Remote Sensing

29 pages, 44178 KiB

Open AccessEditor’s ChoiceArticle

Cloud-Free Global Maps of Essential Vegetation Traits Processed from the TOA Sentinel-3 Catalogue in Google Earth Engine

by Dávid D. Kovács, Pablo Reyes-Muñoz, Matías Salinero-Delgado, Viktor Ixion Mészáros, Katja Berger and Jochem Verrelst

Remote Sens. 2023, 15(13), 3404; https://doi.org/10.3390/rs15133404 - 5 Jul 2023

Cited by 13 | Viewed by 5434

Abstract

Global mapping of essential vegetation traits (EVTs) through data acquired by Earth-observing satellites provides a spatially explicit way to analyze the current vegetation states and dynamics of our planet. Although significant efforts have been made, there is still a lack of global and [...] Read more.

Global mapping of essential vegetation traits (EVTs) through data acquired by Earth-observing satellites provides a spatially explicit way to analyze the current vegetation states and dynamics of our planet. Although significant efforts have been made, there is still a lack of global and consistently derived multi-temporal trait maps that are cloud-free. Here we present the processing chain for the spatiotemporally continuous production of four EVTs at a global scale: (1) fraction of absorbed photosynthetically active radiation (FAPAR), (2) leaf area index (LAI), (3) fractional vegetation cover (FVC), and (4) leaf chlorophyll content (LCC). The proposed workflow presents a scalable processing approach to the global cloud-free mapping of the EVTs. Hybrid retrieval models, named S3-TOA-GPR-1.0-WS, were implemented into Google Earth Engine (GEE) using Sentinel-3 Ocean and Land Color Instrument (OLCI) Level-1B for the mapping of the four EVTs along with associated uncertainty estimates. We used the Whittaker smoother (WS) for the temporal reconstruction of the four EVTs, which led to continuous data streams, here applied to the year 2019. Cloud-free maps were produced at 5 km spatial resolution at 10-day time intervals. The consistency and plausibility of the EVT estimates for the resulting annual profiles were evaluated by per-pixel intra-annually correlating against corresponding vegetation products of both MODIS and Copernicus Global Land Service (CGLS). The most consistent results were obtained for LAI, which showed intra-annual correlations with an average Pearson correlation coefficient (R) of 0.57 against the CGLS LAI product. Globally, the EVT products showed consistent results, specifically obtaining higher correlation than

R >

0.5 with reference products between 30 and 60° latitude in the Northern Hemisphere. Additionally, intra-annual goodness-of-fit statistics were also calculated locally against reference products over four distinct vegetated land covers. As a general trend, vegetated land covers with pronounced phenological dynamics led to high correlations between the different products. However, sparsely vegetated fields as well as areas near the equator linked to smaller seasonality led to lower correlations. We conclude that the global gap-free mapping of the four EVTs was overall consistent. Thanks to GEE, the entire OLCI L1B catalogue can be processed efficiently into the EVT products on a global scale and made cloud-free with the WS temporal reconstruction method. Additionally, GEE facilitates the workflow to be operationally applicable and easily accessible to the broader community. Full article

(This article belongs to the Special Issue Remote Sensing of Vegetation Biochemical and Biophysical Parameters)

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19 pages, 42392 KiB

Open AccessEditor’s ChoiceArticle

Detection of Solar Photovoltaic Power Plants Using Satellite and Airborne Hyperspectral Imaging

by Christoph Jörges, Hedwig Sophie Vidal, Tobias Hank and Heike Bach

Remote Sens. 2023, 15(13), 3403; https://doi.org/10.3390/rs15133403 - 5 Jul 2023

Cited by 17 | Viewed by 6342

Abstract

Solar photovoltaic panels (PV) provide great potential to reduce greenhouse gas emissions as a renewable energy technology. The number of solar PV has increased significantly in recent years and is expected to increase even further. Therefore, accurate and global mapping and monitoring of [...] Read more.

Solar photovoltaic panels (PV) provide great potential to reduce greenhouse gas emissions as a renewable energy technology. The number of solar PV has increased significantly in recent years and is expected to increase even further. Therefore, accurate and global mapping and monitoring of PV modules with remote sensing methods is important for predicting energy production potentials, revealing socio-economic drivers, supporting urban planning, and estimating ecological impacts. Hyperspectral imagery provides crucial information to identify PV modules based on their physical absorption and reflection properties. This study investigated spectral signatures of spaceborne PRISMA data of 30 m low resolution for the first time, as well as airborne AVIRIS-NG data of 5.3 m medium resolution for the detection of solar PV. The study region is located around Irlbach in southern Germany. A physics-based approach using the spectral indices nHI, NSPI, aVNIR, PEP, and VPEP was used for the classification of the hyperspectral images. By validation with a solar PV ground truth dataset of the study area, a user’s accuracy of 70.53% and a producer’s accuracy of 88.06% for the PRISMA hyperspectral data, and a user’s accuracy of 65.94% and a producer’s accuracy of 82.77% for AVIRIS-NG were achieved. Full article

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35 pages, 5319 KiB

Open AccessEditor’s ChoiceArticle

Crop Mapping without Labels: Investigating Temporal and Spatial Transferability of Crop Classification Models Using a 5-Year Sentinel-2 Series and Machine Learning

by Tomáš Rusňák, Tomáš Kasanický, Peter Malík, Ján Mojžiš, Ján Zelenka, Michal Sviček, Dominik Abrahám and Andrej Halabuk

Remote Sens. 2023, 15(13), 3414; https://doi.org/10.3390/rs15133414 - 5 Jul 2023

Cited by 11 | Viewed by 3120

Abstract

Multitemporal crop classification approaches have demonstrated high performance within a given season. However, cross-season and cross-region crop classification presents a unique transferability challenge. This study addresses this challenge by adopting a domain generalization approach, e.g., by training models on multiple seasons to improve [...] Read more.

Multitemporal crop classification approaches have demonstrated high performance within a given season. However, cross-season and cross-region crop classification presents a unique transferability challenge. This study addresses this challenge by adopting a domain generalization approach, e.g., by training models on multiple seasons to improve generalization to new, unseen target years. We utilize a comprehensive five-year Sentinel-2 dataset over different agricultural regions in Slovakia and a diverse crop scheme (eight crop classes). We evaluate the performance of different machine learning classification algorithms, including random forests, support vector machines, quadratic discriminant analysis, and neural networks. Our main findings reveal that the transferability of models across years differs between regions, with the Danubian lowlands demonstrating better performance (overall accuracies ranging from 91.5% in 2022 to 94.3% in 2020) compared to eastern Slovakia (overall accuracies ranging from 85% in 2022 to 91.9% in 2020). Quadratic discriminant analysis, support vector machines, and neural networks consistently demonstrated high performance across diverse transferability scenarios. The random forest algorithm was less reliable in generalizing across different scenarios, particularly when there was a significant deviation in the distribution of unseen domains. This finding underscores the importance of employing a multi-classifier analysis. Rapeseed, grasslands, and sugar beet consistently show stable transferability across seasons. We observe that all periods play a crucial role in the classification process, with July being the most important and August the least important. Acceptable performance can be achieved as early as June, with only slight improvements towards the end of the season. Finally, employing a multi-classifier approach allows for parcel-level confidence determination, enhancing the reliability of crop distribution maps by assuming higher confidence when multiple classifiers yield similar results. To enhance spatiotemporal generalization, our study proposes a two-step approach: (1) determine the optimal spatial domain to accurately represent crop type distribution; and (2) apply interannual training to capture variability across years. This approach helps account for various factors, such as different crop rotation practices, diverse observational quality, and local climate-driven patterns, leading to more accurate and reliable crop classification models for nationwide agricultural monitoring. Full article

(This article belongs to the Special Issue Crop Quantitative Monitoring with Remote Sensing)

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35 pages, 2670 KiB

Open AccessEditor’s ChoiceReview

Unmanned Aerial Vehicles for Search and Rescue: A Survey

by Mingyang Lyu, Yibo Zhao, Chao Huang and Hailong Huang

Remote Sens. 2023, 15(13), 3266; https://doi.org/10.3390/rs15133266 - 25 Jun 2023

Cited by 130 | Viewed by 28983

Abstract

In recent years, unmanned aerial vehicles (UAVs) have gained popularity due to their flexibility, mobility, and accessibility in various fields, including search and rescue (SAR) operations. The use of UAVs in SAR can greatly enhance the task success rates in reaching inaccessible or [...] Read more.

In recent years, unmanned aerial vehicles (UAVs) have gained popularity due to their flexibility, mobility, and accessibility in various fields, including search and rescue (SAR) operations. The use of UAVs in SAR can greatly enhance the task success rates in reaching inaccessible or dangerous areas, performing challenging operations, and providing real-time monitoring and modeling of the situation. This article aims to help readers understand the latest progress and trends in this field by synthesizing and organizing papers related to UAV search and rescue. An introduction to the various types and components of UAVs and their importance in SAR operations is settled first. Additionally, we present a comprehensive review of sensor integrations in UAVs for SAR operations, highlighting their roles in target perception, localization, and identification. Furthermore, we elaborate on the various applications of UAVs in SAR, including on-site monitoring and modeling, perception and localization of targets, and SAR operations such as task assignment, path planning, and collision avoidance. We compare different approaches and methodologies used in different studies, assess the strengths and weaknesses of various approaches, and provide insights on addressing the research questions relating to specific UAV operations in SAR. Overall, this article presents a comprehensive overview of the significant role of UAVs in SAR operations. It emphasizes the vital contributions of drones in enhancing mission success rates, augmenting situational awareness, and facilitating efficient and effective SAR activities. Additionally, the article discusses potential avenues for enhancing the performance of UAVs in SAR. Full article

(This article belongs to the Special Issue The Application of Unmanned Aerial Systems in Search and Rescue Activities)

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17 pages, 2862 KiB

Open AccessEditor’s ChoiceArticle

Satellite Sensed Data-Dose Response Functions: A Totally New Approach for Estimating Materials’ Deterioration from Space

by Georgios Kouremadas, John Christodoulakis, Costas Varotsos and Yong Xue

Remote Sens. 2023, 15(12), 3194; https://doi.org/10.3390/rs15123194 - 20 Jun 2023

Viewed by 2915

Abstract

When construction materials are exposed to the atmospheric environment, they are subject to deterioration, which varies according to the time period of exposure and the location. A tool named Dose–Response Functions (DRFs) has been developed to estimate this deterioration. DRFs use specific air [...] Read more.

When construction materials are exposed to the atmospheric environment, they are subject to deterioration, which varies according to the time period of exposure and the location. A tool named Dose–Response Functions (DRFs) has been developed to estimate this deterioration. DRFs use specific air pollutants and climatic parameters as input data. Existing DRFs in the literature use only ground-based measurements as input data. This fact constitutes a limitation for the application of this tool because it is too expensive to establish and maintain such a large network of ground-based stations for pollution monitoring. In this study, we present the development of new DRFs using only satellite data as an input named Satellite Sensed Data Dose-Response Functions (SSD-DRFs). Due to the global coverage provided by satellites, this new tool for monitoring the corrosion/soiling of materials overcomes the previous limitation because it can be applied to any area of interest. To develop SSD-DRFs, we used measurements from MODIS (Moderate Resolution Imaging Spectroradiometer) and AIRS (Atmospheric Infrared Sounder) on board Aqua and OMI (Ozone Monitoring Instrument) on Aura. According to the obtained results, SSD-DRFs were developed for the case of carbon steel, zinc, limestone and modern glass materials. SSD-DRFs are shown to produce more reliable corrosion/soiling estimates than “traditional” DRFs using ground-based data. Furthermore, research into the development of the SSD-DRFs revealed that the different corrosion mechanisms taking place on the surface of a material do not act additively with each other but rather synergistically. Full article

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19 pages, 7596 KiB

Open AccessEditor’s ChoiceArticle

Assessing the Magnitude of the Amazonian Forest Blowdowns and Post-Disturbance Recovery Using Landsat-8 and Time Series of PlanetScope Satellite Constellation Data

by Dazhou Ping, Ricardo Dalagnol, Lênio Soares Galvão, Bruce Nelson, Fabien Wagner, David M. Schultz and Polyanna da C. Bispo

Remote Sens. 2023, 15(12), 3196; https://doi.org/10.3390/rs15123196 - 20 Jun 2023

Cited by 5 | Viewed by 8170

Abstract

Blowdown events are a major natural disturbance in the central Amazon Forest, but their impact and subsequent vegetation recovery have been poorly understood. This study aimed to track post-disturbance regeneration after blowdown events in the Amazon Forest. We analyzed 45 blowdown sites identified [...] Read more.

