Remote Sensing

18 pages, 5877 KB

Open AccessEditor’s ChoiceArticle

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

by Xiaochen Ren, Biqiang Zhao, Zhipeng Ren and Bo Xiong

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

Cited by 16 | Viewed by 5524

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 KB

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

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

Cited by 16 | Viewed by 3600

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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20 pages, 7086 KB

Open AccessEditor’s ChoiceArticle

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

by Gustavo A. Mesías-Ruiz, José M. Peña, Ana I. de Castro, Irene Borra-Serrano and José Dorado

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

Cited by 11 | Viewed by 3094

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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31 pages, 4586 KB

Open AccessEditor’s ChoiceArticle

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

by Fei Li, Tan Yigitcanlar, Madhav Nepal, Kien Nguyen Thanh and Fatih Dur

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

Cited by 62 | Viewed by 12865

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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27 pages, 59331 KB

Open AccessEditor’s ChoiceArticle

AerialFormer: Multi-Resolution Transformer for Aerial Image Segmentation

by Taisei Hanyu, Kashu Yamazaki, Minh Tran, Roy A. McCann, Haitao Liao, Chase Rainwater, Meredith Adkins, Jackson Cothren and Ngan Le

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

Cited by 74 | Viewed by 9621

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 KB

Open AccessEditor’s ChoiceArticle

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

by Wenmei Li, Hao Xia, Jiadong Zhang, Yu Wang, Yan Jia and Yuhong He

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

Cited by 23 | Viewed by 4325

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 KB

Open AccessEditor’s ChoiceReview

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

by Katarzyna Strząbała, Paweł Ćwiąkała and Edyta Puniach

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

Cited by 39 | Viewed by 10258

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 KB

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 Park, Sami Khanal, Kaiguang Zhao and Kyuhyun Byun

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

Cited by 23 | Viewed by 6517

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 KB

Open AccessEditor’s ChoiceArticle

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

by Gianfranco Cianchini, Massimo Calcara, Angelo De Santis, Alessandro Piscini, Serena D’Arcangelo, Cristiano Fidani, Dario Sabbagh, Martina Orlando, Loredana Perrone, Saioa A. Campuzano, Mariagrazia De Caro, Adriano Nardi and Maurizio Soldani

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

Cited by 19 | Viewed by 5234

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 KB

Open AccessEditor’s ChoiceArticle

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

by Chloe Brown, Sofie Sjögersten, Martha J. Ledger, Faizal Parish and Doreen Boyd

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

Cited by 9 | Viewed by 4533

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 KB

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 Heydari, Jody C. Vogeler, Orion S. E. Cardenas-Ritzert, Steven K. Filippelli, Melissa McHale and Melinda Laituri

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

Cited by 8 | Viewed by 4589

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 KB

Open AccessEditor’s ChoiceArticle

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

by Margot Verhulst, Stien Heremans, Matthew B. Blaschko and Ben Somers

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

Cited by 9 | Viewed by 3578

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 KB

Open AccessEditor’s ChoiceArticle

Cell-Resolved PV Soiling Measurement Using Drone Images

by Peter Winkel, Stefan Wilbert, Marc Röger, Julian J. Krauth, Niels Algner, Bijan Nouri, Fabian Wolfertstetter, Jose Antonio Carballo, M. Carmen Alonso-Garcia, Jesus Polo, Aránzazu Fernández-García and Robert Pitz-Paal

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

Cited by 10 | Viewed by 6474

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 KB

Open AccessEditor’s ChoiceArticle

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

by Jianhua Zhang, Shucheng You, Aixia Liu, Lijian Xie, Chenhao Huang, Xu Han, Penghan Li, Yixuan Wu and Jinsong Deng

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

Cited by 19 | Viewed by 3426

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 KB

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, Joaquim J. Sousa, Pedro Couto, Albino Bento and Luís Pádua

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

Cited by 20 | Viewed by 3976

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 KB

Open AccessEditor’s ChoiceArticle

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

by Angela Gabrielly Pires Silva, Lênio Soares Galvão, Laerte Guimarães Ferreira Júnior, Nathália Monteiro Teles, Vinícius Vieira Mesquita and Isadora Haddad

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

Cited by 13 | Viewed by 3594

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 KB

Open AccessEditor’s ChoiceArticle

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

by Mir Md Tasnim Alam, Anita Simic Milas, Mateo Gašparović and Henry Poku Osei

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

Cited by 30 | Viewed by 5337

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 KB

Open AccessEditor’s ChoiceArticle

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

by Elena C. Reinisch, Charles J. Abolt, Erika M. Swanson, Bertrand Rouet-Leduc, Emily E. Snyder, Kavya Sivaraj and Kurt C. Solander

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

Cited by 6 | Viewed by 9291

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 KB

Open AccessEditor’s ChoiceArticle

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

by Wilson B. Salls, Blake A. Schaeffer, Nima Pahlevan, Megan M. Coffer, Bridget N. Seegers, P. Jeremy Werdell, Hannah Ferriby, Richard P. Stumpf, Caren E. Binding and Darryl J. Keith

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

Cited by 31 | Viewed by 10379

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 KB

Open AccessEditor’s ChoiceArticle

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

by Conglin Pang, Liqing Zhou and Xianfeng Huang

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

Cited by 24 | Viewed by 11026

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 KB

Open AccessEditor’s ChoiceArticle

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

by Sanjay Somanath, Vasilis Naserentin, Orfeas Eleftheriou, Daniel Sjölie, Beata Stahre Wästberg and Anders Logg

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

Cited by 33 | Viewed by 8032

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 KB

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, Daniel Moya, Eva Marino, José Luis Tomé, Álvaro Fajardo-Cantos, Javier González-Romero, Manuel Esteban Lucas-Borja and Jorge de las Heras

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

Cited by 17 | Viewed by 5866

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 KB

Open AccessEditor’s ChoiceReview

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

by Qinghua Ye, Yuzhe Wang, Lin Liu, Linan Guo, Xueqin Zhang, Liyun Dai, Limin Zhai, Yafan Hu, Nauman Ali, Xinhui Ji, Youhua Ran, Yubao Qiu, Lijuan Shi, Tao Che, Ninglian Wang, Xin Li and Liping Zhu

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

Cited by 32 | Viewed by 9054

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 KB

Open AccessEditor’s ChoiceArticle

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

by Jesse Casana and Carolin Ferwerda

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

Cited by 9 | Viewed by 5873

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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24 pages, 20771 KB

Open AccessEditor’s ChoiceArticle

Overestimated Fog-Top Entrainment in WRF Simulation Leading to Unrealistic Dissipation of Sea Fog: A Case Study

by Li Zhang, Hao Shi, Shanhong Gao and Shun Li

Remote Sens. 2024, 16(10), 1656; https://doi.org/10.3390/rs16101656 - 7 May 2024

Cited by 6 | Viewed by 3433

Abstract

Entrainment at the top of the planetary boundary layer (PBL) is of significant importance because it controls the upward growth of the PBL height. An option called ysu_topdown_pblmix, which provides a parameterization of fog-top entrainment, has been proposed for valley fog modeling and [...] Read more.

Entrainment at the top of the planetary boundary layer (PBL) is of significant importance because it controls the upward growth of the PBL height. An option called ysu_topdown_pblmix, which provides a parameterization of fog-top entrainment, has been proposed for valley fog modeling and introduced into the YSU (Yonsei University) PBL scheme in the Weather Research and Forecasting (WRF) model. However, enabling this option in simulations of sea fog over the Yellow Sea typically results in unrealistic dissipation near the fog bottom and even within the entire fog layer. In this study, we theoretically examine the composition of the option ysu_topdown_pblmix, and then argue that one term in this option might be redundant for sea-fog modeling. The fog-top variables are employed in this term to determine the basic entrainment in the dry PBL, which is already parameterized by the surface variables in the original YSU PBL scheme. This term likely leads to an overestimation of the fog-top entrainment rate, so we refer to it as redundant. To explore the connection between the redundant term and unrealistic dissipation, a widespread sea-fog episode over the Yellow Sea is employed as a case study based on the WRF model. The simulation results clearly attribute the unrealistic dissipation to the extra entrainment rate that the redundant term induces. Fog-top entrainment is unexpectedly overestimated due to this extra entrainment rate, resulting in a significantly drier and warmer bias within the interior of sea fog. When sea fog develops and reaches a temperature lower than the sea surface, the sea surface functions as a warming source to heat the fog bottom jointly with the downward heat flux brought by the fog-top entrainment, leading the dissipation to initially occur near the fog bottom and then gradually expand upwards. We suggest a straightforward method to modify the option ysu_topdown_pblmix for sea-fog modeling that eliminates the redundant term. The improvement effect of this method was supported by the results of sensitivity tests. However, more sea-fog cases are required to validate the modification method. Full article

