Remote Sensing

20 pages, 9177 KiB

Open AccessArticle

Study on the Identification of Habitat Suitability Areas for the Dominant Locust Species Dasyhippus Barbipes in Inner Mongolia

by Xianwei Zhang, Wenjiang Huang, Huichun Ye and Longhui Lu

Remote Sens. 2023, 15(6), 1718; https://doi.org/10.3390/rs15061718 - 22 Mar 2023

Cited by 3 | Viewed by 1353

Abstract

Grassland locusts harm a large amount of grassland every year. Grassland locusts have caused devastating disasters across grassland resources and have greatly impacted the lives of herdsmen. Due to the impacts of climate change and human activity, the distribution of grassland locust habitats [...] Read more.

Grassland locusts harm a large amount of grassland every year. Grassland locusts have caused devastating disasters across grassland resources and have greatly impacted the lives of herdsmen. Due to the impacts of climate change and human activity, the distribution of grassland locust habitats changes constantly. The monitoring and identification of locust habitats is of great significance for the production and utilization of grassland resources. In order to further understand the behavior of these grassland pests and carry out precise prevention and control strategies, researchers have often used survey points to reveal the distribution of habitat-suitability areas or establish the high density of locusts (more than 15 locusts/m²) to identify the different risk levels of habitat-suitability areas for grassland locusts. However, the results of these two methods have often been too large, which is not conducive to the precise control of grassland locusts in large areas. Starting from the sample points of our locust investigation, we conducted a hierarchical prediction of the density of locusts and used the probability value of locust occurrence, as predicted by a maximum entropy model, to categorize the habitat-suitability areas according to the probability thresholds of suitable species growth. The results were in good agreement with the actual situation and there was little difference between the prediction results for locust densities greater than 15 locusts/m² in the middle- and high-density habitat-suitability areas and those for all survey points, while there was a big difference between the prediction results for densities in the middle- and low-density habitat-suitability areas and those for all survey points. These results could provide a basis for the efficient and accurate control of grassland locusts and could have practical significance for future guidance. Full article

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22 pages, 22379 KiB

Open AccessArticle

A Partial Reconstruction Method for SAR Altimeter Coastal Waveforms Based on Adaptive Threshold Judgment

by Xiaonan Liu, Weiya Kong, Hanwei Sun and Yaobing Lu

Remote Sens. 2023, 15(6), 1717; https://doi.org/10.3390/rs15061717 - 22 Mar 2023

Viewed by 1154

Abstract

Due to land contamination and human activities, the sea surface height (SSH) data retrieved from altimeter coastal waveforms have poor precision and cannot provide effective information for various tasks. The along-track high-resolution characteristic of the new synthetic aperture radar (SAR) altimeter makes the [...] Read more.

Due to land contamination and human activities, the sea surface height (SSH) data retrieved from altimeter coastal waveforms have poor precision and cannot provide effective information for various tasks. The along-track high-resolution characteristic of the new synthetic aperture radar (SAR) altimeter makes the retracking methods of traditional coastal waveforms difficult to apply. This study proposes a partial reconstruction method for SAR altimeter coastal waveforms. By making adaptive threshold judgments of model matching errors and repairing the contaminated waveforms based on the nearest linear prediction, the success rate of retracking and retrieval precision of SSH are significantly improved. The data from the coastal experimental areas of the Sentinel-3B satellite altimeter are processed. The results indicate that the mean proportion of waveform quality improvement brought by partial reconstruction is 80.30%, the mean retracking success rate of reconstructed waveforms is 85.60%, and the mean increasing percentage is 30.98%. The noise levels of SSH data retrieved by different methods are calculated to evaluate the processing precision. It is shown that the 20 Hz SSH precisions of the original and reconstructed coastal waveforms are 12.75 cm and 6.32 cm, respectively, and the corresponding 1 Hz SSH precisions are 2.85 cm and 1.41 cm, respectively. The results validate that the proposed partial reconstruction method has improved the SSH precision by a factor of two, and the comparison results with mean sea surface (MSS) model data further verify this conclusion. Full article

(This article belongs to the Special Issue Radar Signal Processing and Imaging for Ocean Remote Sensing)

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17 pages, 4534 KiB

Open AccessArticle

Weakening the Flicker Noise in GPS Vertical Coordinate Time Series Using Hybrid Approaches

by Bing Yang, Zhiqiang Yang, Zhen Tian and Pei Liang

Remote Sens. 2023, 15(6), 1716; https://doi.org/10.3390/rs15061716 - 22 Mar 2023

Cited by 1 | Viewed by 1195

Abstract

Noises in the GPS vertical coordinate time series, mainly including the white and flicker noise, have been proven to impair the accuracy and reliability of GPS products. Various methods were adopted to weaken the white and flicker noises in the GPS time series, [...] Read more.

Noises in the GPS vertical coordinate time series, mainly including the white and flicker noise, have been proven to impair the accuracy and reliability of GPS products. Various methods were adopted to weaken the white and flicker noises in the GPS time series, such as the complementary ensemble empirical mode decomposition (CEEMD), wavelet denoising (WD), and variational mode decomposition (VMD). However, a single method only works at a limited frequency band of the time series, and the corresponding denoising ability is insufficient, especially for the flicker noise. Hence, in this study, we try to build two combined methods: CEEMD & WD and VMD & WD, to weaken the flicker noise in the GPS positioning time series from the Crustal Movement Observation Network of China. First, we handled the original signal using CEEMD or VMD with the appropriate parameters. Then, the processed signal was further denoised by WD. The results show that the average flicker noise in the time series was reduced from 19.90 mm/year^0.25 to 2.8 mm/year^0.25. This relates to a reduction of 86% after applying the two methods to process the GPS data, which indicates our solutions outperform CEEMD by 6.84% and VMD by 16.88% in weakening the flicker noise, respectively. Those apparent decreases in the flicker noises for the two combined methods are attributed to the differences in the frequencies between the WD and the other two methods, which were verified by analyzing the power spectrum density (PSD). With the help of WD, CEEMD & WD and VMD & WD can identify more flicker noise hidden in the low-frequency signals obtained by CEEMD and VMD. Finally, we found that the two combined methods have almost identical effects on removing the flicker noise in the time series for 226 GPS stations in China, testified by the Wilcoxon rank sum test. Full article

(This article belongs to the Special Issue Remote Sensing in Space Geodesy and Cartography Methods)

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36 pages, 12517 KiB

Open AccessArticle

Breach Progression Observation in Rockfill Dam Models Using Photogrammetry

by Geir Helge Kiplesund, Fjola Gudrun Sigtryggsdottir and Leif Lia

Remote Sens. 2023, 15(6), 1715; https://doi.org/10.3390/rs15061715 - 22 Mar 2023

Cited by 3 | Viewed by 1747

Abstract

Dam failures are examples of man-made disasters that have stimulated investigation into the processes related to the failure of different dam types. Embankment dam breaching during an overtopping event is one of the major modes of failure for this dam type, comprising both [...] Read more.

