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Advanced 3D Remote Sensing and Image Analysis from Unmanned Aerial Systems

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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 (1 July 2022) | Viewed by 9065

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Special Issue Editors

Dr. Mateo Gašparović

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Guest Editor

Chair of Photogrammetry and Remote Sensing, Faculty of Geodesy, University of Zagreb, Kačićeva 26, Zagreb 10000, Croatia
Interests: remote sensing; photogrammetry; LiDAR; SAR; UAV; GIS; geodesy; data fusion; land cover analysis; image processing; computer vision; 3D reconstruction
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Dr. Gil Rito Gonçalves

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Guest Editor

Department of Mathematics, Faculty of Sciences and Technology, University of Coimbra, 3001-454 Coimbra, Portugal
Interests: unmanned aerial systems; satellite image processing; satellite image analysis; geoinformation; mapping; spatial analysis; geospatial science; digital mapping; remote sensing; geographical information systems; environment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Aerial photogrammetry, LiDAR (Light Detection and Ranging), and 3D Remote Sensing (3D-RS) are developing rapidly nowadays, and these techniques are applied in different advanced applications related to environmental monitoring. Sensor minimization enables the development of new multi-sensor systems, e.g., advanced unmanned aircraft systems (UAVs) that are capable of acquiring a high number of data autonomously. These advanced machines enable new frontiers in aerial photogrammetry, LiDAR, and 3D-RS. For data acquisition, various novel sensors have been developed in past years, from UAV-based LiDAR and multispectral and hyperspectral cameras to thermal cameras. Furthermore, combining several various acquisition sensors lead to the development of the advanced hybrid multi-sensor acquisition platforms oriented to solving specific RS tasks. This large number of heterogeneous 3D spatial data needs new big data processing technologies. The preprocessing, validation, and calibration of the UAV data, as well as multi-sensor data fusion, are crucial for combining the data from various sensors. Known photogrammetry and digital image processing methodology require new concepts, technology, and methods. Novel technology on high-performance computing (HPC), cloud computing, and field-programmable gate array (FPGA) technology can be used to speed up the 3D-RS processing tasks. The advanced application of the UAVs enables new perspectives in natural hazard monitoring, precise agriculture, forest inventory, cultural heritage, archaeology, geology, geodesy, civil engineering, and other geosciences.

In this Special Issue, we would like to invite you to submit original research papers, comprehensive reviews, letters, and communications covering all aspects of the advanced application of 3D sensing and imaging for unmanned aircraft systems, primarily focused on solving complex research questions that are closely related to novel photogrammetry, LiDAR, 3D remote sensing methods, and technology. Potential topics include, but are not limited to, the following:

UAV-based photogrammetry and digital image processing;
Very high-resolution multi-sensor data fusion;
The preprocessing, validation, and calibration of UAV-based LiDAR and multispectral and hyperspectral as well as thermal data;
Machine learning in 3D data;
The advanced application of high-performance computing (HPC), cloud computing, and field-programmable gate array (FPGA) technology in photogrammetry, LiDAR, and RS;
Big data technologies and cloud computing in UAV photogrammetry and 3D-RS;
The advanced application of UAVs in precise agriculture, forest inventory, geology, geodesy, civil engineering, architecture, hydrology, ecology, and other geosciences;
3D-RS in natural hazard and environmental risk management;
UAVs in cultural heritage and archaeology;
Challenges and future perspectives of 3D sensing and imaging for UAVs

Dr. Mateo Gašparović
Dr. Gil Rito Gonçalves
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Photogrammetry
Remote sensing
Light detection and ranging (LiDAR)
Unmanned aerial vehicle (UAV)
Machine learning
Image classification
Multi-sensor data fusion
Multispectral, hyperspectral, and thermal image analysis
UAV-based Digital surface model (DSM), Digital terrain model (DTM) and Building information modeling (BIM)
UAS for environmental monitoring and natural hazards mapping

Published Papers (3 papers)

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Research

19 pages, 6525 KiB

Open AccessArticle

A UAS and Machine Learning Classification Approach to Suitability Prediction of Expanding Natural Habitats for Endangered Flora Species

by Mladen Jurišić, Dorijan Radočaj, Ivan Plaščak and Irena Rapčan

Remote Sens. 2022, 14(13), 3054; https://doi.org/10.3390/rs14133054 - 25 Jun 2022

Cited by 1 | Viewed by 1571

Abstract

In this study, we propose integrating unmanned aerial systems (UASs) and machine learning classification for suitability prediction of expanding habitats for endangered flora species to prevent further extinction. Remote sensing imaging of the protected steppe-like grassland in Bilje using the DJI P4 Multispectral UAS ensured non-invasive data collection. A total of 129 individual flora units of five endangered flora species, including small pasque flower (Pulsatilla pratensis (L.) Miller ssp. nigricans (Störck) Zämelis), green-winged orchid (Orchis morio (L.)), Hungarian false leopardbane (Doronicum hungaricum Rchb.f.), bloody cranesbill (Geranium sanguineum (L.)) and Hungarian iris (Iris variegate (L.)) were detected and georeferenced. Habitat suitability in the projected area, designated for the expansion of the current area of steppe-like grassland in Bilje, was predicted using the binomial machine learning classification algorithm based on three groups of environmental abiotic criteria: vegetation, soil, and topography. Four machine learning classification methods were evaluated: random forest, XGBoost, neural network, and generalized linear model. The random forest method outperformed the other classification methods for all five flora species and achieved the highest receiver operating characteristic (ROC) values, ranging from 0.809 to 0.999. Soil compaction was the least favorable criterion for the habitat suitability of all five flora species, indicating the need to perform soil tillage operations to potentially enable the expansion of their coverage in the projected area. However, potential habitat suitability was detected for the critically endangered flora species of Hungarian false leopardbane, indicating its habitat-related potential for expanding and preventing further extinction. In addition to the current methods of predicting current coverage and population count of endangered species using UASs, the proposed method could serve as a basis for decision making in nature conservation and land management. Full article

