Remote Sensing

18 pages, 28843 KiB

Open AccessArticle

Lifting Scheme-Based Sparse Density Feature Extraction for Remote Sensing Target Detection

by Ling Tian, Yu Cao, Zishan Shi, Bokun He, Chu He and Deshi Li

Remote Sens. 2021, 13(9), 1862; https://doi.org/10.3390/rs13091862 - 10 May 2021

Cited by 2 | Viewed by 3038

The design of backbones is of great significance for enhancing the location and classification precision in the remote sensing target detection task. Recently, various approaches have been proposed on altering the feature extraction density in the backbones to enlarge the receptive field, make [...] Read more.

The design of backbones is of great significance for enhancing the location and classification precision in the remote sensing target detection task. Recently, various approaches have been proposed on altering the feature extraction density in the backbones to enlarge the receptive field, make features prominent, and reduce computational complexity, such as dilated convolution and deformable convolution. Among them, one of the most widely used methods is strided convolution, but it loses the information about adjacent feature points which leads to the omission of some useful features and the decrease of detection precision. This paper proposes a novel sparse density feature extraction method based on the relationship between the lifting scheme and convolution, which improves the detection precision while keeping the computational complexity almost the same as the strided convolution. Experimental results on remote sensing target detection indicate that our proposed method improves both detection performance and network efficiency. Full article

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Full article ">Figure 1
Typical remote sensing target detection flowchart. There are mainly two types of target detection networks, including two-stage (containing both Stage1 in the dashed box and Stage2) and one-stage networks (containing Stage2 only). For both of them, convolutional neural networks are the most common backbones for feature extraction. The vanilla convolution with the stride of one is repeatedly used in the backbone for normal density feature extraction. The sparse density feature extraction module is for receptive field enlargement and dimension reduction, concrete existing approaches of which are vanilla convolutional layer followed by a pooling layer (which is denoted as downsampling after extraction approach in this paper), dilated convolution, deformable convolution, and the most frequently used method—strided convolution, etc. Full article ">Figure 2
The method of downsampling after extraction is generally implemented with a vanilla convolutional layer and a pooling layer, while the lifting scheme is another alternative implementation. The symbols <math display="inline"><semantics> <mi mathvariant="bold-italic">x</mi> </semantics></math>, <math display="inline"><semantics> <mi mathvariant="bold-italic">h</mi> </semantics></math>, and <math display="inline"><semantics> <mi mathvariant="bold-italic">y</mi> </semantics></math> denote the input signal, convolutional kernel, and output signal, respectively. The lifting scheme contains three steps: split, prediction and update. These two methods have the same <math display="inline"><semantics> <mi mathvariant="bold-italic">x</mi> </semantics></math> and <math display="inline"><semantics> <mi mathvariant="bold-italic">y</mi> </semantics></math> theoretically, and <math display="inline"><semantics> <mrow> <mi mathvariant="bold-italic">h</mi> <mo>=</mo> <mo>[</mo> <msub> <mi>h</mi> <mn>0</mn> </msub> <mo>,</mo> <msub> <mi>h</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>h</mi> <mn>2</mn> </msub> <mo>]</mo> </mrow> </semantics></math> in (a) is transformed into the prediction and update operators as showned in (b). Full article ">Figure 3
Two stage detection network flowchart based on lifting scheme layer. Different from the existing two-stage detection networks, the lifting scheme layer is used as the sparse density feature extraction module in the backbone CNN, which is superior to the frequently used strided convolution in detection precision. A series of candidate boxes are generated in the first stage, and then they are classified in the second stage. The candidate box that is classified as a ship and exceeds the IOU threshold is reserved in the output. Full article ">Figure 4
Samples from the datasets. Three remote sensing ship datasets are used in the experiments to evaluate the proposed method, including two SAR datasets SSDD and AIR-SarSHIP-1.0, and one optical remote sensing image dataset DOTA-ship. Full article ">Figure 5
Precision-recall curves on three remote sensing image datasets of two pairs of detection networks, including (1) Cascade R-CNN and Cascade R-CNN-LS, (2) Faster R-CNN and Faster R-CNN-LS. For all the datasets, Cascade R-CNN-LS consistently performs the best, and the lifting scheme-based method is superior to the strided convolution in both experiments with the baselines of Cascade R-CNN and Faster R-CNN. Full article ">Figure 6
Detection results on SSDD samples. The 1st column displays the samples from the ground truth, while the 2nd and 3rd columns are the detection results of the relative samples of Cascade R-CNN and Cascade R-CNN-LS, respectively. The lifting scheme-based network tends to detect small objects better than the strided convolution as the 2nd and the 5th rows show. Full article ">Figure 7
Detection results on AIR-SarSHIP-1.0 samples. The 1st column displays the samples from the ground truth, while the 2nd and 3rd columns are the detection results of the relative samples of Cascade R-CNN and Cascade R-CNN-LS, respectively. The lifting scheme-based network tends to detect small objects better than the strided convolution as the 1st and the 3rd rows show. Full article ">Figure 8
Detection results on DOTA-ship samples. The 1st column displays the samples from the ground truth, while the 2nd and 3rd columns are the detection results of the relative samples of Cascade R-CNN and Cascade R-CNN-LS, respectively. The lifting scheme-based network detects targets correctly in these samples, while Cascade R-CNN fails to detect some small, middle, and large size targets and has false alarms. Full article ">

25 pages, 32847 KiB

Open AccessArticle

Analysis of Activity in an Open-Pit Mine by Using InSAR Coherence-Based Normalized Difference Activity Index

by Jihyun Moon and Hoonyol Lee

Remote Sens. 2021, 13(9), 1861; https://doi.org/10.3390/rs13091861 - 10 May 2021

Cited by 14 | Viewed by 4011

Abstract

In this study, time-series of Sentinel-1A/B Interferometric Synthetic Aperture Radar (InSAR) coherence images were used to monitor the mining activity of Musan open-pit mine, the largest iron mine in North Korea. First, the subtraction of SRTM DEM (2000) from TanDEM-X DEM (2010–2015) has [...] Read more.

In this study, time-series of Sentinel-1A/B Interferometric Synthetic Aperture Radar (InSAR) coherence images were used to monitor the mining activity of Musan open-pit mine, the largest iron mine in North Korea. First, the subtraction of SRTM DEM (2000) from TanDEM-X DEM (2010–2015) has identified two major accumulation areas, one in the east (+112.33 m) and the other in the west (+84.03 m), and a major excavation area (−42.54 m) at the center of the mine. A total of 89 high-quality coherence images with a 12-day baseline from 2015 to 2020 were converted to the normalized difference activity index (NDAI), a newly developed activity indicator robust to spatial and temporal decorrelation. An RGB composite of annually averaged NDAI maps (red for 2019, green for 2018, and blue for 2017) showed that overall activity has diminished since 2018. Dumping slopes were categorized into shrinking, expanding, or transitional, according to the color pattern. Migration and expansion of excavation sites were also found on the pit floor. Time series of 12-day NDAI graphs revealed the date of activities with monthly accuracy. It is believed that NDAI with continuous acquisition of Sentinel-1A/B data can provide detailed monitoring of various types of activities in open-pit mines especially with limited in situ data. Full article

(This article belongs to the Special Issue Selected Papers from the “International Symposium on Remote Sensing 2021”)

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14 pages, 1257 KiB

Open AccessTechnical Note

NDFTC: A New Detection Framework of Tropical Cyclones from Meteorological Satellite Images with Deep Transfer Learning

by Shanchen Pang, Pengfei Xie, Danya Xu, Fan Meng, Xixi Tao, Bowen Li, Ying Li and Tao Song

Remote Sens. 2021, 13(9), 1860; https://doi.org/10.3390/rs13091860 - 10 May 2021

Cited by 32 | Viewed by 3991

Abstract

Accurate detection of tropical cyclones (TCs) is important to prevent and mitigate natural disasters associated with TCs. Deep transfer learning methods have advantages in detection tasks, because they can further improve the stability and accuracy of the detection model. Therefore, on the basis [...] Read more.

Accurate detection of tropical cyclones (TCs) is important to prevent and mitigate natural disasters associated with TCs. Deep transfer learning methods have advantages in detection tasks, because they can further improve the stability and accuracy of the detection model. Therefore, on the basis of deep transfer learning, we propose a new detection framework of tropical cyclones (NDFTC) from meteorological satellite images by combining the deep convolutional generative adversarial networks (DCGAN) and You Only Look Once (YOLO) v3 model. The algorithm process of NDFTC consists of three major steps: data augmentation, a pre-training phase, and transfer learning. First, to improve the utilization of finite data, DCGAN is used as the data augmentation method to generate images simulated to TCs. Second, to extract the salient characteristics of TCs, the generated images obtained from DCGAN are inputted into the detection model YOLOv3 in the pre-training phase. Furthermore, based on the network-based deep transfer learning method, we train the detection model with real images of TCs and its initial weights are transferred from the YOLOv3 trained with generated images. Training with real images helps to extract universal characteristics of TCs and using transferred weights as initial weights can improve the stability and accuracy of the model. The experimental results show that the NDFTC has a better performance, with an accuracy (ACC) of 97.78% and average precision (AP) of 81.39%, in comparison to the YOLOv3, with an ACC of 93.96% and AP of 80.64%. Full article

(This article belongs to the Special Issue Deep Learning and Computer Vision in Remote Sensing)

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22 pages, 8592 KiB

Open AccessArticle

Canopy Parameter Estimation of Citrus grandis var. Longanyou Based on LiDAR 3D Point Clouds

by Xiangyang Liu, Yaxiong Wang, Feng Kang, Yang Yue and Yongjun Zheng

Remote Sens. 2021, 13(9), 1859; https://doi.org/10.3390/rs13091859 - 10 May 2021

Cited by 15 | Viewed by 4149

Abstract

The characteristic parameters of Citrus grandis var. Longanyou canopies are important when measuring yield and spraying pesticides. However, the feasibility of the canopy reconstruction method based on point clouds has not been confirmed with these canopies. Therefore, LiDAR point cloud data for C. [...] Read more.

