Remote Sensing

23 pages, 7089 KiB

Open AccessArticle

A Deep Learning-Based Method for Extracting Standing Wood Feature Parameters from Terrestrial Laser Scanning Point Clouds of Artificially Planted Forest

by Xingyu Shen, Qingqing Huang, Xin Wang, Jiang Li and Benye Xi

Remote Sens. 2022, 14(15), 3842; https://doi.org/10.3390/rs14153842 - 08 Aug 2022

Cited by 10 | Viewed by 2837

Abstract

The use of 3D point cloud-based technology for quantifying standing wood and stand parameters can play a key role in forestry ecological benefit assessment and standing tree cultivation and utilization. With the advance of 3D information acquisition techniques, such as light detection and [...] Read more.

The use of 3D point cloud-based technology for quantifying standing wood and stand parameters can play a key role in forestry ecological benefit assessment and standing tree cultivation and utilization. With the advance of 3D information acquisition techniques, such as light detection and ranging (LiDAR) scanning, the stand information of trees in large areas and complex terrain can be obtained more efficiently. However, due to the diversity of the forest floor, the morphological diversity of the trees, and the fact that forestry is often planted as large-scale plantations, efficiently segmenting the point cloud of artificially planted forests and extracting standing wood feature parameters remains a considerable challenge. An effective method based on energy segmentation and PointCNN is proposed in this work to address this issue. The network is enhanced for learning point cloud features by geometric feature balance model (GFBM), enabling the efficient segmentation of tree point clouds from forestry point cloud data collected by terrestrial laser scanning (TLS) in outdoor environments. The 3D Forest software is then used to obtain single wood point cloud after semantic segmentation, and the extracted single wood point cloud is finally employed to extract standing wood feature parameters using TreeQSM. The point cloud semantic segmentation method is the most important part of our research. According to our findings, this method can segment datasets of two different artificially planted woodland point clouds with an overall accuracy of 0.95 and a tree segmentation accuracy of 0.93. When compared with the manual measurements, the root-mean-square error (RMSE) for tree height in the two datasets are 0.30272 and 0.21015 m, and the RMSEs for the diameter at breast height are 0.01436 and 0.01222 m, respectively. Our method is a robust framework based on deep learning that is applicable to forestry for extracting the feature parameters of artificially planted trees. It solves the problem of segmenting tree point clouds in artificially planted trees and provides a reliable data processing method for tree information extraction, trunk shape analysis, etc. Full article

(This article belongs to the Special Issue Terrestrial Laser Scanning of Forest Structure)

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21 pages, 13565 KiB

Open AccessArticle

Evolution and Structure of a Dry Microburst Line Observed by Multiple Remote Sensors in a Plateau Airport

by Xuan Huang, Jiafeng Zheng, Yuzhang Che, Gaili Wang, Tao Ren, Zhiqiang Hua, Weidong Tian, Zhikun Su and Lianxia Su

Remote Sens. 2022, 14(15), 3841; https://doi.org/10.3390/rs14153841 - 08 Aug 2022

Cited by 2 | Viewed by 1738

Abstract

The civilian airplane is a common transportation mode for the local people in the Qinghai-Tibet Plateau (QTP). Due to the profound dynamic and thermal effects, the QTP can trigger strong windstorms during the warm season, during which downbursts can cause severe low-level wind [...] Read more.

The civilian airplane is a common transportation mode for the local people in the Qinghai-Tibet Plateau (QTP). Due to the profound dynamic and thermal effects, the QTP can trigger strong windstorms during the warm season, during which downbursts can cause severe low-level wind shear and threaten aviation safety. However, the study of downbursts over QTP has not been given much attention. This study analyzes and interprets a typical traveling dry microburst line that happened at the Xining Caojiapu International Airport (ZLXN) on 14 May 2020, intending to show a better understanding of the dry downbursts over QTP and explore the synergetic usage of different remote sensing technologies for downburst detection and warning in plateau airports. Specifically, the characteristics of synoptic conditions, the convective system formation process, and the structure and evolution of downbursts and relevant low-level winds are comprehensively investigated. The results show that, under the control of an upstream shallow trough, features of the local atmosphere state, including a dry-adiabatic stratification, a shallow temperature inversion, increases in solar radiation heating, and strong vertical shears of horizontal winds, can be favorable atmospheric prerequisites for the formation and development of dry storms and downbursts. Low-reflectivity storm cells of the Mesoscale Convective System (MCS) organize to form narrow bow echoes, and downbursts show features of radial wind convergences and rapid descending reflectivity cores with hanging virga as observed by a Doppler weather radar. Moreover, details of gales, gust fronts, convergences, turbulences, wind collisions, and outflow interactions triggered by the downburst line are also detected and interpreted by a scanning Doppler wind lidar from different perspectives. In addition, the findings in this work have been compared with the results observed in Denver, U.S., and some simulation studies. Finally, a few conceptual models of low-level wind evolutions influenced by the dry downburst line are given. Full article

(This article belongs to the Special Issue Synergetic Remote Sensing of Clouds and Precipitation)

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21 pages, 9224 KiB

Open AccessArticle

Orographic Construction of a Numerical Weather Prediction Spectral Model Based on ASTER Data and Its Application to Simulation of the Henan 20·7 Extreme Rainfall Event

by Yingjie Wang, Jianping Wu, Xiangrong Yang, Jun Peng and Xiaotian Pan

Remote Sens. 2022, 14(15), 3840; https://doi.org/10.3390/rs14153840 - 08 Aug 2022

Viewed by 1505

Abstract

Numerical weather prediction (NWP) has become an important method of predicting extreme weather events, but orography is one of the key factors affecting the performance of NWPs. In this paper, based on Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) elevation data, a [...] Read more.

Numerical weather prediction (NWP) has become an important method of predicting extreme weather events, but orography is one of the key factors affecting the performance of NWPs. In this paper, based on Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) elevation data, a method for constructing a global orographic dataset suitable for NWP spectral models is investigated. The Yin-He global spectrum model (YHGSM) is used to simulate the early and peak periods of the extreme rainfall event on 20 July 2021 in Henan Province, China, and the heavy rain in Beijing in order to verify the effectiveness and superiority of the proposed orographic construction method. It is demonstrated that in a few cases the direct two-dimensional filter can sometimes simulate more intense rainfall, but in general, the bidirectional one-dimensional filter is better than the direct two-dimensional filter in orographic processing, and the bidirectional one-dimensional filter can filter out more of the small-scale orographic information. The effect of the higher orographic resolution before conversion to spectral space is not very obvious, but it is demonstrated that the simulation results are better for the heavy-rainfall level. In conclusion, in most cases, the simulations conducted using the new global orographic dataset based on ASTER data are better than those obtained using the model’s original orography, especially for torrential and extreme rainfall. These conclusions provide a reference for future predictions of and research on extreme rainfall events. Full article

(This article belongs to the Special Issue Prediction of Extreme Weather Events)

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21 pages, 4696 KiB

Open AccessArticle

Evaluation of Methods for Estimating Lake Surface Water Temperature Using Landsat 8

by Krzysztof Dyba, Sofia Ermida, Mariusz Ptak, Jan Piekarczyk and Mariusz Sojka

Remote Sens. 2022, 14(15), 3839; https://doi.org/10.3390/rs14153839 - 08 Aug 2022

Cited by 5 | Viewed by 3690

Abstract

Changes in lake water temperature, observed with the greatest intensity during the last two decades, may significantly affect the functioning of these unique ecosystems. Currently, in situ studies in Poland are conducted only for 38 lakes using the single-point method. The aim of [...] Read more.

