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State-of-the-Art on Satellite and UAV Remote Sensing in Geoscience Research

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Engineering Remote Sensing".

Deadline for manuscript submissions: closed (30 September 2024) | Viewed by 9247

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Special Issue Editors

Dr. Francesca Ardizzone

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Guest Editor

National Research Council of Italy, Research Institute of Geo-Hydrological Protection (CNR IRPI), Via della Madonna Alta 126, 06128 Perugia, Italy
Interests: landslide; landslide hazard; landslide risk; remote sensing; geodatabase
Special Issues, Collections and Topics in MDPI journals

Dr. Gabriella Caroti

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Guest Editor

Department of Civil and Industrial Engineering, University of Pisa, 56122 Pisa, Italy
Interests: geomatics; photogrammetry; terrestrial laser scanning, cultural heritage surveying
Special Issues, Collections and Topics in MDPI journals

Dr. Yuankun Xu

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Guest Editor

Department of Earth and Planetary Science, University of California, Berkeley, 201 McCone Hall, Berkeley, CA 94720, USA
Interests: radar remote sensing; InSAR; hydromechanics; flow dynamics; natural hazards

Special Issue Information

Dear Colleagues,

Satellite and UAV (Unmanned Aerial Vehicle) remote sensing provide essential observations for geoscientists to better understand the earth system and the interaction between human activities and our living environment. The advancement of remote sensing in geosciences encompasses efforts spanning from the development of remote sensing sensors and platforms, the processing, interpretation, and dissemination of observational data, to the physical modeling of earth system dynamics. We welcome studies that utilize state-of-the-art remote sensing techniques to advance our knowledge in geosciences across a broad range of spectrums. These topics include yet are not limited to (1) development, calibration, and validation of satellite and airborne instruments, such as optical, radar, and lidar sensors; (2) advanced and novel data processing methodology and algorithms, including photogrammetry, structure from motion, and point cloud processing, InSAR time series analysis; (3) geoscience research using remotely sensed datasets from individual cases to regional-scale characterization, such as landcover change, geological mapping, groundwater and soil moisture assessment, geodetic deformation survey, natural hazard monitoring, and archaeology; (4) mechanistic characterization of surface and subsurface processes using remote sensing and numerical modeling, and (5) the integration of multiple components above.

Dr. Francesca Ardizzone
Dr. Gabriella Caroti
Dr. Yuankun Xu
Guest Editors

Manuscript Submission Information

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Keywords

remote sensing
photogrammetry
lidar point cloud
UAV
InSAR
natural hazards
landcover change
soil moisture
geologic mapping
geomorphology

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Published Papers (7 papers)

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Research

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18 pages, 46116 KiB

Open AccessArticle

Structural Complexity Significantly Impacts Canopy Reflectance Simulations as Revealed from Reconstructed and Sentinel-2-Monitored Scenes in a Temperate Deciduous Forest

by Yi Gan, Quan Wang and Guangman Song

Remote Sens. 2024, 16(22), 4296; https://doi.org/10.3390/rs16224296 - 18 Nov 2024

Viewed by 316

Abstract

Detailed three-dimensional (3D) radiative transfer models (RTMs) enable a clear understanding of the interactions between light, biochemistry, and canopy structure, but they are rarely explicitly evaluated due to the availability of 3D canopy structure data, leading to a lack of knowledge on how canopy structure/leaf characteristics affect radiative transfer processes within forest ecosystems. In this study, the newly released 3D RTM Eradiate was extensively evaluated based on both virtual scenes reconstructed using the quantitative structure model (QSM) by adding leaves to point clouds generated from terrestrial laser scanning (TLS) data, and real scenes monitored by Sentinel-2 in a typical temperate deciduous forest. The effects of structural parameters on reflectance were investigated through sensitivity analysis, and the performance of the 3D model was compared with the 5-Scale and PROSAIL radiative transfer models. The results showed that the Eradiate-simulated reflectance achieved good agreement with the Sentinel-2 reflectance, especially in the visible and near-infrared spectral regions. Furthermore, the simulated reflectance, particularly in the blue and shortwave infrared spectral bands, was clearly shown to be influenced by canopy structure using the Eradiate model. This study demonstrated that the Eradiate RTM, based on the 3D explicit representation, is capable of providing accurate radiative transfer simulations in the temperate deciduous forest and hence provides a basis for understanding tree interactions and their effects on ecosystem structure and functions. Full article

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

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23 pages, 10879 KiB

Open AccessArticle

Reconstruction of Coal Mining Subsidence Field by Fusion of SAR and UAV LiDAR Deformation Data

by Bin Yang, Weibing Du, Youfeng Zou, Hebing Zhang, Huabin Chai, Wei Wang, Xiangyang Song and Wenzhi Zhang

Remote Sens. 2024, 16(18), 3383; https://doi.org/10.3390/rs16183383 - 12 Sep 2024

