Remote Sensing

19 pages, 3969 KiB

Open AccessArticle

Assessing the Predictive Power of Democratic Republic of Congo’s National Spaceborne Biomass Map over Independent Test Samples

by Augustin Lamulamu, Pierre Ploton, Luca Birigazzi, Liang Xu, Sassan Saatchi and Jean-Paul Kibambe Lubamba

Remote Sens. 2022, 14(16), 4126; https://doi.org/10.3390/rs14164126 - 22 Aug 2022

Cited by 4 | Viewed by 2332

Abstract

Remotely sensed maps of forest carbon stocks have enormous potential for supporting greenhouse gas (GHG) inventory and monitoring in tropical countries. However, most countries have not used maps as the reference data for GHG inventory due to the lack of confidence in the [...] Read more.

Remotely sensed maps of forest carbon stocks have enormous potential for supporting greenhouse gas (GHG) inventory and monitoring in tropical countries. However, most countries have not used maps as the reference data for GHG inventory due to the lack of confidence in the accuracy of maps and of data to perform local validation. Here, we use the first national forest inventory (NFI) data of the Democratic Republic of Congo to perform an independent assessment of the country’s latest national spaceborne carbon stocks map. We compared plot-to-plot variations and areal estimates of forest aboveground biomass (AGB) derived from NFI data and from the map across jurisdictional and ecological domains. Across all plots, map predictions were nearly unbiased and captured c. 60% of the variation in NFI plots AGB. Map performance was not uniform along the AGB gradient, and saturated around c. 290 Mg ha⁻¹, increasingly underestimating forest AGB above this threshold. Splitting NFI plots by land cover types, we found map predictions unbiased in the dominant terra firme Humid forest class, while plot-to-plot variations were poorly captured (R² of c. 0.33, or c. 0.20 after excluding disturbed plots). In contrast, map predictions underestimated AGB by c. 33% in the small AGB woodland savanna class but captured a much greater share of plot-to-plot AGB variation (R² of c. 0.41, or 0.58 after excluding disturbed plots). Areal estimates from the map and NFI data depicted a similar trend with a slightly smaller (but statistically indiscernible) mean AGB from the map across the entire study area (i.e., 252.7 vs. 280.6 Mg ha⁻¹), owing to the underestimation of mean AGB in the woodland savanna domain (31.8 vs. 57.3 Mg ha⁻¹), which was broadly consistent with the results obtained at the provincial level. This study provides insights and outlooks for country-wide AGB mapping efforts in the tropics and the computation of emission factors in Democratic Republic of Congo for carbon monitoring initiatives. Full article

(This article belongs to the Special Issue Accelerating REDD+ Initiatives in Africa Using Remote Sensing)

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21 pages, 9552 KiB

Open AccessArticle

An Improved Coastal Marine Gravity Field Based on the Mean Sea Surface Height Constraint Factor Method

by Wensong Zhang, Jianguo Yan and Fei Li

Remote Sens. 2022, 14(16), 4125; https://doi.org/10.3390/rs14164125 - 22 Aug 2022

Viewed by 1764

Abstract

Construction of a high spatial resolution and high precision marine gravity field in coastal areas is constrained by the low quality and sparse coverage of altimetry data, except for limited shipborne and airborne gravity surveys. To address this problem, a mean sea surface [...] Read more.

Construction of a high spatial resolution and high precision marine gravity field in coastal areas is constrained by the low quality and sparse coverage of altimetry data, except for limited shipborne and airborne gravity surveys. To address this problem, a mean sea surface height constraint factor (MSSHCF) method based on the ordinary kriging method and the remove-restore technique is proposed from the perspective of interpolation. In this method, the data is standardized during the interpolation process to reduce the error and mean sea surface as variables related to the marine gravity field are added to the semi-variance function in ordinary kriging to obtain a marine gravity field with a spatial resolution of 1′ × 1′. Validation experiments show that the MSSHCF method more closely agrees with the referenced SS V28, DTU17 global marine gravity models than the ordinary kriging method. Our results were further validated against shipborne data; the accuracy of the MSSHCF method is 0.13 and 0.33 mGal higher than that of the ordinary kriging method in two experimental areas. The effects of ocean depth and offshore distance on the results were also assessed. These results show that the proposed method is more accurate than the ordinary kriging method, when the distance and depth varied. Therefore, our study demonstrates that the MSSHCF method is an innovative and feasible tool for extracting gravity fields along coastal, beach, and island areas. Full article

(This article belongs to the Special Issue Remote Sensing Observation on Coastal Change)

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18 pages, 3958 KiB

Open AccessArticle

Evaluation of NeQuick2 Model over Mid-Latitudes of Northern Hemisphere

by Lingxuan Wang, Erhu Wei, Si Xiong, Tengxu Zhang and Ziyu Shen

Remote Sens. 2022, 14(16), 4124; https://doi.org/10.3390/rs14164124 - 22 Aug 2022

Cited by 3 | Viewed by 2236

Abstract

NeQuick2 is a three-dimensional ionospheric electron density empirical model that uses numerical integration to calculate the total electron content along any line-of-sight (LOS). As one of the most commonly used three-dimensional ionospheric models, it is necessary to objectively evaluate the accuracy and stability [...] Read more.

NeQuick2 is a three-dimensional ionospheric electron density empirical model that uses numerical integration to calculate the total electron content along any line-of-sight (LOS). As one of the most commonly used three-dimensional ionospheric models, it is necessary to objectively evaluate the accuracy and stability of NeQuick2 over a long period, especially over the mid-latitudes of the northern hemisphere where most of the ground-based GNSS stations are distributed. Therefore, different methods are used in this study to evaluate the accuracy of the NeQuick2 model from 2008 to 2021, including comparison with the International Global Navigation Satellite System Global Ionosphere Maps (IGSG), Jason2 Vertical Electron content (VTEC), and self-consistent evaluation. The comparison with IGSG shows that the standard deviation (STD) value is about 2.59 TECU. The accuracy of the IGSG and NeQuick2 model over ocean regions shows that the bias of IGSG is more significant than that of the NeQuick2 model. The mean STD value is 2.09 TECU for IGSG, and the corresponding value is 3.18 TECU for the NeQuick2 model, which is about 50% worse than IGSG. The dSTEC assessment results indicate that the variation in bias for IGSG is more stable than that of the NeQuick2 model. The mean STD value is 0.86 and 1.52 TECU for IGSG and NeQuick2 model, respectively. The conclusion could be made that NeQuick2 model represents the average ionosphere electron content and its accuracy fluctuates with solar conditions. Compared with the IGSG, the NeQuick2 model always underestimates TEC value, especially in low solar activity periods and compared with Jason2, the TEC values obtained by NeQuick2 model are overestimated, but the degree of overestimation is smaller than that of IGSG. Full article

(This article belongs to the Special Issue Carbon, Water and Climate Monitoring Using Space Geodesy Observations)

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18 pages, 9650 KiB

Open AccessArticle

Mapping Morphodynamic Variabilities of a Meso-Tidal Flat in Shanghai Based on Satellite-Derived Data

by Rui Yuan, Hezhenjia Zhang, Cheng Qiu, Yuefeng Wang, Xingjie Guo, Yaping Wang and Shenliang Chen

Remote Sens. 2022, 14(16), 4123; https://doi.org/10.3390/rs14164123 - 22 Aug 2022

Cited by 7 | Viewed by 2277

Abstract

Morphodynamic variabilities of tidal flats (e.g., the variations of pattern, area, and topography) are a key issue in the management of coastal intertidal zones. In this study, the morphodynamic variabilities of the Lin-gang (Shanghai) tidal flat were investigated using waterlines extracted from multi-source [...] Read more.

