bluuebunny/crossref_metadata_2025 huggingface dataset:
On the Impacts of Historical and Future Climate Changes to the Sustainability of the Main Sardinian Forests
Authors: Cipolla Sara Simona , Montaldo Nicola
Published: October 2022

The Mediterranean Basin is affected by climate changes that may have negative effects on forests. This study aimed to evaluate the ability of 17 forests located in the Island of Sardinia to resist or adapt to the past and future climate. Sardinia is experiencing a decreasing anthropic pressure on forests, but drought-triggered dieback in trees was recently observed and confirmed by the analysis of 20 years of satellite tree-cover data (MOD44B). Significant negative trends in yearly tree cover have affected the broad-leaved vegetation, while significative positive trends were found in the bushy sclerophyllous vegetation. Vegetation behavior resulted in being related to the mean annual precipitation (MAP); for MAP < 700 mm, we found a decline in the tall broad-leaved stands and an increase in the short ones, and the opposite was found for bushy sclerophyllous vegetations. In forests with MAP > 700 mm, both stands are stable, regardless of the growing trends in the vapor-pressure deficit (VPD) and temperature. No significative correlation between bushy sclerophyllous tree cover and the climate drivers was found, while broad-leaved tree cover is positively related to MAP1990–2019 and negatively related to the growing annual VPD. We modeled those relationships, and then we used them to coarsely predict the effects of twelve future scenarios (derived from HADGEM2-AO (CMIP5) and HadGEM3-GC31-LL (CMIP6) models) on forest tree covers. All scenarios show an annual VPD increase, and the higher its increase, the higher the trees-cover loss. The future changes in precipitation were contrasting. SC6, in line with past precipitation trends, predicts a further drop in the mean annual precipitation (−7.6%), which would correspond to an average 2.1-times-greater reduction in the tree cover (−16.09%). The future changes in precipitation for CMIP6 scenarios agree on a precipitation reduction in the range of −3.4% (SC7) to −14.29% (S12). However, although the reduction in precipitation predicted in SC12 is almost double that predicted in SC6, the consequent average reduction in TC is comparable and stands at −16%. On the contrary, SC2 predicts a turnaround with an abrupt increase of precipitation (+21.5%) in the upcoming years, with a reduction in the number of forests in water-limited areas and an increase in the percentage of tree cover in almost all forests.
MAEANet: Multiscale Attention and Edge-Aware Siamese Network for Building Change Detection in High-Resolution Remote Sensing Images
Authors: Yang Bingjie , Huang Yuancheng , Su Xin , Guo Haonan
Published: October 2022

In recent years, using deep learning for large area building change detection has proven to be very efficient. However, the current methods for pixel-wise building change detection still have some limitations, such as a lack of robustness to false-positive changes and confusion about the boundary of dense buildings. To address these problems, a novel deep learning method called multiscale attention and edge-aware Siamese network (MAEANet) is proposed. The principal idea is to integrate both multiscale discriminative and edge structure information to improve the quality of prediction results. To effectively extract multiscale discriminative features, we design a contour channel attention module (CCAM) that highlights the edge of the changed region and combine it with the classical convolutional block attention module (CBAM) to construct multiscale attention (MA) module, which mainly contains channel, spatial and contour attention mechanisms. Meanwhile, to consider the structure information of buildings, we introduce the edge-aware (EA) module, which combines discriminative features with edge structure features to alleviate edge confusion in dense buildings. We conducted the experiments using LEVIR-CD and BCDD datasets. The proposed MA and EA modules can improve the F1-Score of the basic architecture by 1.13% on the LEVIR CD and by 1.39% on the BCDD with an accepted computation overhead. The experimental results demonstrate that the proposed MAEANet is effective and outperforms other state-of-the-art methods concerning metrics and visualization.
Probabilistic Tracking of Annual Cropland Changes over Large, Complex Agricultural Landscapes Using Google Earth Engine
Authors: Xiong Sitian , Baltezar Priscilla , Crowley Morgan A. , Cecil Michael , Crema Stefano C. , Baldwin Eli , Cardille Jeffrey A. , Estes Lyndon
Published: October 2022

Cropland expansion is expected to increase across sub-Saharan African (SSA) countries in the next thirty years to meet growing food needs across the continent. These land transformations will have cascading social and ecological impacts that can be monitored using novel Earth observation techniques that produce datasets complementary to national cropland surveys. In this study, we present a flexible Bayesian data synthesis workflow on Google Earth Engine (GEE) that can be used to fuse optical and synthetic aperture radar data and demonstrate its ability to track agricultural change at national scales. We adapted the previously developed Bayesian Updating of Land Cover (Unsupervised) algorithm (BULC-U) by integrating a shapelet and slope thresholding algorithm to identify the locations and dates of cropland expansion and implemented a tiling scheme to allow the processing of large volumes of imagery. We apply this approach to map annual cropland change from 2000 to 2015 for Zambia (750,000 km2), a country that is experiencing rapid growth in agricultural land. We applied our cropland mapping approach to a time series of unsupervised classifications developed from Landsat 5, 7, 8, Sentinel-1, and ALOS PALSAR within 1476 tiles covering Zambia. The annual cropland changes maps reveal active cropland expansion between 2000 to 2015 in Zambia, especially in the Southern, Central, and Eastern provinces. Our accuracy assessment estimates that we have identified 27.5% to 69.6% of the total cropland expansion from 2000 to 2015 in Zambia (commission errors between 6.1% to 37.6%), depending on the slope threshold. Our results demonstrate the usefulness of Bayesian data fusion and shapelet, slope-based thresholding to synthesize optical and synthetic aperture radar for monitoring agricultural changes in situations where training data are scarce. In addition, the annual cropland maps provide one of the first spatially continuous, annually incremented accounts of cropland growth in this region. Our flexible, cloud-based workflow using GEE enables multi-sensor, national-scale agricultural change monitoring at low cost for users.
Monitoring Non-Linear Ground Motion above Underground Gas Storage Using GNSS and PSInSAR Based on Sentinel-1 Data
Authors: Struhár Juraj , Rapant Petr , Kačmařík Michal , Hlaváčová Ivana , Lazecký Milan
Published: October 2022

