terrestrial laser scanning (TLS) data to calculate the area, volume, and mass changes for Haxilegen Glacier No. 51 from 1964 to 2018. Haxilegen Glacier No. 51 lost 0.34 km2 (at a rate of 0.006 km2 a−1 or 0.42% a−1) of its area during the period 1964–2018. The glacier experienced clearly negative surface elevation changes and geodetic mass balance. Thinning occurred almost across the entire glacier surface, with a mean value of −0.43 ± 0.12 m a−1. The calculated average geodetic mass balance was −0.36 ± 0.12 m w.e. a−1. Without considering the error bounds of mass balance estimates, glacier mass loss over the past 50 years was in line with the observed and modeled mass balance (−0.37 ± 0.22 m w.e. a−1) that was published for short time intervals since 1999 but was slightly less negative than glacier mass loss in the entire eastern Tien Shan. Our results indicate that Riegl VZ®-6000 TLS can be widely used for mass balance measurements of unmonitored individual glaciers.
Fusion Retrieval of Sea Surface Barometric Pressure from the Microwave Humidity and Temperature Sounder and Microwave Temperature Sounder-II Onboard the Fengyun-3 Satellite
Authors: He Qiurui , Wang Zhenzhan , Li Jiaoyang
Published: January 2022

Both the Microwave Humidity and Temperature Sounder (MWHTS) and the Microwave Temperature Sounder-II (MWTS-II) operate on the Fengyun-3 (FY-3) satellite platform, which provides an opportunity to retrieve the sea surface barometric pressure (SSP) with high accuracy by fusing the observations from the 60 GHz, 118.75 GHz, and 183.31 GHz channels. The theory of retrieving SSP using passive microwave observations is analyzed, and the sensitivity test experiments of MWHTS and MWTS-II to SSP as well as the test experiments of the contributions of MWHTS and MWTS-II to SSP retrieval are carried out. The theoretical channel combination is established based on the theoretical analysis, and the SSP retrieval experiment is carried out based on the Deep Neural Network (DNN) for the theoretical channel combination. The experimental results show that the retrieval accuracy of SSP using the theoretical channel combination is higher than that of MWHTS or MWTS-II. In addition, based on the test results of the contributions of MWHTS and MWTS-II to the retrieval SSP, the optimal theoretical channel combination can be built, and can further improve the retrieval accuracy of SSP from the theoretical channel combination.
Two Simulated Spectral Databases of Lunar Regolith: Method, Validation, and Application
Authors: Zhou Ping , Zhao Zhe , Wei Guangyuan , Huo Hong-Yuan
Published: January 2022

Our simulated lunar regolith spectra database, based on the Hapke AMSA radiative transfer model (RTM), is a large supplement to the limited number of lunar spectra data. By analyzing the multiple solutions and applicable scopes of the Hapke model by means of Newton interpolation and the least square optimization method, an improved method was found for the simulation of spectra, but it remained challenging to use to invert mineral abundance. Then, we simulated the spectra, mineral abundance, particle size and maturity of 57 mare and highland samples of the Lunar Soil Characterization Consortium (LSCC) in size groups of 10 µm, 10–20 µm and 20–45 µm. The simulated and measured spectra fit well with each other, with correlation coefficients greater than 0.99 and root mean square errors at a magnitude of 10-3. The parameters of mineral abundance, particle size and maturity are highly consistent with the measured values. Having confirmed the reliability of our simulation method, we analyzed the mechanism, reliability and applicability of the “spectral characteristic angle parameter” proposed by Lucey using the simulated spectral data of lunar regolith. Lucey’s method is only suitable for macro analysis of the entire moon, and the error is large when it is used for areas with high abundance of forsterite or ilmenite. In the spectral simulation of lunar regolith, olivine was subdivided into forsterite and fayalite, and the two end-members were mixed to approximately estimate the effect of the chemical composition of olivine on the spectra, which has been confirmed to be feasible.
On the Scale Effect of Relationship Identification between Land Surface Temperature and 3D Landscape Pattern: The Application of Random Forest
Authors: Wu Qiong , Li Zhaoyi , Yang Changbao , Li Hongqing , Gong Liwei , Guo Fengxiang
Published: January 2022