(This article belongs to the Special Issue Severe Weather Observations and Meteorology Modeling Development Using Remote Sensing)

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12 pages, 3256 KB

Open AccessEditor’s ChoiceArticle

Miniaturizing Hyperspectral Lidar System Employing Integrated Optical Filters

by Haibin Sun, Yicheng Wang, Zhipei Sun, Shaowei Wang, Shengli Sun, Jianxin Jia, Changhui Jiang, Peilun Hu, Haima Yang, Xing Yang, Mika Karjalnen, Juha Hyyppä and Yuwei Chen

Remote Sens. 2024, 16(9), 1642; https://doi.org/10.3390/rs16091642 - 4 May 2024

Cited by 3 | Viewed by 3553

Abstract

Hyperspectral LiDAR (HSL) has been utilized as an efficacious technique in object classification and recognition based on its unique capability to obtain ranges and spectra synchronously. Different kinds of HSL prototypes with varied structures have been promoted and measured its performance. However, almost [...] Read more.

Hyperspectral LiDAR (HSL) has been utilized as an efficacious technique in object classification and recognition based on its unique capability to obtain ranges and spectra synchronously. Different kinds of HSL prototypes with varied structures have been promoted and measured its performance. However, almost all of these HSL prototypes employ complex and large spectroscopic devices, such as an Acousto-Optic Tunable Filter and Liquid-Crystal Tunable Filter, which makes this HSL system bulky and expensive, and then hinders its extensive application in many fields. In this paper, a smart and smaller spectroscopic component, an intergraded optical filter (IOF), is promoted to miniaturize these HSL systems. The system calibration, range precision, and spectral profile experiments were carried out to test the HSL prototype. Although the IOF employed here only covered a wavelength range of 699–758 nm with a six-channel passband and showed a transmittance of less than 50%, the HSL prototype showed excellent performance in ranging and spectral profile collecting. The spectral profiles collected are well in accordance with those acquired based on the AOTF. The spectral profiles of the fruits, vegetables, plants, and ore samples collected by the HSL based on an IOF can effectively reveal the status of the plants, the component materials, and ore species. Finally, we also showed the integrated design of the HSL based on a three-dimensional IOF and combined with a detector. The performance and designs of this HSL system based on an IOF show great potential for miniaturizing in some specific applications. Full article

(This article belongs to the Special Issue Remote Sensing and Lidar Data for Forest Monitoring)

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29 pages, 2637 KB

Open AccessEditor’s ChoiceArticle

Four Years of Atmospheric Boundary Layer Height Retrievals Using COSMIC-2 Satellite Data

by Ginés Garnés-Morales, Maria João Costa, Juan Antonio Bravo-Aranda, María José Granados-Muñoz, Vanda Salgueiro, Jesús Abril-Gago, Sol Fernández-Carvelo, Juana Andújar-Maqueda, Antonio Valenzuela, Inmaculada Foyo-Moreno, Francisco Navas-Guzmán, Lucas Alados-Arboledas, Daniele Bortoli and Juan Luis Guerrero-Rascado

Remote Sens. 2024, 16(9), 1632; https://doi.org/10.3390/rs16091632 - 3 May 2024

Cited by 12 | Viewed by 6722

Abstract

This work aimed to study the atmospheric boundary layer height (ABLH) from COSMIC-2 refractivity data, endeavoring to refine existing ABLH detection algorithms and scrutinize the resulting spatial and seasonal distributions. Through validation analyses involving different ground-based methodologies (involving data from lidar, ceilometer, microwave [...] Read more.

This work aimed to study the atmospheric boundary layer height (ABLH) from COSMIC-2 refractivity data, endeavoring to refine existing ABLH detection algorithms and scrutinize the resulting spatial and seasonal distributions. Through validation analyses involving different ground-based methodologies (involving data from lidar, ceilometer, microwave radiometers, and radiosondes), the optimal ABLH determination relied on identifying the lowest refractivity gradient negative peak with a magnitude at least

τ

% times the minimum refractivity gradient magnitude, where

τ

is a fitting parameter representing the minimum peak strength relative to the absolute minimum refractivity gradient. Different

τ

values were derived accounting for the moment of the day (daytime, nighttime, or sunrise/sunset) and the underlying surface (land or sea). Results show discernible relations between ABLH and various features, notably, the land cover and latitude. On average, ABLH is higher over oceans (≈1.5 km), but extreme values (maximums > 2.5 km, and minimums < 1 km) are reached over intertropical lands. Variability is generally subtle over oceans, whereas seasonality and daily evolution are pronounced over continents, with higher ABLHs during daytime and local wintertime (summertime) in intertropical (middle) latitudes. Full article

(This article belongs to the Special Issue Observation of Atmospheric Boundary-Layer Based on Remote Sensing)

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17 pages, 7831 KB

Open AccessEditor’s ChoiceArticle

Landslide Mapping in Calitri (Southern Italy) Using New Multi-Temporal InSAR Algorithms Based on Permanent and Distributed Scatterers

by Nicola Angelo Famiglietti, Pietro Miele, Marco Defilippi, Alessio Cantone, Paolo Riccardi, Giulia Tessari and Annamaria Vicari

Remote Sens. 2024, 16(9), 1610; https://doi.org/10.3390/rs16091610 - 30 Apr 2024

Cited by 16 | Viewed by 9823

Abstract

Landslides play a significant role in the morpho-evolutional processes of slopes, affecting them globally under various geological conditions. Often unnoticed due to low velocities, they cause diffuse damage and loss of economic resources to the infrastructure or villages built on them. Recognizing and [...] Read more.

Landslides play a significant role in the morpho-evolutional processes of slopes, affecting them globally under various geological conditions. Often unnoticed due to low velocities, they cause diffuse damage and loss of economic resources to the infrastructure or villages built on them. Recognizing and mapping mass movements is crucial for mitigating economic and social impacts. Conventional monitoring techniques prove challenging for large areas, necessitating resource-intensive ground-based networks. Leveraging abundant synthetic aperture radar (SAR) sensors, satellite techniques offer cost-effective solutions. Among the various methods based on SAR products for detecting landslides, multi-temporal differential interferometry SAR techniques (MTInSAR) stand out for their precise measurement capabilities and spatiotemporal evolution analysis. They have been widely used in several works in the last decades. Using information from the official Italian landslide database (IFFI), this study employs Sentinel-1 imagery and two new processing chains, E-PS and E-SBAS algorithms, to detect deformation areas on the slopes of Calitri, a small town in Southern Italy; these algorithms assess the cumulated displacements and their state of activity. Taking into account the non-linear trends of the scatterers, these innovative algorithms have helped to identify a dozen clusters of points that correspond well with IFFI polygons. Full article

(This article belongs to the Special Issue Landslide Inventory Mapping and Monitoring Using Remote Sensing Techniques)

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17 pages, 27416 KB

Open AccessEditor’s ChoiceArticle

Landsat 8 and 9 Underfly International Surface Reflectance Validation Collaboration

by Joshua Mann, Emily Maddox, Mahesh Shrestha, Jeffrey Irwin, Jeffrey Czapla-Myers, Aaron Gerace, Eon Rehman, Nina Raqueno, Craig Coburn, Guy Byrne, Mark Broomhall and Andrew Walsh

Remote Sens. 2024, 16(9), 1492; https://doi.org/10.3390/rs16091492 - 23 Apr 2024

Cited by 6 | Viewed by 5686

Abstract

During the launch and path to its final orbit, the Landsat 9 satellite performed a once in a mission lifetime maneuver as it passed beneath Landsat 8, resulting in near coincident data collection. This maneuver provided ground validation teams from across the globe [...] Read more.