The characteristic parameters of Citrus grandis var. Longanyou canopies are important when measuring yield and spraying pesticides. However, the feasibility of the canopy reconstruction method based on point clouds has not been confirmed with these canopies. Therefore, LiDAR point cloud data for C. grandis var. Longanyou were obtained to facilitate the management of groves of this species. Then, a cloth simulation filter and European clustering algorithm were used to realize individual canopy extraction. After calculating canopy height and width, canopy reconstruction and volume calculation were realized using six approaches: by a manual method and using five algorithms based on point clouds (convex hull, CH; convex hull by slices; voxel-based, VB; alpha-shape, AS; alpha-shape by slices, ASBS). ASBS is an innovative algorithm that combines AS with slices optimization, and can best approximate the actual canopy shape. Moreover, the CH algorithm had the shortest run time, and the R² values of V_CH, V_VB, V_AS, and V_ASBS algorithms were above 0.87. The volume with the highest accuracy was obtained from the ASBS algorithm, and the CH algorithm had the shortest computation time. In addition, a theoretical but preliminarily system suitable for the calculation of the canopy volume of C. grandis var. Longanyou was developed, which provides a theoretical reference for the efficient and accurate realization of future functional modules such as accurate plant protection, orchard obstacle avoidance, and biomass estimation. Full article

(This article belongs to the Special Issue 3D Point Clouds for Agriculture Applications)

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23 pages, 14071 KiB

Open AccessArticle

Optical Remote Sensing Image Denoising and Super-Resolution Reconstructing Using Optimized Generative Network in Wavelet Transform Domain

by Xubin Feng, Wuxia Zhang, Xiuqin Su and Zhengpu Xu

Remote Sens. 2021, 13(9), 1858; https://doi.org/10.3390/rs13091858 - 10 May 2021

Cited by 52 | Viewed by 5159

Abstract

High spatial quality (HQ) optical remote sensing images are very useful for target detection, target recognition and image classification. Due to the influence of imaging equipment accuracy and atmospheric environment, HQ images are difficult to acquire, while low spatial quality (LQ) remote sensing [...] Read more.

High spatial quality (HQ) optical remote sensing images are very useful for target detection, target recognition and image classification. Due to the influence of imaging equipment accuracy and atmospheric environment, HQ images are difficult to acquire, while low spatial quality (LQ) remote sensing images are very easy to acquire. Hence, denoising and super-resolution (SR) reconstruction technology are the most important solutions to improve the quality of remote sensing images very effectively, which can lower the cost as much as possible. Most existing methods usually only employ denoising or SR technology to obtain HQ images. However, due to the complex structure and the large noise of remote sensing images, the quality of the remote sensing image obtained only by denoising method or SR method cannot meet the actual needs. To address these problems, a method of reconstructing HQ remote sensing images based on Generative Adversarial Network (GAN) named “Restoration Generative Adversarial Network with ResNet and DenseNet” (RRDGAN) is proposed, which can acquire better quality images by incorporating denoising and SR into a unified framework. The generative network is implemented by fusing Residual Neural Network (ResNet) and Dense Convolutional Network (DenseNet) in order to consider denoising and SR problems at the same time. Then, total variation (TV) regularization is used to furthermore enhance the edge details, and the idea of Relativistic GAN is explored to make the whole network converge better. Our RRDGAN is implemented in wavelet transform (WT) domain, since different frequency parts could be handled separately in the wavelet domain. The experimental results on three different remote sensing datasets shows the feasibility of our proposed method in acquiring remote sensing images. Full article

(This article belongs to the Section Remote Sensing Image Processing)

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21 pages, 12259 KiB

Open AccessArticle

Develop of New Tools for 4D Monitoring: Case Study of Cliff in Apulia Region (Italy)

by Domenica Costantino, Francesco Settembrini, Massimiliano Pepe and Vincenzo Saverio Alfio

Remote Sens. 2021, 13(9), 1857; https://doi.org/10.3390/rs13091857 - 10 May 2021

Cited by 5 | Viewed by 2229

Abstract

The monitoring of areas at risk is one of the topics of great interest in the scientific world in order to preserve natural areas of particular environmental value. The present work aims to develop a suitable survey and analysis methodology, in order to [...] Read more.

The monitoring of areas at risk is one of the topics of great interest in the scientific world in order to preserve natural areas of particular environmental value. The present work aims to develop a suitable survey and analysis methodology, in order to optimise multi-temporal processing. In particular, the phenomenon investigated the monitoring of cliffs in southern Apulia (Italy). To achieve this objective, different algorithms were tested and implemented in an in-house software called ICV. The implementation involved the use of different calculation procedures, combined and aimed at the analysis of the phenomenon in question. The validation of the experimentation was shown through the elaboration of a series of datasets of a particular area within the investigated coastline. Full article

(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)

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18 pages, 9142 KiB

Open AccessArticle

SNR-Based Water Height Retrieval in Rivers: Application to High Amplitude Asymmetric Tides in the Garonne River

by Pierre Zeiger, Frédéric Frappart, José Darrozes, Nicolas Roussel, Philippe Bonneton, Natalie Bonneton and Guillaume Detandt

Remote Sens. 2021, 13(9), 1856; https://doi.org/10.3390/rs13091856 - 10 May 2021

Cited by 13 | Viewed by 2955

Abstract

Signal-to-noise ratio (SNR) time series acquired by a geodetic antenna were analyzed to retrieve water heights during asymmetric tides on a narrow river using the Interference Pattern Technique (IPT) from Global Navigation Satellite System Reflectometry (GNSS-R). The dynamic SNR method was selected because [...] Read more.

Signal-to-noise ratio (SNR) time series acquired by a geodetic antenna were analyzed to retrieve water heights during asymmetric tides on a narrow river using the Interference Pattern Technique (IPT) from Global Navigation Satellite System Reflectometry (GNSS-R). The dynamic SNR method was selected because the elevation rate of the reflecting surface during rising tides is high in the Garonne River with macro tidal conditions. A new process was developed to filter out the noise introduced by the environmental conditions on the reflected signal due to the narrowness of the river compared to the size of the Fresnel areas, the presence of vegetation on the river banks, and the presence of boats causing multiple reflections. This process involved the removal of multipeaks in the Lomb-Scargle Periodogram (LSP) output and an iterative least square estimation (LSE) of the output heights. Evaluation of the results was performed against pressure-derived water heights. The best results were obtained using all GNSS bands (L1, L2, and L5) simultaneously: R = 0.99, ubRMSD = 0.31 m. We showed that the quality of the retrieved heights was consistent, whatever the vertical velocity of the reflecting surface, and was highly dependent on the number of satellites visible. The sampling period of our solution was 1 min with a 5-min moving window, and no tide models or fit were used in the inversion process. This highlights the potential of the dynamic SNR method to detect and monitor extreme events with GNSS-R, including those affecting inland waters such as flash floods. Full article

(This article belongs to the Special Issue Radar Based Water Level Estimation)

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Location of the study area. (a) Location of Podensac on the Garonne River; (b) the Gironde/Garonne/Dordogne estuary in southwest France; (c) drone image of the Garonne River taken by V. Marie (EPOC), showing the first waves of the tidal bore, its direction of propagation, and the platform location; (d) photo of the Garonne River from the platform with the GNSS antenna installed. The narrowness of the river and the vegetation on riverbanks are visible in both images. Full article ">Figure 2
1-Hz resampled pressure water level time series of the Garonne River at Podensac. (a) During the GNSS-R SNR acquisition with red rectangle indicating tidal bores; (b) during four consecutive tide periods. These figures are representative of the entire pressure water level time series and do not show the tidal oscillations over a longer period, as the acquisition was performed during spring tides only when both the tidal range is maximum and tidal bores can form. Full article ">Figure 3
Flowchart of the dynamic SNR method with our improvements adapted from [<a href="#B29-remotesensing-13-01856" class="html-bibr">29</a>]. (a) Processing chain with the addition of a two-step filtering of the dominant frequencies (in orange); (b,c) respective examples of a single-peak output and a multipeak output from LSP for the same satellite track (G01). Red line materializes the level of filtering which depends on parameter k (here k = 0.6), and <math display="inline"><semantics> <mover accent="true"> <mi>f</mi> <mo>˜</mo> </mover> </semantics></math> ~100 Hz. Full article ">Figure 4
Dominant frequencies extracted from GPS (green) and GLONASS (orange) satellites using the LSP, and river heights inverted with the dynamic SNR method (blue) compared to pressure water levels (red). (a,b) <math display="inline"><semantics> <mrow> <mo> </mo> <mover accent="true"> <mi>f</mi> <mo>˜</mo> </mover> </mrow> </semantics></math> and h from raw LSP output respectively; (c) frequencies filtered out after multipeak rejection with parameter k = 0.6 and iterative LSE; (d) concordant time series of water levels estimated with iterative LSE. Grey areas are masks due to tidal bore occurrence (17 h) and data gaps (21–22 h). Full article ">Figure 5
Final results using the adapted dynamic SNR inversion. (a) Comparison of h calculated using L1 only (orange), L2 only (green), and L1 + L2 + L5 (blue) frequencies for GPS and GLONASS satellites with pressure water levels (red); (b) output <math display="inline"><semantics> <mover accent="true"> <mi>h</mi> <mo>˙</mo> </mover> </semantics></math> with L1 + L2 + L5 bands; (c) number of GPS and GLONASS satellites for the calculation of h and <math display="inline"><semantics> <mover accent="true"> <mi>h</mi> <mo>˙</mo> </mover> </semantics></math>. The value of <math display="inline"><semantics> <mover accent="true"> <mi>h</mi> <mo>˙</mo> </mover> </semantics></math> was derived from the relative antenna height h, thus it was negative during rising tides as the relative antenna height decreased. Full article ">Figure 6
Statistical results depending on the number of satellites and the vertical velocity. (a) R and ubRMSD computed according to the number of satellites for the inversion of h and <math display="inline"><semantics> <mover accent="true"> <mi>h</mi> <mo>˙</mo> </mover> </semantics></math>; (b) Idem according to the vertical velocity class (intervals computed with range 1 × 10−4 m.s−1). Full article ">