Changes in lake water temperature, observed with the greatest intensity during the last two decades, may significantly affect the functioning of these unique ecosystems. Currently, in situ studies in Poland are conducted only for 38 lakes using the single-point method. The aim of this study was to develop a method for remote sensing monitoring of lake water temperature in a spatio-temporal context based on Landsat 8 imagery. For this purpose, using data obtained for 28 lakes from the period 2013–2020, linear regression (LM) and random forest (RF) models were developed to estimate surface water temperature. In addition, analysis of Landsat Level-2 Surface Temperature Science Product (LST-L2) data provided by United States Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA) was performed. The remaining 10 lakes not previously used in the model development stage were used to validate model performance. The results showed that the most accurate estimation is possible using the RF method for which RMSE = 1.83 °C and R² = 0.89, while RMSE = 3.68 °C and R² = 0.8 for the LST-L2 method. We found that LST-L2 contains a systematic error in the coastal zone, which can be corrected and eventually improve the quality of estimation. The satellite-based method makes it possible to determine water temperature for all lakes in Poland at different times and to understand the influence of climatic factors affecting temperature at the regional scale. On the other hand, spatial presentation of thermics within individual lakes enables understanding the influence of local factors and morphometric conditions. Full article

(This article belongs to the Special Issue Remote Sensing Image Processing in Poland)

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25 pages, 13571 KiB

Open AccessArticle

Backscatter Characteristics Analysis for Flood Mapping Using Multi-Temporal Sentinel-1 Images

by Minmin Huang and Shuanggen Jin

Remote Sens. 2022, 14(15), 3838; https://doi.org/10.3390/rs14153838 - 08 Aug 2022

Cited by 3 | Viewed by 2388

Abstract

Change detection between images of pre-flood and flooding periods is a critical process for flood mapping using satellite images. Flood mapping from SAR images is based on backscattering coefficient differences. The change rules of the backscattering coefficient with different flooding depths of ground [...] Read more.

Change detection between images of pre-flood and flooding periods is a critical process for flood mapping using satellite images. Flood mapping from SAR images is based on backscattering coefficient differences. The change rules of the backscattering coefficient with different flooding depths of ground objects are essential prior knowledge for flood mapping, while their absence greatly limits the precision. Therefore, minimizing the backscattering coefficient differences caused by non-flood factors is of great significance for improving the accuracy of flood mapping. In this paper, non-flood factor influences, i.e., monthly variations of ground objects and polarization and satellite orbits, on the backscattering coefficient are studied with multi-temporal Sentinel-1 images for five ground objects in Kouzi Village, Shouguang City, Shandong Province, China. Sentinel-1 images in different rainfalls are used to study the variation of the backscattering coefficient with flooding depths. Since it is difficult to measure the flooding depth of historical rainfall events, a hydrological analysis based on the Geographic Information System (GIS) and Remote Sensing (RS) is used to estimate the flooding depth. The results showed that the monthly variations of the maximum backscattering coefficients of farmland and construction and the backscattering coefficient differences caused by the satellite orbit were larger than the minimum backscattering coefficient differences caused by inundation. The flood extraction rules of five objects based on Sentinel-1 were obtained and analyzed, which improved flood extraction knowledge from qualitative to semi-quantitative analysis. Full article

(This article belongs to the Special Issue Remote Sensing in Urban Natural Hazards Monitoring)

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13 pages, 3455 KiB

Open AccessArticle

Real-Time Software for the Efficient Generation of the Clumping Index and Its Application Based on the Google Earth Engine

by Yu Li and Hongliang Fang

Remote Sens. 2022, 14(15), 3837; https://doi.org/10.3390/rs14153837 - 08 Aug 2022

Cited by 3 | Viewed by 2123

Abstract

Canopy clumping index (CI) is a key structural parameter related to vegetation phenology and the absorption of radiation, and it is usually retrieved from remote sensing data based on an empirical relationship with the Normalized Difference between Hotspot and Darkspot (NDHD) index. A [...] Read more.

Canopy clumping index (CI) is a key structural parameter related to vegetation phenology and the absorption of radiation, and it is usually retrieved from remote sensing data based on an empirical relationship with the Normalized Difference between Hotspot and Darkspot (NDHD) index. A rapid production software was developed to implement the CI algorithm based on the Google Earth Engine (GEE) to update current CI products and promote the application of CI in different fields. Daily, monthly, and yearly global CI products are continuously generated and updated in real-time by the software. Users can directly download the product or work with CI without paying attention to data generation. For the application case study, a change detection algorithm, LandTrendr, was implemented on the GEE to examine the global CI trend from 2000 to 2020. The results indicate that the area of increase trend (28.7%, ΔCI > 0.02) is greater than that of the decrease trend (17.1%, ΔCI < −0.02). Our work contributes toward the retrieval, application, and validation of CI. Full article

(This article belongs to the Collection Google Earth Engine Applications)

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20 pages, 8892 KiB

Open AccessArticle

Finite-Region Approximation of EM Fields in Layered Biaxial Anisotropic Media

by Zhuangzhuang Kang, Hongnian Wang and Changchun Yin

Remote Sens. 2022, 14(15), 3836; https://doi.org/10.3390/rs14153836 - 08 Aug 2022

Cited by 1 | Viewed by 1353

Abstract

A new algorithm is developed to accurately compute the electromagnetic (EM) fields in the layered biaxial anisotropic media. We enclose the computational region in an infinitely long rectangular region by four vertical truncation planes and establish the corresponding algorithm to approximate the EM [...] Read more.

A new algorithm is developed to accurately compute the electromagnetic (EM) fields in the layered biaxial anisotropic media. We enclose the computational region in an infinitely long rectangular region by four vertical truncation planes and establish the corresponding algorithm to approximate the EM fields in the entire space. The EM fields in this region are expanded as a two-dimensional (2-D) Fourier series of the transverse variables. By using the spectral state variable method, the generalized reflection coefficient matrices and transmission matrices are then derived to determine the Fourier coefficients per layer. Therefore, we can obtain the spatial-domain EM fields by summing the 2-D Fourier series. To enhance the accuracy and efficiency of this algorithm, we apply the method of images to estimate the influence of the artificial boundaries on the EM fields at the observer. We then further develop a quantitative principle to choose the proper size of the region according to the desired error tolerance. With the proper choice, the summation of the series can achieve satisfactory accuracy. This algorithm is finally applied to simulate the responses of the triaxial logging tool in transversely isotropic and biaxial anisotropic media and is verified through comparisons to the other method. Full article

(This article belongs to the Special Issue Microwave Remote Sensing for Quantitative Parameters Retrieval: Methods and Applications)

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20 pages, 3378 KiB

Open AccessArticle

Assessment and Hydrological Validation of Merged Near-Real-Time Satellite Precipitation Estimates Based on the Gauge-Free Triple Collocation Approach

by Daling Cao, Hongtao Li, Enguang Hou, Sulin Song and Chengguang Lai

Remote Sens. 2022, 14(15), 3835; https://doi.org/10.3390/rs14153835 - 08 Aug 2022

Cited by 5 | Viewed by 1365

Abstract

Obtaining accurate near-real-time precipitation data and merging multiple precipitation estimates require sufficient in-situ rain gauge networks. The triple collocation (TC) approach is a novel error assessment method that does not require rain gauge data and provides reasonable precipitation estimates by merging data; this [...] Read more.