During the launch and path to its final orbit, the Landsat 9 satellite performed a once in a mission lifetime maneuver as it passed beneath Landsat 8, resulting in near coincident data collection. This maneuver provided ground validation teams from across the globe the opportunity of collecting surface in situ data to compare directly to Landsat 8 and Landsat 9 data. Ground validation teams identified surface targets that would yield reflectance and/or thermal values that could be used in Landsat Level 2 product validation and set out to collect at these locations using surface validation methodologies the teams developed. The values were collected from each team and compared directly with each other across each of the different bands of both Landsat 8 and 9. The results proved consistency across the Landsat 8 and 9 platforms and also agreed well in surface reflectance underestimation of the Coastal Aerosol, Blue, and SWIR2 bands. Full article

(This article belongs to the Special Issue Landsat 9 Pre-launch, Commissioning, and Early On-Orbit Imaging Performance)

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28 pages, 14693 KB

Open AccessEditor’s ChoiceArticle

Wildlife Real-Time Detection in Complex Forest Scenes Based on YOLOv5s Deep Learning Network

by Zhibin Ma, Yanqi Dong, Yi Xia, Delong Xu, Fu Xu and Feixiang Chen

Remote Sens. 2024, 16(8), 1350; https://doi.org/10.3390/rs16081350 - 11 Apr 2024

Cited by 48 | Viewed by 11575

Abstract

With the progressively deteriorating global ecological environment and the gradual escalation of human activities, the survival of wildlife has been severely impacted. Hence, a rapid, precise, and reliable method for detecting wildlife holds immense significance in safeguarding their existence and monitoring their status. [...] Read more.

With the progressively deteriorating global ecological environment and the gradual escalation of human activities, the survival of wildlife has been severely impacted. Hence, a rapid, precise, and reliable method for detecting wildlife holds immense significance in safeguarding their existence and monitoring their status. However, due to the rare and concealed nature of wildlife activities, the existing wildlife detection methods face limitations in efficiently extracting features during real-time monitoring in complex forest environments. These models exhibit drawbacks such as slow speed and low accuracy. Therefore, we propose a novel real-time monitoring model called WL-YOLO, which is designed for lightweight wildlife detection in complex forest environments. This model is built upon the deep learning model YOLOv5s. In WL-YOLO, we introduce a novel and lightweight feature extraction module. This module is comprised of a deeply separable convolutional neural network integrated with compression and excitation modules in the backbone network. This design is aimed at reducing the number of model parameters and computational requirements, while simultaneously enhancing the feature representation of the network. Additionally, we introduced a CBAM attention mechanism to enhance the extraction of local key features, resulting in improved performance of WL-YOLO in the natural environment where wildlife has high concealment and complexity. This model achieved a mean accuracy (mAP) value of 97.25%, an F1-score value of 95.65%, and an accuracy value of 95.14%. These results demonstrated that this model outperforms the current mainstream deep learning models. Additionally, compared to the YOLOv5m base model, WL-YOLO reduces the number of parameters by 44.73% and shortens the detection time by 58%. This study offers technical support for detecting and protecting wildlife in intricate environments by introducing a highly efficient and advanced wildlife detection model. Full article

(This article belongs to the Topic Computer Vision and Image Processing, 2nd Edition)

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19 pages, 1771 KB

Open AccessEditor’s ChoiceArticle

Volume Structure Retrieval Using Drone-Based SAR Interferometry with Wide Fractional Bandwidth

by Sumin Kim, Gerhard Krieger and Michelangelo Villano

Remote Sens. 2024, 16(8), 1352; https://doi.org/10.3390/rs16081352 - 11 Apr 2024

Cited by 7 | Viewed by 8882

Abstract

Synthetic aperture radar (SAR) interferometry (InSAR) is a well-established remote sensing technique capable of providing accurate topographic information of an area. Multiple scattering occurring at different heights within a single resolution cell, however, cannot be resolved using a single baseline and results in [...] Read more.

Synthetic aperture radar (SAR) interferometry (InSAR) is a well-established remote sensing technique capable of providing accurate topographic information of an area. Multiple scattering occurring at different heights within a single resolution cell, however, cannot be resolved using a single baseline and results in a degradation of the height accuracy. Techniques such as polarimetric InSAR and SAR tomography tackle this problem using additional measurements to obtain the three-dimensional structure of the volume. However, polarimetry-based methods assume orthogonal and deterministic scattering mechanisms and tomography requires numerous observations. This paper presents a novel approach to retrieving the three-dimensional structure of semitransparent media using wide fractional bandwidth InSAR. The frequency dependency of the InSAR coherence is exploited to retrieve the three-dimensional structure, assuming that signals with wide fractional bandwidth are available, as is the case for unmanned aerial vehicles (UAVs) or drones. As the extinction is, in general, frequency-dependent, a third observation may improve the inversion results. Simulations of different scenarios show that the frequency profile can be inverted to obtain information about the three-dimensional scattering structure. The acquired data also enable retrieval of the phase center depth as a function of frequency. This method allows for frequent and accurate monitoring of semitransparent targets, e.g., forests and vegetation, using a swarm of two or three drones. Full article

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23 pages, 33239 KB

Open AccessEditor’s ChoiceArticle

Lunar Surface Resource Exploration: Tracing Lithium, 7 Li and Black Ice Using Spectral Libraries and Apollo Mission Samples

by Susana del Carmen Fernández, Fernando Alberquilla, Julia María Fernández, Enrique Díez, Javier Rodríguez, Rubén Muñiz, Javier F. Calleja, Francisco Javier de Cos and Jesús Martínez-Frías

Remote Sens. 2024, 16(7), 1306; https://doi.org/10.3390/rs16071306 - 8 Apr 2024

Cited by 5 | Viewed by 5769

Abstract

This is an exercise to explore the concentration of lithium, lithium-7 isotope and the possible presence of black dirty ice on the lunar surface using spectral data obtained from the Clementine mission. The main interest in tracing the lithium and presence of dark [...] Read more.

This is an exercise to explore the concentration of lithium, lithium-7 isotope and the possible presence of black dirty ice on the lunar surface using spectral data obtained from the Clementine mission. The main interest in tracing the lithium and presence of dark ice on the lunar surface is closely related to future human settlement missions on the moon. We investigate the distribution of lithium and 7 Li isotope on the lunar surface by employing spectral data from the Clementine images. We utilized visible (VIS–NIR) imagery at wavelengths of 450, 750, 900, 950 and 1000 nm, along with near-infrared (NIR–SWIR) at 1100, 1250, 1500, 2000, 2600 and 2780 nm, encompassing 11 bands in total. This dataset offers a comprehensive coverage of about 80% of the lunar surface, with resolutions ranging from 100 to 500 m, spanning latitudes from 80°S to 80°N. In order to extract quantitative abundance of lithium, ground-truth sites were used to calibrate the Clementine images. Samples (specifically, 12045, 15058, 15475, 15555, 62255, 70035, 74220 and 75075) returned from Apollo missions 12, 15, 16 and 17 have been correlated to the Clementine VIS–NIR bands and five spectral ratios. The five spectral ratios calculated synthesize the main spectral features of sample spectra that were grouped by their lithium and 7 Li content using Principal Component Analysis. The ratios spectrally characterize substrates of anorthosite, silica-rich basalts, olivine-rich basalts, high-Ti mare basalts and Orange and Glasses soils. Our findings reveal a strong linear correlation between the spectral parameters and the lithium content in the eight Apollo samples. With the values of the 11 Clementine bands and the 5 spectral ratios, we performed linear regression models to estimate the concentration of lithium and 7 Li. Also, we calculated Digital Terrain Models (Altitude, Slope, Aspect, DirectInsolation and WindExposition) from LOLA-DTM to discover relations between relief and spatial distribution of the extended models of lithium and 7 Li. The analysis was conducted in a mask polygon around the Apollo 15 landing site. This analysis seeks to uncover potential 7 Li enrichment through spallation processes, influenced by varying exposure to solar wind. To explore the possibility of finding ice mixed with regolith (often referred to as `black ice’), we extended results to the entire Clementine coverage spectral indices, calculated with a library (350–2500 nm) of ice samples contaminated with various concentrations of volcanic particles. Full article

(This article belongs to the Special Issue Future of Lunar Exploration)

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22 pages, 7233 KB

Open AccessEditor’s ChoiceArticle

High-Resolution Canopy Height Mapping: Integrating NASA’s Global Ecosystem Dynamics Investigation (GEDI) with Multi-Source Remote Sensing Data

by Cesar Alvites, Hannah O’Sullivan, Saverio Francini, Marco Marchetti, Giovanni Santopuoli, Gherardo Chirici, Bruno Lasserre, Michela Marignani and Erika Bazzato

Remote Sens. 2024, 16(7), 1281; https://doi.org/10.3390/rs16071281 - 5 Apr 2024

Cited by 34 | Viewed by 14555

Abstract

Accurate structural information about forests, including canopy heights and diameters, is crucial for quantifying tree volume, biomass, and carbon stocks, enabling effective forest ecosystem management, particularly in response to changing environmental conditions. Since late 2018, NASA’s Global Ecosystem Dynamics Investigation (GEDI) mission has [...] Read more.