21 pages, 8833 KiB

Open AccessArticle

Airborne LiDAR-Derived Digital Elevation Model for Archaeology

by Benjamin Štular, Edisa Lozić and Stefan Eichert

Remote Sens. 2021, 13(9), 1855; https://doi.org/10.3390/rs13091855 - 10 May 2021

Cited by 53 | Viewed by 7760

Abstract

The use of topographic airborne LiDAR data has become an essential part of archaeological prospection, and the need for an archaeology-specific data processing workflow is well known. It is therefore surprising that little attention has been paid to the key element of processing: [...] Read more.

The use of topographic airborne LiDAR data has become an essential part of archaeological prospection, and the need for an archaeology-specific data processing workflow is well known. It is therefore surprising that little attention has been paid to the key element of processing: an archaeology-specific DEM. Accordingly, the aim of this paper is to describe an archaeology-specific DEM in detail, provide a tool for its automatic precision assessment, and determine the appropriate grid resolution. We define an archaeology-specific DEM as a subtype of DEM, which is interpolated from ground points, buildings, and four morphological types of archaeological features. We introduce a confidence map (QGIS plug-in) that assigns a confidence level to each grid cell. This is primarily used to attach a confidence level to each archaeological feature, which is useful for detecting data bias in archaeological interpretation. Confidence mapping is also an effective tool for identifying the optimal grid resolution for specific datasets. Beyond archaeological applications, the confidence map provides clear criteria for segmentation, which is one of the unsolved problems of DEM interpolation. All of these are important steps towards the general methodological maturity of airborne LiDAR in archaeology, which is our ultimate goal. Full article

(This article belongs to the Special Issue Perspectives on Digital Elevation Model Applications)

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21 pages, 60660 KiB

Open AccessArticle

Small Object Detection in Remote Sensing Images with Residual Feature Aggregation-Based Super-Resolution and Object Detector Network

by Syed Muhammad Arsalan Bashir and Yi Wang

Remote Sens. 2021, 13(9), 1854; https://doi.org/10.3390/rs13091854 - 10 May 2021

Cited by 50 | Viewed by 7973

Abstract

This paper deals with detecting small objects in remote sensing images from satellites or any aerial vehicle by utilizing the concept of image super-resolution for image resolution enhancement using a deep-learning-based detection method. This paper provides a rationale for image super-resolution for small [...] Read more.

This paper deals with detecting small objects in remote sensing images from satellites or any aerial vehicle by utilizing the concept of image super-resolution for image resolution enhancement using a deep-learning-based detection method. This paper provides a rationale for image super-resolution for small objects by improving the current super-resolution (SR) framework by incorporating a cyclic generative adversarial network (GAN) and residual feature aggregation (RFA) to improve detection performance. The novelty of the method is threefold: first, a framework is proposed, independent of the final object detector used in research, i.e., YOLOv3 could be replaced with Faster R-CNN or any object detector to perform object detection; second, a residual feature aggregation network was used in the generator, which significantly improved the detection performance as the RFA network detected complex features; and third, the whole network was transformed into a cyclic GAN. The image super-resolution cyclic GAN with RFA and YOLO as the detection network is termed as SRCGAN-RFA-YOLO, which is compared with the detection accuracies of other methods. Rigorous experiments on both satellite images and aerial images (ISPRS Potsdam, VAID, and Draper Satellite Image Chronology datasets) were performed, and the results showed that the detection performance increased by using super-resolution methods for spatial resolution enhancement; for an IoU of 0.10, AP of 0.7867 was achieved for a scale factor of 16. Full article

(This article belongs to the Special Issue Convolutional Neural Networks for Object Detection)

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28 pages, 11976 KiB

Open AccessArticle

Sequence Image Datasets Construction via Deep Convolution Networks

by Xing Jin, Ping Tang and Zheng Zhang

Remote Sens. 2021, 13(9), 1853; https://doi.org/10.3390/rs13091853 - 10 May 2021

Cited by 2 | Viewed by 2888

Abstract

Remote-sensing time-series datasets are significant for global change research and a better understanding of the Earth. However, remote-sensing acquisitions often provide sparse time series due to sensor resolution limitations and environmental factors such as cloud noise for optical data. Image transformation is the [...] Read more.

Remote-sensing time-series datasets are significant for global change research and a better understanding of the Earth. However, remote-sensing acquisitions often provide sparse time series due to sensor resolution limitations and environmental factors such as cloud noise for optical data. Image transformation is the method that is often used to deal with this issue. This paper considers the deep convolution networks to learn the complex mapping between sequence images, called adaptive filter generation network (AdaFG), convolution long short-term memory network (CLSTM), and cycle-consistent generative adversarial network (CyGAN) for construction of sequence image datasets. AdaFG network uses a separable 1D convolution kernel instead of 2D kernels to capture the spatial characteristics of input sequence images and then is trained end-to-end using sequence images. CLSTM network can map between different images using the state information of multiple time-series images. CyGAN network can map an image from a source domain to a target domain without additional information. Our experiments, which were performed with unmanned aerial vehicle (UAV) and Landsat-8 datasets, show that the deep convolution networks are effective to produce high-quality time-series image datasets, and the data-driven deep convolution networks can better simulate complex and diverse nonlinear data information. Full article

(This article belongs to the Special Issue Advanced Machine Learning Techniques for High-Resolution Remote Sensing Data Analysis)

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17 pages, 4135 KiB

Open AccessTechnical Note

Day and Night Clouds Detection Using a Thermal-Infrared All-Sky-View Camera

by Yiren Wang, Dong Liu, Wanyi Xie, Ming Yang, Zhenyu Gao, Xinfeng Ling, Yong Huang, Congcong Li, Yong Liu and Yingwei Xia

Remote Sens. 2021, 13(9), 1852; https://doi.org/10.3390/rs13091852 - 10 May 2021

Cited by 16 | Viewed by 6384

Abstract

The formation and evolution of clouds are associated with their thermodynamical and microphysical progress. Previous studies have been conducted to collect images using ground-based cloud observation equipment to provide important cloud characteristics information. However, most of this equipment cannot perform continuous observations during [...] Read more.

The formation and evolution of clouds are associated with their thermodynamical and microphysical progress. Previous studies have been conducted to collect images using ground-based cloud observation equipment to provide important cloud characteristics information. However, most of this equipment cannot perform continuous observations during the day and night, and their field of view (FOV) is also limited. To address these issues, this work proposes a day and night clouds detection approach integrated into a self-made thermal-infrared (TIR) all-sky-view camera. The TIR camera consists of a high-resolution thermal microbolometer array and a fish-eye lens with a FOV larger than 160°. In addition, a detection scheme was designed to directly subtract the contamination of the atmospheric TIR emission from the entire infrared image of such a large FOV, which was used for cloud recognition. The performance of this scheme was validated by comparing the cloud fractions retrieved from the infrared channel with those from the visible channel and manual observation. The results indicated that the current instrument could obtain accurate cloud fraction from the observed infrared image, and the TIR all-sky-view camera developed in this work exhibits good feasibility for long-term and continuous cloud observation. Full article

(This article belongs to the Special Issue Forecasting Cloudiness Using Remote Sensing Techniques and Sky Camera Imagery)

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19 pages, 2747 KiB

Open AccessArticle

Monitoring Terrestrial Water Storage Changes with the Tongji-Grace2018 Model in the Nine Major River Basins of the Chinese Mainland

by Zhiwei Chen, Xingfu Zhang and Jianhua Chen

Remote Sens. 2021, 13(9), 1851; https://doi.org/10.3390/rs13091851 - 10 May 2021

Cited by 14 | Viewed by 3259

Abstract

Data from the Gravity Recovery and Climate Experiment (GRACE) satellite mission can be used to monitor changes in terrestrial water storage (TWS). In this study, we exploit the TWS observations from a new temporal gravity field model, Tongji-Grace2018, which was developed using an [...] Read more.