Dam failures are examples of man-made disasters that have stimulated investigation into the processes related to the failure of different dam types. Embankment dam breaching during an overtopping event is one of the major modes of failure for this dam type, comprising both earthfill and rockfill dams. This paper presents the results of a series of laboratory tests on breach initiation and progression in rockfill dams. Especially eight breaching tests of 1 m-high 1:10 scale embankment dams constructed of scaled well-graded rockfill were conducted. Tests were performed with and without an impervious core and under different inflow discharges. Controlling instrumentation includes up to nine video cameras used for image analysis and photogrammetry. A previously little-used technique of dynamic 3D photogrammetry has been applied to prepare 3D models every 5 s throughout the breaching process, allowing us to track in detail breach development. These dynamic 3D models along with pressure sensor data, flow data, and side-view video are used to provide data on erosion rates throughout the breaching process. One important purpose of this research is to test methods of observing a rapidly changing morphology such as an embankment dam breach that can easily be scaled up to large-scale and prototype-scale tests. The resulting data sets are further intended for the verification of existing empirical and numerical models for slope stability and breach development as well as the development of new models. Full article

(This article belongs to the Special Issue Application of Digital Aerial Photogrammetry in Geomorphological Studies)

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27 pages, 7002 KiB

Open AccessArticle

Passive Electro-Optical Tracking of Resident Space Objects for Distributed Satellite Systems Autonomous Navigation

by Khaja Faisal Hussain, Kathiravan Thangavel, Alessandro Gardi and Roberto Sabatini

Remote Sens. 2023, 15(6), 1714; https://doi.org/10.3390/rs15061714 - 22 Mar 2023

Cited by 9 | Viewed by 2135 | Correction

Abstract

Autonomous navigation (AN) and manoeuvring are increasingly important in distributed satellite systems (DSS) in order to avoid potential collisions with space debris and other resident space objects (RSO). In order to accomplish collision avoidance manoeuvres, tracking and characterization of RSO is crucial. At [...] Read more.

Autonomous navigation (AN) and manoeuvring are increasingly important in distributed satellite systems (DSS) in order to avoid potential collisions with space debris and other resident space objects (RSO). In order to accomplish collision avoidance manoeuvres, tracking and characterization of RSO is crucial. At present, RSO are tracked and catalogued using ground-based observations, but space-based space surveillance (SBSS) represents a valid alternative (or complementary asset) due to its ability to offer enhanced performances in terms of sensor resolution, tracking accuracy, and weather independence. This paper proposes a particle swarm optimization (PSO) algorithm for DSS AN and manoeuvring, specifically addressing RSO tracking and collision avoidance requirements as an integral part of the overall system design. More specifically, a DSS architecture employing hyperspectral sensors for Earth observation is considered, and passive electro-optical sensors are used, in conjunction with suitable mathematical algorithms, to accomplish autonomous RSO tracking and classification. Simulation case studies are performed to investigate the tracking and system collision avoidance capabilities in both space-based and ground-based tracking scenarios. Results corroborate the effectiveness of the proposed AN technique and highlight its potential to supplement either conventional (ground-based) or SBSS tracking methods. Full article

(This article belongs to the Special Issue Autonomous Space Navigation)

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27 pages, 1846 KiB

Open AccessArticle

Nearest Neighboring Self-Supervised Learning for Hyperspectral Image Classification

by Yao Qin, Yuanxin Ye, Yue Zhao, Junzheng Wu, Han Zhang, Kenan Cheng and Kun Li

Remote Sens. 2023, 15(6), 1713; https://doi.org/10.3390/rs15061713 - 22 Mar 2023

Cited by 4 | Viewed by 1704

Abstract

Recently, state-of-the-art classification performance of natural images has been obtained by self-supervised learning (S2L) as it can generate latent features through learning between different views of the same images. However, the latent semantic information of similar images has hardly been exploited by these [...] Read more.

Recently, state-of-the-art classification performance of natural images has been obtained by self-supervised learning (S2L) as it can generate latent features through learning between different views of the same images. However, the latent semantic information of similar images has hardly been exploited by these S2L-based methods. Consequently, to explore the potential of S2L between similar samples in hyperspectral image classification (HSIC), we propose the nearest neighboring self-supervised learning (N2SSL) method, by interacting between different augmentations of reliable nearest neighboring pairs (RN2Ps) of HSI samples in the framework of bootstrap your own latent (BYOL). Specifically, there are four main steps: pretraining of spectral spatial residual network (SSRN)-based BYOL, generation of nearest neighboring pairs (N2Ps), training of BYOL based on RN2P, final classification. Experimental results of three benchmark HSIs validated that S2L on similar samples can facilitate subsequent classification. Moreover, we found that BYOL trained on an un-related HSI can be fine-tuned for classification of other HSIs with less computational cost and higher accuracy than training from scratch. Beyond the methodology, we present a comprehensive review of HSI-related data augmentation (DA), which is meaningful to future research of S2L on HSIs. Full article

(This article belongs to the Special Issue Computational Intelligence for Remote Sensing Image Analysis and Applications)

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23 pages, 1961 KiB

Open AccessArticle

Visible Near-Infrared Spectroscopy and Pedotransfer Function Well Predict Soil Sorption Coefficient of Glyphosate

by Sonia Akter, Lis Wollesen de Jonge, Per Møldrup, Mogens Humlekrog Greve, Trine Nørgaard, Peter Lystbæk Weber, Cecilie Hermansen, Abdul Mounem Mouazen and Maria Knadel

Remote Sens. 2023, 15(6), 1712; https://doi.org/10.3390/rs15061712 - 22 Mar 2023

Cited by 1 | Viewed by 1335

Abstract

The soil sorption coefficient (K_d) of glyphosate mainly controls its transport and fate in the environment. Laboratory-based analysis of K_d is laborious and expensive. This study aimed to test the feasibility of visible near-infrared spectroscopy (vis–NIRS) as an alternative method [...] Read more.

The soil sorption coefficient (K_d) of glyphosate mainly controls its transport and fate in the environment. Laboratory-based analysis of K_d is laborious and expensive. This study aimed to test the feasibility of visible near-infrared spectroscopy (vis–NIRS) as an alternative method for glyphosate K_d estimation at a country scale and compare its accuracy against pedotransfer function (PTF). A total of 439 soils with a wide range of K_d values (37–2409 L kg⁻¹) were collected from Denmark (DK) and southwest Greenland (GR). Two modeling scenarios were considered to predict K_d: a combined model developed on DK and GR samples and individual models developed on either DK or GR samples. Partial least squares regression (PLSR) and artificial neural network (ANN) techniques were applied to develop vis–NIRS models. Results from the best technique were validated using a prediction set and compared with PTF for each scenario. The PTFs were built with soil texture, OC, pH, Fe_ox, and P_ox. The ratio of performance to interquartile distance (RPIQ) was 1.88, 1.70, and 1.50 for the combined (ANN), DK (ANN), and GR (PLSR) validation models, respectively. vis–NIRS obtained higher predictive ability for K_d than PTFs for the combined dataset, whereas PTF resulted in slightly better estimations of K_d on the DK and GR samples. However, the differences in prediction accuracy between vis–NIRS and PTF were statistically insignificant. Considering the multiple advantages of vis–NIRS, e.g., being rapid and non-destructive, it can provide a faster and easier alternative to PTF for estimating glyphosate K_d. Full article

(This article belongs to the Special Issue The Use of Proximal and Remote Sensing Techniques for the Detection and Mapping of Contaminants in Soils)

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16 pages, 9790 KiB

Open AccessArticle

A Data Assimilation Method Combined with Machine Learning and Its Application to Anthropogenic Emission Adjustment in CMAQ

by Congwu Huang, Tao Niu, Hao Wu, Yawei Qu, Tijian Wang, Mengmeng Li, Rong Li and Hongli Liu

Remote Sens. 2023, 15(6), 1711; https://doi.org/10.3390/rs15061711 - 22 Mar 2023

Viewed by 1433

Abstract

Anthropogenic emissions play an important role in air quality forecasting. To improve the forecasting accuracy, the use of nudging as the data assimilation method, combined with extremely randomized trees (ExRT) as the machine learning method, was developed and applied to adjust the anthropogenic [...] Read more.