Obtaining accurate near-real-time precipitation data and merging multiple precipitation estimates require sufficient in-situ rain gauge networks. The triple collocation (TC) approach is a novel error assessment method that does not require rain gauge data and provides reasonable precipitation estimates by merging data; this study assesses the TC approach for producing reliable near-real-time satellite-based precipitation estimate (SPE) products and the utility of the merged SPEs for hydrological modeling of ungauged areas. Three widely used near-real-time SPEs, including the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) early/late run (E/L) series, and the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Dynamic Infrared Rain Rate (PDIR) products, are used in the Beijiang basin in south China. The results show that the TC-based merged SPEs generally outperform all original SPEs, with higher consistency with the in-situ observations, and show superiority over the simple equal-weighted merged SPEs used for comparison; these findings indicate the superiority of the TC approach for utilizing the error characteristics of input SPEs for multi-SPE merging for ungauged areas. The validation of the hydrological modeling utility based on the Génie Rural à 4 paramètres Journalier (GR4J) model shows that the streamflow modeled by the TC-based merged SPEs has the best performance among all SPEs, especially for modeling low streamflow because the integration with the PDIR outperforms the IMERG products in low streamflow modeling. The TC merging approach performs satisfactorily for producing reliable near-real-time SPEs without gauge data, showing great potential for near-real-time applications, such as modeling rainstorms and monitoring floods and flash droughts in ungauged areas. Full article

(This article belongs to the Special Issue Remote Sensing of Hydrological Extremes: Current Progress and Future Prospect)

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15 pages, 6575 KiB

Open AccessArticle

Role of the Nyainrong Microcontinent in Seismogenic Mechanism and Stress Partitioning: Insights from the 2021 Nagqu Mw 5.7 Earthquake

by Xiaoge Liu, Lei Xie, Yujiang Li, Bingquan Han, Zhidan Chen and Wenbin Xu

Remote Sens. 2022, 14(15), 3834; https://doi.org/10.3390/rs14153834 - 08 Aug 2022

Viewed by 1492

Abstract

The Nyainrong microcontinent carries key information about the ongoing evolution of the central Tibetan Plateau. The 2021 Mw 5.7 Nagqu earthquake is the largest instrumentally recorded event inside this microcontinent, which provides an ideal opportunity to elucidate the influence of this ancient microcontinent [...] Read more.

The Nyainrong microcontinent carries key information about the ongoing evolution of the central Tibetan Plateau. The 2021 Mw 5.7 Nagqu earthquake is the largest instrumentally recorded event inside this microcontinent, which provides an ideal opportunity to elucidate the influence of this ancient microcontinent on the seismogenic mechanisms, stress heterogeneity and strain partitioning across the Tibetan Plateau. Here, we constrain the seismogenic fault geometry and distributed fault slip using Interferometric Synthetic Aperture Radar (InSAR) observations. By using the regional focal mechanism solutions, we invert the stress regimes surrounding the Nyainrong microcontinent. Our analysis demonstrates that the mainshock was caused by a normal fault with a comparable sinistral strike-slip component on a North-West dipping fault plane. The Nyainrong microcontinent is surrounded by a dominant normal faulting stress regime to the northeast and a dominant strike-slip stress regime to the southwest. Moreover, the clockwise rotation of the maximum horizontal stress (SHmax) from the southwest to the northeast is ~20°. This indicates that the Nyainrong microcontinent is involved in the mainshock occurrence as well as regional stress heterogeneity, and strain partitioning. Our results highlight the significance of the ancient microcontinent in the tectonic evolution of the Tibetan Plateau. Full article

(This article belongs to the Special Issue Role of Remote Sensing in Investigating Fault Zone Properties over Various Spatial-Temporal Scales)

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19 pages, 67087 KiB

Open AccessArticle

Estimating Fractional Vegetation Cover Changes in Desert Regions Using RGB Data

by Lu Xie, Xiang Meng, Xiaodi Zhao, Liyong Fu, Ram P. Sharma and Hua Sun

Remote Sens. 2022, 14(15), 3833; https://doi.org/10.3390/rs14153833 - 08 Aug 2022

Cited by 8 | Viewed by 2480

Abstract

Fractional vegetation cover (FVC) is an important indicator of ecosystem changes. Both satellite remote sensing and ground measurements are common methods for estimating FVC. However, desert vegetation grows sparsely and scantly and spreads widely in desert regions, making it challenging to accurately estimate [...] Read more.

Fractional vegetation cover (FVC) is an important indicator of ecosystem changes. Both satellite remote sensing and ground measurements are common methods for estimating FVC. However, desert vegetation grows sparsely and scantly and spreads widely in desert regions, making it challenging to accurately estimate its vegetation cover using satellite data. In this study, we used RGB images from two periods: images from 2006 captured with a small, light manned aircraft with a resolution of 0.1 m and images from 2019 captured with an unmanned aerial vehicle (UAV) with a resolution of 0.02 m. Three pixel-based machine learning algorithms, namely gradient enhancement decision tree (GBDT), k-nearest neighbor (KNN) and random forest (RF), were used to classify the main vegetation (woody and grass species) and calculate the coverage. An independent data set was used to evaluate the accuracy of the algorithms. Overall accuracies of GBDT, KNN and RF for 2006 image classification were 0.9140, 0.9190 and 0.9478, respectively, with RF achieving the best classification results. Overall accuracies of GBDT, KNN and RF for 2019 images were 0.8466, 0.8627 and 0.8569, respectively, with the KNN algorithm achieving the best results for vegetation cover classification. The vegetation coverage in the study area changed significantly from 2006 to 2019, with an increase in grass coverage from 15.47 ± 1.49% to 27.90 ± 2.79%. The results show that RGB images are suitable for mapping FVC. Determining the best spatial resolution for different vegetation features may make estimation of desert vegetation coverage more accurate. Vegetation cover changes are also important in terms of understanding the evolution of desert ecosystems. Full article

(This article belongs to the Special Issue UAS-Based Lidar and Imagery Data for Forest)

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19 pages, 12200 KiB

Open AccessArticle

A Strategy for Variable-Scale InSAR Deformation Monitoring in a Wide Area: A Case Study in the Turpan–Hami Basin, China

by Yuedong Wang, Guangcai Feng, Zhiwei Li, Shuran Luo, Haiyan Wang, Zhiqiang Xiong, Jianjun Zhu and Jun Hu

Remote Sens. 2022, 14(15), 3832; https://doi.org/10.3390/rs14153832 - 08 Aug 2022

Cited by 8 | Viewed by 2504

Abstract

In recent years, increasing available synthetic aperture radar (SAR) satellite data and gradually developing interferometric SAR (InSAR) technology have provided the possibility for wide-scale ground-deformation monitoring using InSAR. Traditionally, the InSAR data are processed by the existing time-series InSAR (TS–InSAR) technology, which has [...] Read more.

