bluuebunny/crossref_metadata_2025 huggingface dataset:
Estimating the Changes in Glaciers and Glacial Lakes in the Xixabangma Massif, Central Himalayas, between 1974 and 2018 from Multisource Remote Sensing Data
Authors: Wang Yingzheng , Li Jia , Wu Lixin , Guo Lei , Hu Jun , Zhang Xin
Published: October 2021

The continuous melting of valley glaciers can impact the water levels of glacial lakes and create glacial lake outburst floods (GLOFs). The Xixabangma massif is one of the most populated areas in the Himalayas and has suffered from multiple GLOFs. To estimate the glacier melting rate in the past four decades and analyze the outburst risk of glacial lakes in the Xixabangma massif, we determined changes in glacier mass balance, glacier area and glacial lake area based on KH-9 images, TanDEM-X images, Landsat images, SRTM DEM and ICESat-2 elevations. Our results show that, from 1974 to 2018, the total glacier area shrank from 954.01 km2 to 752.46 km2, whereas the total glacial lake area grew from 20.90 km2 to 38.71 km2. From 1974 to 2000, 2000 to 2013 and 2013 to 2018, the region-wide glacier mass balance values were −0.16 m w.e./a, −0.31 m w.e./a and −0.29 m w.e./a, respectively. Three glacial lakes, named Gangxico, Galongco and Jialongco, respectively, expanded by 127.14%, 373.45% and 436.36% from 1974 to 2018, and the mass loss rates of their parent glaciers from 2000 to 2013 increased by 81.72%, 122.22% and 160.00% relative to those during 1974 to 2000. The dams of these three lakes are unstable, and their drainage valleys directly connect to a major town and its infrastructure. Due to current high-water levels, possible external events such as ice collapse, landslide, heavy rainfall and earthquakes can easily trigger GLOFs. Hence, we deemed that the Gangxico, Galongco and Jialongco glacial lakes are dangerous and require special attention.
Discontinuity Detection in GNSS Station Coordinate Time Series Using Machine Learning
Authors: Crocetti Laura , Schartner Matthias , Soja Benedikt
Published: October 2021

Global navigation satellite systems (GNSS) provide globally distributed station coordinate time series that can be used for a variety of applications such as the definition of a terrestrial reference frame. A reliable estimation of the coordinate time series trends gives valuable information about station movements during the measured time period. Detecting discontinuities of various origins in such time series is crucial for accurate and robust velocity estimation. At present, there is no fully automated standard method for detecting discontinuities. Instead, discontinuity-catalogues are frequently used, which provide information about when a device was changed or an earthquake occurred. However, it is known that these catalogues suffer from incompleteness. This study investigates the suitability of machine learning classification algorithms that are fully data-driven to detect discontinuities caused by earthquakes in station coordinate time series without the need for external information. For this study, Japan was selected as a testing area. Ten different machine learning algorithms have been tested. It is found that Random Forest achieves the best performance with an F1 score of 0.77, a recall of 0.78, and a precision of 0.76. Overall, 525 of 565 recorded earthquakes in the test data were correctly classified. It is further highlighted that splitting the time series into chunks of 21 days leads to the best performance. Furthermore, it is beneficial to combine the three (normalized) components of the GNSS solution into one sample, and that adding the value range as an additional feature improves the result. Thus, this work demonstrates how it is possible to use machine learning algorithms to detect discontinuities in GNSS time series.
Texture Is Important in Improving the Accuracy of Mapping Photovoltaic Power Plants: A Case Study of Ningxia Autonomous Region, China
Authors: Zhang Xunhe , Zeraatpisheh Mojtaba , Rahman Md Mizanur , Wang Shujian , Xu Ming
Published: October 2021

Photovoltaic (PV) technology is becoming more popular due to climate change because it allows for replacing fossil-fuel power generation to reduce greenhouse gas emissions. Consequently, many countries have been attempting to generate electricity through PV power plants over the last decade. Monitoring PV power plants through satellite imagery, machine learning models, and cloud-based computing systems that may ensure rapid and precise locating with current status on a regional basis are crucial for environmental impact assessment and policy formulation. The effect of fusion of the spectral, textural with different neighbor sizes, and topographic features that may improve machine learning accuracy has not been evaluated yet in PV power plants’ mapping. This study mapped PV power plants using a random forest (RF) model on the Google Earth Engine (GEE) platform. We combined textural features calculated from the Grey Level Co-occurrence Matrix (GLCM), reflectance, thermal spectral features, and Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and Modified Normalized Difference Water Index (MNDWI) from Landsat-8 imagery and elevation, slope, and aspect from Shuttle Radar Topography Mission (SRTM) as input variables. We found that the textural features from GLCM prominent enhance the accuracy of the random forest model in identifying PV power plants where a neighbor size of 30 pixels showed the best model performance. The addition of texture features can improve model accuracy from a Kappa statistic of 0.904 ± 0.05 to 0.938 ± 0.04 and overall accuracy of 97.45 ± 0.14% to 98.32 ± 0.11%. The topographic and thermal features contribute a slight improvement in modeling. This study extends the knowledge of the effect of various variables in identifying PV power plants from remote sensing data. The texture characteristics of PV power plants at different spatial resolutions deserve attention. The findings of our study have great significance for collecting the geographic information of PV power plants and evaluating their environmental impact.
A Novel Method for Estimating Spatial Distribution of Forest Above-Ground Biomass Based on Multispectral Fusion Data and Ensemble Learning Algorithm
Authors: Li Xinyu , Zhang Meng , Long Jiangping , Lin Hui
Published: October 2021