Morphodynamic variabilities of tidal flats (e.g., the variations of pattern, area, and topography) are a key issue in the management of coastal intertidal zones. In this study, the morphodynamic variabilities of the Lin-gang (Shanghai) tidal flat were investigated using waterlines extracted from multi-source satellite images acquired from 2013 to 2020. The waterlines were evaluated against in situ measurements. The results of our investigation indicated that the tidal flat was in a state of rapid accretion from 2015 to 2018, and in a state of erosion from 2018 to 2020. We found that the accretion of the tidal flat was most likely due to the protection of local vegetation, which prevents the sea bottom from eroding. However, storms have primarily been causing erosion since 2018. The potential mechanisms of the geomorphological variations were further analyzed using the empirical orthogonal function (EOF) method. The analysis revealed that the variation in the tidal flat was dominated by two modes. The first mode accounted for 55% of the variation, while the second mode accounted for 18%. The spatial distribution of the first mode was highly related to the artificial vegetation, indicating that the local variations in the vegetation prevented the sea bottom from eroding, which was dominant in the accretional phase from 2015 to 2018. The second model reflected the extreme meteorological events that resulted in potential changes in the tidal flat’s pattern (i.e., transitioning to an erosion phase from 2018 to 2020). The satellite-derived topographies were demonstrated to be an effective means of mapping the evolution of a meso-tidal flat. Full article

(This article belongs to the Special Issue Satellite-Based Assessment of Geomorphological Dynamics of Coastline Using Optical and Near-Infrared Instruments)

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18 pages, 3835 KiB

Open AccessArticle

Validation of an Empirical Subwaveform Retracking Strategy for SAR Altimetry

by Marcello Passaro, Laura Rautiainen, Denise Dettmering, Marco Restano, Michael G. Hart-Davis, Florian Schlembach, Jani Särkkä, Felix L. Müller, Christian Schwatke and Jérôme Benveniste

Remote Sens. 2022, 14(16), 4122; https://doi.org/10.3390/rs14164122 - 22 Aug 2022

Cited by 9 | Viewed by 3035

Abstract

The sea level retrievals from the latest generation of radar altimeters (the SAR altimeters) are still challenging in the coastal zone and areas covered by sea ice and require a dedicated fitting (retracking) strategy for the waveforms. In the framework of the European [...] Read more.

The sea level retrievals from the latest generation of radar altimeters (the SAR altimeters) are still challenging in the coastal zone and areas covered by sea ice and require a dedicated fitting (retracking) strategy for the waveforms. In the framework of the European Space Agency’s Baltic + Sea Level (ESA Baltic SEAL) project, an empirical retracking strategy (ALES + SAR), including a dedicated sea state bias correction, has been designed to improve the sea level observations in the Baltic Sea, characterised by a jagged coastline and seasonal sea ice coverage, without compromising the quality of open ocean data. In this work, the performances of ALES + SAR are validated against in-situ data in the Baltic Sea. Moreover, variance, crossover differences and power spectral density of the open ocean data are evaluated on a global scale. The results show that ALES + SAR performances are of comparable quality to the ones obtained using physical-based retrackers, with relevant advantages in coastal and sea ice areas in terms of quality and quantity of the sea level data. Full article

(This article belongs to the Special Issue Advances in Satellite Altimetry)

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10 pages, 1869 KiB

Open AccessCommunication

Real Valued MUSIC Method for Height Measurement of Meter Wave Polarimetric MIMO Radar Based on Matrix Reconstruction

by Guimei Zheng, Chen Chen and Yuwei Song

Remote Sens. 2022, 14(16), 4121; https://doi.org/10.3390/rs14164121 - 22 Aug 2022

Cited by 2 | Viewed by 1674

Abstract

Combining the advantages of diversity provided by polarization MIMO radar and good decoherence ability of matrix reconstruction technology, a method for height measurements based on matrix reconstruction after real valued processing is developed. To solve height measurement problem in meter wave polarization MIMO [...] Read more.

Combining the advantages of diversity provided by polarization MIMO radar and good decoherence ability of matrix reconstruction technology, a method for height measurements based on matrix reconstruction after real valued processing is developed. To solve height measurement problem in meter wave polarization MIMO radar, we first derive the corresponding flat ground signal model; then, the received data matrix is reconstructed to eliminate the influence of multipath coherent signal on height measurements. Then, the reconstructed data matrix is transformed into a real valued matrix using a unitary matrix. In order to reduce the influence of noise on the signal subspace and reduce the data dimension, singular value decomposition technology is applied to receive the signal data. Finally, the elevation and height of the target are estimated according to the principle that the signal subspace is orthogonal to the noise subspace. The proposed method does not require prior knowledge, such as the reflection coefficient, wave path difference and polarization information. Simulation experiments show that the proposed algorithm has better estimation performance and less computational complexity than conventional algorithms. Full article

(This article belongs to the Topic Advances in Array Signal Processing with Errors: Models, Algorithms and Applications)

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17 pages, 1364 KiB

Open AccessArticle

A Robust Sparse Imaging Algorithm Using Joint MIMO Array Manifold and Array Channel Outliers

by Jieru Ding, Zhiyi Wang, Xinghui Wu and Min Wang

Remote Sens. 2022, 14(16), 4120; https://doi.org/10.3390/rs14164120 - 22 Aug 2022

Cited by 2 | Viewed by 1808

Abstract

The multiple-input multiple-output (MIMO) radar imaging technology has attracted many scholars due to its many inherent advantages, such as avoiding complex motion compensation and imaging a quickly maneuvering target, compared to inverse synthetic aperture radar (ISAR) imaging. Although some imaging algorithms, such as [...] Read more.

The multiple-input multiple-output (MIMO) radar imaging technology has attracted many scholars due to its many inherent advantages, such as avoiding complex motion compensation and imaging a quickly maneuvering target, compared to inverse synthetic aperture radar (ISAR) imaging. Although some imaging algorithms, such as the 2D fast iterative shrinkage thresholding algorithm (2D-FISTA), can meet the demand for super-resolution, they are not directly suited to MIMO radar imaging, for which the MIMO manifold needs to be considered. In this paper, based on the above questions, we propose the MIMO radar imaging algorithm, utilizing the sparsity of the scattering map in space and the MIMO array manifold, even achieving a good performance in the presence of MIMO channel error. The sparse reconstruction algorithm is developed with the alternative direction method of multipliers (ADMM) with the help of 2D-FISTA and the

l_{p}

-norm. Then, two algorithms are derived: one is the exact sparse recovery algorithm, and the other is the inexact sparse recovery algorithm. Although the exact sparse recovery algorithm can converge to a more accurate precision than the inexact algorithm, the latter can converge at a faster speed. Finally, the results on simulation data validated the effectiveness of the algorithm. Full article

(This article belongs to the Special Issue Theory and Applications of MIMO Radar)

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28 pages, 6899 KiB

Open AccessArticle

Assessment and Prediction of Impact of Flight Configuration Factors on UAS-Based Photogrammetric Survey Accuracy

by Yajie Liu, Kevin Han and William Rasdorf

Remote Sens. 2022, 14(16), 4119; https://doi.org/10.3390/rs14164119 - 22 Aug 2022

Cited by 16 | Viewed by 3035

Abstract

Recent advances in computer vision and camera-equipped unmanned aerial systems (UAS) for 3D modeling enable UAS-based photogrammetry surveys with high spatial-temporal resolutions. To generate consistent and high-quality 3D models using UASs, understanding how influence factors (i.e., flight height, image overlap, etc.) affect the [...] Read more.

