bluuebunny/crossref_metadata_2025 huggingface dataset:
Soil Salinity Estimation for South Kazakhstan Based on SAR Sentinel-1 and Landsat-8,9 OLI Data with Machine Learning Models
Authors: Mukhamediev Ravil I. , Merembayev Timur , Kuchin Yan , Malakhov Dmitry , Zaitseva Elena , Levashenko Vitaly , Popova Yelena , Symagulov Adilkhan , Sagatdinova Gulshat , Amirgaliyev Yedilkhan
Published: August 2023

Climate change, uneven distribution of water resources and anthropogenic impact have led to salinization and land degradation in the southern regions of Kazakhstan. Identification of saline lands and their mapping is a laborious process associated with a complex of ground measurements. Data from remote sensing are widely used to solve this problem. In this paper, the problem of assessing the salinity of the lands of the South Kazakhstan region using remote sensing data is considered. The aim of the study is to analyze the applicability of machine learning methods to assess the salinity of agricultural lands in southern Kazakhstan based on remote sensing. The authors present a salinity dataset obtained from field studies and containing more than 200 laboratory measurements of soil salinity. Moreover, the authors describe the results of applying several regression reconstruction algorithms (XGBoost, LightGBM, random forest, Support vector machines, Elastic net, etc.), where synthetic aperture radar (SAR) data from the Sentinel-1 satellite and optical data in the form of spectral salinity indices are used as input data. The obtained results show that, in general, these input data can be used to estimate salinity of the wetted arable land. XGBoost regressor (R2 = 0.282) showed the best results. Supplementing the radar data with the values of salinity spectral index improves the result significantly (R2 = 0.356). For the local datasets, the best result shown by the model is R2 = 0.473 (SAR) and R2 = 0.654 (SAR with spectral indexes), respectively. The study also revealed a number of problems that justify the need for a broader range of ground surveys and consideration of multi-year factors affecting soil salinity. Key results of the article: (i) a set of salinity data for different geographical zones of southern Kazakhstan is presented for the first time; (ii) a method is proposed for determining soil salinity on the basis of synthetic aperture radar supplemented with optical data, and this resulted in the improved prediction of the results for the region under consideration; (iii) a comparison of several types of machine learning models was made and it was found that boosted models give, on average, the best prediction result; (iv) a method for optimizing the number of model input parameters using explainable machine learning is proposed; (v) it is shown that the results obtained in this work are in better agreement with ground-based measurements of electrical conductivity than the results of the previously proposed global model.
The Influence of Ocean Processes on Fine-Scale Changes in the Yellow Sea Cold Water Mass Boundary Area Structure Based on Acoustic Observations
Authors: Nie Lingyun , Li Jianchao , Wu Hao , Zhang Wenchao , Tian Yongjun , Liu Yang , Sun Peng , Ye Zhenjiang , Ma Shuyang , Gao Qinfeng
Published: August 2023

The boundary of Yellow Sea Cold Water Mass (YSCWM) is a key ocean frontal structure influencing the regional ecosystem. Complex oceanic processes such as tidal currents, upwelling, and internal waves influence fine-scale hydrological structures, comprehensively resulting in a significantly highly productive area for plankton and fisheries. However, detailed research requires inaccessible high-resolution data. To investigate the fine-scale and high-frequency effects of oceanic processes on the local hydrological and ecological environment, we conducted comprehensive cruise acoustic observations and intensive station surveys of the hydrological environment around the YSCWM boundary in summer 2021 and 2022, and found that: (1) fine-scale hydrological structures across the YSCWM boundary were directly captured through this specific intensive station observation design; (2) clear zooplankton diel vertical migration (DVM) phenomena match well with the thermocline variation, showing that acoustics are effective indicators that reflect the water mass layering structure in summer in the YS; and (3) the shear excited by internal waves during propagation and flood tides enhances the upward and downward mixing of the water mass near the thermocline, thus thickening and weakening the layer, an effect that will be more pronounced when both are present at the same time, with ebb tide having the opposite effect. Topographically influenced tidal upwelling also causes significant vertical fluctuations in isotherms. This represents a new way of studying the fine-scale hydrodynamic–hydrologic–ecological aspects of key regions through acoustic remote sensing.
Enhancing Solar-Induced Fluorescence Interpretation: Quantifying Fractional Sunlit Vegetation Cover Using Linear Spectral Unmixing
Authors: Moncholi-Estornell Adrián , Cendrero-Mateo Maria Pilar , Antala Michal , Cogliati Sergio , Moreno José , Van Wittenberghe Shari
Published: August 2023