In recent years, increasing available synthetic aperture radar (SAR) satellite data and gradually developing interferometric SAR (InSAR) technology have provided the possibility for wide-scale ground-deformation monitoring using InSAR. Traditionally, the InSAR data are processed by the existing time-series InSAR (TS–InSAR) technology, which has inefficient calculation and redundant results. In this study, we propose a wide-area InSAR variable-scale deformation detection strategy (hereafter referred to as the WAVS–InSAR strategy). The strategy combines stacking technology for fast ground-deformation rate calculation and advanced TS–InSAR technology for obtaining fine deformation time series. It adopts an adaptive recognition algorithm to identify the spatial distribution and area of deformation regions (regions of interest, ROI) in the wide study area and uses a novel wide-area deformation product organization structure to generate variable-scale deformation products. The Turpan–Hami basin in western China is selected as the wide study area (277,000 km²) to verify the proposed WAVS–InSAR strategy. The results are as follows: (1) There are 32 deformation regions with an area of ≥1 km² and a deformation magnitude of greater than ±2 cm/year in the Turpan–Hami basin. The deformation area accounts for 2.4‰ of the total monitoring area. (2) A large area of ground subsidence has occurred in the farmland areas of the ROI, which is caused by groundwater overexploitation. The popularization and application of facility agriculture in the ROI have increased the demand for irrigation water. Due to the influence of the tectonic fault, the water supply of the ROI is mainly dependent on groundwater. Huge water demand has led to a continuous net deficit in aquifers, leading to land subsidence. The WAVS–InSAR strategy will be helpful for InSAR deformation monitoring at a national/regional scale and promoting the engineering application of InSAR technology. Full article

(This article belongs to the Special Issue Remote Sensing for Natural Hazards Assessment and Control)

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18 pages, 18039 KiB

Open AccessArticle

Surface Deformation Analysis of the Houston Area Using Time Series Interferometry and Emerging Hot Spot Analysis

by Shuhab D. Khan, Otto C. A. Gadea, Alyssa Tello Alvarado and Osman A. Tirmizi

Remote Sens. 2022, 14(15), 3831; https://doi.org/10.3390/rs14153831 - 08 Aug 2022

Cited by 14 | Viewed by 9218

Abstract

Cities in the northern Gulf of Mexico, such as Houston, have experienced one of the fastest rates of subsidence, with groundwater/hydrocarbon withdrawal being considered the primary cause. This work reports substantial ground subsidence in a few parts of Greater Houston and adjoining areas [...] Read more.

Cities in the northern Gulf of Mexico, such as Houston, have experienced one of the fastest rates of subsidence, with groundwater/hydrocarbon withdrawal being considered the primary cause. This work reports substantial ground subsidence in a few parts of Greater Houston and adjoining areas not reported before. Observation of surface deformation using interferometric synthetic aperture radar (InSAR) data obtained from Sentinel-1A shows total subsidence of up to 9 cm in some areas from 2016 to 2020. Most of the area within the Houston city limits shows no substantial subsidence, but growing suburbs around the city, such as Katy in the west, Spring and The Woodlands in the north and northwest, and Fresno in the south, show subsidence. In this study, we performed emerging hot spot analysis on InSAR displacement products to identify areas undergoing significant subsidence. To investigate the contributions of groundwater to subsidence, we apply optimized hot spot analysis to groundwater level data collected over the past 31 years from over 71,000 water wells and look at the correlation with fault surface deformation patterns. To evaluate the contribution of oil/gas pumping, we applied optimized hot spot analysis to known locations of oil and gas wells. The high rate of water pumping in the suburbs is the main driver of subsidence, but oil/gas withdrawal plays an important role in areas such as Mont Belvieu. Displacement time series shows that the Clodine, Hockley, and Woodgate faults are active, whereas the Long Point Fault shows no motion, although it was once very active. Full article

(This article belongs to the Special Issue Ground and Structural Deformations Monitoring Systems Integrating Remote Sensing and Ground-Based Data)

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16 pages, 1055 KiB

Open AccessArticle

UAV-Based Hyperspectral Imagery for Detection of Root, Butt, and Stem Rot in Norway Spruce

by Benjamin Allen, Michele Dalponte, Hans Ole Ørka, Erik Næsset, Stefano Puliti, Rasmus Astrup and Terje Gobakken

Remote Sens. 2022, 14(15), 3830; https://doi.org/10.3390/rs14153830 - 08 Aug 2022

Cited by 3 | Viewed by 2100

Abstract

Numerous species of pathogenic wood decay fungi, including members of the genera Heterobasidion and Armillaria, exist in forests in the northern hemisphere. Detection of these fungi through field surveys is often difficult due to a lack of visual symptoms and is cost-prohibitive [...] Read more.

Numerous species of pathogenic wood decay fungi, including members of the genera Heterobasidion and Armillaria, exist in forests in the northern hemisphere. Detection of these fungi through field surveys is often difficult due to a lack of visual symptoms and is cost-prohibitive for most applications. Remotely sensed data can offer a lower-cost alternative for collecting information about vegetation health. This study used hyperspectral imagery collected from unmanned aerial vehicles (UAVs) to detect the presence of wood decay in Norway spruce (Picea abies L. Karst) at two sites in Norway. UAV-based sensors were tested as they offer flexibility and potential cost advantages for small landowners. Ground reference data regarding pathogenic wood decay were collected by harvest machine operators and field crews after harvest. Support vector machines were used to classify the presence of root, butt, and stem rot infection. Classification accuracies as high as 76% with a kappa value of 0.24 were obtained with 490-band hyperspectral imagery, while 29-band imagery provided a lower classification accuracy (~60%, kappa = 0.13). Full article

(This article belongs to the Special Issue UAS-Based Lidar and Imagery Data for Forest)

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19 pages, 4018 KiB

Open AccessArticle

A-BFPN: An Attention-Guided Balanced Feature Pyramid Network for SAR Ship Detection

by Xiuqin Li, Dong Li, Hongqing Liu, Jun Wan, Zhanye Chen and Qinghua Liu

Remote Sens. 2022, 14(15), 3829; https://doi.org/10.3390/rs14153829 - 08 Aug 2022

Cited by 14 | Viewed by 2282

Abstract

Thanks to the excellent feature representation capabilities of neural networks, target detection methods based on deep learning are now widely applied in synthetic aperture radar (SAR) ship detection. However, the multi-scale variation, small targets with complex background such as islands, sea clutter, and [...] Read more.

Thanks to the excellent feature representation capabilities of neural networks, target detection methods based on deep learning are now widely applied in synthetic aperture radar (SAR) ship detection. However, the multi-scale variation, small targets with complex background such as islands, sea clutter, and inland facilities in SAR images increase the difficulty for SAR ship detection. To increase the detection performance, in this paper, a novel deep learning network for SAR ship detection, termed as attention-guided balanced feature pyramid network (A-BFPN), is proposed to better exploit semantic and multilevel complementary features, which consists of the following two main steps. First, in order to reduce interferences from complex backgrounds, the enhanced refinement module (ERM) is developed to enable BFPN to learn the dependency features from the channel and space dimensions, respectively, which enhances the representation of ship objects. Second, the channel attention-guided fusion network (CAFN) model is designed to obtain optimized multi-scale features and reduce serious aliasing effects in hybrid feature maps. Finally, we illustrate the effectiveness of the proposed method, adopting the existing SAR Ship Detection Dataset (SSDD) and Large-Scale SAR Ship Detection Dataset-v1.0 (LS-SSDD-v1.0). Experimental results show that the proposed method is superior to the existing algorithms, especially for multi-scale small ship targets under complex background. Full article

(This article belongs to the Special Issue Curvilinear Flight Synthetic Aperture Radar (SAR): Analysis, Methods, and Applications)

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26 pages, 29341 KiB

Open AccessArticle

Scanning Inside Volcanoes with Synthetic Aperture Radar Echography Tomographic Doppler Imaging

by Filippo Biondi

Remote Sens. 2022, 14(15), 3828; https://doi.org/10.3390/rs14153828 - 08 Aug 2022

Cited by 2 | Viewed by 2438

Abstract

A problem with synthetic aperture radars (SARs) is that due to the poor penetrating action of electromagnetic waves within solid bodies, the ability to see through distributed targets is precluded. In this context, indeed, imaging is only possible for targets distributed on the [...] Read more.