Several methods allow accurate measurement of terrain surface motions. Global navigation satellite systems (GNSSes) and interferometry with synthetic aperture radar (InSAR) stand out in terms of measurement accuracy among them. In principle, both methods make it possible to evaluate a three-dimensional vector of the motion of points on the terrain surface. In this work, we dealt with the evaluation of motions in the up–down (U–D) and east–west direction (E–W) over underground gas storage (UGS) from InSAR. One crucial step in breaking down PSInSAR line of sight (LOS) measurements to U–D and E–W components is getting time series derived from individual tracks to the same time frame. This is usually performed by interpolation, but we used an innovative approach: we analyzed individual time series using the Lomb–Scargle periodogram (LSP), which is suitable for periodic noisy and irregularly sampled data; we selected the most significant period, created LSP models, and used them instead of the original time series. Then, it was possible to derive time series values for any arbitrary time step. To validate the results, we installed one GNSS receiver in the Tvrdonice UGS test area to perform independent measurements. The results show a good agreement in the evaluation of motions by both methods. The correlation coefficient between horizontal components from both PSInSAR and GNSS was 0.95 in the case of the E–W component, with an RMSE of 1.75 mm; for U–D they were 0.78 and 2.35 mm, respectively. In addition to comparing the motions in the U–D and E–W directions, we also created a comparison by converting GNSS measurements to a line of sight of the Sentinel-1 satellite to evaluate the conformity of InSAR and GNSS measurements. Based on descending track, the correlation coefficient between LOS from both methods is, on average, 0.97, with an RMSE of 2.70 mm.
Analysis of Spatiotemporal Variation and Drivers of Ecological Quality in Fuzhou Based on RSEI
Authors: Geng Jianwei , Yu Kunyong , Xie Zhen , Zhao Gejin , Ai Jingwen , Yang Liuqing , Yang Honghui , Liu Jian
Published: October 2022

Background: High-speed urbanization has brought about a number of ecological and environmental problems, as well as the use of remote sensing to monitor the urban ecological environment and explore the main factors affecting its changes. It is important to promote the sustainable development of cities. Methods: In this study, we quantify the ecological quality of the study area from 2000 to 2020 based on the remote sensing ecological index (RSEI) and analyze its drivers through Geodetector and geographically weighted regression. Results: The RSEI of Fuzhou City from 2000 to 2020 showed an increasing followed by a decreasing trend, with obvious spatial autocorrelation. The main driving factors causing the spatial divergence of the RSEI were elevation (q = 0.48–0.63), slope (0.42–0.59), and GDP (0.3–0.42), and the driving effect and range of each factor changed with time. Conclusion: In this paper, we explore changes in the ecological environment in Fuzhou City over the past 20 years, as well as the scope and magnitude of the drivers, providing an important reference basis to improve the ecological environment quality of the city.
Unbiased Area Estimation Using Copernicus High Resolution Layers and Reference Data
Authors: Kleinewillinghöfer Luca , Olofsson Pontus , Pebesma Edzer , Meyer Hanna , Buck Oliver , Haub Carsten , Eiselt Beatrice
Published: October 2022

Land cover area estimates can be derived via design-based approaches using a probability (random) reference sample. The collection of samples is usually costly and requires an effective sampling design. Earth-Observation-based mapping approaches do not have this requirement but can be biased in providing area estimates. Combining reference samples with remote sensing products can reduce sampling efforts and provide a more effective method to estimate land cover. The Copernicus High-Resolution Layer (HRL) provides remote-sensing-based data across Europe to support area estimation. Different methods are tested to estimate areas of imperviousness in four selected countries in Europe to demonstrate the use and shortcomings of existing reference information from the LUCAS survey program and the HRL Imperviousness products from 2015 and 2018.
Updating Inventory, Deformation, and Development Characteristics of Landslides in Hunza Valley, NW Karakoram, Pakistan by SBAS-InSAR
Authors: Su Xiaojun , Zhang Yi , Meng Xingmin , Rehman Mohib Ur , Khalid Zainab , Yue Dongxia
Published: October 2022