Data from the Gravity Recovery and Climate Experiment (GRACE) satellite mission can be used to monitor changes in terrestrial water storage (TWS). In this study, we exploit the TWS observations from a new temporal gravity field model, Tongji-Grace2018, which was developed using an optimized short-arc approach at Tongji University. We analyzed the changes in the TWS and groundwater storage (GWS) in each of the nine major river basins of the Chinese mainland from April 2002 to August 2016, using Tongji-Grace2018, the Global Land Data Assimilation System (GLDAS) hydrological model, in situ observations, and additional auxiliary data (such as precipitation and temperature). Our results indicate that the TWS of the Songliao, Yangtze, Pearl, and Southeastern River Basins are all increasing, with the most drastic TWS growth occurring in the Southeastern River Basin. The TWS of the Yellow, Haihe, Huaihe, and Southwestern River Basins are all decreasing, with the most drastic TWS loss occurring in the Haihe River Basin. The Continental River Basin TWS has remained largely unchanged over time. With the exception of the Songliao and Pearl River Basins, the GWS results produced by the Tongji-Grace2018 model are consistent with the in situ observations of these basins. The correlation coefficients for the Tongji-Grace2018 model results and the in situ observations for the Yellow, Huaihe, Yangtze, Southwestern, and Continental River Basins are higher than 0.710. Overall, the GWS results for the Songliao, Yellow, Haihe, Huaihe, Southwestern, and Continental River Basins all exhibit a downward trend, with the most severe groundwater loss occurring in the Haihe and Huaihe River Basins. However, the Yangtze and Southeastern River Basins both have upward-trending modeled and measured GWS values. This study demonstrates the effectiveness of the Tongji-Grace2018 model for the reliable estimation of TWS and GWS changes on the Chinese mainland, and may contribute to the management of available water resources. Full article

(This article belongs to the Special Issue Terrestrial Hydrology Using GRACE and GRACE-FO)

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The nine major river basins in China. (1) Songliao River Basin (SLRB); (2) Yellow River Basin, (YERB); (3) Haihe River Basin (HARB); (4) Huaihe River Basin (HURB); (5) Yangtze River Basin, (YARB); (6) Pearl River Basin (PERB); (7) Southeastern River Basin (SERB); (8) Southwestern River Basin (SWRB); (9) Continental River Basin (CORB). The black dots represent the locations of the in situ observations. Full article ">Figure 2
The spatial variations in (a) the monthly average precipitation values and (b) the monthly average temperature values on the Chinese mainland. Full article ">Figure 3
The workflow in this study. Full article ">Figure 4
Spatial variations in the TWS in China for different combinations of the Duan de-striping method and the Gaussian filter radius. (a) The Tongji-Grace2018 model without applying any filters; (b) The Tongji-Grace2018 model after applying the 150 km Gaussian filter; (c) The Tongji-Grace2018 model after applying the Duan de-striping method and the 150 km Gaussian filter; (d) The Tongji-Grace2018 model after applying the Duan de-striping method and the 200 km Gaussian filter; (e) The Tongji-Grace2018 model after applying the Duan de-striping method and the 250 km Gaussian filter; (f) The Tongji-Grace2018 model after applying the Duan de-striping method and the 300 km Gaussian filter. Full article ">Figure 5
The TWS time series for each river basin. The red curve represents the regional TWS changes (Tongji-Grace2018 model), the purple curve represents the 13 month moving average of the TWS changes, the black straight line is the TWS trend line, the green curve is the SWS (SMS + SWES) (GLDAS hydrological model), and the blue column is the monthly rainfall. The grey column is the annual rainfall anomaly, which is obtained by subtracting the average yearly rainfall value from the annual rainfall. The yellow column is the annual temperature anomaly, which is obtained by subtracting the average yearly temperature from the annual temperature. Full article ">Figure 6
Comparison of the GWS results from the GRACE models and from the in situ observations in each river basin; ρ represents the correlation coefficient between the measured and modeled GWS values from January of 2005 to August of 2016. Full article ">Figure 6 Cont.
Comparison of the GWS results from the GRACE models and from the in situ observations in each river basin; ρ represents the correlation coefficient between the measured and modeled GWS values from January of 2005 to August of 2016. Full article ">

21 pages, 7309 KiB

Open AccessArticle

Towards the Spectral Mapping of Plastic Debris on Beaches

by Jenna A. Guffogg, Mariela Soto-Berelov, Simon D. Jones, Chris J. Bellman, Jennifer L. Lavers and Andrew K. Skidmore

Remote Sens. 2021, 13(9), 1850; https://doi.org/10.3390/rs13091850 - 10 May 2021

Cited by 16 | Viewed by 4795

Abstract

Floating and washed ashore marine plastic debris (MPD) is a growing environmental challenge. It has become evident that secluded locations including the Arctic, Antarctic, and remote islands are being impacted by plastic pollution generated thousands of kilometers away. Optical remote sensing of MPD [...] Read more.

Floating and washed ashore marine plastic debris (MPD) is a growing environmental challenge. It has become evident that secluded locations including the Arctic, Antarctic, and remote islands are being impacted by plastic pollution generated thousands of kilometers away. Optical remote sensing of MPD is an emerging field that can aid in monitoring remote environments where in-person observation and data collection is not always feasible. Here we evaluate MPD spectral features in the visible to shortwave infrared regions for detecting varying quantities of MPD that have accumulated on beaches using a spectroradiometer. Measurements were taken from a range of in situ MPD accumulations ranging from 0.08% to 7.94% surface coverage. Our results suggest that spectral absorption features at 1215 nm and 1732 nm are useful for detecting varying abundance levels of MPD in a complex natural environment, however other absorption features at 931 nm, 1045 nm and 2046 nm could not detect in situ MPD. The reflectance of some in situ MPD accumulations was statistically different from samples that only contained organic debris and sand between 1.56% and 7.94% surface cover; however other samples with similar surface cover did not have reflectance that was statistically different from samples containing no MPD. Despite MPD being detectable against a background of sand and organic beach debris, a clear relationship between the surface cover of MPD and the strength of key absorption features could not be established. Additional research is needed to advance our understanding of the factors, such as type of MPD assemblage, that contribute to the bulk reflectance of MPD contaminated landscapes. Full article

(This article belongs to the Section Ocean Remote Sensing)

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16 pages, 3997 KiB

Open AccessArticle

Evaluation of Light Pollution in Global Protected Areas from 1992 to 2018

by Haowei Mu, Xuecao Li, Xiaoping Du, Jianxi Huang, Wei Su, Tengyun Hu, Yanan Wen, Peiyi Yin, Yuan Han and Fei Xue

Remote Sens. 2021, 13(9), 1849; https://doi.org/10.3390/rs13091849 - 9 May 2021

Cited by 37 | Viewed by 8422

Abstract

Light pollution, a phenomenon in which artificial nighttime light (NTL) changes the form of brightness and darkness in natural areas such as protected areas (PAs), has become a global concern due to its threat to global biodiversity. With ongoing global urbanization and climate [...] Read more.

Light pollution, a phenomenon in which artificial nighttime light (NTL) changes the form of brightness and darkness in natural areas such as protected areas (PAs), has become a global concern due to its threat to global biodiversity. With ongoing global urbanization and climate change, the light pollution status in global PAs deserves attention for mitigation and adaptation. In this study, we developed a framework to evaluate the light pollution status in global PAs, using the global NTL time series data. First, we classified global PAs (30,624) into three pollution categories: non-polluted (5974), continuously polluted (8141), and discontinuously polluted (16,509), according to the time of occurrence of lit pixels in/around PAs from 1992 to 2018. Then, we explored the NTL intensity (e.g., digital numbers) and its trend in those polluted PAs and identified those hotspots of PAs at the global scale with consideration of global urbanization. Our study shows that global light pollution is mainly distributed within the range of 30°N and 60°N, including Europe, north America, and East Asia. Although the temporal trend of NTL intensity in global PAs is increasing, Japan and the United States of America (USA) have opposite trends due to the implementation of well-planned ecological conservation policies and declining population growth. For most polluted PAs, the lit pixels are close to their boundaries (i.e., less than 10 km), and the NTL in/around these lit areas has become stronger over the past decades. The identified hotspots of PAs (e.g., Europe, the USA, and East Asia) help support decisions on global biodiversity conservation, particularly with global urbanization and climate change. Full article

(This article belongs to the Special Issue Light Pollution Monitoring Using Remote Sensing Data)

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30 pages, 6983 KiB

Open AccessArticle

Estimation of Long-Term Surface Downward Longwave Radiation over the Global Land from 2000 to 2018

by Chunjie Feng, Xiaotong Zhang, Yu Wei, Weiyu Zhang, Ning Hou, Jiawen Xu, Shuyue Yang, Xianhong Xie and Bo Jiang

Remote Sens. 2021, 13(9), 1848; https://doi.org/10.3390/rs13091848 - 9 May 2021

Cited by 9 | Viewed by 3651

Abstract

It is of great importance for climate change studies to construct a worldwide, long-term surface downward longwave radiation (L_d, 4–100 μm) dataset. Although a number of global L_d datasets are available, their low accuracies and coarse spatial resolutions limit [...] Read more.