A problem with synthetic aperture radars (SARs) is that due to the poor penetrating action of electromagnetic waves within solid bodies, the ability to see through distributed targets is precluded. In this context, indeed, imaging is only possible for targets distributed on the scene surface. This work describes an imaging method based on the analysis of micro-motions present in volcanoes and generated by the Earth’s underground heat. Processing the coherent vibrational information embedded in a single SAR image, in the single-look-complex configuration, the sound information is exploited, penetrating tomographic imaging over a depth of about 3 km from the Earth’s surface. Measurement results are calculated by processing a single-look-complex image from the COSMO-SkyMed Second Generation satellite constellation of Vesuvius. Tomographic maps reveal the presence of the magma chamber, together with the main and the secondary volcanic conduits. This technique certainly paves the way for completely new exploitation of SAR images to scan inside the Earth’s surface. Full article

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

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18 pages, 5169 KiB

Open AccessArticle

Long-Term Investigation of Aerosols in the Urmia Lake Region in the Middle East by Ground-Based and Satellite Data in 2000–2021

by Abbas Ranjbar Saadat Abadi, Nasim Hossein Hamzeh, Karim Shukurov, Christian Opp and Umesh Chandra Dumka

Remote Sens. 2022, 14(15), 3827; https://doi.org/10.3390/rs14153827 - 08 Aug 2022

Cited by 10 | Viewed by 2497

Abstract

Dried lake beds are some of the largest sources of dust in the world and have caused environmental problems in the surrounding areas in recent decades. In the present work, we studied the monthly and annual occurrence of dust storms at selected weather [...] Read more.

Dried lake beds are some of the largest sources of dust in the world and have caused environmental problems in the surrounding areas in recent decades. In the present work, we studied the monthly and annual occurrence of dust storms at selected weather stations around Urmia Lake in northwestern (NW) Iran. Furthermore, we investigated the variations in the daily aerosol optical depth (AOD at 550 nm) and the Ångström exponent (at 412/470 nm), as well as the vertical profile of the total aerosol extinction coefficient and AOD at 532 nm, using space-borne MODIS (Moderate Resolution Imaging Spectroradiometer) Aqua and CALIPSO Satellite LiDAR data over the Urmia Lake region (36–39°N, 44–47°E). The monthly variations of AOD₅₅₀ and AOD₅₃₂ for the regions 37–39°N and 46–59°E were compared, and it was found that the CALIPSO AOD₅₃₂ and MODIS AOD₅₃₂ (reconstructed using the Ångström exponent) were in good agreement. In general, the dust storms during 2000–2021 increased the AOD₅₅₀ above average around the Urmia Lake. The vertical profile of aerosols showed that the largest contribution to total aerosol loading over the Urmia Lake was from 1.5–3 km, 1.5–4 km, 1.5–5 km, and 1.5–3 km during winter, spring, summer, and autumn seasons, respectively. Full article

(This article belongs to the Topic Recent Progress in Aerosol Remote Sensing and Products)

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22 pages, 6241 KiB

Open AccessArticle

Forest Damage by Super Typhoon Rammasun and Post-Disturbance Recovery Using Landsat Imagery and the Machine-Learning Method

by Xu Zhang, Hongbo Jiao, Guangsheng Chen, Jianing Shen, Zihao Huang and Haiyan Luo

Remote Sens. 2022, 14(15), 3826; https://doi.org/10.3390/rs14153826 - 08 Aug 2022

Cited by 1 | Viewed by 1928

Abstract

Typhoon Rammasun landed on the southern coastal region of Guangdong and Hainan Provinces on 18 July 2014, and is the strongest recorded typhoon since the 1970s in China. It caused enormous losses in human lives, property, and crop yields in two provinces; however, [...] Read more.

Typhoon Rammasun landed on the southern coastal region of Guangdong and Hainan Provinces on 18 July 2014, and is the strongest recorded typhoon since the 1970s in China. It caused enormous losses in human lives, property, and crop yields in two provinces; however, its impact on forests and subsequent recovery has not yet been assessed. Here we detected forest damage area and severity from Typhoon Rammasun using Landsat 8 OLI imagery, the Random Forest (RF) machine-learning algorithm, and univariate image differencing (UID) methods, and the controlling factors on damage severity and canopy greenness recovery were further analyzed. The accuracy evaluations against sample plot data indicated that the RF approach can more accurately detect the affected forest area and damage severity than the UID-based methods, with higher overall accuracy (94%), Kappa coefficient (0.92), and regression coefficient (R² = 0.81; p < 0.01). The affected forest area in Guangdong and Hainan was 13,556 km² and 3914 km², accounting for 13.8% and 18.5% total forest area, respectively. The highest affected forest fractions reached 70% in some cities or counties. The proportions of severe damage category accounted for 20.85% and 21.31% of all affected forests in Guangdong and Hainan, respectively. Our study suggests that increasing tree density and choosing less sensitive tree species would reduce damage from typhoons in vulnerable areas such as fringe, scattered, and high-slope forests. The canopy greenness of damaged forests recovered rapidly within three months for both provinces; however, management strategies should still be applied in the severely damaged areas to sustain forest functions since the persistent forest canopy structure and biomass may require a longer time to recover. Full article

(This article belongs to the Special Issue Forest Disturbance Monitoring Using Satellite Remote Sensing)

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18 pages, 2631 KiB

Open AccessArticle

DGPolarNet: Dynamic Graph Convolution Network for LiDAR Point Cloud Semantic Segmentation on Polar BEV

by Wei Song, Zhen Liu, Ying Guo, Su Sun, Guidong Zu and Maozhen Li

Remote Sens. 2022, 14(15), 3825; https://doi.org/10.3390/rs14153825 - 08 Aug 2022

Cited by 3 | Viewed by 1967

Abstract

Semantic segmentation in LiDAR point clouds has become an important research topic for autonomous driving systems. This paper proposes a dynamic graph convolution neural network for LiDAR point cloud semantic segmentation using a polar bird’s-eye view, referred to as DGPolarNet. LiDAR point clouds [...] Read more.

Semantic segmentation in LiDAR point clouds has become an important research topic for autonomous driving systems. This paper proposes a dynamic graph convolution neural network for LiDAR point cloud semantic segmentation using a polar bird’s-eye view, referred to as DGPolarNet. LiDAR point clouds are converted to polar coordinates, which are rasterized into regular grids. The points mapped onto each grid distribute evenly to solve the problem of the sparse distribution and uneven density of LiDAR point clouds. In DGPolarNet, a dynamic feature extraction module is designed to generate edge features of perceptual points of interest sampled by the farthest point sampling and K-nearest neighbor methods. By embedding edge features with the original point cloud, local features are obtained and input into PointNet to quantize the points and predict semantic segmentation results. The system was tested on the Semantic KITTI dataset, and the segmentation accuracy reached 56.5% Full article

(This article belongs to the Special Issue Advances in Scene Understanding, 3D Reconstruction and Simulation for Autonomous Things)

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26 pages, 1955 KiB

Open AccessReview

Image-Based Obstacle Detection Methods for the Safe Navigation of Unmanned Vehicles: A Review

by Samira Badrloo, Masood Varshosaz, Saied Pirasteh and Jonathan Li

Remote Sens. 2022, 14(15), 3824; https://doi.org/10.3390/rs14153824 - 08 Aug 2022

Cited by 25 | Viewed by 6589

Abstract

Mobile robots lack a driver or a pilot and, thus, should be able to detect obstacles autonomously. This paper reviews various image-based obstacle detection techniques employed by unmanned vehicles such as Unmanned Surface Vehicles (USVs), Unmanned Aerial Vehicles (UAVs), and Micro Aerial Vehicles [...] Read more.