The Hunza Valley, in the northwestern Karakoram Mountains, North Pakistan, is a typical region with many towns and villages, and a dense population and is prone to landslides. The present study completed landslide identification, updating a comprehensive landslide inventory and analysis. First, the ground surface deformation was detected in the Hunza Valley by SBAS-InSAR from ascending and descending datasets, respectively. Then, the locations and boundaries were interpreted and delineated, and a comprehensive inventory of 118 landslides, including the 53 most recent InSAR identified active landslides and 65 landslides cited from the literature, was completed. This study firstly named all 118 landslides, considering the demand for globally intensive research and hazard mitigation. Finally, the deformation, spatial–topographic development, and distribution characteristics in the Hunza Valley scale and three large significant landslides were analyzed. Information on 72 reported landslides was used to construct an empirical power law relationship linking landslide area (AL) to volume (VL) (VL = 0.067 × AL1.351), and this formula predicted the volume of 118 landslides in this study. We discovered that the landslides from the literature, which were interpreted from optical images, had lower levels of velocity, area, elevation, and height. The SBAS-InSAR-detected active landslide was characterized by higher velocity, larger area, higher elevation, larger slope gradient, larger NDVI (normalized difference vegetation index), and greater height. The melting glacier water and rainfall infiltration from cracks on the landslide’s upper part may promote the action of a push from gravity on the upper part. Simultaneously, the coupling of actions from river erosion and active tectonics could have an impact on the stability of the slope toe. The up-to-date comprehensive identification and understanding of the characteristics and mechanism of landslide development in this study provide a reference for the next step in landslide disaster prevention and risk assessment.
Application of Random Forest Algorithm on Tornado Detection
Authors: Zeng Qiangyu , Qing Zhipeng , Zhu Ming , Zhang Fugui , Wang Hao , Liu Yin , Shi Zhao , Yu Qiu
Published: October 2022

Tornadoes are highly destructive small-scale extreme weather processes in the troposphere. The weather radar is one of the most effective remote sensing devices for the monitoring and early warning of tornadoes. The existing tornado detection algorithms based on radar data are unsupervised and have strict multi-altitude constraints, such as the tornado detection algorithm based on tornado vortex signatures (TDA-TVS), which may lead to high false alarm rates, and the performance of the detection algorithm is greatly affected by the radar data quality control algorithm. A novel TDA-RF algorithm based on the random forest (RF) classification algorithm is proposed for real-time tornado identification of the S-band China new generation of Doppler weather radar (CINRAD-SA). The TDA-RF algorithm uses velocity features to identify tornadoes and adds features related to reflectivity and velocity spectrum width in radar level-II data. Historical CINRAD-SA tornado data from 2006–2015 are used to construct the tornado dataset and train the TDA-RF model. The performance of TDA-RF is evaluated using CINRAD-SA data from five tornadoes of 2016–2020 with enhanced Fujita(EF) scale ratings ranging from EF0 to EF4 and distances from 10 to 130 km to the radar. TDA-RF performs well overall with the probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) of 71%, 29%, and 55%, respectively. Moreover, the TDA-RF improves POD and CSI, and reduces FAR compared to the TDA-TVS. The maximum tornado early-warning time of TDA-RF is 17 min, and the average is 6 min; TDA-RF can provide classification probability according to the tornado generation and development process to facilitate tracking ability.
AC-WGAN-GP: Generating Labeled Samples for Improving Hyperspectral Image Classification with Small-Samples
Authors: Sun Caihao , Zhang Xiaohua , Meng Hongyun , Cao Xianghai , Zhang Jinhua
Published: October 2022

The lack of labeled samples severely restricts the classification performance of deep learning on hyperspectral image classification. To solve this problem, Generative Adversarial Networks (GAN) are usually used for data augmentation. However, GAN have several problems with this task, such as the poor quality of the generated samples and an unstable training process. Thereby, knowing how to construct a GAN to generate high-quality hyperspectral training samples is meaningful for the small-sample classification task of hyperspectral data. In this paper, an Auxiliary Classifier based Wasserstein GAN with Gradient Penalty (AC-WGAN-GP) was proposed. The framework includes AC-WGAN-GP, an online generation mechanism, and a sample selection algorithm. The proposed method has the following distinctive advantages. First, the input of the generator is guided by prior knowledge and a separate classifier is introduced to the architecture of AC-WGAN-GP to produce reliable labels. Second, an online generation mechanism ensures the diversity of generated samples. Third, generated samples that are similar to real data are selected. Experiments on three public hyperspectral datasets show that the generated samples follow the same distribution as the real samples and have enough diversity, which effectively expands the training set. Compared to other competitive methods, the proposed framework achieved better classification accuracy with a small number of labeled samples.
Multibranch Unsupervised Domain Adaptation Network for Cross Multidomain Orchard Area Segmentation
Authors: Liu Ming , Ren Dong , Sun Hang , Yang Simon X.
Published: October 2022