Viewed by 862

Abstract

The geological environment damage caused by coal mining subsidence has become an important factor affecting the sustainable development of mining areas. Reconstruction of the Coal Mining Subsidence Field (CMSF) is the key to preventing geological disasters, and the needs of CMSF reconstruction cannot be met by solely relying on a single remote sensing technology. The combination of Unmanned Aerial Vehicle (UAV) and Synthetic Aperture Radar (SAR) has complementary advantages; however, the data fusion strategy by refining the SAR deformation field through UAV still needs to be updated constantly. This paper proposed a Prior Weighting (PW) method based on Satellite Aerial (SA) heterogeneous remote sensing. The method can be used to fuse SAR and UAV Light Detection and Ranging (LiDAR) data for ground subsidence parameter inversion. Firstly, the subsidence boundary of Differential Interferometric SAR (DInSAR) combined with the large gradient subsidence of Pixel Offset Tracking (POT) was developed to initialize the SAR preliminary CMSF. Secondly, the SAR preliminary CMSF was refined by UAV LiDAR data; the weights of SAR and UAV LiDAR data are 0.4 and 0.6 iteratively. After the data fusion, the subsidence field was reconstructed. The results showed that the overall CMSF accuracy improved from ±144 mm to ±51 mm. The relative errors of the surface subsidence factor and main influence angle tangent calculated by the physical model and in situ measured data are 1.3% and 1.7%. It shows that the proposed SAR/UAV fusion method has significant advantages in the reconstruction of CMSF, and the PW method contributes to the prevention and control of mining subsidence. Full article

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

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26 pages, 14101 KiB

Open AccessArticle

Precision Irrigation Soil Moisture Mapper: A Thermal Inertia Approach to Estimating Volumetric Soil Water Content Using Unmanned Aerial Vehicles and Multispectral Imagery

by Kevin J. Wienhold, Dongfeng Li and Zheng N. Fang

Remote Sens. 2024, 16(10), 1660; https://doi.org/10.3390/rs16101660 - 8 May 2024

Cited by 1 | Viewed by 1283

Abstract

To address the issue of estimating soil moisture at a hyper-resolution scale, a methodology referred to as Precision Irrigation Soil Moisture Mapper (PrISMM), that includes three key components, is developed: high-resolution remotely sensed optical and thermal data, surface energy balance modeling, and site-specific soil analysis. An Unmanned Aerial Vehicle/System (UAV or UAS) collects high-resolution multispectral imagery in the Dallas–Fort Worth metropolitan study area. Orthomosaics are converted to thermal inertia estimates in a spatially distributed format using the remotely sensed data combined with a set of surface energy balance modeling equations. Using thermal and physical properties of soil gained from site-specific soil analysis, thermal inertia estimates were further converted from thermal inertia to daily volumetric soil water content (VSWC) with a horizonal resolution of 8.6 cm. A ground truthing dataset of measured VSWC values taken from a Time Domain Reflectometer was compared with model results, producing a reasonable correlation with an average coefficient of determination of (R²) = 0.79, an average root mean square error (RMSE) = 0.0408, and mean absolute error (MAE) = 0.0308. This study highlights a practical approach of estimating VSWC for irrigation purposes while providing superior spatio-temporal coverage over in situ methods. The authors envision that PrISMM can be implemented in water usage management by relating VSWC with weather forecasts and evapotranspiration rates to develop time-based spatially distributed irrigation management plans. Full article

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

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32 pages, 32412 KiB

Open AccessArticle

Monitoring and Quantifying Soil Erosion and Sedimentation Rates in Centimeter Accuracy Using UAV-Photogrammetry, GNSS, and t-LiDAR in a Post-Fire Setting

by Simoni Alexiou, Ioannis Papanikolaou, Sascha Schneiderwind, Valerie Kehrle and Klaus Reicherter