Accurate structural information about forests, including canopy heights and diameters, is crucial for quantifying tree volume, biomass, and carbon stocks, enabling effective forest ecosystem management, particularly in response to changing environmental conditions. Since late 2018, NASA’s Global Ecosystem Dynamics Investigation (GEDI) mission has monitored global canopy structure using a satellite Light Detection and Ranging (LiDAR) instrument. While GEDI has collected billions of LiDAR shots across a near-global range (between 51.6°N and >51.6°S), their spatial distribution remains dispersed, posing challenges for achieving complete forest coverage. This study proposes and evaluates an approach that generates high-resolution canopy height maps by integrating GEDI data with Sentinel-1, Sentinel-2, and topographical ancillary data through three machine learning (ML) algorithms: random forests (RF), gradient tree boost (GB), and classification and regression trees (CART). To achieve this, the secondary aims included the following: (1) to assess the performance of three ML algorithms, RF, GB, and CART, in predicting canopy heights, (2) to evaluate the performance of our canopy height maps using reference canopy height from canopy height models (CHMs), and (3) to compare our canopy height maps with other two existing canopy height maps. RF and GB were the top-performing algorithms, achieving the best 13.32% and 16% root mean squared error for broadleaf and coniferous forests, respectively. Validation of the proposed approach revealed that the 100th and 98th percentile, followed by the average of the 75th, 90th, 95th, and 100th percentiles (AVG), were the most accurate GEDI metrics for predicting real canopy heights. Comparisons between predicted and reference CHMs demonstrated accurate predictions for coniferous stands (R-squared = 0.45, RMSE = 29.16%). Full article

(This article belongs to the Special Issue Vegetation Structure Monitoring with Multi-Source Remote Sensing Data)

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27 pages, 8398 KB

Open AccessEditor’s ChoiceArticle

Gaussian Process Regression Hybrid Models for the Top-of-Atmosphere Retrieval of Vegetation Traits Applied to PRISMA and EnMAP Imagery

by Ana B. Pascual-Venteo, Jose L. Garcia, Katja Berger, José Estévez, Jorge Vicent, Adrián Pérez-Suay, Shari Van Wittenberghe and Jochem Verrelst

Remote Sens. 2024, 16(7), 1211; https://doi.org/10.3390/rs16071211 - 29 Mar 2024

Cited by 22 | Viewed by 5432

Abstract

The continuous monitoring of the terrestrial Earth system by a growing number of optical satellite missions provides valuable insights into vegetation and cropland characteristics. Satellite missions typically provide different levels of data, such as level 1 top-of-atmosphere (TOA) radiance and level 2 bottom-of-atmosphere [...] Read more.

The continuous monitoring of the terrestrial Earth system by a growing number of optical satellite missions provides valuable insights into vegetation and cropland characteristics. Satellite missions typically provide different levels of data, such as level 1 top-of-atmosphere (TOA) radiance and level 2 bottom-of-atmosphere (BOA) reflectance products. Exploiting TOA radiance data directly offers the advantage of bypassing the complex atmospheric correction step, where errors can propagate and compromise the subsequent retrieval process. Therefore, the objective of our study was to develop models capable of retrieving vegetation traits directly from TOA radiance data from imaging spectroscopy satellite missions. To achieve this, we constructed hybrid models based on radiative transfer model (RTM) simulated data, thereby employing the vegetation SCOPE RTM coupled with the atmosphere LibRadtran RTM in conjunction with Gaussian process regression (GPR). The retrieval evaluation focused on vegetation canopy traits, including the leaf area index (LAI), canopy chlorophyll content (CCC), canopy water content (CWC), the fraction of absorbed photosynthetically active radiation (FAPAR), and the fraction of vegetation cover (FVC). Employing band settings from the upcoming Copernicus Hyperspectral Imaging Mission (CHIME), two types of hybrid GPR models were assessed: (1) one trained at level 1 (L1) using TOA radiance data and (2) one trained at level 2 (L2) using BOA reflectance data. Both the TOA- and BOA-based GPR models were validated against in situ data with corresponding hyperspectral data obtained from field campaigns. The TOA-based hybrid GPR models revealed a range of performance from moderate to optimal results, thus reaching

R^{2}

= 0.92 (LAI),

R^{2}

= 0.72 (CCC) and 0.68 (CWC),

R^{2}

= 0.94 (FAPAR), and

R^{2}

= 0.95 (FVC). To demonstrate the models’ applicability, the TOA- and BOA-based GPR models were subsequently applied to imagery from the scientific precursor missions PRISMA and EnMAP. The resulting trait maps showed sufficient consistency between the TOA- and BOA-based models, with relative errors between

4 %

and

16 %

(

R^{2}

between 0.68 and 0.97). Altogether, these findings illuminate the path for the development and enhancement of machine learning hybrid models for the estimation of vegetation traits directly tailored at the TOA level. Full article

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

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18 pages, 4949 KB

Open AccessEditor’s ChoiceArticle

Combining Multi-View UAV Photogrammetry, Thermal Imaging, and Computer Vision Can Derive Cost-Effective Ecological Indicators for Habitat Assessment

by Qiao Hu, Ligang Zhang, Jeff Drahota, Wayne Woldt, Dana Varner, Andy Bishop, Ted LaGrange, Christopher M. U. Neale and Zhenghong Tang

Remote Sens. 2024, 16(6), 1081; https://doi.org/10.3390/rs16061081 - 20 Mar 2024

Cited by 11 | Viewed by 4897

Abstract

Recent developments in Unmanned Aircraft Vehicles (UAVs), thermal imaging, and Auto-machine learning (AutoML) have shown high potential for precise wildlife surveys but have rarely been studied for habitat assessment. Here, we propose a framework that leverages these advanced techniques to achieve cost-effective habitat [...] Read more.

Recent developments in Unmanned Aircraft Vehicles (UAVs), thermal imaging, and Auto-machine learning (AutoML) have shown high potential for precise wildlife surveys but have rarely been studied for habitat assessment. Here, we propose a framework that leverages these advanced techniques to achieve cost-effective habitat quality assessment from the perspective of actual wildlife community usage. The framework exploits vision intelligence hidden in the UAV thermal images and AutoML methods to achieve cost-effective wildlife distribution mapping, and then derives wildlife use indicators to imply habitat quality variance. We conducted UAV-based thermal wildlife surveys at three wetlands in the Rainwater Basin, Nebraska. Experiments were set to examine the optimal protocols, including various flight designs (61 and 122 m), feature types, and AutoML. The results showed that UAV images collected at 61 m with a spatial resolution of 7.5 cm, combined with Faster R-CNN, returned the optimal wildlife mapping (more than 90% accuracy). Results also indicated that the vision intelligence exploited can effectively transfer the redundant AutoML adaptation cycles into a fully automatic process (with around 33 times efficiency improvement for data labeling), facilitating cost-effective AutoML adaptation. Eventually, the derived ecological indicators can explain the wildlife use status well, reflecting potential within- and between-habitat quality variance. Full article

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

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21 pages, 25991 KB

Open AccessEditor’s ChoiceArticle

CUS3D: A New Comprehensive Urban-Scale Semantic-Segmentation 3D Benchmark Dataset

by Lin Gao, Yu Liu, Xi Chen, Yuxiang Liu, Shen Yan and Maojun Zhang

Remote Sens. 2024, 16(6), 1079; https://doi.org/10.3390/rs16061079 - 19 Mar 2024

Cited by 7 | Viewed by 5306

Abstract

With the continuous advancement of the construction of smart cities, the availability of large-scale and semantically enriched datasets is essential for enhancing the machine’s ability to understand urban scenes. Mesh data have a distinct advantage over point cloud data for large-scale scenes, as [...] Read more.