Although unsupervised domain adaptation (UDA) has been extensively studied in remote sensing image segmentation tasks, most UDA models are designed based on single-target domain settings. Large-scale remote sensing images often have multiple target domains in practical applications, and the simple extension of single-target UDA models to multiple target domains is unstable and costly. Multi-target unsupervised domain adaptation (MTUDA) is a more practical scenario that has great potential for solving the problem of crossing multiple domains in remote sensing images. However, existing MTUDA models neglect to learn and control the private features of the target domain, leading to missing information and negative migration. To solve these problems, this paper proposes a multibranch unsupervised domain adaptation network (MBUDA) for orchard area segmentation. The multibranch framework aligns multiple domain features, while preventing private features from interfering with training. We introduce multiple ancillary classifiers to help the model learn more robust latent target domain data representations. Additionally, we propose an adaptation enhanced learning strategy to reduce the distribution gaps further and enhance the adaptation effect. To evaluate the proposed method, this paper utilizes two settings with different numbers of target domains. On average, the proposed method achieves a high IoU gain of 7.47% over the baseline (single-target UDA), reducing costs and ensuring segmentation model performance in multiple target domains.
Multidirectional Shift Rasterization (MDSR) Algorithm for Effective Identification of Ground in Dense Point Clouds
Authors: Štroner Martin , Urban Rudolf , Línková Lenka
Published: October 2022

With the ever-increasing popularity of unmanned aerial vehicles and other platforms providing dense point clouds, filters for the identification of ground points in such dense clouds are needed. Many filters have been proposed and are widely used, usually based on the determination of an original surface approximation and subsequent identification of points within a predefined distance from such surface. We presented a new filter, the multidirectional shift rasterization (MDSR) algorithm, which is based on a different principle, i.e., on the identification of just the lowest points in individual grid cells, shifting the grid along both the planar axis and subsequent tilting of the entire grid. The principle was presented in detail and both visually and numerically compared with other commonly used ground filters (PMF, SMRF, CSF, and ATIN) on three sites with different ruggedness and vegetation density. Visually, the MDSR filter showed the smoothest and thinnest ground profiles, with the ATIN the only filter comparably performing. The same was confirmed when comparing the ground filtered by other filters with the MDSR-based surface. The goodness of fit with the original cloud is demonstrated by the root mean square deviations (RMSDs) of the points from the original cloud found below the MDSR-generated surface (ranging, depending on the site, between 0.6 and 2.5 cm). In conclusion, this paper introduced a newly developed MDSR filter that outstandingly performed at all sites, identifying the ground points with great accuracy while filtering out the maximum of vegetation and above-ground points and outperforming the aforementioned widely used filters. The filter dilutes the cloud somewhat; in such dense point clouds, however, this can be perceived as a benefit rather than as a disadvantage.
Assessment and Calibration of ERA5 Severe Winds in the Atlantic Ocean Using Satellite Data
Authors: Campos Ricardo M. , Gramcianinov Carolina B. , de Camargo Ricardo , da Silva Dias Pedro L.
Published: October 2022

In this paper, we analyze the surface winds of ECMWF ERA5 reanalysis in the Atlantic Ocean. The first part addresses a reanalysis validation, studying the spatial distribution of the errors and the performance as a function of the percentiles, with a further investigation under cyclonic conditions. The second part proposes and compares two calibration models, a simple least-squares linear regression (LR) and the quantile mapping method (QM). Our results indicate that ERA5 provides high-quality winds for non-extreme conditions, especially at the eastern boundaries, with bias between −0.5 and 0.3 m/s and RMSE below 1.5 m/s. The reanalysis errors are site-dependent, where large RMSE and severe underestimation are found in tropical latitudes and locations following the warm currents. The most extreme winds in tropical cyclones show the worst results, with RMSE above 5 m/s. Apart from these areas, the strong winds at extratropical locations are well represented. The bias-correction models have proven to be very efficient in removing systematic bias. The LR works well for low-to-mild wind intensities while the QM is better for the upper percentiles and winds above 15 m/s—an improvement of 10% in RMSE and 50% for the bias compared to the original reanalysis is reported.
Retrieval of Chlorophyll-a Concentrations Using Sentinel-2 MSI Imagery in Lake Chagan Based on Assessments with Machine Learning Models
Authors: Shi Xuming , Gu Lingjia , Jiang Tao , Zheng Xingming , Dong Wen , Tao Zui
Published: October 2022

Chlorophyll-a (Chl-a) is an important characterized parameter of lakes. Monitoring it accurately through remote sensing is thus of great significance for early warnings of water eutrophication. Sentinel Multispectral Imager (MSI) images from May to September between 2020 and 2021 were used along with in-situ measurements to estimate Chl-a in Lake Chagan, which is located in Jilin Province, Northeast China. In this study, the extreme gradient boosting (XGBoost) and Random Forest (RF) models, which had similar performances, were generated by six single bands and six band combinations. The RF model was then selected based on the assessments (R2 = 0.79, RMSE = 2.51 μg L−1, MAPE = 9.86%), since its learning of the input features in the model conformed to the bio-optical properties of Case 2 waters. The study considered Chl-a concentrations in Lake Chagan as a seasonal pattern according to the K-Nearest-Neighbors (KNN) classification. The RF model also showed relatively stable performance for three seasons (spring, summer and autumn) and it was applied to map Chl-a in the whole lake. The research presents a more reliable machine learning (ML) model with higher precision than previous empirical models, as shown by the effects of the input features linked with the biological mechanisms of Chl-a. Its robustness was revealed by the temporal and spatial distributions of Chl-a concentrations, which were consistent with in-situ measurements in the map. This research was capable of revealing the current ecological situation in Lake Chagan and can serve as a reference in remote sensing of inland lakes.
A Universal Landslide Detection Method in Optical Remote Sensing Images Based on Improved YOLOX
Authors: Hou Heyi , Chen Mingxia , Tie Yongbo , Li Weile
Published: October 2022