Recent advances in computer vision and camera-equipped unmanned aerial systems (UAS) for 3D modeling enable UAS-based photogrammetry surveys with high spatial-temporal resolutions. To generate consistent and high-quality 3D models using UASs, understanding how influence factors (i.e., flight height, image overlap, etc.) affect the 3D modeling accuracy and their levels of significance are important. However, there is little to no quantitative analysis that studies how these influence factors interact with and affect the accuracy when changing the values of the influence factors. Moreover, there is little to no research that assesses more than three influence factors. Therefore, to fill this gap, this paper aims to evaluate and predict the accuracy generated by different flight combinations. This paper presents a study that (1) assessed the significance levels of five influence factors (flight height, average image quality, image overlap, ground control point (GCP) quantity, and camera focal lengths), (2) investigated how they interact and impact 3D modeling accuracy using the multiple regression (MR) method, and (3) used the developed MR models for predicting horizontal and vertical accuracies. To build the MR model, 160 datasets were created from 40 flight missions collected at a site with a facility and open terrain. For validating the prediction model, five testing datasets were collected and used at a larger site with a complex building and open terrain. The results show that the findings of this study can be applied to surveyors’ better design flight configurations that result in the highest accuracies, given different site conditions and constraints. The results also provide a reasonable prediction of accuracy given different flight configurations. Full article

(This article belongs to the Section Engineering Remote Sensing)

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Graphical abstract

17 pages, 3745 KiB

Open AccessArticle

Reduction of Species Identification Errors in Surveys of Marine Wildlife Abundance Utilising Unoccupied Aerial Vehicles (UAVs)

by Eyal Bigal, Ori Galili, Itai van Rijn, Massimiliano Rosso, Christophe Cleguer, Amanda Hodgson, Aviad Scheinin and Dan Tchernov

Remote Sens. 2022, 14(16), 4118; https://doi.org/10.3390/rs14164118 - 22 Aug 2022

Cited by 2 | Viewed by 2454

Abstract

The advent of unoccupied aerial vehicles (UAVs) has enhanced our capacity to survey wildlife abundance, yet new protocols are still required for collecting, processing, and analysing image-type observations. This paper presents a methodological approach to produce informative priors on species misidentification probabilities based [...] Read more.

The advent of unoccupied aerial vehicles (UAVs) has enhanced our capacity to survey wildlife abundance, yet new protocols are still required for collecting, processing, and analysing image-type observations. This paper presents a methodological approach to produce informative priors on species misidentification probabilities based on independent experiments. We performed focal follows of known dolphin species and distributed our imagery amongst 13 trained observers. Then, we investigated the effects of reviewer-related variables and image attributes on the accuracy of species identification and level of certainty in observations. In addition, we assessed the number of reviewers required to produce reliable identification using an agreement-based framework compared with the majority rule approach. Among-reviewer variation was an important predictor of identification accuracy, regardless of previous experience. Image resolution and sea state exhibited the most pronounced effects on the proportion of correct identifications and the reviewers’ mean level of confidence. Agreement-based identification resulted in substantial data losses but retained a broader range of image resolutions and sea states than the majority rule approach and produced considerably higher accuracy. Our findings suggest a strong dependency on reviewer-related variables and image attributes, which, unless considered, may compromise identification accuracy and produce unreliable estimators of abundance. Full article

(This article belongs to the Special Issue Remote Sensing Applied to Marine Species Distribution)

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23 pages, 6614 KiB

Open AccessArticle

Analysis and Demonstration of First Cross-Support Interferometry Tracking in China Mars Mission

by Songtao Han, Haijun Man, Mei Wang, Zhijin Zhou, Jianfeng Cao, Wei Gao, Lue Chen and Jinsong Ping

Remote Sens. 2022, 14(16), 4117; https://doi.org/10.3390/rs14164117 - 22 Aug 2022

Viewed by 1691

Abstract

Delta-Differential One-Way Ranging (DeltaDOR) is widely used in deep spacecraft navigation, and cross support could enhance navigation accuracy with more interferometry baselines and longer baseline. In China Mars mission Tianwen-1, formal joint cross-support interferometry tracking between China Satellite Launch and TT&C General (CLTC) [...] Read more.

Delta-Differential One-Way Ranging (DeltaDOR) is widely used in deep spacecraft navigation, and cross support could enhance navigation accuracy with more interferometry baselines and longer baseline. In China Mars mission Tianwen-1, formal joint cross-support interferometry tracking between China Satellite Launch and TT&C General (CLTC) and European Space Operations Center (ESOC) under commercial contract was conducted around the critical stages of the mission, such as Mars orbit insertion. Cross-support interferometry is a new challenge to CLTC, as the correlator for routine DeltaDOR measurements do not fit for cross support, because of observable definition, blind station clock searching, and so on. This paper discusses the new method and algorithm adopted in joint cross support, especially for spacecraft tone signal processing and clock estimation when correlating with the data of two stations from different agencies. Results of the cross-support interferometry tracking activities are also analyzed. Observables from CLTC and ESOC are consistent with each other, and the difference in observables is in the order of tens of ps. All the baselines are induced to evaluate the accuracy of the spacecraft orbit determined and predicted by CLTC, and the DeltaDOR residuals have a root-mean-square (RMS) better than 0.5 ns (the goal is 1 ns), which could enhance the confidence of the orbit accuracy and the effectiveness of control parameters during critical orbit operation. Full article

(This article belongs to the Special Issue Recent Progress and Applications on Multi-Dimensional SAR)

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18 pages, 28955 KiB

Open AccessArticle

A Hybrid Model Based on Superpixel Entropy Discrimination for PolSAR Image Classification

by Jili Sun, Lingdong Geng and Yize Wang

Remote Sens. 2022, 14(16), 4116; https://doi.org/10.3390/rs14164116 - 22 Aug 2022

Cited by 3 | Viewed by 1703

Abstract

Superpixel segmentation is widely used in polarimetric synthetic aperture radar (PolSAR) image classification. However, the classification method using simple majority voting cannot easily handle evidence conflicts in a single superpixel. At present, there is no method to evaluate the quality of superpixel classification. [...] Read more.

Superpixel segmentation is widely used in polarimetric synthetic aperture radar (PolSAR) image classification. However, the classification method using simple majority voting cannot easily handle evidence conflicts in a single superpixel. At present, there is no method to evaluate the quality of superpixel classification. To solve the above problems, this paper proposes a hybrid classification model based on superpixel entropy discrimination (SED), and constructs a two-level cascade classifier. Firstly, a light gradient boosting machine (LGBM) was used to process large-dimensional input features, and simple linear iterative clustering (SLIC) was integrated to obtain the primary classification results based on superpixels. Secondly, information entropy was introduced to evaluate the quality of superpixel classification, and a complex-valued convolutional neural network (CV-CNN) was used to reclassify the high-entropy superpixels to obtain the secondary classification results. Experiments with two measured PolSAR datasets show that the overall accuracy of both classification methods exceeded 97%. This method suppressed the evidence conflict in a single superpixel and the inaccuracy of superpixel segmentation. The test time of our proposed method was shorter than that of CV-CNN, and using only 55% of CV-CNN test data could achieve the same accuracy as using CV-CNN for the whole image. Full article

(This article belongs to the Special Issue Recent Progress and Applications on Multi-Dimensional SAR)

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18 pages, 6020 KiB

Open AccessArticle

Dictionary Learning- and Total Variation-Based High-Light-Efficiency Snapshot Multi-Aperture Spectral Imaging

by Feng Huang, Peng Lin, Rongjin Cao, Bin Zhou and Xianyu Wu

Remote Sens. 2022, 14(16), 4115; https://doi.org/10.3390/rs14164115 - 22 Aug 2022

Cited by 4 | Viewed by 1911

Abstract

Conventional multispectral imaging systems based on bandpass filters struggle to record multispectral videos with high spatial resolutions because of their limited light efficiencies. This paper proposes a multi-aperture multispectral imaging system based on notch filters that overcomes this limitation by allowing light from [...] Read more.