Solar-induced chlorophyll fluorescence (SIF) is closely related to plant photosynthetic activity and has been used in different studies as a proxy for vegetation health status. However, in order to use SIF as a relevant indicator of plant physiological stress, it is necessary to accurately quantify the amount of light absorbed by the photosynthetic plant pigments, called the absorbed photosynthetically active radiation (APAR). The ratio between fluorescence emission and light absorption (i.e., SIF and APAR) is known as the fluorescence quantum efficiency (FQE). In this work, simultaneous measurements of SIF and reflected radiance were performed both at the leaf and canopy levels for Salvia farinacea and Datura stramonium plants. With the aim of disentangling the proportion of sunlit and shaded absorbed PAR, an ad hoc experimental setup was designed to provide a wide range of fraction vegetation cover (FVC) canopy settings. A linear spectral unmixing method was proposed to estimate the contribution of soil, sunlit, and shaded vegetation from the total reflectance spectrum measured at the canopy level. Later, the retrieved sunlit FVC (FVCsunlit) was used to estimate the (dominant) green APAR flux, and this was combined with the integral of the spectrally resolved fluorescence to calculate the FQE. The results of this study demonstrated that under no-stress conditions and independently of the FVC, similar FQE values were observed when SIF was properly normalised by the green APAR flux. The results obtained showed that the reflectance spectra retrieved using a linear unmixing method had a maximum RMSE of less than 0.03 along the spectrum. The FVCsunlit evaluation showed an RMSE of 14% with an R2 of 0.84. Moreover, the FQE values obtained at the top of the canopy (TOC) were found statistically comparable to the reference values at the leaf level. These results support further efforts to improve the interpretation of fluorescence based on field spectroscopy and the further upscaling to imaging spectroscopy at airborne and satellite levels.
A Signal Model Based on the Space–Time Coding Array and a Novel Imaging Method Based on the Hybrid Correlation Algorithm for F-SCAN SAR
Authors: Liu Yuqing , Wang Pengbo , Men Zhirong , Guo Yanan , He Tao , Bao Rui , Cui Lei
Published: August 2023

The F-SCAN principle is a better alternative to the scan-on-receive technique (SCORE) based on digital beamforming (DBF), which can avoid low gain caused by a conventional broad beam in the case of a wide swath. In F-SCAN SAR, a pencil beam scans the entire target area from far to near, providing high energy independent of the position and ensuring a low range ambiguity-to-signal ratio (RASR). Moreover, echo compression can be achieved via appropriate system parameter configuration, significantly shortening the receive window and reducing the amount of data. A wider range swath can, therefore, be achieved. However, for this novel F-SCAN SAR working mode, signal modeling and imaging processing are key issues that needed to be addressed. In this paper, the far-field synthetic antenna pattern of the space–time coding array (STCA) is first derived and analyzed, based on which the signal modeling of the F-SCAN SAR is carried out. Then, according to the signal model and echo characteristics, a novel imaging processing method based on the hybrid correlation algorithm is presented for the F-SCAN SAR. First, the dechirp operation is performed to compensate for the quadratic phase of the range time. The range compressed result is obtained after a range Fourier transform, where different range targets are successfully separated and range aliasing is avoided. Then, the modified azimuth reference function is correlated with the echo at each range cell to complete range cell migration correction (RCMC) and azimuth compensation. The received signal parameters and the Doppler parameters of each range cell are derived to update the azimuth reference function. Finally, accurate focused results are obtained in the range-frequency, azimuth-time domain. The simulation results indicate that the signal model based on the STCA can satisfy the requirements of the F-SCAN principle, and the proposed imaging algorithm can complete the precise focusing processing of the F-SCAN SAR echo.
The Effect of Spatially Correlated Errors on Sea Surface Height Retrieval from SWOT Altimetry
Authors: Yaremchuk Max , Beattie Christopher , Panteleev Gleb , D’Addezio Joseph M. , Smith Scott
Published: August 2023

The upcoming technology of wide-swath altimetry from space will enable monitoring the ocean surface at 4–5 times better spatial resolution and 2–3 times better accuracy than traditional nadir altimeters. This development will provide a chance to directly observe submesoscale sea surface height (SSH) variations that have a typical magnitude of a few centimeters. Taking full advantage of this opportunity requires correct treatment of the correlated SSH errors caused by uncertainties in environmental conditions beneath the satellite and in the geometry and orientation of the on-board interferometer. These observation errors are highly correlated both along and across the surface swath scanned by the satellite, and this presents a significant challenge for accurate processing. In particular, the SWOT precision matrix has off-diagonal elements that are too numerous to allow standard approaches to remain tractable. In this study, we explore the utility of a block-diagonal approximation to the SWOT precision matrix in order to reconstruct SSH variability in the region east of Greenland. An extensive set of 2dVar assimilation experiments demonstrates that the sparse approximation proposed for the precision matrix provides accurate SSH retrievals when the background-to-observation error ratio ν does not exceed 3 and significant wave height is below 2.5 m. We also quantify the range of ν and significant wave heights over which the retrieval accuracy of the exact spatially correlated SWOT error model will outperform the uncorrelated model. In particular, the estimated range is found to be substantially wider (ν<10 with significant wave heights below 8–10 m), indicating the potential benefits of further improving the accuracy of approximations for the SWOT precision matrix.
Advantages of Using Transfer Learning Technology with a Quantative Measurement
Authors: Hattula Emilia , Zhu Lingli , Raninen Jere , Oksanen Juha , Hyyppä Juha
Published: August 2023