It is of great importance for climate change studies to construct a worldwide, long-term surface downward longwave radiation (L_d, 4–100 μm) dataset. Although a number of global L_d datasets are available, their low accuracies and coarse spatial resolutions limit their applications. This study generated a daily L_d dataset with a 5-km spatial resolution over the global land surface from 2000 to 2018 using atmospheric parameters, which include 2-m air temperature (Ta), relative humidity (RH) at 1000 hPa, total column water vapor (TCWV), surface downward shortwave radiation (S_d), and elevation, based on the gradient boosting regression tree (GBRT) method. The generated L_d dataset was evaluated using ground measurements collected from AmeriFlux, AsiaFlux, baseline surface radiation network (BSRN), surface radiation budget network (SURFRAD), and FLUXNET networks. The validation results showed that the root mean square error (RMSE), mean bias error (MBE), and correlation coefficient (R) values of the generated daily L_d dataset were 17.78 W m⁻², 0.99 W m⁻², and 0.96 (p < 0.01). Comparisons with other global land surface radiation products indicated that the generated L_d dataset performed better than the clouds and earth’s radiant energy system synoptic (CERES-SYN) edition 4.1 dataset and ERA5 reanalysis product at the selected sites. In addition, the analysis of the spatiotemporal characteristics for the generated L_d dataset showed an increasing trend of 1.8 W m⁻² per decade (p < 0.01) from 2003 to 2018, which was closely related to Ta and water vapor pressure. In general, the generated L_d dataset has a higher spatial resolution and accuracy, which can contribute to perfect the existing radiation products. Full article

(This article belongs to the Special Issue Advances on Land–Ocean Heat Fluxes Using Remote Sensing)

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20 pages, 4168 KiB

Open AccessArticle

Comparing PlanetScope to Landsat-8 and Sentinel-2 for Sensing Water Quality in Reservoirs in Agricultural Watersheds

by Abubakarr S. Mansaray, Andrew R. Dzialowski, Meghan E. Martin, Kevin L. Wagner, Hamed Gholizadeh and Scott H. Stoodley

Remote Sens. 2021, 13(9), 1847; https://doi.org/10.3390/rs13091847 - 9 May 2021

Cited by 46 | Viewed by 7496

Abstract

Agricultural runoff transports sediments and nutrients that deteriorate water quality erratically, posing a challenge to ground-based monitoring. Satellites provide data at spatial-temporal scales that can be used for water quality monitoring. PlanetScope nanosatellites have spatial (3 m) and temporal (daily) resolutions that may [...] Read more.

Agricultural runoff transports sediments and nutrients that deteriorate water quality erratically, posing a challenge to ground-based monitoring. Satellites provide data at spatial-temporal scales that can be used for water quality monitoring. PlanetScope nanosatellites have spatial (3 m) and temporal (daily) resolutions that may help improve water quality monitoring compared to coarser-resolution satellites. This work compared PlanetScope to Landsat-8 and Sentinel-2 in their ability to detect key water quality parameters. Spectral bands of each satellite were regressed against chlorophyll a, turbidity, and Secchi depth data from 13 reservoirs in Oklahoma over three years (2017–2020). We developed significant regression models for each satellite. Landsat-8 and Sentinel-2 explained more variation in chlorophyll a than PlanetScope, likely because they have more spectral bands. PlanetScope and Sentinel-2 explained relatively similar amounts of variations in turbidity and Secchi Disk data, while Landsat-8 explained less variation in these parameters. Since PlanetScope is a commercial satellite, its application may be limited to cases where the application of coarser-resolution satellites is not feasible. We identified scenarios where PS may be more beneficial than Landsat-8 and Sentinel-2. These include measuring water quality parameters that vary daily, in small ponds and narrow coves of reservoirs, and at reservoir edges. Full article

(This article belongs to the Special Issue Remote Sensing for Water Resources Assessment in Agriculture)

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20 pages, 7700 KiB

Open AccessArticle

Identifying Spatial and Temporal Variations in Concrete Bridges with Ground Penetrating Radar Attributes

by Vivek Kumar, Isabel M. Morris, Santiago A. Lopez and Branko Glisic

Remote Sens. 2021, 13(9), 1846; https://doi.org/10.3390/rs13091846 - 9 May 2021

Cited by 10 | Viewed by 3172

Abstract

Estimating variations in material properties over space and time is essential for the purposes of structural health monitoring (SHM), mandated inspection, and insurance of civil infrastructure. Properties such as compressive strength evolve over time and are reflective of the overall condition of the [...] Read more.

Estimating variations in material properties over space and time is essential for the purposes of structural health monitoring (SHM), mandated inspection, and insurance of civil infrastructure. Properties such as compressive strength evolve over time and are reflective of the overall condition of the aging infrastructure. Concrete structures pose an additional challenge due to the inherent spatial variability of material properties over large length scales. In recent years, nondestructive approaches such as rebound hammer and ultrasonic velocity have been used to determine the in situ material properties of concrete with a focus on the compressive strength. However, these methods require personnel expertise, careful data collection, and high investment. This paper presents a novel approach using ground penetrating radar (GPR) to estimate the variability of in situ material properties over time and space for assessment of concrete bridges. The results show that attributes (or features) of the GPR data such as raw average amplitudes can be used to identify differences in compressive strength across the deck of a concrete bridge. Attributes such as instantaneous amplitudes and intensity of reflected waves are useful in predicting the material properties such as compressive strength, porosity, and density. For compressive strength, one alternative approach of the Maturity Index (MI) was used to estimate the present values and compare with GPR estimated values. The results show that GPR attributes could be successfully used for identifying spatial and temporal variation of concrete properties. Finally, discussions are presented regarding their suitability and limitations for field applications. Full article

(This article belongs to the Special Issue Trends in GPR and Other NDTs for Transport Infrastructure Assessment)

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29 pages, 3572 KiB

Open AccessArticle

Quantifying the Response of German Forests to Drought Events via Satellite Imagery

by Marius Philipp, Martin Wegmann and Carina Kübert-Flock

Remote Sens. 2021, 13(9), 1845; https://doi.org/10.3390/rs13091845 - 9 May 2021

Cited by 15 | Viewed by 5340

Abstract

Forest systems provide crucial ecosystem functions to our environment, such as balancing carbon stocks and influencing the local, regional and global climate. A trend towards an increasing frequency of climate change induced extreme weather events, including drought, is hereby a major challenge for [...] Read more.

Forest systems provide crucial ecosystem functions to our environment, such as balancing carbon stocks and influencing the local, regional and global climate. A trend towards an increasing frequency of climate change induced extreme weather events, including drought, is hereby a major challenge for forest management. Within this context, the application of remote sensing data provides a powerful means for fast, operational and inexpensive investigations over large spatial scales and time. This study was dedicated to explore the potential of satellite data in combination with harmonic analyses for quantifying the vegetation response to drought events in German forests. The harmonic modelling method was compared with a z-score standardization approach and correlated against both, meteorological and topographical data. Optical satellite imagery from Landsat and the Moderate Resolution Imaging Spectroradiometer (MODIS) was used in combination with three commonly applied vegetation indices. Highest correlation scores based on the harmonic modelling technique were computed for the 6th harmonic degree. MODIS imagery in combination with the Normalized Difference Vegetation Index (NDVI) generated hereby best results for measuring spectral response to drought conditions. Strongest correlation between remote sensing data and meteorological measures were observed for soil moisture and the self-calibrated Palmer Drought Severity Index (scPDSI). Furthermore, forests regions over sandy soils with pine as the dominant tree type were identified to be particularly vulnerable to drought. In addition, topographical analyses suggested mitigated drought affects along hill slopes. While the proposed approaches provide valuable information about vegetation dynamics as a response to meteorological weather conditions, standardized in-situ measurements over larger spatial scales and related to drought quantification are required for further in-depth quality assessment of the used methods and data. Full article

(This article belongs to the Section Forest Remote Sensing)

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26 pages, 16537 KiB

Open AccessArticle

Terrain Proxy-Based Site Classification for Seismic Zonation in North Korea within a Geospatial Data-Driven Workflow

by Han-Saem Kim, Chang-Guk Sun, Moon-Gyo Lee and Hyung-Ik Cho

Remote Sens. 2021, 13(9), 1844; https://doi.org/10.3390/rs13091844 - 9 May 2021

Cited by 4 | Viewed by 3419

Abstract

Numerous seismic activities occur in North Korea. However, it is difficult to perform seismic hazard assessment and obtain zonal data in the Korean Peninsula, including North Korea, when applying parametric or nonparametric methods. Remote sensing can be implemented for soil characterization or spatial [...] Read more.

Numerous seismic activities occur in North Korea. However, it is difficult to perform seismic hazard assessment and obtain zonal data in the Korean Peninsula, including North Korea, when applying parametric or nonparametric methods. Remote sensing can be implemented for soil characterization or spatial zonation studies on irregular, surficial, and subsurface systems of inaccessible areas. Herein, a data-driven workflow for extracting the principal features using a digital terrain model (DTM) is proposed. In addition, geospatial grid information containing terrain features and the average shear wave velocity in the top 30 m of the subsurface (V_S₃₀) are employed using geostatistical interpolation methods; machine learning (ML)-based regression models were optimized and V_S₃₀-based seismic zonation in the test areas in North Korea were forecasted. The interrelationships between V_S₃₀ and terrain proxy (elevation, slope, and landform class) in the training area in South Korea were verified to define the input layer in regression models. The landform class represents a new proxy of V_S₃₀ and was subgrouped according to the correlation with grid-based V_S₃₀. The geospatial grid information was generated via the optimum geostatistical interpolation method (i.e., sequential Gaussian simulation (SGS)). The best-fitting model among four ML methods was determined by evaluating cost function-based prediction performance, performing uncertainty analysis for the empirical correlations of V_S₃₀, and studying spatial correspondence with the borehole-based V_S₃₀ map. Subsequently, the best-fitting regression models were designed by training the geospatial grid in South Korea. Then, DTM and its terrain features were constructed along with V_S₃₀ maps for three major cities (Pyongyang, Kaesong, and Nampo) in North Korea. A similar distribution of the V_S₃₀ grid obtained using SGS was shown in the multilayer perceptron-based V_S₃₀ map. Full article

(This article belongs to the Special Issue Spatial Modelling of Natural Hazards and Water Resources through Remote Sensing, GIS and Machine Learning Methods)

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18 pages, 5859 KiB

Open AccessArticle

Distribution and Attribution of Terrestrial Snow Cover Phenology Changes over the Northern Hemisphere during 2001–2020

by Xiaona Chen, Yaping Yang, Yingzhao Ma and Huan Li

Remote Sens. 2021, 13(9), 1843; https://doi.org/10.3390/rs13091843 - 9 May 2021

Cited by 22 | Viewed by 3362

Abstract

Snow cover phenology has exhibited dramatic changes in the past decades. However, the distribution and attribution of the hemispheric scale snow cover phenology anomalies remain unclear. Using satellite-retrieved snow cover products, ground observations, and reanalysis climate variables, this study explored the distribution and [...] Read more.