Conventional multispectral imaging systems based on bandpass filters struggle to record multispectral videos with high spatial resolutions because of their limited light efficiencies. This paper proposes a multi-aperture multispectral imaging system based on notch filters that overcomes this limitation by allowing light from most of the spectrum to pass through. Based on this imaging principle, a prototype multi-aperture multispectral imaging system comprising notch filters was built and demonstrated. Further, a dictionary learning- and total variation-based spectral super-resolution algorithm was developed to reconstruct spectral images. The simulation results obtained using public multispectral datasets showed that, compared to the dictionary learning-based spectral super-resolution algorithm, the proposed algorithm reconstructed the spectral information with a higher accuracy and removed noise, and the verification experiments confirmed the performance efficiency of the prototype system. The experimental results showed that the proposed imaging system can capture images with high spatial and spectral resolutions under low illumination conditions. The proposed algorithm improved the spectral resolution of the acquired data from 9 to 31 bands, and the average peak signal-to-noise ratio remained above 43 dB, which is 13 dB higher than those of the state-of-the-art coded aperture snapshot spectral imaging methods. Simultaneously, the frame rate of the imaging system was up to 5000 frames/s under natural daylight. Full article

(This article belongs to the Special Issue Machine Vision and Advanced Image Processing in Remote Sensing)

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18 pages, 19099 KiB

Open AccessArticle

Investigating the Effects of Snow Cover and Vegetation on Soil Temperature Using Remote Sensing Indicators in the Three River Source Region, China

by Xiaoqing Tan, Siqiong Luo, Hongmei Li, Xiaohua Hao, Jingyuan Wang, Qingxue Dong and Zihang Chen

Remote Sens. 2022, 14(16), 4114; https://doi.org/10.3390/rs14164114 - 22 Aug 2022

Cited by 13 | Viewed by 2821

Abstract

Soil temperature is an important physical variable that characterises geothermal conditions and influences geophysical, biological and chemical processes in the earth sciences. Soil temperature is not only affected by climatic and geographical factors; it is also modulated by local factors such as snow [...] Read more.

Soil temperature is an important physical variable that characterises geothermal conditions and influences geophysical, biological and chemical processes in the earth sciences. Soil temperature is not only affected by climatic and geographical factors; it is also modulated by local factors such as snow cover and vegetation. This paper investigates the relationship between snow cover and vegetation and soil temperature with the help of two classical remote sensing indicators, the Snow Cover Days (SCD) based Advanced Very High Resolution Radiometer and the Normalized Difference Vegetation Index (NDVI)-based Global Inventory Modelling and Mapping Studies, to analyse the influence of local factors on soil temperature in the Three River Source Region (TRSR). Combing multi-layer geothermal observations from 23 stations in the TRSR with meteorological dataset, soil properties datasets, snow cover and vegetation indices, a non-linear model, the Random Forest model, is used to establish a multi-layer soil temperature dataset to analyse the influence of surface cover factors in each depth. The results showed that the annual SCD had a decreasing trend during 1982–2015 and was negatively correlated with the annual mean soil temperature; the annual NDVI had no significant trend, but it was positively correlated with the annual mean soil temperature. Regionally, there was a significant decrease in SCD in the mountainous areas bordering the source areas of the three rivers, and there was a trend of increasing NDVI in the northwest and decreasing vegetation in the southwest in the TRSR. The stronger the correlation with soil temperature in areas with a larger SCD, the more the snow has a cooling effect on the shallower soil temperatures due to the high albedo of the accumulated snow and the repeated melting and heat absorption of the snow in the area. The snow has an insulating effect on the 40 cm soil layer by impeding the cooling effect of the atmosphere in winter. In sparsely vegetated areas, vegetation lowers ground albedo and warms the soil, but in July and August, in areas with more vegetation, NDVI is negatively correlated with soil temperature, with heavy vegetation intercepting summer radiant energy and having a cooling effect on the soil. Full article

(This article belongs to the Special Issue Remote Sensing for Mountain Vegetation and Snow Cover)

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21 pages, 7586 KiB

Open AccessArticle

Fast Tree Detection and Counting on UAVs for Sequential Aerial Images with Generating Orthophoto Mosaicing

by Pengcheng Han, Cunbao Ma, Jian Chen, Lin Chen, Shuhui Bu, Shibiao Xu, Yong Zhao, Chenhua Zhang and Tatsuya Hagino

Remote Sens. 2022, 14(16), 4113; https://doi.org/10.3390/rs14164113 - 22 Aug 2022

Cited by 13 | Viewed by 4236

Abstract

Individual tree counting (ITC) is a popular topic in the remote sensing application field. The number and planting density of trees are significant for estimating the yield and for futher planing, etc. Although existing studies have already achieved great performance on tree detection [...] Read more.

Individual tree counting (ITC) is a popular topic in the remote sensing application field. The number and planting density of trees are significant for estimating the yield and for futher planing, etc. Although existing studies have already achieved great performance on tree detection with satellite imagery, the quality is often negatively affected by clouds and heavy fog, which limits the application of high-frequency inventory. Nowadays, with ultra high spatial resolution and convenient usage, Unmanned Aerial Vehicles (UAVs) have become promising tools for obtaining statistics from plantations. However, for large scale areas, a UAV cannot capture the whole region of interest in one photo session. In this paper, a real-time orthophoto mosaicing-based tree counting framework is proposed to detect trees using sequential aerial images, which is very effective for fast detection of large areas. Firstly, to guarantee the speed and accuracy, a multi-planar assumption constrained graph optimization algorithm is proposed to estimate the camera pose and generate orthophoto mosaicing simultaneously. Secondly, to avoid time-consuming box or mask annotations, a point supervised method is designed for tree counting task, which greatly speeds up the entire workflow. We demonstrate the effectiveness of our method by performing extensive experiments on oil-palm and acacia trees. To avoid the delay between data acquisition and processing, the proposed framework algorithm is embedded into the UAV for completing tree counting tasks, which also reduces the quantity of data transmission from the UAV system to the ground station. We evaluate the proposed pipeline using sequential UAV images captured in Indonesia. The proposed pipeline achieves an F1-score of 98.2% for acacia tree detection and 96.3% for oil-palm tree detection with online orthophoto mosaicing generation. Full article

(This article belongs to the Special Issue Deep Learning in Remote Sensing Application)

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21 pages, 4003 KiB

Open AccessArticle

Canopy Height Mapping by Sentinel 1 and 2 Satellite Images, Airborne LiDAR Data, and Machine Learning

by Catherine Torres de Almeida, Jéssica Gerente, Jamerson Rodrigo dos Prazeres Campos, Francisco Caruso Gomes Junior, Lucas Antonio Providelo, Guilherme Marchiori and Xinjian Chen

Remote Sens. 2022, 14(16), 4112; https://doi.org/10.3390/rs14164112 - 22 Aug 2022

Cited by 9 | Viewed by 4023

Abstract

Continuous mapping of vegetation height is critical for many forestry applications, such as planning vegetation management in power transmission line right-of-way. Satellite images from different sensors, including SAR (Synthetic Aperture Radar) from Sentinel 1 (S1) and multispectral from Sentinel 2 (S2), can be [...] Read more.

Continuous mapping of vegetation height is critical for many forestry applications, such as planning vegetation management in power transmission line right-of-way. Satellite images from different sensors, including SAR (Synthetic Aperture Radar) from Sentinel 1 (S1) and multispectral from Sentinel 2 (S2), can be used for producing high-resolution vegetation height maps at a broad scale. The main objective of this study is to assess the potential of S1 and S2 satellite data, both in a single and a multisensor approach, for modeling canopy height in a transmission line right-of-way located in the Atlantic Forest of Paraná, Brazil. For integrating S1 and S2 data, we used three machine learning algorithms (LR: Linear Regression, CART: Classification and Regression Trees, and RF: Random Forest) and airborne LiDAR (Light Detection and Ranging) measurements as the reference height. The best models were obtained using the RF algorithm and 20 m resolution features from only S2 data (cross-validated RMSE of 4.92 m and R² of 0.58) or multisensor data (cross-validated RMSE of 4.86 m and R² of 0.60). Although the multisensor model presented the best performance, it was not statistically different from the single-S2 model. Thus, the use of only S2 to estimate canopy height has practical advantages, as it reduces the need to process SAR images and the uncertainties due to S1 noise or differences between the acquisition dates of S2 and S1. Full article

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19 pages, 6816 KiB

Open AccessArticle

Assessing Spatiotemporal Dynamics of Land Use and Cover Change and Carbon Storage in China’s Ecological Conservation Pilot Zone: A Case Study in Fujian Province

by Shengpeng Li, Yingui Cao, Jianling Liu, Shufei Wang and Wenxiang Zhou

Remote Sens. 2022, 14(16), 4111; https://doi.org/10.3390/rs14164111 - 22 Aug 2022

Cited by 16 | Viewed by 3252

Abstract

Many strategies have been put forward to seek green and low-carbon development, some of which are achieved through land use and cover change (LUCC). A series of land management policies related to LUCC and corresponding changes in carbon dynamics were released with the [...] Read more.