The number of people living in cities is continuously growing, and the buildings in topographic maps are in need of frequent updates, which are costly to perform manually. This makes automatic building extraction a significant research subject. Transfer learning, on the other hand, offers solutions in situations where the data of a target area are scarce, making it a profitable research subject. Moreover, in previous studies, there was a lack of metrics in quantifying the accuracy improvement with transfer learning techniques. This paper investigated various transfer learning techniques and their combinations with U-Net for the semantic segmentation of buildings from true orthophotos. The results were analyzed using quantitative methods. Open-source remote sensing data from Poland were used for pretraining a model for building segmentation. The fine-tuning techniques including a fine-tuning contracting path, a fine-tuning expanding path, a retraining contracting path, and a retraining expanding path were studied. These fine-tuning techniques and their combinations were tested with three local datasets from the diverse environment in Finland: urban, suburban, and rural areas. Knowledge from the pretrained model was transferred to the local datasets from Helsinki (urban), Kajaani (suburban), and selected areas in Finland (rural area). Three models with no transfer learning were trained from scratch with three sets of local data to compare the fine-tuning results. Our experiment focused on how various transfer learning techniques perform on datasets from different environments (urban, suburban, and rural areas) and multiple locations (southern, northern, and across Finland). A quantitative assessment of performance improvement by using transfer learning techniques was conducted. Despite the differences in datasets, the results showed that using transfer learning techniques could achieve at least 5% better accuracy than a model trained from scratch with several different transfer learning techniques. In addition, the effect of the sizes of training datasets was also studied.
Impact of the Detection Channels Added by Fengyun Satellite MWHS-II at 183 GHz on Global Numerical Weather Prediction
Authors: Ju Yali , He Jieying , Ma Gang , Huang Jing , Guo Yang , Liu Guiqing , Zhang Minjie , Gong Jiandong , Zhang Peng
Published: August 2023

Fine spectral detection can basically solve the problem of low vertical resolution at the 183 GHz water-vapor absorption line, and it is expected to become one of the main methods for next-generation geostationary and polar-orbiting satellites. Here, using data from Microwave Humidity Sounder II (MWHS-II) onboard the Chinese Fengyun 3D (FY-3D) satellite in the Global/Regional Assimilation and Prediction System (GRAPES) Four-Dimensional Variational (4D-Var) system of the China Meteorological Administration (CMA), we explore the assimilation application of the water-vapor absorption line at 183.31 ± 1 GHz, 183.31 ± 3 GHz and 183.31 ± 7 GHz, as well as 183.31 ± 1.8 GHz and 183.31 ± 4.5 GHz, two added channels, to assess the impact of adding the 183.31 ± 1.8 GHz and 183.31 ± 4.5 GHz sampling channels on data assimilation and numerical weather prediction. Our findings reveal a significant increase in the specific-humidity increment, which in the middle–upper troposphere is numerically much larger than in the lower troposphere. Specifically, the assimilation of 183.31 ± 1.8 GHz observations, positioned near the center of the water-vapor absorption line, results in a pronounced adjustment compared with the 183.31 ± 4.5 GHz observations. And under the strong constraint of the numerical model, the Root Mean Square Error (RMSE) of the wind field diminishes more significantly (by an average of 2–4%) after assimilating the water-vapor observations at greater heights.
Quantifying Water Impoundment-Driven Air Temperature Changes in the Dammed Jinsha River, Southwest China
Authors: Li Xinzhe , Zhou Jia , Huang Yangbin , Wang Ruyun , Lu Tao
Published: August 2023

A number of previous studies have contributed to a better understanding of the thermal impacts of dam-related reservoirs on stream temperature, but very few studies have focused on air temperature, especially at the catchment scale. In addition, due to the lack of quantitative analysis, the identification of the effects of water impoundment on regional air temperature is still lacking. We investigated the impacts of reservoirs on the regional air temperature changes before and after two large dam constructions in the lower Jinsha River located in southwest China, by using a 40 year record of reanalysis data at 90 m resolutions. Furthermore, the long short-term memory (LSTM) model was also employed to construct an impoundment effect on the temperature (IET) index. Research results indicate that compared to the pre-impoundment period (1980–2012), the variations in the air temperature at the catchment scale were reduced during the post-impoundment period (2013–2019). The annual maximum air temperature decreased by 0.4 °C relative to the natural regimes. In contrast, the cumulative effects of dam-related reservoirs increased the annual mean and minimum air temperature by 0.1 °C and 1.0 °C, respectively. Warming effects prevailed during the dry season and in the regions with high elevations, while cooling effects dominated within a 4 km buffer of the reservoirs. Therefore, this study offers important insights about the impacts of anthropogenic impoundments on air temperature changes, which could be useful for policymakers to have a more informed and profound understanding of local climate changes in dammed areas.
Vehicle Detection in Multisource Remote Sensing Images Based on Edge-Preserving Super-Resolution Reconstruction
Authors: Zhu Hong , Lv Yanan , Meng Jian , Liu Yuxuan , Hu Liuru , Yao Jiaqi , Lu Xionghanxuan
Published: August 2023