Snow cover phenology has exhibited dramatic changes in the past decades. However, the distribution and attribution of the hemispheric scale snow cover phenology anomalies remain unclear. Using satellite-retrieved snow cover products, ground observations, and reanalysis climate variables, this study explored the distribution and attribution of snow onset date, snow end date, and snow duration days over the Northern Hemisphere from 2001 to 2020. The latitudinal and altitudinal distributions of the 20-year averaged snow onset date, snow end date, and snow duration days are well represented by satellite-retrieved snow cover phenology matrixes. The validation results by using 850 ground snow stations demonstrated that satellite-retrieved snow cover phenology matrixes capture the spatial variability of the snow onset date, snow end date, and snow duration days at the 95% significance level during the overlapping period of 2001–2017. Moreover, a delayed snow onset date and an earlier snow end date (1.12 days decade⁻¹, p < 0.05) are detected over the Northern Hemisphere during 2001–2020 based on the satellite-retrieved snow cover phenology matrixes. In addition, the attribution analysis indicated that snow end date dominates snow cover phenology changes and that an increased melting season temperature is the key driving factor of snow end date anomalies over the NH during 2001–2020. These results are helpful in understanding recent snow cover change and can contribute to climate projection studies. Full article

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24 pages, 3358 KiB

Open AccessArticle

Analyzing the Performance of GPS Data for Earthquake Prediction

by Valeri Gitis, Alexander Derendyaev and Konstantin Petrov

Remote Sens. 2021, 13(9), 1842; https://doi.org/10.3390/rs13091842 - 9 May 2021

Cited by 18 | Viewed by 4573

Abstract

The results of earthquake prediction largely depend on the quality of data and the methods of their joint processing. At present, for a number of regions, it is possible, in addition to data from earthquake catalogs, to use space geodesy data obtained with [...] Read more.

The results of earthquake prediction largely depend on the quality of data and the methods of their joint processing. At present, for a number of regions, it is possible, in addition to data from earthquake catalogs, to use space geodesy data obtained with the help of GPS. The purpose of our study is to evaluate the efficiency of using the time series of displacements of the Earth’s surface according to GPS data for the systematic prediction of earthquakes. The criterion of efficiency is the probability of successful prediction of an earthquake with a limited size of the alarm zone. We use a machine learning method, namely the method of the minimum area of alarm, to predict earthquakes with a magnitude greater than 6.0 and a hypocenter depth of up to 60 km, which occurred from 2016 to 2020 in Japan, and earthquakes with a magnitude greater than 5.5. and a hypocenter depth of up to 60 km, which happened from 2013 to 2020 in California. For each region, we compare the following results: random forecast of earthquakes, forecast obtained with the field of spatial density of earthquake epicenters, forecast obtained with spatio-temporal fields based on GPS data, based on seismological data, and based on combined GPS data and seismological data. The results confirm the effectiveness of using GPS data for the systematic prediction of earthquakes. Full article

(This article belongs to the Special Issue Earthquakes and Co-seismic Mass Movements Remote Sensing: From Prediction to Crisis Management )

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12 pages, 3810 KiB

Open AccessCommunication

Impact of Assimilating FY-3D MWTS-2 Upper Air Sounding Data on Forecasting Typhoon Lekima (2019)

by Zeyi Niu, Lei Zhang, Peiming Dong, Fuzhong Weng and Wei Huang

Remote Sens. 2021, 13(9), 1841; https://doi.org/10.3390/rs13091841 - 9 May 2021

Cited by 11 | Viewed by 2569

Abstract

In this study, the Fengyun-3D (FY-3D) clear-sky microwave temperature sounder-2 (MWTS-2) radiances were directly assimilated in the regional mesoscale Weather Research and Forecasting (WRF) model using the Gridpoint Statistical Interpolation (GSI) data assimilation system. The assimilation experiments were conducted to compare the track [...] Read more.

In this study, the Fengyun-3D (FY-3D) clear-sky microwave temperature sounder-2 (MWTS-2) radiances were directly assimilated in the regional mesoscale Weather Research and Forecasting (WRF) model using the Gridpoint Statistical Interpolation (GSI) data assimilation system. The assimilation experiments were conducted to compare the track errors of typhoon Lekima from uses of the Advanced Microwave Sounding Unit-A (AMSU-A) radiances (EXP_AD) with those from FY-3D MWTS-2 upper-air sounding data at channels 5–7 (EXP_AMD). The clear-sky mean bias-corrected observation-minus-background (O-B) values of FY-3D MWTS-2 channels 5, 6, and 7 are 0.27, 0.10 and 0.57 K, respectively, which are smaller than those without bias corrections. Compared with the control experiment, which was the forecast of the WRF model without use of satellite data, the assimilation of satellite radiances can improve the forecast performance and reduce the mean track error by 8.7% (~18.4 km) and 30% (~58.6 km) beyond 36 h through the EXP_AD and EXP_AMD, respectively. The direction of simulated steering flow changed from southwest in the EXP_AD to southeast in the EXP_AMD, which can be pivotal to forecasting the landfall of typhoon Lekima (2019) three days in advance. Assimilation of MWTS-2 upper-troposphere channels 5–7 has great potential to improve the track forecasts for typhoon Lekima. Full article

(This article belongs to the Section Environmental Remote Sensing)

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Figure 1
Model domain configuration and the best track of typhoon Lekima during the period from 0000 UTC on 2 August 2019, to 1800 UTC on 14 August 2019. Lekima made first landfall at 1800 UTC on 9 August 2019 in the Zhejiang Province. TD, TS, STS, TY, and L stand for tropical depression, tropical storm, severe tropical storm, typhoon, and extra-tropical cyclone, respectively. The red points stand for the FY-3D MWTS-2 data points after data thinning in the GSI with (a) a 1.5 h data assimilation time window and (b) a 3 h data assimilation time window at 0600 UTC on 6 August 2019. Full article ">Figure 2
(a–f) O–B for FY-3D MWTS-2 channels 5, 6, and 7 before (left panel) and after (right panel) bias corrections at 0600 UTC on 7 August 2019. Full article ">Figure 3
Domain-averaged RMSEs of vertical profiles of the (a) temperature, (b) water vapor mixing ratio, (c) U and (d) V increments of the EXP_AD (green) and EXP_AMD (red) at the initial time at 0600 UTC on 7 August 2019. Full article ">Figure 4
(a) The best track (white) and the track forecasts from CTRL (blue), EXP_AD (green), and EXP_AMD (red) from 0600 UTC on 7 August 2019 to 0600 UTC on 10 August 2019. The background image shows the Fengyun-4A (FY-4A) Advanced Geostationary Radiation Imager (AGRI) True Color Imagery on 0600 UTC 7 August 2019. The mean wind flow, wind speed (shaded), and calculated steering flow (solid circle with black arrow) by the 48 h forecasts of the (b) EXP_AD and (c) EXP_AMD on 0600 UTC 9 August 2019. Full article ">Figure 4 Cont.
(a) The best track (white) and the track forecasts from CTRL (blue), EXP_AD (green), and EXP_AMD (red) from 0600 UTC on 7 August 2019 to 0600 UTC on 10 August 2019. The background image shows the Fengyun-4A (FY-4A) Advanced Geostationary Radiation Imager (AGRI) True Color Imagery on 0600 UTC 7 August 2019. The mean wind flow, wind speed (shaded), and calculated steering flow (solid circle with black arrow) by the 48 h forecasts of the (b) EXP_AD and (c) EXP_AMD on 0600 UTC 9 August 2019. Full article ">Figure 5
(a–h) 72 h track errors (unit: km) of the CTRL (blue), EXP_AD (green), and EXP_AMD (red) with the model initialization times at 0600 UTC (left panel) and 1800 UTC (right panel) from 5 to 8 August 2019. The x-axis represents the forecast hours (unit: h). Full article ">Figure 6
(a) Mean track errors (unit: km) calculated by averaging track errors of eight different initial times from <a href="#remotesensing-13-01841-f005" class="html-fig">Figure 5</a> and (b) mean track forecast improvement (unit: %) relative to the CTRL (blue) for EXP_AD (green) and EXP_AMD (red). The x-axis represents the forecast hours. Full article ">

20 pages, 11430 KiB

Open AccessArticle

The Intra-Tidal Characteristics of Tidal Front and Their Spring–Neap Tidal and Seasonal Variations in Bungo Channel, Japan

by Menghong Dong and Xinyu Guo

Remote Sens. 2021, 13(9), 1840; https://doi.org/10.3390/rs13091840 - 9 May 2021

Viewed by 2914

Abstract

The intra-tidal variations of a tidal front in Bungo Channel, Japan and their dependence on the spring–neap tidal cycle and month were analyzed utilizing high-resolution (~2 km) hourly sea surface temperature (SST) data obtained from a Himawari-8 geostationary satellite from April 2016 to [...] Read more.