With the continuous advancement of the construction of smart cities, the availability of large-scale and semantically enriched datasets is essential for enhancing the machine’s ability to understand urban scenes. Mesh data have a distinct advantage over point cloud data for large-scale scenes, as they can provide inherent geometric topology information and consume less memory space. However, existing publicly available large-scale scene mesh datasets are limited in scale and semantic richness and do not cover a wide range of urban semantic information. The development of 3D semantic segmentation algorithms depends on the availability of datasets. Moreover, existing large-scale 3D datasets lack various types of official annotation data, which hinders the widespread applicability of benchmark applications and may cause label errors during data conversion. To address these issues, we present a comprehensive urban-scale semantic segmentation benchmark dataset. It is suitable for various research pursuits on semantic segmentation methodologies. This dataset contains finely annotated point cloud and mesh data types for 3D, as well as high-resolution original 2D images with detailed 2D semantic annotations. It is constructed from a 3D reconstruction of 10,840 UVA aerial images and spans a vast area of approximately 2.85 square kilometers that covers both urban and rural scenes. The dataset is composed of 152,298,756 3D points and 289,404,088 triangles. Each 3D point, triangular mesh, and the original 2D image in the dataset are carefully labeled with one of the ten semantic categories. Six typical 3D semantic segmentation methods were compared on the CUS3D dataset, with KPConv demonstrating the highest overall performance. The mIoU is 59.72%, OA is 89.42%, and mAcc is 97.88%. Furthermore, the experimental results on the impact of color information on semantic segmentation suggest that incorporating both coordinate and color features can enhance the performance of semantic segmentation. The current limitations of the CUS3D dataset, particularly in class imbalance, will be the primary target for future dataset enhancements. Full article

(This article belongs to the Section Urban Remote Sensing)

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22 pages, 43461 KB

Open AccessEditor’s ChoiceArticle

Few-Shot Learning for Crop Mapping from Satellite Image Time Series

by Sina Mohammadi, Mariana Belgiu and Alfred Stein

Remote Sens. 2024, 16(6), 1026; https://doi.org/10.3390/rs16061026 - 14 Mar 2024

Cited by 16 | Viewed by 4750

Abstract

Recently, deep learning methods have achieved promising crop mapping results. Yet, their classification performance is constrained by the scarcity of labeled samples. Therefore, the development of methods capable of exploiting label-rich environments to classify crops in label-scarce environments using only a few labeled [...] Read more.

Recently, deep learning methods have achieved promising crop mapping results. Yet, their classification performance is constrained by the scarcity of labeled samples. Therefore, the development of methods capable of exploiting label-rich environments to classify crops in label-scarce environments using only a few labeled samples per class is required. Few-shot learning (FSL) methods have achieved this goal in computer vision for natural images, but they remain largely unexplored in crop mapping from time series data. In order to address this gap, we adapted eight FSL methods to map infrequent crops cultivated in the selected study areas from France and a large diversity of crops from a complex agricultural area situated in Ghana. The FSL methods are commonly evaluated using class-balanced unlabeled sets from the target domain data (query sets), leading to overestimated classification results. This is unrealistic since these sets can have an arbitrary number of samples per class. In our work, we used the Dirichlet distribution to model the class proportions in few-shot query sets as random variables. We demonstrated that transductive information maximization based on

α

-divergence (

α

-TIM) performs better than the competing methods, including dynamic time warping (DTW), which is commonly used to tackle the lack of labeled samples.

α

-TIM achieved, for example, a macro F1-score of 59.6% in Ghana in a 24-way 20-shot setting (i.e., 20 labeled samples from each of the 24 crop types) and a macro F1-score of 75.9% in a seven-way 20-shot setting in France, outperforming the second best-performing methods by 2.7% and 5.7%, respectively. Moreover,

α

-TIM outperformed a baseline deep learning model, highlighting the benefits of effectively integrating the query sets into the learning process. Full article

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25 pages, 16942 KB

Open AccessEditor’s ChoiceArticle

TAG-Net: Target Attitude Angle-Guided Network for Ship Detection and Classification in SAR Images

by Dece Pan, Youming Wu, Wei Dai, Tian Miao, Wenchao Zhao, Xin Gao and Xian Sun

Remote Sens. 2024, 16(6), 944; https://doi.org/10.3390/rs16060944 - 7 Mar 2024

Cited by 9 | Viewed by 3478

Abstract

Synthetic aperture radar (SAR) ship detection and classification has gained unprecedented attention due to its important role in maritime transportation. Many deep learning-based detectors and classifiers have been successfully applied and achieved great progress. However, ships in SAR images present discrete and multi-centric [...] Read more.

Synthetic aperture radar (SAR) ship detection and classification has gained unprecedented attention due to its important role in maritime transportation. Many deep learning-based detectors and classifiers have been successfully applied and achieved great progress. However, ships in SAR images present discrete and multi-centric features, and their scattering characteristics and edge information are sensitive to variations in target attitude angles (TAAs). These factors pose challenges for existing methods to obtain satisfactory results. To address these challenges, a novel target attitude angle-guided network (TAG-Net) is proposed in this article. The core idea of TAG-Net is to leverage TAA information as guidance and use an adaptive feature-level fusion strategy to dynamically learn more representative features that can handle the target imaging diversity caused by TAA. This is achieved through a TAA-aware feature modulation (TAFM) module. It uses the TAA information and foreground information as prior knowledge and establishes the relationship between the ship scattering characteristics and TAA information. This enables a reduction in the intra-class variability and highlights ship targets. Additionally, considering the different requirements of the detection and classification tasks for the scattering information, we propose a layer-wise attention-based task decoupling detection head (LATD). Unlike general deep learning methods that use shared features for both detection and classification tasks, LATD extracts multi-level features and uses layer attention to achieve feature decoupling and select the most suitable features for each task. Finally, we introduce a novel salient-enhanced feature balance module (SFB) to provide richer semantic information and capture the global context to highlight ships in complex scenes, effectively reducing the impact of background noise. A large-scale ship detection dataset (LSSDD+) is used to verify the effectiveness of TAG-Net, and our method achieves state-of-the-art performance. Full article

(This article belongs to the Special Issue SAR Data Processing and Applications Based on Machine Learning Method)

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28 pages, 20313 KB

Open AccessEditor’s ChoiceArticle

Machine Learning-Based Wetland Vulnerability Assessment in the Sindh Province Ramsar Site Using Remote Sensing Data

by Rana Waqar Aslam, Hong Shu, Iram Naz, Abdul Quddoos, Andaleeb Yaseen, Khansa Gulshad and Saad S. Alarifi

Remote Sens. 2024, 16(5), 928; https://doi.org/10.3390/rs16050928 - 6 Mar 2024

Cited by 131 | Viewed by 11365

Abstract

Wetlands provide vital ecological and socioeconomic services but face escalating pressures worldwide. This study undertakes an integrated spatiotemporal assessment of the multifaceted vulnerabilities shaping Khinjhir Lake, an ecologically significant wetland ecosystem in Pakistan, using advanced geospatial and machine learning techniques. Multi-temporal optical remote [...] Read more.

Wetlands provide vital ecological and socioeconomic services but face escalating pressures worldwide. This study undertakes an integrated spatiotemporal assessment of the multifaceted vulnerabilities shaping Khinjhir Lake, an ecologically significant wetland ecosystem in Pakistan, using advanced geospatial and machine learning techniques. Multi-temporal optical remote sensing data from 2000 to 2020 was analyzed through spectral water indices, land cover classification, change detection and risk mapping to examine moisture variability, land cover modifications, area changes and proximity-based threats over two decades. The random forest algorithm attained the highest accuracy (89.5%) for land cover classification based on rigorous k-fold cross-validation, with a training accuracy of 91.2% and a testing accuracy of 87.3%. This demonstrates the model’s effectiveness and robustness for wetland vulnerability modeling in the study area, showing 11% shrinkage in open water bodies since 2000. Inventory risk zoning revealed 30% of present-day wetland areas under moderate to high vulnerability. The cellular automata–Markov (CA–Markov) model predicted continued long-term declines driven by swelling anthropogenic pressures like the 29 million population growth surrounding Khinjhir Lake. The research demonstrates the effectiveness of integrating satellite data analytics, machine learning algorithms and spatial modeling to generate actionable insights into wetland vulnerability to guide conservation planning. The findings provide a robust baseline to inform policies aimed at ensuring the health and sustainable management and conservation of Khinjhir Lake wetlands in the face of escalating human and climatic pressures that threaten the ecological health and functioning of these vital ecosystems. Full article

(This article belongs to the Special Issue Remote Sensing for Mapping and Monitoring Wetlands and Their Ecosystems)

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17 pages, 9275 KB

Open AccessEditor’s ChoiceArticle

Mapping Soil Organic Carbon Stock Using Hyperspectral Remote Sensing: A Case Study in the Sele River Plain in Southern Italy

by Nicolas Francos, Paolo Nasta, Carolina Allocca, Benedetto Sica, Caterina Mazzitelli, Ugo Lazzaro, Guido D’Urso, Oscar Rosario Belfiore, Mariano Crimaldi, Fabrizio Sarghini, Eyal Ben-Dor and Nunzio Romano

Remote Sens. 2024, 16(5), 897; https://doi.org/10.3390/rs16050897 - 3 Mar 2024

Cited by 11 | Viewed by 6771

Abstract

Mapping soil organic carbon (SOC) stock can serve as a resilience indicator for climate change. As part of the carbon dioxide (CO₂) sink, soil has recently become an integral part of the global carbon agenda to mitigate climate change. We used [...] Read more.

