Remote Sensing

23 pages, 10008 KiB

Open AccessEditor’s ChoiceReview

Multi-Global Navigation Satellite System for Earth Observation: Recent Developments and New Progress

by Shuanggen Jin^1,2

, Xuyang Meng¹, Gino Dardanelli^3,*

and Yunlong Zhu¹

¹ School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454003, China

² Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030, China

³ Department of Engineering, University of Palermo, Viale delle Scienze, 90128 Palermo, Italy

Remote Sens. 2024, 16(24), 4800; https://doi.org/10.3390/rs16244800 - 23 Dec 2024

Viewed by 1855

Abstract

The Global Navigation Satellite System (GNSS) has made important progress in Earth observation and applications. With the successful design of the BeiDou Navigation Satellite System (BDS), four global navigation satellite systems are available worldwide, together with Galileo, GLONASS, and GPS. These systems have [...] Read more.

The Global Navigation Satellite System (GNSS) has made important progress in Earth observation and applications. With the successful design of the BeiDou Navigation Satellite System (BDS), four global navigation satellite systems are available worldwide, together with Galileo, GLONASS, and GPS. These systems have been widely employed in positioning, navigation, and timing (PNT). Furthermore, GNSS refraction, reflection, and scattering signals can remotely sense the Earth’s surface and atmosphere with powerful implications for environmental remote sensing. In this paper, the recent developments and new application progress of multi-GNSS in Earth observation are presented and reviewed, including the methods of BDS/GNSS for Earth observations, GNSS navigation and positioning performance (e.g., GNSS-PPP and GNSS-NRTK), GNSS ionospheric modelling and space weather monitoring, GNSS meteorology, and GNSS-reflectometry and its applications. For instance, the static Precise Point Positioning (PPP) precision of most MGEX stations was improved by 35.1%, 18.7%, and 8.7% in the east, north, and upward directions, respectively, with PPP ambiguity resolution (AR) based on factor graph optimization. A two-layer ionospheric model was constructed using IGS station data through three-dimensional ionospheric model constraints and TEC accuracy was increased by about 20–27% with the GIM model. Ten-minute water level change with centimeter-level accuracy was estimated with ground-based multiple GNSS-R data based on a weighted iterative least-squares method. Furthermore, a cyclone and its positions were detected by utilizing the GNSS-reflectometry from the space-borne Cyclone GNSS (CYGNSS) mission. Over the years, GNSS has become a dominant technology among Earth observation with powerful applications, not only for conventional positioning, navigation and timing techniques, but also for integrated remote sensing solutions, such as monitoring typhoons, river water level changes, geological geohazard warnings, low-altitude UAV navigation, etc., due to its high performance, low cost, all time and all weather. Full article

(This article belongs to the Special Issue State of the Art of GNSS and SAR/InSAR Techniques for Geomatic Applications)

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29 pages, 5124 KiB

Open AccessEditor’s ChoiceReview

Combination of Remote Sensing and Artificial Intelligence in Fruit Growing: Progress, Challenges, and Potential Applications

by Danielle Elis Garcia Furuya^1,*, Édson Luis Bolfe^1,2

, Taya Cristo Parreiras²

, Jayme Garcia Arnal Barbedo¹

, Thiago Teixeira Santos¹

and Luciano Gebler³

¹ Brazilian Agricultural Research Corporation, Embrapa Agricultura Digital, Campinas 13083-886, São Paulo, Brazil

² Institute of Geosciences, State University of Campinas (Unicamp), Campinas 13083-970, São Paulo, Brazil

³ Brazilian Agricultural Research Corporation, Embrapa Uva e Vinho, Vacaria 95200-970, Rio Grande do Sul, Brazil

Remote Sens. 2024, 16(24), 4805; https://doi.org/10.3390/rs16244805 - 23 Dec 2024

Cited by 2 | Viewed by 2589

Abstract

Fruit growing is important in the global agricultural economy, contributing significantly to food security, job creation, and rural development. With the advancement of technologies, mapping fruits using remote sensing and machine learning (ML) and deep learning (DL) techniques has become an essential tool [...] Read more.

Fruit growing is important in the global agricultural economy, contributing significantly to food security, job creation, and rural development. With the advancement of technologies, mapping fruits using remote sensing and machine learning (ML) and deep learning (DL) techniques has become an essential tool to optimize production, monitor crop health, and predict harvests with greater accuracy. This study was developed in four main stages. In the first stage, a comprehensive review of the existing literature was made from July 2018 (first article found) to June 2024, totaling 117 articles. In the second stage, a general analysis of the data obtained was made, such as the identification of the most studied fruits with the techniques of interest. In the third stage, a more in-depth analysis was made focusing on apples and grapes, with 27 and 30 articles, respectively. The analysis included the use of remote sensing (orbital and proximal) imagery and ML/DL algorithms to map crop areas, detect diseases, and monitor crop development, among other analyses. The fourth stage shows the data’s potential application in a Southern Brazilian region, known for apple and grape production. This study demonstrates how the integration of modern technologies can transform fruit farming, promoting more sustainable and efficient agriculture through remote sensing and artificial intelligence technologies. Full article

(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)

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16 pages, 9121 KiB

Open AccessEditor’s ChoiceTechnical Note

A Benchmark Dataset for Aircraft Detection in Optical Remote Sensing Imagery

by Jianming Hu¹

, Xiyang Zhi^1,*

, Bingxian Zhang², Tianjun Shi¹, Qi Cui¹ and Xiaogang Sun³

¹ Research Center for Space Optical Engineering, Harbin Institute of Technology, Harbin 150001, China

² Beijing Institute of Space Mechanics and Electricity, Beijing 100076, China

³ School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin 150001, China

Remote Sens. 2024, 16(24), 4699; https://doi.org/10.3390/rs16244699 - 17 Dec 2024

Viewed by 1675

Abstract

The problem is that existing aircraft detection datasets rarely simultaneously consider the diversity of target features and the complexity of environmental factors, which has become an important factor restricting the effectiveness and reliability of aircraft detection algorithms. Although a large amount of research [...] Read more.

The problem is that existing aircraft detection datasets rarely simultaneously consider the diversity of target features and the complexity of environmental factors, which has become an important factor restricting the effectiveness and reliability of aircraft detection algorithms. Although a large amount of research has been devoted to breaking through few-sample-driven aircraft detection technology, most algorithms still struggle to effectively solve the problems of missed target detection and false alarms caused by numerous environmental interferences in bird-eye optical remote sensing scenes. To further aircraft detection research, we have established a new dataset, Aircraft Detection in Complex Optical Scene (ADCOS), sourced from various platforms including Google Earth, Microsoft Map, Worldview-3, Pleiades, Ikonos, Orbview-3, and Jilin-1 satellites. It integrates 3903 meticulously chosen images of over 400 famous airports worldwide, containing 33,831 annotated instances employing the oriented bounding box (OBB) format. Notably, this dataset encompasses a wide range of various targets characteristics including multi-scale, multi-direction, multi-type, multi-state, and dense arrangement, along with complex relationships between targets and backgrounds like cluttered backgrounds, low contrast, shadows, and occlusion interference conditions. Furthermore, we evaluated nine representative detection algorithms on the ADCOS dataset, establishing a performance benchmark for subsequent algorithm optimization. The latest dataset will soon be available on the Github website. Full article

(This article belongs to the Section Earth Observation Data)

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18 pages, 5411 KiB

Open AccessEditor’s ChoiceArticle

Leveraging Neural Radiance Fields for Large-Scale 3D Reconstruction from Aerial Imagery

by Max Hermann^1,2,*

, Hyovin Kwak², Boitumelo Ruf²

and Martin Weinmann¹

¹ Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany

² Fraunhofer IOSB, 76131 Karlsruhe, Germany

Remote Sens. 2024, 16(24), 4655; https://doi.org/10.3390/rs16244655 - 12 Dec 2024

Viewed by 2114

Abstract

Since conventional photogrammetric approaches struggle with with low-texture, reflective, and transparent regions, this study explores the application of Neural Radiance Fields (NeRFs) for large-scale 3D reconstruction of outdoor scenes, since NeRF-based methods have recently shown very impressive results in these areas. We evaluate [...] Read more.

Since conventional photogrammetric approaches struggle with with low-texture, reflective, and transparent regions, this study explores the application of Neural Radiance Fields (NeRFs) for large-scale 3D reconstruction of outdoor scenes, since NeRF-based methods have recently shown very impressive results in these areas. We evaluate three approaches: Mega-NeRF, Block-NeRF, and Direct Voxel Grid Optimization, focusing on their accuracy and completeness compared to ground truth point clouds. In addition, we analyze the effects of using multiple sub-modules, estimating the visibility by an additional neural network and varying the density threshold for the extraction of the point cloud. For performance evaluation, we use benchmark datasets that correspond to the setting off standard flight campaigns and therefore typically have nadir camera perspective and relatively little image overlap, which can be challenging for NeRF-based approaches that are typically trained with significantly more images and varying camera angles. We show that despite lower quality compared to classic photogrammetric approaches, NeRF-based reconstructions provide visually convincing results in challenging areas. Furthermore, our study shows that in particular increasing the number of sub-modules and predicting the visibility using an additional neural network improves the quality of the resulting reconstructions significantly. Full article

(This article belongs to the Special Issue Deep Learning Applications of 3D Reconstruction and Visualization from Remote Sensing Imagery)

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20 pages, 4062 KiB

Open AccessEditor’s ChoiceArticle

A CNN-Based Framework for Automatic Extraction of High-Resolution River Bankfull Width

by Wenqi Li^1,2,†

, Chendi Zhang^2,3,†

, David Puhl⁴

, Xiao Pan^5,6

, Marwan A. Hassan^4,*

, Stephen Bird⁷, Kejun Yang² and Yang Zhao⁸

¹ Changjiang River Scientific Research Institute, Wuhan 430010, China

² State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu 610065, China

³ Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences (CAS), Beijing 100101, China

⁴ Department of Geography, The University of British Columbia, Vancouver, BC V6T 1Z2, Canada

⁵ Department of Civil Engineering, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada

⁶ Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong

⁷ Fluvial Systems Research Inc., White Rock, BC V4B 0A7, Canada

⁸ Sichuan Zipingpu Development Co., Ltd., Chengdu 610091, China

^† These authors contributed equally to this work.

Remote Sens. 2024, 16(23), 4614; https://doi.org/10.3390/rs16234614 - 9 Dec 2024

Cited by 1 | Viewed by 1365

Abstract

River width is a crucial parameter that correlates and reflects the hydrological, geomorphological, and ecological characteristics of the channel. However, the width data with high spatial resolution is limited owing to the difficulties in extracting channel width under complex and variable riverine surroundings. [...] Read more.

River width is a crucial parameter that correlates and reflects the hydrological, geomorphological, and ecological characteristics of the channel. However, the width data with high spatial resolution is limited owing to the difficulties in extracting channel width under complex and variable riverine surroundings. To address this issue, we aimed to develop an automatic framework specifically for delineating river channels and measuring the bankfull widths at small spatial intervals along the channel. The DeepLabV3+ Convolutional Neural Network (CNN) model was employed to accurately delineate channel boundaries and a Voronoi Diagram approach was complemented as the river width algorithm (RWA) to calculate river bankfull widths. The CNN model was trained by images across four river types and performed well with all the evaluating metrics (mIoU, Accuracy, F1-score, and Recall) higher than 0.97, referring to the accuracy over 97% in prediction. The RWA outperformed other existing river width calculation methods by showing lower errors. The application of the framework in the Lillooet River, Canada, presented the capacity of this methodology to obtain detailed distributions of hydraulic and hydrological parameters, including flow resistance, flow energy, and sediment transport capacity, based on high-resolution channel widths. Our work highlights the significant potential of the newly developed framework in acquiring high-resolution channel width information and characterizing fluvial dynamics based on these widths along river channels, which contributes to facilitating cost-effective integrated river management. Full article

(This article belongs to the Special Issue Remote Sensing in Geomatics (Second Edition))

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20 pages, 3750 KiB

Open AccessEditor’s ChoiceArticle

An Automatic Modulation Recognition Algorithm Based on Time–Frequency Features and Deep Learning with Fading Channels

by Xiaoya Zuo¹

, Yuan Yang¹

, Rugui Yao^1,*

, Ye Fan^1,2 and Lu Li³

¹ School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710129, China

² Research and Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen 518057, China

³ China Electronics Technology Corporation 29th Research Institute, Chengdu 610036, China

Remote Sens. 2024, 16(23), 4550; https://doi.org/10.3390/rs16234550 - 4 Dec 2024

Cited by 4 | Viewed by 1901

Abstract

Automatic modulation recognition (AMR) stands as a crucial core technology within the realm of signal processing and perception, playing a significant part in harsh electromagnetic environments. The time–frequency image (TFI) of communication signals can manifest modulation characteristics and serve as a foundation for [...] Read more.

Automatic modulation recognition (AMR) stands as a crucial core technology within the realm of signal processing and perception, playing a significant part in harsh electromagnetic environments. The time–frequency image (TFI) of communication signals can manifest modulation characteristics and serve as a foundation for signal modulation recognition and classification. However, under the influence of the electromagnetic environment, communication signals are exposed to varying degrees of interference, which poses a challenge to the recognition of modulation types. Taking into account the effects of interference and channel fading, this paper introduces a communication signal modulation recognition algorithm based on deep learning (DL) and time–frequency analysis. This approach employs short-time Fourier transform (STFT) to generate time–frequency diagrams from time-domain signals. Subsequently, it binarizes the image and feeds it as input data to the neural network. Our research presents a composite deep convolutional neural network (CNN) architecture known as the composite dense-residual neural network (CDRNN). This architecture focuses on enhancing the feature extraction and identification, aiming to achieve accurate recognition of modulation types in harsh electromagnetic environments. Finally, simulation results validate that the proposed deep learning algorithm holds remarkable advantages in boosting the accuracy of modulation type recognition with better adaptability. The algorithm shows better performance even in harsh electromagnetic environments. When the signal-to-noise ratio (SNR) is 18 dB, the recognition accuracy can reach 92.1%. Full article

(This article belongs to the Special Issue Signal Processing Theory and Methods in Remote Sensing (Second Edition))

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51 pages, 19385 KiB

Open AccessEditor’s ChoiceReview

Remote Sensing in Bridge Digitalization: A Review

by Joan R. Casas^1,*

, Rolando Chacón¹

, Necati Catbas²

, Belén Riveiro³

and Daniel Tonelli⁴

¹ Department of Civil and Environmental Engineering, UPC-BarcelonaTech, 08034 Barcelona, Spain

² Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, FL 32816, USA

³ CINTECX, GeoTECH Group, Campus Universitario de Vigo, Universidade de Vigo, As Lagoas, Marcosende, 36310 Vigo, Spain

⁴ Department of Civil, Environmental and Mechanical Engineering, University of Trento, 38123 Trento, Italy

Remote Sens. 2024, 16(23), 4438; https://doi.org/10.3390/rs16234438 - 27 Nov 2024

Viewed by 1903

Abstract

A review of the application of remote sensing technologies in the SHM and management of existing bridges is presented, showing their capabilities and advantages, as well as the main drawbacks when specifically applied to bridge assets. The main sensing technologies used as corresponding [...] Read more.

A review of the application of remote sensing technologies in the SHM and management of existing bridges is presented, showing their capabilities and advantages, as well as the main drawbacks when specifically applied to bridge assets. The main sensing technologies used as corresponding platforms are discussed. This is complemented by the presentation of five case studies emphasizing the wide field of application in several bridge typologies and the justification for the selection of the optimal techniques depending on the objectives of the monitoring and assessment of a particular bridge. The review shows the potentiality of remote sensing technologies in the decision-making process regarding optimal interventions in bridge management. The data gathered by them are the mandatory precursors for determining the relevant performance indicators needed for the quality control of these important infrastructure assets. Full article

(This article belongs to the Special Issue Structural Health Monitoring and Damage Assessment by Advanced Remote Sensing Techniques and Methods)

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17 pages, 12379 KiB

Open AccessEditor’s ChoiceArticle

Artificial-Intelligence-Based Classification to Unveil Geodynamic Processes in the Eastern Alps

by Christian Bignami^1,*

, Alessandro Pignatelli¹

, Giulia Romoli¹

and Carlo Doglioni^1,2

¹ Istituto Nazionale di Geofisica e Vulcanologia, 00143 Rome, Italy

² Earth Science Department, Sapienza University of Rome, 00185 Rome, Italy

Remote Sens. 2024, 16(23), 4364; https://doi.org/10.3390/rs16234364 - 22 Nov 2024

Viewed by 926

Abstract

InSAR has emerged as a leading technique for studying and monitoring ground movements over large areas and across various geodynamic environments. Recent advancements in SAR sensor technology have enabled the acquisition of dense spatial datasets, providing substantial information at regional and national scales. [...] Read more.