Remote Sens. 2024, 16(5), 802; https://doi.org/10.3390/rs16050802 - 25 Feb 2024

Cited by 1 | Viewed by 2948

Abstract

Remote sensing techniques, namely Unmanned Aerial Vehicle (UAV) photogrammetry and t-LiDAR (terrestrial Light Detection and Ranging), two well-established techniques, were applied for seven years in a mountainous Mediterranean catchment in Greece (Ilioupoli test site, Athens), following a wildfire event in 2015. The goal was to monitor and quantify soil erosion and sedimentation rates with cm accuracy. As the frequency of wildfires in the Mediterranean has increased, this study aims to present a methodological approach for monitoring and quantifying soil erosion and sedimentation rates in post-fire conditions, through high spatial resolution field measurements acquired using a UAV survey and a t-LiDAR (or TLS—Terrestrial Laser Scanning), in combination with georadar profiles (Ground Penetration Radar—GPR) and GNSS. This test site revealed that 40 m³ of sediment was deposited following the first intense autumn rainfall events, a value that was decreased by 50% over the next six months (20 m³). The UAV–SfM technique revealed only 2 m³ of sediment deposition during the 2018–2019 analysis, highlighting the decrease in soil erosion rates three years after the wildfire event. In the following years (2017–2021), erosion and sedimentation decreased further, confirming the theoretical pattern, whereas sedimentation over the first year after the fire was very high and then sharply lessened as vegetation regenerated. The methodology proposed in this research can serve as a valuable guide for achieving high-precision sediment yield deposition measurements based on a detailed analysis of 3D modeling and a point cloud comparison, specifically leveraging the dense data collection facilitated by UAV–SfM and TLS technology. The resulting point clouds effectively replicate the fine details of the topsoil microtopography within the upland dam basin, as highlighted by the profile analysis. Overall, this research clearly demonstrates that after monitoring the upland area in post-fire conditions, the UAV–SfM method and LiDAR cm-scale data offer a realistic assessment of the retention dam’s life expectancy and management planning. These observations are especially crucial for assessing the impacts in the wildfire-affected areas, the implementation of mitigation strategies, and the construction and maintenance of retention dams. Full article

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

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15 pages, 9026 KiB

Open AccessArticle

Non-Destructive Estimation of Deciduous Forest Metrics: Comparisons between UAV-LiDAR, UAV-DAP, and Terrestrial LiDAR Leaf-Off Point Clouds Using Two QSMs

by Yi Gan, Quan Wang and Guangman Song

Remote Sens. 2024, 16(4), 697; https://doi.org/10.3390/rs16040697 - 16 Feb 2024

Viewed by 1308

Abstract

Timely acquisition of forest structure is crucial for understanding the dynamics of ecosystem functions. Despite the fact that the combination of different quantitative structure models (QSMs) and point cloud sources (ALS and DAP) has shown great potential to characterize tree structure, few studies have addressed their pros and cons in alpine temperate deciduous forests. In this study, different point clouds from UAV-mounted LiDAR and DAP under leaf-off conditions were first processed into individual tree point clouds, and then explicit 3D tree models of the forest were reconstructed using the TreeQSM and AdQSM methods. Structural metrics obtained from the two QSMs were evaluated based on terrestrial LiDAR (TLS)-based surveys. The results showed that ALS-based predictions of forest structure outperformed DAP-based predictions at both plot and tree levels. TreeQSM performed with comparable accuracy to AdQSM for estimating tree height, regardless of ALS (plot level: 0.93 vs. 0.94; tree level: 0.92 vs. 0.92) and DAP (plot level: 0.86 vs. 0.86; tree level: 0.89 vs. 0.90) point clouds. These results provide a robust and efficient workflow that takes advantage of UAV monitoring for estimating forest structural metrics and suggest the effectiveness of LiDAR in temperate deciduous forests. Full article

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

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18 pages, 2670 KiB

Open AccessArticle

Absolute Calibration of a UAV-Mounted Ultra-Wideband Software-Defined Radar Using an External Target in the Near-Field

by Asem Melebari, Piril Nergis, Sepehr Eskandari, Pedro Ramos Costa and Mahta Moghaddam

Remote Sens. 2024, 16(2), 231; https://doi.org/10.3390/rs16020231 - 6 Jan 2024

Cited by 2 | Viewed by 1186

Abstract

We describe a method to calibrate a Software-Defined Radar (SDRadar) system mounted on an uncrewed aerial vehicle (UAV) with an ultra-wideband (UWB) waveform operated in the near-field region. Radar calibration is a prerequisite for using the full capabilities of the radar system to retrieve geophysical parameters accurately. We introduce a framework and process to calibrate the SDRadar with the UWB waveform in the 675 MHz–3 GHz range in the near-field region. Furthermore, we present the framework for computing the near-field radar cross section (RCS) of an external passive calibration target, a trihedral corner reflector (CR), using HFSS software and with consideration for specific antennas. The calibration performance was evaluated with various distances between the calibration target and radar antennas. The necessity for the knowledge of the near-field RCS to calibrate SDRadar was demonstrated, which sets this work apart from the standard method of using a trihedral CR for backscatter radar calibration. We were able to achieve approximately

0.5

dB accuracy when calibrating the SDRadar in the anechoic chamber using a trihedral CR. In outdoor field conditions, where the ground rough surface scattering effects are present, the calibration performance was lower, approximately

1.5

dB. A solution is proposed to overcome the ground effect by elevating the CR above the ground level, which enables applying time-gating around the CR echo, excluding the reflection from the ground. Full article

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

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Review

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22 pages, 2453 KiB

Open AccessReview

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

by Bashar Alsadik, Florian J. Ellsäßer, Muheeb Awawdeh, Abdulla Al-Rawabdeh, Lubna Almahasneh, Sander Oude Elberink, Doaa Abuhamoor and Yolla Al Asmar

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

Abstract

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

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

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