As an essential technology for intelligent transportation management and traffic risk prevention and control, vehicle detection plays a significant role in the comprehensive evaluation of the intelligent transportation system. However, limited by the small size of vehicles in satellite remote sensing images and lack of sufficient texture features, its detection performance is far from satisfactory. In view of the unclear edge structure of small objects in the super-resolution (SR) reconstruction process, deep convolutional neural networks are no longer effective in extracting small-scale feature information. Therefore, a vehicle detection network based on remote sensing images (VDNET-RSI) is constructed in this article. The VDNET-RSI contains a two-stage convolutional neural network for vehicle detection. In the first stage, a partial convolution-based padding adopts the improved Local Implicit Image Function (LIIF) to reconstruct high-resolution remote sensing images. Then, the network associated with the results from the first stage is used in the second stage for vehicle detection. In the second stage, the super-resolution module, detection heads module and convolutional block attention module adopt the increased object detection framework to improve the performance of small object detection in large-scale remote sensing images. The publicly available DIOR dataset is selected as the experimental dataset to compare the performance of VDNET-RSI with that of the state-of-the-art models in vehicle detection based on satellite remote sensing images. The experimental results demonstrated that the overall precision of VDNET-RSI reached 62.9%, about 6.3%, 38.6%, 39.8% higher than that of YOLOv5, Faster-RCNN and FCOS, respectively. The conclusions of this paper can provide a theoretical basis and key technical support for the development of intelligent transportation.
Geoid Studies in Two Test Areas in Greece Using Different Geopotential Models towards the Estimation of a Reference Geopotential Value
Authors: Grigoriadis Vassilios N. , Andritsanos Vassilios D. , Natsiopoulos Dimitrios A. , Vergos Georgios S. , Tziavos Ilias N.
Published: August 2023

In the present study, we first examine the contribution of different, satellite-only or combined, global geopotential models in geoid computation employing the remove–compute–restore approach. For this reason, two test areas of about 100 km2 each, one in northern and one in central Greece, were selected, and gravity measurements were conducted. These new gravity measurements were used along with the selected geopotential models to produce the reduced gravity field for the study areas. The classical and spectral residual terrain modeling effect is also removed to derive the residual gravity field. The latter is used for geoid computation using the 1D fast Fourier transform. The validation of the geoid models is carried out with gravity/GNSS/leveling measurements, which were conducted in two traverses located in the study areas. Special attention is given to the tidal approach, the geoid separation term as well as the coordinate reference system. Next, the northern study area is extended by incorporating gravity measurements obtained during the last five decades, and geoid models are recomputed. Lastly, using the geoid models computed, reference geopotential values are computed for both areas. From the results achieved for both study areas, the combined model XGM2019 provides the best overall statistical results with differences of 0.065 m and 0.036 m in terms of root mean square error. The incorporation of not recent data into the solutions leads to a degradation in accuracy by about 1.2 cm in terms of standard deviation. Lastly, the computed reference geopotential values present discrepancies between the two study areas, revealing network inconsistencies as well as the dependency on the geopotential model used for the geoid computations.
New Functionalities and Regional/National Use Cases of the Anomaly Hotspots of Agricultural Production (ASAP) Platform
Authors: Rembold Felix , Meroni Michele , Otieno Viola , Kipkogei Oliver , Mwangi Kenneth , de Sousa Afonso João Maria , Ihadua Isidro Metódio Tuleni Johannes , José Amílcar Ernesto A. , Zoungrana Louis Evence , Taieb Amjed Hadj , Urbano Ferdinando , Dimou Maria , Kerdiles Hervé , Vojnovic Petar , Zampieri Matteo , Toreti Andrea
Published: August 2023

The Anomaly hotSpots of Agricultural Production (ASAP) Decision Support System was launched operationally in 2017 for providing timely early warning information on agricultural production based on Earth Observation and agro-climatic data in an open and easy to use online platform. Over the last three years, the system has seen several methodological improvements related to the input indicators and to system functionalities. These include: an improved dataset of rainfall estimates for Africa; a new satellite indicator of biomass optimised for near-real-time monitoring; an indicator of crop and rangeland water stress derived from a water balance accounting scheme; the inclusion of seasonal precipitation forecasts; national and sub-national crop calendars adapted to ASAP phenology; and a new interface for the visualisation and analysis of high spatial resolution Sentinel and Landsat data. In parallel to these technical improvements, stakeholders and users uptake was consolidated through the set up of regionally adapted versions of the ASAP system for Eastern Africa in partnership with the Intergovernmental Authority on Development (IGAD) Climate Prediction and Applications Centre (ICPAC), for North Africa with the Observatoire du Sahara et du Sahel (OSS), and through the collaboration with the Angolan National Institute of Meteorology and Geophysics (INAMET), that used the ASAP system to inform about agricultural drought. Finally, ASAP indicators have been used as inputs for quantitative crop yield forecasting with machine learning at the province level for Algeria’s 2021 and 2022 winter crop seasons that were affected by drought.
Deep Learning-Based Detection of Urban Forest Cover Change along with Overall Urban Changes Using Very-High-Resolution Satellite Images
Authors: Javed Aisha , Kim Taeheon , Lee Changhui , Oh Jaehong , Han Youkyung
Published: August 2023