Optical remote sensing technology has been widely used in forest resources inventory. Due to the influence of satellite orbits, sensor parameters, sensor errors, and atmospheric effects, there are great differences in vegetation spectral information captured by different satellite sensor images. Spectral fusion technology can couple the advantages of different multispectral sensor images to produce new multispectral data with high spatial and spectral resolution, it has great potential for improving the spectral sensitivity of forest vegetation and alleviating the spectral saturation. However, how to quickly and effectively select the multi-spectral fusion data suitable for forest above-ground biomass (AGB) estimation is a very critical issue. This study proposes a scheme (RF-S) to comprehensively evaluate multispectral fused images and develop the appropriate model for forest AGB estimation, on the basis of random forest (RF) and the stacking ensemble algorithm. First, four classic fusion methods are used to fuse the preprocessed GaoFen-2 (GF-2) multispectral image with Sentinel-2 image to generate 12 fused Sentinel-like images. Secondly, we apply a comprehensive evaluation method to quickly select the optimal fused image for the follow-up research. Subsequently, two feature combination optimization methods are used to select feature variables from the three feature sets. Finally, the stacking ensemble algorithm based on model dynamic integration and hyperparameter automatic optimization, as well as some classic machine learners, are used to construct the forest AGB estimation model. The results show that the fused image NND_B3 (based on nearest neighbor diffusion pan sharpening method and Band3_Red) selected by the evaluation method proposed in this study has the best performance in AGB estimation. Using the stacking ensemble method and NND_B3 image, we get the highest estimation accuracy, with the adjusted R2 and relative root mean square error (RMSEr) of 0.6306 and 15.53%, respectively. The AGB estimation RMSEr of NND_B3 is 19.95% and 24.90% lower than those of GF-2 and Sentinel-2, respectively. We also found that the multi-window texture factor has better performance in the area with low AGB, and it can suppress the overestimation significantly. The AGB spatial distribution estimated using the NND_B3 image matches the field observations well, indicating that the multispectral fusion image combined with the Stacking algorithm can increase the accuracy and saturation of the AGB estimates.
Monitoring Cropping Intensity Dynamics across the North China Plain from 1982 to 2018 Using GLASS LAI Products
Authors: Zhao Yan , Feng Jianzhong , Luo Lei , Bai Linyan , Wan Hong , Ren Hongge
Published: October 2021

China is a large grain producer and consumer. Thus, obtaining information about the cropping intensity (CI) in cultivated land, as well as understanding the intensified utilization of cultivated land, is important to ensuring an increased grain production and food security for China. This study aims to detect and map the changes in CI over a period of 36 years across China’s core grain-producing area—the North China Plain (NCP)— using remotely sensed leaf area index (LAI) time series data acquired by the Global LAnd Surface Satellite (GLASS) products. We first selected 2132 sample points that consisted entirely, or almost entirely, of cultivated cropland from all pixels; the biennial LAI curves for the sample points were then extracted; the Savitzky–Golay filter and second-order difference algorithm were then applied to reconstruct the biennial LAI curves and obtain the number of peaks in these curves. In addition, the multiple cropping index (MCI) was calculated to represent the CI. Finally, the spatial distribution of the CI of cultivated land on the NCP was mapped from 1982 to 2018 using a geo-statistical kriging approach. Spatially, the results indicate that the CI of cultivated land over the NCP exhibits a distinct spatial pattern that conforms to “high in the south, low in the north”. The single cropping system (SCS) mainly occurred in the higher latitude area ranging from 37.04°N to 42.54°N, and the double cropping system (DCS) mainly existed in the lower latitude area between 31.95°N and 39.97°N. Temporally, the CI increased over the study period, but there were some large fluctuations in CI from 1982 to 1998 and it maintained relatively stable since 2000. Across the NCP, 68.14% of cultivated land experienced a significant increase in CI during the 36-year period, while only 3.87% showed a significant decrease. We also found that, between 1982 and 2018, the northern boundary of the area for DCS underwent a significant westward expansion and northward movement. Our results show a good degree of consistency with statistical data and previous research and also help to improve the reliability of satellite-based identification of CI using low spatial resolution LAI products. The results provide important information that can be used for analyzing and evaluating the rational utilization of cultivated land resources; thus, ensuring food security and realizing agricultural sustainability not only for the NCP, but for China as a whole. These results also highlight the value of satellite remote sensing to the long-term monitoring of cropping intensity at large scales.
Detection of Flood Damage in Urban Residential Areas Using Object-Oriented UAV Image Analysis Coupled with Tree-Based Classifiers
Authors: Zawadzka Joanna , Truckell Ian , Khouakhi Abdou , Rivas Casado Mónica
Published: October 2021