Mapping soil organic carbon (SOC) stock can serve as a resilience indicator for climate change. As part of the carbon dioxide (CO₂) sink, soil has recently become an integral part of the global carbon agenda to mitigate climate change. We used hyperspectral remote sensing to model the SOC stock in the Sele River plain located in the Campania region in southern Italy. To this end, a soil spectral library (SSL) for the Campania region was combined with an aerial hyperspectral image acquired with the AVIRIS–NG sensor mounted on a Twin Otter aircraft at an altitude of 1433 m. The products of this study were four raster layers with a high spatial resolution (1 m), representing the SOC stocks and three other related soil attributes: SOC content, clay content, and bulk density (BD). We found that the clay minerals’ spectral absorption at 2200 nm has a significant impact on predicting the examined soil attributes. The predictions were performed by using AVIRIS–NG sensor data over a selected plot and generating a quantitative map which was validated with in situ observations showing high accuracies in the ground-truth stage (OC stocks [RPIQ = 2.19, R² = 0.72, RMSE = 0.07]; OC content [RPIQ = 2.27, R² = 0.80, RMSE = 1.78]; clay content [RPIQ = 1.6 R² = 0.89, RMSE = 25.42]; bulk density [RPIQ = 1.97, R² = 0.84, RMSE = 0.08]). The results demonstrated the potential of combining SSLs with remote sensing data of high spectral/spatial resolution to estimate soil attributes, including SOC stocks. Full article

(This article belongs to the Special Issue Remote Sensing of Carbon Fluxes and Stocks II)

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24 pages, 10745 KB

Open AccessEditor’s ChoiceArticle

Modeling Land Use Transformations and Flood Hazard on Ibaraki’s Coastal in 2030: A Scenario-Based Approach Amid Population Fluctuations

by Mohammadreza Safabakhshpachehkenari and Hideyuki Tonooka

Remote Sens. 2024, 16(5), 898; https://doi.org/10.3390/rs16050898 - 3 Mar 2024

Cited by 6 | Viewed by 9235

Abstract

Coastal areas, influenced by human activity and natural factors, face major environmental shifts, including climate-induced flood risks. This highlights the importance of forecasting coastal land use for effective flood defense and ecological conservation. Japan’s distinct demographic path necessitates flexible strategies for managing its [...] Read more.

Coastal areas, influenced by human activity and natural factors, face major environmental shifts, including climate-induced flood risks. This highlights the importance of forecasting coastal land use for effective flood defense and ecological conservation. Japan’s distinct demographic path necessitates flexible strategies for managing its urban development. The study examines the Ibaraki Coastal region to analyze the impacts of land-use changes in 2030, predicting and evaluating future floods from intensified high tides and waves in scenario-based forecasts. The future roughness map is derived from projected land-use changes, and we utilize this information in DioVISTA 3.5.0 software to simulate flood scenarios. Finally, we analyzed the overlap between simulated floods and each land-use category. The results indicate since 2020, built-up areas have increased by 52.37 sq. km (39%). In scenarios of constant or shrinking urban areas, grassland increased by 28.54 sq. km (42%), and urban land cover decreased by 7.47 sq. km (5.6%) over ten years. Our research examines two separate peaks in water levels associated with urban flooding. Using 2030 land use maps and a peak height of 4 m, which is the lower limit of the maximum run-up height due to storm surge expected in the study area, 4.71 sq. km of residential areas flooded in the urban growth scenario, compared to 4.01 sq. km in the stagnant scenario and 3.96 sq. km in the shrinkage scenario. With the upper limit of 7.2 m, which is the extreme case in most of the study area, these areas increased to 49.91 sq. km, 42.52 sq. km, and 42.31 sq. km, respectively. The simulation highlights future flood-prone urban areas for each scenario, guiding targeted flood prevention efforts. Full article

(This article belongs to the Special Issue Future Prediction and Scenario Analysis of Urbanization Using Remote Sensing and GIS)

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42 pages, 20744 KB

Open AccessEditor’s ChoiceReview

A Comprehensive Survey of Unmanned Aerial Vehicles Detection and Classification Using Machine Learning Approach: Challenges, Solutions, and Future Directions

by Md Habibur Rahman, Mohammad Abrar Shakil Sejan, Md Abdul Aziz, Rana Tabassum, Jung-In Baik and Hyoung-Kyu Song

Remote Sens. 2024, 16(5), 879; https://doi.org/10.3390/rs16050879 - 1 Mar 2024

Cited by 137 | Viewed by 32040

Abstract

Autonomous unmanned aerial vehicles (UAVs) have several advantages in various fields, including disaster relief, aerial photography and videography, mapping and surveying, farming, as well as defense and public usage. However, there is a growing probability that UAVs could be misused to breach vital [...] Read more.

Autonomous unmanned aerial vehicles (UAVs) have several advantages in various fields, including disaster relief, aerial photography and videography, mapping and surveying, farming, as well as defense and public usage. However, there is a growing probability that UAVs could be misused to breach vital locations such as airports and power plants without authorization, endangering public safety. Because of this, it is critical to accurately and swiftly identify different types of UAVs to prevent their misuse and prevent security issues arising from unauthorized access. In recent years, machine learning (ML) algorithms have shown promise in automatically addressing the aforementioned concerns and providing accurate detection and classification of UAVs across a broad range. This technology is considered highly promising for UAV systems. In this survey, we describe the recent use of various UAV detection and classification technologies based on ML and deep learning (DL) algorithms. Four types of UAV detection and classification technologies based on ML are considered in this survey: radio frequency-based UAV detection, visual data (images/video)-based UAV detection, acoustic/sound-based UAV detection, and radar-based UAV detection. Additionally, this survey report explores hybrid sensor- and reinforcement learning-based UAV detection and classification using ML. Furthermore, we consider method challenges, solutions, and possible future research directions for ML-based UAV detection. Moreover, the dataset information of UAV detection and classification technologies is extensively explored. This investigation holds potential as a study for current UAV detection and classification research, particularly for ML- and DL-based UAV detection approaches. Full article

(This article belongs to the Special Issue UAV Agricultural Management: Recent Advances and Future Prospects)

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24 pages, 33015 KB

Open AccessEditor’s ChoiceArticle

An Extended Polar Format Algorithm for Joint Envelope and Phase Error Correction in Widefield Staring SAR with Maneuvering Trajectory

by Yujie Liang, Yi Liang, Xiaoge Wang, Junhui Li and Mengdao Xing

Remote Sens. 2024, 16(5), 856; https://doi.org/10.3390/rs16050856 - 29 Feb 2024

Cited by 1 | Viewed by 3076

Abstract

Polar format algorithm (PFA) is a widely used high-resolution SAR imaging algorithm that can be implemented in advanced widefield staring synthetic aperture radar (WFS-SAR). However, existing algorithms have limited analysis in wavefront curvature error (WCE) and are challenging to apply to WFS-SAR with [...] Read more.