(This article belongs to the Special Issue Advanced 3D Remote Sensing and Image Analysis from Unmanned Aerial Systems)

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18 pages, 12482 KiB

Open AccessArticle

Comparative Assessment of Pixel and Object-Based Approaches for Mapping of Olive Tree Crowns Based on UAV Multispectral Imagery

by Ante Šiljeg, Lovre Panđa, Fran Domazetović, Ivan Marić, Mateo Gašparović, Mirko Borisov and Rina Milošević

Remote Sens. 2022, 14(3), 757; https://doi.org/10.3390/rs14030757 - 06 Feb 2022

Cited by 23 | Viewed by 2928

Abstract

Pixel-based (PB) and geographic-object-based (GEOBIA) classification approaches allow the extraction of different objects from multispectral images (MS). The primary goal of this research was the analysis of UAV imagery applicability and accuracy assessment of MLC and SVM classification algorithms within PB and GEOBIA classification approaches. The secondary goal was to use different accuracy assessment metrics to determine which of the two tested classification algorithms (SVM and MLC) most reliably distinguishes olive tree crowns and which approach is more accurate (PB or GEOBIA). The third goal was to add false polygon samples for Correctness (COR), Completeness (COM) and Overall Quality (OQ) metrics and use them to calculate the Total Accuracy (TA). The methodology can be divided into six steps, from data acquisition to selection of the best classification algorithm after accuracy assessment. High-quality DOP (digital orthophoto) and UAV_MS were generated. A new accuracy metric, called Total Accuracy (TA), combined both false and true positive polygon samples, thus providing a more comprehensive insight into the assessed classification accuracy. The SVM (GEOBIA) was the most reliable classification algorithm for extracting olive tree crowns from UAV_MS imagery. The assessment carried out indicated that application of GEOBIA-SVM achieved a TA_COR of 0.527, TA_COM of 0.811, TA_OQ of 0.745, Overall Accuracy (OA) of 0.926 or 0.980 and Area Under Curve (AUC) value of 0.904 or 0.929. The calculated accuracy metrics confirmed that the GEOBIA approach (SVM and MLC) achieved more accurate olive tree crown extraction than the PB approach (SVM and MLC) if applied to classifying VHR UAV_MS imagery. The SVM classification algorithm extracted olive tree crowns more accurately than MLC in both approaches. However, the accuracy assessment has proven that PB classification algorithms can also achieve satisfactory accuracy. Full article

(This article belongs to the Special Issue Advanced 3D Remote Sensing and Image Analysis from Unmanned Aerial Systems)

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19 pages, 4424 KiB

Open AccessArticle

Estimating Floodplain Vegetative Roughness Using Drone-Based Laser Scanning and Structure from Motion Photogrammetry

by Elizabeth M. Prior, Charles A. Aquilina, Jonathan A. Czuba, Thomas J. Pingel and W. Cully Hession

Remote Sens. 2021, 13(13), 2616; https://doi.org/10.3390/rs13132616 - 03 Jul 2021

Cited by 10 | Viewed by 3465

Abstract

Vegetation heights derived from drone laser scanning (DLS), and structure from motion (SfM) photogrammetry at the Virginia Tech StREAM Lab were utilized to determine hydraulic roughness (Manning’s roughness coefficients). We determined hydraulic roughness at three spatial scales: reach, patch, and pixel. For the reach scale, one roughness value was set for the channel, and one value for the entire floodplain. For the patch scale, vegetation heights were used to classify the floodplain into grass, scrub, and small and large trees, with a single roughness value for each. The roughness values for the reach and patch methods were calibrated using a two-dimensional (2D) hydrodynamic model (HEC-RAS) and data from in situ velocity sensors. For the pixel method, we applied empirical equations that directly estimated roughness from vegetation height for each pixel of the raster (no calibration necessary). Model simulations incorporating these roughness datasets in 2D HEC-RAS were validated against water surface elevations (WSE) from seventeen groundwater wells for seven high-flow events during the Fall of 2018 and 2019, and compared to marked flood extents. The reach method tended to overestimate while the pixel method tended to underestimate the flood extent. There were no visual differences between DLS and SfM within the pixel and patch methods when comparing flood extents. All model simulations were not significantly different with respect to the well WSEs (p > 0.05). The pixel methods had the lowest WSE RMSEs (SfM: 0.136 m, DLS: 0.124 m). The other methods had RMSE values 0.01–0.02 m larger than the DLS pixel method. Models with DLS data also had lower WSE RMSEs by 0.01 m when compared to models utilizing SfM. This difference might not justify the increased cost of a DLS setup over SfM (~150,000 vs. ~2000 USD for this study), though our use of the DLS DEM to determine SfM vegetation heights might explain this minimal difference. We expect a poorer performance of the SfM-derived vegetation heights/roughness values if we were using a SfM DEM, although further work is needed. These results will help improve hydrodynamic modeling efforts, which are becoming increasingly important for management and planning in response to climate change, specifically in regions were high flow events are increasing. Full article

(This article belongs to the Special Issue Advanced 3D Remote Sensing and Image Analysis from Unmanned Aerial Systems)

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