InSAR has emerged as a leading technique for studying and monitoring ground movements over large areas and across various geodynamic environments. Recent advancements in SAR sensor technology have enabled the acquisition of dense spatial datasets, providing substantial information at regional and national scales. Despite these improvements, classifying and interpreting such vast datasets remains a significant challenge. InSAR analysts and geologists frequently have to manually analyze the time series from Persistent Scatterer Interferometry (PSI) to model the complexity of geological and tectonic phenomena. This process is time-consuming and impractical for large-scale monitoring. Utilizing Artificial Intelligence (AI) to classify and detect deformation processes presents a promising solution. In this study, vertical ground deformation time series from northeastern Italy were obtained from the European Ground Motion Service and classified by experts into different deformation categories. Convolutional and pre-trained neural networks were then trained and tested using both numerical time-series data and trend images. The application of the best performing trained network to test data showed an accuracy of

83 %

. Such a result demonstrates that neural networks can successfully identify areas experiencing distinct geodynamic processes, emphasizing the potential of AI to improve PSI data interpretation. Full article

(This article belongs to the Special Issue Signal Processing and Machine Learning for Space Geodesy Applications)

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22 pages, 2412 KiB

Open AccessEditor’s ChoiceReview

Remote Sensing Technologies Using UAVs for Pest and Disease Monitoring: A Review Centered on Date Palm Trees

by Bashar Alsadik¹

, Florian J. Ellsäßer¹, Muheeb Awawdeh²

, Abdulla Al-Rawabdeh²

, Lubna Almahasneh³, Sander Oude Elberink^1,*

, Doaa Abuhamoor³ and Yolla Al Asmar¹

¹ Geo-Information Science and Earth Observation Faculty, University of Twente, 7522 NH Enschede, The Netherlands

² Laboratory of Applied Geoinformatics, Department of Earth and Environmental Sciences, Yarmouk University, Irbid 21163, Jordan

³ Geographic Information Systems and Remote Sensing Department, Environment and Climate Change Directorate, National Agricultural Research Center (NARC), Amman 19381, Jordan

Remote Sens. 2024, 16(23), 4371; https://doi.org/10.3390/rs16234371 - 22 Nov 2024

Cited by 3 | Viewed by 3124

Abstract

This review is aimed at exploring the use of remote sensing technology with a focus on Unmanned Aerial Vehicles (UAVs) in monitoring and management of palm pests and diseases with a special focus on date palms. It highlights the most common sensor types, [...] Read more.

This review is aimed at exploring the use of remote sensing technology with a focus on Unmanned Aerial Vehicles (UAVs) in monitoring and management of palm pests and diseases with a special focus on date palms. It highlights the most common sensor types, ranging from passive sensors such as RGB, multispectral, hyperspectral, and thermal as well as active sensors such as light detection and ranging (LiDAR), expounding on their unique functions and gains as far as the detection of pest infestation and disease symptoms is concerned. Indices derived from UAV multispectral and hyperspectral sensors are used to assess their usefulness in vegetation health monitoring and plant physiological changes. Other UAVs are equipped with thermal sensors to identify water stress and temperature anomalies associated with the presence of pests and diseases. Furthermore, the review discusses how LiDAR technology can be used to capture detailed 3D canopy structures as well as volume changes that may occur during the progressing stages of a date palm infection. Besides, the paper examines how machine learning algorithms have been incorporated into remote sensing technologies to ensure high accuracy levels in detecting diseases or pests. This paper aims to present a comprehensive outline for future research focusing on modern methodologies, technological improvements, and direction for the efficient application of UAV-based remote sensing in managing palm tree pests and diseases. Full article

(This article belongs to the Special Issue State-of-the-Art on Satellite and UAV Remote Sensing in Geoscience Research)

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24 pages, 14942 KiB

Open AccessEditor’s ChoiceArticle

The Ground-Penetrating Radar Image Matching Method Based on Central Dense Structure Context Features

by Jie Xu^1,2, Qifeng Lai^1,*, Dongyan Wei¹, Xinchun Ji^1,3, Ge Shen¹ and Hong Yuan¹

¹ Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

² School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China

³ School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, China

Remote Sens. 2024, 16(22), 4291; https://doi.org/10.3390/rs16224291 - 18 Nov 2024

Cited by 3 | Viewed by 1495

Abstract

Subsurface structural distribution can be detected using Ground-Penetrating Radar (GPR). The distribution can be considered as road fingerprints for vehicle positioning. Similar to the principle of visual image matching for localization, the position coordinates of the vehicle can be calculated by matching real-time [...] Read more.

Subsurface structural distribution can be detected using Ground-Penetrating Radar (GPR). The distribution can be considered as road fingerprints for vehicle positioning. Similar to the principle of visual image matching for localization, the position coordinates of the vehicle can be calculated by matching real-time GPR images with pre-constructed reference GPR images. However, GPR images, due to their low resolution, cannot extract well-defined geometric features such as corners and lines. Thus, traditional visual image processing algorithms perform inadequately when applied to GPR image matching. To address this issue, this paper innovatively proposes a GPR image matching and localization method based on a novel feature descriptor, termed as central dense structure context (CDSC) features. The algorithm utilizes the strip-like elements in GPR images to improve the accuracy of GPR image matching. First, a CDSC feature descriptor is designed. By applying threshold segmentation and extremum point extraction to the GPR image, stratified strip-like elements and pseudo-corner points are obtained. The pseudo-corner points are treated as the centers, and the surrounding strip-like elements are described in context to form the GPR feature descriptors. Then, based on the feature description method, feature descriptors for both the real-time image and the reference image are calculated separately. By searching for the nearest matching point pairs and removing erroneous pairs, GPR image matching and localization are achieved. The proposed algorithm was evaluated on datasets collected from urban roads and railway tracks, achieving localization errors of 0.06 m (RMSE) and 1.22 m (RMSE), respectively. Compared to the traditional Speeded Up Robust Features (SURF) visual image matching algorithm, localization errors were reduced by 86.6% and 95.7% in urban road and railway track scenarios, respectively. Full article

(This article belongs to the Special Issue Advanced Ground-Penetrating Radar (GPR) Technologies and Applications)

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22 pages, 8045 KiB

Open AccessEditor’s ChoiceArticle

A GIS Plugin for the Assessment of Deformations in Existing Bridge Portfolios via MTInSAR Data

by Mirko Calò

, Sergio Ruggieri^*

, Andrea Nettis

and Giuseppina Uva

Department DICATECh, Polytechnic University of Bari, Via Orabona, 4, 70126 Bari, Italy

Remote Sens. 2024, 16(22), 4293; https://doi.org/10.3390/rs16224293 - 18 Nov 2024

Cited by 8 | Viewed by 1035

Abstract

The paper presents a GIS plugin, named Bridge Assessment System via MTInSAR (BAS-MTInSAR), aimed at assessing deformations in existing simply supported concrete girder bridges through Multi-Temporal Interferometry Synthetic Aperture Radar (MTInSAR). Existing bridges require continuous maintenance to ensure functionality toward external effects undermining [...] Read more.

The paper presents a GIS plugin, named Bridge Assessment System via MTInSAR (BAS-MTInSAR), aimed at assessing deformations in existing simply supported concrete girder bridges through Multi-Temporal Interferometry Synthetic Aperture Radar (MTInSAR). Existing bridges require continuous maintenance to ensure functionality toward external effects undermining the safety of these structures, such as aging, material degradation, and environmental factors. Although effective and standardized methodologies exist (e.g., structural monitoring, periodic onsite inspections), new emerging technologies could be employed to provide time- and cost-effective information on the current state of structures and to drive prompt interventions to mitigate risk. One example is represented by MTInSAR data, which can provide near-continuous information about structural displacements over time. To easily manage these data, the paper presents BAS-MTInSAR. The tool allows users to insert information of the focused bridge (displacement time series, structural information, temperature data) and, through a user-friendly GUI, observe the occurrence of abnormal deformations. In addition, the tool implements a procedure of multisource data management and defines proper thresholds to assess bridge behavior against current code prescriptions. BAS-MTInSAR is fully described throughout the text and was tested on a real case study, showing the main potentialities of the tool in managing bridge portfolios. Full article

(This article belongs to the Special Issue Structural Health Monitoring and Damage Assessment by Advanced Remote Sensing Techniques and Methods)

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19 pages, 19888 KiB

Open AccessEditor’s ChoiceArticle

Improvement of Coal Mining-Induced Subsidence-Affected (MISA) Zone Irregular Boundary Delineation by MT-InSAR Techniques, UAV Photogrammetry, and Field Investigation

by Linan Liu¹

, Nengxiong Xu^1,*

, Wendy Zhou²

, Yan Qin¹

and Shilong Luan¹

¹ School of Engineering and Technology, China University of Geosciences (Beijing), Beijing 100083, China

² Department of Geology & Geological Engineering, Colorado School of Mines, Golden, CO 80401, USA

Remote Sens. 2024, 16(22), 4221; https://doi.org/10.3390/rs16224221 - 12 Nov 2024

Cited by 2 | Viewed by 1505

Abstract

Coal mining-induced ground subsidence is a severe hazard that can damage property, infrastructure, and the environment in the vicinity when the deformation is not negligible. The boundary of a mining-induced subsidence-affected zone refers to the area beyond which the ground subsidence is less [...] Read more.

Coal mining-induced ground subsidence is a severe hazard that can damage property, infrastructure, and the environment in the vicinity when the deformation is not negligible. The boundary of a mining-induced subsidence-affected zone refers to the area beyond which the ground subsidence is less concerned. Accurately measuring mining-induced ground deformation is essential for delineating the irregular boundary of the impacted area. This study employs multitemporal interferometric synthetic aperture radar (MT-InSAR) techniques, including differential InSAR (DInSAR), InSAR stacking, and interferometric point target analysis (IPTA), to analyze coal mine subsidence and delineate the boundaries of the mining-impacted zones. DInSAR accurately reconstructs, locates, and detects the trend in mining-induced subsidence and correlates well with documented mining operations. The InSAR stacking method maps the spatial variation of the ground’s average line-of-sight (LOS) velocity over the mining area, delineating the boundary of the impacted zone. IPTA analysis combining multilook and single-pixel phases achieves millimeter-level surface measurement above tunnel alignments and measures unevenly distributed deformation fields. This study considers an average of 4 cm per year of surface deformation in the LOS direction as the subsidence threshold value for delineating the boundary of the mining-induced subsidence-affected (MISA) zone during the active coal mining stage. Interestingly, there are twin transportation tunnels near the mining area. The twin tunnels completed before the coal mining activities started were functioning well, but damage was observed after the mining began. Our study reveals the tunnels are located within the InSAR-derived MISA zone, although the tunnels approach the MISA boundary. As direct signs of subsidence, ground fissures have been identified near the tunnels via field investigations and UAV photogrammetry. Furthermore, the derived distribution of ground fissures validates and verifies InSAR measurements. The integrated approach of MT-InSAR, UVA photogrammetry, and field investigation developed in this study can be applied to delineate the irregular boundary of the MISA zone and study the accumulating effects of mining-induced subsidence on the performance of infrastructure in areas proximate to coal mining activities. Full article

(This article belongs to the Special Issue Remote Sensing for Sustainability and Durability of Transportation Infrastructures)

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19 pages, 2630 KiB

Open AccessEditor’s ChoiceArticle

Enhancing Long-Term Robustness of Inter-Space Laser Links in Space Gravitational Wave Detection: An Adaptive Weight Optimization Method for Multi-Attitude Sensors Data Fusion

by Zhao Cui^1,2,3,†

, Xue Wang^2,3

, Jinke Yang^2,3

, Haoqi Shi^1,3,4

, Bo Liang^1,3

, Xingguang Qian^1,3,4

, Zongjin Ye^1,3,4

, Jianjun Jia^1,2,3

, Yikun Wang^1,3,†

and Jianyu Wang^1,2,3,*

¹ Key School of Physics and Photoelectric Engineering, Key Laboratory of Gravitational Wave Precision Measurement of Zhejiang Province, Taiji Laboratory for Gravitational Wave Universe, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China

² Key Lab of Space Active Opto-Electronic Technology and Systems, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China

³ University of Chinese Academy of Sciences, Beijing 100049, China

⁴ Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201800, China

^† These authors contributed equally to this work.

Remote Sens. 2024, 16(22), 4179; https://doi.org/10.3390/rs16224179 - 8 Nov 2024

Cited by 1 | Viewed by 727

Abstract

The stable and high-precision acquisition of attitude data is crucial for sustaining the long-term robustness of laser links to detect gravitational waves in space. We introduce an effective method that utilizes an adaptive weight optimization approach for the fusion of attitude data obtained [...] Read more.

The stable and high-precision acquisition of attitude data is crucial for sustaining the long-term robustness of laser links to detect gravitational waves in space. We introduce an effective method that utilizes an adaptive weight optimization approach for the fusion of attitude data obtained from charge-coupled device (CCD) spot-positioning-based attitude measurements, differential power sensing (DPS), and differential wavefront sensing (DWS). This approach aims to obtain more robust and lower-noise-level attitude data. A system is designed based on the Michelson interferometer for link simulations; validation experiments are also conducted. The experimental results demonstrate that the fused data exhibit higher robustness. Even in the case of a single sensor failure, valid attitude data can still be obtained. Additionally, the fused data have lower noise levels, with root mean square errors of 9.5%, 37.4%, and 93.4% for the single CCD, DPS, and DWS noise errors, respectively. Full article

(This article belongs to the Special Issue Advances in Multiple Sensor Fusion and Classification for Object Detection and Tracking)

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18 pages, 6544 KiB

Open AccessEditor’s ChoiceArticle

Remote Inspection of Bridges with the Integration of Scanning Total Station and Unmanned Aerial Vehicle Data

by Piotr Olaszek^1,*

, Edgar Maciejewski², Anna Rakoczy²

, Rafael Cabral³

, Ricardo Santos⁴

and Diogo Ribeiro⁴

¹ Road and Bridge Research Institute, ul. Instytutowa 1, 03-302 Warszaw, Poland

² Faculty of Civil Engineering, Warsaw University of Technology, al. Armii Ludowej 16, 00-637 Warsaw, Poland

³ CONSTRUCT-LESE, Department of Civil Engineering, University of Porto, R. Dr. Roberto Frias s/n, 4200-465 Porto, Portugal

⁴ CONSTRUCT-LESE, Department of Civil Engineering, Polytechnic of Porto, R. Dr. António Bernardino de Almeida 431, 4249-015 Porto, Portugal

Remote Sens. 2024, 16(22), 4176; https://doi.org/10.3390/rs16224176 - 8 Nov 2024

Cited by 2 | Viewed by 1554

Abstract

Remote visual inspections are valuable tools for maintaining bridges in safe operation. In the case of old structures with incomplete documentation, the verification of dimensions is also an essential aspect. This paper presents an attempt to use a Scanning Total Station (STS) and [...] Read more.