Urban forests globally face severe degradation due to human activities and natural disasters, making deforestation an urgent environmental challenge. Remote sensing technology and very-high-resolution (VHR) bitemporal satellite imagery enable change detection (CD) for monitoring forest changes. However, deep learning techniques for forest CD concatenate bitemporal images into a single input, limiting the extraction of informative deep features from individual raw images. Furthermore, they are developed for middle to low-resolution images focused on specific forests such as the Amazon or a single element in the urban environment. Therefore, in this study, we propose deep learning-based urban forest CD along with overall changes in the urban environment by using VHR bitemporal images. Two networks are used independently: DeepLabv3+ for generating binary forest cover masks, and a deeply supervised image fusion network (DSIFN) for the generation of a binary change mask. The results are concatenated for semantic CD focusing on forest cover changes. To carry out the experiments, full scene tests were performed using the VHR bitemporal imagery of three urban cities acquired via three different satellites. The findings reveal significant changes in forest covers alongside urban environmental changes. Based on the accuracy assessment, the networks used in the proposed study achieved the highest F1-score, kappa, IoU, and accuracy values compared with those using other techniques. This study contributes to monitoring the impacts of climate change, rapid urbanization, and natural disasters on urban environments especially urban forests, as well as relations between changes in urban environment and urban forests.
An Automatic Method for Delimiting Deformation Area in InSAR Based on HNSW-DBSCAN Clustering Algorithm
Authors: Han Jianfeng , Guo Xuefei , Jiao Runcheng , Nan Yun , Yang Honglei , Ni Xuan , Zhao Danning , Wang Shengyu , Ma Xiaoxue , Yan Chi , Ma Chi , Zhao Jia
Published: August 2023

InSAR (Interferometric Synthetic Aperture Radar) is widely recognized as a crucial remote sensing tool for monitoring various geological disasters because it provides all-day and all-weather monitoring. Nevertheless, the current interpretation methods for InSAR heavily depend on the interpreter’s experience, which hinders efficiency and fails to meet the requirements for the timely detection of geologic hazards. Furthermore, the results obtained through current InSAR processing carry inherent noise interference, further complicating the interpretation process. To address those issues, this paper proposes an approach that enables automatic and rapid identification of deformation zones. The proposed method leverages IPTA (Interferometric Point Target Analysis) technology for SAR data processing. It combines the power of HNSW (Hierarchical Navigable Small Word) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithms to cluster deformation results. Compared with traditional methods, the computational efficiency of the proposed method is improved by 11.26 times, and spatial noise is suppressed. Additionally, the clustering results are fused with slope units determined using DEM (Digital Elevation Model), which facilitates the automatic identification of slopes experiencing deformation. The experimental verification in the western mountainous area of Beijing has identified 716 hidden danger areas, and this method is superior to the traditional technology in speed and automation.
A Scalable Method to Improve Large-Scale Lidar Topographic Differencing Results
Authors: Jung Minyoung , Jung Jinha
Published: August 2023

Differencing digital terrain models (DTMs) generated from multitemporal airborne light detection and ranging (lidar) data provide accurate and detailed information about three-dimensional (3D) changes on the Earth. However, noticeable spurious errors along flight paths are often included in the differencing results, hindering the accurate analysis of the topographic changes. This paper proposes a new scalable method to alleviate the problematic systematic errors with a high degree of automation in consideration of the practical limitations raised when processing the rapidly increasing amount of large-scale lidar datasets. The proposed method focused on estimating the displacements caused by vertical positioning errors, which are the most critical error source, and adjusting the DTMs already produced as basic lidar products without access to the point cloud and raw data from the laser scanner. The feasibility and effectiveness of the proposed method were evaluated with experiments with county-level multitemporal airborne lidar datasets in Indiana, USA. The experimental results demonstrated that the proposed method could estimate the vertical displacement reasonably along the flight paths and improve the county-level lidar differencing results by reducing the problematic errors and increasing consistency across the flight paths. The improved differencing results presented in this paper are expected to provide more consistent information about topographic changes in Indiana. In addition, the proposed method can be a feasible solution to upcoming problems induced by rapidly increasing large-scale multitemporal lidar given recent active government-driven lidar data acquisition programs, such as the U.S. Geological Survey (USGS) 3D Elevation Program (3DEP).
Comment on Blagoveshchenskaya et al. Artificial Ducts Created via High-Power HF Radio Waves at EISCAT. Remote Sens. 2023, 15, 2300
Authors: Rietveld Michael , Senior Andrew
Published: August 2023

The claim that incoherent scatter radar data show electron density enhancements of 50–80% during some ionospheric heating experiments in a recent paper is questioned. The backscatter from the monostatic radar can indeed be enhanced during these experiments, but the conclusion that a large electron density increase is the cause is almost certainly wrong. Some natural plasma line data are presented in support of our claim. Previously published studies of similar events and a possible explanation for the observed increases in backscattered power are pointed out.
Detection Method of Infected Wood on Digital Orthophoto Map–Digital Surface Model Fusion Network
Authors: Wang Guangbiao , Zhao Hongbo , Chang Qing , Lyu Shuchang , Liu Binghao , Wang Chunlei , Feng Wenquan
Published: August 2023