Blowdown events are a major natural disturbance in the central Amazon Forest, but their impact and subsequent vegetation recovery have been poorly understood. This study aimed to track post-disturbance regeneration after blowdown events in the Amazon Forest. We analyzed 45 blowdown sites identified after September 2020 at Amazonas, Mato Grosso, and Colombia jurisdictions using Landsat-8 and PlanetScope NICFI satellite imagery. Non-photosynthetic vegetation (NPV), green vegetation (GV), and shade fractions were calculated for each image and sensor using spectral mixture analysis in Google Earth Engine. The results showed that PlanetScope NICFI data provided more regular and higher-spatial-resolution observations of blowdown areas than Landsat-8, allowing for more accurate characterization of post-disturbance vegetation recovery. Specifically, NICFI data indicated that just four months after the blowdown event, nearly half of ΔNPV, which represents the difference between the NPV after blowdown and the NPV before blowdown, had disappeared. ΔNPV and GV values recovered to pre-blowdown levels after approximately 15 months of regeneration. Our findings highlight that the precise timing of blowdown detection has huge implications on quantification of the magnitude of damage. Landsat data may miss important changes in signal due to the difficulty of obtaining regular monthly observations. These findings provide valuable insights into vegetation recovery dynamics following blowdown events. Full article

(This article belongs to the Special Issue Remote Sensing of the Amazon Region)

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21 pages, 11097 KiB

Open AccessEditor’s ChoiceArticle

Implementing Cloud Computing for the Digital Mapping of Agricultural Soil Properties from High Resolution UAV Multispectral Imagery

by Samuel Pizarro, Narcisa G. Pricope, Deyanira Figueroa, Carlos Carbajal, Miriam Quispe, Jesús Vera, Lidiana Alejandro, Lino Achallma, Izamar Gonzalez, Wilian Salazar, Hildo Loayza, Juancarlos Cruz and Carlos I. Arbizu

Remote Sens. 2023, 15(12), 3203; https://doi.org/10.3390/rs15123203 - 20 Jun 2023

Cited by 11 | Viewed by 5445

Abstract

The spatial heterogeneity of soil properties has a significant impact on crop growth, making it difficult to adopt site-specific crop management practices. Traditional laboratory-based analyses are costly, and data extrapolation for mapping soil properties using high-resolution imagery becomes a computationally expensive procedure, taking [...] Read more.

The spatial heterogeneity of soil properties has a significant impact on crop growth, making it difficult to adopt site-specific crop management practices. Traditional laboratory-based analyses are costly, and data extrapolation for mapping soil properties using high-resolution imagery becomes a computationally expensive procedure, taking days or weeks to obtain accurate results using a desktop workstation. To overcome these challenges, cloud-based solutions such as Google Earth Engine (GEE) have been used to analyze complex data with machine learning algorithms. In this study, we explored the feasibility of designing and implementing a digital soil mapping approach in the GEE platform using high-resolution reflectance imagery derived from a thermal infrared and multispectral camera Altum (MicaSense, Seattle, WA, USA). We compared a suite of multispectral-derived soil and vegetation indices with in situ measurements of physical-chemical soil properties in agricultural lands in the Peruvian Mantaro Valley. The prediction ability of several machine learning algorithms (CART, XGBoost, and Random Forest) was evaluated using R², to select the best predicted maps (R² > 0.80), for ten soil properties, including Lime, Clay, Sand, N, P, K, OM, Al, EC, and pH, using multispectral imagery and derived products such as spectral indices and a digital surface model (DSM). Our results indicate that the predictions based on spectral indices, most notably, SRI, GNDWI, NDWI, and ExG, in combination with CART and RF algorithms are superior to those based on individual spectral bands. Additionally, the DSM improves the model prediction accuracy, especially for K and Al. We demonstrate that high-resolution multispectral imagery processed in the GEE platform has the potential to develop soil properties prediction models essential in establishing adaptive soil monitoring programs for agricultural regions. Full article

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27 pages, 13888 KiB

Open AccessEditor’s ChoiceArticle

A New Blind Selection Approach for Lunar Landing Zones Based on Engineering Constraints Using Sliding Window

by Hengxi Liu, Yongzhi Wang, Shibo Wen, Jianzhong Liu, Jiaxiang Wang, Yaqin Cao, Zhiguo Meng and Yuanzhi Zhang

Remote Sens. 2023, 15(12), 3184; https://doi.org/10.3390/rs15123184 - 19 Jun 2023

Cited by 2 | Viewed by 2301

Abstract

Deep space exploration has risen in interest among scientists in recent years, with soft landings being one of the most straightforward ways to acquire knowledge about the Moon. In general, landing mission success depends on the selection of landing zones, and there are [...] Read more.

Deep space exploration has risen in interest among scientists in recent years, with soft landings being one of the most straightforward ways to acquire knowledge about the Moon. In general, landing mission success depends on the selection of landing zones, and there are currently few effective quantitative models that can be used to select suitable landing zones. When automatic landing zones are selected, the grid method used for data partitioning tends to miss potentially suitable landing sites between grids. Therefore, this study proposes a new engineering-constrained approach for landing zone selection using LRO LOLA-based slope data as original data based on the sliding window method, which solves the spatial omission problem of the grid method. Using the threshold ratio, mean, coefficient of variation, Moran’s I, and overall rating, this method quantifies the suitability of each sliding window. The k-means clustering algorithm is adopted to determine the suitability threshold for the overall rating. The results show that 20 of 22 lunar soft landing sites are suitable for landing. Additionally, 43 of 50 landing sites preselected by the experts (suitable landing sites considering a combination of conditions) are suitable for landing, accounting for 90.9% and 86% of the total number, respectively, for a window size of 0.5° × 0.5°. Among them, there are four soft landing sites: Surveyor 3, 6, 7, and Apollo 15, which are not suitable for landing in the evaluation results of the grid method. However, they are suitable for landing in the overall evaluation results of the sliding window method, which significantly reduces the spatial omission problem of the grid method. In addition, four candidate landing regions, including Aristarchus Crater, Marius Hills, Moscoviense Basin, and Orientale Basin, were evaluated for landing suitability using the sliding window method. The suitability of the landing area within the candidate range of small window sizes was 0.90, 0.97, 0.49, and 0.55. This indicates the capacity of the method to analyze an arbitrary range during blind landing zone selection. The results can quantify the slope suitability of the landing zones from an engineering perspective and provide different landing window options. The proposed method for selecting lunar landing zones is clearly superior to the gridding method. It enhances data processing for automatic lunar landing zone selection and progresses the selection process from qualitative to quantitative. Full article

(This article belongs to the Special Issue Advances in Exploring the Moon, Mars, and Asteroids Using Spacecraft Remote Sensing and Other Toolkits)

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20 pages, 5006 KiB

Open AccessEditor’s ChoiceArticle

An Intelligent Detection Method for Small and Weak Objects in Space

by Yuman Yuan, Hongyang Bai, Panfeng Wu, Hongwei Guo, Tianyu Deng and Weiwei Qin

Remote Sens. 2023, 15(12), 3169; https://doi.org/10.3390/rs15123169 - 18 Jun 2023

Cited by 10 | Viewed by 2829

Abstract

In the case of a boom in space resource development, space debris will increase dramatically and cause serious problems for the spacecraft in orbit. To address this problem, a novel context sensing-YOLOv5 (CS-YOLOv5) is proposed for small and weak space object detection, which [...] Read more.

In the case of a boom in space resource development, space debris will increase dramatically and cause serious problems for the spacecraft in orbit. To address this problem, a novel context sensing-YOLOv5 (CS-YOLOv5) is proposed for small and weak space object detection, which could realize the extraction of local context information and the enhancement and fusion of spatial information. To enhance the expression ability of feature information and the identification ability of the network, we propose the cross-layer context fusion module (CCFM) through multiple branches in parallel to learn the context information of different scales. At the same time, to map the small-scale features sequentially to the features of the previous layer, we design the adaptive weighting module (AWM) to assist the CCFM in further enhancing the expression of features. Additionally, to solve the problem that the spatial information of small objects is easily lost, we designed the spatial information enhancement module (SIEM) to adaptively learn the weak spatial information of small objects that need to be protected. To further enhance the generalization ability of CS-YOLOv5, we propose a contrast mosaic data augmentation to enrich the diversity of the sample. Extensive experiments are conducted on self-built datasets, which strongly prove the effectiveness of our method in space object detection. Full article

(This article belongs to the Special Issue Behavioural Characterisation of Resident Space Objects for Space Situational Awareness)

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17 pages, 5355 KiB

Open AccessEditor’s ChoiceTechnical Note

Flying Laboratory of Imaging Systems: Fusion of Airborne Hyperspectral and Laser Scanning for Ecosystem Research

by Jan Hanuš, Lukáš Slezák, Tomáš Fabiánek, Lukáš Fajmon, Tomáš Hanousek, Růžena Janoutová, Daniel Kopkáně, Jan Novotný, Karel Pavelka, Miroslav Pikl, František Zemek and Lucie Homolová

Remote Sens. 2023, 15(12), 3130; https://doi.org/10.3390/rs15123130 - 15 Jun 2023

Cited by 3 | Viewed by 2476

Abstract

Synergies of optical, thermal and laser scanning remotely sensed data provide valuable information to study the structure and functioning of terrestrial ecosystems. One of the few fully operational airborne multi-sensor platforms for ecosystem research in Europe is the Flying Laboratory of Imaging Systems [...] Read more.

Synergies of optical, thermal and laser scanning remotely sensed data provide valuable information to study the structure and functioning of terrestrial ecosystems. One of the few fully operational airborne multi-sensor platforms for ecosystem research in Europe is the Flying Laboratory of Imaging Systems (FLIS), operated by the Global Change Research Institute of the Czech Academy of Sciences. The system consists of three commercial imaging spectroradiometers. One spectroradiometer covers the visible and near-infrared, and the other covers the shortwave infrared part of the electromagnetic spectrum. These two provide full spectral data between 380–2450 nm, mainly for the assessment of biochemical properties of vegetation, soil and water. The third spectroradiometer covers the thermal long-wave infrared part of the electromagnetic spectrum and allows for mapping of surface emissivity and temperature properties. The fourth instrument onboard is the full waveform laser scanning system, which provides data on landscape orography and 3D structure. Here, we describe the FLIS design, data acquisition plan and primary data pre-processing. The synchronous acquisition of multiple data sources provides a complex analytical and data framework for the assessment of vegetation ecosystems (such as plant species composition, plant functional traits, biomass and carbon stocks), as well as for studying the role of greenery or blue-green infrastructure on the thermal behaviour of urban systems. In addition, the FLIS airborne infrastructure supports calibration and validation activities for existing and upcoming satellite missions (e.g., FLEX, PRISMA). Full article

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16 pages, 4820 KiB

Open AccessEditor’s ChoiceArticle

Validation of Himawari-8 Sea Surface Temperature Retrievals Using Infrared SST Autonomous Radiometer Measurements

by Haifeng Zhang, Helen Beggs, Christopher Griffin and Pallavi Devidas Govekar

Remote Sens. 2023, 15(11), 2841; https://doi.org/10.3390/rs15112841 - 30 May 2023

Cited by 6 | Viewed by 2408

Abstract

This study has evaluated five years (2016–2020) of Himawari-8 (H8) Sea Surface Temperature (SST) Level 2 Pre-processed (L2P) data produced by the Australian Bureau of Meteorology (Bureau) against shipborne radiometer SST measurements obtained from the Infrared SST Autonomous Radiometer (ISAR) onboard research vessel [...] Read more.

This study has evaluated five years (2016–2020) of Himawari-8 (H8) Sea Surface Temperature (SST) Level 2 Pre-processed (L2P) data produced by the Australian Bureau of Meteorology (Bureau) against shipborne radiometer SST measurements obtained from the Infrared SST Autonomous Radiometer (ISAR) onboard research vessel RV Investigator. Before being used, all data sets employed in this study have gone through careful quality control, and only the most trustworthy measurements are retained. With a large matchup database (31,871 collocations in total, including 16,418 during daytime and 15,453 during night-time), it is found that the Bureau H8 SST product is of good quality, with a mean bias ± standard deviation (SD) of −0.12 °C ± 0.47 °C for the daytime and −0.04 °C ± 0.37 °C for the night-time. The performance of the H8 data under different environmental conditions, determined by the observations obtained concurrently from RV Investigator, is examined. Daytime and night-time satellite data behave slightly differently. During the daytime, a cold bias can be seen under almost all environmental conditions, including for most values of wind speed, SST, and relative humidity. On the other hand, the performance of the night-time H8 SST product is consistently more stable under most meteorological conditions with the mean bias usually close to zero. Full article

(This article belongs to the Section Earth Observation Data)

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25 pages, 22618 KiB

Open AccessEditor’s ChoiceArticle

MT-InSAR and Dam Modeling for the Comprehensive Monitoring of an Earth-Fill Dam: The Case of the Benínar Dam (Almería, Spain)

by Miguel Marchamalo-Sacristán, Antonio Miguel Ruiz-Armenteros, Francisco Lamas-Fernández, Beatriz González-Rodrigo, Rubén Martínez-Marín, José Manuel Delgado-Blasco, Matus Bakon, Milan Lazecky, Daniele Perissin, Juraj Papco and Joaquim J. Sousa

Remote Sens. 2023, 15(11), 2802; https://doi.org/10.3390/rs15112802 - 28 May 2023

Cited by 10 | Viewed by 3453

Abstract

The Benínar Dam, located in Southeastern Spain, is an earth-fill dam that has experienced filtration issues since its construction in 1985. Despite the installation of various monitoring systems, the data collected are sparse and inadequate for the dam’s lifetime. The present research integrates [...] Read more.