Many strategies have been put forward to seek green and low-carbon development, some of which are achieved through land use and cover change (LUCC). A series of land management policies related to LUCC and corresponding changes in carbon dynamics were released with the implementation of the Ecological Conservation Pilot Zone Program (ECPZP) in China. We explored the spatiotemporal dynamics of LUCC and carbon storage in the first ECPZP implementation region (Fujian province) at the time before and after ECPZP implementation using a simplified carbon pools model and quantified the relative impacts of human activities and climate change on net primary productivity (NPP) employing residual analysis. This can fill the gap of land use and vegetation changes and the corresponding carbon dynamics in the ECPZP region and can serve as a reference for future land management policy revisions and ECPZP project extensions. The results showed that: (1) In 1990–2020, woodland, cultivated land, and grassland were the leading land use type in Fujian province. The area of LUCC was 11,707.75 km², and it was predominantly caused by the conversion from cultivated land to built-up land, and the interconversion between woodland and grassland. (2) An increase of 9.74 Tg in carbon storage was mainly caused by vegetation conversion from 1990 to 2020. (3) The statistically significant increased area of climate change-induced NPP was 2.3% primarily in the northwest, but the decreased area of it statistically significantly was only 0.1%. Correspondingly, the increased area of statistically significant human activity-induced NPP was 8.7% primarily in the southeast, but the decreased area of statistically significance was 6.5%, mostly in the central region. In addition, the statistically significant areas of NPP caused by the combination of human activities and climate change differed by 1.8%. To sum up, ECPZP makes full use of the vertical mountain landscape and property right reform to effectively secure ecological space and local income. Moreover, urbanization-related policies are an essential impetus for LUCC and carbon balance. The impact of other built-up land expansion on environmental change needs to be paid particular attention to. Moreover, land-use activities in the centre of the study region that are not conducive to NPP growth should be judiciously assessed in the future. Full article

(This article belongs to the Special Issue Remote Sensing in Land Use and Management)

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18 pages, 19536 KiB

Open AccessArticle

A Novel GAN-Based Anomaly Detection and Localization Method for Aerial Video Surveillance at Low Altitude

by Danilo Avola, Irene Cannistraci, Marco Cascio, Luigi Cinque, Anxhelo Diko, Alessio Fagioli, Gian Luca Foresti, Romeo Lanzino, Maurizio Mancini, Alessio Mecca and Daniele Pannone

Remote Sens. 2022, 14(16), 4110; https://doi.org/10.3390/rs14164110 - 22 Aug 2022

Cited by 21 | Viewed by 3816

Abstract

The last two decades have seen an incessant growth in the use of Unmanned Aerial Vehicles (UAVs) equipped with HD cameras for developing aerial vision-based systems to support civilian and military tasks, including land monitoring, change detection, and object classification. To perform most [...] Read more.

The last two decades have seen an incessant growth in the use of Unmanned Aerial Vehicles (UAVs) equipped with HD cameras for developing aerial vision-based systems to support civilian and military tasks, including land monitoring, change detection, and object classification. To perform most of these tasks, the artificial intelligence algorithms usually need to know, a priori, what to look for, identify. or recognize. Actually, in most operational scenarios, such as war zones or post-disaster situations, areas and objects of interest are not decidable a priori since their shape and visual features may have been altered by events or even intentionally disguised (e.g., improvised explosive devices (IEDs)). For these reasons, in recent years, more and more research groups are investigating the design of original anomaly detection methods, which, in short, are focused on detecting samples that differ from the others in terms of visual appearance and occurrences with respect to a given environment. In this paper, we present a novel two-branch Generative Adversarial Network (GAN)-based method for low-altitude RGB aerial video surveillance to detect and localize anomalies. We have chosen to focus on the low-altitude sequences as we are interested in complex operational scenarios where even a small object or device can represent a reason for danger or attention. The proposed model was tested on the UAV Mosaicking and Change Detection (UMCD) dataset, a one-of-a-kind collection of challenging videos whose sequences were acquired between 6 and 15 m above sea level on three types of ground (i.e., urban, dirt, and countryside). Results demonstrated the effectiveness of the model in terms of Area Under the Receiving Operating Curve (AUROC) and Structural Similarity Index (SSIM), achieving an average of 97.2% and 95.7%, respectively, thus suggesting that the system can be deployed in real-world applications. Full article

(This article belongs to the Special Issue AI Interpretation of Satellite, Aerial, Ground, and Underwater Image and Video Sequences)

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18 pages, 11728 KiB

Open AccessArticle

The Validation of Soil Moisture from Various Sources and Its Influence Factors in the Tibetan Plateau

by Na Li, Changyan Zhou and Ping Zhao

Remote Sens. 2022, 14(16), 4109; https://doi.org/10.3390/rs14164109 - 22 Aug 2022

Cited by 9 | Viewed by 2200

Abstract

The tempo-spatial continuous soil moisture (SM) datasets of satellite remote sensing, land surface models, and reanalysis products are very important for correlational research in the Tibetan Plateau (TP) meteorology. Based on the in situ observed SM, AMSR2, SMAP, GLDAS-Noah, and ERA5 SM are [...] Read more.

The tempo-spatial continuous soil moisture (SM) datasets of satellite remote sensing, land surface models, and reanalysis products are very important for correlational research in the Tibetan Plateau (TP) meteorology. Based on the in situ observed SM, AMSR2, SMAP, GLDAS-Noah, and ERA5 SM are assessed at regional and site scales in the TP during the non-frozen period from 2015 to 2016. The results indicate that SMAP and ERA5 SM (AMSR2 and GLDAS-Noah SM) present an overestimation (underestimation) of the TP regional average. Specifically, SMAP (ERA5) SM performs best in Maqu and south-central TP (Naqu, Pali, and southeast TP), with a Spearman’s rank correlation (

ρ

) greater than 0.57 and an unbiased root mean square error (ubRMSE) less than 0.05 m³/m³. In Shiquanhe, GLDAS-Noah SM performs best among the four SM products. At the site scale, SMAP SM has relatively high

ρ

and low ubRMSE values at the most sites, except the sites at the Karakoram Mountains and Himalayan Mountains. The four SM products show underestimation in different degrees at Shiquanhe. The

ρ

values between AMSR2 SM and rainfall are the highest in most study subregions, especially in Naqu and Pali. For the other SM products, they have the highest positive correlations with a normalized difference vegetation index (NDVI). Besides, land surface temperature (LST) has significant negative (positive) correlations with SM products in the summer (other seasons). Through the multiple linear stepwise regression analysis, NDVI has negative (positive) impacts on SM products in the spring (other seasons), while LST shows the opposite conditions. NDVI (rainfall) is identified as the main influencing factor on the in situ observed, SMAP, GLDAS-Noah, and ERA5 (AMSR2) SM in this study. Compared to previous studies, these results comprehensively present the applicability of SM products in the TP and further reveal their main influencing factors. Full article

(This article belongs to the Special Issue Precipitation and Evapotranspiration Mechanisms in Drylands and Their Remote Sensing Retrieval & Simulation)

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2 pages, 165 KiB

Open AccessCorrection

Correction: Yang et al. Detecting Spatiotemporal Features and Rationalities of Urban Expansions within the Guangdong–Hong Kong–Macau Greater Bay Area of China from 1987 to 2017 Using Time-Series Landsat Images and Socioeconomic Data. Remote Sens. 2019, 11, 2215

by Chao Yang, Qingquan Li, Tianhong Zhao, Huizeng Liu, Wenxiu Gao, Tiezhu Shi, Minglei Guan and Guofeng Wu

Remote Sens. 2022, 14(16), 4108; https://doi.org/10.3390/rs14164108 - 22 Aug 2022

Viewed by 1225

Abstract

The authors wish to make the following corrections to the paper [...] Full article

15 pages, 4627 KiB

Open AccessArticle

IoT Enabled Deep Learning Based Framework for Multiple Object Detection in Remote Sensing Images

by Imran Ahmed, Misbah Ahmad, Abdellah Chehri, Mohammad Mehedi Hassan and Gwanggil Jeon

Remote Sens. 2022, 14(16), 4107; https://doi.org/10.3390/rs14164107 - 22 Aug 2022

Cited by 17 | Viewed by 3347

Abstract

Advanced collaborative and communication technologies play a significant role in intelligent services and applications, including artificial intelligence, Internet of Things (IoT), remote sensing, robotics, future generation wireless, and aerial access networks. These technologies improve connectivity, energy efficiency, and quality of services of various [...] Read more.