Anthropogenic emissions play an important role in air quality forecasting. To improve the forecasting accuracy, the use of nudging as the data assimilation method, combined with extremely randomized trees (ExRT) as the machine learning method, was developed and applied to adjust the anthropogenic emissions in the Community Multiscale Air Quality modeling system (CMAQ). This nudging–ExRT method can iterate with the forecast and is suitable for linear and nonlinear emissions. For example, an episode between 15 and 30 January 2019 was simulated for China’s Beijing–Tianjin–Hebei (BTH) region. For PM_2.5, the correlation coefficient of the site averaged concentration (Ra) increased from 0.85 to 0.94, and the root mean square error (RMSEa) decreased from 24.41 to 9.97 µg/m³. For O₃, the Ra increased from 0.75 to 0.81, and the RMSEa decreased from 13.91 to 12.07 µg/m³. These results showed that nudging–ExRT can significantly improve forecasting skills and can be applied to routine air quality forecasting in the future. Full article

(This article belongs to the Special Issue Recent Advances in Air Quality Modeling, Forecasting and Data Assimilation)

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17 pages, 16249 KiB

Open AccessArticle

Evaluation and Error Decomposition of IMERG Product Based on Multiple Satellite Sensors

by Yunping Li, Ke Zhang, Andras Bardossy, Xiaoji Shen and Yujia Cheng

Remote Sens. 2023, 15(6), 1710; https://doi.org/10.3390/rs15061710 - 22 Mar 2023

Cited by 1 | Viewed by 1173

Abstract

The Integrated Multisatellite Retrievals for GPM (IMERG) is designed to derive precipitation by merging data from all the passive microwave (PMW) and infrared (IR) sensors. While the input source errors originating from the PMW and IR sensors are important, their structure, characteristics, and [...] Read more.

The Integrated Multisatellite Retrievals for GPM (IMERG) is designed to derive precipitation by merging data from all the passive microwave (PMW) and infrared (IR) sensors. While the input source errors originating from the PMW and IR sensors are important, their structure, characteristics, and algorithm improvement remain unclear. Our study utilized a four-component error decomposition (4CED) method and a systematic and random error decomposition method to evaluate the detectability of IMERG dataset and identify the precipitation errors based on the multi-sensors. The 30 min data from 30 precipitation stations in the Tunxi Watershed were used to evaluate the IMERG data from 2018 to 2020. The input source includes five types of PMW sensors and IR instruments. The results show that the sample ratio for IR (Morph, IR + Morph, and IR only) is much higher than that for PMW (AMSR2, SSMIS, GMI, MHS, and ATMS), with a ratio of 72.8% for IR sources and a ratio of 27.2% for PMW sources. The high false ratio of the IR sensor leads to poor detectability performance of the false alarm ratio (FAR, 0.5854), critical success index (CSI, 0.3014), and Brier score (BS, 0.1126). As for the 4CED, Morph and Morph + IR have a large magnitude of high total bias (TB), hit overestimate bias (HOB), hit underestimate bias (HUB), false bias (FB), and miss bias (MB), which is related to the prediction ability and sample size. In addition, systematic error is the prominent component for AMSR2, SSMIS, GMI, and Morph + IR, indicating some inherent error (retrieval algorithm) that needs to be removed. These findings can support improving the retrieval algorithm and reducing errors in the IMERG dataset. Full article

(This article belongs to the Topic Advanced Research in Precipitation Measurements)

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10 pages, 4671 KiB

Open AccessCommunication

Improving Pre-Training and Fine-Tuning for Few-Shot SAR Automatic Target Recognition

by Chao Zhang, Hongbin Dong and Baosong Deng

Remote Sens. 2023, 15(6), 1709; https://doi.org/10.3390/rs15061709 - 22 Mar 2023

Cited by 1 | Viewed by 1385

Abstract

SAR-ATR (synthetic aperture radar-automatic target recognition) is a hot topic in remote sensing. This work suggests a few-shot target recognition approach (FTL) based on the concept of transfer learning to accomplish accurate target recognition of SAR images in a few-shot scenario since the [...] Read more.

SAR-ATR (synthetic aperture radar-automatic target recognition) is a hot topic in remote sensing. This work suggests a few-shot target recognition approach (FTL) based on the concept of transfer learning to accomplish accurate target recognition of SAR images in a few-shot scenario since the classic SAR ATR method has significant data reliance. At the same time, the strategy introduces a model distillation method to improve the model’s performance further. This method is composed of three parts. First, the data engine, which uses the style conversion model and optical image data to generate image data similar to SAR style and realize cross-domain conversion, can effectively solve the problem of insufficient training data of the SAR image classification model. Second is model training, which uses SAR image data sets to pre-train the model. Here, we introduce the deep Brownian distance covariance (Deep BDC) pooling layer to optimize the image feature representation so that the model can learn the image representation by measuring the difference between the joint feature function of the embedded feature and the edge product. Third, model fine-tuning, which freezes the model structure, except the classifier, and fine-tunes it by using a small amount of novel data. The knowledge distillation approach is also introduced simultaneously to train the model repeatedly, sharpen the knowledge, and enhance model performance. According to experimental results on the MSTAR benchmark dataset, the proposed method is demonstrably better than the SOTA method in the few-shot SAR ATR issue. The recognition accuracy is about 80% in the case of 10-way 10-shot. Full article

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27 pages, 8792 KiB

Open AccessArticle

Drought Disasters in China from 1991 to 2018: Analysis of Spatiotemporal Trends and Characteristics

by Xiaofeng Wang, Pingping Luo, Yue Zheng, Weili Duan, Shuangtao Wang, Wei Zhu, Yuzhu Zhang and Daniel Nover

Remote Sens. 2023, 15(6), 1708; https://doi.org/10.3390/rs15061708 - 22 Mar 2023

Cited by 14 | Viewed by 2143

Abstract

Droughts have emerged as a global problem in contemporary societies. China suffers from different degrees of drought almost every year, with increasing drought severity each year. Droughts in China are seasonal and can severely impact crops. This study used spatiotemporal trend and characteristics [...] Read more.

Droughts have emerged as a global problem in contemporary societies. China suffers from different degrees of drought almost every year, with increasing drought severity each year. Droughts in China are seasonal and can severely impact crops. This study used spatiotemporal trend and characteristics analysis of drought disaster data from 1991 to 2018 in Chinese provinces, in addition to the Mann–Kendall test and wavelet analysis. The drought disaster data included the crop damage area, drought-affected area of the crops, and crop failure area. The outputs of the crops decreased by 10%, 30%, and 80%, respectively. The population with reduced drinking water caused by drought, and the domestic animals with reduced drinking water caused by drought, were numbered in the tens of thousands. The results of the study show that the crop damage areas owing to drought disasters, drought-affected areas of crops, and crop failure areas in China were mainly distributed in the northern, eastern, northeaster, and southwestern regions. The number of people and domestic animals with reduced drinking water owing to drought in China were mainly concentrated in the northern and southwestern regions. These indicators showed a general increasing trend. Tibet, Fujian, Shandong, Jiangsu, Anhui, and Henan provinces and autonomous regions also showed a slightly increasing trend. In particular, the number of domestic animals with reduced drinking water caused by drought in the Inner Mongolia Autonomous Region showed a clear increasing trend with a significant Z-value of 2.2629. The results of this research can be used to provide scientific evidence for predicting future trends in drought and for practising the best management of drought prevention and resistance. Full article

(This article belongs to the Topic Green Technology, Environmental Management and Corporate Social Responsibility from a Global Perspective)

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17 pages, 8071 KiB

Open AccessArticle

Remote Seismoacoustic Monitoring of Tropical Cyclones in the Sea of Japan

by Grigory Dolgikh, Stanislav Dolgikh, Vladimir Chupin, Aleksandr Davydov and Aleksandr Mishakov

Remote Sens. 2023, 15(6), 1707; https://doi.org/10.3390/rs15061707 - 22 Mar 2023

Viewed by 893

Abstract

In the course of processing and analysing data from a two-coordinate laser strainmeter, obtained during the propagation of the Hagupit typhoon over the Sea of Japan, we researched the possibility of sensing the direction of tropical cyclones/typhoons and also tracking their movements. We [...] Read more.