The Benínar Dam, located in Southeastern Spain, is an earth-fill dam that has experienced filtration issues since its construction in 1985. Despite the installation of various monitoring systems, the data collected are sparse and inadequate for the dam’s lifetime. The present research integrates Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) and dam modeling to validate the monitoring of this dam, opening the way to enhanced integrated monitoring systems. MT-InSAR was proved to be a reliable and continuous monitoring system for dam deformation, surpassing previously installed systems in terms of precision. MT-InSAR allowed the almost-continuous monitoring of this dam since 1992, combining ERS, Envisat, and Sentinel-1A/B data. Line-of-sight (LOS) velocities of settlement in the crest of the dam evolved from maximums of −6 mm/year (1992–2000), −4 mm/year (2002–2010), and −2 mm/year (2015–2021) with median values of −2.6 and −3.0 mm/year in the first periods (ERS and Envisat) and −1.3 mm/year in the Sentinel 1-A/B period. These results are consistent with the maximum admissible modeled deformation from construction, confirming that settlement was more intense in the dam’s early stages and decreased over time. MT-InSAR was also used to integrate the monitoring of the dam basin, including critical slopes, quarries, and infrastructures, such as roads, tracks, and spillways. This study allows us to conclude that MT-InSAR and dam modeling are important elements for the integrated monitoring systems of embankment dams. This conclusion supports the complete integration of MT-InSAR and 3D modeling into the monitoring systems of embankment dams, as they are a key complement to traditional geotechnical monitoring and can overcome the main limitations of topographical monitoring. Full article

(This article belongs to the Special Issue Dam Stability Monitoring with Satellite Geodesy)

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17 pages, 12058 KiB

Open AccessEditor’s ChoiceArticle

Drone-Based Imaging Polarimetry of Dark Lake Patches from the Viewpoint of Flying Polarotactic Insects with Ecological Implication

by Dénes Száz, Péter Takács, Balázs Bernáth, György Kriska, András Barta, István Pomozi and Gábor Horváth

Remote Sens. 2023, 15(11), 2797; https://doi.org/10.3390/rs15112797 - 27 May 2023

Cited by 9 | Viewed by 2006

Abstract

Aquatic insects detect water by the horizontal polarization of water-reflected light and thus are attracted to such light. Recently, in the Hungarian Lake Balaton we observed dark water patches forming between every autumn and spring because of the inflow of black suspended/dissolved organic [...] Read more.

Aquatic insects detect water by the horizontal polarization of water-reflected light and thus are attracted to such light. Recently, in the Hungarian Lake Balaton we observed dark water patches forming between every autumn and spring because of the inflow of black suspended/dissolved organic matter into the bright lake water. Earlier, the polarization characteristics of such water surfaces were mapped by imaging polarimeters from the ground. In order to measure the reflection-polarization patterns of these dark lake patches from the higher viewpoint of flying polarotactic aquatic insects, we designed a drone-based imaging polarimeter. We found that the dark lake patches reflected light with very high (60% ≤ d ≤ 80%) degrees of horizontal polarization at the Brewster’s angle, while the bright lake water was only weakly (d < 20%) horizontally polarizing. There was a large contrast in both the radiance and degree of polarization between dark lake patches and bright lake water, while there was no such contrast in the angle of polarization. The ecological implication of these findings could be that these dark lake patches attract water-seeking polarotactic insects, which may oviposit more frequently in them than in the brighter lake water. However, it might not matter if they lay their eggs in these dark patches rather than the bright lake water, because this may simply increase the abundance of breeding flying insects in areas where dark patches are common. Full article

(This article belongs to the Topic Drones for Coastal and Coral Reef Environments)

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20 pages, 21214 KiB

Open AccessEditor’s ChoiceArticle

Line Scan Hyperspectral Imaging Framework for Open Source Low-Cost Platforms

by Akram Al-Hourani, Sivacarendran Balendhran, Sumeet Walia and Tetiana Hourani

Remote Sens. 2023, 15(11), 2787; https://doi.org/10.3390/rs15112787 - 26 May 2023

Cited by 5 | Viewed by 6478

Abstract

With advancements in computer processing power and deep learning techniques, hyperspectral imaging is continually being explored for improved sensing applications in various fields. However, the high cost associated with such imaging platforms impedes their widespread use in spite of the availability of the [...] Read more.

With advancements in computer processing power and deep learning techniques, hyperspectral imaging is continually being explored for improved sensing applications in various fields. However, the high cost associated with such imaging platforms impedes their widespread use in spite of the availability of the needed processing power. In this paper, we develop a novel theoretical framework required for an open source ultra-low-cost hyperspectral imaging platform based on the line scan method suitable for remote sensing applications. Then, we demonstrate the design and fabrication of an open source platform using consumer-grade commercial off-the-shelf components that are both affordable and easily accessible to researchers and users. At the heart of the optical system is a consumer-grade spectroscope along with a basic galvanometer mirror that is widely used in laser scanning devices. The utilized pushbroom scanning method provides a very high spectral resolution of 2.8 nm, as tested against commercial spectral sensors. Since the resolution is limited by the slit width of the spectroscope, we also provide a deconvolution method for the line scan in order to improve the monochromatic spatial resolution. Finally, we provide a cost-effective testing method for the hyperspectral imaging platform where the results validate both the spectral and spatial performances of the platform. Full article

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24 pages, 7856 KiB

Open AccessEditor’s ChoiceArticle

Ionospheric–Thermospheric Responses to Geomagnetic Storms from Multi-Instrument Space Weather Data

by Rasim Shahzad, Munawar Shah, M. Arslan Tariq, Andres Calabia, Angela Melgarejo-Morales, Punyawi Jamjareegulgarn and Libo Liu

Remote Sens. 2023, 15(10), 2687; https://doi.org/10.3390/rs15102687 - 22 May 2023

Cited by 17 | Viewed by 4611

Abstract

We analyze vertical total electron content (vTEC) variations from the Global Navigation Satellite System (GNSS) at different latitudes in different continents of the world during the geomagnetic storms of June 2015, August 2018, and November 2021. The resulting ionospheric perturbations at the low [...] Read more.

We analyze vertical total electron content (vTEC) variations from the Global Navigation Satellite System (GNSS) at different latitudes in different continents of the world during the geomagnetic storms of June 2015, August 2018, and November 2021. The resulting ionospheric perturbations at the low and mid-latitudes are investigated in terms of the prompt penetration electric field (PPEF), the equatorial electrojet (EEJ), and the magnetic H component from INTERMAGNET stations near the equator. East and Southeast Asia, Russia, and Oceania exhibited positive vTEC disturbances, while South American stations showed negative vTEC disturbances during all the storms. We also analyzed the vTEC from the Swarm satellites and found similar results to the retrieved vTEC data during the June 2015 and August 2018 storms. Moreover, we observed that ionospheric plasma tended to increase rapidly during the local afternoon in the main phase of the storms and has the opposite behavior at nighttime. The equatorial ionization anomaly (EIA) crest expansion to higher latitudes is driven by PPEF during daytime at the main and recovery phases of the storms. The magnetic H component exhibits longitudinal behavior along with the EEJ enhancement near the magnetic equator. Full article

(This article belongs to the Special Issue Satellite Observations of the Global Ionosphere and Plasma Dynamics)

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29 pages, 2126 KiB

Open AccessFeature PaperEditor’s ChoiceReview

Climate-Change-Driven Droughts and Tree Mortality: Assessing the Potential of UAV-Derived Early Warning Metrics

by Ewane Basil Ewane, Midhun Mohan, Shaurya Bajaj, G. A. Pabodha Galgamuwa, Michael S. Watt, Pavithra Pitumpe Arachchige, Andrew T. Hudak, Gabriella Richardson, Nivedhitha Ajithkumar, Shruthi Srinivasan, Ana Paula Dalla Corte, Daniel J. Johnson, Eben North Broadbent, Sergio de-Miguel, Margherita Bruscolini, Derek J. N. Young, Shahid Shafai, Meshal M. Abdullah, Wan Shafrina Wan Mohd Jaafar, Willie Doaemo, Carlos Alberto Silva and Adrian Cardil Show full author list Hide full author list

Remote Sens. 2023, 15(10), 2627; https://doi.org/10.3390/rs15102627 - 18 May 2023

Cited by 15 | Viewed by 5482

Abstract

Protecting and enhancing forest carbon sinks is considered a natural solution for mitigating climate change. However, the increasing frequency, intensity, and duration of droughts due to climate change can threaten the stability and growth of existing forest carbon sinks. Extreme droughts weaken plant [...] Read more.

Protecting and enhancing forest carbon sinks is considered a natural solution for mitigating climate change. However, the increasing frequency, intensity, and duration of droughts due to climate change can threaten the stability and growth of existing forest carbon sinks. Extreme droughts weaken plant hydraulic systems, can lead to tree mortality events, and may reduce forest diversity, making forests more vulnerable to subsequent forest disturbances, such as forest fires or pest infestations. Although early warning metrics (EWMs) derived using satellite remote sensing data are now being tested for predicting post-drought plant physiological stress and mortality, applications of unmanned aerial vehicles (UAVs) are yet to be explored extensively. Herein, we provide twenty-four prospective approaches classified into five categories: (i) physiological complexities, (ii) site-specific and confounding (abiotic) factors, (iii) interactions with biotic agents, (iv) forest carbon monitoring and optimization, and (v) technological and infrastructural developments, for adoption, future operationalization, and upscaling of UAV-based frameworks for EWM applications. These UAV considerations are paramount as they hold the potential to bridge the gap between field inventory and satellite remote sensing for assessing forest characteristics and their responses to drought conditions, identifying and prioritizing conservation needs of vulnerable and/or high-carbon-efficient tree species for efficient allocation of resources, and optimizing forest carbon management with climate change adaptation and mitigation practices in a timely and cost-effective manner. Full article

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

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22 pages, 6387 KiB

Open AccessEditor’s ChoiceArticle

Source Model of the 2023 Turkey Earthquake Sequence Imaged by Sentinel-1 and GPS Measurements: Implications for Heterogeneous Fault Behavior along the East Anatolian Fault Zone

by Shuiping Li, Xin Wang, Tingye Tao, Yongchao Zhu, Xiaochuan Qu, Zhenxuan Li, Jianwei Huang and Shunyue Song

Remote Sens. 2023, 15(10), 2618; https://doi.org/10.3390/rs15102618 - 18 May 2023

Cited by 48 | Viewed by 7540

Abstract

On 6 February 2023, a devastating doublet of earthquakes with magnitudes of Mw 7.8 and Mw 7.6 successively struck southeastern Turkey near the border of Syria. The earthquake sequence represents the strongest earthquakes in Turkey during the past 80 years and caused an [...] Read more.

On 6 February 2023, a devastating doublet of earthquakes with magnitudes of Mw 7.8 and Mw 7.6 successively struck southeastern Turkey near the border of Syria. The earthquake sequence represents the strongest earthquakes in Turkey during the past 80 years and caused an extensive loss of life and property. In this study, we processed Sentinel-1 and GPS data to derive the complete surface displacement caused by the earthquake sequence. The surface displacements were adopted to invert for the fault geometry and coseismic slip distribution on the seismogenic faults of the earthquake sequence. The results indicate that the coseismic rupture of the Turkey earthquake sequence was dominated by left-lateral strike slips with a maximum slip of ~10 m on the East Anatolian Fault Zone (EAFZ) and the Sürgü fault (SF). Significant surface ruptures are recognized based on the geodetic inversion, which is consistent with the analysis of post-earthquake satellite images. The cumulative released moment of the two earthquakes reached 9.62 × 10²⁰ Nm, which corresponds to an event of Mw 7.95. Additionally, the interseismic fault slip rates and locking depths along the central and western segments of the EAFZ were estimated using the high-resolution long-term velocity field. The results reveal significant lateral variations of fault slip rates and locking depths along the central and western segments of the EAFZ. Generally, the estimated fault locking zone showed good spatial consistency with the coseismic fault rupture of the Mw 7.8 shock on the EAFZ. The static coulomb failure stress (CFS) change due to the Mw 7.8 earthquakes suggests that the subsequent Mw 7.6 event was certainly promoted by the Mw 7.8 shock. The stress transfers from the fault EAFZ to the fault SF were realized by unclamping the interface of the fault SF, which significantly reduces the effective normal stress on the fault plane. Large CFS increases in the western Puturge segment of the EAFZ, which was not ruptured in the 2020 Mw 6.8 and the 2023 Mw 7.8 earthquakes, highlight the future earthquake risk in this fault segment. Full article

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

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20 pages, 4694 KiB

Open AccessEditor’s ChoiceArticle

Vegetation Growth Response and Trends after Water Deficit Exposure in the Loess Plateau, China

by Yuanyuan Luo, Wei Liang, Jianwu Yan, Weibin Zhang, Fen Gou, Chengxi Wang and Xiaoru Liang

Remote Sens. 2023, 15(10), 2593; https://doi.org/10.3390/rs15102593 - 16 May 2023

Cited by 7 | Viewed by 4461

Abstract

Understanding the impact of water availability on vegetation growth in the context of climate change is crucial for assessing the resilience of vegetation to environmental shifts. In this study, the relationship between vegetation growth and water availability was studied using a variety of [...] Read more.

Understanding the impact of water availability on vegetation growth in the context of climate change is crucial for assessing the resilience of vegetation to environmental shifts. In this study, the relationship between vegetation growth and water availability was studied using a variety of indicators. The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and Solar-Induced Chlorophyll Fluorescence (SIF) were utilized as vegetation growth indicators, while the standardized precipitation evapotranspiration index (SPEI) and soil moisture indicators served as water use indices. To investigate the vegetation response to water deficit in the Loess Plateau during the growing season from 2000 to 2020, Spearman’s rank correlation coefficients were calculated using a 5-year sliding window approach. The spatial and temporal heterogeneity of vegetation response to water deficit during the growing seasons were also explored. The results showed that: (1) with the improvement of moisture conditions, vegetation growth recovered significantly, and there was no expansion trend for vegetation water deficit. (2) The most sensitive timescale of vegetation to water deficit was 6–8 months; the response degree and sensitivity of vegetation to water surplus and deficit were the highest from June to August; and broadleaved forest was the vegetation type most sensitive to water deficit in the early growing season, while grass was the vegetation type most sensitive to water deficit during the mid and late growing seasons. (3) Soil moisture emerged as the dominant factor influencing vegetation growth in the Loess Plateau, followed by precipitation, albeit to a lesser extent. These findings contribute to understanding the mechanism and characteristics of the response of vegetation to climate fluctuations induced by global climate change. Full article

(This article belongs to the Special Issue Remote Sensing of Arid/Semiarid Lands II)

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10 pages, 2896 KiB

Open AccessEditor’s ChoiceCommunication

Enhanced Doppler Resolution and Sidelobe Suppression Performance for Golay Complementary Waveforms

by Jiahua Zhu, Yongping Song, Nan Jiang, Zhuang Xie, Chongyi Fan and Xiaotao Huang

Remote Sens. 2023, 15(9), 2452; https://doi.org/10.3390/rs15092452 - 6 May 2023

Cited by 58 | Viewed by 2560

Abstract

An enhanced Doppler resolution and sidelobe suppression have long been practical issues for moving target detection using Golay complementary waveforms. In this paper, Golay complementary waveform radar returns are combined with a proposed processor, the pointwise thresholding processor (PTP). Compared to the pointwise [...] Read more.