Remote visual inspections are valuable tools for maintaining bridges in safe operation. In the case of old structures with incomplete documentation, the verification of dimensions is also an essential aspect. This paper presents an attempt to use a Scanning Total Station (STS) and Unmanned Aerial Vehicle (UAV) for the inspection and inventory of bridge dimensions. The STS’s measurements are conducted by applying two methods: the direct method using a total station (TS) and advanced geometric analyses of the collected point cloud. The UAV’s measurements use a Structure from Motion (SfM) method. Verification tests were conducted on a steel truss railway bridge over the largest river in Poland. The measurements concerned both the basic dimensions of the bridge and the details of a selected truss connection. The STS identified a significant deviation in the actual geometry of the measured connection and the design documentation. The UAV’s inspection confirmed these findings. The integration of STS and UAV technologies has demonstrated significant advantages, including STS’s high accuracy in direct measurements, with deviations within acceptable engineering tolerances (below a few mm), and the UAV’s efficiency in covering large areas, achieving over 90% compliance with reference dimensions. This combined approach not only reduces operating costs and enhances safety by minimizing the need for heavy machinery or scaffolding but also provides a more comprehensive understanding of the structural condition. Full article

(This article belongs to the Special Issue Structural Health Monitoring and Damage Assessment by Advanced Remote Sensing Techniques and Methods)

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27 pages, 31281 KiB

Open AccessEditor’s ChoiceArticle

Tracking Moisture Dynamics in a Karst Rock Formation Combining Multi-Frequency 3D GPR Data: A Strategy for Protecting the Polychrome Hall Paintings in Altamira Cave

by Vicente Bayarri^1,2

, Alfredo Prada³, Francisco García^4,*

, Carmen De Las Heras³ and Pilar Fatás³

¹ GIM Geomatics, S.L. C/Conde Torreanaz 8, 39300 Torrelavega, Spain

² Polytechnic School, Universidad Europea del Atlántico, Parque Científico y Tecnológico de Cantabria, C/Isabel Torres 21, 39011 Santander, Spain

³ Museo Nacional y Centro de Investigación de Altamira, Marcelino Sanz de Sautuola, S/N, 39330 Santillana del Mar, Spain

⁴ Department of Cartographic Engineering, Geodesy and Photogrammetry, Universitat Politècnica de València, Camino de Vera, s/n, 46022 Valencia, Spain

Remote Sens. 2024, 16(20), 3905; https://doi.org/10.3390/rs16203905 - 21 Oct 2024

Cited by 3 | Viewed by 1625

Abstract

This study addresses the features of the internal structure of the geological layers adjacent to the Polychrome Hall ceiling of the Cave of Altamira (Spain) and their link to the distribution of moisture and geological discontinuities mainly as fractures, joints, bedding planes and [...] Read more.

This study addresses the features of the internal structure of the geological layers adjacent to the Polychrome Hall ceiling of the Cave of Altamira (Spain) and their link to the distribution of moisture and geological discontinuities mainly as fractures, joints, bedding planes and detachments, using 3D Ground Penetrating Radar (GPR) mapping. In this research, 3D GPR data were collected with 300 MHz, 800 MHz and 1.6 GHz center frequency antennas. The data recorded with these three frequency antennas were combined to further our understanding of the layout of geological discontinuities and how they link to the moisture or water inputs that infiltrate and reach the ceiling surface where the rock art of the Polychrome Hall is located. The same 1 × 1 m² area was adopted for 3D data acquisition with the three antennas, obtaining 3D isosurface (isoattribute-surface) images of internal distribution of moisture and structural features of the Polychrome Hall ceiling. The results derived from this study reveal significant insights into the overlying karst strata of Polychrome Hall, particularly the interface between the Polychrome Layer and the underlying Dolomitic Layer. The results show moisture patterns associated with geological features such as fractures, joints, detachments of strata and microcatchments, elucidating the mechanisms driving capillary rise and water infiltration coming from higher altitudes. The study primarily identifies areas of increased moisture content, correlating with earlier observations and enhancing our understanding of water infiltration patterns. This underscores the utility of 3D GPR as an essential tool for informing and putting conservation measures into practice. By delineating subsurface structures and moisture dynamics, this research contributes to a deeper analysis of the deterioration processes directly associated with the infiltration water both in this ceiling and in the rest of the Cave of Altamira, providing information to determine its future geological and hydrogeological evolution. Full article

(This article belongs to the Special Issue Multi-Data Applied to Near-Surface Geophysics (Second Edition))

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34 pages, 8862 KiB

Open AccessEditor’s ChoiceArticle

A Novel Detection Transformer Framework for Ship Detection in Synthetic Aperture Radar Imagery Using Advanced Feature Fusion and Polarimetric Techniques

by Mahmoud Ahmed¹

, Naser El-Sheimy^2,* and Henry Leung¹

¹ Department of Electrical and Software Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada

² Department of Geomatics Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada

Remote Sens. 2024, 16(20), 3877; https://doi.org/10.3390/rs16203877 - 18 Oct 2024

Cited by 6 | Viewed by 2629

Abstract

Ship detection in synthetic aperture radar (SAR) imagery faces significant challenges due to the limitations of traditional methods, such as convolutional neural network (CNN) and anchor-based matching approaches, which struggle with accurately detecting smaller targets as well as adapting to varying environmental conditions. [...] Read more.

Ship detection in synthetic aperture radar (SAR) imagery faces significant challenges due to the limitations of traditional methods, such as convolutional neural network (CNN) and anchor-based matching approaches, which struggle with accurately detecting smaller targets as well as adapting to varying environmental conditions. These methods, relying on either intensity values or single-target characteristics, often fail to enhance the signal-to-clutter ratio (SCR) and are prone to false detections due to environmental factors. To address these issues, a novel framework is introduced that leverages the detection transformer (DETR) model along with advanced feature fusion techniques to enhance ship detection. This feature enhancement DETR (FEDETR) module manages clutter and improves feature extraction through preprocessing techniques such as filtering, denoising, and applying maximum and median pooling with various kernel sizes. Furthermore, it combines metrics like the line spread function (LSF), peak signal-to-noise ratio (PSNR), and F1 score to predict optimal pooling configurations and thus enhance edge sharpness, image fidelity, and detection accuracy. Complementing this, the weighted feature fusion (WFF) module integrates polarimetric SAR (PolSAR) methods such as Pauli decomposition, coherence matrix analysis, and feature volume and helix scattering (Fvh) components decomposition, along with FEDETR attention maps, to provide detailed radar scattering insights that enhance ship response characterization. Finally, by integrating wave polarization properties, the ability to distinguish and characterize targets is augmented, thereby improving SCR and facilitating the detection of weakly scattered targets in SAR imagery. Overall, this new framework significantly boosts DETR’s performance, offering a robust solution for maritime surveillance and security. Full article

(This article belongs to the Special Issue Target Detection with Fully-Polarized Radar)

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24 pages, 15074 KiB

Open AccessEditor’s ChoiceArticle

The Standardized Spectroscopic Mixture Model

by Christopher Small^1,*

and Daniel Sousa²

¹ Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY 10964, USA

² Department of Geography, San Diego State University, San Diego, CA 92182, USA

Remote Sens. 2024, 16(20), 3768; https://doi.org/10.3390/rs16203768 - 11 Oct 2024

Cited by 2 | Viewed by 992

Abstract

The standardized spectral mixture model combines the specificity of a physically based representation of a spectrally mixed pixel with the generality and portability of a spectral index. Earlier studies have used spectrally and geographically diverse collections of broadband and spectroscopic imagery to show [...] Read more.

The standardized spectral mixture model combines the specificity of a physically based representation of a spectrally mixed pixel with the generality and portability of a spectral index. Earlier studies have used spectrally and geographically diverse collections of broadband and spectroscopic imagery to show that the reflectance of the majority of ice-free landscapes on Earth can be represented as linear mixtures of rock and soil substrates (S), photosynthetic vegetation (V) and dark targets (D) composed of shadow and spectrally absorptive/transmissive materials. However, both broadband and spectroscopic studies of the topology of spectral mixing spaces raise questions about the completeness and generality of the Substrate, Vegetation, Dark (SVD) model for imaging spectrometer data. This study uses a spectrally diverse collection of 40 granules from the EMIT imaging spectrometer to verify the generality and stability of the spectroscopic SVD model and characterize the SVD topology and plane of substrates to assess linearity of spectral mixing. New endmembers for soil and non-photosynthetic vegetation (NPV; N) allow the planar SVD model to be extended to a tetrahedral SVDN model to better accommodate the 3D topology of the mixing space. The SVDN model achieves smaller misfit than the SVD, but does so at the expense of implausible fractions beyond [0, 1]. However, a refined spectroscopic SVD model still achieves small (<0.03) RMS misfit, negligible sensitivity to endmember variability and strongly linear scaling over more than an order of magnitude range of spatial resolution. Full article

(This article belongs to the Special Issue New Advancements in the Field of Remote Sensing in Land Surface Processes)

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23 pages, 32897 KiB

Open AccessEditor’s ChoiceArticle

On the Suitability of Different Satellite Land Surface Temperature Products to Study Surface Urban Heat Islands

by Alexandra Hurduc^1,*

, Sofia L. Ermida^1,2

and Carlos C. DaCamara¹

¹ Instituto Dom Luiz (IDL), Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisbon, Portugal

² Instituto Português do Mar e da Atmosfera (IPMA), 1749-077 Lisbon, Portugal

Remote Sens. 2024, 16(20), 3765; https://doi.org/10.3390/rs16203765 - 10 Oct 2024

Cited by 4 | Viewed by 2467

Abstract

Remote sensing satellite data have been a crucial tool in understanding urban climates. The variety of sensors with different spatiotemporal characteristics and retrieval methodologies gave rise to a multitude of approaches when analyzing the surface urban heat island effect (SUHI). Although there are [...] Read more.

Remote sensing satellite data have been a crucial tool in understanding urban climates. The variety of sensors with different spatiotemporal characteristics and retrieval methodologies gave rise to a multitude of approaches when analyzing the surface urban heat island effect (SUHI). Although there are considerable advantages that arise from these different characteristics (spatiotemporal resolution, time of observation, etc.), it also means that there is a need for understanding the ability of sensors in capturing spatial and temporal SUHI patterns. For this, several land surface temperature products are compared for the cities of Madrid and Paris, retrieved from five sensors: the Spinning Enhanced Visible and InfraRed Imager onboard Meteosat Second Generation, the Advanced Very-High-Resolution Radiometer onboard Metop, the Moderate-resolution Imaging Spectroradiometer onboard both Aqua and Terra, and the Thermal Infrared Sensor onboard Landsat 8 and 9. These products span a wide range of LST algorithms, including split-window, single-channel, and temperature–emissivity separation methods. Results show that the diurnal amplitude of SUHI may not be well represented when considering daytime and nighttime polar orbiting platforms. Also, significant differences arise in SUHI intensity and spatial and temporal variability due to the different methods implemented for LST retrieval. Full article

(This article belongs to the Section AI Remote Sensing)

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21 pages, 6212 KiB

Open AccessEditor’s ChoiceArticle

Validation and Error Minimization of Global Ecosystem Dynamics Investigation (GEDI) Relative Height Metrics in the Amazon

by Alyson East^1,*

, Andrew Hansen¹

, Patrick Jantz²

, Bryce Currey^3,4, David W. Roberts¹ and Dolors Armenteras⁵

¹ Department of Ecology, Montana State University, Bozeman, MT 59717, USA

² School of Informatics, Computing and Cyber Systems, Northern Arizona University, Flagstaff, AZ 86011, USA

³ Department of Land Resources and Environmental Sciences, Montana State University, Bozeman, MT 59717, USA

⁴ NASA Goddard Space Flight Center, Biospheric Sciences Lab, Greenbelt, MD 20771, USA

⁵ Departamento de Biología, Facultad de Ciencias, Universidad Nacional de Colombia, Bogotá 111321, Colombia

Remote Sens. 2024, 16(19), 3550; https://doi.org/10.3390/rs16193550 - 24 Sep 2024

Cited by 6 | Viewed by 1980

Abstract

Global Ecosystem Dynamics Investigation (GEDI) is a relatively new technology for global forest research, acquiring LiDAR measurements of vertical vegetation structure across Earth’s tropical, sub-tropical, and temperate forests. Previous GEDI validation efforts have largely focused on top of canopy accuracy, and findings vary [...] Read more.

Global Ecosystem Dynamics Investigation (GEDI) is a relatively new technology for global forest research, acquiring LiDAR measurements of vertical vegetation structure across Earth’s tropical, sub-tropical, and temperate forests. Previous GEDI validation efforts have largely focused on top of canopy accuracy, and findings vary by geographic region and forest type. Despite this, many applications utilize measurements of vertical vegetation distribution from the lower canopy, with a wide diversity of uses for GEDI data appearing in the literature. Given the variability in data requirements across research applications and ecosystems, and the regional variability in GEDI data quality, it is imperative to understand GEDI error to draw strong inferences. Here, we quantify the accuracy of GEDI relative height metrics through canopy layers for the Brazilian Amazon. To assess the accuracy of on-orbit GEDI L2A relative height metrics, we utilize the GEDI waveform simulator to compare detailed airborne laser scanning (ALS) data from the Sustainable Landscapes Brazil project to GEDI data collected by the International Space Station. We also assess the impacts of data filtering based on biophysical and GEDI sensor conditions and geolocation correction on GEDI error metrics (RMSE, MAE, and Bias) through canopy levels. GEDI data accuracy attenuates through the lower percentiles in the relative height (RH) curve. While top of canopy (RH₉₈) measurements have relatively high accuracy (R² = 0.76, RMSE = 5.33 m), the accuracy of data decreases lower in the canopy (RH₅₀: R² = 0.54, RMSE = 5.59 m). While simulated geolocation correction yielded marginal improvements, this decrease in accuracy remained constant despite all error reduction measures. Some error rates for the Amazon are double those reported in studies from other regions. These findings have broad implications for the application of GEDI data, especially in studies where forest understory measurements are particularly challenging to acquire (e.g., dense tropical forests) and where understory accuracy is highly important. Full article

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28 pages, 20281 KiB

Open AccessEditor’s ChoiceArticle

Spatiotemporal Prediction of Conflict Fatality Risk Using Convolutional Neural Networks and Satellite Imagery

by Seth Goodman^1,*

, Ariel BenYishay^1,2 and Daniel Runfola^3,4

¹ AidData, Global Research Institute, William & Mary, Williamsburg, VA 23185, USA

² Department of Economics, William & Mary, Williamsburg, VA 23185, USA

³ Department of Applied Science, William & Mary, Williamsburg, VA 23185, USA

⁴ Geospatial Evaluation and Observation Lab, Data Science Program, William & Mary, Williamsburg, VA 23185, USA

Remote Sens. 2024, 16(18), 3411; https://doi.org/10.3390/rs16183411 - 13 Sep 2024

Cited by 1 | Viewed by 1631

Abstract

As both satellite imagery and image-based machine learning methods continue to improve and become more accessible, they are being utilized in an increasing number of sectors and applications. Recent applications using convolutional neural networks (CNNs) and satellite imagery include estimating socioeconomic and development [...] Read more.

As both satellite imagery and image-based machine learning methods continue to improve and become more accessible, they are being utilized in an increasing number of sectors and applications. Recent applications using convolutional neural networks (CNNs) and satellite imagery include estimating socioeconomic and development indicators such as poverty, road quality, and conflict. This article builds on existing work leveraging satellite imagery and machine learning for estimation or prediction, to explore the potential to extend these methods temporally. Using Landsat 8 imagery and data from the Armed Conflict Location & Event Data Project (ACLED) we produce subnational predictions of the risk of conflict fatalities in Nigeria during 2015, 2017, and 2019 using distinct models trained on both yearly and six-month windows of data from the preceding year. We find that predictions at conflict sites leveraging imagery from the preceding year for training can predict conflict fatalities in the following year with an area under the receiver operating characteristic curve (AUC) of over 75% on average. While models consistently outperform a baseline comparison, and performance in individual periods can be strong (AUC > 80%), changes based on ground conditions such as the geographic scope of conflict can degrade performance in subsequent periods. In addition, we find that training models using an entire year of data slightly outperform models using only six months of data. Overall, the findings suggest CNN-based methods are moderately effective at detecting features in Landsat satellite imagery associated with the risk of fatalities from conflict events across time periods. Full article

(This article belongs to the Special Issue Weakly Supervised Deep Learning in Exploiting Remote Sensing Big Data)

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15 pages, 4826 KiB

Open AccessEditor’s ChoiceArticle

Assessing Evapotranspiration Changes in Response to Cropland Expansion in Tropical Climates

by Leonardo Laipelt^1,*, Julia Brusso Rossi¹

, Bruno Comini de Andrade¹, Morris Scherer-Warren² and Anderson Ruhoff¹

¹ Instituto de Pesquisas Hidráulicas, Universidade Federal do Rio Grande do Sul, Porto Alegre 91501-970, RS, Brazil

² Agência Nacional de Águas e Saneamento Básico (ANA), Brasília 70610-200, DF, Brazil

Remote Sens. 2024, 16(18), 3404; https://doi.org/10.3390/rs16183404 - 13 Sep 2024

Viewed by 1496

Abstract

The expansion of cropland in tropical regions has significantly accelerated in recent decades, triggering an escalation in water demand and changing the total water loss to the atmosphere (evapotranspiration). Additionally, the increase in areas dedicated to agriculture in tropical climates coincides with an [...] Read more.