Pine wilt disease (PWD) is a worldwide affliction that poses a significant menace to forest ecosystems. The swift and precise identification of pine trees under infection holds paramount significance in the proficient administration of this ailment. The progression of remote sensing and deep learning methodologies has propelled the utilization of target detection and recognition techniques reliant on remote sensing imagery, emerging as the prevailing strategy for pinpointing affected trees. Although the existing object detection algorithms have achieved remarkable success, virtually all methods solely rely on a Digital Orthophoto Map (DOM), which is not suitable for diseased trees detection, leading to a large false detection rate in the detection of easily confused targets, such as bare land, houses, brown herbs and so on. In order to improve the ability of detecting diseased trees and preventing the spread of the epidemic, we construct a large-scale PWD detection dataset with both DOM and Digital Surface Model (DSM) images and propose a novel detection framework, DDNet, which makes full use of the spectral features and geomorphological spatial features of remote sensing targets. The experimental results show that the proposed joint network achieves an AP50 2.4% higher than the traditional deep learning network.
Spectral De-Aliasing Method of Micro-Motion Signals Based on a Complex-Valued U-Net Network
Authors: Long Ming , Yang Jun , Xia Saiqiang , Lv Mingjiu , Cheng Bolin , Chen Wenfeng
Published: August 2023

Spectrum aliasing occurs in signal echoes when the sampling frequency does not comply with the Nyquist Sampling Theorem. In this scenario, the extraction of micro-motion parameters becomes challenging. This paper proposes a spectral de-aliasing method for micro-motion signals based on a complex-valued U-Net network. Zero interpolation is employed to insert zeros into the echo, effectively increasing the sampling frequency. After zero interpolation, the micro-motion signal contains both real micro-motion signal frequency components and new frequency components. Short-Time Fourier Transform (STFT) is then applied to transform the zero-interpolated echo from the time domain to the time–frequency domain. Furthermore, a complex-valued U-Net training model is utilized to eliminate redundant frequency components generated by zero interpolation, thereby achieving the frequency reconstruction of micro-motion signal echoes. Finally, the training models are employed to process the measured data. The theoretical analysis, simulations, and experimental results demonstrate that this method is robust and feasible, and is capable of addressing the problem of micro-motion signal echo spectrum aliasing in narrowband radar.
Comparative Analysis of Gully Morphology Extraction Suitability Using Unmanned Aerial Vehicle and Google Earth Imagery
Authors: Zhang Chunmei , Wang Chunmei , Long Yongqing , Pang Guowei , Shen Huazhen , Wang Lei , Yang Qinke
Published: August 2023

Gully erosion is considered to be a highly destructive form of soil erosion, often leading to the occurrence of natural calamities like landslides and mudslides. Remote sensing images have been extensively utilized in gully erosion research, and the suitability of extracting gully morphology parameters in various topographic regions needs to be clarified. Based on field measurements, this paper focuses on two widely used high-resolution remote sensing images: Unmanned Aerial Vehicle (UAV) and Google Earth (GE) imagery. It systematically examines the accuracy of gully morphological characteristic extraction using remote sensing in two regions with different terrain characteristics. The results show the following: (1) Compared to interpreting wide gullies with unclear shoulder lines, centimeter-level UAV imagery is more suitable for interpreting narrow gullies with clear shoulder lines. Conversely, the interpretability of sub-meter-level GE imagery is exactly the opposite. (2) The error in interpreting gully head points (GHPs) based on UAV images is less than 1 m, while the errors in gully length (GL), width (GW), perimeter (GP) and area (GA) are all below 3%, and these errors are hardly affected by gully morphology. (3) The error of GHPs based on GE images is concentrated within the range of 1–3 m. Meanwhile, the errors associated with GL, GP and GA are less than 10%. Conversely, the error of GW exceeds 11%. Furthermore, the aforementioned errors tend to increase as the gully width decreases and the complexity of the gully shoulder line increases. These findings shed light on the suitability of two commonly used remote sensing images for gully morphology extraction and provide valuable guidance for image selection in future research endeavors in this field.
Carotenoid Content Estimation in Tea Leaves Using Noisy Reflectance Data
Authors: Sonobe Rei , Hirono Yuhei
Published: August 2023

Quantifying carotenoid content in agriculture is essential for assessing crop nutritional value, improving crop quality, promoting human health, understanding plant stress responses, and facilitating breeding and genetic improvement efforts. Hyperspectral reflectance imaging is a nondestructive and rapid tool for estimating the carotenoid content. In spectrometer reflectance measurements, there are various sources of noise that can compromise the accuracy of carotenoid content estimations. Recently, various machine learning algorithms have been identified as robust against various types of noise, eliminating the need for denoising processes. Specifically, Cubist and the one-dimensional convolutional neural network (1D-CNN) have been used in evaluating vegetation properties based on reflectance data. We used regression models based on Cubist and 1D-CNN to estimate carotenoid content from reflectance data (the spectral resolution was resampled in 5 nm bands across the entire wavelength domain from 400 to 850 nm) with various degrees of Gaussian and spike noise added. The Cubist-based model was the most robust for this purpose: it achieved a ratio of performance to deviation of 1.41, a root mean square error of 1.11 µg/cm2, and a coefficient of determination (R2) of 0.496 when applied to reflectance data with a combination of Gaussian (mean: 0; variance: 0.04) and spike noise (density: 0.05; amplitude: 0.05).
Insight into the Characteristics and Triggers of Loess Landslides during the 2013 Heavy Rainfall Event in the Tianshui Area, China
Authors: Shao Xiaoyi , Ma Siyuan , Xu Chong , Xu Yueren
Published: August 2023