Timely clearing-up interventions are essential for effective recovery of flood-damaged housing, however, time-consuming door-to-door inspections for insurance purposes need to take place before major repairs can be done to adequately assess the losses caused by flooding. With the increased probability of flooding, there is a heightened need for rapid flood damage assessment methods. High resolution imagery captured by unmanned aerial vehicles (UAVs) offers an opportunity for accelerating the time needed for inspections, either through visual interpretation or automated image classification. In this study, object-oriented image segmentation coupled with tree-based classifiers was implemented on a 10 cm resolution RGB orthoimage, captured over the English town of Cockermouth a week after a flood triggered by storm Desmond, to automatically detect debris associated with damages predominantly to residential housing. Random forests algorithm achieved a good level of overall accuracy of 74%, with debris being correctly classified at the rate of 58%, and performing well for small debris (67%) and skips (64%). The method was successful at depicting brightly-colored debris, however, was prone to misclassifications with brightly-colored vehicles. Consequently, in the current stage, the methodology could be used to facilitate visual interpretation of UAV images. Methods to improve accuracy have been identified and discussed.
Flood Mitigation in the Transboundary Chenab River Basin: A Basin-Wise Approach from Flood Forecasting to Management
Authors: Ali Sikandar , Cheema Muhammad , Waqas Muhammad , Waseem Muhammad , Leta Megersa , Qamar Muhammad , Awan Usman , Bilal Muhammad , Rahman Muhammad
Published: October 2021

Rapid and reliable flood information is crucial for minimizing post-event catastrophes in the complex river basins of the world. The Chenab River basin is one of the complex river basins of the world, facing adverse hydrometeorological conditions with unpredictable hydrologic response. Resultantly, many vicinities along the river undergo destructive inundation, resulting in huge life and economic losses. In this study, Hydrologic Engineering Centre–Hydrologic Modeling System (HEC-HMS) and HEC–River Analysis System (HEC-RAS) models were used for flood forecasting and inundation modeling of the Chenab River basin. The HEC-HMS model was used for peak flow simulation of 2014 flood event using Global Precipitation Mission (GMP) Integrated Multisatellite Retrievals-Final (IMERG-F), Tropical Rainfall Measuring Mission_Real Time (TRMM_3B42RT), and Global Satellite Mapping of Precipitation_Near Real Time (GSMaP_NRT) precipitation products. The calibration and validation of the HEC-RAS model were carried out for flood events of 1992 and 2014, respectively. The comparison of observed and simulated flow at the outlet indicated that IMERG-F has good peak flow simulation results. The simulated inundation extent revealed an overall accuracy of more than 90% when compared with satellite imagery. The HEC-RAS model performed well at Manning’s n of 0.06 for the river and the floodplain. From the results, it can be concluded that remote sensing integrated with HEC-HMS and HEC-RAS models could be one of the workable solutions for flood forecasting, inundation modeling, and early warning. The concept of integrated flood management (IFM) has also been translated into practical implementation for joint Indo-Pak management for flood mitigation in the transboundary Chenab River basin.
S-NPP VIIRS Thermal Emissive Bands 10-Year On-Orbit Calibration and Performance
Authors: Pérez Díaz Carlos L. , Xiong Xiaoxiong , Li Yonghong , Chiang Kwofu
Published: October 2021

The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership Program (S-NPP) satellite, launched in late 2011, has reached the decade landmark under successful operations. VIIRS has 22 spectral bands, 7 of which are thermal emissive bands (TEB) that cover the 3.70 to 11.84 μm wavelength range. Over the years, VIIRS TEB observations have been used to generate several data products (e.g., surface/cloud/atmospheric temperatures, cloud top altitude, and water vapor properties). The VIIRS TEB calibration uses a quadratic algorithm and is referenced to an on-board blackbody with temperature measurements traceable to the National Institute of Standards and Technology standard. This manuscript provides an overview of the VIIRS instrument operations and TEB calibration activities and algorithms used in the level 1B data and describes the TEB on-orbit performance for S-NPP VIIRS. The 10-year on-orbit performance of the S-NPP VIIRS TEB has generally been stable, and the degradations in the S-NPP TEB detector responses are minor after a decade in orbit. The noise characterization performance repeatedly meets the design requirements for all TEB detectors as well. On-orbit changes in the TEB response-versus-scan-angle, based on pitch maneuver observations, have been demonstrated to be extremely small. Moreover, multiple time series over select ground targets have shown that the sensor’s on-orbit performance is quite stable.
3D Ocean Water Wave Surface Analysis on Airborne LiDAR Bathymetric Point Clouds
Authors: Roshandel Sajjad , Liu Weiquan , Wang Cheng , Li Jonathan
Published: October 2021