Mobile robots lack a driver or a pilot and, thus, should be able to detect obstacles autonomously. This paper reviews various image-based obstacle detection techniques employed by unmanned vehicles such as Unmanned Surface Vehicles (USVs), Unmanned Aerial Vehicles (UAVs), and Micro Aerial Vehicles (MAVs). More than 110 papers from 23 high-impact computer science journals, which were published over the past 20 years, were reviewed. The techniques were divided into monocular and stereo. The former uses a single camera, while the latter makes use of images taken by two synchronised cameras. Monocular obstacle detection methods are discussed in appearance-based, motion-based, depth-based, and expansion-based categories. Monocular obstacle detection approaches have simple, fast, and straightforward computations. Thus, they are more suited for robots like MAVs and compact UAVs, which usually are small and have limited processing power. On the other hand, stereo-based methods use pair(s) of synchronised cameras to generate a real-time 3D map from the surrounding objects to locate the obstacles. Stereo-based approaches have been classified into Inverse Perspective Mapping (IPM)-based and disparity histogram-based methods. Whether aerial or terrestrial, disparity histogram-based methods suffer from common problems: computational complexity, sensitivity to illumination changes, and the need for accurate camera calibration, especially when implemented on small robots. In addition, until recently, both monocular and stereo methods relied on conventional image processing techniques and, thus, did not meet the requirements of real-time applications. Therefore, deep learning networks have been the centre of focus in recent years to develop fast and reliable obstacle detection solutions. However, we observed that despite significant progress, deep learning techniques also face difficulties in complex and unknown environments where objects of varying types and shapes are present. The review suggests that detecting narrow and small, moving obstacles and fast obstacle detection are the most challenging problem to focus on in future studies. Full article

(This article belongs to the Special Issue Trends, Innovative Developments and Disruptive Applications in UAV Remote Sensing)

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18 pages, 7867 KiB

Open AccessArticle

A Spatial Pattern Extraction and Recognition Toolbox Supporting Machine Learning Applications on Large Hydroclimatic Datasets

by Han Wang and Yunqing Xuan

Remote Sens. 2022, 14(15), 3823; https://doi.org/10.3390/rs14153823 - 08 Aug 2022

Cited by 2 | Viewed by 2497

Abstract

This paper presents the development and applications of a new, open-source toolbox that aims to provide automatic identification and classification of hydroclimatic patterns by their spatial features, i.e., location, size, orientation, and shape, as well as the physical features, i.e., the areal average, [...] Read more.

This paper presents the development and applications of a new, open-source toolbox that aims to provide automatic identification and classification of hydroclimatic patterns by their spatial features, i.e., location, size, orientation, and shape, as well as the physical features, i.e., the areal average, total volume, and spatial distribution. The highlights of this toolbox are: (1) incorporating an efficient algorithm for automatically identifying and classifying the spatial features that are linked to hydroclimatic extremes; (2) use as a frontend for supporting AI-based training in tracking and forecasting extremes; and (3) direct support for short-term nowcasting of extreme rainfall via tracking rainstorm centres and movement. The key design and implementation of the toolbox are discussed alongside three case studies demonstrating the application of the toolbox and its potential in helping build machine learning applications in hydroclimatic sciences. Finally, the availability of the toolbox and its source code is included. Full article

(This article belongs to the Topic Hydro-Meteorological Hazards: Forecasting, Assessment and Risk Management)

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22 pages, 6088 KiB

Open AccessArticle

Quantitative Evaluation of Reclamation Intensity Based on Regional Planning Theory and Human–Marine Coordination Since 1974: A Case Study of Shandong, China

by Baijing Liu, Meng Gong, Xiaoqing Wu and Ziyang Wang

Remote Sens. 2022, 14(15), 3822; https://doi.org/10.3390/rs14153822 - 08 Aug 2022

Cited by 1 | Viewed by 1628

Abstract

Increased reclamation activity has adversely affected the conservation of coastal environments. The interactions between reclamation activities and their interference with the natural and functional properties of coastal zones increase the difficulty of marine spatial planning and eco-environmental management. In this study, an evaluation [...] Read more.

Increased reclamation activity has adversely affected the conservation of coastal environments. The interactions between reclamation activities and their interference with the natural and functional properties of coastal zones increase the difficulty of marine spatial planning and eco-environmental management. In this study, an evaluation method for describing the intensity of the reclamation activity (RAI) based on regional planning theory and human–marine coordination theory was proposed, and a quantitative evaluation index system was constructed. The method was applied to Shandong Province in China via geographic information system (GIS) spatial analysis. The results reveal that there was an obvious increase in the RAI from 1974 to 2021, with the total reclamation scale index and coordination of reclamation activities index being the most prominent. In addition, it was found that 2007–2017 was the peak period of infilling reclamation in Shandong Province. The natural coastlines are mainly occupied by enclosed mariculture and saltern, which should be strictly managed. The proposed index system can effectively identify the spatiotemporal characteristics of the reclamation intensity and can be used to efficiently determine management priorities. It provides a theoretical basis for regional reclamation management and can be conveniently adopted by management departments for coastal environmental protection. Full article

(This article belongs to the Special Issue Remote Sensing of Interaction between Human and Natural Ecosystem)

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20 pages, 4234 KiB

Open AccessArticle

Supervised Machine Learning Algorithms for Ground Motion Time Series Classification from InSAR Data

by S. Mohammad Mirmazloumi, Angel Fernandez Gambin, Riccardo Palamà, Michele Crosetto, Yismaw Wassie, José A. Navarro, Anna Barra and Oriol Monserrat

Remote Sens. 2022, 14(15), 3821; https://doi.org/10.3390/rs14153821 - 08 Aug 2022

Cited by 7 | Viewed by 2465

Abstract

The increasing availability of Synthetic Aperture Radar (SAR) images facilitates the generation of rich Differential Interferometric SAR (DInSAR) data. Temporal analysis of DInSAR products, and in particular deformation Time Series (TS), enables advanced investigations for ground deformation identification. Machine Learning algorithms offer efficient [...] Read more.

The increasing availability of Synthetic Aperture Radar (SAR) images facilitates the generation of rich Differential Interferometric SAR (DInSAR) data. Temporal analysis of DInSAR products, and in particular deformation Time Series (TS), enables advanced investigations for ground deformation identification. Machine Learning algorithms offer efficient tools for classifying large volumes of data. In this study, we train supervised Machine Learning models using 5000 reference samples of three datasets to classify DInSAR TS in five deformation trends: Stable, Linear, Quadratic, Bilinear, and Phase Unwrapping Error. General statistics and advanced features are also computed from TS to assess the classification performance. The proposed methods reported accuracy values greater than 0.90, whereas the customized features significantly increased the performance. Besides, the importance of customized features was analysed in order to identify the most effective features in TS classification. The proposed models were also tested on 15000 unlabelled data and compared to a model-based method to validate their reliability. Random Forest and Extreme Gradient Boosting could accurately classify reference samples and positively assign correct labels to random samples. This study indicates the efficiency of Machine Learning models in the classification and management of DInSAR TSs, along with shortcomings of the proposed models in classification of nonmoving targets (i.e., false alarm rate) and a decreasing accuracy for shorter TS. Full article

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17 pages, 5966 KiB

Open AccessArticle

Classification of Precipitation Types Based on Machine Learning Using Dual-Polarization Radar Measurements and Thermodynamic Fields

by Kyuhee Shin, Kwonil Kim, Joon Jin Song and GyuWon Lee

Remote Sens. 2022, 14(15), 3820; https://doi.org/10.3390/rs14153820 - 08 Aug 2022

Cited by 3 | Viewed by 1993

Abstract

An accurate classification of the precipitation type is important for forecasters, particularly in the winter season. We explored the capability of three supervised machine learning (ML) methods (decision tree, random forest, and support vector machine) to determine ground precipitation types (no precipitation, rain, [...] Read more.