Advanced collaborative and communication technologies play a significant role in intelligent services and applications, including artificial intelligence, Internet of Things (IoT), remote sensing, robotics, future generation wireless, and aerial access networks. These technologies improve connectivity, energy efficiency, and quality of services of various smart city applications, particularly in transportation, monitoring, healthcare, public services, and surveillance. A large amount of data can be obtained by IoT systems and then examined by deep learning methods for various applications, e.g., object detection or recognition. However, it is a challenging and complex task in smart remote monitoring applications (aerial and drone). Nevertheless, it has gained special consideration in recent years and has performed a pivotal role in different control and monitoring applications. This article presents an IoT-enabled smart surveillance solution for multiple object detection through segmentation. In particular, we aim to provide the concept of collaborative drones, deep learning, and IoT for improving surveillance applications in smart cities. We present an artificial intelligence-based system using the deep learning based segmentation model PSPNet (Pyramid Scene Parsing Network) for segmenting multiple objects. We used an aerial drone data set, implemented data augmentation techniques, and leveraged deep transfer learning to boost the system’s performance. We investigate and analyze the performance of the segmentation paradigm with different CNN (Convolution Neural Network) based architectures. The experimental results illustrate that data augmentation enhances the system’s performance by producing good accuracy results of multiple object segmentation. The accuracy of the developed system is 92% with VGG-16 (Visual Geometry Group), 93% with ResNet-50 (Residual Neural Network), and 95% with MobileNet. Full article

(This article belongs to the Special Issue New Developments in Remote Sensing for the Environment)

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17 pages, 2740 KiB

Open AccessArticle

A Robust Underwater Multiclass Fish-School Tracking Algorithm

by Tao Liu, Shuangyan He, Haoyang Liu, Yanzhen Gu and Peiliang Li

Remote Sens. 2022, 14(16), 4106; https://doi.org/10.3390/rs14164106 - 21 Aug 2022

Cited by 9 | Viewed by 3197

Abstract

State-of-the-art multiple-object tracking methods are frequently applied to people or vehicle tracking, but rarely involve underwater-object tracking. Compared with the processing in non-underwater photos or videos, underwater fish tracking is challenging due to variations in light conditions, water turbidity levels, shape deformations, and [...] Read more.

State-of-the-art multiple-object tracking methods are frequently applied to people or vehicle tracking, but rarely involve underwater-object tracking. Compared with the processing in non-underwater photos or videos, underwater fish tracking is challenging due to variations in light conditions, water turbidity levels, shape deformations, and the similar appearances of fish. This article proposes a robust underwater fish-school tracking algorithm (FSTA). The FSTA is based on the tracking-by-detection paradigm. To solve the problem of low recognition accuracy in an underwater environment, we add an amendment detection module that uses prior knowledge to modify the detection result. Second, we introduce an underwater data association algorithm for aquatic non-rigid organisms that recombines representation and location information to refine the data matching process and improve the tracking results. The Resnet50-IBN network is used as a re-identification network to track fish. We introduce a triplet loss function based on a centroid to train the feature extraction network. The multiple-object tracking accuracy (MOTA) of the FSTA is 79.1% on the underwater dataset, which shows that it can achieve state-of-the-art performance in a complex real-world marine environment. Full article

(This article belongs to the Special Issue Artificial Intelligence for Object Detection in Optical, Radar and Lidar Images)

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15 pages, 5890 KiB

Open AccessArticle

Successful Derivation of Absorbing Aerosol Index from the Environmental Trace Gases Monitoring Instrument (EMI)

by Fuying Tang, Weihe Wang, Fuqi Si, Haijin Zhou, Yuhan Luo and Yuanyuan Qian

Remote Sens. 2022, 14(16), 4105; https://doi.org/10.3390/rs14164105 - 21 Aug 2022

Cited by 4 | Viewed by 2818

Abstract

We retrieved the absorbing aerosol index (AAI) based on the measured reflectance from the Environmental Trace Gases Monitoring Instrument (EMI) for the first time. EMI is a push-broom spectrometer onboard the Chinese GeoFen-5 satellite launched on 9 May 2018, which was initially developed [...] Read more.

We retrieved the absorbing aerosol index (AAI) based on the measured reflectance from the Environmental Trace Gases Monitoring Instrument (EMI) for the first time. EMI is a push-broom spectrometer onboard the Chinese GeoFen-5 satellite launched on 9 May 2018, which was initially developed to determine the global distribution of atmospheric composition. The EMI initial AAI results were corrected from physical stripes and yielded an offset of 5.92 as calibration errors from a background value based on the statistical method that count the EMI AAI over the Pacific Ocean under cloudless scenes. We also evaluated the consistency of the EMI AAI and data with the TROPOspheric Monitoring Instrument (TROPOMI) observations. A comparison between the monthly average EMI AAI data and TROPOMI AAI revealed regional consistencies between these instruments with a similar spatial distribution of AAI (correlation coefficient, r > 0.9). The daily-scale results demonstrated that EMI was also consistent with TROPOMI AAI (r = 0.9). The spatial distribution of EMI AAI is consistent with Aerosol Optical Depth (AOD) from TROPOMI. The daily variation of EMI AAI in an Australian wildfire event was consistent with TROPOMI (r = 0.92). Overall, we demonstrated that EMI AAI can be efficiently used to detect large aerosol events for reconstructing the spatial variability of Ultraviolet (UV) absorbing aerosols. Full article

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14 pages, 3072 KiB

Open AccessArticle

Optical Turbulence Characteristics in the Upper Troposphere–Lower Stratosphere over the Lhasa within the Asian Summer Monsoon Anticyclone

by Kun Zhang, Feifei Wang, Ningquan Weng, Xiaoqing Wu, Xuebin Li and Tao Luo

Remote Sens. 2022, 14(16), 4104; https://doi.org/10.3390/rs14164104 - 21 Aug 2022

Cited by 3 | Viewed by 1931

Abstract

The high elevation, complex topography, and unique atmospheric circulations of the Tibetan Plateau (TP) make its optical turbulence characteristics different from those in low-elevation regions. In this study, the characteristics of the atmospheric refractive index structure constant (

C_{n}^{2}

) profiles [...] Read more.

The high elevation, complex topography, and unique atmospheric circulations of the Tibetan Plateau (TP) make its optical turbulence characteristics different from those in low-elevation regions. In this study, the characteristics of the atmospheric refractive index structure constant (

C_{n}^{2}

) profiles in the Lhasa area at different strength states of the Asian summer monsoon anticyclone (ASMA) are analyzed based on precious in situ sounding data measured over the Lhasa in August 2018.