In the course of processing and analysing data from a two-coordinate laser strainmeter, obtained during the propagation of the Hagupit typhoon over the Sea of Japan, we researched the possibility of sensing the direction of tropical cyclones/typhoons and also tracking their movements. We tackled the set of problems on the basis of further development of the technology for sensing the direction of primary and secondary microseisms’ generation zones, the “voice of the sea” microseisms, and clarifying the connection between their formation zones and movement of tropical cyclones. In our work, we identified the formation zones of primary and secondary microseisms, which were registered by the two-coordinate laser strainmeter. We established that, from the registered microseisms, we could determine the main characteristics of wind waves generated by a typhoon, but we could not identify its location. By processing the two-coordinate laser strainmeter data in the range of the “voice of the sea” microseisms, we established the possibility of sensing the direction of the “voice of the sea” microseisms’ formation zones, which are associated with zones of the highest energy capacity of typhoons, and this allowed us to tracking the direction of the typhoons’ movement. Full article

(This article belongs to the Special Issue Linking Upper Ocean Dynamics with Extreme Weather and Climate Events over the Ocean)

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20 pages, 40396 KiB

Open AccessArticle

Convolutional Neural Network-Driven Improvements in Global Cloud Detection for Landsat 8 and Transfer Learning on Sentinel-2 Imagery

by Shulin Pang, Lin Sun, Yanan Tian, Yutiao Ma and Jing Wei

Remote Sens. 2023, 15(6), 1706; https://doi.org/10.3390/rs15061706 - 22 Mar 2023

Cited by 6 | Viewed by 1846

Abstract

A stable and reliable cloud detection algorithm is an important step of optical satellite data preprocessing. Existing threshold methods are mostly based on classifying spectral features of isolated individual pixels and do not contain or incorporate the spatial information. This often leads to [...] Read more.

A stable and reliable cloud detection algorithm is an important step of optical satellite data preprocessing. Existing threshold methods are mostly based on classifying spectral features of isolated individual pixels and do not contain or incorporate the spatial information. This often leads to misclassifications of bright surfaces, such as human-made structures or snow/ice. Multi-temporal methods can alleviate this problem, but cloud-free images of the scene are difficult to obtain. To deal with this issue, we extended four deep-learning Convolutional Neural Network (CNN) models to improve the global cloud detection accuracy for Landsat imagery. The inputs are simplified as all discrete spectral channels from visible to short wave infrared wavelengths through radiometric calibration, and the United States Geological Survey (USGS) global Landsat 8 Biome cloud-cover assessment dataset is randomly divided for model training and validation independently. Experiments demonstrate that the cloud mask of the extended U-net model (i.e., UNmask) yields the best performance among all the models in estimating the cloud amounts (cloud amount difference, CAD = −0.35%) and capturing the cloud distributions (overall accuracy = 94.9%) for Landsat 8 imagery compared with the real validation masks; in particular, it runs fast and only takes about 41 ± 5.5 s for each scene. Our model can also actually detect broken and thin clouds over both dark and bright surfaces (e.g., urban and barren). Last, the UNmask model trained for Landsat 8 imagery is successfully applied in cloud detections for the Sentinel-2 imagery (overall accuracy = 90.1%) via transfer learning. These prove the great potential of our model in future applications such as remote sensing satellite data preprocessing. Full article

(This article belongs to the Special Issue "Atmospheric Environmental Remote Sensing Society (AERSS)": Celebrating the Establishment)

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30 pages, 24057 KiB

Open AccessArticle

RadWet: An Improved and Transferable Mapping of Open Water and Inundated Vegetation Using Sentinel-1

by Gregory Oakes, Andy Hardy and Pete Bunting

Remote Sens. 2023, 15(6), 1705; https://doi.org/10.3390/rs15061705 - 22 Mar 2023

Cited by 2 | Viewed by 2197

Abstract

Mapping the spatial and temporal dynamics of tropical herbaceous wetlands is vital for a wide range of applications. Inundated vegetation can account for over three-quarters of the total inundated area, yet widely used EO mapping approaches are limited to the detection of open [...] Read more.

Mapping the spatial and temporal dynamics of tropical herbaceous wetlands is vital for a wide range of applications. Inundated vegetation can account for over three-quarters of the total inundated area, yet widely used EO mapping approaches are limited to the detection of open water bodies. This paper presents a new wetland mapping approach, RadWet, that automatically defines open water and inundated vegetation training data using a novel mixture of radar, terrain, and optical imagery. Training data samples are then used to classify serial Sentinel-1 radar imagery using an ensemble machine learning classification routine, providing information on the spatial and temporal dynamics of inundation every 12 days at a resolution of 30 m. The approach was evaluated over the period 2017–2022, covering a range of conditions (dry season to wet season) for two sites: (1) the Barotseland Floodplain, Zambia (31,172 km²) and (2) the Upper Rupununi Wetlands in Guyana (11,745 km²). Good agreement was found at both sites using random stratified accuracy assessment data (n = 28,223) with a median overall accuracy of 89% in Barotseland and 80% in the Upper Rupununi, outperforming existing approaches. The results revealed fine-scale hydrological processes driving inundation patterns as well as temporal patterns in seasonal flood pulse timing and magnitude. Inundated vegetation dominated wet season wetland extent, accounting for a mean 80% of total inundation. RadWet offers a new way in which tropical wetlands can be routinely monitored and characterised. This can provide significant benefits for a range of application areas, including flood hazard management, wetland inventories, monitoring natural greenhouse gas emissions and disease vector control. Full article

(This article belongs to the Special Issue Advances of Remote Sensing and GIS Technology in Surface Water Bodies)

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28 pages, 31701 KiB

Open AccessArticle

Present-Day Surface Deformation in North-East Italy Using InSAR and GNSS Data

by Giulia Areggi, Giuseppe Pezzo, John Peter Merryman Boncori, Letizia Anderlini, Giuliana Rossi, Enrico Serpelloni, David Zuliani and Lorenzo Bonini

Remote Sens. 2023, 15(6), 1704; https://doi.org/10.3390/rs15061704 - 22 Mar 2023

Cited by 1 | Viewed by 2036

Abstract

Geodetic data can detect and estimate deformation signals and rates due to natural and anthropogenic phenomena. In the present study, we focus on northeastern Italy, an area characterized by ~1.5–3 mm/yr of convergence rates due to the collision of Adria-Eurasia plates and active [...] Read more.

Geodetic data can detect and estimate deformation signals and rates due to natural and anthropogenic phenomena. In the present study, we focus on northeastern Italy, an area characterized by ~1.5–3 mm/yr of convergence rates due to the collision of Adria-Eurasia plates and active subsidence along the coasts. To define the rates and trends of tectonic and subsidence signals, we use a Multi-Temporal InSAR (MT-InSAR) approach called the Stanford Method for Persistent Scatterers (StaMPS), which is based on the detection of coherent and temporally stable pixels in a stack of single-master differential interferograms. We use Sentinel-1 SAR images along ascending and descending orbits spanning the 2015–2019 temporal interval as inputs for Persistent Scatterers InSAR (PSI) processing. We apply spatial-temporal filters and post-processing steps to reduce unrealistic results. Finally, we calibrate InSAR measurements using GNSS velocities derived from permanent stations available in the study area. Our results consist of mean ground velocity maps showing the displacement rates along the radar Line-Of-Sight for each satellite track, from which we estimate the east–west and vertical velocity components. Our results provide a detailed and original view of active vertical and horizontal displacement rates over the whole region, allowing the detection of spatial velocity gradients, which are particularly relevant to a better understanding of the seismogenic potential of the area. As regards the subsidence along the coasts, our measurements confirm the correlation between subsidence and the geological setting of the study area, with rates of ~2–4 mm/yr between the Venezia and Marano lagoons, and lower than 1 mm/yr near Grado. Full article

(This article belongs to the Special Issue Ground Deformation Detection and Geomatic Applications by InSAR and GNSS Techniques II)

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16 pages, 5184 KiB

Open AccessCommunication

Distinguishing Buildings from Vegetation in an Urban-Chaparral Mosaic Landscape with LiDAR-Informed Discriminant Analysis

by Thomas J. Yamashita, David B. Wester, Michael E. Tewes, John H. Young, Jr. and Jason V. Lombardi

Remote Sens. 2023, 15(6), 1703; https://doi.org/10.3390/rs15061703 - 22 Mar 2023

Cited by 1 | Viewed by 1080

Abstract

Identification of buildings from remotely sensed imagery in urban and suburban areas is a challenging task. Light detection and Ranging (LiDAR) provides an opportunity to accurately identify buildings by identification of planar surfaces. Dense vegetation can limit the number of light particles that [...] Read more.