Using deep learning-based object detection algorithms for landslide hazards detection is very popular and effective. However, most existing algorithms are designed for landslides in a specific geographical range. This paper constructs a set of landslide detection models YOLOX-Pro, based on the improved YOLOX (You Only Look Once) target detection model to address the poor detection of complex mixed landslides. Wherein the VariFocal is used to replace the binary cross entropy in the original classification loss function to solve the uneven distribution of landslide samples and improve the detection recall; the coordinate attention (CA) mechanism is added to enhance the detection accuracy. Firstly, 1200 historical landslide optical remote sensing images in thirty-eight areas of China were extracted from Google Earth to create a mixed sample set for landslide detection. Next, the three attention mechanisms were compared to form the YOLOX-Pro model. Then, we tested the performance of YOLOX-Pro by comparing it with four models: YOLOX, YOLOv5, Faster R-CNN, and Single Shot MultiBox Detector (SSD). The results show that the YOLOX-Pro(m) has significantly improved the detection accuracy of complex and small landslides than the other models, with an average precision (AP0.75) of 51.5%, APsmall of 36.50%, and ARsmall of 49.50%. In addition, optical remote sensing images of a 12.32 km2 group-occurring landslides area located in Mibei village, Longchuan County, Guangdong, China, and 750 Unmanned Aerial Vehicle (UAV) images collected from the Internet were also used for landslide detection. The research results proved that the proposed method has strong generalization and good detection performance for many types of landslides, which provide a technical reference for the broad application of landslide detection using UAV.
Remote Sensing on Alfalfa as an Approach to Optimize Production Outcomes: A Review of Evidence and Directions for Future Assessments
Authors: Tedesco Danilo , Nieto Luciana , Hernández Carlos , Rybecky Juan F. , Min Doohong , Sharda Ajay , Hamilton Kevin J. , Ciampitti Ignacio A.
Published: October 2022

Alfalfa (Medicago sativa L.) is one of the most relevant forage crops due to its importance for livestock. Timely harvesting is critical to secure adequate forage quality. However, farmers face challenges not only to decide the optimal harvesting time but to predict the optimum levels for both forage production and quality. Fortunately, remote sensing technologies can significantly contribute to obtaining production and quality insights, providing scalability, and supporting complex farming decision-making. Therefore, we aim to develop a systematic review of the current scientific literature to identify the current status of research in remote sensing for alfalfa and to evaluate new perspectives for enhancing prediction of both biomass and quality (herein defined as crude protein and fibers) for alfalfa. Twelve papers were included in the database from a total of 198 studies included in the initial screening process. The main findings were (i) more than two-thirds of the studies focused on predicting biomass; (ii) half of the studies used terrestrial platforms, with only 33% using drones and 17% using satellite for remote sensing; (iii) no studies have used satellites assessed alfalfa quality traits; (iv) improved biomass and quality estimations were obtained when remote sensing data was combined with environmental information; (v) due to a direct relationship between biomass and quality, modeling them algorithmically improves the accuracy of estimation as well; (vi) from spectral wavelengths, dry biomass was better estimated in regions near 398, 551, 670, 730, 780, 865, and 1077 nm, wet biomass in regions near 478, 631, 670, 730, 780, 834, 933, 1034, and 1538 nm, and quality traits identified with narrow and very specific wavelengths (e.g., 398, 461, 551, 667, 712, and 1077 nm). Our findings might serve as a foundation to guide further research and the development of handheld sensors for assessing alfalfa biomass and quality.
Dynamic Convolution Self-Attention Network for Land-Cover Classification in VHR Remote-Sensing Images
Authors: Wang Xuan , Zhang Yue , Lei Tao , Wang Yingbo , Zhai Yujie , Nandi Asoke K.
Published: October 2022

The current deep convolutional neural networks for very-high-resolution (VHR) remote-sensing image land-cover classification often suffer from two challenges. First, the feature maps extracted by network encoders based on vanilla convolution usually contain a lot of redundant information, which easily causes misclassification of land cover. Moreover, these encoders usually require a large number of parameters and high computational costs. Second, as remote-sensing images are complex and contain many objects with large-scale variances, it is difficult to use the popular feature fusion modules to improve the representation ability of networks. To address the above issues, we propose a dynamic convolution self-attention network (DCSA-Net) for VHR remote-sensing image land-cover classification. The proposed network has two advantages. On one hand, we designed a lightweight dynamic convolution module (LDCM) by using dynamic convolution and a self-attention mechanism. This module can extract more useful image features than vanilla convolution, avoiding the negative effect of useless feature maps on land-cover classification. On the other hand, we designed a context information aggregation module (CIAM) with a ladder structure to enlarge the receptive field. This module can aggregate multi-scale contexture information from feature maps with different resolutions using a dense connection. Experiment results show that the proposed DCSA-Net is superior to state-of-the-art networks due to higher accuracy of land-cover classification, fewer parameters, and lower computational cost. The source code is made public available.
Sugarcane Biomass Prediction with Multi-Mode Remote Sensing Data Using Deep Archetypal Analysis and Integrated Learning
Authors: Wang Zhuowei , Lu Yusheng , Zhao Genping , Sun Chuanliang , Zhang Fuhua , He Su
Published: October 2022