The expansion of cropland in tropical regions has significantly accelerated in recent decades, triggering an escalation in water demand and changing the total water loss to the atmosphere (evapotranspiration). Additionally, the increase in areas dedicated to agriculture in tropical climates coincides with an increased frequency of drought events, leading to a series of conflicts among water users. However, detailed studies on the impacts of changes in water use due to agriculture expansion, including irrigation, are still lacking. Furthermore, the higher presence of clouds in tropical environments poses challenges for the availability of high-resolution data for vegetation monitoring via satellite images. This study aims to analyze 37 years of agricultural expansion using the Landsat collection and a satellite-based model (geeSEBAL) to assess changes in evapotranspiration resulting from cropland expansion in tropical climates, focusing on the São Marcos River Basin in Brazil. It also used a methodology for estimating daily evapotranspiration on days without satellite images. The results showed a 34% increase in evapotranspiration from rainfed areas, mainly driven by soybean cultivation. In addition, irrigated areas increased their water use, despite not significantly changing water use at the basin scale. Conversely, natural vegetation areas decreased their evapotranspiration rates by 22%, suggesting possible further implications with advancing changes in land use and land cover. Thus, this study underscores the importance of using satellite-based evapotranspiration estimates to enhance our understanding of water use across different land use types and scales, thereby improving water management strategies on a large scale. Full article

(This article belongs to the Special Issue Remote Sensing for Agricultural Water Management (RSAWM) (Second Edition))

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29 pages, 6780 KiB

Open AccessEditor’s ChoiceArticle

Phenological and Biophysical Mediterranean Orchard Assessment Using Ground-Based Methods and Sentinel 2 Data

by Pierre Rouault¹

, Dominique Courault^1,*

, Guillaume Pouget¹, Fabrice Flamain¹, Papa-Khaly Diop¹, Véronique Desfonds¹, Claude Doussan¹

, André Chanzy¹

, Marta Debolini²

, Matthew McCabe³

and Raul Lopez-Lozano¹

¹ UMR 1114 EMMAH INRAE, Avignon University, Domaine St Paul, 84914 Avignon, France

² CMCC Foundation—Euro-Mediterranean Centre on Climate Change, IAFES Division, 07100 Sassari, Italy

³ Division of Biological and Environmental Sciences and Engineering, King Abdullah University of Science and Technology, Thuwal 23955-6900, Makkah, Saudi Arabia

Remote Sens. 2024, 16(18), 3393; https://doi.org/10.3390/rs16183393 - 12 Sep 2024

Cited by 3 | Viewed by 2059

Abstract

A range of remote sensing platforms provide high spatial and temporal resolution insights which are useful for monitoring vegetation growth. Very few studies have focused on fruit orchards, largely due to the inherent complexity of their structure. Fruit trees are mixed with inter-rows [...] Read more.

A range of remote sensing platforms provide high spatial and temporal resolution insights which are useful for monitoring vegetation growth. Very few studies have focused on fruit orchards, largely due to the inherent complexity of their structure. Fruit trees are mixed with inter-rows that can be grassed or non-grassed, and there are no standard protocols for ground measurements suitable for the range of crops. The assessment of biophysical variables (BVs) for fruit orchards from optical satellites remains a significant challenge. The objectives of this study are as follows: (1) to address the challenges of extracting and better interpreting biophysical variables from optical data by proposing new ground measurements protocols tailored to various orchards with differing inter-row management practices, (2) to quantify the impact of the inter-row at the Sentinel pixel scale, and (3) to evaluate the potential of Sentinel 2 data on BVs for orchard development monitoring and the detection of key phenological stages, such as the flowering and fruit set stages. Several orchards in two pedo-climatic zones in southeast France were monitored for three years: four apricot and nectarine orchards under different management systems and nine cherry orchards with differing tree densities and inter-row surfaces. We provide the first comparison of three established ground-based methods of assessing BVs in orchards: (1) hemispherical photographs, (2) a ceptometer, and (3) the Viticanopy smartphone app. The major phenological stages, from budburst to fruit growth, were also determined by in situ annotations on the same fields monitored using Viticanopy. In parallel, Sentinel 2 images from the two study sites were processed using a Biophysical Variable Neural Network (BVNET) model to extract the main BVs, including the leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and fraction of green vegetation cover (FCOVER). The temporal dynamics of the normalised FAPAR were analysed, enabling the detection of the fruit set stage. A new aggregative model was applied to data from hemispherical photographs taken under trees and within inter-rows, enabling us to quantify the impact of the inter-row at the Sentinel 2 pixel scale. The resulting value compared to BVs computed from Sentinel 2 gave statistically significant correlations (0.57 for FCOVER and 0.45 for FAPAR, with respective RMSE values of 0.12 and 0.11). Viticanopy appears promising for assessing the PAI (plant area index) and FCOVER for orchards with grassed inter-rows, showing significant correlations with the Sentinel 2 LAI (R² of 0.72, RMSE 0.41) and FCOVER (R² 0.66 and RMSE 0.08). Overall, our results suggest that Sentinel 2 imagery can support orchard monitoring via indicators of development and inter-row management, offering data that are useful to quantify production and enhance resource management. Full article

(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)

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19 pages, 7218 KiB

Open AccessEditor’s ChoiceArticle

Relationship between Vegetation and Soil Moisture Anomalies Based on Remote Sensing Data: A Semiarid Rangeland Case

by Juan José Martín-Sotoca^1,2,*, Ernesto Sanz^1,2

, Antonio Saa-Requejo^1,3

, Rubén Moratiel^1,4

, Andrés F. Almeida-Ñauñay^1,2 and Ana M. Tarquis^1,2

¹ Centro de Estudios e Investigación para la Gestión de Riesgos Agrarios y Medioambientales (CEIGRAM), Universidad Politécnica de Madrid, 28040 Madrid, Spain

² Grupo de Sistemas Complejos, Universidad Politécnica de Madrid, 28040 Madrid, Spain

³ Evaluación de Recursos Naturales, Universidad Politécnica de Madrid, 28040 Madrid, Spain

⁴ Grupo AgSystems, Universidad Politécnica de Madrid, 28040 Madrid, Spain

Remote Sens. 2024, 16(18), 3369; https://doi.org/10.3390/rs16183369 - 11 Sep 2024

Cited by 1 | Viewed by 1387

Abstract

The dynamic of rangelands results from complex interactions between vegetation, soil, climate, and human activity. This scenario makes rangeland’s condition challenging to monitor, and degradation assessment should be carefully considered when studying grazing pressures. In the present work, we study the interaction of [...] Read more.

The dynamic of rangelands results from complex interactions between vegetation, soil, climate, and human activity. This scenario makes rangeland’s condition challenging to monitor, and degradation assessment should be carefully considered when studying grazing pressures. In the present work, we study the interaction of vegetation and soil moisture in semiarid rangelands using vegetation and soil moisture indices. We aim to study the feasibility of using soil moisture negative anomalies as a warning index for vegetation or agricultural drought. Two semiarid agricultural regions were selected in Spain for this study: Los Vélez (Almería) and Bajo Aragón (Teruel). MODIS images, with 250 m and 500 m spatial resolution, from 2002 to 2019, were acquired to calculate the Vegetation Condition Index (VCI) and the Water Condition Index (WCI) based on the Normalised Difference Vegetation Index (NDVI) and soil moisture component (W), respectively. The Optical Trapezoid Model (OPTRAM) estimated this latter W index. From them, the anomaly (Z-score) for each index was calculated, being

Z_{V C I}

and

Z_{W C I}

, respectively. The probability of coincidence of their negative anomalies was calculated every 10 days (10-day periods). The results show that for specific months, the

Z_{W C I}

had a strong probability of informing in advance, where the negative

Z_{V C I}

will decrease. Soil moisture content and vegetation indices show more similar dynamics in the months with lower temperatures (from autumn to spring). In these months, given the low temperatures, precipitation leads to vegetation growth. In the following months, water availability depends on evapotranspiration and vegetation type as the temperature rises and the precipitation falls. The stronger relationship between vegetation and precipitation from autumn to the beginning of spring is reflected in the feasibility of

Z_{W C I}

to aid the prediction of

Z_{V C I}

. During these months, using

Z_{W C I}

as a warning index is possible for both areas studied. Notably, November to the beginning of February showed an average increase of 20–30% in the predictability of vegetation anomalies, knowing moisture soil anomalies four lags in advance. We found other periods of relevant increment in the predictability, such as March and April for Los Vélez, and from July to September for Bajo Aragón. Full article

(This article belongs to the Special Issue Advances in Remote Sensing for Regional Soil Moisture Monitoring)

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16 pages, 10159 KiB

Open AccessEditor’s ChoiceArticle

Contribution of Climatic Change and Human Activities to Vegetation Dynamics over Southwest China during 2000–2020

by Gang Qi^1,2, Nan Cong³, Man Luo^1,2, Tangzhen Qiu^1,2, Lei Rong^1,2, Ping Ren^1,2 and Jiangtao Xiao^1,2,*

¹ Key Lab of Land Resources Evaluation and Monitoring in Southwest China, Ministry of Education, Sichuan Normal University, Chengdu 610066, China

² School of Geography and Resources Sciences, Sichuan Normal University, Chengdu 610066, China

³ CAS Key Laboratory of Ecosystem Network Observation and Modeling, Lhasa Plateau Ecosystem Research Station, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

Remote Sens. 2024, 16(18), 3361; https://doi.org/10.3390/rs16183361 - 10 Sep 2024

Cited by 3 | Viewed by 1579

Abstract

Southwest China is an important carbon sink area in China. It is critical to track and assess how human activity (HA) and climate change (CC) affect plant alterations in order to create effective and sustainable vegetation restoration techniques. This study used MODIS NDVI [...] Read more.

Southwest China is an important carbon sink area in China. It is critical to track and assess how human activity (HA) and climate change (CC) affect plant alterations in order to create effective and sustainable vegetation restoration techniques. This study used MODIS NDVI data, vegetation type data, and meteorological data to examine the regional and temporal variations in the normalized difference vegetation index (NDVI) in Southwest China from 2000 to 2020. Using trend analysis, the study looks at the temporal and geographical variability in the NDVI. Partial correlation analysis was also used to assess the effects of precipitation, extreme climate indicators, and mean temperature on the dynamics of the vegetation. A new residual analysis technique was created to categorize the effects of CC and HA on NDVI changes while taking extreme climate into consideration. The findings showed that the NDVI in Southwest China grew at a rate of 0.02 per decade between 2000 and 2020. According to the annual NDVI, there was a regional rise in around 85.59% of the vegetative areas, with notable increases in 36.34% of these regions. Temperature had a major influence on the northern half of the research region, but precipitation and extreme climate had a notable effect on the southern half. The rates at which climatic variables and human activity contributed to changes in the NDVI were 0.0008/10a and 0.0034/10a, respectively. These rates accounted for 19.1% and 80.9% of the variances, respectively. The findings demonstrate that most areas displayed greater HA-induced NDVI increases, with the exception of the western Sichuan Plateau. This result suggests that when formulating vegetation restoration and conservation strategies, special attention should be paid to the impact of human activities on vegetation to ensure the sustainable development of ecosystems. Full article

(This article belongs to the Special Issue Quantitative Remote Sensing for Vegetation Phenology and Regional Landscape Patterns)

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18 pages, 232655 KiB

Open AccessEditor’s ChoiceArticle

SFA-Net: Semantic Feature Adjustment Network for Remote Sensing Image Segmentation

by Gyutae Hwang^1,†

, Jiwoo Jeong^1,†

and Sang Jun Lee^2,*

¹ Division of Electronic Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea

² Future Semiconductor Convergence Technology Research Center, Division of Electronic Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea

^† These authors contributed equally to this work.

Remote Sens. 2024, 16(17), 3278; https://doi.org/10.3390/rs16173278 - 3 Sep 2024

Cited by 8 | Viewed by 3685

Abstract

Advances in deep learning and computer vision techniques have made impacts in the field of remote sensing, enabling efficient data analysis for applications such as land cover classification and change detection. Convolutional neural networks (CNNs) and transformer architectures have been utilized in visual [...] Read more.

Advances in deep learning and computer vision techniques have made impacts in the field of remote sensing, enabling efficient data analysis for applications such as land cover classification and change detection. Convolutional neural networks (CNNs) and transformer architectures have been utilized in visual perception algorithms due to their effectiveness in analyzing local features and global context. In this paper, we propose a hybrid transformer architecture that consists of a CNN-based encoder and transformer-based decoder. We propose a feature adjustment module that refines the multiscale feature maps extracted from an EfficientNet backbone network. The adjusted feature maps are integrated into the transformer-based decoder to perform the semantic segmentation of the remote sensing images. This paper refers to the proposed encoder–decoder architecture as a semantic feature adjustment network (SFA-Net). To demonstrate the effectiveness of the SFA-Net, experiments were thoroughly conducted with four public benchmark datasets, including the UAVid, ISPRS Potsdam, ISPRS Vaihingen, and LoveDA datasets. The proposed model achieved state-of-the-art accuracy on the UAVid, ISPRS Vaihingen, and LoveDA datasets for the segmentation of the remote sensing images. On the ISPRS Potsdam dataset, our method achieved comparable accuracy to the latest model while reducing the number of trainable parameters from 113.8 M to 10.7 M. Full article

(This article belongs to the Special Issue Deep Learning for Remote Sensing and Geodata)

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31 pages, 5838 KiB

Open AccessEditor’s ChoiceReview

Monitoring Heavy Metals and Metalloids in Soils and Vegetation by Remote Sensing: A Review

by Viktoriia Lovynska^1,2

, Bagher Bayat¹

, Roland Bol¹

, Shirin Moradi¹

, Mehdi Rahmati¹

, Rahul Raj¹

, Svitlana Sytnyk^2,3

, Oliver Wiche⁴

, Bei Wu¹

and Carsten Montzka^1,*

¹ Institute of Bio- and Geosciences: Agrosphere (IBG-3), Forschungszentrum Jülich, 52425 Jülich, Germany

² Laboratory of Forestry, Dnipro State Agrarian and Economic University, 49009 Dnipro, Ukraine

³ Chemical Ecology Group, Bielefeld University, 33615 Bielefeld, Germany

⁴ Applied Geoecology Group, Faculty of Natural and Environmental Sciences, Zittau/Görlitz University of Applied Sciences, 02763 Zittau, Germany

Remote Sens. 2024, 16(17), 3221; https://doi.org/10.3390/rs16173221 - 30 Aug 2024

Cited by 8 | Viewed by 5398

Abstract

Heavy metal contamination in soils and vegetation poses a significant problem due to its toxicity and persistence. Toxic effects on vegetation include not only impaired growth, reduced yields, and even plant death but also biodiversity loss and ecosystem degradation. Addressing this issue requires [...] Read more.

Heavy metal contamination in soils and vegetation poses a significant problem due to its toxicity and persistence. Toxic effects on vegetation include not only impaired growth, reduced yields, and even plant death but also biodiversity loss and ecosystem degradation. Addressing this issue requires comprehensive monitoring and remediation efforts to mitigate the environmental, human health, and ecological impacts. This review examines the state-of-the-art methodologies and advancements in remote sensing applications for detecting and monitoring heavy metal contamination in soil and its subsequent effects on vegetation. By synthesizing the current research findings and technological developments, this review offers insights into the efficacy and potential of remote sensing for monitoring heavy metal contamination in terrestrial ecosystems. However, current studies focus on regression and AI methods to link spectral reflectances and indices to heavy metal concentrations, which poses limited transferability to other areas, times, spectral discretizations, and heavy metal elements. We conclude that one important way forward is the more thorough understanding and simulation of the related physico-chemical processes in soils and plants and their effects on the spectral signatures. This would offer a profound basis for remote sensing applications for individual circumstances and would allow disentangling heavy metal effects from other stressors such as droughts or soil salinity. Full article

(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)

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18 pages, 5877 KiB

Open AccessEditor’s ChoiceArticle

Ionospheric TEC Prediction in China during Storm Periods Based on Deep Learning: Mixed CNN-BiLSTM Method

by Xiaochen Ren^1,2,3,4

, Biqiang Zhao^1,2,3,4,*, Zhipeng Ren^1,2,3,4 and Bo Xiong^1,3,5

¹ Key Laboratory of Earth and Planetary Physics, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China

² Institutions of Earth Science, Chinese Academy of Sciences, Beijing 100029, China

³ Beijing National Observatory of Space Environment, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China

⁴ College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100029, China

⁵ School of Mathematics and Physics, North China Electric Power University, Baoding 071003, China

Remote Sens. 2024, 16(17), 3160; https://doi.org/10.3390/rs16173160 - 27 Aug 2024

Cited by 3 | Viewed by 1552

Abstract

Applying deep learning to high-precision ionospheric parameter prediction is a significant and growing field within the realm of space weather research. This paper proposes an improved model, Mixed Convolutional Neural Network (CNN)—Bidirectional Long Short-Term Memory (BiLSTM), for predicting the Total Electron Content (TEC) [...] Read more.