An enhanced Doppler resolution and sidelobe suppression have long been practical issues for moving target detection using Golay complementary waveforms. In this paper, Golay complementary waveform radar returns are combined with a proposed processor, the pointwise thresholding processor (PTP). Compared to the pointwise minimization processor (PMP) illustrated in a previous work, which could only achieve a Doppler resolution comparable to existing methods, this approach essentially increases the Doppler resolution to a very high level in theory. This study also introduced a further filtering process for the delay-Doppler map of the PTP, and simulations verified that the method results in a delay-Doppler map virtually free of range sidelobes. Full article

(This article belongs to the Special Issue Advanced Array Signal Processing for Target Imaging and Detection)

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16 pages, 17072 KiB

Open AccessEditor’s ChoiceArticle

A Multi-Pixel Split-Window Approach to Sea Surface Temperature Retrieval from Thermal Imagers with Relatively High Radiometric Noise: Preliminary Studies

by Gian Luigi Liberti, Mattia Sabatini, David S. Wethey and Daniele Ciani

Remote Sens. 2023, 15(9), 2453; https://doi.org/10.3390/rs15092453 - 6 May 2023

Cited by 4 | Viewed by 2898

Abstract

In the following decade(s), a set of satellite missions carrying thermal infrared (TIR) imagers with a relatively high noise equivalent differential temperature (NEdT) are expected, e.g., the high resolution TIR imagers flying on the future Thermal infraRed Imaging Satellite for High-resolution Natural resource [...] Read more.

In the following decade(s), a set of satellite missions carrying thermal infrared (TIR) imagers with a relatively high noise equivalent differential temperature (NEdT) are expected, e.g., the high resolution TIR imagers flying on the future Thermal infraRed Imaging Satellite for High-resolution Natural resource Assessment (TRISHNA), Land Surface Temperature Monitoring (LSTM) and NASA-JPL/ASI Surface Biology and Geology Thermal (SBG) missions or the secondary payload on board the ESA Earth Explorer 10 Harmony. The instruments on board these missions are expected to be characterized by an NEdT of ⪆0.1 K. In order to reduce the impact of radiometric noise on the retrieved sea surface temperature (SST), this study investigates the possibility of applying a multi-pixel atmospheric correction based on the hypotheses that (i) the spatial variability scales of radiatively active atmospheric variables are, on average, larger than those of the SST and (ii) the effect of atmosphere is accounted for via the split window (SW) difference. Based on 32 Sentinel 3 SLSTR case studies selected in oceanic regions where SST features are mainly driven by meso to sub-mesoscale turbulence (e.g., corresponding to major western boundary currents), this study documents that the local spatial variability of the SW difference term on the scale of ≃3 × 3 km² is comparable with the noise associated with the SW difference. Similarly, the power spectra of the SW term are shown to have, for small scales, the behavior of white noise spectra. On this basis, we suggest to average the SW term and to use it for the atmospheric correction procedure to reduce the impact of radiometric noise. In principle, this methodology can be applied on proper scales that can be dynamically defined for each pixel. The applicability of our findings to high-resolution TIR missions is discussed and an example of an application to ECOSTRESS data is reported. Full article

(This article belongs to the Special Issue Atmospheric Correction of Remote Sensing Imagery)

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16 pages, 10668 KiB

Open AccessEditor’s ChoiceCommunication

Ionospheric Response to the 6 February 2023 Turkey–Syria Earthquake

by Artem Vesnin, Yury Yasyukevich, Natalia Perevalova and Erman Şentürk

Remote Sens. 2023, 15(9), 2336; https://doi.org/10.3390/rs15092336 - 28 Apr 2023

Cited by 27 | Viewed by 4830

Abstract

Two strong earthquakes occurred in Turkey on 6 February 2023, at 01:17:34 (nighttime, Mw = 7.8) and at 10:24:50 UT (daytime, Mw = 7.5). The seismo-ionospheric impact is an important part of the near-Earth environment state. This paper provides the first results on [...] Read more.

Two strong earthquakes occurred in Turkey on 6 February 2023, at 01:17:34 (nighttime, Mw = 7.8) and at 10:24:50 UT (daytime, Mw = 7.5). The seismo-ionospheric impact is an important part of the near-Earth environment state. This paper provides the first results on the ionospheric effects associated with the aforementioned earthquakes. We used data from global navigation satellite system (GNSS) receivers and ionosondes. We found that both earthquakes generated circle disturbance in the ionosphere, detected by GNSS data. The amplitude of the ionospheric response caused by daytime M7.5 earthquake exceeded by five times that caused by nighttime M7.8 earthquake: 0.5 TECU/min and 0.1 TECU/min, respectively, according to the ROTI data. The velocities of the earthquake-related ionospheric waves were ~2000 m/s, as measured by ROTI, for the M7.5 earthquake. TEC variations with 2–10 min periods showed velocities from 1500 to 900 m/s as disturbances evolved. Ionospheric disturbances occurred around epicenters and propagated to the south by means of 2–10 min TEC variations. ROTI data showed a more symmetric distribution with irregularities observed both to the South and to the North from 10:24:50 UT epicenter. The ionospheric effects were recorded over 750 km from the epicenters. Ionosonde located 420/490 km from the epicenters did not catch ionospheric effects. The results show significant asymmetry in the propagation of coseismic ionospheric disturbances. We observed coseismic ionospheric disturbances associated with Rayleigh mode and acoustic modes, but we did not observe disturbances associated with acoustic gravity mode. Full article

(This article belongs to the Special Issue Recent Advances in Observation and Simulation of the Lithosphere-Atmosphere-Space Coupling)

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19 pages, 3507 KiB

Open AccessEditor’s ChoiceArticle

Validation of NASA Sea Surface Temperature Satellite Products Using Saildrone Data

by Kalliopi Koutantou, Philip Brunner and Jorge Vazquez-Cuervo

Remote Sens. 2023, 15(9), 2277; https://doi.org/10.3390/rs15092277 - 25 Apr 2023

Cited by 9 | Viewed by 4816

Abstract

Sea Surface Temperature (SST) is at the core of many processes in the oceans. Various remote sensing platforms have been used to obtain SST products of different scales, but their validation remains a topic of ongoing research. One promising platform is an uncrewed [...] Read more.

Sea Surface Temperature (SST) is at the core of many processes in the oceans. Various remote sensing platforms have been used to obtain SST products of different scales, but their validation remains a topic of ongoing research. One promising platform is an uncrewed surface vehicle called Saildrone. We use the data from eight Saildrone deployments of the USA West Coast 2019 campaign to validate MODIS level-2 and Multi-scale Ultra-high Resolution (MUR) level-4 satellite SST products at 1 km spatial resolution and to assess the robustness of the quality levels of MODIS level-2 products over the California Coast. Pixel-based SST comparisons between Saildrone and the satellite products were performed, as well as thermal gradient comparisons computed both at the pixel-base level and using kriging interpolation. The results generally showed better accuracies for the MUR products. The characterization of the MODIS quality level proved to be valid in areas covered by bad-quality MODIS pixels but less valid in areas covered by lower-quality pixels. The latter implies possible errors in the MODIS quality level characterization and MUR interpolation processes. We have demonstrated the ability of the Saildrones to accurately validate near-shore satellite SST products and provide important information for the quality assessment of satellite products. Full article

(This article belongs to the Special Issue Advances in Retrieval, Operationalization, Monitoring and Application of Sea Surface Temperature II)

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20 pages, 8340 KiB

Open AccessEditor’s ChoiceArticle

Analysis of Spatial and Temporal Criteria for Altimeter Collocation of Significant Wave Height and Wind Speed Data in Deep Waters

by Ricardo M. Campos

Remote Sens. 2023, 15(8), 2203; https://doi.org/10.3390/rs15082203 - 21 Apr 2023

Cited by 5 | Viewed by 2256

Abstract

This paper investigates the spatial and temporal variability of significant wave height (Hs) and wind speed (U10) using altimeter data from the Australian Ocean Data Network (AODN) and buoy data from the National Data Buoy Center (NDBC). The main goal is to evaluate [...] Read more.

This paper investigates the spatial and temporal variability of significant wave height (Hs) and wind speed (U10) using altimeter data from the Australian Ocean Data Network (AODN) and buoy data from the National Data Buoy Center (NDBC). The main goal is to evaluate spatial and temporal criteria for collocating altimeter data to fixed-point positions and to provide practical guidance on altimeter collocation in deep waters. The results show that a temporal criterion of 30 min and a spatial criterion between 25 km and 50 km produce the best results for altimeter collocation, in close agreement with buoy data. Applying a 25 km criterion leads to slightly better error metrics but at the cost of fewer matchups, whereas using 50 km augments the resulting collocated dataset while keeping the differences to buoy measurements very low. Furthermore, the study demonstrates that using the single closest altimeter record to the buoy position leads to worse results compared to the collocation method based on temporal and spatial averaging. The final validation of altimeter data against buoy observations shows an RMSD of 0.21 m, scatter index of 0.09, and correlation coefficient of 0.98 for Hs, confirming the optimal choice of temporal and spatial criteria employed and the high quality of the calibrated AODN altimeter dataset. Full article

(This article belongs to the Special Issue Remote Sensing of Wave Fields under Extreme Weather Conditions (in Tropical and Extra-Tropical Cyclones and Polar Lows))

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18 pages, 3883 KiB

Open AccessEditor’s ChoiceArticle

Methods of Analyzing the Error and Rectifying the Calibration of a Solar Tracking System for High-Precision Solar Tracking in Orbit

by Yingqiu Shao, Zhanfeng Li, Xiaohu Yang, Yu Huang, Bo Li, Guanyu Lin and Jifeng Li

Remote Sens. 2023, 15(8), 2213; https://doi.org/10.3390/rs15082213 - 21 Apr 2023

Cited by 2 | Viewed by 1791

Abstract

Reliability is the most critical characteristic of space missions, for example in capturing and tracking moving targets. To this end, two methods are designed to track sunlight using solar remote-sensing instruments (SRSIs). The primary method is to use the offset angles of the [...] Read more.

Reliability is the most critical characteristic of space missions, for example in capturing and tracking moving targets. To this end, two methods are designed to track sunlight using solar remote-sensing instruments (SRSIs). The primary method is to use the offset angles of the guide mirror for closed-loop tracking, while the alternative method is to use the sunlight angles, calculated from the satellite attitude, solar vector, and mechanical installation correction parameters, for open-loop tracking. By comprehensively analyzing the error and rectifying the calibration of the solar tracking system, we demonstrate that the absolute value of the azimuth tracking precision is less than 0.0121° and the pitch is less than 0.0037° with the primary method. Correspondingly, they are 0.0992° and 0.0960° with the alternative method. These precisions meet the requirements of SRSIs. In addition, recalibration due to mechanical vibration during the satellite’s launch may invalidate the above methods, leading to the failure of SRSIs. Hence, we propose a dedicated injection parameter strategy to rectify the sunlight angles to capture and track the sunlight successfully. The stable and effective results in the ultraviolet to near-infrared spectrum validate the SRSI’s high-precision sunlight tracking performance. Furthermore, the above methods can also be applied to all orbital inclinations and may provide a solution for capturing and tracking moving targets. Full article

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41 pages, 2368 KiB

Open AccessEditor’s ChoiceArticle

Fast, Efficient, and Viable Compressed Sensing, Low-Rank, and Robust Principle Component Analysis Algorithms for Radar Signal Processing

by Reinhard Panhuber

Remote Sens. 2023, 15(8), 2216; https://doi.org/10.3390/rs15082216 - 21 Apr 2023

Cited by 8 | Viewed by 2436

Abstract

Modern radar signal processing techniques make strong use of compressed sensing, affine rank minimization, and robust principle component analysis. The corresponding reconstruction algorithms should fulfill the following desired properties: complex valued, viable in the sense of not requiring parameters that are unknown in [...] Read more.