The use of multi-mode remote sensing data for biomass prediction is of potential value to aid planting management and yield maximization. In this study, an advanced biomass estimation approach for sugarcane fields is proposed based on multi-source remote sensing data. Since feature interpretability in agricultural data mining is significant, a feature extraction method of deep archetypal analysis (DAA) that has good model interpretability is introduced and aided by principal component analysis (PCA) for feature mining from the multi-mode multispectral and light detection and ranging (LiDAR) remote sensing data pertaining to sugarcane. In addition, an integrated regression model integrating random forest regression, support vector regression, K-nearest neighbor regression and deep network regression is developed after feature extraction by DAA to precisely predict biomass of sugarcane. In this study, the biomass prediction performance achieved using the proposed integrated learning approach is found to be predominantly better than that achieved by using conventional linear methods in all the time periods of plant growth. Of more significance, according to model interpretability of DAA, only a small set of informative features maintaining their physical meanings (four informative spectral indices and four key LiDAR metrics) can be extracted which eliminates the redundancy of multi-mode data and plays a vital role in accurate biomass prediction. Therefore, the findings in this study provide hands-on experience to planters with indications of the key or informative spectral or LiDAR metrics relevant to the biomass to adjust the corresponding planting management design.
Internal Solitary Waves in the White Sea: Hot-Spots, Structure, and Kinematics from Multi-Sensor Observations
Authors: Kozlov Igor E. , Atadzhanova Oksana A. , Zimin Alexey V.
Published: October 2022

A detailed picture of internal solitary waves (ISWs) in the White Sea is presented based on an analysis of historical spaceborne synthetic aperture radar (SAR) data and field measurements. The major hot-spot of ISW generation locates in the southwestern (SW) Gorlo Strait (GS), characterized by the presence of strong tides, complex topography, and two distinct fronts. Here, pronounced high-frequency isopycnal depressions of 5–8 m were regularly observed during flood and flood/ebb slackening. Other regions of pronounced ISW activity are found near Solovetsky Islands and in the northwestern Onega Bay. The spatial and kinematic properties of the observed ISWs are linked to water depth, with larger wave trains and higher propagation speeds being observed over the deep regions. Direct estimates of ISW propagation speeds from sequential and single SAR images agree well, while theoretical ones obtained using a two-layer model overestimate the observed values by 2–3 times. This is explained by the effective modulation of ISW propagation speed during the tidal cycle by background currents that are not accounted for in the model. Enhanced values of diapycnic diffusion coefficient in the pycnocline layer were registered near the frontal zones, where intense 14–17 m high ISWs were regularly observed.
Using UAV to Identify the Optimal Vegetation Index for Yield Prediction of Oil Seed Rape (Brassica napus L.) at the Flowering Stage
Authors: Lukas Vojtěch , Huňady Igor , Kintl Antonín , Mezera Jiří , Hammerschmiedt Tereza , Sobotková Julie , Brtnický Martin , Elbl Jakub
Published: October 2022

Suitability of the vegetation indices of normalized difference vegetation index (NDVI), blue normalized difference vegetation index (BNDVI), and normalized difference yellowness index (NDYI) obtained by means of UAV at the flowering stage of oil seed rape for the prediction of seed yield and usability of these vegetation indices in the identification of anomalies in the condition of the flowering growth were verified based on the regression analysis. Correlation analysis was performed to find the degree of yield dependence on the values of NDVI, BNDVI, and NDYI indices, which revealed a strong, significant linear positive dependence of seed yield on BNDVI (R = 0.98) and NDYI (R = 0.95). The level of correlation between the NDVI index and the seed yield was weaker (R = 0.70) than the others. Regression analysis was performed for a closer determination of the functional dependence of NDVI, BNDVI, and NDYI indices and the yield of seeds. Coefficients of determination in the linear regression model of NDVI, BNDVI, and NDYI indices reached the following values: R2 = 0.48 (NDVI), R2 = 0.95 (BNDVI), and R2 = 0.90 (NDYI). Thus, it was shown that increased density of yellow flowers decreased the relationship between NDVI and crop yield. The NDVI index is not appropriate for assessing growth conditions and prediction of yields at the flowering stage of oil seed rape. High accuracy of yield prediction was achieved with the use of BNDVI and NDYI. The performed analysis of NDVI, BNDVI, and NDYI demonstrated that particularly the BNDVI and NDYI indices can be used to identify problems in the development of oil seed rape growth at the stage of flowering, for their precise localization, and hence to targeted and effective remedial measures in line with the principles of precision agriculture.
Deep Learning for InSAR Phase Filtering: An Optimized Framework for Phase Unwrapping
Authors: Murdaca Gianluca , Rucci Alessio , Prati Claudio
Published: October 2022

