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Special Issue "Frontiers in Spectral Imaging and 3D Technologies for Geospatial Solutions"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: 31 December 2018

Special Issue Editors

Guest Editor
Dr. Eija Honkavaara

Finnish Geospatial Research Institute, Geodeetinrinne 2, FI-02430 Masala, Finland
Website | E-Mail
Interests: photogrammetry; hyperspectral imaging; radiometry; calibration; machine vision; UAV
Guest Editor
Dr. Konstantinos Karantzalos

National Technical University of Athens, Zografou 15780, Griechenland
Website | E-Mail
Interests: fusion; vision; machine learning; land cover mapping; geospatial applications
Guest Editor
Dr. Xinlian Liang

National Land Survey of Finland, Finnish Geospatial Research Institute Geodeetinrinne 2, P.O. Box 15 02431 Masala, Finland
Website | E-Mail
Interests: Remote sensing of vegetation; Forest inventories; Thematic information extraction; Multitemporal analyses; 3D modeling; Mobile mapping; LiDAR; Laser scanning; Point cloud
Guest Editor
Dr. Erica Nocerino

Bruno Kessler Foundation, Via Santa Croce, 77, 38122 Trento, Italy
Website | E-Mail
Interests: photogrammetry; laser scanning; calibration; 3D modelling; indoor and outdoor mapping; image processing
Guest Editor
Dr. Ilkka Pölönen

Faculty of Information Technology, University of Jyväskylä P.O. Box 35, FIN-40014 University of Jyväskylä, Finland Building: Agora, Room: C411.2
Website | E-Mail
Phone: +358 40 024 8140
Interests: spectral imaging; data analysis; manifold learning; mathematical modelling
Guest Editor
Dr. Petri Rönnholm

Aalto University, P.O. Box 14100, 00076 AALTO, Finland
Website | E-Mail
Interests: photogrammetry; laser scanning; digital image processing; registration

Special Issue Information

Dear Colleagues,

Spectral imaging and 3D sensor technologies have developed explosively in recent years for a variety of geospatial applications, ranging, but not limited to, agriculture (precision farming, vegetative index and water supply mapping), forestry (forest inventory, forest health monitoring), mapping, environmental monitoring (surface geology survey, pollutant and hazardous substances mapping), industry (large industrial plant monitoring), etc. New cutting-edge hardware and software solutions are emerging, and integration of multi-modal information is allowing increasingly accurate, automated and fast remote sensing.

The main ambition of this Special Issue is to promote discussion on new developments in the field of combined use of spectral and 3D remote sensing technologies, comprising sensing technologies, thematic information extraction, and geospatial solutions. It aims at bringing together research presented in the first ISPRS SPEC3D workshop "Frontiers in Spectral imaging and 3D Technologies for Geospatial Solutions" organized in Finland in 25–27 October, 2017, as well as high quality scientific contributions from the global research community. Researchers and experts are kindly encouraged to submit innovative papers related to integrated use of spectral and 3D technologies, focused on, but not limited to, the following topics:

1. New aspects of sensors, systems and calibration: 3D spectral information capture using spectral imaging, LIDAR, micro-LIDAR and -RADAR, low-cost 3D and spectral sensors, emerging platforms (aerial, UAV, robotic, mobile, portable, etc.), and geometric and radiometric sensor and system integration and calibration.

2. Processing and interpretation requirements for novel spectroscopic and 3D data, including georeferencing, radiometric calibration, multisource data fusion, video data analysis, time-series and change detection, context awareness, big data, and crowd sourcing, as well as aspects of automation, fast response and real-time processing.

3. Geospatial solutions utilizing the new sensors and data in indoor and outdoor applications, such as mapping and monitoring in natural and built environments, forestry and agriculture, biodiversity, industrial and civil applications, robotics, and virtual and augmented reality.