C_{n}^{2}

in the upper troposphere–lower stratosphere fluctuates significantly within a few days during the ASMA, particularly in the upper troposphere. The effect of the ASMA on

C_{n}^{2}

varies among the upper troposphere, tropopause, and lower stratosphere. The stronger and closer the ASMA is to Lhasa, the more pronounced is the “upper highs and lower lows” pressure field structure, which is beneficial for decreasing the potential temperature lapse rate. The decrease in static stability is an important condition for developing optical turbulence, elevating the tropopause height, and reducing the tropopause temperature. However, if strong high-pressure activity occurs at the lower pressure layer, such as at 500 hPa, an “upper highs and lower highs” pressure field structure forms over the Lhasa, increasing the potential temperature lapse rate and suppressing the convective intensity. Being almost unaffected by low-level atmospheric high-pressure activities, the ASMA, as the main influencing factor, mainly inhibits

C_{n}^{2}

in the tropopause and lower stratosphere. The variations of turbulence intensity in UTLS caused by ASMA activities also have a great influence on astronomical parameters, which will have certain guiding significance for astronomical site testing and observations. Full article

(This article belongs to the Special Issue Land-Atmosphere Interactions and Effects on the Climate of the Tibetan Plateau and Surrounding Regions)

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15 pages, 3821 KiB

Open AccessArticle

Imbalanced Underwater Acoustic Target Recognition with Trigonometric Loss and Attention Mechanism Convolutional Network

by Yanxin Ma, Mengqi Liu, Yi Zhang, Bingbing Zhang, Ke Xu, Bo Zou and Zhijian Huang

Remote Sens. 2022, 14(16), 4103; https://doi.org/10.3390/rs14164103 - 21 Aug 2022

Cited by 12 | Viewed by 2152

Abstract

A balanced dataset is generally beneficial to underwater acoustic target recognition. However, the imbalanced class distribution is always meted out in a real scene. To address this, a weighted cross entropy loss function based on trigonometric function is proposed. Then, the proposed loss [...] Read more.

A balanced dataset is generally beneficial to underwater acoustic target recognition. However, the imbalanced class distribution is always meted out in a real scene. To address this, a weighted cross entropy loss function based on trigonometric function is proposed. Then, the proposed loss function is applied in a multi-scale residual convolutional neural network (named MR-CNN-A network) embedded with an attention mechanism for the recognition task. Firstly, a multi-scale convolution kernel is used to obtain multi-scale features. Then, an attention mechanism is used to fuse these multi-scale feature maps. Furthermore, a cosx-function-weighted cross-entropy loss function is used to deal with the class imbalance in underwater acoustic data. This function adjusts the loss ratio of each sample by adjusting the loss interval of every mini-batch based on cosx term to achieve a balanced total loss for each class. Two imbalanced underwater acoustic data sets, ShipsEar and autonomous underwater vehicle (self-collected data) are used to evaluate the proposed network. The experimental results show that the proposed network outperforms the support vector machine and a simple convolutional neural network. Compared with the other three loss functions, the proposed loss function achieves better stability and adaptability. The results strongly demonstrate the validity of the proposed loss function and the network. Full article

(This article belongs to the Special Issue Advancement in Undersea Remote Sensing)

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23 pages, 37659 KiB

Open AccessArticle

Using UAV and Structure-From-Motion Photogrammetry for the Detection of Boulder Movement by Storms on a Rocky Shore Platform in Laghdira, Northwest Morocco

by Mouncef Sedrati, Juan A. Morales, Abdelmounim El M’rini, Edward J. Anthony, Glen Bulot, Romain Le Gall and Abdelkarim Tadibaght

Remote Sens. 2022, 14(16), 4102; https://doi.org/10.3390/rs14164102 - 21 Aug 2022

Cited by 12 | Viewed by 3124

Abstract

The detachment and mobilization of boulders from rocky shore platforms by waves involves complex geomorphic and hydrodynamic processes. Understanding these processes requires precise information on the rates and patterns of movement of these megaclasts scaled against the wave conditions that generate boulder mobility. [...] Read more.

The detachment and mobilization of boulders from rocky shore platforms by waves involves complex geomorphic and hydrodynamic processes. Understanding these processes requires precise information on the rates and patterns of movement of these megaclasts scaled against the wave conditions that generate boulder mobility. Repeat photogrammetry and structure-from-motion (SfM) models commonly used in geomorphic analyses are an interesting option for monitoring boulder dynamics. In this study, we used unmanned aerial vehicle (UAV)-based digital photogrammetry and SfM differential models to identify recent boulder movements over a rocky shore platform in Laghdira, Morocco. Combining these results with data on storm occurrence in the study area allowed us to identify storm waves as the unique driver of the dislodged and mobilized boulders. The identified storm event had a significant wave height of 5.2 m. The UAV models were built from imagery captured in September and December 2019 using a DJI MAVIC PRO PLATINUM, and we used QGIS to produce 2D and 3D model outputs. The exploitation of the 2D model differentials allowed us to appreciate the response of the boulders to the storm waves and to determine platform volumetric changes and, therefore, boulder mobility. The 3D models were valuable in determining the mode of transport of the boulders. Mobility patterns included sliding, overturning with no further mobility, and rotation and saltation, as well as boulder breakup. Storm waves did not have a preferential impact on any particular boulder shape, size category, or position at the outer edge of the platform. These results highlight the utility of combining UAV surveys with identified storm events, which are much more frequent than tsunamis, in determining observed boulder initiation and mobility. Full article

(This article belongs to the Special Issue Advances in Remote Sensing in Coastal Geomorphology)

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19 pages, 4867 KiB

Open AccessArticle

Global 10 m Land Use Land Cover Datasets: A Comparison of Dynamic World, World Cover and Esri Land Cover

by Zander S. Venter, David N. Barton, Tirthankar Chakraborty, Trond Simensen and Geethen Singh

Remote Sens. 2022, 14(16), 4101; https://doi.org/10.3390/rs14164101 - 21 Aug 2022

Cited by 133 | Viewed by 28680

Abstract

The European Space Agency’s Sentinel satellites have laid the foundation for global land use land cover (LULC) mapping with unprecedented detail at 10 m resolution. We present a cross-comparison and accuracy assessment of Google’s Dynamic World (DW), ESA’s World Cover (WC) and Esri’s [...] Read more.

The European Space Agency’s Sentinel satellites have laid the foundation for global land use land cover (LULC) mapping with unprecedented detail at 10 m resolution. We present a cross-comparison and accuracy assessment of Google’s Dynamic World (DW), ESA’s World Cover (WC) and Esri’s Land Cover (Esri) products for the first time in order to inform the adoption and application of these maps going forward. For the year 2020, the three global LULC maps show strong spatial correspondence (i.e., near-equal area estimates) for water, built area, trees and crop LULC classes. However, relative to one another, WC is biased towards over-estimating grass cover, Esri towards shrub and scrub cover and DW towards snow and ice. Using global ground truth data with a minimum mapping unit of 250 m², we found that Esri had the highest overall accuracy (75%) compared to DW (72%) and WC (65%). Across all global maps, water was the most accurately mapped class (92%), followed by built area (83%), tree cover (81%) and crops (78%), particularly in biomes characterized by temperate and boreal forests. The classes with the lowest accuracies, particularly in the tundra biome, included shrub and scrub (47%), grass (34%), bare ground (57%) and flooded vegetation (53%). When using European ground truth data from LUCAS (Land Use/Cover Area Frame Survey) with a minimum mapping unit of <100 m², we found that WC had the highest accuracy (71%) compared to DW (66%) and Esri (63%), highlighting the ability of WC to resolve landscape elements with more detail compared to DW and Esri. Although not analyzed in our study, we discuss the relative advantages of DW due to its frequent and near real-time data delivery of both categorical predictions and class probability scores. We recommend that the use of global LULC products should involve critical evaluation of their suitability with respect to the application purpose, such as aggregate changes in ecosystem accounting versus site-specific change detection in monitoring, considering trade-offs between thematic resolution, global versus. local accuracy, class-specific biases and whether change analysis is necessary. We also emphasize the importance of not estimating areas from pixel-counting alone but adopting best practices in design-based inference and area estimation that quantify uncertainty for a given study area. Full article

(This article belongs to the Special Issue Remote Sensing of Land Use and Land Change with Google Earth Engine)

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21 pages, 12944 KiB

Open AccessArticle

Spectral-Spatial Interaction Network for Multispectral Image and Panchromatic Image Fusion

by Zihao Nie, Lihui Chen, Seunggil Jeon and Xiaomin Yang

Remote Sens. 2022, 14(16), 4100; https://doi.org/10.3390/rs14164100 - 21 Aug 2022

Cited by 6 | Viewed by 1984

Abstract

Recently, with the rapid development of deep learning (DL), an increasing number of DL-based methods are applied in pansharpening. Benefiting from the powerful feature extraction capability of deep learning, DL-based methods have achieved state-of-the-art performance in pansharpening. However, most DL-based methods simply fuse [...] Read more.