Interferometric Synthetic Aperture Radar (InSAR) data processing applications, such as deformation monitoring and topographic mapping, require an interferometric phase filtering step. Indeed, the filtering quality significantly impacts the deformation and terrain height estimation accuracy. However, the existing classical and deep learning-based phase filtering methods provide artefacts in the filtered areas where a large amount of noise prevents retrieving the original signal. In this way, we can no longer distinguish the underlying informative signal for the next processing step. This paper proposes a deep convolutional neural network filtering method, developing a novel learning strategy to preserve the initial phase noise input into these crucial areas. Thanks to the encoder–decoder powerful phase feature extraction ability, the network can predict an accurate coherence and filtered interferometric phase, ensuring reliable final results. Furthermore, we also address a novel Synthetic Aperture Radar (SAR) interferograms simulation strategy that, using initial parameters estimated from real SAR images, considers physical behaviors typical of a real acquisition. According to the results achieved on simulated and real InSAR data, the proposed filtering method significantly outperforms the classical and deep learning-based ones.
The Effect of Controlled Tile Drainage on Growth and Grain Yield of Spring Barley as Detected by UAV Images, Yield Map and Soil Moisture Content
Authors: Duffková Renata , Poláková Lucie , Lukas Vojtěch , Fučík Petr
Published: October 2022

Controlled tile drainage (CTD) practices are a promising tool for improving water balance, water quality and increasing crop yield by raising shallow groundwater level and capillary rise due to drainage flow retardation. We tested the effect of CTD on growth and grain yield of spring barley, at a study site in central Bohemia using vegetation indices from unmanned aerial vehicle (UAV) imagery and Sentinel-2 satellite imagery. Tile drainage flow was slowed by fixed water level control structures that increased soil moisture in the surrounding area according to the terrain slope. Vegetation indices based on red-edge spectral bands in combination with near-infrared and red bands were selected, of which the Normalized Red Edge-Red Index (NRERI) showed the closest relationships with shoot biomass parameters (dry biomass, nitrogen concentration and uptake, nitrogen nutrition index) from point sampling at the tillering stage. The CTD sites showed significantly more biomass using NRERI compared to free tile drainage (FTD) sites. In contrast, in the period prior to the implementation of CTD practices, Sentinel-2 satellite imagery did not demonstrate higher biomass based on NRERI at CTD sites compared to FTD sites. The grain yields of spring barley as determined from the yield map also increased due to CTD (by 0.3 t/ha, i.e., by 4%). The positive impact of CTD on biomass development and grain yield of spring barley was confirmed by the increase in soil moisture at depths of 20, 40 and 60 cm compared to FTD. The largest increase in soil water content of 3.5 vol% due to CTD occurred at the depth of 40 cm, which also had a higher degree of saturation of available water capacity and the occurrence of crop water stress was delayed by 14 days compared to FTD.
A Cluster-Based Partition Method of Remote Sensing Data for Efficient Distributed Image Processing
Authors: Wang Lei , Yu Bo , Chen Fang , Li Congrong , Li Bin , Wang Ning
Published: October 2022

Data stream partitioning is a fundamental and important mechanism for distributed systems. However, use of an inappropriate partition scheme may generate a data skew problem, which can influence the execution efficiency of many application tasks. Processing of skewed partitions usually takes a longer time, need more computational resources to complete the task and can even become a performance bottleneck. To solve such data skew issues, this paper proposes a novel partition method to divide on demand the image tiles uniformly into partitions. The partitioning problem is then transformed into a uniform and compact clustering problem whereby the image tiles are regarded as image pixels without spectrum and texture information. First, the equal area conversion principle was used to select the seed points of the partitions and then the image tiles were aggregated in an image layout, thus achieving an initial partition scheme. Second, the image tiles of the initial partition were finely adjusted in the vertical and horizontal directions in separate steps to achieve a uniform distribution among the partitions. Two traditional partition methods were adopted to evaluate the efficiency of the proposed method in terms of the image segmentation testing, data shuffle testing, and image clipping testing. The results demonstrated that the proposed partition method solved the data skew problem observed in the hash partition method. In addition, this method is designed specifically for processing of image tiles and makes the related processing operations for large-scale images faster and more efficient.
Beach Profile, Water Level, and Wave Runup Measurements Using a Standalone Line-Scanning, Low-Cost (LLC) LiDAR System
Authors: O’Connor Christopher S. , Mieras Ryan S.
Published: October 2022