The intra-tidal variations of a tidal front in Bungo Channel, Japan and their dependence on the spring–neap tidal cycle and month were analyzed utilizing high-resolution (~2 km) hourly sea surface temperature (SST) data obtained from a Himawari-8 geostationary satellite from April 2016 to August 2020. A gradient-based front detection method was utilized to define the position and intensity of the front. Similar to previous ship-based studies, SST data were utilized to identify tidal fronts between a well-mixed strait and its surrounding stratified area. The hourly SST data confirmed the theoretical intra-tidal movement of the tidal front, which is mainly controlled by tidal current advection. Notably, the intensity of the front increases during the ebb current phase, which carries the front toward the stratified area, but decreases during the flood current phase that drives the front in the opposite direction. Due to a strong dependence on tidal currents, the intra-tidal variations appear in a fortnight cycle, and the fortnightly variations of the front are dependent on the month in which the background stratification and residual current changes occur. Additionally, tidal current convergence and divergence are posited to cause tidal front intensification and weakening. Full article

(This article belongs to the Special Issue Prediction of Ocean Circulation and Its Variability – Expanding the Horizons)

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11 pages, 2051 KiB

Open AccessCommunication

Seasonal Trends in Movement Patterns of Birds and Insects Aloft Simultaneously Recorded by Radar

by Xu Shi, Baptiste Schmid, Philippe Tschanz, Gernot Segelbacher and Felix Liechti

Remote Sens. 2021, 13(9), 1839; https://doi.org/10.3390/rs13091839 - 9 May 2021

Cited by 12 | Viewed by 4514

Abstract

Airspace is a key but not well-understood habitat for many animal species. Enormous amounts of insects and birds use the airspace to forage, disperse, and migrate. Despite numerous studies on migration, the year-round flight activities of both birds and insects are still poorly [...] Read more.

Airspace is a key but not well-understood habitat for many animal species. Enormous amounts of insects and birds use the airspace to forage, disperse, and migrate. Despite numerous studies on migration, the year-round flight activities of both birds and insects are still poorly studied. We used a 2 year dataset from a vertical-looking radar in Central Europe and developed an iterative hypothesis-testing algorithm to investigate the general temporal pattern of migratory and local movements. We estimated at least 3 million bird and 20 million insect passages over a 1 km transect annually. Most surprisingly, peak non-directional bird movement intensities during summer were of the same magnitude as seasonal directional movement peaks. Birds showed clear peaks in seasonally directional movements during day and night, coinciding well with the main migration period documented in this region. Directional insect movements occurred throughout the year, paralleling non-directional movements. In spring and summer, insect movements were non-directional; in autumn, their movements concentrated toward the southwest, similar to birds. Notably, the nocturnal movements of insects did not appear until April, while directional movements mainly occurred in autumn. This simple monitoring reveals how little we still know about the movement of biomass through airspace. Full article

(This article belongs to the Section Ecological Remote Sensing)

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Full article ">Figure 1
A graphical description of the iterative Rayleigh’s approach to calculate the proportion of directional (blue dots) and non-directional movement (green dots) for each day and night. Echoes (dots) are binned per flight direction and incrementally stacked (layers 1 to n). The mth layer is the first layer that is directional (Rayleigh test: p < 0.05). Note that if the first layer is already directional (m = 1), then the proportion of directional movements reaches 1 (all movements are migratory), and if none of the layers are directional, the proportion is 0 (all movements are non-migratory). Full article ">Figure 2
Results of the simulation tests showing on the x-axis, the input values for the proportion of directional and non-directional movements (0 = 100% non-directional, 1 = 100% directional), and on the y-axis, the estimated proportions based on our method. Colored lines represent different sample sizes. For this graph, the bin width was five degrees and assumed standard deviation for directional movements was 40°. For further graphs (SD = 20° and 60°), see the <a href="#app1-remotesensing-13-01839" class="html-app">supplemental material</a>. Full article ">Figure 3
Year-round birds’ (a–d) and insects’ (e–h) daily mean flight directions in 2016 and 2017. The plots with a white background represent diurnal movements and the plots with a light yellow background represent nocturnal movements. Note that in the first half of a year (left column), directions in ±22.5 degrees from NE (45 degrees from N) are shaded in grey, and this is the same for directions in ±22.5 degrees from SW (225 degrees from N) in the second half of a year (right column). Full article ">Figure 4
Year-round predicted trend in directional (blue) and non-directional (green) flight activity of birds (above) and insects (below): (a) trends in birds during day and (b) night; (c) trends in insects during day and (d) night. X-axis represents every odd-numbered month. See <a href="#app1-remotesensing-13-01839" class="html-app">Figure S1 in the electronic supplementary material</a> for the data of the two years. Full article ">

23 pages, 11653 KiB

Open AccessEditor’s ChoiceArticle

Assessing the Accuracy of ALOS/PALSAR-2 and Sentinel-1 Radar Images in Estimating the Land Subsidence of Coastal Areas: A Case Study in Alexandria City, Egypt

by Noura Darwish, Mona Kaiser, Magaly Koch and Ahmed Gaber

Remote Sens. 2021, 13(9), 1838; https://doi.org/10.3390/rs13091838 - 9 May 2021

Cited by 19 | Viewed by 5856

Abstract

Recently, the Differential Interferometric Synthetic Aperture Radar (DInSAR) technique is widely used for quantifying the land surface deformation, which is very important to assess the potential impact on social and economic activities. Radar satellites operate in different wavelengths and each provides different levels [...] Read more.

Recently, the Differential Interferometric Synthetic Aperture Radar (DInSAR) technique is widely used for quantifying the land surface deformation, which is very important to assess the potential impact on social and economic activities. Radar satellites operate in different wavelengths and each provides different levels of vertical displacement accuracy. In this study, the accuracies of Sentinel-1 (C-band) and ALOS/PALSAR-2 (L-band) were investigated in terms of estimating the land subsidence rate along the study area of Alexandria City, Egypt. A total of nine Sentinel-1 and 11 ALOS/PALSAR-2 scenes were used for such assessment. The small baseline subset (SBAS) processing scheme, which detects the land deformation with a high spatial and temporal coverage, was performed. The results show that the threshold coherence values of the generated interferograms from ALOS-2 data are highly concentrated between 0.2 and 0.3, while a higher threshold value of 0.4 shows no coherent pixels for about 80% of Alexandria’s urban area. However, the coherence values of Sentinel-1 interferograms ranged between 0.3 and 1, with most of the urban area in Alexandria showing coherent pixels at a 0.4 value. In addition, both data types produced different residual topography values of almost 0 m with a standard deviation of 13.5 m for Sentinel-1 and −20.5 m with a standard deviation of 33.24 m for ALOS-2 using the same digital elevation model (DEM) and wavelet number. Consequently, the final deformation was estimated using high coherent pixels with a threshold of 0.4 for Sentinel-1, which is comparable to a threshold of about 0.8 when using ALOS-2 data. The cumulative vertical displacement along the study area from 2017 to 2020 reached −60 mm with an average of −12.5 mm and mean displacement rate of −1.73 mm/year. Accordingly, the Alexandrian coastal plain and city center are found to be relatively stable, with land subsidence rates ranging from 0 to −5 mm/year. The maximum subsidence rate reached −20 mm/year and was found along the boundary of Mariout Lakes and former Abu Qir Lagoon. Finally, the affected buildings recorded during the field survey were plotted on the final land subsidence maps and show high consistency with the DInSAR results. For future developmental urban plans in Alexandria City, it is recommended to expand towards the western desert fringes instead of the south where the present-day ground lies on top of the former wetland areas. Full article

(This article belongs to the Special Issue ALOS-2/PALSAR-2 Calibration, Validation, Science and Applications)

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21 pages, 5666 KiB

Open AccessArticle

Towards Vine Water Status Monitoring on a Large Scale Using Sentinel-2 Images

by Eve Laroche-Pinel, Sylvie Duthoit, Mohanad Albughdadi, Anne D. Costard, Jacques Rousseau, Véronique Chéret and Harold Clenet

Remote Sens. 2021, 13(9), 1837; https://doi.org/10.3390/rs13091837 - 9 May 2021

Cited by 18 | Viewed by 4188

Abstract

Wine growing needs to adapt to confront climate change. In fact, the lack of water becomes more and more important in many regions. Whereas vineyards have been located in dry areas for decades, so they need special resilient varieties and/or a sufficient water [...] Read more.

Wine growing needs to adapt to confront climate change. In fact, the lack of water becomes more and more important in many regions. Whereas vineyards have been located in dry areas for decades, so they need special resilient varieties and/or a sufficient water supply at key development stages in case of severe drought. With climate change and the decrease of water availability, some vineyard regions face difficulties because of unsuitable variety, wrong vine management or due to the limited water access. Decision support tools are therefore required to optimize water use or to adapt agronomic practices. This study aimed at monitoring vine water status at a large scale with Sentinel-2 images. The goal was to provide a solution that would give spatialized and temporal information throughout the season on the water status of the vines. For this purpose, thirty six plots were monitored in total over three years (2018, 2019 and 2020). Vine water status was measured with stem water potential in field measurements from pea size to ripening stage. Simultaneously Sentinel-2 images were downloaded and processed to extract band reflectance values and compute vegetation indices. In our study, we tested five supervised regression machine learning algorithms to find possible relationships between stem water potential and data acquired from Sentinel-2 images (bands reflectance values and vegetation indices). Regression model using Red, NIR, Red-Edge and SWIR bands gave promising result to predict stem water potential (

R^{2} = 0.40

,

R M S E = 0.26

). Full article

(This article belongs to the Special Issue Remote and Proximal Sensing for Precision Agriculture and Viticulture)

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18 pages, 6987 KiB

Open AccessArticle

Determination of Key Phenological Phases of Winter Wheat Based on the Time-Weighted Dynamic Time Warping Algorithm and MODIS Time-Series Data

by Fa Zhao, Guijun Yang, Xiaodong Yang, Haiyan Cen, Yaohui Zhu, Shaoyu Han, Hao Yang, Yong He and Chunjiang Zhao

Remote Sens. 2021, 13(9), 1836; https://doi.org/10.3390/rs13091836 - 8 May 2021

Cited by 16 | Viewed by 3502

Abstract

Accurate determination of phenological information of crops is essential for field management and decision-making. Remote sensing time-series data are widely used for extracting phenological phases. Existing methods mainly extract phenological phases directly from individual remote sensing time-series, which are easily affected by clouds, [...] Read more.