Applying deep learning to high-precision ionospheric parameter prediction is a significant and growing field within the realm of space weather research. This paper proposes an improved model, Mixed Convolutional Neural Network (CNN)—Bidirectional Long Short-Term Memory (BiLSTM), for predicting the Total Electron Content (TEC) in China. This model was trained using the longest available Global Ionospheric Maps (GIM)-TEC from 1998 to 2023 in China, and underwent an interpretability analysis and accuracy evaluation. The results indicate that historical TEC maps play the most critical role, followed by Kp, ap, AE, F10.7, and time factor. The contributions of Dst and Disturbance Index (DI) to improving accuracy are relatively small but still essential. In long-term predictions, the contributions of the geomagnetic index, solar activity index, and time factor are higher. In addition, the model performs well in short-term predictions, accurately capturing the occurrence, evolution, and classification of ionospheric storms. However, as the predicted length increases, the accuracy gradually decreases, and some erroneous predictions may occur. The northeast region exhibits lower accuracy but a higher F1 score, which may be attributed to the frequency of ionospheric storm occurrences in different locations. Overall, the model effectively predicts the trends and evolution processes of ionospheric storms. Full article

(This article belongs to the Special Issue Applications of Remote Sensing in Monitoring Ionospheric and Atmospheric Physics (Third Edition))

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22 pages, 5669 KiB

Open AccessEditor’s ChoiceArticle

Multi-Stage Feature Fusion of Multispectral and SAR Satellite Images for Seasonal Crop-Type Mapping at Regional Scale Using an Adapted 3D U-Net Model

by Lucas Wittstruck

, Thomas Jarmer^*

and Björn Waske

Institute of Computer Science, University of Osnabrueck, Wachsbleiche 27, D-49090 Osnabrueck, Germany

Remote Sens. 2024, 16(17), 3115; https://doi.org/10.3390/rs16173115 - 23 Aug 2024

Cited by 2 | Viewed by 1698

Abstract

Earth observation missions such as Sentinel and Landsat support the large-scale identification of agricultural crops by providing free radar and multispectral satellite images. The extraction of representative image information as well as the combination of different image sources for improved feature selection still [...] Read more.

Earth observation missions such as Sentinel and Landsat support the large-scale identification of agricultural crops by providing free radar and multispectral satellite images. The extraction of representative image information as well as the combination of different image sources for improved feature selection still represent a major challenge in the field of remote sensing. In this paper, we propose a novel three-dimensional (3D) deep learning U-Net model to fuse multi-level image features from multispectral and synthetic aperture radar (SAR) time series data for seasonal crop-type mapping at a regional scale. For this purpose, we used a dual-stream U-Net with a 3D squeeze-and-excitation fusion module applied at multiple stages in the network to progressively extract and combine multispectral and SAR image features. Additionally, we introduced a distinctive method for generating patch-based multitemporal multispectral composites by selective image sampling within a 14-day window, prioritizing those with minimal cloud cover. The classification results showed that the proposed network provided the best overall accuracy (94.5%) compared to conventional two-dimensional (2D) and three-dimensional U-Net models (2D: 92.6% and 3D: 94.2%). Our network successfully learned multi-modal dependencies between the multispectral and SAR satellite images, leading to improved field mapping of spectrally similar and heterogeneous classes while mitigating the limitations imposed by persistent cloud coverage. Additionally, the feature representations extracted by the proposed network demonstrated their transferability to a new cropping season, providing a reliable mapping of spatio-temporal crop type patterns. Full article

(This article belongs to the Special Issue Remote Sensing: 15th Anniversary)

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31 pages, 4586 KiB

Open AccessEditor’s ChoiceArticle

A Novel Urban Heat Vulnerability Analysis: Integrating Machine Learning and Remote Sensing for Enhanced Insights

by Fei Li¹, Tan Yigitcanlar^1,*

, Madhav Nepal¹, Kien Nguyen Thanh²

and Fatih Dur¹

¹ City 4.0 Lab, School of Architecture and Built Environment, Faculty of Engineering, Queensland University of Technology, 2 George Street, Brisbane, QLD 4000, Australia

² School of Electrical Engineering and Robotics, Faculty of Engineering, Queensland University of Technology, 2 George Street, Brisbane, QLD 4000, Australia

Remote Sens. 2024, 16(16), 3032; https://doi.org/10.3390/rs16163032 - 18 Aug 2024

Cited by 13 | Viewed by 5285

Abstract

Rapid urbanization and climate change exacerbate the urban heat island effect, increasing the vulnerability of urban residents to extreme heat. Although many studies have assessed urban heat vulnerability, there is a significant lack of standardized criteria and references for selecting indicators, building models, [...] Read more.

Rapid urbanization and climate change exacerbate the urban heat island effect, increasing the vulnerability of urban residents to extreme heat. Although many studies have assessed urban heat vulnerability, there is a significant lack of standardized criteria and references for selecting indicators, building models, and validating those models. Many existing approaches do not adequately meet urban planning needs due to insufficient spatial resolution, temporal coverage, and accuracy. To address this gap, this paper introduces the U-HEAT framework, a conceptual model for analyzing urban heat vulnerability. The primary objective is to outline the theoretical foundations and potential applications of U-HEAT, emphasizing its conceptual nature. This framework integrates machine learning (ML) with remote sensing (RS) to identify urban heat vulnerability at both long-term and detailed levels. It combines retrospective and forward-looking mapping for continuous monitoring and assessment, providing essential data for developing comprehensive strategies. With its active learning capacity, U-HEAT enables model refinement and the evaluation of policy impacts. The framework presented in this paper offers a standardized and sustainable approach, aiming to enhance practical analysis tools. It highlights the importance of interdisciplinary research in bolstering urban resilience and stresses the need for sustainable urban ecosystems capable of addressing the complex challenges posed by climate change and increased urban heat. This study provides valuable insights for researchers, urban administrators, and planners to effectively combat urban heat challenges. Full article

(This article belongs to the Special Issue Estimating the Ecological Services of Urban Green Infrastructures Using Remote Sensing)

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20 pages, 7086 KiB

Open AccessEditor’s ChoiceArticle

Cognitive Computing Advancements: Improving Precision Crop Protection through UAV Imagery for Targeted Weed Monitoring

by Gustavo A. Mesías-Ruiz^1,2

, José M. Peña¹

, Ana I. de Castro³

, Irene Borra-Serrano¹

and José Dorado^1,*

¹ Institute of Agricultural Sciences, Spanish National Research Council (ICA-CSIC), Serrano 115b, 28006 Madrid, Spain

² School of Agricultural, Food and Biosystems Engineering (ETSIAAB), Polytechnic University of Madrid (UPM), Av. Puerta de Hierro 2, 28040 Madrid, Spain

³ Environment and Agronomy Department, National Agricultural and Food Research and Technology Institute (INIA-CSIC), Ctra. Coruña km 7.5, 28008 Madrid, Spain

Remote Sens. 2024, 16(16), 3026; https://doi.org/10.3390/rs16163026 - 18 Aug 2024

Cited by 5 | Viewed by 1740

Abstract

Early detection of weeds is crucial to manage weeds effectively, support decision-making and prevent potential crop losses. This research presents an innovative approach to develop a specialized cognitive system for classifying and detecting early-stage weeds at the species level. The primary objective was [...] Read more.

Early detection of weeds is crucial to manage weeds effectively, support decision-making and prevent potential crop losses. This research presents an innovative approach to develop a specialized cognitive system for classifying and detecting early-stage weeds at the species level. The primary objective was to create an automated multiclass discrimination system using cognitive computing, regardless of the weed growth stage. Initially, the model was trained and tested on a dataset of 31,002 UAV images, including ten weed species manually identified by experts at the early phenological stages of maize (BBCH14) and tomato (BBCH501). The images were captured at 11 m above ground level. This resulted in a classification accuracy exceeding 99.1% using the vision transformer Swin-T model. Subsequently, generative modeling was employed for data augmentation, resulting in new classification models based on the Swin-T architecture. These models were evaluated on an unbalanced dataset of 36,556 UAV images captured at later phenological stages (maize BBCH17 and tomato BBCH509), achieving a weighted average F1-score ranging from 94.8% to 95.3%. This performance highlights the system’s adaptability to morphological variations and its robustness in diverse crop scenarios, suggesting that the system can be effectively implemented in real agricultural scenarios, significantly reducing the time and resources required for weed identification. The proposed data augmentation technique also proved to be effective in implementing the detection transformer architecture, significantly improving the generalization capability and enabling accurate detection of weeds at different growth stages. The research represents a significant advancement in weed monitoring across phenological stages, with potential applications in precision agriculture and sustainable crop management. Furthermore, the methodology showcases the versatility of the latest generation models for application in other knowledge domains, facilitating time-efficient model development. Future research could investigate the applicability of the model in different geographical regions and with different types of crops, as well as real-time implementation for continuous field monitoring. Full article

(This article belongs to the Special Issue Remote Sensing and Associated Artificial Intelligence in Agricultural Applications)

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27 pages, 59331 KiB

Open AccessEditor’s ChoiceArticle

AerialFormer: Multi-Resolution Transformer for Aerial Image Segmentation

by Taisei Hanyu^1,*,†

, Kashu Yamazaki^1,†

, Minh Tran¹, Roy A. McCann¹, Haitao Liao¹, Chase Rainwater¹, Meredith Adkins²

, Jackson Cothren¹

and Ngan Le¹

¹ College of Engineering, University of Arkansas, Fayetteville, AR 72701, USA

² Institute for Integrative and Innovative Research, University of Arkansas, Fayetteville, AR 72701, USA

^† These authors contributed equally to this work.

Remote Sens. 2024, 16(16), 2930; https://doi.org/10.3390/rs16162930 - 9 Aug 2024

Cited by 23 | Viewed by 4275

Abstract

When performing remote sensing image segmentation, practitioners often encounter various challenges, such as a strong imbalance in the foreground–background, the presence of tiny objects, high object density, intra-class heterogeneity, and inter-class homogeneity. To overcome these challenges, this paper introduces AerialFormer, a hybrid model [...] Read more.

When performing remote sensing image segmentation, practitioners often encounter various challenges, such as a strong imbalance in the foreground–background, the presence of tiny objects, high object density, intra-class heterogeneity, and inter-class homogeneity. To overcome these challenges, this paper introduces AerialFormer, a hybrid model that strategically combines the strengths of Transformers and Convolutional Neural Networks (CNNs). AerialFormer features a CNN Stem module integrated to preserve low-level and high-resolution features, enhancing the model’s capability to process details of aerial imagery. The proposed AerialFormer is designed with a hierarchical structure, in which a Transformer encoder generates multi-scale features and a multi-dilated CNN (MDC) decoder aggregates the information from the multi-scale inputs. As a result, information is taken into account in both local and global contexts, so that powerful representations and high-resolution segmentation can be achieved. The proposed AerialFormer was benchmarked on three benchmark datasets, including iSAID, LoveDA, and Potsdam. Comprehensive experiments and extensive ablation studies show that the proposed AerialFormer remarkably outperforms state-of-the-art methods. Full article

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

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23 pages, 2216 KiB

Open AccessEditor’s ChoiceArticle

Complex-Valued 2D-3D Hybrid Convolutional Neural Network with Attention Mechanism for PolSAR Image Classification

by Wenmei Li¹

, Hao Xia¹

, Jiadong Zhang¹, Yu Wang², Yan Jia¹ and Yuhong He^3,*

¹ School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210023, China

² College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China

³ Department of Geography, Geomatics and Environment, University of Toronto, Mississauga, ON L5L 1C6, Canada

Remote Sens. 2024, 16(16), 2908; https://doi.org/10.3390/rs16162908 - 9 Aug 2024

Cited by 7 | Viewed by 2169

Abstract

The recently introduced complex-valued convolutional neural network (CV-CNN) has shown considerable advancements for polarimetric synthetic aperture radar (PolSAR) image classification by effectively incorporating both magnitude and phase information. However, a solitary 2D or 3D CNN encounters challenges such as insufficiently extracting scattering channel [...] Read more.

The recently introduced complex-valued convolutional neural network (CV-CNN) has shown considerable advancements for polarimetric synthetic aperture radar (PolSAR) image classification by effectively incorporating both magnitude and phase information. However, a solitary 2D or 3D CNN encounters challenges such as insufficiently extracting scattering channel dimension features or excessive computational parameters. Moreover, these networks’ default is that all information is equally important, consuming vast resources for processing useless information. To address these issues, this study presents a new hybrid CV-CNN with the attention mechanism (CV-2D/3D-CNN-AM) to classify PolSAR ground objects, possessing both excellent computational efficiency and feature extraction capability. In the proposed framework, multi-level discriminative features are extracted from preprocessed data through hybrid networks in the complex domain, along with a special attention block to filter the feature importance from both spatial and channel dimensions. Experimental results performed on three PolSAR datasets demonstrate our present approach’s superiority over other existing ones. Furthermore, ablation experiments confirm the validity of each module, highlighting our model’s robustness and effectiveness. Full article

(This article belongs to the Special Issue Advances in Synthetic Aperture Radar Data Processing and Application)

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39 pages, 28523 KiB

Open AccessEditor’s ChoiceReview

Identification of Landslide Precursors for Early Warning of Hazards with Remote Sensing

by Katarzyna Strząbała

, Paweł Ćwiąkała^*

and Edyta Puniach

AGH University of Krakow, Faculty of Geo-Data Science, Geodesy, and Environmental Engineering, 30-059 Cracow, Poland

Remote Sens. 2024, 16(15), 2781; https://doi.org/10.3390/rs16152781 - 30 Jul 2024

Cited by 9 | Viewed by 4542

Abstract

Landslides are a widely recognized phenomenon, causing huge economic and human losses worldwide. The detection of spatial and temporal landslide deformation, together with the acquisition of precursor information, is crucial for hazard prediction and landslide risk management. Advanced landslide monitoring systems based on [...] Read more.

Landslides are a widely recognized phenomenon, causing huge economic and human losses worldwide. The detection of spatial and temporal landslide deformation, together with the acquisition of precursor information, is crucial for hazard prediction and landslide risk management. Advanced landslide monitoring systems based on remote sensing techniques (RSTs) play a crucial role in risk management and provide important support for early warning systems (EWSs) at local and regional scales. The purpose of this article is to present a review of the current state of knowledge in the development of RSTs used for identifying landslide precursors, as well as detecting, monitoring, and predicting landslides. Almost 200 articles from 2010 to 2024 were analyzed, in which the authors utilized RSTs to detect potential precursors for early warning of hazards. The applications, challenges, and trends of RSTs, largely dependent on the type of landslide, deformation pattern, hazards posed by the landslide, and the size of the area of interest, were also discussed. Although the article indicates some limitations of the RSTs used so far, integrating different techniques and technological developments offers the opportunity to create reliable EWSs and improve existing ones. Full article

(This article belongs to the Special Issue Remote Sensing in Engineering Geology (Third Edition))

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16 pages, 3823 KiB

Open AccessEditor’s ChoiceArticle

Remote Sensing of Chlorophyll-a and Water Quality over Inland Lakes: How to Alleviate Geo-Location Error and Temporal Discrepancy in Model Training

by Jongmin Park¹, Sami Khanal²

, Kaiguang Zhao^3,† and Kyuhyun Byun^4,*,†

¹ Department of Environmental Engineering, Korea National University of Transportation, Chungju 27469, Republic of Korea

² Department of Food, Agricultural, and Biological Engineering, The Ohio State University, Columbus, OH 43210, USA

³ School of Environment and Natural Resources, The Ohio State University, Columbus, OH 43210, USA

⁴ Department of Environmental Engineering, Incheon National University, Incheon 22012, Republic of Korea

^† These authors contributed equally to this work.