The 2013 heavy rainfall event (from June to July) in the Tianshui area triggered the most serious rainfall-induced group-occurring landslides since 1984, causing extensive casualties and economic losses. To better understand the characteristics and triggers of these loess landslides, we conducted a detailed analysis of the landslides and relevant influencing factors. Based on the detailed rainfall-induced landslide database obtained using visual interpretation of remote sensing images before and after rainfall, the correlation between the landslide occurrence and different influencing factors such as terrain, geomorphology, geology, and rainfall condition was analyzed. This rainfall event triggered approximately 54,000 landslides with a total area of 67.9 km2, mainly consisting of shallow loess landslides with elongated type, shallow rockslides, collapses, and mudflows. The landslides exhibited a clustered distribution, with the majority concentrated in two specific areas (i.e., Niangniangba and Shetang). The abundance index of landslides was closely associated with the hillslope gradient, total rainfall, and drainage (river) density. The landslide area density (LAD) was positively correlated with these influential factors, characterized by either an exponential or a linear relationship. The Middle Devonian Shujiaba formation (D2S) was identified to be highly susceptible to landslides, and the landslide events therein accounted for 35% of the total landslide occurrences within 22% of the study area. In addition, the E-SE aspect was more prone to landslides, while the W-NW aspect exhibited a low abundance of landslides.
A Novel AGV Path Planning Approach for Narrow Channels Based on the Bi-RRT Algorithm with a Failure Rate Threshold
Authors: Wu Bin , Zhang Wei , Chi Xiaonan , Jiang Di , Yi Yang , Lu Yi
Published: August 2023

The efficiency of the rapidly exploring random tree (RRT) falls short when efficiently guiding targets through constricted-passage environments, presenting issues such as sluggish convergence speed and elevated path costs. To overcome these algorithmic limitations, we propose a narrow-channel path-finding algorithm (named NCB-RRT) based on Bi-RRT with the addition of our proposed research failure rate threshold (RFRT) concept. Firstly, a three-stage search strategy is employed to generate sampling points guided by real-time sampling failure rates. By means of the balance strategy, two randomly growing trees are established to perform searching, which improves the success rate of the algorithm in narrow channel environments, accelerating the convergence speed and reducing the number of iterations required. Secondly, the parent node re-selection and path pruning strategy are integrated. This shortens the path length and greatly reduces the number of redundant nodes and inflection points. Finally, the path is optimized by utilizing segmented quadratic Bezier curves to achieve a smooth trajectory. This research shows that the NCB-RRT algorithm is better able to adapt to the complex narrow channel environment, and the performance is also greatly improved in terms of the path length and the number of inflection points. Compared with the RRT, RRT* and Bi-RRT algorithms, the success rate is increased by 2400%, 1900% and 11.11%, respectively.
MCMNET: Multi-Scale Context Modeling Network for Temporal Action Detection
Authors: Zhang Haiping , Zhou Fuxing , Ma Conghao , Wang Dongjing , Zhang Wanjun
Published: August 2023

Temporal action detection is a very important and challenging task in the field of video understanding, especially for datasets with significant differences in action duration. The temporal relationships between the action instances contained in these datasets are very complex. For such videos, it is necessary to capture information with a richer temporal distribution as much as possible. In this paper, we propose a dual-stream model that can model contextual information at multiple temporal scales. First, the input video is divided into two resolution streams, followed by a Multi-Resolution Context Aggregation module to capture multi-scale temporal information. Additionally, an Information Enhancement module is added after the high-resolution input stream to model both long-range and short-range contexts. Finally, the outputs of the two modules are merged to obtain features with rich temporal information for action localization and classification. We conducted experiments on three datasets to evaluate the proposed approach. On ActivityNet-v1.3, an average mAP (mean Average Precision) of 32.83% was obtained. On Charades, the best performance was obtained, with an average mAP of 27.3%. On TSU (Toyota Smarthome Untrimmed), an average mAP of 33.1% was achieved.
Isolation and Characterization of Triterpenoid Saponins from Ficus natalensis subsp. leprieurii Leaves
Authors: Alanazi Samyah , Elish Shaimaa E. A. , Temraz Abeer , El-Tantawy Walid H. , Baky Mostafa H.
Published: August 2023