Water wave monitoring is a vital issue for coastal research and plays a key role in geomorphological changes, erosion and sediment transportation, coastal hazards, risk assessment, and decision making. However, despite missing data and the difficulty of capturing the data of nearshore fieldwork, the analysis of water wave surface parameters is still able to be discussed. In this paper, we propose a novel approach for accurate detection and analysis of water wave surface from Airborne LiDAR Bathymetry (ALB) large-scale point clouds data. In our proposed method we combined the modified Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering method with a connectivity constraint and a multi-level analysis of ocean water surface. We adapted for most types of wave shape anatomies in shallow waters, nearshore, and onshore of the coastal zone. We used a wavelet analysis filter to detect the water wave surface. Then, through the Fourier Transformation Approach, we estimated the parameters of wave height, wavelength, and wave orientation. The comparison between the LiDAR measure estimation technique and available buoy data was then presented. We quantified the performance of the algorithm by measuring the precision and recall for the waves identification without evaluating the degree of over-segmentation. The proposed method achieves 87% accuracy of wave identification in the shallow water of coastal zones.
Deep Learning-Based Instance Segmentation Method of Litchi Canopy from UAV-Acquired Images
Authors: Mo Jiawei , Lan Yubin , Yang Dongzi , Wen Fei , Qiu Hongbin , Chen Xin , Deng Xiaoling
Published: October 2021

Instance segmentation of fruit tree canopies from images acquired by unmanned aerial vehicles (UAVs) is of significance for the precise management of orchards. Although deep learning methods have been widely used in the fields of feature extraction and classification, there are still phenomena of complex data and strong dependence on software performances. This paper proposes a deep learning-based instance segmentation method of litchi trees, which has a simple structure and lower requirements for data form. Considering that deep learning models require a large amount of training data, a labor-friendly semi-auto method for image annotation is introduced. The introduction of this method allows for a significant improvement in the efficiency of data pre-processing. Facing the high requirement of a deep learning method for computing resources, a partition-based method is presented for the segmentation of high-resolution digital orthophoto maps (DOMs). Citrus data is added to the training set to alleviate the lack of diversity of the original litchi dataset. The average precision (AP) is selected to evaluate the metric of the proposed model. The results show that with the help of training with the litchi-citrus datasets, the best AP on the test set reaches 96.25%.
Complementary Differences in Primary Production and Phenology among Vegetation Types Increase Ecosystem Resilience to Climate Change and Grazing Pressure in an Iconic Mediterranean Ecosystem
Authors: Giralt-Rueda Juan Miguel , Santamaria Luis
Published: October 2021

Plant primary production is a key factor in ecosystem dynamics. In environments with high climatic variability such as the Mediterranean region, plant primary production shows strong seasonal and inter-annual fluctuations, which both drive and interplay with herbivore grazing. Knowledge on the responses of different vegetation types to the variability in both rainfall and grazing pressure by wild and domestic ungulates is a necessary starting point for the sustainable management of these ecosystems. In this work we combine a 15 year series of remote sensing data on plant production (NDVI) with meteorological (daily precipitation data) and ungulate abundance (annual counts of four species of wild and domestic ungulates: red deer, fallow deer, cattle, and horses) in an iconic protected area (the Doñana National Park, SW Spain) to (i) estimate the impact of intra- and inter-annual variation in rainfall and herbivore pressure on primary production, for each of four main vegetation types; and (ii) evaluate the potential impact of different policy (i.e., herbivore management) strategies under expected climate change scenarios. Our results show that the production of different vegetation types differed strongly in their responses to phenology (a surrogate of the effect of climatology on vegetation development), water availability (rainfall accumulated until the phenological peak), and grazing pressure. Although the density of domestic ungulates shows a linear, negative effect on the primary production of three of the four vegetation types, differences in primary production and phenology among vegetation types increase ecosystem resilience to both climatological variability and grazing pressure. Such resilience may, however, be reduced under the conditions predicted by climate change models, if the moderate predicted reduction in rainfall levels combines with moderate to high densities of domestic ungulates, resulting in important reductions in primary production that may compromise plant regeneration, leading to irreversible degradation. New management strategies taking advantage of habitat heterogeneity and phenological alternation, more flexible stocking rates, and the redistribution of management units should be considered to mitigate these effects. The use of available remote sensing data and techniques in combination with statistical models represents a valuable tool for developing, monitoring, and refining such strategies.
Dynamic Assessment of the Impact of Flood Disaster on Economy and Population under Extreme Rainstorm Events
Authors: Su Xin , Shao Weiwei , Liu Jiahong , Jiang Yunzhong , Wang Kaibo
Published: October 2021

In the context of climate change and rapid urbanization, flood disaster loss caused by extreme rainstorm events is becoming more and more serious. An accurate assessment of flood disaster loss has become a key issue. In this study, extreme rainstorm scenarios with 50- and 100-year return periods based on the Chicago rain pattern were designed. The dynamic change process of flood disaster loss was obtained by using a 1D–2D coupled model, Hazard Rating (HR) method, machine learning, and ArcPy script. The results show that under extreme rainstorm events, the direct economic loss and affected population account for about 3% of the total GDP and 16% of the total population, respectively, and built-up land is the main disaster area. In addition, the initial time and the peak time of flood disaster loss increases with an increasing flood hazard degree and decreases with the increase in the return period. The total loss increases with the increase in the return period, and the unit loss decreases with the increase in the return period. Compared with a static assessment, a dynamic assessment can better reveal the development law of flood disaster loss, which has great significance for flood risk management and the mitigation of flood disaster loss.
Evaluation of Satellite Precipitation Estimates over the South West Pacific Region
Authors: Wild Ashley , Chua Zhi-Weng , Kuleshov Yuriy
Published: October 2021