Urbanization processes greatly change urban landscape patterns and the urban thermal environment. Significant multi-scale correlation exists between the land surface temperature (LST) and landscape pattern. Compared with traditional linear regression methods, the regression model based on random forest has the advantages of higher accuracy and better learning ability, and can remove the linear correlation between regression features. Taking Beijing’s metropolitan area as an example, this paper conducted multi-scale relationship analysis between 3D landscape patterns and LST using Pearson Correlation Coefficient (PCC), Multiple Linear Regression and Random Forest Regression (RFR). The results indicated that LST was relatively high in the central area of Beijing, and decreased from the center to the surrounding areas. The interpretation effect of 3D landscape metrics on LST was more obvious than that of the 2D landscape metrics, and 3D landscape diversity and evenness played more important roles than the other metrics in the change of LST. The multi-scale relationship between LST and the landscape pattern was discovered in the fourth ring road of Beijing, the effect of the extent of change on the landscape pattern is greater than that of the grain size change, and the interpretation effect and correlation of landscape metrics on LST increase with the increase in the rectangle size. Impervious surfaces significantly increased the LST, while the impervious surfaces located at low building areas were more likely to increase LST than those located at tall building areas. It seems that increasing the distance between buildings to improve the rate of energy exchange between urban and rural areas can effectively decrease LST. Vegetation and water can effectively reduce LST, but large, clustered and irregularly shaped patches have a better effect on land surface cooling than small and discrete patches. The Coefficients of Rectangle Variation (CORV) power function fitting results of landscape metrics showed that the optimal rectangle size for studying the relationship between the 3D landscape pattern and LST is about 700 m. Our study is useful for future urban planning and provides references to mitigate the daytime urban heat island (UHI) effect.
Estimation of Terrestrial Water Storage Variations in Sichuan-Yunnan Region from GPS Observations Using Independent Component Analysis
Authors: Liu Bin , Yu Wenkun , Dai Wujiao , Xing Xuemin , Kuang Cuilin
Published: January 2022

GPS can be used to measure land motions induced by mass loading variations on the Earth’s surface. This paper presents an independent component analysis (ICA)-based inversion method that uses vertical GPS coordinate time series to estimate the change of terrestrial water storage (TWS) in the Sichuan-Yunnan region in China. The ICA method was applied to extract the hydrological deformation signals from the vertical coordinate time series of GPS stations in the Sichuan-Yunnan region from the Crustal Movement Observation Network of China (CMONC). These vertical deformation signals were then inverted to TWS variations. Comparative experiments were conducted based on Gravity Recovery and Climate Experiment (GRACE) data and a hydrological model for validation. The results demonstrate that the TWS changes estimated from GPS(ICA) deformations are highly correlated with the water variations derived from the GRACE data and hydrological model in Sichuan-Yunnan region. The TWS variations are overestimated by the vertical GPS observations the northwestern Sichuan-Yunnan region. The anomalies are likely caused by inaccurate atmospheric loading correction models or residual tropospheric errors in the region with high topographic variability and can be reduced by ICA preprocessing.
Spatially Varying Relationships between Land Subsidence and Urbanization: A Case Study in Wuhan, China
Authors: Wang Zhengyu , Liu Yaolin , Zhang Yang , Liu Yanfang , Wang Baoshun , Zhang Guangxia
Published: January 2022