Modern radar signal processing techniques make strong use of compressed sensing, affine rank minimization, and robust principle component analysis. The corresponding reconstruction algorithms should fulfill the following desired properties: complex valued, viable in the sense of not requiring parameters that are unknown in practice, fast convergence, low computational complexity, and high reconstruction performance. Although a plethora of reconstruction algorithms are available in the literature, these generally do not meet all of the aforementioned desired properties together. In this paper, a set of algorithms fulfilling these conditions is presented. The desired requirements are met by a combination of turbo-message-passing algorithms and smoothed

ℓ_{0}

-refinements. Their performance is evaluated by use of extensive numerical simulations and compared with popular conventional algorithms. Full article

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14 pages, 4294 KiB

Open AccessEditor’s ChoiceArticle

Stratospheric Water Vapor from the Hunga Tonga–Hunga Ha’apai Volcanic Eruption Deduced from COSMIC-2 Radio Occultation

by William J. Randel, Benjamin R. Johnston, John J. Braun, Sergey Sokolovskiy, Holger Vömel, Aurelien Podglajen and Bernard Legras

Remote Sens. 2023, 15(8), 2167; https://doi.org/10.3390/rs15082167 - 20 Apr 2023

Cited by 11 | Viewed by 3007

Abstract

The eruption of the Hunga Tonga–Hunga Ha’apai (HTHH) volcano on 15 January 2022 injected large amounts of water vapor (H₂O) directly into the stratosphere. While normal background levels of stratospheric H₂O are not detectable in radio occultation (RO) measurements, [...] Read more.

The eruption of the Hunga Tonga–Hunga Ha’apai (HTHH) volcano on 15 January 2022 injected large amounts of water vapor (H₂O) directly into the stratosphere. While normal background levels of stratospheric H₂O are not detectable in radio occultation (RO) measurements, effects of the HTHH eruption are clearly observed as anomalous refractivity profiles from COSMIC-2, suggesting the possibility of detecting the HTHH H₂O signal. To separate temperature and H₂O effects on refractivity, we use co-located temperature observations from the Microwave Limb Sounder (MLS) to constrain a simplified H₂O retrieval. Our results show enhancements of H₂O up to ~2500–3500 ppmv in the stratosphere (~29–33 km) in the days following the HTHH eruption, with propagating patterns that follow the dispersing volcanic plume. The stratospheric H₂O profiles derived from RO are in reasonable agreement with limited radiosonde observations over Australia. The H₂O profiles during the first few days after the eruption show descent of the plume at a rate of ~−1 km/day, likely due to strong radiative cooling (~−10 K/day) induced by high H₂O concentrations; slower descent (~−200 m/day) is observed over the following week as the plume disperses. The total mass of H₂O injected by HTHH is estimated as 110 ± 14 Tg from measurements in the early plumes during 16–18 January, which equates to approximately 8% of the background global mass of stratospheric H₂O. These RO measurements provide novel quantification of the unprecedented H₂O amounts and the plume evolution during the first week after the HTHH eruption. Full article

(This article belongs to the Section Atmospheric Remote Sensing)

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24 pages, 16057 KiB

Open AccessEditor’s ChoiceArticle

KaRIn Noise Reduction Using a Convolutional Neural Network for the SWOT Ocean Products

by Anaëlle Tréboutte, Elisa Carli, Maxime Ballarotta, Benjamin Carpentier, Yannice Faugère and Gérald Dibarboure

Remote Sens. 2023, 15(8), 2183; https://doi.org/10.3390/rs15082183 - 20 Apr 2023

Cited by 13 | Viewed by 3164

Abstract

The SWOT (Surface Water Ocean Topography) mission will provide high-resolution and two-dimensional measurements of sea surface height (SSH). However, despite its unprecedented precision, SWOT’s Ka-band Radar Interferometer (KaRIn) still exhibits a substantial amount of random noise. In turn, the random noise limits the [...] Read more.

The SWOT (Surface Water Ocean Topography) mission will provide high-resolution and two-dimensional measurements of sea surface height (SSH). However, despite its unprecedented precision, SWOT’s Ka-band Radar Interferometer (KaRIn) still exhibits a substantial amount of random noise. In turn, the random noise limits the ability of SWOT to capture the smallest scales of the ocean’s topography and its derivatives. In that context, this paper explores the feasibility, strengths and limits of a noise-reduction algorithm based on a convolutional neural network. The model is based on a U-Net architecture and is trained and tested with simulated data from the North Atlantic. Our results are compared to classical smoothing methods: a median filter, a Lanczos kernel smoother and the SWOT de-noising algorithm developed by Gomez-Navarro et al. Our U-Net model yields better results for all the evaluation metrics: 2 mm root mean square error, sub-millimetric bias, variance reduction by factor of 44 (16 dB) and an accurate power spectral density down to 10–20 km wavelengths. We also tested various scenarios to infer the robustness and the stability of the U-Net. The U-Net always exhibits good performance and can be further improved with retraining if necessary. This robustness in simulation is very encouraging: our findings show that the U-Net architecture is likely one of the best candidates to reduce the noise of flight data from KaRIn. Full article

(This article belongs to the Special Issue Applications of Satellite Altimetry in Ocean Observation)

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27 pages, 4773 KiB

Open AccessEditor’s ChoiceArticle

The InflateSAR Campaign: Developing Refugee Vessel Detection Capabilities with Polarimetric SAR

by Peter Lanz, Armando Marino, Morgan David Simpson, Thomas Brinkhoff, Frank Köster and Matthias Möller

Remote Sens. 2023, 15(8), 2008; https://doi.org/10.3390/rs15082008 - 10 Apr 2023

Cited by 4 | Viewed by 2732

Abstract

In the efforts to mitigate the ongoing humanitarian crisis at the European sea borders, this work builds detection capabilities to help find refugee boats in distress. For this paper, we collected dual-pol and quad-pol synthetic aperture radar (SAR) data over a 12 m [...] Read more.

In the efforts to mitigate the ongoing humanitarian crisis at the European sea borders, this work builds detection capabilities to help find refugee boats in distress. For this paper, we collected dual-pol and quad-pol synthetic aperture radar (SAR) data over a 12 m rubber inflatable in a test-bed lake near Berlin, Germany. To consider a real scenario, we prepared the vessel so that its backscattering emulated that of a vessel fully occupied with people. Further, we collected SAR imagery over the ocean with different sea states, categorized by incidence angle and by polarization. These were used to emulate the conditions for a vessel located in ocean waters. This setup enabled us to test nine well-known vessel-detection systems (VDS), to explore the capabilities of new detection algorithms and to benchmark different combinations of detectors (detector fusion) with respect to different sensor and scene parameters (e.g., the polarization, wind speed, wind direction and boat orientation). This analysis culminated in designing a system that is specifically tailored to accommodate different situations and sea states. Full article

(This article belongs to the Special Issue Remote Sensing for Marine Environmental Disaster Response)

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24 pages, 2950 KiB

Open AccessEditor’s ChoiceArticle

Can Plot-Level Photographs Accurately Estimate Tundra Vegetation Cover in Northern Alaska?

by Hana L. Sellers, Sergio A. Vargas Zesati, Sarah C. Elmendorf, Alexandra Locher, Steven F. Oberbauer, Craig E. Tweedie, Chandi Witharana and Robert D. Hollister

Remote Sens. 2023, 15(8), 1972; https://doi.org/10.3390/rs15081972 - 8 Apr 2023

Cited by 3 | Viewed by 2695

Abstract

Plot-level photography is an attractive time-saving alternative to field measurements for vegetation monitoring. However, widespread adoption of this technique relies on efficient workflows for post-processing images and the accuracy of the resulting products. Here, we estimated relative vegetation cover using both traditional field [...] Read more.

Plot-level photography is an attractive time-saving alternative to field measurements for vegetation monitoring. However, widespread adoption of this technique relies on efficient workflows for post-processing images and the accuracy of the resulting products. Here, we estimated relative vegetation cover using both traditional field sampling methods (point frame) and semi-automated classification of photographs (plot-level photography) across thirty 1 m² plots near Utqiaġvik, Alaska, from 2012 to 2021. Geographic object-based image analysis (GEOBIA) was applied to generate objects based on the three spectral bands (red, green, and blue) of the images. Five machine learning algorithms were then applied to classify the objects into vegetation groups, and random forest performed best (60.5% overall accuracy). Objects were reliably classified into the following classes: bryophytes, forbs, graminoids, litter, shadows, and standing dead. Deciduous shrubs and lichens were not reliably classified. Multinomial regression models were used to gauge if the cover estimates from plot-level photography could accurately predict the cover estimates from the point frame across space or time. Plot-level photography yielded useful estimates of vegetation cover for graminoids. However, the predictive performance varied both by vegetation class and whether it was being used to predict cover in new locations or change over time in previously sampled plots. These results suggest that plot-level photography may maximize the efficient use of time, funding, and available technology to monitor vegetation cover in the Arctic, but the accuracy of current semi-automated image analysis is not sufficient to detect small changes in cover. Full article

(This article belongs to the Special Issue Advanced Technologies in Wetland and Vegetation Ecological Monitoring)

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22 pages, 8449 KiB

Open AccessEditor’s ChoiceArticle

Accuracy Assessment of High-Resolution Globally Available Open-Source DEMs Using ICESat/GLAS over Mountainous Areas, A Case Study in Yunnan Province, China

by Menghua Li, Xiebing Yin, Bo-Hui Tang and Mengshi Yang

Remote Sens. 2023, 15(7), 1952; https://doi.org/10.3390/rs15071952 - 6 Apr 2023

Cited by 11 | Viewed by 3142

Abstract

The Open-Source Digital Elevation Model (DEM) is fundamental data of the geoscientific community. However, the variation of its accuracy with land cover type and topography has not been thoroughly studied. This study evaluates the accuracy of five globally covered and open-accessed DEM products [...] Read more.

The Open-Source Digital Elevation Model (DEM) is fundamental data of the geoscientific community. However, the variation of its accuracy with land cover type and topography has not been thoroughly studied. This study evaluates the accuracy of five globally covered and open-accessed DEM products (TanDEM-X90 m, SRTEM, NASADEM, ASTER GDEM, and AW3D30) in the mountain area using ICESat/GLAS data as the GCPs. The robust evaluation indicators were utilized to compare the five DEMs’ accuracy and explore the relationship between these errors and slope, aspect, landcover types, and vegetation coverage, thereby revealing the consistency differences in DEM quality under different geographical feature conditions. The Taguchi method is introduced to quantify the impact of these surface characteristics on DEM errors. The results show that the slope is the main factor affecting the accuracy of DEM products, accounting for about 90%, 81%, 85%, 83%, and 65% for TanDEM-X90, SRTM, NASADEM, ASTER GDEM, and AW3D30, respectively. TanDEM-X90 has the highest accuracy in very flat areas (slope < 2°), NASADEM and SRTM have the greatest accuracy in flat areas (2 ≤ slope < 5°), while AW3D30 accuracy is the best in other cases and shows the best consistency on slopes. This study makes a new attempt to quantify the factors affecting the accuracy of DEM, and the results can guide the selection of open-source DEMs in related geoscience research. Full article

(This article belongs to the Special Issue Quantifying Geomorphological Processes Using Remote Sensing Techniques)

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23 pages, 9006 KiB

Open AccessEditor’s ChoiceArticle

Terrestrial Laser Scanning for Non-Destructive Estimation of Aboveground Biomass in Short-Rotation Poplar Coppices

by María Menéndez-Miguélez, Guillermo Madrigal, Hortensia Sixto, Nerea Oliveira and Rafael Calama

Remote Sens. 2023, 15(7), 1942; https://doi.org/10.3390/rs15071942 - 5 Apr 2023

Cited by 7 | Viewed by 2499

Abstract

Poplar plantations in high-density and short-rotation coppices (SRC) are a suitable way for the fast production of wood that can be transformed into bioproducts or bioenergy. Optimal management of these coppices requires accurate assessment of the total standing biomass. However, traditional field inventory [...] Read more.

Poplar plantations in high-density and short-rotation coppices (SRC) are a suitable way for the fast production of wood that can be transformed into bioproducts or bioenergy. Optimal management of these coppices requires accurate assessment of the total standing biomass. However, traditional field inventory is a challenging task, given the existence of multiple shoots, the difficulty of identifying terminal shoots, and the extreme high density. As an alternative, in this work, we propose to develop individual stool and plot biomass models using metrics derived from terrestrial laser scanning (TLS) as predictors. To this aim, we used data from a SRC poplar plantation, including nine plots and 154 individual stools. Every plot was scanned from different positions, and individual stools were felled, weighed, and dried to compute aboveground biomass (AGB). Individual stools were segmented from the cloud point, and different TLS metrics at stool and plot level were derived following processes of bounding box, slicing, and voxelization. These metrics were then used, either alone or combined with field-measured metrics, to fit biomass models. Our results indicate that at individual-stool level, the biomass models combining TLS metrics and easy to measure in field metrics (stool diameter) perform similarly to the traditional allometric models based on field inventories, while at plot scales, TLS-derived models show superiority over traditional models. Our proposed methodology permits accurate and non-destructive estimates of the biomass fixed in SRC plantations. Full article

(This article belongs to the Special Issue Remote Sensing for Forest Morphological and Physiological Traits Monitoring)

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19 pages, 2390 KiB

Open AccessEditor’s ChoiceTechnical Note

Radiometric Terrain Flattening of Geocoded Stacks of SAR Imagery

by Piyush S. Agram, Michael S. Warren, Scott A. Arko and Matthew T. Calef

Remote Sens. 2023, 15(7), 1932; https://doi.org/10.3390/rs15071932 - 4 Apr 2023

Cited by 5 | Viewed by 3002

Abstract

We have described an efficient approach to radiometrically flatten geocoded stacks of calibrated synthetic aperture radar (SAR) data for terrain-related effects. We have used simulation to demonstrate that, for the Sentinel-1 mission, one static radiometric terrain-flattening factor derived from actual SAR imaging metadata [...] Read more.