An accurate classification of the precipitation type is important for forecasters, particularly in the winter season. We explored the capability of three supervised machine learning (ML) methods (decision tree, random forest, and support vector machine) to determine ground precipitation types (no precipitation, rain, mixed, and snow) for winter precipitation. We provided information on the particle characteristics within a radar sampling volume and the environmental condition to the ML model with the simultaneous use of polarimetric radar variables and thermodynamic variables. The ML algorithms were optimized using predictor selection and hyperparameter tuning in order to maximize the computational efficiency and accuracy. The random forest (RF) had the highest skill scores in all precipitation types and outperformed the operational scheme. The spatial distribution of the precipitation type from the RF model showed a good agreement with the surface observation. As a result, RF is recommended for the real-time precipitation type classification due to its easy implementation, computational efficiency, and satisfactory accuracy. In addition to the validation, this study confirmed the strong dependence of precipitation type on wet-bulb temperature and a 1000–850 hPa layer thickness. The results also suggested that the base heights of the radar echo are useful in discriminating non-precipitating area. Full article

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16 pages, 5168 KiB

Open AccessArticle

Single-Epoch Ambiguity Resolution of a Large-Scale CORS Network with Multi-Frequency and Multi-Constellation GNSS

by Shengyue Ji, Guofeng Liu, Duojie Weng, Zhenjie Wang, Kaifei He and Wu Chen

Remote Sens. 2022, 14(15), 3819; https://doi.org/10.3390/rs14153819 - 08 Aug 2022

Viewed by 1339

Abstract

Ambiguity resolution at Continuously Operating Reference Station (CORS) network sites is the key step in the whole processing chain of Network Real Time Kinematic (NRTK). An appropriate ambiguity-resolution speed is important, and single-epoch ambiguity resolution has not been realized yet, especially for large-scale [...] Read more.

Ambiguity resolution at Continuously Operating Reference Station (CORS) network sites is the key step in the whole processing chain of Network Real Time Kinematic (NRTK). An appropriate ambiguity-resolution speed is important, and single-epoch ambiguity resolution has not been realized yet, especially for large-scale CORS. We attempt to realize single-epoch ambiguity resolution for a large-scale CORS network by neglecting tropospheric delay through forming difference between satellites with close mapping functions whether they belong to the same or different GNSSs. As only two frequency bands are shared among GPS, Galileo and BeiDou, the biggest challenge is how to get this single-epoch ambiguity solution for wide-lane combinations of L1 and L5 when the difference is formed between satellites of different GNSSs. The proposed method includes five steps for ambiguity resolution for different combinations: extra wide-lane, wide-lane, inter-GNSS wide-lane, subset narrow-lane and narrow-lane. The single-epoch ambiguity-resolution performance is assessed based on GNSS observations from two long-distance baselines formed with IGS stations, BRUX-REDU and BAUT-LEIJ, separated by distances of approximately 104 km and 151 km, respectively. The numerical results show that the fixing rate of the single-epoch ambiguity resolution can reach more than 90%, so for a large-scale CORS network, single-epoch ambiguity resolution is feasible and can be realized in the future. Full article

(This article belongs to the Special Issue Remote Sensing in Navigation: State-of-the-Art)

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22 pages, 5491 KiB

Open AccessArticle

Lateral Border of a Small River Plume: Salinity Structure, Instabilities and Mass Transport

by Alexander Osadchiev, Alexandra Gordey, Alexandra Barymova, Roman Sedakov, Vladimir Rogozhin, Roman Zhiba and Roman Dbar

Remote Sens. 2022, 14(15), 3818; https://doi.org/10.3390/rs14153818 - 08 Aug 2022

Cited by 6 | Viewed by 1645

Abstract

The interfaces between small river plumes and ambient seawater have extremely sharp horizontal and vertical salinity gradients, often accompanied by velocity shear. It results in formation of instabilities at the lateral borders of small plumes. In this study, we use high-resolution aerial remote [...] Read more.

The interfaces between small river plumes and ambient seawater have extremely sharp horizontal and vertical salinity gradients, often accompanied by velocity shear. It results in formation of instabilities at the lateral borders of small plumes. In this study, we use high-resolution aerial remote sensing supported by in situ measurements to study these instabilities. We describe their spatial and temporal characteristics and then reconstruct their relation to density gradient and velocity shear. We report that Rayleigh–Taylor instabilities, with spatial scales ~5–50 m, are common features of the sharp plume-sea interfaces and their sizes are proportional to the Atwood number determined by the cross-shore density gradient. Kelvin–Helmholtz instabilities have a smaller size (~3–7 m) and are formed at the plume border in case of velocity shear >20–30 cm/s. Both instabilities induce mass transport across the plume-sea interfaces, which modifies salinity structure of the plume borders and induces lateral mixing of small river plumes. In addition, aerial observations revealed wind-driven Stokes transport across the sharp plume-sea interface, which occurs in the shallow (~2–3 cm) surface layer. This process limitedly affects salinity structure and mixing at the plume border, however, it could be an important issue for the spread of river-borne floating particles in the ocean. Full article

(This article belongs to the Special Issue Seawater Bio-Optical Characteristics from Satellite Ocean Color Data)

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14 pages, 6962 KiB

Open AccessArticle

Monitoring Land Vegetation from Geostationary Satellite Advanced Himawari Imager (AHI)

by Shengqi Li, Xiuzhen Han and Fuzhong Weng

Remote Sens. 2022, 14(15), 3817; https://doi.org/10.3390/rs14153817 - 08 Aug 2022

Cited by 3 | Viewed by 1622

Abstract

For many years, the Advanced Very High-Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) instruments have been widely used to monitor the condition of surface vegetation. Since the polar-orbiting satellite provides limited daily samples on surface, a completed spatial coverage of land [...] Read more.

For many years, the Advanced Very High-Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) instruments have been widely used to monitor the condition of surface vegetation. Since the polar-orbiting satellite provides limited daily samples on surface, a completed spatial coverage of land vegetation is often relied on over multiple days of observations. In this study, observations from the Japanese geostationary satellite imager Advanced Himawari Imagers (AHI) are used to derive the surface vegetation index. The AHI reflectance at visible and near-infrared bands are first corrected to the surface reflectance by using the 6S radiative transfer model. The AHI surface reflectance from various viewing angles and solar geometry is further normalized to form an angular-independent reflectance by using a BRDF model. Finally, the surface vegetation index is calculated and synthesized from the daytime AHI data. It is found that the high-frequency AHI observations can significantly reduce the impact of clouds on compositing land NDVI and require a shorter time for a complete coverage of surface conditions. Also, a single NDVI image from AHI exhibits spatial distribution similar to that from 16 days of MODIS data. Full article

(This article belongs to the Special Issue Atmospheric and Surface Modeling, Data Assimilation, and Forecasting of Remote Sensing)

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12 pages, 725 KiB

Open AccessTechnical Note

An Ornithologist’s Guide for Including Machine Learning in a Workflow to Identify a Secretive Focal Species from Recorded Audio

by Ming Liu, Qiyu Sun, Dustin E. Brewer, Thomas M. Gehring and Jesse Eickholt

Remote Sens. 2022, 14(15), 3816; https://doi.org/10.3390/rs14153816 - 08 Aug 2022

Cited by 1 | Viewed by 1661

Abstract

Reliable and efficient avian monitoring tools are required to identify population change and then guide conservation initiatives. Autonomous recording units (ARUs) could increase both the amount and quality of monitoring data, though manual analysis of recordings is time consuming. Machine learning could help [...] Read more.