Land subsidence has become an increasing global concern over the past few decades due to natural and anthropogenic factors. However, although several studies have examined factors affecting land subsidence in recent years, few have focused on the spatial heterogeneity of relationships between land subsidence and urbanization. In this paper, we adopted the small baseline subset-synthetic aperture radar interferometry (SBAS-InSAR) method using Sentinel-1 radar satellite images to map land subsidence from 2015 to 2018 and characterized its spatial pattern in Wuhan. The bivariate Moran’s I index was used to test and visualize the spatial correlations between land subsidence and urbanization. A geographically weighted regression (GWR) model was employed to explore the strengths and directions of impacts of urbanization on land subsidence. Our findings showed that land subsidence was obvious and unevenly distributed in the study area, the annual deformation rate varied from −42.85 mm/year to +29.98 mm/year, and its average value was −1.0 mm/year. A clear spatial pattern for land subsidence in Wuhan was mapped, and several apparent subsidence funnels were primarily located in central urban areas. All urbanization indicators were found to be significantly spatially correlated with land subsidence at different scales. In addition, the GWR model results showed that all urbanization indicators were significantly associated with land subsidence across the whole study area in Wuhan. The results of bivariate Moran’s I and GWR results confirmed that the relationships between land subsidence and urbanization spatially varied in Wuhan at multiple spatial scales. Although scale dependence existed in both the bivariate Moran’s I and GWR models for land subsidence and urbanization indicators, a “best” spatial scale could not be confirmed because the disturbance of factors varied over different sampling scales. The results can advance the understanding of the relationships between land subsidence and urbanization, and they will provide guidance for subsidence control and sustainable urban planning.
Human vs. Machine, the Eyes Have It. Assessment of Stemphylium Leaf Blight on Onion Using Aerial Photographs from an NIR Camera
Authors: McDonald Mary Ruth , Tayviah Cyril Selasi , Gossen Bruce D.
Published: January 2022

Aerial surveillance could be a useful tool for early detection and quantification of plant diseases, however, there are often confounding effects of other types of plant stress. Stemphylium leaf blight (SLB), caused by the fungus Stemphylium vesicarium, is a damaging foliar disease of onion. Studies were conducted to determine if near-infrared photographic images could be used to accurately assess SLB severity in onion research trials in the Holland Marsh in Ontario, Canada. The site was selected for its uniform soil and level topography. Aerial photographs were taken in 2015 and 2016 using an Xnite-Canon SX230NDVI with a near-infrared filter, mounted on a modified Cine Star—8 MK Heavy Lift RTF octocopter UAV. Images were taken at 15–20 m above the ground, providing an average of 0.5 cm/pixel and a field of view of 15 × 20 m. Photography and ground assessments of disease were carried out on the same day. NDVI (normalized difference vegetation index), green NDVI, chlorophyll index and plant senescence reflective index (PSRI) were calculated from the images. There were differences in SLB incidence and severity in the field plots and differences in the vegetative indices among the treatments, but there were no correlations between disease assessments and any of the indices.
PLS-R Calibration Models for Wine Spirit Volatile Phenols Prediction by Near-Infrared Spectroscopy
Authors: Anjos Ofélia , Caldeira Ilda , Fernandes Tiago A. , Pedro Soraia Inês , Vitória Cláudia , Oliveira-Alves Sheila , Catarino Sofia , Canas Sara
Published: January 2022

Near-infrared spectroscopic (NIR) technique was used, for the first time, to predict volatile phenols content, namely guaiacol, 4-methyl-guaiacol, eugenol, syringol, 4-methyl-syringol and 4-allyl-syringol, of aged wine spirits (AWS). This study aimed to develop calibration models for the volatile phenol’s quantification in AWS, by NIR, faster and without sample preparation. Partial least square regression (PLS-R) models were developed with NIR spectra in the near-IR region (12,500–4000 cm−1) and those obtained from GC-FID quantification after liquid-liquid extraction. In the PLS-R developed method, cross-validation with 50% of the samples along a validation test set with 50% of the remaining samples. The final calibration was performed with 100% of the data. PLS-R models with a good accuracy were obtained for guaiacol (r2 = 96.34; RPD = 5.23), 4-methyl-guaiacol (r2 = 96.1; RPD = 5.07), eugenol (r2 = 96.06; RPD = 5.04), syringol (r2 = 97.32; RPD = 6.11), 4-methyl-syringol (r2 = 95.79; RPD = 4.88) and 4-allyl-syringol (r2 = 95.97; RPD = 4.98). These results reveal that NIR is a valuable technique for the quality control of wine spirits and to predict the volatile phenols content, which contributes to the sensory quality of the spirit beverages.
Sensitive Metal-Semiconductor Nanothermocouple Fabricated by FIB to Investigate Laser Beams with Nanometer Spatial Resolution
Authors: Łaszcz Adam , Czerwinski Andrzej , Pruszyńska-Karbownik Emilia , Wzorek Marek , Szmigiel Dariusz
Published: January 2022