Remote Sens. 2024, 16(15), 2761; https://doi.org/10.3390/rs16152761 - 29 Jul 2024

Cited by 11 | Viewed by 2915

Abstract

Harmful algal blooms (HABs) threaten lake ecosystems and public health. Early HAB detection is possible by monitoring chlorophyll-a (Chl-a) concentration. Ground-based Chl-a data have limited spatial and temporal coverage but can be geo-registered with temporally coincident satellite imagery to calibrate a remote sensing-based [...] Read more.

Harmful algal blooms (HABs) threaten lake ecosystems and public health. Early HAB detection is possible by monitoring chlorophyll-a (Chl-a) concentration. Ground-based Chl-a data have limited spatial and temporal coverage but can be geo-registered with temporally coincident satellite imagery to calibrate a remote sensing-based predictive model for regional mapping over time. When matching ground and satellite data, positional and temporal discrepancies are unavoidable due particularly to dynamic lake surfaces, thereby biasing the model calibration. This limitation has long been recognized but so far has not been addressed explicitly. To mitigate such effects of data mismatching, we proposed an Akaike Information Criterion (AIC)-like weighted regression algorithm that relies on an error-based heuristic to automatically favor “good” data points and downplay “bad” points. We evaluated the algorithm for estimating Chl-a over inland lakes in Ohio using Harmonized Landsat Sentinel-2. The AIC-like weighted regression estimates showed superior performance with an R² of 0.91 and an error variance (

σ_{E}^{2}

) of 0.29 μg/L, outperforming linear regression (R² = 0.34,

σ_{E}^{2}

= 2.34 μg/L) and random forest (R² = 0.82,

σ_{E}^{2}

= 0.92 μg/L). We also noticed the poorest performance occurred in the spring due to low reflectance variation in clear water and low Chl-a concentration. Our weighted regression scheme is adaptive and generically applicable. Future studies may adopt our scheme to tackle other remote sensing estimation problems (e.g., terrestrial applications) for alleviating the adverse effects of geolocation errors and temporal discrepancies. Full article

(This article belongs to the Special Issue Multi-Source Remote Sensing Data in Hydrology and Water Management)

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31 pages, 11063 KiB

Open AccessEditor’s ChoiceArticle

The Preparation Phase of the 2023 Kahramanmaraş (Turkey) Major Earthquakes from a Multidisciplinary and Comparative Perspective

by Gianfranco Cianchini¹

, Massimo Calcara¹

, Angelo De Santis^1,*

, Alessandro Piscini¹, Serena D’Arcangelo^1,2

, Cristiano Fidani¹

, Dario Sabbagh¹

, Martina Orlando^1,3

, Loredana Perrone¹

, Saioa A. Campuzano^1,2

, Mariagrazia De Caro¹

, Adriano Nardi¹

and Maurizio Soldani¹

¹ Istituto Nazionale di Geofisica e Vulcanologia (INGV), 00143 Rome, Italy

² Departmento de Física de la Tierra y Astrofísica, Universidad Complutense de Madrid (UCM), 28040 Madrid, Spain

³ Dipartimento di Scienze, Università Roma TRE, 00154 Rome, Italy

Remote Sens. 2024, 16(15), 2766; https://doi.org/10.3390/rs16152766 - 29 Jul 2024

Cited by 9 | Viewed by 1828

Abstract

On 6 February 2023, Turkey experienced its most powerful earthquake in over 80 years, with a moment magnitude (Mw) of 7.7. This was then followed by a second earthquake of Mw 7.6 just nine hours later. According to the lithosphere–atmosphere–ionosphere coupling (LAIC) models, [...] Read more.

On 6 February 2023, Turkey experienced its most powerful earthquake in over 80 years, with a moment magnitude (Mw) of 7.7. This was then followed by a second earthquake of Mw 7.6 just nine hours later. According to the lithosphere–atmosphere–ionosphere coupling (LAIC) models, such a significant seismic activity is expected to cause anomalies across various observables, from the Earth’s surface to the ionosphere. This multidisciplinary study investigates the preparatory phase of these two major earthquakes by identifying potential precursors across the lithosphere, atmosphere, and ionosphere. Our comprehensive analysis successfully identified and collected various anomalies, revealing that their cumulative occurrence follows an accelerating trend, either exponential or power-law. Most anomalies appeared to progress from the lithosphere upward through the atmosphere to the ionosphere, suggesting a sequential chain of processes across these geospheres. Notably, some anomalies deviated from this overall trend, manifesting as oscillating variations. We propose that these anomalies support a two-way coupling model preceding major earthquakes, highlighting the potential role of fluid chemistry in facilitating these processes. Full article

(This article belongs to the Section Earth Observation for Emergency Management)

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21 pages, 12265 KiB

Open AccessEditor’s ChoiceArticle

Remote Sensing for Restoration Change Monitoring in Tropical Peat Swamp Forests in Malaysia

by Chloe Brown¹, Sofie Sjögersten², Martha J. Ledger³

, Faizal Parish⁴ and Doreen Boyd^1,*

¹ School of Geography, University of Nottingham, Nottingham NG7 2QL, UK

² School of Biosciences, University of Nottingham, Loughborough LE12 5RD, UK

³ School of Biological Sciences, University of Hong Kong, Hong Kong

⁴ Global Environment Centre, Petaling Jaya 47300, Selangor, Malaysia

Remote Sens. 2024, 16(15), 2690; https://doi.org/10.3390/rs16152690 - 23 Jul 2024

Cited by 3 | Viewed by 2103

Abstract

Effective planning and management strategies for restoring and conserving tropical peat swamp ecosystems require accurate and timely estimates of aboveground biomass (AGB), especially when monitoring the impacts of restoration interventions. The aim of this research is to assess changes in AGB and evaluate [...] Read more.

Effective planning and management strategies for restoring and conserving tropical peat swamp ecosystems require accurate and timely estimates of aboveground biomass (AGB), especially when monitoring the impacts of restoration interventions. The aim of this research is to assess changes in AGB and evaluate the effectiveness of restoration efforts in the North Selangor Peat Swamp Forest (NSPSF), one of the largest remaining peat swamp forests in Peninsular Malaysia, using advanced remote sensing techniques. A Random Forest machine learning method was employed to upscale AGB estimates, derived from a ‘LiDAR AGB model’, to larger landscape-scale areas with Sentinel-2 spectral and textural variables. The time period under investigation (2015–2018) marked a concentrated phase of restoration and regeneration efforts in NSPSF. The results demonstrate an overall increase in tropical peat swamp AGB during these years, where the total amount of estimated AGB stored in NSPSF increased from 19.3 Tg in 2015 to an estimated 19.8 Tg in 2018. The research found that a tailored variable selection approach improved predictions of AGB, with optimised input variables (n = 62) and parameter adjustments producing a good plausible result (R² = 0.80; RMSE = 55.2 Mg/ha). This paper concludes by emphasizing the importance of long-term studies (>5 years) for analyzing the success of tropical peat swamp restoration methods, with a potential for integrating remote sensing technology. Full article

(This article belongs to the Section Environmental Remote Sensing)

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53 pages, 21900 KiB

Open AccessEditor’s ChoiceArticle

Multi-Tier Land Use and Land Cover Mapping Framework and Its Application in Urbanization Analysis in Three African Countries

by Shahriar Shah Heydari¹

, Jody C. Vogeler^1,*

, Orion S. E. Cardenas-Ritzert¹, Steven K. Filippelli¹

, Melissa McHale²

and Melinda Laituri³

¹ Natural Resources Ecology Laboratory, Colorado State University, Fort Collins, CO 80523-1499, USA

² Department of Forest Resources Management, University of British Columbia, Vancouver, BC V6T 1Z4, Canada

³ Department of Ecosystem Science and Sustainability, Colorado State University, Fort Collins, CO 80523-1476, USA

Remote Sens. 2024, 16(14), 2677; https://doi.org/10.3390/rs16142677 - 22 Jul 2024

Cited by 4 | Viewed by 2129

Abstract

The population of Africa is expected to rise to 2.5 billion by 2050, with more than 80% of this increase concentrated in cities. Africa’s anticipated population growth has serious implications for urban resource utilization and management, necessitating multi-level monitoring efforts that can inform [...] Read more.

The population of Africa is expected to rise to 2.5 billion by 2050, with more than 80% of this increase concentrated in cities. Africa’s anticipated population growth has serious implications for urban resource utilization and management, necessitating multi-level monitoring efforts that can inform planning and decision-making. Commonly, broad extent (e.g., country level) urban change analyses only examine a homogenous “developed” or “built-up” area, which may not capture patterns influenced by the heterogeneity of landscape features within urban areas. Contrarily, studies examining landscape heterogeneity at a finer resolution are typically limited in spatial extent (e.g., single city level). The goal of this study was to develop and test a hierarchical integrated mapping framework using globally available Earth Observation data (e.g., Landsat, Sentinel-2, Sentinel-1, and nightlight imagery) and accessible methodologies to produce national-level land use (LU) and urban-level land cover (LC) map products which may support a range of global and local monitoring and planning initiatives. We test our multi-tier methodology across three rapidly urbanizing African countries for the 2016–2020 period: Ethiopia, Nigeria, and South Africa. The initial output of our methodology includes annual national land use maps (Tier 1) for the purpose of delineating the dynamic boundaries of individual urban areas and monitoring national LU change. To complement Tier 1 LU maps, we detailed urban heterogeneity through LC classifications within urban areas (Tier 2) delineated using Tier 1 LU maps. Based on country-optimized sets of selected features that leverage spatial/texture and temporal dimensions of available data, we obtained an overall map accuracy of between 65 and 80% for Tier 1 maps and between 60 and 80% for Tier 2 maps, dependent on the evaluation country, although with consistent performance across study years providing a solid foundation for monitoring changes. We demonstrate the potential applications for our products through various analyses, including urbanization-driven LU change, and examine LC urban patterns across the three African study countries. While our findings allude to general differences in urban patterns across national scales, further analyses are needed to better understand the complex drivers behind urban LC configurations and their change patterns across different countries, city sizes, and rates of urbanization. Our multi-tier mapping framework is a viable strategy for producing harmonious, multi-level LULC products in developing countries using publicly available data and methodologies, which can serve as a basis for a wide range of informative and insightful monitoring analyses. Full article

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20 pages, 3406 KiB

Open AccessEditor’s ChoiceArticle

Temporal Transferability of Tree Species Classification in Temperate Forests with Sentinel-2 Time Series

by Margot Verhulst^1,*, Stien Heremans^1,2

, Matthew B. Blaschko³

and Ben Somers^1,4

¹ Division of Forest, Nature and Landscape (FNL), Department of Earth and Environmental Sciences, KU Leuven, Celestijnenlaan 200E, 3001 Leuven, Belgium

² Research Institute for Nature and Forest (INBO), Havenlaan 88, 1000 Brussels, Belgium

³ Center for Processing Speech and Images (PSI), Department of Electrical Engineering, KU Leuven, Kasteelpark Arenberg 10, 3001 Leuven, Belgium

⁴ KU Leuven Plant Institute (LPI), KU Leuven, Kasteelpark Arenberg 31, 3001 Leuven, Belgium

Remote Sens. 2024, 16(14), 2653; https://doi.org/10.3390/rs16142653 - 20 Jul 2024

Cited by 4 | Viewed by 1995

Abstract

Detailed information on forest tree species is crucial to inform management and policy and support environmental and ecological research. Sentinel-2 imagery is useful for obtaining spatially explicit and frequent information on forest tree species due to its suitable spatial, spectral, and temporal resolutions. [...] Read more.

Detailed information on forest tree species is crucial to inform management and policy and support environmental and ecological research. Sentinel-2 imagery is useful for obtaining spatially explicit and frequent information on forest tree species due to its suitable spatial, spectral, and temporal resolutions. However, classification workflows often do not generalise well to time periods that are not seen by the model during the calibration phase. This study investigates the temporal transferability of dominant tree species classification. To this end, the Random Forest, Support Vector Machine, and Multilayer Perceptron algorithms were used to classify five tree species in Flanders (Belgium) with regularly spaced Sentinel-2 time series from 2018 to 2022. Cross-year single-year input scenarios were compared with same-year single-year input scenarios to quantify the temporal transferability of the five evaluated years. This resulted in a decrease in overall accuracy between 2.30 and 14.92 percentage points depending on the algorithm and evaluated year. Moreover, our results indicate that the cross-year classification performance could be improved by using multi-year training data, reducing the drop in overall accuracy. In some cases, gains in overall accuracy were even observed. This study highlights the importance of including interannual spectral variability during the training stage of tree species classification models to improve their ability to generalise in time. Full article

(This article belongs to the Special Issue Remote Sensing Applications for Forest Ecosystem Monitoring and Spatial Modeling)

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40 pages, 9898 KiB

Open AccessEditor’s ChoiceArticle

Cell-Resolved PV Soiling Measurement Using Drone Images

by Peter Winkel^1,2,*

, Stefan Wilbert¹

, Marc Röger¹

, Julian J. Krauth¹

, Niels Algner¹, Bijan Nouri¹, Fabian Wolfertstetter³, Jose Antonio Carballo⁴

, M. Carmen Alonso-Garcia⁵

, Jesus Polo⁵

, Aránzazu Fernández-García²

and Robert Pitz-Paal^2,6

¹ German Aerospace Center (DLR), Institute of Solar Research, Calle Doctor Carracido 44, 04005 Almería, Spain

² Chair of Solar Technology, Faculty of Mechanical Engineering, RWTH Aachen University, Linder Höhe, 51147 Cologne, Germany

³ Volateq GmbH, Mühlenhof 7-9, 40721 Hilden, Germany

⁴ CIEMAT, Plataforma Solar de Almería, Carretera de Senés km 4, 04200 Tabernas, Spain

⁵ CIEMAT, Photovoltaic Solar Energy Unit, Av. Complutense 40, 28040 Madrid, Spain

⁶ DLR, Institute of Solar Research, Linder Höhe, 51147 Cologne, Germany

Remote Sens. 2024, 16(14), 2617; https://doi.org/10.3390/rs16142617 - 17 Jul 2024

Cited by 3 | Viewed by 2108

Abstract

The maintenance of photovoltaic (PV) power plants is of central importance for their yield. To reach higher efficiencies in PV parks, it is helpful to detect soiling such as dust deposition and to apply this information to optimize cleaning strategies. Furthermore, a periodic [...] Read more.

The maintenance of photovoltaic (PV) power plants is of central importance for their yield. To reach higher efficiencies in PV parks, it is helpful to detect soiling such as dust deposition and to apply this information to optimize cleaning strategies. Furthermore, a periodic inspection of the PV modules with infrared (IR) imagery is of advantage to detect and potentially remove faulty PV modules. Soiling can be erroneously interpreted as PV module defects and hence spatially resolved soiling measurements can improve the results of IR-based PV inspection. So far, soiling measurements are mostly performed only locally in PV fields, thus not supporting the above-mentioned IR inspections. This study presents a method for measuring the soiling of PV modules at cell resolution using RGB images taken by aerial drones under sunny conditions. The increase in brightness observed for soiled cells under evaluation, compared to clean cells, is used to calculate the transmission loss of the soiling layer. Photos of a clean PV module and a soiled module for which the soiling loss is measured electrically are used to determine the relation between the brightness increase and the soiling loss. To achieve this, the irradiance at the time of the image acquisitions and the viewing geometry are considered. The measurement method has been validated with electrical measurements of the soiling loss yielding root mean square deviations in the 1% absolute range. The method has the potential to be applied to entire PV parks in the future. Full article

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

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21 pages, 4551 KiB

Open AccessEditor’s ChoiceArticle

Winter Wheat Mapping Method Based on Pseudo-Labels and U-Net Model for Training Sample Shortage

by Jianhua Zhang^1,2, Shucheng You³, Aixia Liu³, Lijian Xie^1,2, Chenhao Huang^1,2, Xu Han³, Penghan Li^1,2, Yixuan Wu^1,2 and Jinsong Deng^1,2,*

¹ College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China

² Zhejiang Ecological Civilization Academy, Huzhou 313300, China

³ Land Satellite Remote Sensing Application Center, Ministry of Natural Resources (MNR), Beijing 100048, China

Remote Sens. 2024, 16(14), 2553; https://doi.org/10.3390/rs16142553 - 12 Jul 2024

Cited by 11 | Viewed by 1644

Abstract

In recent years, the semantic segmentation model has been widely applied in fields such as the extraction of crops due to its advantages such as strong discrimination ability, high accuracy, etc. Currently, there is no standard set of ground true label data for [...] Read more.