Reliable and efficient avian monitoring tools are required to identify population change and then guide conservation initiatives. Autonomous recording units (ARUs) could increase both the amount and quality of monitoring data, though manual analysis of recordings is time consuming. Machine learning could help to analyze these audio data and identify focal species, though few ornithologists know how to cater this tool for their own projects. We present a workflow that exemplifies how machine learning can reduce the amount of expert review time required for analyzing audio recordings to detect a secretive focal species (Sora; Porzana carolina). The deep convolutional neural network that we trained achieved a precision of 97% and reduced the amount of audio for expert review by ~66% while still retaining 60% of Sora calls. Our study could be particularly useful, as an example, for those who wish to utilize machine learning to analyze audio recordings of a focal species that has not often been recorded. Such applications could help to facilitate the effective conservation of avian populations. Full article

(This article belongs to the Special Issue Feature Paper Special Issue on Ecological Remote Sensing)

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30 pages, 12634 KiB

Open AccessArticle

Data-Free Area Detection and Evaluation for Marine Satellite Data Products

by Shengjia Zhang, Hongchun Zhu, Jie Li, Yanrui Yang and Haiying Liu

Remote Sens. 2022, 14(15), 3815; https://doi.org/10.3390/rs14153815 - 08 Aug 2022

Cited by 1 | Viewed by 1195

Abstract

The uncertainty verification of satellite ocean color products and the bias analysis of multiple data are both indispensable in the evaluation of ocean color products. Incidentally, ocean color products often have missing information that causes the methods mentioned above to be difficult to [...] Read more.

The uncertainty verification of satellite ocean color products and the bias analysis of multiple data are both indispensable in the evaluation of ocean color products. Incidentally, ocean color products often have missing information that causes the methods mentioned above to be difficult to evaluate these data effectively. We propose an analysis and evaluation method based on data-free area. The objective of this study is to evaluate the quality of ocean color products with respect to information integrity and continuity. First, we use an improved Spectral Angle Mapper, also called ISAM. It can automatically obtain the optimal threshold value for each class of objects. Then, based on ISAM, we perform spectral information mining on first-level Yellow Sea and Bohai Sea data obtained from the Geostationary Ocean Color Imager (GOCI), Moderate Resolution Imaging Spectroradiometer (MODIS) and Ocean and Land Color Instrument (OLCI). In this manner, quantitative results of information related to data-free areas of ocean data products are obtained. The findings indicate that the product data of OLCI are optimal with respect to both completeness and continuity. GOCI and MODIS have striking similarities in their quantitative or visualization results for both evaluation metrics. Moreover, a concomitant phenomenon of ocean-covered objects is apparent in the data-free area with temporal and spatial distribution characteristics. The two characteristics are subsequently explored for further analysis. The evaluation method adopted in this study can help to enrich the content of ocean color product evaluation, facilitate the research of cloud detection algorithms and further understand the composition of the data-free regional information of marine data products. The method proposed in this study has a wide application value. Full article

(This article belongs to the Section Ocean Remote Sensing)

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16 pages, 7490 KiB

Open AccessArticle

Airborne Radiometric Surveys and Machine Learning Algorithms for Revealing Soil Texture

by Andrea Maino, Matteo Alberi, Emiliano Anceschi, Enrico Chiarelli, Luca Cicala, Tommaso Colonna, Mario De Cesare, Enrico Guastaldi, Nicola Lopane, Fabio Mantovani, Maurizio Marcialis, Nicola Martini, Michele Montuschi, Silvia Piccioli, Kassandra Giulia Cristina Raptis, Antonio Russo, Filippo Semenza and Virginia Strati

Remote Sens. 2022, 14(15), 3814; https://doi.org/10.3390/rs14153814 - 08 Aug 2022

Cited by 8 | Viewed by 2267

Abstract

Soil texture is key information in agriculture for improving soil knowledge and crop performance, so the accurate mapping of this crucial feature is imperative for rationally planning cultivations and for targeting interventions. We studied the relationship between radioelements and soil texture in the [...] Read more.

Soil texture is key information in agriculture for improving soil knowledge and crop performance, so the accurate mapping of this crucial feature is imperative for rationally planning cultivations and for targeting interventions. We studied the relationship between radioelements and soil texture in the Mezzano Lowland (Italy), a 189 km² agricultural plain investigated through a dedicated airborne gamma-ray spectroscopy survey. The K and Th abundances were used to retrieve the clay and sand content by means of a multi-approach method. Linear (simple and multiple) and non-linear (machine learning algorithms with deep neural networks) predictive models were trained and tested adopting a 1:50,000 scale soil texture map. The comparison of these approaches highlighted that the non-linear model introduces significant improvements in the prediction of soil texture fractions. The predicted maps of the clay and of the sand content were compared with the regional soil maps. Although the macro-structures were equally present, the airborne gamma-ray data permits us shedding light on finer features. Map areas with higher clay content were coincident with paleo-channels crossing the Mezzano Lowland in Etruscan and Roman periods, confirmed by the hydrographic setting of historical maps and by the geo-morphological features of the study area. Full article

(This article belongs to the Special Issue Topsoil Characterization by Means of Remote Sensing)

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19 pages, 2862 KiB

Open AccessArticle

Mapping Forest Stability within Major Biomes Using Canopy Indices Derived from MODIS Time Series

by Tatiana A. Shestakova, Brendan Mackey, Sonia Hugh, Jackie Dean, Elena A. Kukavskaya, Jocelyne Laflamme, Evgeny G. Shvetsov and Brendan M. Rogers

Remote Sens. 2022, 14(15), 3813; https://doi.org/10.3390/rs14153813 - 08 Aug 2022

Cited by 4 | Viewed by 2720

Abstract

Deforestation and forest degradation from human land use, including primary forest loss, are of growing concern. The conservation of old-growth and other forests with important environmental values is central to many international initiatives aimed at protecting biodiversity, mitigating climate change impacts, and supporting [...] Read more.

Deforestation and forest degradation from human land use, including primary forest loss, are of growing concern. The conservation of old-growth and other forests with important environmental values is central to many international initiatives aimed at protecting biodiversity, mitigating climate change impacts, and supporting sustainable livelihoods. Current remote-sensing products largely focus on deforestation rather than forest degradation and are dependent on machine learning, calibrated with extensive field measurements. To help address this, we developed a novel approach for mapping forest ecosystem stability, defined in terms of constancy, which is a key characteristic of long-undisturbed (including primary) forests. Our approach categorizes forests into stability classes based on satellite-data time series related to plant water–carbon relationships. Specifically, we used long-term dynamics of the fraction of photosynthetically active radiation intercepted by the canopy (fPAR) and shortwave infrared water stress index (SIWSI) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) for the period 2003–2018. We calculated a set of variables from annual time series of fPAR and SIWSI for representative forest regions at opposite ends of Earth’s climatic and latitudinal gradients: boreal forests of Siberia (southern taiga, Russia) and tropical rainforests of the Amazon basin (Kayapó territory, Brazil). Independent validation drew upon high-resolution Landsat imagery and forest cover change data. The results indicate that the proposed approach is accurate and applicable across forest biomes and, thereby, provides a timely and transferrable method to aid in the identification and conservation of stable forests. Information on the location of less stable forests is equally relevant for ecological restoration, reforestation, and proforestation activities. Full article

(This article belongs to the Special Issue Forest Disturbance Monitoring Using Satellite Remote Sensing)

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