Rainfall estimation over the Pacific region is difficult due to the large distances between rain gauges and the high convection nature of many rainfall events. This study evaluates space-based rainfall observations over the South West Pacific Region from the Japan Aerospace Exploration Agency’s (JAXA) Global Satellite Mapping of Precipitation (GSMaP), the USA National Oceanographic and Atmospheric Administration’s (NOAA) Climate Prediction Center morphing technique (CMORPH), the Climate Hazards group Infrared Precipitation with Stations (CHIRPS), and the National Aeronautics and Space Administration’s (NASA) Integrated Multi-Satellite Retrievals for GPM (IMERG). The technique of collocation analysis (CA) is used to compare the performance of monthly satellite precipitation estimates (SPEs). Multi-Source Weighted-Ensemble Precipitation (MSWEP) was used as a reference dataset to compare with each SPE. European Centre for Medium-range Weather Forecasts’ (ECMWF) ERA5 reanalysis was also combined with Soil Moisture-2-Rain–ASCAT (SM2RAIN–ASCAT) to perform triple CA for the six sub-regions of Fiji, New Caledonia, Papua New Guinea (PNG), the Solomon Islands, Timor, and Vanuatu. It was found that GSMaP performed best over low rain gauge density areas, including mountainous areas of PNG (the cross-correlation, CC = 0.64), and the Solomon Islands (CC = 0.74). CHIRPS had the most consistent performance (high correlations and low errors) across all six sub-regions in the study area. Based on the results, recommendations are made for the use of SPEs over the South West Pacific Region.
Optimizing Top Dressing Nitrogen Fertilization Using VENμS and Sentinel-2 L1 Data
Authors: Bonfil David J. , Michael Yaron , Shiff Shilo , Lensky Itamar M.
Published: October 2021

Environmental and economic constraints are forcing farmers to be more precise in the rates and timing of nitrogen (N) fertilizer application to wheat. In practice, N is frequently applied without knowledge of the precise amount needed or the likelihood of significant protein enhancement. The objective of this study was to help farmers optimize top dress N application by adopting the use of within-field reference N strips. We developed an assisting app on the Google Earth Engine (GEE) platform to map the spatial variability of four different vegetation indices (VIs) in each field by calculating the mean VI, masking extreme values (three standard deviations, 3σ) of each field, and presenting the anomaly as a deviation of ±σ and ±2σ or deviation of percentage. VIs based on red-edge bands (REIP, NDRE, ICCI) were very useful for the detection of wheat above ground N uptake and in-field anomalies. VENµS high temporal and spatial resolutions provide advantages over Sentinel-2 in monitoring agricultural fields during the growing season, representing the within-field variations and for decision making, but the spatial coverage and accessibility of Sentinel-2 data are much better. Sentinel-2 data is already available on the GEE platform and was found to be of much help for the farmers in optimizing topdressing N application to wheat, applying it only where it will increase grain yield and/or grain quality. Therefore, the GEE anomaly app can be used for top-N dressing application decisions. Nevertheless, there are some issues that must be tested, and more research is required. To conclude, satellite images can be used in the GEE platform for anomaly detection, rendering results within a few seconds. The ability to use L1 VENµS or Sentinel-2 data without atmospheric correction through GEE opens the opportunity to use these data for several applications by farmers and others.
Next Generation Gravity Mission Elements of the Mass Change and Geoscience International Constellation: From Orbit Selection to Instrument and Mission Design
Authors: Massotti Luca , Siemes Christian , March Günther , Haagmans Roger , Silvestrin Pierluigi
Published: October 2021

ESA’s Next Generation Gravity Mission (NGGM) is a candidate Mission of Opportunity for ESA–NASA cooperation in the frame of the Mass Change and Geosciences International Constellation (MAGIC). The mission aims at enabling long-term monitoring of the temporal variations of Earth’s gravity field at relatively high temporal (down to 3 days) and increased spatial resolutions (up to 100 km) at longer time intervals. This implies also that time series of GRACE and GRACE-FO can be extended towards a climate series. Such variations carry information about mass change induced by the water cycle and the related mass exchange among atmosphere, oceans, cryosphere, land and solid Earth and will complete our picture of global and climate change. The main observable is the variation of the distance between two satellites measured by a ranging instrument. This is complemented by accelerometers that measure the nongravitational accelerations, which need to be reduced from ranging measurements to obtain the gravity signal. The preferred satellite constellation comprises one satellite pair in a near-polar and another in an inclined circular orbit. The paper focuses on the orbit selection methods for optimizing the spatial sampling for multiple temporal resolutions and then on the methodology for deriving the engineering requirements for the space segment, together with a discussion on the main mission parameters.
Ecogeomorphological Transformations of Aeolian Form—The Case of a Parabolic Dune, Poland
Authors: Zarychta Roksana , Zarychta Adrian , Bzdęga Katarzyna
Published: October 2021