We have described an efficient approach to radiometrically flatten geocoded stacks of calibrated synthetic aperture radar (SAR) data for terrain-related effects. We have used simulation to demonstrate that, for the Sentinel-1 mission, one static radiometric terrain-flattening factor derived from actual SAR imaging metadata per imaging geometry is sufficient for flattening interferometrically compliant stacks of SAR data. We have quantified the loss of precision due to the application of static flattening factors, and show that these are well below the stated requirements of change-detection algorithms. Finally, we have discussed the implications of applying radiometric terrain flattening to geocoded SAR data instead of the traditional approach of flattening data provided in the original SAR image geometry. The proposed approach allows for efficient and consistent generation of five different Committee of Earth-Observation Satellites (CEOS) Analysis-Ready Dataset (ARD) families—Geocoded Single-Look Complex (GSLC), Interferometric Radar (InSAR), Normalized Radar Backscatter (NRB), Polarimetric Radar (POL) and Ocean Radar Backscatter (ORB) from SAR missions in a common framework. Full article

(This article belongs to the Section Engineering Remote Sensing)

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30 pages, 19069 KiB

Open AccessEditor’s ChoiceArticle

Insights into the Magmatic Feeding System of the 2021 Eruption at Cumbre Vieja (La Palma, Canary Islands) Inferred from Gravity Data Modeling

by Fuensanta G. Montesinos, Sergio Sainz-Maza, David Gómez-Ortiz, José Arnoso, Isabel Blanco-Montenegro, Maite Benavent, Emilio Vélez, Nieves Sánchez and Tomás Martín-Crespo

Remote Sens. 2023, 15(7), 1936; https://doi.org/10.3390/rs15071936 - 4 Apr 2023

Cited by 12 | Viewed by 4492

Abstract

This study used spatiotemporal land gravity data to investigate the 2021 eruption that occurred in the Cumbre Vieja volcano (La Palma, Canary Islands). First, we produced a density model by inverting the local gravity field using data collected in July 2005 and July [...] Read more.

This study used spatiotemporal land gravity data to investigate the 2021 eruption that occurred in the Cumbre Vieja volcano (La Palma, Canary Islands). First, we produced a density model by inverting the local gravity field using data collected in July 2005 and July 2021. This model revealed a low-density body beneath the western flank of the volcano that explains a highly fractured and altered structure related to the active hydrothermal system. Then, we retrieved changes in gravity and GNSS vertical displacements from repeated measurements made in a local network before (July 2021) and after (January 2022) the eruption. After correcting the vertical surface displacements, the gravity changes produced by mass variation during the eruptive process were used to build a forward model of the magmatic feeding system consisting of dikes and sills based on an initial model defined by the paths of the earthquake hypocenters preceding the eruption. Our study provides a final model of the magma plumbing system, which establishes a spatiotemporal framework tracing the path of magma ascent from the crust–mantle boundary to the surface from 11–19 September 2021, where the shallowest magma path was strongly influenced by the low-density body identified in the inversion process. Full article

(This article belongs to the Special Issue Investigation of Volcanic Activity Using Potential and Deformation Fields Retrieved from Proximal and Remote Sensing Techniques)

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16 pages, 6277 KiB

Open AccessEditor’s ChoiceArticle

A 3D Interferometer-Type Lightning Mapping Array for Observation of Winter Lightning in Japan

by Junchen Yang, Daohong Wang, Haitao Huang, Ting Wu, Nobuyuki Takagi and Kazuo Yamamoto

Remote Sens. 2023, 15(7), 1923; https://doi.org/10.3390/rs15071923 - 3 Apr 2023

Cited by 7 | Viewed by 2806

Abstract

We have developed and deployed a 3D Interferometer-type Lightning Mapping Array (InLMA) for observing winter lightning in Japan. InLMA consists of three broadband interferometers installed at three stations with a distance from 3 to 5 km. At each interferometer station, three discone antennas [...] Read more.

We have developed and deployed a 3D Interferometer-type Lightning Mapping Array (InLMA) for observing winter lightning in Japan. InLMA consists of three broadband interferometers installed at three stations with a distance from 3 to 5 km. At each interferometer station, three discone antennas were installed, forming a right triangle with a separation of 75 m along their two orthogonal baselines. The output of each InLMA antenna is passed through a 400 MHz low-pass filter and then recorded at 1 GS/s with 16-bit accuracy. A new method has been proposed for finding 3D solutions of a lightning mapping system that consists of multiple interferometers. Using the InLMA, we have succeeded in mapping a positive cloud-to-ground (CG) lightning flash in winter, particularly its preliminary breakdown (PB) process. A study on individual PB pulse processes allows us to infer that each PB pulse process contains many small-scale discharges scattering in a height range of about 150 m. These small-scale discharges in a series of PB pulses appear to be continuous in space, though discontinuous in time. We have also examined the positive return stroke in the CG flash and found a 3D average return stroke speed of 7.5 × 10⁷ m/s. Full article

(This article belongs to the Special Issue Advances in Instrumentation and Algorithms for Atmospheric Electricity Applications)

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20 pages, 4886 KiB

Open AccessEditor’s ChoiceArticle

Accuracy Assessment of Surveying Strategies for the Characterization of Microtopographic Features That Influence Surface Water Flooding

by Rakhee Ramachandran, Yadira Bajón Fernández, Ian Truckell, Carlos Constantino, Richard Casselden, Paul Leinster and Mónica Rivas Casado

Remote Sens. 2023, 15(7), 1912; https://doi.org/10.3390/rs15071912 - 2 Apr 2023

Cited by 6 | Viewed by 3186

Abstract

With the increase in rainfall intensity, population, and urbanised areas, surface water flooding (SWF) is an increasing concern impacting properties, businesses, and human lives. Previous studies have shown that microtopography significantly influences flow paths, flow direction, and velocity, impacting flood extent and depth, [...] Read more.

With the increase in rainfall intensity, population, and urbanised areas, surface water flooding (SWF) is an increasing concern impacting properties, businesses, and human lives. Previous studies have shown that microtopography significantly influences flow paths, flow direction, and velocity, impacting flood extent and depth, particularly for the shallow flow associated with urban SWF. This study compares two survey strategies commonly used by flood practitioners, S1 (using Unmanned Aerial Systems-based RGB data) and S2 (using manned aircraft with LiDAR scanners), to develop guidelines on where to use each strategy to better characterise microtopography for a range of flood features. The difference between S1 and S2 in elevation and their accuracies were assessed using both traditional and robust statistical measures. The results showed that the difference in elevation between S1 and S2 varies between 11 cm and 37 cm on different land use and microtopographic flood features. Similarly, the accuracy of S1 ranges between 3 cm and 70 cm, and the accuracy of S2 ranges between 3.8 cm and 30.3 cm on different microtopographic flood features. Thus, this study suggests that the flood features of interest in any given flood study would be key to select the most suitable survey strategy. A decision framework was developed to inform data collection and integration of the two surveying strategies to better characterise microtopographic features. The findings from this study will help improve the microtopographic representation of flood features in flood models and, thus, increase the ability to identify high flood-risk prompt areas accurately. It would also help manage and maintain drainage assets, spatial planning of sustainable drainage systems, and property level flood resilience and insurance to better adapt to the effects of climate change. This study is another step towards standardising flood extent and impact surveying strategies. Full article

(This article belongs to the Special Issue State-of-the-Art Remote Sensing Technologies for Environmental Monitoring)

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14 pages, 2757 KiB

Open AccessEditor’s ChoiceCommunication

Underwater 3D Scanning System for Cultural Heritage Documentation

by Christian Bräuer-Burchardt, Christoph Munkelt, Michael Bleier, Matthias Heinze, Ingo Gebhart, Peter Kühmstedt and Gunther Notni

Remote Sens. 2023, 15(7), 1864; https://doi.org/10.3390/rs15071864 - 31 Mar 2023

Cited by 18 | Viewed by 4159

Abstract

Three-dimensional capturing of underwater archeological sites or sunken shipwrecks can support important documentation purposes. In this study, a novel 3D scanning system based on structured illumination is introduced, which supports cultural heritage documentation and measurement tasks in underwater environments. The newly developed system [...] Read more.

Three-dimensional capturing of underwater archeological sites or sunken shipwrecks can support important documentation purposes. In this study, a novel 3D scanning system based on structured illumination is introduced, which supports cultural heritage documentation and measurement tasks in underwater environments. The newly developed system consists of two monochrome measurement cameras, a projection unit that produces aperiodic sinusoidal fringe patterns, two flashlights, a color camera, an inertial measurement unit (IMU), and an electronic control box. The opportunities and limitations of the measurement principles of the 3D scanning system are discussed and compared to other 3D recording methods such as laser scanning, ultrasound, and photogrammetry, in the context of underwater applications. Some possible operational scenarios concerning cultural heritage documentation are introduced and discussed. A report on application activities in water basins and offshore environments including measurement examples and results of the accuracy measurements is given. The study shows that the new 3D scanning system can be used for both the topographic documentation of underwater sites and to generate detailed true-scale 3D models including the texture and color information of objects that must remain under water. Full article

(This article belongs to the Special Issue Documenting and Monitoring Underwater Cultural Heritage Using Remote Sensing)

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19 pages, 1145 KiB

Open AccessEditor’s ChoiceArticle

CRSNet: Cloud and Cloud Shadow Refinement Segmentation Networks for Remote Sensing Imagery

by Chao Zhang, Liguo Weng, Li Ding, Min Xia and Haifeng Lin

Remote Sens. 2023, 15(6), 1664; https://doi.org/10.3390/rs15061664 - 20 Mar 2023

Cited by 32 | Viewed by 3468

Abstract

Cloud detection is a critical task in remote sensing image tasks. Due to the influence of ground objects and other noises, the traditional detection methods are prone to miss or false detection and rough edge segmentation in the detection process. To avoid the [...] Read more.

Cloud detection is a critical task in remote sensing image tasks. Due to the influence of ground objects and other noises, the traditional detection methods are prone to miss or false detection and rough edge segmentation in the detection process. To avoid the defects of traditional methods, Cloud and Cloud Shadow Refinement Segmentation Networks are proposed in this paper. The network can correctly and efficiently detect smaller clouds and obtain finer edges. The model takes ResNet-18 as the backbone to extract features at different levels, and the Multi-scale Global Attention Module is used to strengthen the channel and spatial information to improve the accuracy of detection. The Strip Pyramid Channel Attention Module is used to learn spatial information at multiple scales to detect small clouds better. Finally, the high-dimensional feature and low-dimensional feature are fused by the Hierarchical Feature Aggregation Module, and the final segmentation effect is obtained by up-sampling layer by layer. The proposed model attains excellent results compared to methods with classic or special cloud segmentation tasks on Cloud and Cloud Shadow Dataset and the public dataset CSWV. Full article

(This article belongs to the Special Issue Advanced Application of Artificial Intelligence and Machine Vision in Remote Sensing II)

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17 pages, 11877 KiB

Open AccessEditor’s ChoiceArticle

Absolute Localization of Targets Using a Phase-Measuring Sidescan Sonar in Very Shallow Waters

by Mark Borrelli, Bryan Legare, Bryan McCormack, Pedro Paulo Guy Martins dos Santos and Daniel Solazzo

Remote Sens. 2023, 15(6), 1626; https://doi.org/10.3390/rs15061626 - 17 Mar 2023

Cited by 7 | Viewed by 2434

Abstract

The detection, classification, and localization of targets or features on the seafloor in acoustic data are critical to many disciplines. This is most important in cases where human safety is in jeopardy, such as hazards to navigation, mitigation of mine countermeasures, or unexploded [...] Read more.

The detection, classification, and localization of targets or features on the seafloor in acoustic data are critical to many disciplines. This is most important in cases where human safety is in jeopardy, such as hazards to navigation, mitigation of mine countermeasures, or unexploded ordnance. This study quantifies the absolute localization of targets, in the form of inert unexploded ordnance, in very shallow waters (2–3 m) on two intertidal bottom types in a meso-tidal environment (tide range = ~3.0 m). The two sites, a sandy intertidal flat and a mixed sand and gravel beach with abundant cobble-sized material, were seeded at low tide with targets (wax-filled 60-, 81-, 105- and 155-mm, projectile and mortar shells). An RTK-GPS was used to collect positional data for the targets and an unoccupied aerial system (UAS) survey was conducted on both sites. At the next high-tide, a vessel-based acoustic survey was performed, and at the subsequent low tide, the targets were re-surveyed with RTK-GPS. We focus here on the sidescan backscatter from a phase-measuring sidescan sonar (PMSS) and the sources of uncertainty for absolute localization. A total of 1426 calls of acoustic targets were made within the sidescan backscatter data, yielding an accuracy of 0.41 ± 0.26 m, with 98.9% of all calls <1 m from their absolute location. Distance from nadir was the most significant source of uncertainty, and targets between 3–7 m had the lowest uncertainty (0.32 ± 0.23 m) with increasing values toward and away from nadir. Bathymetry and bathymetry-mode backscatter were less useful for the detection and classification of targets compared to sidescan backscatter, but once detected, the accuracy of absolute localization were similar. This is likely due to target calls from these two datasets that were orders of magnitude less and that focused on the larger sized targets, thus more work is needed to better understand these differences. Lastly, the absolute localization of targets detected on sandy and cobble bottoms for all datasets were statistically similar. These acoustic instruments, their datasets, and methods presented herein can better document the absolute localization within acoustic data for many uses in very shallow waters. Full article

(This article belongs to the Special Issue Remote Sensing for Shallow and Deep Waters Mapping and Monitoring)

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39 pages, 7209 KiB

Open AccessEditor’s ChoiceArticle

AVHRR NDVI Compositing Method Comparison and Generation of Multi-Decadal Time Series—A TIMELINE Thematic Processor

by Sarah Asam, Christina Eisfelder, Andreas Hirner, Philipp Reiners, Stefanie Holzwarth and Martin Bachmann

Remote Sens. 2023, 15(6), 1631; https://doi.org/10.3390/rs15061631 - 17 Mar 2023

Cited by 11 | Viewed by 4139

Abstract

Remote sensing image composites are crucial for a wide range of remote sensing applications, such as multi-decadal time series analysis. The Advanced Very High Resolution Radiometer (AVHRR) instrument has provided daily data since the early 1980s at a spatial resolution of 1 km, [...] Read more.