In recent years, the semantic segmentation model has been widely applied in fields such as the extraction of crops due to its advantages such as strong discrimination ability, high accuracy, etc. Currently, there is no standard set of ground true label data for major crops in China, and the visual interpretation process is usually time-consuming and laborious. The sample size also makes it difficult to support the model to learn enough ground features, resulting in poor generalisation ability of the model, which in turn makes the model difficult to apply in fine extraction tasks of large-area crops. In this study, a method to establish a pseudo-label sample set based on the random forest algorithm to train a semantic segmentation model (U-Net) was proposed to perform winter wheat extraction. With the help of the GEE platform, Winter Wheat Canopy Index (WCI) indicators were employed in this method to initially extract winter wheat, and training samples (i.e., pseudo labels) were built for the semantic segmentation model through the iterative process of “generating random sample points—random forest model training—winter wheat extraction”; on this basis, the U-net model was trained with multi-time series remote sensing images; finally, the U-Net model was employed to obtain the spatial distribution map of winter wheat in Henan Province in 2022. The results illustrated that: (1) Pseudo-label data were constructed using the random forest model in typical regions, achieving an overall accuracy of 97.53% under validation with manual samples, proving that its accuracy meets the requirements for U-Net model training. (2) Utilizing the U-Net model, U-Net++ model, and random forest model constructed based on pseudo-label data for 2022, winter wheat mapping was conducted in Henan Province. The extraction accuracy of the three models is in the order of U-Net model > U-Net++ model > random forest model. (3) Using the U-Net model to predict the winter wheat planting areas in Henan Province in 2019, although the extraction accuracy decreased compared to 2022, it still exceeded that of the random forest model. Additionally, the U-Net++ model did not achieve higher classification accuracy. (4) Experimental results demonstrate that deep learning models constructed based on pseudo-labels exhibit higher classification accuracy. Compared to traditional machine learning models like random forest, they have higher spatiotemporal adaptability and robustness, further validating the scientific and practical feasibility of pseudo-labels and their generation strategies, which are expected to provide a feasible technical pathway for intelligent extraction of winter wheat spatial distribution information in the future. Full article

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19 pages, 5751 KiB

Open AccessEditor’s ChoiceArticle

Combining UAV-Based Multispectral and Thermal Infrared Data with Regression Modeling and SHAP Analysis for Predicting Stomatal Conductance in Almond Orchards

by Nathalie Guimarães^1,2,3,*

, Joaquim J. Sousa^1,4

, Pedro Couto^1,2,3

, Albino Bento⁵

and Luís Pádua^1,2,3

¹ Engineering Department, University of Trás-os-Montes e Alto Douro (UTAD), 5000-801 Vila Real, Portugal

² Centre for the Research and Technology of Agro-Environmental and Biological Sciences (CITAB), University of Trás-os-Montes e Alto Douro (UTAD), 5000-801 Vila Real, Portugal

³ Institute for Innovation, Capacity Building and Sustainability of Agri-Food Production, University of Trás-os-Montes e Alto Douro (UTAD), 5000-801 Vila Real, Portugal

⁴ Centre for Robotics in Industry and Intelligent Systems, INESC-TEC, 4200-465 Porto, Portugal

⁵ Centro de Investigação de Montanha (CIMO), Instituto Politécnico de Bragança, 5300-253 Bragança, Portugal

Remote Sens. 2024, 16(13), 2467; https://doi.org/10.3390/rs16132467 - 5 Jul 2024

Cited by 5 | Viewed by 1872

Abstract

Understanding and accurately predicting stomatal conductance in almond orchards is critical for effective water-management strategies, especially under challenging climatic conditions. In this study, machine-learning (ML) regression models trained on multispectral (MSP) and thermal infrared (TIR) data acquired from unmanned aerial vehicles (UAVs) are [...] Read more.

Understanding and accurately predicting stomatal conductance in almond orchards is critical for effective water-management strategies, especially under challenging climatic conditions. In this study, machine-learning (ML) regression models trained on multispectral (MSP) and thermal infrared (TIR) data acquired from unmanned aerial vehicles (UAVs) are used to address this challenge. Through an analysis of spectral indices calculated from UAV-based data and feature-selection methods, this study investigates the predictive performance of three ML models (extra trees, ET; stochastic gradient descent, SGD; and extreme gradient boosting, XGBoost) in predicting stomatal conductance. The results show that the XGBoost model trained with both MSP and TIR data had the best performance (R² = 0.87) and highlight the importance of integrating surface-temperature information in addition to other spectral indices to improve prediction accuracy, up to 11% more when compared to the use of only MSP data. Key features, such as the green–red vegetation index, chlorophyll red-edge index, and the ratio between canopy temperature and air temperature (T_c-T_a), prove to be relevant features for model performance and highlight their importance for the assessment of water stress dynamics. Furthermore, the implementation of Shapley additive explanations (SHAP) values facilitates the interpretation of model decisions and provides valuable insights into the contributions of the features. This study contributes to the advancement of precision agriculture by providing a novel approach for stomatal conductance prediction in almond orchards, supporting efforts towards sustainable water management in changing environmental conditions. Full article

(This article belongs to the Special Issue Remote Sensing for Crop Nutrients and Related Traits)

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26 pages, 9310 KiB

Open AccessEditor’s ChoiceArticle

Discrimination of Degraded Pastures in the Brazilian Cerrado Using the PlanetScope SuperDove Satellite Constellation

by Angela Gabrielly Pires Silva¹, Lênio Soares Galvão^1,*

, Laerte Guimarães Ferreira Júnior², Nathália Monteiro Teles², Vinícius Vieira Mesquita²

and Isadora Haddad¹

¹ Divisão de Observação da Terra e Geoinformática (DIOTG), Instituto Nacional de Pesquisas Espaciais (INPE), São José dos Campos 12245-970, SP, Brazil

² Laboratório de Processamento de Imagens e Geoprocessamento (LAPIG), Universidade Federal de Goiás—UFG, Campus II, LAPIG, Goiânia 74001-970, GO, Brazil

Remote Sens. 2024, 16(13), 2256; https://doi.org/10.3390/rs16132256 - 21 Jun 2024

Cited by 5 | Viewed by 1939

Abstract

Pasture degradation poses significant economic, social, and environmental impacts in the Brazilian savanna ecosystem. Despite these impacts, effectively detecting varying intensities of agronomic and biological degradation through remote sensing remains challenging. This study explores the potential of the eight-band PlanetScope SuperDove satellite constellation [...] Read more.

Pasture degradation poses significant economic, social, and environmental impacts in the Brazilian savanna ecosystem. Despite these impacts, effectively detecting varying intensities of agronomic and biological degradation through remote sensing remains challenging. This study explores the potential of the eight-band PlanetScope SuperDove satellite constellation to discriminate between five classes of pasture degradation: non-degraded pasture (NDP); pastures with low- (LID) and moderate-intensity degradation (MID); severe agronomic degradation (SAD); and severe biological degradation (SBD). Using a set of 259 cloud-free images acquired in 2022 across five sites located in central Brazil, the study aims to: (i) identify the most suitable period for discriminating between various degradation classes; (ii) evaluate the Random Forest (RF) classification performance of different SuperDove attributes; and (iii) compare metrics of accuracy derived from two predicted scenarios of pasture degradation: a more challenging one involving five classes (NDP, LID, MID, SAD, and SBD), and another considering only non-degraded and severely degraded pastures (NDP, SAD, and SBD). The study assessed individual and combined sets of SuperDove attributes, including band reflectance, vegetation indices, endmember fractions from spectral mixture analysis (SMA), and image texture variables from Gray-level Co-occurrence Matrix (GLCM). The results highlighted the effectiveness of the transition from the rainy to the dry season and the period towards the beginning of a new seasonal rainy cycle in October for discriminating pasture degradation. In comparison to the dry season, more favorable discrimination scenarios were observed during the rainy season. In the dry season, increased amounts of non-photosynthetic vegetation (NPV) complicate the differentiation between NDP and SBD, which is characterized by high soil exposure. Pastures exhibiting severe biological degradation showed greater sensitivity to water stress, manifesting earlier reflectance changes in the visible and near-infrared bands of SuperDove compared to other classes. Reflectance-based classification yielded higher overall accuracy (OA) than the approaches using endmember fractions, vegetation indices, or texture metrics. Classifications using combined attributes achieved an OA of 0.69 and 0.88 for the five-class and three-class scenarios, respectively. In the five-class scenario, the highest F1-scores were observed for NDP (0.61) and classes of agronomic (0.71) and biological (0.88) degradation, indicating the challenges in separating low and moderate stages of pasture degradation. An initial comparison of RF classification results for the five categories of degraded pastures, utilizing reflectance data from MultiSpectral Instrument (MSI)/Sentinel-2 (400–2500 nm) and SuperDove (400–900 nm), demonstrated an enhanced OA (0.79 versus 0.66) with Sentinel-2 data. This enhancement is likely to be attributed to the inclusion of shortwave infrared (SWIR) spectral bands in the data analysis. Our findings highlight the potential of satellite constellation data, acquired at high spatial resolution, for remote identification of pasture degradation. Full article

(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)

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22 pages, 9510 KiB

Open AccessEditor’s ChoiceArticle

Retrieval of Crop Canopy Chlorophyll: Machine Learning vs. Radiative Transfer Model

by Mir Md Tasnim Alam^1,*

, Anita Simic Milas¹

, Mateo Gašparović²

and Henry Poku Osei¹

¹ School of Earth, Environment and Society, Bowling Green State University, Bowling Green, OH 43403, USA

² Faculty of Geodesy, University of Zagreb, 10000 Zagreb, Croatia

Remote Sens. 2024, 16(12), 2058; https://doi.org/10.3390/rs16122058 - 7 Jun 2024

Cited by 7 | Viewed by 3220

Abstract

In recent years, the utilization of machine learning algorithms and advancements in unmanned aerial vehicle (UAV) technology have caused significant shifts in remote sensing practices. In particular, the integration of machine learning with physical models and their application in UAV–satellite data fusion have [...] Read more.

In recent years, the utilization of machine learning algorithms and advancements in unmanned aerial vehicle (UAV) technology have caused significant shifts in remote sensing practices. In particular, the integration of machine learning with physical models and their application in UAV–satellite data fusion have emerged as two prominent approaches for the estimation of vegetation biochemistry. This study evaluates the performance of five machine learning regression algorithms (MLRAs) for the mapping of crop canopy chlorophyll at the Kellogg Biological Station (KBS) in Michigan, USA, across three scenarios: (1) application to Landsat 7, RapidEye, and PlanetScope satellite images; (2) application to UAV–satellite data fusion; and (3) integration with the PROSAIL radiative transfer model (hybrid methods PROSAIL + MLRAs). The results indicate that the majority of the five MLRAs utilized in UAV–satellite data fusion perform better than the five PROSAIL + MLRAs. The general trend suggests that the integration of satellite data with UAV-derived information, including the normalized difference red-edge index (NDRE), canopy height model, and leaf area index (LAI), significantly enhances the performance of MLRAs. The UAV–RapidEye dataset exhibits the highest coefficient of determination (R²) and the lowest root mean square errors (RMSE) when employing kernel ridge regression (KRR) and Gaussian process regression (GPR) (R² = 0.89 and 0.89 and RMSE = 8.99 µg/cm² and 9.65 µg/cm², respectively). Similar performance is observed for the UAV–Landsat and UAV–PlanetScope datasets (R² = 0.86 and 0.87 for KRR, respectively). For the hybrid models, the maximum performance is attained with the Landsat data using KRR and GPR (R² = 0.77 and 0.51 and RMSE = 33.10 µg/cm² and 42.91 µg/cm², respectively), followed by R² = 0.75 and RMSE = 39.78 µg/cm² for the PlanetScope data upon integrating partial least squares regression (PLSR) into the hybrid model. Across all hybrid models, the RapidEye data yield the most stable performance, with the R² ranging from 0.45 to 0.71 and RMSE ranging from 19.16 µg/cm² to 33.07 µg/cm². The study highlights the importance of synergizing UAV and satellite data, which enables the effective monitoring of canopy chlorophyll in small agricultural lands. Full article

(This article belongs to the Special Issue Application of Satellite and UAV Data in Precision Agriculture)

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24 pages, 11944 KiB

Open AccessEditor’s ChoiceArticle

Advancing the Limits of InSAR to Detect Crustal Displacement from Low-Magnitude Earthquakes through Deep Learning

by Elena C. Reinisch^1,*, Charles J. Abolt²

, Erika M. Swanson², Bertrand Rouet-Leduc^2,†, Emily E. Snyder^2,‡, Kavya Sivaraj² and Kurt C. Solander²

¹ Los Alamos National Laboratory, Intelligence and Space Research Division, Los Alamos, NM 87545, USA

² Los Alamos National Laboratory, Earth and Environmental Sciences Division, Los Alamos, NM 87545, USA

^† Current address: Disaster Prevention Research Institute, Kyoto University, Kyoto 611-0011, Japan.

^‡ Current address: TerranearPMC, Exton, PA 19341, USA.

Remote Sens. 2024, 16(11), 2019; https://doi.org/10.3390/rs16112019 - 4 Jun 2024

Cited by 4 | Viewed by 2227

Abstract

Detecting surface deformation associated with low-magnitude (

M_{w} \leq 5

) seismicity using interferometric synthetic aperture radar (InSAR) is challenging due to the subtlety of the signal and the often challenging imaging environments. However, low-magnitude earthquakes are potential precursors to larger seismic [...] Read more.

Detecting surface deformation associated with low-magnitude (

M_{w} \leq 5

) seismicity using interferometric synthetic aperture radar (InSAR) is challenging due to the subtlety of the signal and the often challenging imaging environments. However, low-magnitude earthquakes are potential precursors to larger seismic events, and thus characterizing the crustal displacement associated with them is crucial for regional seismic hazard assessment. We combine InSAR time-series techniques with a Deep Learning (DL) autoencoder denoiser to detect the magnitude and extent of crustal deformation from the

M_{w} = 3.4

Gallina, New Mexico earthquake that occurred on 30 July 2020. Although InSAR alone cannot detect event-related deformation from such a low-magnitude seismic event, application of the DL method reveals maximum displacements as small as (±2.5 mm) in the vicinity of both the fault and earthquake epicenter without prior knowledge of the fault system. This finding improves small-scale displacement discernment with InSAR by an order of magnitude relative to previous studies. We additionally estimate best-fitting fault parameters associated with the observed deformation. The application of the DL technique unlocks the potential for low-magnitude earthquake studies, providing new insights into local fault geometries and potential risks from higher-magnitude earthquakes. This technique also permits low-magnitude event monitoring in areas where seismic networks are sparse, allowing for the possibility of global fault deformation monitoring. Full article

(This article belongs to the Special Issue Remote Sensing Monitoring for Earthquakes, Tectonics and Seismic Hazards)

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29 pages, 7749 KiB

Open AccessEditor’s ChoiceArticle

Expanding the Application of Sentinel-2 Chlorophyll Monitoring across United States Lakes

by Wilson B. Salls^1,*, Blake A. Schaeffer¹, Nima Pahlevan^2,3, Megan M. Coffer^4,5

, Bridget N. Seegers^2,6, P. Jeremy Werdell², Hannah Ferriby⁷

, Richard P. Stumpf⁸

, Caren E. Binding⁹ and Darryl J. Keith¹⁰

¹ U.S. Environmental Protection Agency Office of Research and Development, Research Triangle Park, NC 27711, USA

² NASA Goddard Space Flight Center, Ocean Ecology Lab, Greenbelt, MD 20771, USA

³ Science Systems and Applications, Inc., Lanham, MD 20706, USA

⁴ National Oceanic and Atmospheric Administration, NESDIS Center for Satellite Applications and Research, College Park, MD 20740, USA

⁵ Global Science & Technology, Inc., Greenbelt, MD 20770, USA

⁶ Morgan State University, Baltimore, MD 21251, USA

⁷ Tetra Tech, Research Triangle Park, NC 27709, USA

⁸ National Oceanic and Atmospheric Administration, National Centers for Coastal Ocean Science, Silver Spring, MD 20910, USA

⁹ Environment and Climate Change Canada, Water Science and Technology Directorate, Burlington, ON L7S 1A1, Canada

¹⁰ U.S. Environmental Protection Agency Office of Research and Development, Narragansett, RI 02882, USA

Remote Sens. 2024, 16(11), 1977; https://doi.org/10.3390/rs16111977 - 30 May 2024

Cited by 10 | Viewed by 3171

Abstract

Eutrophication of inland lakes poses various societal and ecological threats, making water quality monitoring crucial. Satellites provide a comprehensive and cost-effective supplement to traditional in situ sampling. The Sentinel-2 MultiSpectral Instrument (S2 MSI) offers unique spectral bands positioned to quantify chlorophyll a, [...] Read more.