The range of natural environmental degradation caused by anthropogenic activity may include geomorphological forms such as dunes resulting from the build-up activity of the wind. In effect, such environmental transformation affects changes connected not only with their relief, but also with the presence and health of diverse plant and animal inhabitants. The subject of the survey was a parabolic dune with asymmetric shape, the sand of which was subjected to exploitation over many years. Terrain data acquired by means of GNSS (Global Navigation Satellite Systems) served to elaborate the present relief of the surveyed dune and to reconstruct its primary relief. These were mainly places where the impacts of human activities were recorded. For this purpose, ordinary kriging (OK) estimation was performed. Simultaneously, satellite data and UAV (Unmanned Aerial Vehicle) imaging were acquired, and subjected to image fusion in order to acquire near infrared bands (NIR), red, green, blue in high spatial resolution. These in turn were applied so as to estimate the condition of the vegetation overplanting the dune and surrounding terrain. The correctness of the modelling was verified by cross-validation (CV), which disclosed low error values. Such values in present and primary relief were, respectively, mean error (ME) at −0.009 and −0.014, root mean square error (RMSE) at 0.564 and 0.304 and root mean square standardised error (RMSSE) at 0.999 and 1.077. Image fusion, with use of pansharpening allowed a colour-infrared composition (CIR) and a Modified Chlorophyll Absorption in Reflectance Index 1 (MCARI1) to be obtained. Their analysis disclosed that vegetation on the dune is characterised by worse health condition as compared with the surrounding area thereof. The proposed approach enabled the environmental condition of the surveyed dune to be analysed, and thereby it allows for a determination of the consequences of further uncontrolled sand recovery without taking into account the historical cartographic materials customarily considered to be the main source of information.
PeaceGAN: A GAN-Based Multi-Task Learning Method for SAR Target Image Generation with a Pose Estimator and an Auxiliary Classifier
Authors: Oh Jihyong , Kim Munchurl
Published: October 2021

Although generative adversarial networks (GANs) are successfully applied to diverse fields, training GANs on synthetic aperture radar (SAR) data is a challenging task due to speckle noise. On the one hand, in a learning perspective of human perception, it is natural to learn a task by using information from multiple sources. However, in the previous GAN works on SAR image generation, information on target classes has only been used. Due to the backscattering characteristics of SAR signals, the structures of SAR images are strongly dependent on their pose angles. Nevertheless, the pose angle information has not been incorporated into GAN models for SAR images. In this paper, we propose a novel GAN-based multi-task learning (MTL) method for SAR target image generation, called PeaceGAN, that has two additional structures, a pose estimator and an auxiliary classifier, at the side of its discriminator in order to effectively combine the pose and class information via MTL. Extensive experiments showed that the proposed MTL framework can help the PeaceGAN’s generator effectively learn the distributions of SAR images so that it can better generate the SAR target images more faithfully at intended pose angles for desired target classes in comparison with the recent state-of-the-art methods.
Impact of Tropospheric Mismodelling in GNSS Precise Point Positioning: A Simulation Study Utilizing Ray-Traced Tropospheric Delays from a High-Resolution NWM
Authors: Zus Florian , Balidakis Kyriakos , Dick Galina , Wilgan Karina , Wickert Jens
Published: October 2021

In GNSS analysis, the tropospheric delay is parameterized by applying mapping functions (MFs), zenith delays, and tropospheric gradients. Thereby, the wet and hydrostatic MF are derived under the assumption of a spherically layered atmosphere. The coefficients of the closed-form expression are computed utilizing a climatology or numerical weather model (NWM) data. In this study, we analyze the impact of tropospheric mismodelling on estimated parameters in precise point positioning (PPP). To do so, we mimic PPP in an artificial environment, i.e., we make use of a linearized observation equation, where the observed minus modelled term equals ray-traced tropospheric delays from a high-resolution NWM. The estimated parameters (station coordinates, clocks, zenith delays, and tropospheric gradients) are then compared with the known values. The simulation study utilized a cut-off elevation angle of 3° and the standard downweighting of low elevation angle observations. The results are representative of a station located in central Europe and the warm season. In essence, when climatology is utilized in GNSS analysis, the root mean square error (RMSE) of the estimated zenith delay and station up-component equal about 2.9 mm and 5.7 mm, respectively. The error of the GNSS estimates can be reduced significantly if the correct zenith hydrostatic delay and the correct hydrostatic MF are utilized in the GNSS analysis. In this case, the RMSE of the estimated zenith delay and station up-component is reduced to about 2.0 mm and 2.9 mm, respectively. The simulation study revealed that the choice of wet MF, when calculated under the assumption of a spherically layered troposphere, does not matter too much. In essence, when the ‘correct’ wet MF is utilized in the GNSS analysis, the RMSE of the estimated zenith delay and station up-component remain at about 1.8 mm and 2.4 mm, respectively. Finally, as a by-product of the simulation study, we developed a modified wet MF, which is no longer based on the assumption of a spherically layered atmosphere. We show that with this modified wet MF in the GNSS analysis, the RMSE of the estimated zenith delay and station up-component can be reduced to about 0.5 mm and 1.0 mm, respectively. In practice, its success depends on the ability of current (future) NWM to predict the fourth coefficient of the developed closed-form expression. We provide some evidence that current NWMs are able to do so.
Environmental Sensitivity of Large Stealth Longwave Antenna Systems
Authors: Miś Tomasz Aleksander , Modelski Józef
Published: October 2021