Ficus natalensis subsp. leprieurii also known as the natal fig is a fruit-producing tree belonging to the family Moraceae and widely distributed in African countries and cultivated in Egypt. F. natalensis is common with a myriad of traditional and medicinal importance. Owing to the increased demand for natural products with new structural compositions, the current study aimed to separate and elucidate the structure of triterpenoid saponins from F. natalensis leaves. Our previous biological investigation of F. natalensis leaves revealed its potent antioxidant and anti-inflammatory characteristics, and its ability to alleviate Cd-induced reproductive toxicity. Phytochemical investigation of F. natalensis leaves revealed the separation and structure elucidation of seven (1–7) compounds belonging to triterpenoid saponins using NMR and MS data and identified for the first time in F. natalensis. The isolated compounds were identified as 3-O-β-D-glucopyranosyl (1 → 4) β-D-glucopyranosyl (1 → 4)–α-L-rhamnopyranosyl-quinovic acid-28-O-β-D-glucopyranosyl (1 → 4)–α-L-rhamnopyranosyl (1 → 2)–α-L-arabinopyranoside ester (1), 3-O-β-D-glucopyranosyl (1 → 4) α-L rhamnopyranosyl-quinovic acid-28-O-β-D-glucopyranosyl (1 → 4)–α-L-rhamnopyranosyl (1 → 2) α-L-arabinopyranoside (2), 3-O-β-D-glucopyranosyl–quinovic acid-28-O-β-D-glucopyranosyl ester (3), as 3-O-α-L-rhamnopyranosyl-quinovic acid-28-O-β-D-glucopyranoside ester (4), 3-O-β-D-glucopyranosyl oleanolic acid (5), 3-methoxy-oleanolic acid-28-O-α-L-rhamnopyranoside (6), and 3-O-α-L-rhamnopyranosyl-oleanolic acid-28-O-β-D-glucopyranoside ester (7). Among the identified compounds, compounds 1 and 2 were identified for the first time in nature according to Reaxys and Web of Science database.
Chitosan and Metal Oxide Functionalized Chitosan as Efficient Sensors for Lead (II) Detection in Wastewater
Authors: Boultif Walid , Dehchar Charif , Belhocine Youghourta , Zouaoui Emna , Rahali Seyfeddine , Zouari Salah Eddine , Sbei Najoua , Seydou Mahamadou
Published: August 2023

The work presented in this paper describes the preparation and the electrochemical application of functionalized chitosan-entrapped carbon paste electrodes (CH/CPE) for lead ions (Pb2+) detection in industrial wastewater. The chitosan was first functionalized using TiO2 and CuO, which were both metal oxides that were obtained by extracting it from waste products derived from shrimp shells. The analytical performance of the as-prepared electrodes, CH/CPE, TiO2-CH/CPE, and NiO-CH/CPE, for the detection of lead (II) was examined using electrochemical impedance spectroscopy (EIS) technique in the 0.1 M KNO3 electrolyte solution. The effect of experimental conditions, including polarization potential, frequency, and pH, are optimized to maximize the sensitivity of the measurements. The developed impedimetric sensors provided a linear response over a concentration range of 10−6 to 10−4 M with a detection limit of 3.10−7 M based on S/N = 3. The DFT computational analysis demonstrated that chitosan biopolymer possesses the ability to adsorb Pb (II) ions that are present in wastewater. Chitosan and the derivatives of chitosan, have the potential to remove heavy metals from industrial effluent in a manner that is both economical and eco-friendly to the environment. Chitosan is a biopolymer that is abundantly renewable.
Design of Adaptive Kalman Consensus Filters (a-KCF)
Authors: Ye Shalin , Wu Shufan
Published: August 2023

This paper addresses the problem of designing an adaptive Kalman consensus filter (a-KCF) which embedded in multiple mobile agents that are distributed in a 2D domain. The role of such filters is to provide adaptive estimation of the states of a dynamic linear system through communication over a wireless sensor network. It is assumed that each sensing device (embedded in each agent) provides partial state measurements and transmits the information to its instant neighbors in the communication topology. An adaptive consensus algorithm is then adopted to enforce the agreement on the state estimates among all connected agents. The basis of a-KCF design is derived from the classic Kalman filtering theorem; the adaptation of the consensus gain for each local filter in the disagreement terms improves the convergence of the associated difference between the estimation and the actual states of the dynamic linear system, reducing it to zero with appropriate norms. Simulation results testing the performance of a-KCF confirm the validation of our design.
Social Support, Active Ageing Perception and Practices among Educational Staff in the Primary Educational Service, Nakhon-Si-Thammarat Province, Southern Thailand
Authors: Jeenmuang Kawintida , Kaewsawas Supreecha , Thanapop Chamnong , Thanapop Sasithorn
Published: August 2023

The phenomenon of an ageing population coupled with diminishing birth rates has given rise to an increasingly aged workforce in numerous nations. The undertaking to extend the working age beyond retirement holds great significance and poses formidable challenges in the fostering of readiness for active ageing among individuals within the age cohort of 45–59. This cross-sectional study aimed to assess WHO’s active ageing perception and practices and the social support of the educational personnel in the government sector of Nakhon-Si-Thammarat Province, southern Thailand. Data were collected through a multi-stage random sampling of 271 participants between October and November 2021. Descriptive statistics and point-biserial analyses were used to examine the correlations between the variables. The study participants consisted predominantly of females (81.2%), with an average age of 50.4 years (SD 4.3). The majority of the participants were married (56.5%) and had obtained a bachelor’s degree (78.2%). Most of the participants had not been diagnosed with noncommunicable diseases (52.0%) and had a good level of active ageing perception (95.9%), a moderate level of practice (68.6%), and a good level of social support (90.4%). Extra income and social support were correlated with practices at a low level (r = 0.252; p-value < 0.001 and r = 0.214; p-value < 0.001, respectively). In summary, the maintenance of a stable income emerged as a compelling incentive to engage in active ageing initiatives. The proactive promotion of active ageing practices among educational staff is of the utmost significance to the Thai government, particularly in the context of population ageing, with the anticipation of heightened awareness and backing from both families and educational institutions.