Recently, with the rapid development of deep learning (DL), an increasing number of DL-based methods are applied in pansharpening. Benefiting from the powerful feature extraction capability of deep learning, DL-based methods have achieved state-of-the-art performance in pansharpening. However, most DL-based methods simply fuse multi-spectral (MS) images and panchromatic (PAN) images by concatenating, which can not make full use of the spectral information and spatial information of MS and PAN images, respectively. To address this issue, we propose a spectral-spatial interaction Network (SSIN) for pansharpening. Different from previous works, we extract the features of PAN and MS, respectively, and then interact them repetitively to incorporate spectral and spatial information progressively. In order to enhance the spectral-spatial information fusion, we further propose spectral-spatial attention (SSA) module to yield a more effective spatial-spectral information transfer in the network. Extensive experiments on QuickBird, WorldView-4, and WorldView-2 images demonstrate that our SSIN significantly outperforms other methods in terms of both objective assessment and visual quality. Full article

(This article belongs to the Special Issue Artificial Intelligence-Based Learning Approaches for Remote Sensing)

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21 pages, 3880 KiB

Open AccessArticle

Uniform and Competency-Based 3D Keypoint Detection for Coarse Registration of Point Clouds with Homogeneous Structure

by Fariborz Ghorbani, Hamid Ebadi, Norbert Pfeifer and Amin Sedaghat

Remote Sens. 2022, 14(16), 4099; https://doi.org/10.3390/rs14164099 - 21 Aug 2022

Cited by 6 | Viewed by 2381

Abstract

Recent advances in 3D laser scanner technology have provided a large amount of accurate geo-information as point clouds. The methods of machine vision and photogrammetry are used in various applications such as medicine, environmental studies, and cultural heritage. Aerial laser scanners (ALS), terrestrial [...] Read more.

Recent advances in 3D laser scanner technology have provided a large amount of accurate geo-information as point clouds. The methods of machine vision and photogrammetry are used in various applications such as medicine, environmental studies, and cultural heritage. Aerial laser scanners (ALS), terrestrial laser scanners (TLS), mobile mapping laser scanners (MLS), and photogrammetric cameras via image matching are the most important tools for producing point clouds. In most applications, the process of point cloud registration is considered to be a fundamental issue. Due to the high volume of initial point cloud data, 3D keypoint detection has been introduced as an important step in the registration of point clouds. In this step, the initial volume of point clouds is converted into a set of candidate points with high information content. Many methods for 3D keypoint detection have been proposed in machine vision, and most of them were based on thresholding the saliency of points, but less attention had been paid to the spatial distribution and number of extracted points. This poses a challenge in the registration process when dealing with point clouds with a homogeneous structure. As keypoints are selected in areas of structural complexity, it leads to an unbalanced distribution of keypoints and a lower registration quality. This research presents an automated approach for 3D keypoint detection to control the quality, spatial distribution, and the number of keypoints. The proposed method generates a quality criterion by combining 3D local shape features, 3D local self-similarity, and the histogram of normal orientation and provides a competency index. In addition, the Octree structure is applied to control the spatial distribution of the detected 3D keypoints. The proposed method was evaluated for the keypoint-based coarse registration of aerial laser scanner and terrestrial laser scanner data, having both cluttered and homogeneous regions. The obtained results demonstrate the proper performance of the proposed method in the registration of these types of data, and in comparison to the standard algorithms, the registration error was diminished by up to 56%. Full article

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15 pages, 2450 KiB

Open AccessArticle

Quantifying the Relationship between 2D/3D Building Patterns and Land Surface Temperature: Study on the Metropolitan Shanghai

by Rui Zhou, Hongchao Xu, Hao Zhang, Jie Zhang, Miao Liu, Tianxing He, Jun Gao and Chunlin Li

Remote Sens. 2022, 14(16), 4098; https://doi.org/10.3390/rs14164098 - 21 Aug 2022

Cited by 23 | Viewed by 2847

Abstract

In the context of urban warming associated with rapid urbanization, the relationship between urban landscape patterns and land surface temperature (LST) has been paid much attention. However, few studies have comprehensively explored the effects of two/three-dimensional (2D/3D) building patterns on LST, particularly by [...] Read more.

In the context of urban warming associated with rapid urbanization, the relationship between urban landscape patterns and land surface temperature (LST) has been paid much attention. However, few studies have comprehensively explored the effects of two/three-dimensional (2D/3D) building patterns on LST, particularly by comparing their relative contribution to the spatial variety of LST. This study adopted the ordinary least squares regression, spatial autoregression and variance partitioning methods to investigate the relationship between 2D/3D building patterns and summertime LST across 2016–2017 in Shanghai. The 2D and 3D building patterns in this study were quantified by four 2D and six 3D metrics. The results showed that: (1) During the daytime, 2D/3D building metrics had significant correlation with LST. However, 3D building patterns played a significant role in predicting LST. They explained 51.0% and 10.2% of the variance in LST, respectively. (2) The building coverage ratio, building density, mean building projection area, the standard deviation of building height, and mean building height highly correlated with LST. Specifically, the building coverage ratio was the main predictor, which was obviously positively correlated with LST. The correlation of building density and average projected area with LST was positive and significant, while the correlation of building height standard deviation and average building height with LST was negative. The increase in average height and standard deviation of buildings and the decrease in building coverage ratio, average projected area, and density of buildings, can effectively improve the urban thermal environment at the census tract level. (3) Spatial autocorrelation analysis can elaborate the spatial relationship between building patterns and LST. The findings from our research will provide important insights for urban planners and decision makers to mitigate urban heat island problems through urban planning and building design. Full article

(This article belongs to the Special Issue Remote Sensing in Applied Ecology)

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23 pages, 8832 KiB

Open AccessArticle

Mapping Two Decades of New York State Forest Aboveground Biomass Change Using Remote Sensing

by Haifa Tamiminia, Bahram Salehi, Masoud Mahdianpari, Colin M. Beier and Lucas Johnson

Remote Sens. 2022, 14(16), 4097; https://doi.org/10.3390/rs14164097 - 21 Aug 2022

Cited by 4 | Viewed by 3356

Abstract

Forest aboveground biomass (AGB) provides valuable information about the carbon cycle, carbon sink monitoring, and understanding of climate change factors. Remote sensing data coupled with machine learning models have been increasingly used for forest AGB estimation over local and regional extents. Landsat series [...] Read more.

Forest aboveground biomass (AGB) provides valuable information about the carbon cycle, carbon sink monitoring, and understanding of climate change factors. Remote sensing data coupled with machine learning models have been increasingly used for forest AGB estimation over local and regional extents. Landsat series provide a 50-year data archive, which is a valuable source for historical mapping over large areas. As such, this paper proposed a machine learning-based workflow for historical AGB estimation and its change analysis from 2001 to 2019 for the New York State’s forests using Landsat historical imagery, airborne LiDAR, and forest plot data. As the object-based image analysis (OBIA) is able to incorporate spectral, contextual, and textural features into the regression model, the proposed method utilizes an OBIA approach and a random forest (RF) regression model implemented on the Google Earth Engine (GEE) cloud computing platform. Results demonstrated that there is a considerable decrease of 983.79 × 10⁶ Mg/ha in the AGB of deciduous forests from 2001 to 2006, followed by an increase of 618.28 × 10⁶ Mg/ha from 2006 to 2011, continued with an increase of 229.12 × 10⁶ Mg/ha of deciduous forests from 2011–2016. Finally, the results demonstrated a slight change in AGB from 2016 to 2019. The transferability of the proposed framework provides a practical solution for monitoring forests in other states or even on a national scale. Full article

(This article belongs to the Special Issue New Developments in Remote Sensing for the Environment)

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Remote Sens., Volume 14, Issue 16 (August-2 2022) – 284 articles

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