This article presents an analysis of measurements collected during a six-month-long experimental deployment of a surface-placed horizontal magnetic loop antenna. The changes in the measured parameters of the loop are investigated in relation to the surrounding environment’s composition, temperature and water content. Basic functions describing these changes are formulated. The results are confronted with outcomes from similar experiments from previous years and different locations, showing good compliance. The developed functions and antenna system can be used for environmental monitoring of soil composition and humidity over large areas and volumes, helpful in, for example, flood awareness.
Corn Grain Yield Prediction and Mapping from Unmanned Aerial System (UAS) Multispectral Imagery
Authors: Sunoj S. , Cho Jason , Guinness Joe , van Aardt Jan , Czymmek Karl J. , Ketterings Quirine M.
Published: October 2021

Harvester-mounted yield monitor sensors are expensive and require calibration and data cleaning. Therefore, we evaluated six vegetation indices (VI) from unmanned aerial system (Quantix™ Mapper) imagery for corn (Zea mays L.) yield prediction. A field trial was conducted with N sidedress treatments applied at four growth stages (V4, V6, V8, or V10) compared against zero-N and N-rich controls. Normalized difference vegetation index (NDVI) and enhanced vegetation index 2 (EVI2), based on flights at R4, resulted in the most accurate yield estimations, as long as sidedressing was performed before V6. Yield estimations based on earlier flights were less accurate. Estimations were most accurate when imagery from both N-rich and zero-N control plots were included, but elimination of the zero-N data only slightly reduced the accuracy. Use of a ratio approach (VITrt/VIN-rich and YieldTrt/YieldN-rich) enables the extension of findings across fields and only slightly reduced the model performance. Finally, a smaller plot size (9 or 75 m2 compared to 150 m2) resulted in a slightly reduced model performance. We concluded that accurate yield estimates can be obtained using NDVI and EVI2, as long as there is an N-rich strip in the field, sidedressing is performed prior to V6, and sensing takes place at R3 or R4.
Entropy Metrics of Radar Signatures of Sea Surface Scattering for Distinguishing Targets
Authors: Jiang Rui , Li Li-Na , Sun Qiang , Hong Si-Zhang , Gao Jian-Jie , Xu Xin-Hui
Published: October 2021

This paper analyzes sea clutter by a random series without assuming the scattering being independent. We quantitated the complexity of sea clutter by applying multiscale sample entropy. We found that above certain wave heights or wind speeds, and for HH or VV polarization, the target can be distinguished from sea clutter by regarding (i) the sample entropy at large scale factors or (ii) the complexity index (CI) as entropy metrics. This is because the backscattering amplitudes of range bins with the primary target were found equipped with the lowest sample entropy at large scale factors or the lowest CI compared to that of range bins with sea clutter only. To further cover low-to-moderate sea states, we constructed a polarized complexity index (PCI) based on the polarization signatures of the multiscale sample entropy of sea clutter. We demonstrated that the PCI is yet another alternative entropy metric and can achieve a superb performance on distinguishing targets within 1993’s IPIX radar data sets. In each data set, the range bins with the primary target turned to have the lowest PCI compared to that of range bins with sea clutter alone. Moreover, in our experiment using 1993’s IPIX radar data sets, the PCIs of range bins with sea clutter only were almost the same and stable in each data set, further suggesting that the proposed PCI metric can be applied in the presence of no or multiple targets through proper fitting curves.
Time Series Analysis of Land Cover Change in Dry Mountains: Insights from the Tajik Pamirs
Authors: Vanselow Kim André , Zandler Harald , Samimi Cyrus
Published: October 2021

Greening and browning trends in vegetation have been observed in many regions of the world in recent decades. However, few studies focused on dry mountains. Here, we analyze trends of land cover change in the Western Pamirs, Tajikistan. We aim to gain a deeper understanding of these changes and thus improve remote sensing studies in dry mountainous areas. The study area is characterized by a complex set of attributes, making it a prime example for this purpose. We used generalized additive mixed models for the trend estimation of a 32-year Landsat time series (1988–2020) of the modified soil adjusted vegetation index, vegetation data, and environmental and socio-demographic data. With this approach, we were able to cope with the typical challenges that occur in the remote sensing analysis of dry and mountainous areas, including background noise and irregular data. We found that greening and browning trends coexist and that they vary according to the land cover class, topography, and geographical distribution. Greening was detected predominantly in agricultural and forestry areas, indicating direct anthropogenic drivers of change. At other sites, greening corresponds well with increasing temperature. Browning was frequently linked to disastrous events, which are promoted by increasing temperatures.
Feasibility of the Spatiotemporal Fusion Model in Monitoring Ebinur Lake’s Suspended Particulate Matter under the Missing-Data Scenario
Authors: Liu Changjiang , Duan Pan , Zhang Fei , Jim Chi-Yung , Tan Mou Leong , Chan Ngai Weng
Published: October 2021