Identification of buildings from remotely sensed imagery in urban and suburban areas is a challenging task. Light detection and Ranging (LiDAR) provides an opportunity to accurately identify buildings by identification of planar surfaces. Dense vegetation can limit the number of light particles that reach the ground, potentially creating false planar surfaces within a vegetation stand. We present an application of discriminant analysis (a commonly used statistical tool in decision theory) to classify polygons (derived from LiDAR) as either buildings or a non-building planar surfaces. We conducted our analysis in southern Texas where thornscrub vegetation often prevents a LiDAR beam from fully penetrating the vegetation canopy in and around residential areas. Using discriminant analysis, we grouped potential building polygons into building and non-building classes using the point densities of ground, unclassified, and building points. Our technique was 95% accurate at distinguishing buildings from non-buildings. Therefore, we recommend its use in any locale where distinguishing buildings from surrounding vegetation may be affected by the proximity of dense vegetation to buildings. Full article

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26 pages, 9597 KiB

Open AccessArticle

Continuously Updated Digital Elevation Models (CUDEMs) to Support Coastal Inundation Modeling

by Christopher J. Amante, Matthew Love, Kelly Carignan, Michael G. Sutherland, Michael MacFerrin and Elliot Lim

Remote Sens. 2023, 15(6), 1702; https://doi.org/10.3390/rs15061702 - 22 Mar 2023

Cited by 8 | Viewed by 3592

Abstract

The National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI) generates digital elevation models (DEMs) that range from the local to global scale. Collectively, these DEMs are essential to determining the timing and extent of coastal inundation and improving community [...] Read more.

The National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI) generates digital elevation models (DEMs) that range from the local to global scale. Collectively, these DEMs are essential to determining the timing and extent of coastal inundation and improving community preparedness, event forecasting, and warning systems. We initiated a comprehensive framework at NCEI, the Continuously Updated DEM (CUDEM) Program, with seamless bare-earth, topographic-bathymetric and bathymetric DEMs for the entire United States (U.S.) Atlantic and Gulf of Mexico Coasts, Hawaii, American Territories, and portions of the U.S. Pacific Coast. The CUDEMs are currently the highest-resolution, seamless depiction of the entire U.S. Atlantic and Gulf Coasts in the public domain; coastal topographic-bathymetric DEMs have a spatial resolution of 1/9th arc-second (~3 m) and offshore bathymetric DEMs coarsen to 1/3rd arc-second (~10 m). We independently validate the land portions of the CUDEMs with NASA’s Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) observatory and calculate a corresponding vertical mean bias error of 0.12 m ± 0.75 m at one standard deviation, with an overall RMSE of 0.76 m. We generate the CUDEMs through a standardized process using free and open-source software (FOSS) and provide open-access to our code repository. The CUDEM framework consists of systematic tiled geographic extents, spatial resolutions, and horizontal and vertical datums to facilitate rapid updates of targeted areas with new data collections, especially post-storm and tsunami events. The CUDEM framework also enables the rapid incorporation of high-resolution data collections ingested into local-scale DEMs into NOAA NCEI’s suite of regional and global DEMs. Future research efforts will focus on the generation of additional data products, such as spatially explicit vertical error estimations and morphologic change calculations, to enhance the utility and scientific benefits of the CUDEM Program. Full article

(This article belongs to the Special Issue Remote Sensing in Marine-Coastal Environments)

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21 pages, 14587 KiB

Open AccessArticle

A Low-Cost Deep Learning System to Characterize Asphalt Surface Deterioration

by Diogo Inácio, Henrique Oliveira, Pedro Oliveira and Paulo Correia

Remote Sens. 2023, 15(6), 1701; https://doi.org/10.3390/rs15061701 - 22 Mar 2023

Viewed by 1526

Abstract

Every day millions of people travel on highways for work- or leisure-related purposes. Ensuring road safety is thus of paramount importance, and maintaining good-quality road pavements is essential, requiring an effective maintenance policy. The automation of some road pavement maintenance tasks can reduce [...] Read more.

Every day millions of people travel on highways for work- or leisure-related purposes. Ensuring road safety is thus of paramount importance, and maintaining good-quality road pavements is essential, requiring an effective maintenance policy. The automation of some road pavement maintenance tasks can reduce the time and effort required from experts. This paper proposes a simple system to help speed up road pavement surface inspection and its analysis towards making maintenance decisions. A low-cost video camera mounted on a vehicle was used to capture pavement imagery, which was fed to an automatic crack detection and classification system based on deep neural networks. The system provided two types of output: (i) a cracking percentage per road segment, providing an alert to areas that require attention from the experts; (ii) a segmentation map highlighting which areas of the road pavement surface are affected by cracking. With this data, it became possible to select which maintenance or rehabilitation processes the road pavement required. The system achieved promising results in the analysis of highway pavements, and being automated and having a low processing time, the system is expected to be an effective aid for experts dealing with road pavement maintenance. Full article

(This article belongs to the Special Issue Road Extraction and Distress Assessment by Spaceborne, Airborne and Terrestrial Platforms)

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17 pages, 6041 KiB

Open AccessArticle

Overcoming Domain Shift in Neural Networks for Accurate Plant Counting in Aerial Images

by Javier Rodriguez-Vazquez, Miguel Fernandez-Cortizas, David Perez-Saura, Martin Molina and Pascual Campoy

Remote Sens. 2023, 15(6), 1700; https://doi.org/10.3390/rs15061700 - 22 Mar 2023

Cited by 1 | Viewed by 1728

Abstract

This paper presents a novel semi-supervised approach for accurate counting and localization of tropical plants in aerial images that can work in new visual domains in which the available data are not labeled. Our approach uses deep learning and domain adaptation, designed to [...] Read more.

This paper presents a novel semi-supervised approach for accurate counting and localization of tropical plants in aerial images that can work in new visual domains in which the available data are not labeled. Our approach uses deep learning and domain adaptation, designed to handle domain shifts between the training and test data, which is a common challenge in this agricultural applications. This method uses a source dataset with annotated plants and a target dataset without annotations and adapts a model trained on the source dataset to the target dataset using unsupervised domain alignment and pseudolabeling. The experimental results show the effectiveness of this approach for plant counting in aerial images of pineapples under significative domain shift, achieving a reduction up to 97% in the counting error (1.42 in absolute count) when compared to the supervised baseline (48.6 in absolute count). Full article

(This article belongs to the Special Issue Data-Driven Approaches and State-of-the-Art Machine Learning Techniques in Support of the Remote Sensing and Agriculture)

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23 pages, 19153 KiB

Open AccessArticle

A Modified NLCS Algorithm for High-Speed Bistatic Forward-Looking SAR Focusing with Spaceborne Illuminator

by Yuzhou Liu, Yachao Li, Xuan Song and Xuanqi Wang

Remote Sens. 2023, 15(6), 1699; https://doi.org/10.3390/rs15061699 - 21 Mar 2023

Viewed by 1160

Abstract

The coupling and spatial variation of range and azimuth parameters is the biggest challenge for bistatic forward-looking SAR (BFSAR) imaging. In contrast with the monostatic SAR and translational invariant bistatic SAR (TI-BSAR), the range cell migration (RCM), and Doppler parameters of high-speed bistatic [...] Read more.