Remote sensing image composites are crucial for a wide range of remote sensing applications, such as multi-decadal time series analysis. The Advanced Very High Resolution Radiometer (AVHRR) instrument has provided daily data since the early 1980s at a spatial resolution of 1 km, allowing analyses of climate change-related environmental processes. For monitoring vegetation conditions, the Normalized Difference Vegetation Index (NDVI) is the most widely used metric. However, to actually enable such analyses, a consistent NDVI time series over the AVHRR time-span needs to be created. In this context, the aim of this study is to thoroughly assess the effect of different compositing procedures on AVHRR NDVI composites, as no standard procedure has been established. Thirteen different compositing methods have been implemented; daily, decadal, and monthly composites over Europe and Northern Africa have been calculated for the year 2007, and the resulting data sets have been thoroughly evaluated according to six criteria. The median approach was selected as the best-performing compositing algorithm considering all the investigated aspects. However, the combination of the NDVI value and viewing and illumination angles as the criteria for the best-pixel selection proved to be a promising approach, too. The generated NDVI time series, currently ranging from 1981–2018, shows a consistent behavior and close agreement to the standard MODIS NDVI product. The conducted analyses demonstrate the strong influence of compositing procedures on the resulting AVHRR NDVI composites. Full article

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

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26 pages, 16407 KiB

Open AccessEditor’s ChoiceArticle

Autonomous Detection of Mouse-Ear Hawkweed Using Drones, Multispectral Imagery and Supervised Machine Learning

by Narmilan Amarasingam, Mark Hamilton, Jane E. Kelly, Lihong Zheng, Juan Sandino, Felipe Gonzalez, Remy L. Dehaan and Hillary Cherry

Remote Sens. 2023, 15(6), 1633; https://doi.org/10.3390/rs15061633 - 17 Mar 2023

Cited by 14 | Viewed by 3098

Abstract

Hawkweeds (Pilosella spp.) have become a severe and rapidly invading weed in pasture lands and forest meadows of New Zealand. Detection of hawkweed infestations is essential for eradication and resource management at private and government levels. This study explores the potential of [...] Read more.

Hawkweeds (Pilosella spp.) have become a severe and rapidly invading weed in pasture lands and forest meadows of New Zealand. Detection of hawkweed infestations is essential for eradication and resource management at private and government levels. This study explores the potential of machine learning (ML) algorithms for detecting mouse-ear hawkweed (Pilosella officinarum) foliage and flowers from Unmanned Aerial Vehicle (UAV)-acquired multispectral (MS) images at various spatial resolutions. The performances of different ML algorithms, namely eXtreme Gradient Boosting (XGB), Support Vector Machine (SVM), Random Forest (RF), and K-nearest neighbours (KNN), were analysed in their capacity to detect hawkweed foliage and flowers using MS imagery. The imagery was obtained at numerous spatial resolutions from a highly infested study site located in the McKenzie Region of the South Island of New Zealand in January 2021. The spatial resolution of 0.65 cm/pixel (acquired at a flying height of 15 m above ground level) produced the highest overall testing and validation accuracy of 100% using the RF, KNN, and XGB models for detecting hawkweed flowers. In hawkweed foliage detection at the same resolution, the RF and XGB models achieved highest testing accuracy of 97%, while other models (KNN and SVM) achieved an overall model testing accuracy of 96% and 72%, respectively. The XGB model achieved the highest overall validation accuracy of 98%, while the other models (RF, KNN, and SVM) produced validation accuracies of 97%, 97%, and 80%, respectively. This proposed methodology may facilitate non-invasive detection efforts of mouse-ear hawkweed flowers and foliage in other naturalised areas, enabling land managers to optimise the use of UAV remote sensing technologies for better resource allocation. Full article

(This article belongs to the Special Issue Machine Learning for Multi-Source Remote Sensing Images Analysis)

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19 pages, 2089 KiB

Open AccessEditor’s ChoiceArticle

Mapping Fractional Vegetation Coverage across Wetland Classes of Sub-Arctic Peatlands Using Combined Partial Least Squares Regression and Multiple Endmember Spectral Unmixing

by Heidi Cunnick, Joan M. Ramage, Dawn Magness and Stephen C. Peters

Remote Sens. 2023, 15(5), 1440; https://doi.org/10.3390/rs15051440 - 4 Mar 2023

Cited by 7 | Viewed by 2699

Abstract

Vegetation communities play a key role in governing the atmospheric-terrestrial fluxes of water, carbon, nutrients, and energy. The expanse and heterogeneity of vegetation in sub-arctic peatland systems makes monitoring change at meaningful spatial resolutions and extents challenging. We use a field-collected spectral endmember [...] Read more.

Vegetation communities play a key role in governing the atmospheric-terrestrial fluxes of water, carbon, nutrients, and energy. The expanse and heterogeneity of vegetation in sub-arctic peatland systems makes monitoring change at meaningful spatial resolutions and extents challenging. We use a field-collected spectral endmember reference library to unmix hyperspectral imagery and map vegetation coverage at the level of plant functional type (PFT), across three wetland sites in sub-arctic Alaska. This study explores the optimization and parametrization of multiple endmember spectral mixture analysis (MESMA) models to estimate coverage of PFTs across wetland classes. We use partial least squares regression (PLSR) to identify a parsimonious set of critical bands for unmixing and compare the reference and modeled coverage. Unmixing, using a full set of 110-bands and a smaller set of 4-bands, results in maps that effectively discriminate between PFTs, indicating a small investment in fieldwork results in maps mirroring the true ground cover. Both sets of spectral bands differentiate between PFTs, but the 4-band unmixing library results in more accurate predictive mapping with lower computational cost. Reducing the unmixing reference dataset by constraining the PFT endmembers to those identified in the field-site produces only a small advantage for mapping, suggesting extensive fieldwork may not be necessary for MESMA to have a high explanatory value in these remote environments. Full article

(This article belongs to the Special Issue Application of Remote Sensing for Monitoring of Peatlands)

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25 pages, 4062 KiB

Open AccessEditor’s ChoiceArticle

Irrigation Timing Retrieval at the Plot Scale Using Surface Soil Moisture Derived from Sentinel Time Series in Europe

by Michel Le Page, Thang Nguyen, Mehrez Zribi, Aaron Boone, Jacopo Dari, Sara Modanesi, Luca Zappa, Nadia Ouaadi and Lionel Jarlan

Remote Sens. 2023, 15(5), 1449; https://doi.org/10.3390/rs15051449 - 4 Mar 2023

Cited by 8 | Viewed by 3571

Abstract

The difficulty of calculating the daily water budget of irrigated fields is often due to the uncertainty surrounding irrigation amounts and timing. The automated detection of irrigation events has the potential to greatly simplify this process, and the combination of high-resolution SAR (Sentinel-1) [...] Read more.

The difficulty of calculating the daily water budget of irrigated fields is often due to the uncertainty surrounding irrigation amounts and timing. The automated detection of irrigation events has the potential to greatly simplify this process, and the combination of high-resolution SAR (Sentinel-1) and optical satellite observations (Sentinel-2) makes the detection of irrigation events feasible through the use of a surface soil moisture (SSM) product. The motivation behind this study is to utilize a large irrigation dataset (collected during the ESA Irrigation + project over five sites in three countries over three years) to analyze the performance of an established algorithm and to test potential improvements. The study’s main findings are (1) the scores decrease with SSM observation frequency; (2) scores decrease as irrigation frequency increases, which was supported by better scores in France (more sprinkler irrigation) than in Germany (more localized irrigation); (3) replacing the original SSM model with the force-restore model resulted in an improvement of about 6% in the F-score and narrowed the error on cumulative seasonal irrigation; (4) the Sentinel-1 configuration (incidence angle, trajectory) did not show a significant impact on the retrieval of irrigation, which supposes that the SSM is not affected by these changes. Other aspects did not allow a definitive conclusion on the irrigation retrieval algorithm: (1) the lower scores obtained with small NDVI compared to large NDVI were counter-intuitive but may have been due to the larger number of irrigation events during high vegetation periods; (2) merging different runs and interpolating all SSM data for one run produced comparable F-scores, but the estimated cumulative sum of irrigation was around −20% lower compared to the reference dataset in the best cases. Full article

(This article belongs to the Special Issue Irrigation Estimates and Management from EO Data)

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30 pages, 18928 KiB

Open AccessEditor’s ChoiceReview

A Bibliometric and Visualized Analysis of Remote Sensing Methods for Glacier Mass Balance Research

by Aijie Yu, Hongling Shi, Yifan Wang, Jin Yang, Chunchun Gao and Yang Lu

Remote Sens. 2023, 15(5), 1425; https://doi.org/10.3390/rs15051425 - 3 Mar 2023

Cited by 5 | Viewed by 5170

Abstract

In recent decades, climate change has led to global warming, glacier melting, glacial lake outbursts, sea level rising, and more extreme weather, and has seriously affected human life. Remote sensing technology has advanced quickly, and it offers effective observation techniques for studying and [...] Read more.

In recent decades, climate change has led to global warming, glacier melting, glacial lake outbursts, sea level rising, and more extreme weather, and has seriously affected human life. Remote sensing technology has advanced quickly, and it offers effective observation techniques for studying and monitoring glaciers. In order to clarify the stage of research development, research hotspots, research frontiers, and limitations and challenges in glacier mass balance based on remote sensing technology, we used the tools of bibliometrics and data visualization to analyze 4817 works of literature related to glacier mass balance based on remote sensing technology from 1990 to 2021 in the Web of Science database. The results showed that (1) China and the United States are the major countries in the study of glacier mass balance based on remote sensing technology. (2) The Chinese Academy of Sciences is the most productive research institution. (3) Current research hotspots focus on “Climate change”, “Inventory”, “Dynamics”, “Model”, “Retreat”, “Glacier mass balance”, “Sea level”, “Radar”, “Volume change”, “Surface velocity”, “Glacier mapping”, “Hazard”, and other keywords. (4) The current research frontiers include water storage change, artificial intelligence, High Mountain Asia (HMA), photogrammetry, debris cover, geodetic method, area change, glacier volume, classification, satellite gravimetry, grounding line retreat, risk assessment, lake outburst flood, glacier elevation change, digital elevation model, geodetic mass balance, (DEM) generation, etc. According to the results of the visual analysis of the literature, we introduced the three commonly used methods of glacier mass balance based on remote sensing observation and summarized the research status and shortcomings of different methods in glacier mass balance. We considered that the future research trend is to improve the spatial and temporal resolution of data and combine a variety of methods and data to achieve high precision and long-term monitoring of glacier mass changes and improve the consistency of results. This research summarizes the study of glacier mass balance using remote sensing, which will provide valuable information for future research across this field. Full article

(This article belongs to the Special Issue Applications of Satellite Geodesy in Hydrology, Glaciology, and Oceanography)

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34 pages, 7094 KiB

Open AccessEditor’s ChoiceArticle

An Ensemble Approach of Feature Selection and Machine Learning Models for Regional Landslide Susceptibility Mapping in the Arid Mountainous Terrain of Southern Peru

by Chandan Kumar, Gabriel Walton, Paul Santi and Carlos Luza

Remote Sens. 2023, 15(5), 1376; https://doi.org/10.3390/rs15051376 - 28 Feb 2023

Cited by 31 | Viewed by 3863

Abstract

This study evaluates the utility of the ensemble framework of feature selection and machine learning (ML) models for regional landslide susceptibility mapping (LSM) in the arid climatic condition of southern Peru. A historical landslide inventory and 24 different landslide influencing factors (LIFs) were [...] Read more.

This study evaluates the utility of the ensemble framework of feature selection and machine learning (ML) models for regional landslide susceptibility mapping (LSM) in the arid climatic condition of southern Peru. A historical landslide inventory and 24 different landslide influencing factors (LIFs) were prepared using remotely sensed and auxiliary datasets. The LIFs were evaluated using multi-collinearity statistics and their relative importance was measured to select the most discriminative LIFs using the ensemble feature selection method, which was developed using Chi-square, gain ratio, and relief-F methods. We evaluated the performance of ten different ML algorithms (linear discriminant analysis, mixture discriminant analysis, bagged cart, boosted logistic regression, k-nearest neighbors, artificial neural network, support vector machine, random forest, rotation forest, and C5.0) using different accuracy statistics (sensitivity, specificity, area under curve (AUC), and overall accuracy (OA)). We used suitable combinations of individual ML models to develop different ensemble ML models and evaluated their performance in LSM. We assessed the impact of LIFs on ML performance. Among all individual ML models, the k-nearest neighbors (sensitivity = 0.72, specificity = 0.82, AUC = 0.86, OA = 78%) and artificial neural network (sensitivity = 0.71, specificity = 0.85, AUC = 0.87, OA = 79%) algorithms showed the best performance using the top five LIFs, while random forest, rotation forest, and C5.0 (sensitivity = 0.76–0.81, specificity = 0.87, AUC = 0.90–0.93, OA = 82–84%) outperformed other models when developed using all twenty-four LIFs. Among ensemble models, the ensemble of k-nearest neighbors and rotation forest, k-nearest neighbors and artificial neural network, and artificial neural network and rotation forest outperformed other models (sensitivity = 0.72–0.73, specificity = 0.83–0.84, AUC = 0.86, OA = 79%) using the top five LIFs. The landslide susceptibility maps derived using these models indicate that ~2–3% and ~10–12% of the total study area fall within the “very high” and “high” susceptibility. The obtained susceptibility maps can be efficiently used to prioritize landslide mitigation activities. Full article

(This article belongs to the Special Issue Advancement of Remote Sensing in Landslide Susceptibility Assessment)

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21 pages, 8362 KiB

Open AccessEditor’s ChoiceArticle

AI-TFNet: Active Inference Transfer Convolutional Fusion Network for Hyperspectral Image Classification

by Jianing Wang, Linhao Li, Yichen Liu, Jinyu Hu, Xiao Xiao and Bo Liu

Remote Sens. 2023, 15(5), 1292; https://doi.org/10.3390/rs15051292 - 26 Feb 2023

Cited by 5 | Viewed by 2336

Abstract

The realization of efficient classification with limited labeled samples is a critical task in hyperspectral image classification (HSIC). Convolutional neural networks (CNNs) have achieved remarkable advances while considering spectral–spatial features simultaneously, while conventional patch-wise-based CNNs usually lead to redundant computations. Therefore, in this [...] Read more.