The focused ion beam (FIB) technique was used to fabricate a nanothermocouple (with a 90 nm wide nanojunction) based on a metal–semiconductor (Pt–Si) structure, which showed a sensitivity up to 10 times larger (with Seebeck coefficient up to 140 µV/K) than typical metal–metal nanothermocouples. In contrast to the fabrication of nanothermocouples which requires a high-tech semiconductor manufacturing line with sophisticated fabrication techniques, environment, and advanced equipment, FIB systems are available in many research laboratories without the need for a high-tech environment, and the described processing is performed relatively quickly by a single operator. The linear response of the manufactured nanothermocouple enabled sensitive measurements even with small changes of temperature when heated with a stream of hot air. A nonlinear response of the nanothermocouple (up to 83.85 mV) was observed during the exposition to an argon-laser beam with a high optical power density (up to 17.4 Wcm−2), which was also used for the laser annealing of metal–semiconductor interfaces. The analysis of the results implies the application of such nanothermocouples, especially for the characterization of laser beams with nanometer spatial resolution. Improvements of the FIB processing should lead to an even higher Seebeck coefficient of the nanothermocouples; e.g., in case of the availability of other suitable metal sources (e.g., Cr).
Simultaneous Determination of Ferulic Acid and Vanillin in Vanilla Extracts Using Voltammetric Sensor Based on Electropolymerized Bromocresol Purple
Authors: Ziyatdinova Guzel , Zhupanova Anastasiya , Davletshin Rustam
Published: January 2022

Natural phenolic antioxidants are one of the widely studied compounds in life sciences due to their important role in oxidative stress prevention and repair. The structural similarity of these antioxidants and their simultaneous presence in the plant samples stipulate the development of methods for their quantification. The current work deals with the simultaneous determination of vanillin and its bioprecursor ferulic acid using a voltammetric sensor for the first time. A sensor based on the layer-by-layer deposition of the polyaminobenzene sulfonic acid functionalized single-walled carbon nanotubes (f-SWCNTs) and electropolymerized bromocresol purple has been developed for this purpose. The best response of co-existing target analytes was registered for the polymer obtained from the 25 µM dye by 10-fold potential cycling from 0.0 to 1.2 V with the scan rate of 100 mV s−1 in 0.1 M phosphate buffer (PB), pH 7.0. Scanning electron microscopy (SEM), cyclic voltammetry and electrochemical impedance spectroscopy (EIS) confirmed the effectivity of the sensor developed. The linear dynamic ranges of 0.10–5.0 µM and 5.0–25 µM for both analytes with the detection limits of 72 nM and 64 nM for ferulic acid and vanillin, respectively, were achieved in differential pulse mode. The sensor was applied for the analysis of vanilla extracts.
Opto-Mechatronics System for Train-Track Micro Deformation Sensing
Authors: Gan Weibing , Tu Shiyu , Tao Yuan , Ai Lingyun , Zhang Cui , Tang Jianguan
Published: January 2022

In this paper, we proposed and experimentally demonstrated an opto-mechatronics system to detect the micro-deformation of tracks caused by running trains. The fiber Bragg grating (FBG) array acting as sensing elements has a low peak reflectivity of around −40 dB. The center wavelengths were designed to alternate between 1551 nm and 1553 nm at 25 °C. Based on dual-wavelength, wavelength-division multiplexing (WDM)/time-division multiplexing (TDM) hybrid networking, we adopted optical time-domain reflectometry (OTDR) technology and a wavelength-scanning interrogation method to achieve FBG array signal demodulation. The field experimental results showed that the average wavelength shift of the FBG array caused by the passage of the lightest rail vehicle was −225 pm. Characteristics of the train-track system, such as track occupancy, train length, number of wheels, train speed, direction, and loading can be accurately obtained in real time. This opto-mechatronics system can meet the requirements of 600 mm spatial resolution, long distance, and large capacity for monitoring the train-track system. This method exhibits great potential for applications in large-scale train-track monitoring, which is meaningful for the safe operation of rail transport.