The coupling and spatial variation of range and azimuth parameters is the biggest challenge for bistatic forward-looking SAR (BFSAR) imaging. In contrast with the monostatic SAR and translational invariant bistatic SAR (TI-BSAR), the range cell migration (RCM), and Doppler parameters of high-speed bistatic forward-looking SAR (HS-BFSAR) have two-dimensional spatial variation characteristics, which makes it difficult to obtain SAR images with satisfactory global focusing. Firstly, based on the configuration of the spaceborne illuminator and high-speed forward-looking receiving platform, the accurate range-Doppler domain expression of the echo signal is derived in this paper. Secondly, using this analytical expression, a range nonlinear chirp scaling (NLCS) is proposed to equalize the RCM and equivalent range frequency modulation (FM) rate so that they can be uniformly processed in the two-dimensional frequency domain. Next, in the azimuth processing, the proposed method decomposes the Doppler contribution of the transmitter and receiver, respectively. Then, an azimuth NLCS is used to eliminate the spatial variation of the azimuth FM rate. Finally, a range-dependent azimuth filter is constructed to achieve azimuth compression. Simulation results validate the efficiency and effectiveness of the proposed algorithm. Full article

(This article belongs to the Special Issue Breakthroughs in Passive Radar Technologies)

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15 pages, 3040 KiB

Open AccessTechnical Note

Going Back to Grassland? Assessing the Impact of Groundwater Decline on Irrigated Agriculture Using Remote Sensing Data

by Haoying Wang

Remote Sens. 2023, 15(6), 1698; https://doi.org/10.3390/rs15061698 - 21 Mar 2023

Cited by 1 | Viewed by 1240

Abstract

Climate change has increased agricultural drought risk in arid/semi-arid regions globally. One of the common adaptation strategies is shifting to more drought-tolerant crops or switching back to grassland permanently. In many drought-prone areas, groundwater dynamics play a critical role in agricultural production and [...] Read more.

Climate change has increased agricultural drought risk in arid/semi-arid regions globally. One of the common adaptation strategies is shifting to more drought-tolerant crops or switching back to grassland permanently. In many drought-prone areas, groundwater dynamics play a critical role in agricultural production and drought management. This study aims to help understand how groundwater level decline affects the propensity of cropland switching back to grassland. Taking Union County of New Mexico (US) as a case study, field-scale groundwater level projections and high-resolution remote sensing data on crop choices are integrated to explore the impact of groundwater level decline in a regression analysis framework. The results show that cropland has been slowly but permanently switching back to grassland as the groundwater level in the Ogallala Aquifer continues to decline in the area. Specifically, for a one-standard-deviation decline in groundwater level (36.95 feet or 11.26 m), the average likelihood of switching back to grassland increases by 1.85% (the 95% confidence interval is [0.07%, 3.58%]). The findings account for the fact that farmers usually explore other options (such as more drought-tolerant crops, land idling, and rotation) before switching back to grassland permanently. The paper concludes by exploring relevant policy implications for land (soil) and water conservation in the long run. Full article

(This article belongs to the Special Issue Remote Sensing for Agriculture, Hydrology, and Ecosystems Response to Climatic Variability)

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16 pages, 1050 KiB

Open AccessArticle

Above- and Belowground Biomass Carbon Stock and Net Primary Productivity Maps for Tidal Herbaceous Marshes of the United States

by Victoria L. Woltz, Camille LaFosse Stagg, Kristin B. Byrd, Lisamarie Windham-Myers, Andre S. Rovai and Zhiliang Zhu

Remote Sens. 2023, 15(6), 1697; https://doi.org/10.3390/rs15061697 - 21 Mar 2023

Cited by 4 | Viewed by 2780

Abstract

Accurate assessments of greenhouse gas emissions and carbon sequestration in natural ecosystems are necessary to develop climate mitigation strategies. Regional and national-level assessments of carbon sequestration require high-resolution data to be available for large areas, increasing the need for remote sensing products that [...] Read more.

Accurate assessments of greenhouse gas emissions and carbon sequestration in natural ecosystems are necessary to develop climate mitigation strategies. Regional and national-level assessments of carbon sequestration require high-resolution data to be available for large areas, increasing the need for remote sensing products that quantify carbon stocks and fluxes. The Intergovernmental Panel on Climate Change (IPCC) provides guidelines on how to quantify carbon flux using land cover land change and biomass carbon stock information. Net primary productivity (NPP), carbon uptake, and storage in vegetation, can also be used to model net carbon sequestration and net carbon export from an ecosystem (net ecosystem carbon balance). While biomass and NPP map products for terrestrial ecosystems are available, there are currently no conterminous United States (CONUS) biomass carbon stock or NPP maps for tidal herbaceous marshes. In this study, we used peak soil adjusted vegetation index (SAVI) values, derived from Landsat 8 composites, and five other vegetation indices, plus a categorical variable for the CONUS region (Pacific Northwest, California, Northeast, Mid-Atlantic, South Atlantic-Gulf, or Everglades), to model spatially explicit aboveground peak biomass stocks in tidal marshes (i.e., tidal palustrine and estuarine herbaceous marshes) for the first time. Tidal marsh carbon conversion factors, root-to-shoot ratios, and vegetation turnover rates, were compiled from the literature and used to convert peak aboveground biomass to peak total (above- and belowground) biomass and NPP. An extensive literature search for aboveground turnover rates produced sparse and variable values; therefore, we used an informed assumption of a turnover rate of one crop per year for all CONUS tidal marshes. Due to the lack of turnover rate data, the NPP map is identical to the peak biomass carbon stock map. In reality, it is probable that turnover rate varies by region, given seasonal length differences; however, the NPP map provides the best available information on spatially explicit CONUS tidal marsh NPP. This study identifies gaps in the scientific knowledge, to support future studies in addressing this lack of turnover data. Across CONUS, average total peak biomass carbon stock in tidal marshes was 848 g C m⁻² (871 g C m⁻² in palustrine and 838 g C m⁻² in estuarine marshes), and based on a median biomass turnover rate of 1, it is expected that the mean NPP annual flux for tidal marshes is similar (e.g., 848 g C m⁻² y⁻¹). Peak biomass carbon stocks in tidal marshes were lowest in the Florida Everglades region and highest in the California regions. These are the first fine-scale national maps of biomass carbon and NPP for tidal wetlands, spanning all of CONUS. These estimates of CONUS total peak biomass carbon stocks and NPP rates for tidal marshes can support regional- and national-scale assessments of greenhouse gas emissions, as well as natural resource management of coastal wetlands, as part of nature-based climate solution efforts. Full article

(This article belongs to the Special Issue Use of Remote Sensing in Valuation of Blue Carbon and Its Co-benefits)

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18 pages, 8883 KiB

Open AccessArticle

A Real-Time Detecting Method for Continuous Urban Flood Scenarios Based on Computer Vision on Block Scale

by Haocheng Huang, Xiaohui Lei, Weihong Liao, Haichen Li, Chao Wang and Hao Wang

Remote Sens. 2023, 15(6), 1696; https://doi.org/10.3390/rs15061696 - 21 Mar 2023

Cited by 2 | Viewed by 1638

Abstract

Due to the frequent and sudden occurrence of urban waterlogging, targeted and rapid risk monitoring is extremely important for urban management. To improve the efficiency and accuracy of urban waterlogging monitoring, a real-time determination method of urban waterlogging based on computer vision technology [...] Read more.