A prototype rapidly deployable, Line-scanning, Low-Cost (LLC) LiDAR system (USD 400 per unit; 2020) was developed to measure coastal hydro-morphodynamic processes. A pilot field study was conducted at the U.S. Army Corps of Engineers, Field Research Facility (FRF) in Duck, North Carolina, USA to evaluate the efficacy of the LLC LiDAR in measuring beach morphology, wave runup, and free-surface elevations against proven approaches. A prototype LLC LiDAR collected continuous cross-shore line scans for 25 min of every half hour, at ~7 revolutions/s and ~1.3° angular resolution, at two locations (one day at each location), spanning 12 m (i) on the backshore berm (35 scans; Series B) and (ii) in the swash/inner surf zone (28 scans; Series C). LLC LiDAR time-averaged beach profiles and wave runup estimates were compared with the same quantities derived from the continuously sampling terrestrial LiDAR scanner installed atop the dune at the FRF (DUNE LiDAR). The average root-mean-square difference (RMSD) between 17 (6) time-averaged LLC and DUNE LiDAR beach profiles was 0.045 m (0.031 m) with a standard deviation of 0.004 m (0.002 m) during Series B (Series C). Small-scale (cm) swash zone bed level changes were resolved over 5-min increments with the LLC LiDAR. The RMSD between LLC- and DUNE LiDAR-derived wave runup excursions over two 25-min segments was 0.542 m (cross-shore) and 0.039 m (elevation) during the rising tide and 0.366 m (cross-shore) and 0.032 m (elevation) during the falling tide. Between 72–79% of the LLC LiDAR wave runup data were more accurate than the RMSD values, thereby demonstrating the LLC LiDAR is an effective, low-cost instrument for measuring wave runup and morphodynamic processes. Co-located water levels were measured with a continuously sampling (16 Hz) RBRsolo3 D|wave16 pressure logger during Series C. LLC LiDAR free-surface elevations at the nadir during one high tide (4.5 h) compared well with pressure-derived free-surface elevations (RMSD = 0.024 m, R2 = 0.85).
Remote Sensing Image-Change Detection with Pre-Generation of Depthwise-Separable Change-Salient Maps
Authors: Li Bin , Wang Guanghui , Zhang Tao , Yang Huachao , Zhang Shubi
Published: October 2022

Remote sensing change detection (CD) identifies changes in each pixel of certain classes of interest from a set of aligned image pairs. It is challenging to accurately identify natural changes in feature categories due to unstructured and temporal changes. This research proposed an effective bi-temporal remote sensing CD comprising an encoder that could extract multiscale features, a decoder that focused on semantic alignment between temporal features, and a classification head. In the decoder, we constructed a new convolutional attention structure based on pre-generation of depthwise-separable change-salient maps (PDACN) that could reduce the attention of the network on unchanged regions and thus reduce the potential pseudo-variation in the data sources caused by semantic differences in illumination and subtle alignment differences. To demonstrate the effectiveness of the PDA attention structure, we designed a lightweight network structure for encoders under both convolution-based and transformer architectures. The experiments were conducted on a single-building CD dataset (LEVIR-CD) and a more complex multivariate change type dataset (SYSU-CD). The results showed that our PDA attention structure generated more discriminative change variance information while the entire network model obtained the best performance results with the same level of network model parameters in the transformer architecture. For LEVIR-CD, we achieved an intersection over union (IoU) of 0.8492 and an F1 score of 0.9185. For SYSU-CD, we obtained an IoU of 0.7028 and an F1 score of 0.8255. The experimental results showed that the method proposed in this paper was superior to some current state-of-the-art CD methods.
Developing a New Parameterization Scheme of Temperature Lapse Rate for the Hydrological Simulation in a Glacierized Basin Based on Remote Sensing
Authors: Song Wanying , Tang Handuo , Sun Xueyan , Xiang Yuxuan , Ma Xiaofei , Zhang Hongbo
Published: October 2022

Temperature lapse rate (TLR) is an important parameter for simulations of snow/glacier melts in alpine watersheds. However, the traditional scheme estimates TLR mainly based on a limited number of stations and may not be suitable for alpine watersheds with scarce observations. To overcome this problem, this study developed a new basin-oriented TLR scheme based on Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature data which was applied in a typical glacierized watershed on the northern slope of the Himalayas. The new TLR scheme was evaluated by comparison with the station-based traditional TLR scheme in terms of performance in simulations of both snow cover area and runoff using the same hydrological model. The results showed that the monthly TLRs estimated by the new scheme presented a more reasonable seasonal pattern than those estimated by the traditional scheme. The model using the new TLR scheme showed better performance in discharge simulations of low-runoff months than that using the traditional TLR scheme. The R-squared value of multiyear mean monthly snow cover area of the former (0.82) was also higher than that of the latter (0.56). This study provides an efficient framework for estimating TLR based on remote sensing for alpine watersheds with scarce observations.
Detection of Bering Sea Slope Mesoscale Eddies Derived from Satellite Altimetry Data by an Attention Network
Authors: Zhang Yuyuan , Liu Na , Zhang Zhiyuan , Liu Min , Fan Long , Li Yunbo , Yang Lei , Lin Lina , Chen Hongxia
Published: October 2022

In the Bering Sea slope, ocean eddies are essential physical processes that carry nutrients to the shelf. The development of the satellite altimeter has facilitated the observation of oceanic eddies. Attention networks are used as the core algorithm for eddy detection to suppress feature responses in irrelevant non-eddy areas, which can address the issue of sample imbalance in high-latitude ocean eddies. Furthermore, data from both the sea surface height (SSH) and geostrophic velocity were employed as model inputs to integrate more eddy-related properties. The results of ocean eddy detection using this method and the dataset allowed more eddies to be detected than with traditional vector geometry-based methods and only SSH-based models. This study also incorporated the results of multiple deep learning models to increase both the overall and single-day eddy detection efficiency. As a result, the algorithms in this paper show that attention networks and geostrophic velocity data are both appropriate for high-latitude ocean eddy identification. This makes a contribution to the further application of deep learning methods to satellite altimetry data.