Accurate determination of phenological information of crops is essential for field management and decision-making. Remote sensing time-series data are widely used for extracting phenological phases. Existing methods mainly extract phenological phases directly from individual remote sensing time-series, which are easily affected by clouds, noise, and mixed pixels. This paper proposes a novel method of phenological phase extraction based on the time-weighted dynamic time warping (TWDTW) algorithm using MODIS Normalized Difference Vegetation Index (NDVI) 5-day time-series data with a spatial resolution of 500 m. Firstly, based on the phenological differences between winter wheat and other land cover types, winter wheat distribution is extracted using the TWDTW classification method, and the results show that the overall classification accuracy and Kappa coefficient reach 94.74% and 0.90, respectively. Then, we extract the pure winter-wheat pixels using a method based on the coefficient of variation, and use these pixels to generate the average phenological curve. Next, the difference between each winter-wheat phenological curve and the average winter-wheat phenological curve is quantitatively calculated using the TWDTW algorithm. Finally, the key phenological phases of winter wheat in the study area, namely, the green-up date (GUD), heading date (HD), and maturity date (MD), are determined. The results show that the phenological phase extraction using the TWDTW algorithm has high accuracy. By verification using phenological station data from the Meteorological Data Sharing Service System of China, the root mean square errors (RMSEs) of the GUD, HD, and MD are found to be 9.76, 5.72, and 6.98 days, respectively. Additionally, the method proposed in this article is shown to have a better extraction performance compared with several other methods. Furthermore, it is shown that, in Hebei Province, the GUD, HD, and MD are mainly affected by latitude and accumulated temperature. As the latitude increases from south to north, the GUD, HD, and MD are delayed, and for each 1° increment in latitude, the GUD, HD, and MD are delayed by 4.84, 5.79, and 6.61 days, respectively. The higher the accumulated temperature, the earlier the phenological phases occur. However, latitude and accumulated temperature have little effect on the length of the phenological phases. Additionally, the lengths of time between GUD and HD, HD and MD, and GUD and MD are stable at 46, 41, and 87 days, respectively. Overall, the proposed TWDTW method can accurately determine the key phenological phases of winter wheat at a regional scale using remote sensing time-series data. Full article

(This article belongs to the Special Issue Remote Sensing and Decision Support for Precision Orchard Production)

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20 pages, 3601 KiB

Open AccessArticle

Multi-Dimensional Drought Assessment in Abbay/Upper Blue Nile Basin: The Importance of Shared Management and Regional Coordination Efforts for Mitigation

by Yared Bayissa, Semu Moges, Assefa Melesse, Tsegaye Tadesse, Anteneh Z. Abiy and Abeyou Worqlul

Remote Sens. 2021, 13(9), 1835; https://doi.org/10.3390/rs13091835 - 8 May 2021

Cited by 8 | Viewed by 3026

Abstract

Drought is one of the least understood and complex natural hazards often characterized by a significant decrease in water availability for a prolonged period. It can be manifested in one or more forms as meteorological, agricultural, hydrological, and/or socio-economic drought. The overarching objective [...] Read more.

Drought is one of the least understood and complex natural hazards often characterized by a significant decrease in water availability for a prolonged period. It can be manifested in one or more forms as meteorological, agricultural, hydrological, and/or socio-economic drought. The overarching objective of this study is to demonstrate and characterize the different forms of droughts and to assess the multidimensional nature of drought in the Abbay/ Upper Blue Nile River (UBN) basin and its national and regional scale implications. In this study, multiple drought indices derived from in situ and earth observation-based hydro-climatic variables were used. The meteorological drought was characterized using the Standardized Precipitation Index (SPI) computed from the earth observation-based gridded CHIRPS (Climate Hazards Group InfraRed Precipitation with Station) rainfall data. Agricultural and hydrological droughts were characterized by using the Soil Moisture Deficit Index (SMDI) and Standardized Runoff-discharge Index (SRI), respectively. The monthly time series of SMDI was derived from model-based gridded soil moisture and SRI from observed streamflow data from 1982 to 2019. The preliminary result illustrates the good performance of the drought indices in capturing the historic severe drought events (e.g., 1984 and 2002) and the spatial extents across the basin. The results further indicated that all forms of droughts (i.e., meteorological, agricultural, and hydrological) occurred concurrently in Abbay/Upper Blue Nile basin with a Pearson correlation coefficient ranges from 0.5 to 0.85 both Kiremt and annual aggregate periods. The concurrent nature of drought is leading to a multi-dimensional socio-economic crisis as indicated by rainfall, and soil moisture deficits, and drying of small streams. Multi-dimensional drought mitigation necessitates regional cooperation and watershed management to protect both the common water sources of the Abbay/Upper Blue Nile basin and the socio-economic activities of the society in the basin. This study also underlines the need for multi-scale drought monitoring and management practices in the basin. Full article

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21 pages, 4012 KiB

Open AccessArticle

Predicting Height to Crown Base of Larix olgensis in Northeast China Using UAV-LiDAR Data and Nonlinear Mixed Effects Models

by Xin Liu, Yuanshuo Hao, Faris Rafi Almay Widagdo, Longfei Xie, Lihu Dong and Fengri Li

Remote Sens. 2021, 13(9), 1834; https://doi.org/10.3390/rs13091834 - 8 May 2021

Cited by 17 | Viewed by 2570

Abstract

As a core content of forest management, the height to crown base (HCB) model can provide a theoretical basis for the study of forest growth and yield. In this study, 8364 trees of Larix olgensis within 118 sample plots from 11 sites were [...] Read more.

As a core content of forest management, the height to crown base (HCB) model can provide a theoretical basis for the study of forest growth and yield. In this study, 8364 trees of Larix olgensis within 118 sample plots from 11 sites were measured to establish a two-level nonlinear mixed effect (NLME) HCB model. All predictors were derived from an unmanned aerial vehicle light detection and ranging (UAV-LiDAR) laser scanning system, which is reliable for extensive forest measurement. The effects of the different individual trees, stand factors, and their combinations on the HCB were analyzed, and the leave-one-site-out cross-validation was utilized for model validation. The results showed that the NLME model significantly improved the prediction accuracy compared to the base model, with a mean absolute error and relative mean absolute error of 0.89% and 9.71%, respectively. In addition, both site-level and plot-level sampling strategies were simulated for NLME model calibration. According to different prediction scale and accuracy requirements, selecting 15 trees randomly per site or selecting the three largest trees and three medium-size trees per plot was considered the most favorable option, especially when both investigations cost and the model’s accuracy are primarily considered. The newly established HCB model will provide valuable tools to effectively utilize the UAV-LiDAR data for facilitating decision making in larch plantations management. Full article

(This article belongs to the Section Forest Remote Sensing)

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27 pages, 9633 KiB

Open AccessArticle

Coupling of Dual Channel Waveform ALS and Sonar for Investigation of Lake Bottoms and Shore Zones

by Jarosław Chormański, Barbara Nowicka, Aleksander Wieckowski, Maurycy Ciupak, Jacek Jóźwiak and Tadeusz Figura

Remote Sens. 2021, 13(9), 1833; https://doi.org/10.3390/rs13091833 - 8 May 2021

Cited by 12 | Viewed by 3025

Abstract

In this work, we proposed to include remote sensing techniques as a part of the methodology for natural lake bottom mapping, with a focus on the littoral zone. Due to the inaccessibility of this zone caused by dense vegetation, measurements of the lake [...] Read more.

In this work, we proposed to include remote sensing techniques as a part of the methodology for natural lake bottom mapping, with a focus on the littoral zone. Due to the inaccessibility of this zone caused by dense vegetation, measurements of the lake bottom and the coastline are also difficult to perform using traditional methods. The authors of this paper present, discuss and verify the applicability of remote sensing active sensors as a tool for measurements in the shore zone of a lake. The single-beam Lowrance HDS-7 ComboGPS echosounder with an 83/200 kHz transducer and a two-beam LiDAR RIEGL VQ-1560i-DW scanner have been used for reservoir bottom measurements of two neighboring lakes, which differ in terms of water transparency. The research has found a strong correlation between both sonar and LiDAR for mapping the bottom depth in a range up to 1.6 m, and allowed LiDAR mapping of approximately 20% of the highly transparent lake, but it has not been found to be useful in water with low transparency. In the light of the conducted research, both devices, sonar and LiDAR, have potential for complementary use by fusing both methods: the sonar for mapping of the sublittoral and the pelagic zone, and the LiDAR for mapping of the littoral zone, overcoming limitation related to vegetation in the lake shore zone. Full article

(This article belongs to the Special Issue Geoinformation Technologies in Civil Engineering and the Environment)

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Remote Sens., Volume 13, Issue 9 (May-1 2021) – 260 articles

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