High-frequency monitoring of suspended particulate matter (SPM) concentration can improve water resource management. Missing high-resolution satellite images could hamper remote-sensing SPM monitoring. This study resolved the problem by applying spatiotemporal fusion technology to obtain high spatial resolution and dense time-series data to fill image-data gaps. Three data sources (MODIS, Landsat 8, and Sentinel 2) and two spatiotemporal fusion methods (the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and the flexible spatiotemporal data fusion (FSDAF)) were used to reconstruct missing satellite images. We compared their fusion accuracy and verified the consistency of fusion images between data sources. For the fusion images, we used random forest (RF) and XGBoost as inversion methods and set “fusion first” and “inversion first” strategies to test the method’s feasibility in Ebinur Lake, Xinjiang, arid northwestern China. Our results showed that (1) the blue, green, red, and NIR bands of ESTARFM fusion image were better than FSDAF, with a good consistency (R2 ≥ 0.54) between the fused Landsat 8, Sentinel 2 images, and their original images; (2) the original image and fusion image offered RF inversion effect better than XGBoost. The inversion accuracy based on Landsat 8 and Sentinel 2 were R2 0.67 and 0.73, respectively. The correlation of SPM distribution maps of the two data sources attained a good consistency of R2 0.51; (3) in retrieving SPM from fused images, the “fusion first” strategy had better accuracy. The optimal combination was ESTARFM (Landsat 8)_RF and ESTARFM (Sentinel 2)_RF, consistent with original SPM maps (R2 = 0.38, 0.41, respectively). Overall, the spatiotemporal fusion model provided effective SPM monitoring under the image-absence scenario, with good consistency in the inversion of SPM. The findings provided the research basis for long-term and high-frequency remote-sensing SPM monitoring and high-precision smart water resource management.
A Fast Hyperspectral Anomaly Detection Algorithm Based on Greedy Bilateral Smoothing and Extended Multi-Attribute Profile
Authors: Liu Senhao , Zhang Lifu , Cen Yi , Chen Likun , Wang Yibo
Published: October 2021

To address the difficulty of separating background materials from similar materials associated with the use of “single-spectral information” for hyperspectral anomaly detection, a fast hyperspectral anomaly detection algorithm based on what we term the “greedy bilateral smoothing and extended multi-attribute profile” (GBSAED) method is proposed to improve detection precision and operation efficiency. This method utilizes “greedy bilateral smoothing” to decompose the low-rank part of a hyperspectral image (HSI) dataset and calculate spectral anomalies. This process improves the operational efficiency. Then, the extended multi-attribute profile is used to extract spatial anomalies and restrict the shape of anomalies. Finally, the two components are combined to limit false alarms and obtain appropriate detection results. This new method considers both spectral and spatial information with an improved structure that ensures operational efficiency. Using five real HSI datasets, this study demonstrates that the GBSAED method is more robust than eight representative algorithms under diverse application scenarios and greatly improves detection precision and operational efficiency.
Short-Term Variability in Alaska Ice-Marginal Lake Area: Implications for Long-Term Studies
Authors: Hengst Anton M. , Armstrong William , Rick Brianna , McGrath Daniel
Published: October 2021

Lakes in direct contact with glaciers (ice-marginal lakes) are found across alpine and polar landscapes. Many studies characterize ice-marginal lake behavior over multi-decadal timescales using either episodic ~annual images or multi-year mosaics. However, ice-marginal lakes are dynamic features that experience short-term (i.e., day to year) variations in area and volume superimposed on longer-term trends. Through aliasing, this short-term variability could result in erroneous long-term estimates of lake change. We develop and implement an automated workflow in Google Earth Engine to quantify monthly behavior of ice-marginal lakes between 2013 and 2019 across south-central Alaska using Landsat 8 imagery. We employ a supervised Mahalanobis minimum-distance land cover classifier incorporating three datasets found to maximize classifier performance: shortwave infrared imagery, the normalized difference vegetation index (NDVI), and spatially filtered panchromatic reflectance. We observe physically-meaningful ice-marginal lake area variance on sub-annual timescales, with the median area fluctuation of an ice-marginal lake found to be 10.8% of its average area. The median signal (slow lake growth) to noise (physically-meaningful short-term area variability) ratio is 1.5:1, indicating that short-term variability is responsible for ~33% of observed area change in the median ice-marginal lake. The magnitude of short-term area variability is similar for ice-marginal and nonglacial lakes, suggesting that the cause of observed variations is not of glacial origin. These data provide a new context for interpreting behaviors observed in multi-decadal studies and encourage attention to sub-annual behavior of ice-marginal lakes even in long-term studies.