Due to the frequent and sudden occurrence of urban waterlogging, targeted and rapid risk monitoring is extremely important for urban management. To improve the efficiency and accuracy of urban waterlogging monitoring, a real-time determination method of urban waterlogging based on computer vision technology was proposed in this study. First, city images were collected and then identified using the ResNet algorithm to determine whether a waterlogging risk existed in the images. Subsequently, the recognition accuracy was improved by image augmentation and the introduction of an attention mechanism (SE-ResNet). The experimental results showed that the waterlogging recognition rate reached 99.50%. In addition, according to the actual water accumulation process, real-time images of the waterlogging area were obtained, and a threshold method using the inverse weight of the time interval (T-IWT) was proposed to determine the times of the waterlogging occurrences from the continuous images. The results showed that the time error of the waterlogging identification was within 30 s. This study provides an effective method for identifying urban waterlogging risks in real-time. Full article

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17 pages, 5871 KiB

Open AccessTechnical Note

Passive Location for 5G OFDM Radiation Sources Based on Virtual Synthetic Aperture

by Tong Zhang, Xin Zhang and Qiang Yang

Remote Sens. 2023, 15(6), 1695; https://doi.org/10.3390/rs15061695 - 21 Mar 2023

Cited by 1 | Viewed by 2366

Abstract

Passive location technology has been greatly developed because of its low power consumption, long detection distance, good concealment, and strong anti-interference ability. Orthogonal frequency-division multiplexing (OFDM) is an efficient multi-carrier transmission technology, which is an important signal form of 5G communication. Researching passive [...] Read more.

Passive location technology has been greatly developed because of its low power consumption, long detection distance, good concealment, and strong anti-interference ability. Orthogonal frequency-division multiplexing (OFDM) is an efficient multi-carrier transmission technology, which is an important signal form of 5G communication. Researching passive locations for OFDM signals can realize the location of base stations, which is of great significance in the military. Space-borne passive location technology has a contradiction between wide coverage and high precision. Therefore, a single-satellite passive location algorithm for OFDM radiation sources based on the virtual synthetic aperture is proposed. The algorithm introduces virtual synthetic aperture technology, using antenna movement to accumulate data coherently over a long time period and synthesizing a long azimuth virtual aperture. In addition, it utilizes fast Fourier transform (FFT) to extract phase information at a specific frequency based on the multi-carrier modulation technology of the OFDM signal. Pilot technology of the communication system is used for phase compensation and noise reduction. Thus, the azimuth linear frequency modulation (LFM) signal containing the location information of the radiation source is obtained. The radiation source location can be obtained by range searching and azimuth focusing. Simulation results verify the effectiveness of the algorithm and show that the algorithm can realize high-precision and wide-coverage location for the OFDM radiation sources using a single antenna, turning the hardware structure into software to reduce the cost and complexity of the system. Full article

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24 pages, 13414 KiB

Open AccessArticle

A Comparison of UAV-Derived Dense Point Clouds Using LiDAR and NIR Photogrammetry in an Australian Eucalypt Forest

by Megan Winsen and Grant Hamilton

Remote Sens. 2023, 15(6), 1694; https://doi.org/10.3390/rs15061694 - 21 Mar 2023

Cited by 1 | Viewed by 2019

Abstract

Light detection and ranging (LiDAR) has been a tool of choice for 3D dense point cloud reconstructions of forest canopy over the past two decades, but advances in computer vision techniques, such as structure from motion (SfM) photogrammetry, have transformed 2D digital aerial [...] Read more.

Light detection and ranging (LiDAR) has been a tool of choice for 3D dense point cloud reconstructions of forest canopy over the past two decades, but advances in computer vision techniques, such as structure from motion (SfM) photogrammetry, have transformed 2D digital aerial imagery into a powerful, inexpensive and highly available alternative. Canopy modelling is complex and affected by a wide range of inputs. While studies have found dense point cloud reconstructions to be accurate, there is no standard approach to comparing outputs or assessing accuracy. Modelling is particularly challenging in native eucalypt forests, where the canopy displays abrupt vertical changes and highly varied relief. This study first investigated whether a remotely sensed LiDAR dense point cloud reconstruction of a native eucalypt forest completely reproduced canopy cover and accurately predicted tree heights. A further comparison was made with a photogrammetric reconstruction based solely on near-infrared (NIR) imagery to gain some insight into the contribution of the NIR spectral band to the 3D SfM reconstruction of native dry eucalypt open forest. The reconstructions did not produce comparable canopy height models and neither reconstruction completely reproduced canopy cover nor accurately predicted tree heights. Nonetheless, the LiDAR product was more representative of the eucalypt canopy than SfM-NIR. The SfM-NIR results were strongly affected by an absence of data in many locations, which was related to low canopy penetration by the passive optical sensor and sub-optimal feature matching in the photogrammetric pre-processing pipeline. To further investigate the contribution of NIR, future studies could combine NIR imagery captured at multiple solar elevations. A variety of photogrammetric pre-processing settings should continue to be explored in an effort to optimise image feature matching. Full article

(This article belongs to the Special Issue Drones, Artificial Intelligence and Advanced Analytics for the Conservation of Threatened Species)

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15 pages, 4052 KiB

Open AccessTechnical Note

Towards a General Monitoring System for Terrestrial Primary Production: A Test Spanning the European Drought of 2018

by Keith J. Bloomfield, Roel van Hoolst, Manuela Balzarolo, Ivan A. Janssens, Sara Vicca, Darren Ghent and I. Colin Prentice

Remote Sens. 2023, 15(6), 1693; https://doi.org/10.3390/rs15061693 - 21 Mar 2023

Cited by 1 | Viewed by 1708

Abstract

(1) Land surface models require inputs of temperature and moisture variables to generate predictions of gross primary production (GPP). Differences between leaf and air temperature vary temporally and spatially and may be especially pronounced under conditions of low soil moisture availability. The Sentinel-3 [...] Read more.

(1) Land surface models require inputs of temperature and moisture variables to generate predictions of gross primary production (GPP). Differences between leaf and air temperature vary temporally and spatially and may be especially pronounced under conditions of low soil moisture availability. The Sentinel-3 satellite mission offers estimates of the land surface temperature (LST), which for vegetated pixels can be adopted as the canopy temperature. Could remotely sensed estimates of LST offer a parsimonious input to models by combining information on leaf temperature and hydration? (2) Using a light use efficiency model that requires only a handful of input variables, we generated GPP simulations for comparison with eddy-covariance inferred estimates available from flux sites within the Integrated Carbon Observation System. Remotely sensed LST and greenness data were input from Sentinel-3. Gridded air temperature data were obtained from the European Centre for Medium-Range Weather Forecasts. We chose the years 2018–2019 to exploit the natural experiment of a pronounced European drought. (3) Simulated GPP showed good agreement with flux-derived estimates. During dry conditions, simulations forced with LST performed better than those with air temperature for shrubland, grassland and savanna sites. (4) This study advances the prospect for a global GPP monitoring system that will rely primarily on remotely sensed inputs. Full article

(This article belongs to the Special Issue Remote Sensing Applications for the Biosphere)

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29 pages, 9750 KiB

Open AccessArticle

Daily Sea Ice Concentration Product over Polar Regions Based on Brightness Temperature Data from the HY-2B SMR Sensor

by Suhui Wu, Lijian Shi, Bin Zou, Tao Zeng, Zhaoqing Dong and Dunwang Lu

Remote Sens. 2023, 15(6), 1692; https://doi.org/10.3390/rs15061692 - 21 Mar 2023

Cited by 1 | Viewed by 1347

Abstract

Polar sea ice profoundly affects atmospheric and oceanic circulation and plays a significant role in climate change. Sea ice concentration (SIC) is a key geophysical parameter used to quantify these changes. In this study, we determined SIC products for the Arctic and Antarctic [...] Read more.