Polar format algorithm (PFA) is a widely used high-resolution SAR imaging algorithm that can be implemented in advanced widefield staring synthetic aperture radar (WFS-SAR). However, existing algorithms have limited analysis in wavefront curvature error (WCE) and are challenging to apply to WFS-SAR with high-resolution and large-swath scenes. This paper proposes an extended polar format algorithm for joint envelope and phase error correction in WFS-SAR imaging with maneuvering trajectory. The impact of the WCE and residual acceleration error (RAE) are analyzed in detail by deriving the specific wavenumber domain signal based on the mapping relationship between the geometry space and wavenumber space. Subsequently, this paper improves the traditional WCE compensation function and introduces a new range cell migration (RCM) recalibration function for joint envelope and phase error correction. The 2D precisely focused SAR image is acquired based on the spatially variant inverse filtering in the final. Simulation experiments validate the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue New Approaches in High-Resolution SAR Imaging)

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38 pages, 53898 KB

Open AccessEditor’s ChoiceReview

Large-Scale 3D Reconstruction from Multi-View Imagery: A Comprehensive Review

by Haitao Luo, Jinming Zhang, Xiongfei Liu, Lili Zhang and Junyi Liu

Remote Sens. 2024, 16(5), 773; https://doi.org/10.3390/rs16050773 - 22 Feb 2024

Cited by 89 | Viewed by 24323

Abstract

Three-dimensional reconstruction is a key technology employed to represent virtual reality in the real world, which is valuable in computer vision. Large-scale 3D models have broad application prospects in the fields of smart cities, navigation, virtual tourism, disaster warning, and search-and-rescue missions. Unfortunately, [...] Read more.

Three-dimensional reconstruction is a key technology employed to represent virtual reality in the real world, which is valuable in computer vision. Large-scale 3D models have broad application prospects in the fields of smart cities, navigation, virtual tourism, disaster warning, and search-and-rescue missions. Unfortunately, most image-based studies currently prioritize the speed and accuracy of 3D reconstruction in indoor scenes. While there are some studies that address large-scale scenes, there has been a lack of systematic comprehensive efforts to bring together the advancements made in the field of 3D reconstruction in large-scale scenes. Hence, this paper presents a comprehensive overview of a 3D reconstruction technique that utilizes multi-view imagery from large-scale scenes. In this article, a comprehensive summary and analysis of vision-based 3D reconstruction technology for large-scale scenes are presented. The 3D reconstruction algorithms are extensively categorized into traditional and learning-based methods. Furthermore, these methods can be categorized based on whether the sensor actively illuminates objects with light sources, resulting in two categories: active and passive methods. Two active methods, namely, structured light and laser scanning, are briefly introduced. The focus then shifts to structure from motion (SfM), stereo matching, and multi-view stereo (MVS), encompassing both traditional and learning-based approaches. Additionally, a novel approach of neural-radiance-field-based 3D reconstruction is introduced. The workflow and improvements in large-scale scenes are elaborated upon. Subsequently, some well-known datasets and evaluation metrics for various 3D reconstruction tasks are introduced. Lastly, a summary of the challenges encountered in the application of 3D reconstruction technology in large-scale outdoor scenes is provided, along with predictions for future trends in development. Full article

(This article belongs to the Section Urban Remote Sensing)

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12 pages, 3883 KB

Open AccessEditor’s ChoiceTechnical Note

Exploring Semantic Prompts in the Segment Anything Model for Domain Adaptation

by Ziquan Wang, Yongsheng Zhang, Zhenchao Zhang, Zhipeng Jiang, Ying Yu, Li Li and Lei Li

Remote Sens. 2024, 16(5), 758; https://doi.org/10.3390/rs16050758 - 21 Feb 2024

Cited by 22 | Viewed by 8869

Abstract

Robust segmentation in adverse weather conditions is crucial for autonomous driving. However, these scenes struggle with recognition and make annotations expensive, resulting in poor performance. As a result, the Segment Anything Model (SAM) was recently proposed to finely segment the spatial structure of [...] Read more.

Robust segmentation in adverse weather conditions is crucial for autonomous driving. However, these scenes struggle with recognition and make annotations expensive, resulting in poor performance. As a result, the Segment Anything Model (SAM) was recently proposed to finely segment the spatial structure of scenes and to provide powerful prior spatial information, thus showing great promise in resolving these problems. However, SAM cannot be applied directly for different geographic scales and non-semantic outputs. To address these issues, we propose SAM-EDA, which integrates SAM into an unsupervised domain adaptation mean-teacher segmentation framework. In this method, we use a “teacher-assistant” model to provide semantic pseudo-labels, which will fill in the holes in the fine spatial structure given by SAM and generate pseudo-labels close to the ground truth, which then guide the student model for learning. Here, the “teacher-assistant” model helps to distill knowledge. During testing, only the student model is used, thus greatly improving efficiency. We tested SAM-EDA on mainstream segmentation benchmarks in adverse weather conditions and obtained a more-robust segmentation model. Full article

(This article belongs to the Special Issue Remote Sensing Image Classification and Semantic Segmentation)

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22 pages, 4906 KB

Open AccessEditor’s ChoiceArticle

Using Remote Sensing Multispectral Imagery for Invasive Species Quantification: The Effect of Image Resolution on Area and Biomass Estimation

by Manuel de Figueiredo Meyer, José Alberto Gonçalves and Ana Maria Ferreira Bio

Remote Sens. 2024, 16(4), 652; https://doi.org/10.3390/rs16040652 - 9 Feb 2024

Cited by 11 | Viewed by 6636

Abstract

This study assesses the applicability of different-resolution multispectral remote sensing images for mapping and estimating the aboveground biomass (AGB) of Carpobrotus edulis, a prominent invasive species in European coastal areas. This study was carried out on the Cávado estuary sand spit (Portugal). [...] Read more.

This study assesses the applicability of different-resolution multispectral remote sensing images for mapping and estimating the aboveground biomass (AGB) of Carpobrotus edulis, a prominent invasive species in European coastal areas. This study was carried out on the Cávado estuary sand spit (Portugal). The performance of three sets of multispectral images with different Ground Sample Distances (GSDs) were compared: 2.5 cm, 5 cm, and 10 cm. The images were classified using the supervised classification algorithm random forest and later improved by applying a sieve filter. Samples of C. edulis were also collected, dried, and weighed to estimate the AGB using the relationship between the dry weight (DW) and vegetation indices (VIs). The resulting regression models were evaluated based on their coefficient of determination (R²), Normalised Root Mean Square Error (NRMSE), p-value, Akaike information criterion (AIC), and the Bayesian information criterion (BIC). The results show that the three tested image resolutions allow for constructing reliable coverage maps of C. edulis, with overall accuracy values of 89%, 85%, and 88% for the classification of the 2.5 cm, 5 cm, and 10 cm GSD images, respectively. The best-performing VI-DW regression models achieved R² = 0.87 and NRMSE = 0.09 for the 2.5 cm resolution; R² = 0.77 and NRMSE = 0.12 for the 5 cm resolution; and R² = 0.64 and NRMSE = 0.15 for the 10 cm resolution. The C. edulis area and total AGB were 3441.10 m² and 28,327.1 kg (with an AGB relative error (RE) = 0.08) for the 2.5 cm resolution; 3070.04 m² and 29,170.8 kg (AGB RE = 0.08) for the 5 cm resolution; and 2305.06 m² and 22,135.7 kg (AGB RE = 0.11) for the 10 cm resolution. Spatial and model differences were analysed in detail to determine their causes. Final analyses suggest that multispectral imagery of up to 5 cm GSD is adequate for estimating C. edulis distribution and biomass. Full article

(This article belongs to the Special Issue Remote Sensing for 2D/3D Mapping)

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25 pages, 25438 KB

Open AccessEditor’s ChoiceArticle

Geomorphology, Mineralogy, and Chronology of Mare Basalts in the Oceanus Procellarum Region

by Cheng Zhang, Jianping Chen, Yiwen Pan, Shuangshuang Wu, Jian Chen, Xiaoxia Hu, Yue Pang, Xueting Liu and Ke Wang

Remote Sens. 2024, 16(4), 634; https://doi.org/10.3390/rs16040634 - 8 Feb 2024

Cited by 3 | Viewed by 8005

Abstract

Mare basalts on the lunar surface are tangible expressions of the complex thermal evolution and geological processes that have occurred within the lunar interior. These basaltic manifestations are highly important because they provide invaluable insights into lunar geological evolution. Notably, the Oceanus Procellarum [...] Read more.