The realization of efficient classification with limited labeled samples is a critical task in hyperspectral image classification (HSIC). Convolutional neural networks (CNNs) have achieved remarkable advances while considering spectral–spatial features simultaneously, while conventional patch-wise-based CNNs usually lead to redundant computations. Therefore, in this paper, we established a novel active inference transfer convolutional fusion network (AI-TFNet) for HSI classification. First, in order to reveal and merge the local low-level and global high-level spectral–spatial contextual features at different stages of extraction, an end-to-end fully hybrid multi-stage transfer fusion network (TFNet) was designed to improve classification performance and efficiency. Meanwhile, an active inference (AI) pseudo-label propagation algorithm for spatially homogeneous samples was constructed using the homogeneous pre-segmentation of the proposed TFNet. In addition, a confidence-augmented pseudo-label loss (CapLoss) was proposed in order to define the confidence of a pseudo-label with an adaptive threshold in homogeneous regions for acquiring pseudo-label samples; this can adaptively infer a pseudo-label by actively augmenting the homogeneous training samples based on their spatial homogeneity and spectral continuity. Experiments on three real HSI datasets proved that the proposed method had competitive performance and efficiency compared to several related state-of-the-art methods. Full article

(This article belongs to the Special Issue Active Learning Methods for Remote Sensing Data Processing)

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20 pages, 12427 KiB

Open AccessEditor’s ChoiceArticle

TChange: A Hybrid Transformer-CNN Change Detection Network

by Yupeng Deng, Yu Meng, Jingbo Chen, Anzhi Yue, Diyou Liu and Jing Chen

Remote Sens. 2023, 15(5), 1219; https://doi.org/10.3390/rs15051219 - 22 Feb 2023

Cited by 19 | Viewed by 5209

Abstract

Change detection is employed to identify regions of change between two different time phases. Presently, the CNN-based change detection algorithm is the mainstream direction of change detection. However, there are two challenges in current change detection methods: (1) the intrascale problem: CNN-based change [...] Read more.

Change detection is employed to identify regions of change between two different time phases. Presently, the CNN-based change detection algorithm is the mainstream direction of change detection. However, there are two challenges in current change detection methods: (1) the intrascale problem: CNN-based change detection algorithms, due to the local receptive field limitation, can only fuse pairwise characteristics in a local range within a single scale, causing incomplete detection of large-scale targets. (2) The interscale problem: Current algorithms generally fuse layer by layer for interscale communication, with one-way flow of information and long propagation links, which are prone to information loss, making it difficult to take into account both large targets and small targets. To address the above issues, a hybrid transformer–CNN change detection network (TChange) for very-high-spatial-resolution (VHR) remote sensing images is proposed. (1) Change multihead self-attention (Change MSA) is built for global intrascale information exchange of spatial features and channel characteristics. (2) An interscale transformer module (ISTM) is proposed to perform direct interscale information exchange. To address the problem that the transformer tends to lose high-frequency features, the use of deep edge supervision is proposed to replace the commonly utilized depth supervision. TChange achieves state-of-the-art scores on the WUH-CD and LEVIR-CD open-source datasets. Furthermore, to validate the effectiveness of Change MSA and the ISTM proposed by TChange, we construct a change detection dataset, TZ-CD, that covers an area of 900

{km}^{2}

and contains numerous large targets and weak change targets. Full article

(This article belongs to the Special Issue Image Change Detection Research in Remote Sensing)

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25 pages, 9423 KiB

Open AccessEditor’s ChoiceArticle

Assessing the Performance of a Handheld Laser Scanning System for Individual Tree Mapping—A Mixed Forests Showcase in Spain

by Frederico Tupinambá-Simões, Adrián Pascual, Juan Guerra-Hernández, Cristóbal Ordóñez, Tiago de Conto and Felipe Bravo

Remote Sens. 2023, 15(5), 1169; https://doi.org/10.3390/rs15051169 - 21 Feb 2023

Cited by 23 | Viewed by 4848

Abstract

The use of mobile laser scanning to survey forest ecosystems is a promising, scalable technology to describe the 3D structure of forests at a high resolution. We use a structurally complex, mixed-species Mediterranean forest to test the performance of a mobile Handheld Laser [...] Read more.

The use of mobile laser scanning to survey forest ecosystems is a promising, scalable technology to describe the 3D structure of forests at a high resolution. We use a structurally complex, mixed-species Mediterranean forest to test the performance of a mobile Handheld Laser Scanning (HLS) system to estimate tree attributes within a forest patch in central Spain. We describe the different stages of the HLS approach: field position, ground data collection, scanning path design, point cloud processing, alignment between detected trees and measured reference trees, and finally, the assessment of main tree structural attributes diameter at breast height (DBH) and tree height considering species and tree size as control factors. We surveyed 418 reference trees to account for omission and commission error rates over a 1 ha plot divided into 16 sections and scanned using two different scanning paths. The HLS-based approach reached a high of 88 and 92% tree detection rate for the best combination of scanning path and point cloud processing modes for the HLS system. The root mean squared errors for DBH estimates varied between species: errors for Pinus pinaster were below 2 cm for Scan 02. Quercus pyrenaica, and Alnus glutinosa showed higher error rates. We observed good agreement between ALS and HLS estimates for tree height, highlighting differences to field measurements. Despite the complexity of the mixed forest area surveyed, our results show that HLS is highly efficient at detecting tree locations, estimating DBH, and supporting tree height measurements as confirmed with airborne laser data used for validation. This study is one of the first HLS-based studies conducted in the Mediterranean mixed forest region, where variability in tree allometries and spacing and the presence of natural regeneration pose challenges for the HLS approach. HLS is a feasible, time-efficient, scalable technology for tree mapping in mixed forests with potential to support forest monitoring programmes such as national forest inventories lacking three-dimensional, remote sensing data to support field measurements. Full article

(This article belongs to the Special Issue Advances in Vegetation Structure Modelling to Support the Sustainable Development Goals Acquisition through Forest Management)

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21 pages, 8080 KiB

Open AccessEditor’s ChoiceArticle

Remote Sensing Monitoring of the Pietrafitta Earth Flows in Southern Italy: An Integrated Approach Based on Multi-Sensor Data

by Davide Mazza, Antonio Cosentino, Saverio Romeo, Paolo Mazzanti, Francesco M. Guadagno and Paola Revellino

Remote Sens. 2023, 15(4), 1138; https://doi.org/10.3390/rs15041138 - 19 Feb 2023

Cited by 10 | Viewed by 2941

Abstract

Earth flows are complex gravitational events characterised by a heterogeneous displacement pattern in terms of scale, style, and orientation. As a result, their monitoring, for both knowledge and emergency purposes, represents a relevant challenge in the field of engineering geology. This paper aims [...] Read more.

Earth flows are complex gravitational events characterised by a heterogeneous displacement pattern in terms of scale, style, and orientation. As a result, their monitoring, for both knowledge and emergency purposes, represents a relevant challenge in the field of engineering geology. This paper aims to assess the capabilities, peculiarities, and limitations of different remote sensing monitoring techniques through their application to the Pietrafitta earth flow (Southern Italy). The research compared and combined data collected during the main landslide reactivations by different ground-based remote sensors such as Robotic Total Station (R-TS), Terrestrial Synthetic Aperture Radar Interferometry (T-InSAR), and Terrestrial Laser Scanner (TLS), with data being derived by satellite-based Digital Image Correlation (DIC) analysis. The comparison between R-TS and T-InSAR measurements showed that, despite their different spatial and temporal resolutions, the observed deformation trends remain approximately coherent. On the other hand, DIC analysis was able to detect a kinematic process, such as the expansion of the landslide channel, which was not detected by the other techniques used. The results suggest that, when faced with complex events, the use of a single monitoring technique may not be enough to fully observe and understand the processes taking place. Therefore, the limitations of each different technique alone can be solved by a multi-sensor monitoring approach. Full article

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

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29 pages, 9416 KiB

Open AccessEditor’s ChoiceArticle

Evaluation of Airborne HySpex and Spaceborne PRISMA Hyperspectral Remote Sensing Data for Soil Organic Matter and Carbonates Estimation

by Theodora Angelopoulou, Sabine Chabrillat, Stefano Pignatti, Robert Milewski, Konstantinos Karyotis, Maximilian Brell, Thomas Ruhtz, Dionysis Bochtis and George Zalidis

Remote Sens. 2023, 15(4), 1106; https://doi.org/10.3390/rs15041106 - 17 Feb 2023

Cited by 26 | Viewed by 4623

Abstract

Remote sensing and soil spectroscopy applications are valuable techniques for soil property estimation. Soil organic matter (SOM) and calcium carbonate are important factors in soil quality, and although organic matter is well studied, calcium carbonates require more investigation. In this study, we validated [...] Read more.

Remote sensing and soil spectroscopy applications are valuable techniques for soil property estimation. Soil organic matter (SOM) and calcium carbonate are important factors in soil quality, and although organic matter is well studied, calcium carbonates require more investigation. In this study, we validated the performance of laboratory soil spectroscopy for estimating the aforementioned properties with referenced in situ data. We also examined the performance of imaging spectroscopy sensors, such as the airborne HySpex and the spaceborne PRISMA. For this purpose, we applied four commonly used machine learning algorithms and six preprocessing methods for the evaluation of the best fitting algorithm.. The study took place over crop areas of Amyntaio in Northern Greece, where extensive soil sampling was conducted. This is an area with a very variable mineralogical environment (from lignite mine to mountainous area). The SOM results were very good at the laboratory scale and for both remote sensing sensors with R² = 0.79 for HySpex and R² = 0.76 for PRISMA. Regarding the calcium carbonate estimations, the remote sensing accuracy was R² = 0.82 for HySpex and R² = 0.36 for PRISMA. PRISMA was still in the commissioning phase at the time of the study, and therefore, the acquired image did not cover the whole study area. Accuracies for calcium carbonates may be lower due to the smaller sample size used for the modeling procedure. The results show the potential for using quantitative predictions of SOM and the carbonate content based on soil and imaging spectroscopy at the air and spaceborne scales and for future applications using larger datasets. Full article

(This article belongs to the Special Issue Remote Sensing for Soil Organic Carbon Mapping and Monitoring)

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19 pages, 10071 KiB

Open AccessEditor’s ChoiceArticle

Extension of Scattering Power Decomposition to Dual-Polarization Data for Tropical Forest Monitoring

by Ryu Sugimoto, Ryosuke Nakamura, Chiaki Tsutsumi and Yoshio Yamaguchi

Remote Sens. 2023, 15(3), 839; https://doi.org/10.3390/rs15030839 - 2 Feb 2023

Cited by 6 | Viewed by 3170

Abstract

A new scattering power decomposition method is developed for accurate tropical forest monitoring that utilizes data in dual-polarization mode instead of quad-polarization (POLSAR) data. This improves the forest classification accuracy and helps to realize rapid deforestation detection because dual-polarization data are more frequently [...] Read more.

A new scattering power decomposition method is developed for accurate tropical forest monitoring that utilizes data in dual-polarization mode instead of quad-polarization (POLSAR) data. This improves the forest classification accuracy and helps to realize rapid deforestation detection because dual-polarization data are more frequently acquired than POLSAR data. The proposed method involves constructing scattering power models for dual-polarization data considering the radar scattering scenario of tropical forests (i.e., ground scattering, volume scattering, and helix scattering). Then, a covariance matrix is created for dual-polarization data and is decomposed to obtain three scattering powers. We evaluated the proposed method by using simulated dual-polarization data for the Amazon, Southeast Asia, and Africa. The proposed method showed an excellent forest classification performance with both user’s accuracy and producer’s accuracy at >98% for window sizes greater than 7 × 14 pixels, regardless of the transmission polarization. It also showed a comparable deforestation detection performance to that obtained by POLSAR data analysis. Moreover, the proposed method showed better classification performance than vegetation indices and was found to be robust regardless of the transmission polarization. When applied to actual dual-polarization data from the Amazon, it provided accurate forest map and deforestation detection. The proposed method will serve tropical forest monitoring very effectively not only for future dual-polarization data but also for accumulated data that have not been fully utilized. Full article

(This article belongs to the Special Issue SAR, Interferometry and Polarimetry Applications in Geoscience)

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