Dr. Eija Honkavaara,
Dr. Konstantinos Karantzalos,
Dr. Xianlian Liang,
Dr. Erica Nocerino,
Dr. Ilkka Pölönen,
Dr. Petri Rönnholm
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 papers will be 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 monthly 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 1800 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

  • Imaging technologies for 3D hyper- and multispectral data capture
  • Multi- and hyperspectral LiDAR
  • Low-cost 3D and spectral sensors
  • Sensor integration in spectral and 3D systems
  • Radiometric and geometric calibration, system calibration
  • Emerging platforms for 3D spectral data capture: UAV, backpack, trolley, handheld, industrial, etc.
  • Georeferencing, radiometric correction, registration, integration
  • Real-time processing, robotics
  • Pointcloud processing integrating spectral and 3D
  • Machine learning and classification with 3D and spectral features
  • Hyperspectral dimensionality reduction, unmixing, source separation, endmember extraction
  • Noise estimation and reduction
  • Hyper- and multispectral multitemporal and video data analysis
  • Utilizing integrated 3D, spectral and multitemporal features in geospatial solutions, such as agriculture, forestry, mining, biodiversity, indoor and outdoor built environments mapping, and engineering applications
  • Benchmarking
  • Thematic information extraction

Published Papers (3 papers)

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Research

Open AccessArticle Estimating Biomass and Nitrogen Amount of Barley and Grass Using UAV and Aircraft Based Spectral and Photogrammetric 3D Features
Remote Sens. 2018, 10(7), 1082; https://doi.org/10.3390/rs10071082
Received: 31 May 2018 / Revised: 27 June 2018 / Accepted: 5 July 2018 / Published: 7 July 2018
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Abstract
The timely estimation of crop biomass and nitrogen content is a crucial step in various tasks in precision agriculture, for example in fertilization optimization. Remote sensing using drones and aircrafts offers a feasible tool to carry out this task. Our objective was to
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The timely estimation of crop biomass and nitrogen content is a crucial step in various tasks in precision agriculture, for example in fertilization optimization. Remote sensing using drones and aircrafts offers a feasible tool to carry out this task. Our objective was to develop and assess a methodology for crop biomass and nitrogen estimation, integrating spectral and 3D features that can be extracted using airborne miniaturized multispectral, hyperspectral and colour (RGB) cameras. We used the Random Forest (RF) as the estimator, and in addition Simple Linear Regression (SLR) was used to validate the consistency of the RF results. The method was assessed with empirical datasets captured of a barley field and a grass silage trial site using a hyperspectral camera based on the Fabry-Pérot interferometer (FPI) and a regular RGB camera onboard a drone and an aircraft. Agricultural reference measurements included fresh yield (FY), dry matter yield (DMY) and amount of nitrogen. In DMY estimation of barley, the Pearson Correlation Coefficient (PCC) and the normalized Root Mean Square Error (RMSE%) were at best 0.95% and 33.2%, respectively; and in the grass DMY estimation, the best results were 0.79% and 1.9%, respectively. In the nitrogen amount estimations of barley, the PCC and RMSE% were at best 0.97% and 21.6%, respectively. In the biomass estimation, the best results were obtained when integrating hyperspectral and 3D features, but the integration of RGB images and 3D features also provided results that were almost as good. In nitrogen content estimation, the hyperspectral camera gave the best results. We concluded that the integration of spectral and high spatial resolution 3D features and radiometric calibration was necessary to optimize the accuracy. Full article
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Open AccessArticle Assessment of Classifiers and Remote Sensing Features of Hyperspectral Imagery and Stereo-Photogrammetric Point Clouds for Recognition of Tree Species in a Forest Area of High Species Diversity
Remote Sens. 2018, 10(5), 714; https://doi.org/10.3390/rs10050714
Received: 21 March 2018 / Revised: 29 April 2018 / Accepted: 4 May 2018 / Published: 5 May 2018
Cited by 1 | PDF Full-text (7403 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Recognition of tree species and geospatial information on tree species composition is essential for forest management. In this study, tree species recognition was examined using hyperspectral imagery from visible to near-infrared (VNIR) and short-wave infrared (SWIR) camera sensors in combination with a 3D