Polar sea ice profoundly affects atmospheric and oceanic circulation and plays a significant role in climate change. Sea ice concentration (SIC) is a key geophysical parameter used to quantify these changes. In this study, we determined SIC products for the Arctic and Antarctic from 2019 to 2021 using data from the Chinese marine satellite Haiyang 2B (HY-2B) with an improved bootstrap algorithm. Then the results were compared with similar operational SIC products and ship-based data. Our findings demonstrate the effectiveness of the improved algorithm for accurately determining SIC in polar regions. Additionally, the results of the study demonstrate that the SIC product obtained through the improved bootstrap algorithm has a high correlation with other similar SIC products. The daily average SIC of the different products showed similar inter-annual trends for both the Arctic and Antarctic regions. Comparison of the different SIC products showed that the Arctic BT-SMR SIC was slightly lower than the BT-SSMIS and BT-AMSR2 SIC products, while the difference between Antarctic SIC products was more pronounced. The lowest MAE was between the BT-SSMIS SIC and BT-SMR SIC in both regions, while the largest MAE was between the NT-SMR and BT-SMR in the Arctic, and between the NT-SSMIS and BT-SMR in the Antarctic. The SIE and SIA time series showed consistent trends, with a greater difference in SIA than SIC and a slight difference in SIA between the BT-AMSR2 and BT-SMR in the Arctic. Evaluation of the different SIC products using ship-based observation data showed a high correlation between the BT-SMR SIC and the ship-based SIC of approximately 0.85 in the Arctic and 0.88 in the Antarctic. The time series of dynamic tie-points better reflected the seasonal variation in sea ice radiation characteristics. This study lays the foundation for the release of long-term SIC product series from the Chinese autonomous HY-2B satellite, which will ensure the continuity of polar sea ice records over the past 40 years despite potential interruptions. Full article

(This article belongs to the Special Issue HY-2 Satellite Microwave Remote Sensing of Ocean for 10 Years: Applications and Advances)

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15 pages, 9427 KiB

Open AccessArticle

The Impacts of Quality-Oriented Dataset Labeling on Tree Cover Segmentation Using U-Net: A Case Study in WorldView-3 Imagery

by Tao Jiang, Maximilian Freudenberg, Christoph Kleinn, Alexander Ecker and Nils Nölke

Remote Sens. 2023, 15(6), 1691; https://doi.org/10.3390/rs15061691 - 21 Mar 2023

Cited by 1 | Viewed by 1630

Abstract

Deep learning has emerged as a prominent technique for extracting vegetation information from high-resolution satellite imagery. However, less attention has been paid to the quality of dataset labeling as compared to research into networks and models, despite data quality consistently having a high [...] Read more.

Deep learning has emerged as a prominent technique for extracting vegetation information from high-resolution satellite imagery. However, less attention has been paid to the quality of dataset labeling as compared to research into networks and models, despite data quality consistently having a high impact on final accuracies. In this work, we trained a U-Net model for tree cover segmentation in 30 cm WorldView-3 imagery and assessed the impact of training data quality on segmentation accuracy. We produced two reference tree cover masks of different qualities by labeling images accurately or roughly and trained the model on a combination of both, with varying proportions. Our results show that models trained with accurately delineated masks achieved higher accuracy (88.06%) than models trained on masks that were only roughly delineated (81.13%). When combining the accurately and roughly delineated masks at varying proportions, we found that the segmentation accuracy increased with the proportion of accurately delineated masks. Furthermore, we applied semisupervised active learning techniques to identify an efficient strategy for selecting images for labeling. This showed that semisupervised active learning saved nearly 50% of the labeling cost when applied to accurate masks, while maintaining high accuracy (88.07%). Our study suggests that accurate mask delineation and semisupervised active learning are essential for efficiently generating training datasets in the context of tree cover segmentation from high-resolution satellite imagery. Full article

(This article belongs to the Special Issue Image Analysis for Forest Environmental Monitoring)

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23 pages, 10592 KiB

Open AccessArticle

Defense against Adversarial Patch Attacks for Aerial Image Semantic Segmentation by Robust Feature Extraction

by Zhen Wang, Buhong Wang, Chuanlei Zhang and Yaohui Liu

Remote Sens. 2023, 15(6), 1690; https://doi.org/10.3390/rs15061690 - 21 Mar 2023

Cited by 2 | Viewed by 1799

Abstract

Deep learning (DL) models have recently been widely used in UAV aerial image semantic segmentation tasks and have achieved excellent performance. However, DL models are vulnerable to adversarial examples, which bring significant security risks to safety-critical systems. Existing research mainly focuses on solving [...] Read more.

Deep learning (DL) models have recently been widely used in UAV aerial image semantic segmentation tasks and have achieved excellent performance. However, DL models are vulnerable to adversarial examples, which bring significant security risks to safety-critical systems. Existing research mainly focuses on solving digital attacks for aerial image semantic segmentation, but adversarial patches with physical attack attributes are more threatening than digital attacks. In this article, we systematically evaluate the threat of adversarial patches on the aerial image semantic segmentation task for the first time. To defend against adversarial patch attacks and obtain accurate semantic segmentation results, we construct a novel robust feature extraction network (RFENet). Based on the characteristics of aerial images and adversarial patches, RFENet designs a limited receptive field mechanism (LRFM), a spatial semantic enhancement module (SSEM), a boundary feature perception module (BFPM) and a global correlation encoder module (GCEM), respectively, to solve adversarial patch attacks from the DL model architecture design level. We discover that semantic features, shape features and global features contained in aerial images can significantly enhance the robustness of the DL model against patch attacks. Extensive experiments on three aerial image benchmark datasets demonstrate that the proposed RFENet has strong resistance to adversarial patch attacks compared with the existing state-of-the-art methods. Full article

(This article belongs to the Special Issue Adversarial Attacks and Defenses for Remote Sensing Data)

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30 pages, 32630 KiB

Open AccessArticle

Spatiotemporal Evolution and Hysteresis Analysis of Drought Based on Rainfed-Irrigated Arable Land

by Enyu Du, Fang Chen, Huicong Jia, Lei Wang and Aqiang Yang

Remote Sens. 2023, 15(6), 1689; https://doi.org/10.3390/rs15061689 - 21 Mar 2023

Cited by 4 | Viewed by 1762

Abstract

Drought poses a serious threat to agricultural production and food security in the context of global climate change. Few studies have explored the response mechanism and lag time of agricultural drought to meteorological drought from the perspective of cultivated land types. This paper [...] Read more.

Drought poses a serious threat to agricultural production and food security in the context of global climate change. Few studies have explored the response mechanism and lag time of agricultural drought to meteorological drought from the perspective of cultivated land types. This paper analyzes the spatiotemporal evolution patterns and hysteresis relationship of meteorological and agricultural droughts in the middle and lower reaches of the Yangtze River in China. Here, the Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index products and surface temperature products were selected to calculate the Temperature Vegetation Dryness Index (TVDI) from 2010 to 2015. Furthermore, we obtained the Standardized Precipitation Evapotranspiration Index (SPEI) and the Palmer Drought Severity Index (PDSI) for the same period. Based on these indices, we analyzed the correlation and the hysteresis relationship between agricultural and meteorological drought in rainfed and irrigated arable land. The results showed that, (1) compared with SPEI, the high spatial resolution PDSI data were deemed more suitable for the subsequent accurate and scientific analysis of the relationship between meteorological and agricultural droughts. (2) When meteorological drought occurs, irrigated arable land is the first to experience agricultural drought, and then alleviates when the drought is most severe in rainfed arable land, indicating that irrigated arable land is more sensitive to drought events when exposed to the same degree of drought risk. However, rainfed arable land is actually more susceptible to agricultural drought due to the intervention of irrigation measures. (3) According to the cross-wavelet transform analysis, agricultural droughts significantly lag behind meteorological droughts by about 33 days during the development process of drought events. (4) The spatial distribution of the correlation coefficient between the PDSI and TVDI shows that the area with negative correlations of rainfed croplands and the area with positive correlations of irrigated croplands account for 77.55% and 68.04% of cropland areas, respectively. This study clarifies and distinguishes the details of the meteorological-to-agricultural drought relationship in rainfed and irrigated arable land, noting that an accurate lag time can provide useful guidance for drought monitoring management and irrigation project planning in the middle and lower reaches of the Yangtze River. Full article

(This article belongs to the Special Issue Remote Sensing for Future Food Security and Sustainable Agriculture: Part II)

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Remote Sens., Volume 15, Issue 6 (March-2 2023) – 247 articles

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