Mare basalts on the lunar surface are tangible expressions of the complex thermal evolution and geological processes that have occurred within the lunar interior. These basaltic manifestations are highly important because they provide invaluable insights into lunar geological evolution. Notably, the Oceanus Procellarum region, which is renowned for its extensive and long-lasting basaltic volcanism, is a premier location to investigate late-stage lunar thermal evolution. The primary aim of this research is to elucidate the geomorphological, compositional, and temporal attributes that define the mare basalts within the Oceanus Procellarum region. To achieve this aim, we comprehensively analyzed the geomorphological features present within the region, leveraging Kaguya/SELENE TC images and digital elevation models. Specifically, these geomorphological features encompass impact craters, wrinkle ridges, sinuous rilles, and volcanic domes. Subsequently, we thoroughly examined the mineralogical attributes of basalts in the Oceanus Procellarum region, leveraging Kaguya/SELENE MI data and compositional map products. To more accurately reflect the actual ages of the mare basalts in the Oceanus Procellarum region, we carefully delineated the geological units within the area and employed the latest crater size-frequency distribution (CSFD) technique to precisely determine their ages. This refined approach allowed for a more comprehensive and accurate understanding of the basaltic rocks in the study area. Overall, our comprehensive study included an in-depth analysis of the volcanic activity and evolution of the Oceanus Procellarum region, along with an examination of the correlation between the mineralogical composition and ages of mare basalts. The findings from this exhaustive investigation reveal a definitive age range for basalt units within the Oceanus Procellarum region from approximately 3.69 Ga to 1.17 Ga. Moreover, the latest mare basalts that formed were pinpointed north of the Aristarchus crater. Significantly, the region has experienced at least five distinct volcanic events, occurring approximately 3.40 Ga, 2.92 Ga, 2.39 Ga, 2.07 Ga, and 1.43 Ga, leading to the formation of multiple basalt units characterized by their unique mineral compositions and elemental abundances. Through the application of remote sensing mineralogical analysis, three primary basalt types were identified: low-titanium, very-low-titanium, and intermediate-titanium basalt. Notably, the younger basalt units exhibit an elevated titanium proportion, indicative of progressive olivine enrichment. Consequently, these younger basalt units exhibit more intricate and complex mineral compositions, offering valuable insights into the dynamic geological processes shaping the lunar surface. Full article

(This article belongs to the Special Issue Planetary Remote Sensing and Applications to Mars and Chang’E-6/7)

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23 pages, 3468 KB

Open AccessEditor’s ChoiceReview

Review of Satellite Remote Sensing and Unoccupied Aircraft Systems for Counting Wildlife on Land

by Marie R. G. Attard, Richard A. Phillips, Ellen Bowler, Penny J. Clarke, Hannah Cubaynes, David W. Johnston and Peter T. Fretwell

Remote Sens. 2024, 16(4), 627; https://doi.org/10.3390/rs16040627 - 8 Feb 2024

Cited by 30 | Viewed by 9984

Abstract

Although many medium-to-large terrestrial vertebrates are still counted by ground or aerial surveys, remote-sensing technologies and image analysis have developed rapidly in recent decades, offering improved accuracy and repeatability, lower costs, speed, expanded spatial coverage and increased potential for public involvement. This review [...] Read more.

Although many medium-to-large terrestrial vertebrates are still counted by ground or aerial surveys, remote-sensing technologies and image analysis have developed rapidly in recent decades, offering improved accuracy and repeatability, lower costs, speed, expanded spatial coverage and increased potential for public involvement. This review provides an introduction for wildlife biologists and managers relatively new to the field on how to implement remote-sensing techniques (satellite and unoccupied aircraft systems) for counting large vertebrates on land, including marine predators that return to land to breed, haul out or roost, to encourage wider application of these technological solutions. We outline the entire process, including the selection of the most appropriate technology, indicative costs, procedures for image acquisition and processing, observer training and annotation, automation, and citizen science campaigns. The review considers both the potential and the challenges associated with different approaches to remote surveys of vertebrates and outlines promising avenues for future research and method development. Full article

(This article belongs to the Special Issue Towards Biodiversity Conservation: Remote Sensing Applications in Ecological Modeling)

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28 pages, 11516 KB

Open AccessEditor’s ChoiceArticle

Segmentation of Individual Tree Points by Combining Marker-Controlled Watershed Segmentation and Spectral Clustering Optimization

by Yuchan Liu, Dong Chen, Shihan Fu, Panagiotis Takis Mathiopoulos, Mingming Sui, Jiaming Na and Jiju Peethambaran

Remote Sens. 2024, 16(4), 610; https://doi.org/10.3390/rs16040610 - 6 Feb 2024

Cited by 33 | Viewed by 9422

Abstract

Accurate identification and segmentation of individual tree points are crucial for assessing forest spatial distribution, understanding tree growth and structure, and managing forest resources. Traditional methods based on Canopy Height Models (CHM) are simple yet prone to over- and/or under-segmentation. To deal with [...] Read more.

Accurate identification and segmentation of individual tree points are crucial for assessing forest spatial distribution, understanding tree growth and structure, and managing forest resources. Traditional methods based on Canopy Height Models (CHM) are simple yet prone to over- and/or under-segmentation. To deal with this problem, this paper introduces a novel approach that combines marker-controlled watershed segmentation with a spectral clustering algorithm. Initially, we determined the local maxima within a series of variable windows according to the lower bound of the prediction interval of the regression equation between tree crown radius and tree height to preliminarily segment individual trees. Subsequently, using this geometric shape analysis method, the under-segmented trees were identified. For these trees, vertical tree crown profile analysis was performed in multiple directions to detect potential treetops which were then considered as inputs for spectral clustering optimization. Our experiments across six plots showed that our method markedly surpasses traditional approaches, achieving an average Recall of 0.854, a Precision of 0.937, and an F1-score of 0.892. Full article

(This article belongs to the Special Issue Advances in Understanding and 3D Semantic Modeling of Large-Scale Urban Scenes from Point Clouds (Second Edition))

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16 pages, 5457 KB

Open AccessEditor’s ChoiceArticle

Edge Effects in Amazon Forests: Integrating Remote Sensing and Modelling to Assess Changes in Biomass and Productivity

by Luise Bauer, Andreas Huth, André Bogdanowski, Michael Müller and Rico Fischer

Remote Sens. 2024, 16(3), 501; https://doi.org/10.3390/rs16030501 - 28 Jan 2024

Cited by 18 | Viewed by 8158

Abstract

The tropical forests in the Amazon store large amounts of carbon and are still considered a carbon sink. There is evidence that deforestation can turn a forest landscape into a carbon source due to land use and forest degradation. Deforestation causes fragmented forest [...] Read more.

The tropical forests in the Amazon store large amounts of carbon and are still considered a carbon sink. There is evidence that deforestation can turn a forest landscape into a carbon source due to land use and forest degradation. Deforestation causes fragmented forest landscapes. It is known from field experiments that forest dynamics at the edge of forest fragments are altered by changes in the microclimate and increased tree mortality (“edge effects”). However, it is unclear how this will affect large fragmented forest landscapes, and thus the entire Amazon region. The aim of this study is to investigate different forest attributes in edge and core forest areas at high resolution, and thus to identify the large-scale impacts of small-scale edge effects. Therefore, a well-established framework combining forest modelling and lidar-generated forest structure information was combined with radar-based forest cover data. Furthermore, forests were also analyzed at the landscape level to investigate changes between highly fragmented and less-fragmented landscapes. This study found that the aboveground biomass in forest edge areas is 27% lower than in forest core areas. In contrast, the net primary productivity is 13% higher in forest edge areas than in forest core areas. In the second step, whole fragmented landscapes were analyzed. Nearly 30% of all forest landscapes are highly fragmented, particularly in the regions of the Arc of Deforestation, on the edge of the Andes and on the Amazon river banks. Less-fragmented landscapes are mainly located in the central Amazon rainforest. The aboveground biomass is 28% lower in highly fragmented forest landscapes than in less-fragmented landscapes. The net primary productivity is 13% higher in highly fragmented forest landscapes than in less-fragmented forest landscapes. In summary, fragmentation of the Amazon rainforest has an impact on forest attributes such as biomass and productivity, with mostly negative effects on forest dynamics. If deforestation continues and the proportion of highly fragmented forest landscapes increase, the effect may be even more intense. By combining lidar, radar and forest modelling, this study shows that it is possible to map forest structure, and thus the degree of forest degradation, over a large area and derive more detailed information about the carbon dynamics of the Amazon region. Full article

(This article belongs to the Special Issue Lidar for Environmental Remote Sensing: Theory and Application)

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