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Recognition of tree species and geospatial information on tree species composition is essential for forest management. In this study, tree species recognition was examined using hyperspectral imagery from visible to near-infrared (VNIR) and short-wave infrared (SWIR) camera sensors in combination with a 3D photogrammetric canopy surface model based on RGB camera stereo-imagery. An arboretum with a diverse selection of 26 tree species from 14 genera was used as a test area. Aerial hyperspectral imagery and high spatial resolution photogrammetric color imagery were acquired from the test area using unmanned aerial vehicle (UAV) borne sensors. Hyperspectral imagery was processed to calibrated reflectance mosaics and was tested along with the mosaics based on original image digital number values (DN). Two alternative classifiers, a k nearest neighbor method (k-nn), combined with a genetic algorithm and a random forest method, were tested for predicting the tree species and genus, as well as for selecting an optimal set of remote sensing features for this task. The combination of VNIR, SWIR, and 3D features performed better than any of the data sets individually. Furthermore, the calibrated reflectance values performed better compared to uncorrected DN values. These trends were similar with both tested classifiers. Of the classifiers, the k-nn combined with the genetic algorithm provided consistently better results than the random forest algorithm. The best result was thus achieved using calibrated reflectance features from VNIR and SWIR imagery together with 3D point cloud features; the proportion of correctly-classified trees was 0.823 for tree species and 0.869 for tree genus. Full article
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Open AccessArticle Assessing Biodiversity in Boreal Forests with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging
Remote Sens. 2018, 10(2), 338; https://doi.org/10.3390/rs10020338
Received: 8 January 2018 / Revised: 13 February 2018 / Accepted: 20 February 2018 / Published: 23 February 2018
Cited by 2 | PDF Full-text (2910 KB) | HTML Full-text | XML Full-text
Abstract
Forests are the most diverse terrestrial ecosystems and their biological diversity includes trees, but also other plants, animals, and micro-organisms. One-third of the forested land is in boreal zone; therefore, changes in biological diversity in boreal forests can shape biodiversity, even at global
[...] Read more.
Forests are the most diverse terrestrial ecosystems and their biological diversity includes trees, but also other plants, animals, and micro-organisms. One-third of the forested land is in boreal zone; therefore, changes in biological diversity in boreal forests can shape biodiversity, even at global scale. Several forest attributes, including size variability, amount of dead wood, and tree species richness, can be applied in assessing biodiversity of a forest ecosystem. Remote sensing offers complimentary tool for traditional field measurements in mapping and monitoring forest biodiversity. Recent development of small unmanned aerial vehicles (UAVs) enable the detailed characterization of forest ecosystems through providing data with high spatial but also temporal resolution at reasonable costs. The objective here is to deepen the knowledge about assessment of plot-level biodiversity indicators in boreal forests with hyperspectral imagery and photogrammetric point clouds from a UAV. We applied individual tree crown approach (ITC) and semi-individual tree crown approach (semi-ITC) in estimating plot-level biodiversity indicators. Structural metrics from the photogrammetric point clouds were used together with either spectral features or vegetation indices derived from hyperspectral imagery. Biodiversity indicators like the amount of dead wood and species richness were mainly underestimated with UAV-based hyperspectral imagery and photogrammetric point clouds. Indicators of structural variability (i.e., standard deviation in diameter-at-breast height and tree height) were the most accurately estimated biodiversity indicators with relative RMSE between 24.4% and 29.3% with semi-ITC. The largest relative errors occurred for predicting deciduous trees (especially aspen and alder), partly due to their small amount within the study area. Thus, especially the structural diversity was reliably predicted by integrating the three-dimensional and spectral datasets of UAV-based point clouds and hyperspectral imaging, and can therefore be further utilized in ecological studies, such as biodiversity monitoring. Full article
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