Eutrophication of inland lakes poses various societal and ecological threats, making water quality monitoring crucial. Satellites provide a comprehensive and cost-effective supplement to traditional in situ sampling. The Sentinel-2 MultiSpectral Instrument (S2 MSI) offers unique spectral bands positioned to quantify chlorophyll a, a water-quality and trophic-state indicator, along with fine spatial resolution, enabling the monitoring of small waterbodies. In this study, two algorithms—the Maximum Chlorophyll Index (MCI) and the Normalized Difference Chlorophyll Index (NDCI)—were applied to S2 MSI data. They were calibrated and validated using in situ chlorophyll a measurements for 103 lakes across the contiguous U.S. Both algorithms were tested using top-of-atmosphere reflectances (ρ_t), Rayleigh-corrected reflectances (ρ_s), and remote sensing reflectances (R_rs). MCI slightly outperformed NDCI across all reflectance products. MCI using ρ_t showed the best overall performance, with a mean absolute error factor of 2.08 and a mean bias factor of 1.15. Conversion of derived chlorophyll a to trophic state improved the potential for management applications, with 82% accuracy using a binary classification. We report algorithm-to-chlorophyll-a conversions that show potential for application across the U.S., demonstrating that S2 can serve as a monitoring tool for inland lakes across broad spatial scales. Full article

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29 pages, 43946 KiB

Open AccessEditor’s ChoiceArticle

A Low-Cost 3D SLAM System Integration of Autonomous Exploration Based on Fast-ICP Enhanced LiDAR-Inertial Odometry

by Conglin Pang¹, Liqing Zhou¹ and Xianfeng Huang^2,3,*

¹ Electronic Information School, Wuhan University, Wuhan 430072, China

² The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430072, China

³ Intellectual Computing Laboratory for Cultural Heritage, Wuhan University, Wuhan 430072, China

Remote Sens. 2024, 16(11), 1979; https://doi.org/10.3390/rs16111979 - 30 May 2024

Cited by 10 | Viewed by 4235

Abstract

Advancements in robotics and mapping technology have spotlighted the development of Simultaneous Localization and Mapping (SLAM) systems as a key research area. However, the high cost of advanced SLAM systems poses a significant barrier to research and development in the field, while many [...] Read more.

Advancements in robotics and mapping technology have spotlighted the development of Simultaneous Localization and Mapping (SLAM) systems as a key research area. However, the high cost of advanced SLAM systems poses a significant barrier to research and development in the field, while many low-cost SLAM systems, operating under resource constraints, fail to achieve high-precision real-time mapping and localization, rendering them unsuitable for practical applications. This paper introduces a cost-effective SLAM system design that maintains high performance while significantly reducing costs. Our approach utilizes economical components and efficient algorithms, addressing the high-cost barrier in the field. First, we developed a robust robotic platform based on a traditional four-wheeled vehicle structure, enhancing flexibility and load capacity. Then, we adapted the SLAM algorithm using the LiDAR-inertial Odometry framework coupled with the Fast Iterative Closest Point (ICP) algorithm to balance accuracy and real-time performance. Finally, we integrated the 3D multi-goal Rapidly exploring Random Tree (RRT) algorithm with Nonlinear Model Predictive Control (NMPC) for autonomous exploration in complex environments. Comprehensive experimental results confirm the system’s capability for real-time, autonomous navigation and mapping in intricate indoor settings, rivaling more expensive SLAM systems in accuracy and efficiency at a lower cost. Our research results are published as open access, facilitating greater accessibility and collaboration. Full article

(This article belongs to the Special Issue High-Precision and High-Reliability Positioning, Navigation, and Timing: Opportunities and Challenges)

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24 pages, 30702 KiB

Open AccessEditor’s ChoiceArticle

Towards Urban Digital Twins: A Workflow for Procedural Visualization Using Geospatial Data

by Sanjay Somanath^1,*

, Vasilis Naserentin^2,3

, Orfeas Eleftheriou³, Daniel Sjölie⁴

, Beata Stahre Wästberg¹

and Anders Logg²

¹ Department of Architecture and Civil Engineering, Chalmers University of Technology, 412 96 Göteborg, Sweden

² Department of Mathematical Sciences, Chalmers University of Technology, 412 96 Göteborg, Sweden

³ Department of Electrical and Computer Engineering, Aristotle University, 541 24 Thessaloniki, Greece

⁴ School of Business, Economics and IT, Division of Informatics, University West, 461 32 Trollhättan, Sweden

Remote Sens. 2024, 16(11), 1939; https://doi.org/10.3390/rs16111939 - 28 May 2024

Cited by 9 | Viewed by 3893

Abstract

A key feature for urban digital twins (DTs) is an automatically generated detailed 3D representation of the built and unbuilt environment from aerial imagery, footprints, LiDAR, or a fusion of these. Such 3D models have applications in architecture, civil engineering, urban planning, construction, [...] Read more.

A key feature for urban digital twins (DTs) is an automatically generated detailed 3D representation of the built and unbuilt environment from aerial imagery, footprints, LiDAR, or a fusion of these. Such 3D models have applications in architecture, civil engineering, urban planning, construction, real estate, Geographical Information Systems (GIS), and many other areas. While the visualization of large-scale data in conjunction with the generated 3D models is often a recurring and resource-intensive task, an automated workflow is complex, requiring many steps to achieve a high-quality visualization. Methods for building reconstruction approaches have come a long way, from previously manual approaches to semi-automatic or automatic approaches. This paper aims to complement existing methods of 3D building generation. First, we present a literature review covering different options for procedural context generation and visualization methods, focusing on workflows and data pipelines. Next, we present a semi-automated workflow that extends the building reconstruction pipeline to include procedural context generation using Python and Unreal Engine. Finally, we propose a workflow for integrating various types of large-scale urban analysis data for visualization. We conclude with a series of challenges faced in achieving such pipelines and the limitations of the current approach. However, the steps for a complete, end-to-end solution involve further developing robust systems for building detection, rooftop recognition, and geometry generation and importing and visualizing data in the same 3D environment, highlighting a need for further research and development in this field. Full article

(This article belongs to the Special Issue 3D and Semantic Reconstruction of the Urban Environment Using Multi-Modal and Multi-Resolution Remote Sensing Data)

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21 pages, 7074 KiB

Open AccessEditor’s ChoiceArticle

Fire Vulnerability, Resilience, and Recovery Rates of Mediterranean Pine Forests Using a 33-Year Time Series of Satellite Imagery

by Esther Peña-Molina^1,*

, Daniel Moya¹

, Eva Marino²

, José Luis Tomé²

, Álvaro Fajardo-Cantos¹

, Javier González-Romero³

, Manuel Esteban Lucas-Borja¹

and Jorge de las Heras¹

¹ Forest Ecology Research Group (ECOFOR), High Technical School of Agricultural and Forestry Engineering and Biotechnology, University of Castilla-La Mancha, University Campus, s/n, 02071 Albacete, Spain

² AGRESTA Sociedad Cooperativa, c/Duque de Fernán Núñez 2, 28012 Madrid, Spain

³ Department of Forestry and Environmental Engineering and Management, Technical University of Madrid, 28040 Madrid, Spain

Remote Sens. 2024, 16(10), 1718; https://doi.org/10.3390/rs16101718 - 13 May 2024

Cited by 4 | Viewed by 3347

Abstract

The modification of fire regimes and their impact on vegetation recovery, soil properties, and fuel structure are current key research areas that attempt to identify the thresholds of vegetation’s susceptibility to wildfires. This study aimed to evaluate the vulnerability of Mediterranean pine forests [...] Read more.

The modification of fire regimes and their impact on vegetation recovery, soil properties, and fuel structure are current key research areas that attempt to identify the thresholds of vegetation’s susceptibility to wildfires. This study aimed to evaluate the vulnerability of Mediterranean pine forests (Pinus halepensis Mill. and Pinus pinaster Aiton) to wildfires, analyzing two major forest fires that occurred in Yeste (Spain) in 1994 and 2017, affecting over 14,000 and 3200 hectares, respectively. Four recovery regions were identified based on fire severity—calculated using the delta Normalized Burn Ratio (dNBR) index—and recurrence: areas with high severity in 2017 but not in 1994 (UB94-HS17), areas with high severity in 1994 but not in 2017 (HS94-UB17), areas with high severity in both fires (HS94-HS17), and areas unaffected by either fire (UB94-UB17). The analysis focused on examining the recovery patterns of three spectral indices—the Normalized Difference Vegetation Index (NDVI), Normalized Moisture Index (NDMI), and Normalized Burn Ratio (NBR)—using the Google Earth Engine platform from 1990 to 2023. Additionally, the Relative Recovery Indicator (RRI), the Ratio of Eighty Percent (R80P), and the Year-on-Year average (YrYr) metrics were computed to assess the spectral recovery rates by region. These three spectral indices showed similar dynamic responses to fire. However, the Mann–Kendall and unit root statistical tests revealed that the NDVI and NDMI exhibited distinct trends, particularly in areas with recurrence (HS94-HS17). The NDVI outperformed the NBR and NDMI in distinguishing variations among regions. These results suggest accelerated vegetation spectral regrowth in the short term. The Vegetation Recovery Capacity After Fire (VRAF) index showed values from low to moderate, while the Vulnerability to Fire (V2FIRE) index exhibited values from medium to high across all recovery regions. These findings enhance our understanding of how vegetation recovers from fire and how vulnerable it is to fire. Full article

(This article belongs to the Special Issue Land Use/Cover Mapping and Trend Analysis Using Google Earth Engine)

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44 pages, 25578 KiB

Open AccessEditor’s ChoiceReview

Remote Sensing and Modeling of the Cryosphere in High Mountain Asia: A Multidisciplinary Review

by Qinghua Ye^1,2

, Yuzhe Wang³

, Lin Liu⁴

, Linan Guo¹, Xueqin Zhang²

, Liyun Dai⁵, Limin Zhai^6,7, Yafan Hu^1,7, Nauman Ali^1,7, Xinhui Ji^1,7, Youhua Ran⁵

, Yubao Qiu⁸

, Lijuan Shi⁸, Tao Che⁵

, Ninglian Wang⁹, Xin Li¹

and Liping Zhu^1,*

¹ State Key Laboratory Tibetan Plateau Earth System, Environment and Resources (TPESER), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China

² Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

³ College of Geography and Environment, Shandong Normal University, Jinan 250358, China

⁴ Earth and Environmental Sciences Programme, Faculty of Science, The Chinese University of Hong Kong, Hong Kong 999077, China

⁵ Key Laboratory of Remote Sensing of Gansu Province, Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China

⁶ Key Lab of Microwave Remote Sensing, National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China

⁷ University of Chinese Academy of Sciences, Beijing 100049, China

⁸ Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

⁹ College of Urban and Environmental Sciences, Northwest University, Xi’an 710127, China

Remote Sens. 2024, 16(10), 1709; https://doi.org/10.3390/rs16101709 - 11 May 2024

Cited by 6 | Viewed by 4586

Abstract

Over the past decades, the cryosphere has changed significantly in High Mountain Asia (HMA), leading to multiple natural hazards such as rock–ice avalanches, glacier collapse, debris flows, landslides, and glacial lake outburst floods (GLOFs). Monitoring cryosphere change and evaluating its hydrological effects are [...] Read more.

Over the past decades, the cryosphere has changed significantly in High Mountain Asia (HMA), leading to multiple natural hazards such as rock–ice avalanches, glacier collapse, debris flows, landslides, and glacial lake outburst floods (GLOFs). Monitoring cryosphere change and evaluating its hydrological effects are essential for studying climate change, the hydrological cycle, water resource management, and natural disaster mitigation and prevention. However, knowledge gaps, data uncertainties, and other substantial challenges limit comprehensive research in climate–cryosphere–hydrology–hazard systems. To address this, we provide an up-to-date, comprehensive, multidisciplinary review of remote sensing techniques in cryosphere studies, demonstrating primary methodologies for delineating glaciers and measuring geodetic glacier mass balance change, glacier thickness, glacier motion or ice velocity, snow extent and water equivalent, frozen ground or frozen soil, lake ice, and glacier-related hazards. The principal results and data achievements are summarized, including URL links for available products and related data platforms. We then describe the main challenges for cryosphere monitoring using satellite-based datasets. Among these challenges, the most significant limitations in accurate data inversion from remotely sensed data are attributed to the high uncertainties and inconsistent estimations due to rough terrain, the various techniques employed, data variability across the same regions (e.g., glacier mass balance change, snow depth retrieval, and the active layer thickness of frozen ground), and poor-quality optical images due to cloudy weather. The paucity of ground observations and validations with few long-term, continuous datasets also limits the utilization of satellite-based cryosphere studies and large-scale hydrological models. Lastly, we address potential breakthroughs in future studies, i.e., (1) outlining debris-covered glacier margins explicitly involving glacier areas in rough mountain shadows, (2) developing highly accurate snow depth retrieval methods by establishing a microwave emission model of snowpack in mountainous regions, (3) advancing techniques for subsurface complex freeze–thaw process observations from space, (4) filling knowledge gaps on scattering mechanisms varying with surface features (e.g., lake ice thickness and varying snow features on lake ice), and (5) improving and cross-verifying the data retrieval accuracy by combining different remote sensing techniques and physical models using machine learning methods and assimilation of multiple high-temporal-resolution datasets from multiple platforms. This comprehensive, multidisciplinary review highlights cryospheric studies incorporating spaceborne observations and hydrological models from diversified techniques/methodologies (e.g., multi-spectral optical data with thermal bands, SAR, InSAR, passive microwave, and altimetry), providing a valuable reference for what scientists have achieved in cryosphere change research and its hydrological effects on the Third Pole. Full article

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

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18 pages, 51367 KiB

Open AccessEditor’s ChoiceArticle

Drone-Acquired Short-Wave Infrared (SWIR) Imagery in Landscape Archaeology: An Experimental Approach

by Jesse Casana^1,*

and Carolin Ferwerda²

¹ Department of Anthropology, Dartmouth College, Hanover, NH 03755, USA

² Spatial Archaeometry Lab (SPARCL), Dartmouth College, Hanover, NH 03755, USA

Remote Sens. 2024, 16(10), 1671; https://doi.org/10.3390/rs16101671 - 9 May 2024

Cited by 3 | Viewed by 3134

Abstract

Many rocks, minerals, and soil types reflect short-wave infrared (SWIR) imagery (900–2500 nm) in distinct ways, and geologists have long relied on this property to aid in the mapping of differing surface lithologies. Although surface archaeological features including artifacts, anthrosols, or structural remains [...] Read more.

Many rocks, minerals, and soil types reflect short-wave infrared (SWIR) imagery (900–2500 nm) in distinct ways, and geologists have long relied on this property to aid in the mapping of differing surface lithologies. Although surface archaeological features including artifacts, anthrosols, or structural remains also likely reflect SWIR wavelengths of light in unique ways, archaeological applications of SWIR imagery are rare, largely due to the low spatial resolution and high acquisition costs of these data. Fortunately, a new generation of compact, drone-deployable sensors now enables the collection of ultra-high-resolution (<10 cm), hyperspectral (>100 bands) SWIR imagery using a consumer-grade drone, while the analysis of these complex datasets is now facilitated by powerful imagery-processing software packages. This paper presents an experimental effort to develop a methodology that would allow archaeologists to collect SWIR imagery using a drone, locate surface artifacts in the resultant data, and identify different artifact types in the imagery based on their reflectance values across the 900–1700 nm spectrum. Our results illustrate both the potential of this novel approach to exploring the archaeological record, as we successfully locate and characterize many surface artifacts in our experimental study, while also highlighting challenges in successful data collection and analysis, largely related to current limitations in sensor and drone technology. These findings show that as underlying hardware sees continued improvements in the coming years, drone-acquired SWIR imagery can become a powerful tool for the discovery, documentation, and analysis of archaeological landscapes. Full article

(This article belongs to the Special Issue Applications of Remote Sensing in Landscape Archaeology)

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