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Article ; Online: Multispectral Light Detection and Ranging Technology and Applications: A Review.

Takhtkeshha, Narges / Mandlburger, Gottfried / Remondino, Fabio / Hyyppä, Juha

2024 Volume 24, Issue 5

Abstract: Light Detection and Ranging (LiDAR) is a well-established active technology for the direct acquisition of 3D data. In recent years, the geometric information collected by LiDAR sensors has been widely combined with optical images to provide supplementary ...

Abstract	Light Detection and Ranging (LiDAR) is a well-established active technology for the direct acquisition of 3D data. In recent years, the geometric information collected by LiDAR sensors has been widely combined with optical images to provide supplementary spectral information to achieve more precise results in diverse remote sensing applications. The emergence of active Multispectral LiDAR (MSL) systems, which operate on different wavelengths, has recently been revolutionizing the simultaneous acquisition of height and intensity information. So far, MSL technology has been successfully applied for fine-scale mapping in various domains. However, a comprehensive review of this modern technology is currently lacking. Hence, this study presents an exhaustive overview of the current state-of-the-art in MSL systems by reviewing the latest technologies for MSL data acquisition. Moreover, the paper reports an in-depth analysis of the diverse applications of MSL, spanning across fields of "ecology and forestry", "objects and Land Use Land Cover (LULC) classification", "change detection", "bathymetry", "topographic mapping", "archaeology and geology", and "navigation". Our systematic review uncovers the potentials, opportunities, and challenges of the recently emerged MSL systems, which integrate spatial-spectral data and unlock the capability for precise multi-dimensional (nD) mapping using only a single-data source.
Language	English
Publishing date	2024-03-04
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2052857-7
ISSN	1424-8220 ; 1424-8220
ISSN (online)	1424-8220
ISSN	1424-8220
DOI	10.3390/s24051669
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Towards 3D basic theories of plant forms.

Lin, Yi / Hyyppä, Juha

Communications biology

2022 Volume 5, Issue 1, Page(s) 703

Abstract: Allometric, metabolic, and biomechanical theories are the critical foundations for scientifically deciphering plant forms. Their concrete laws, however, are found to deviate for plenty of plant specimens. This phenomenon has not been extensively studied, ...

Abstract	Allometric, metabolic, and biomechanical theories are the critical foundations for scientifically deciphering plant forms. Their concrete laws, however, are found to deviate for plenty of plant specimens. This phenomenon has not been extensively studied, due to technical restrictions. This bottleneck now can be overcome by the state-of-the-art three-dimensional (3D) mapping technologies, such as fine-scale terrestrial laser scanning. On these grounds, we proposed to reexamine the basic theories regarding plant forms, and then, we case validated the feasibility of upgrading them into 3D modes. As an in-time enlightening of 3D revolutionizing the related basic subject, our theoretical prospect further sorted out the potential challenges as the cutting points for advancing its future exploration, which may enable 3D reconstruction of the basic theories of plant forms and even boost life science.
MeSH term(s)	Imaging, Three-Dimensional/methods ; Imaging, Three-Dimensional/standards ; Lasers ; Plants/anatomy & histology ; Plants/classification
Language	English
Publishing date	2022-07-14
Publishing country	England
Document type	Journal Article ; Review ; Research Support, Non-U.S. Gov't
ISSN	2399-3642
ISSN (online)	2399-3642
DOI	10.1038/s42003-022-03652-x
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Book ; Online: EXPERIMENT ON PRODUCING DISPARITY MAPS FROM AERIAL STEREO IMAGES USING UNSUPERVISED AND SUPERVISED METHODS

Zhu, L. / Hattula, E. / Raninen, J. / Hyyppä, J.

eISSN: 2194-9034

2022

Abstract: Recent advancement in hardware and software provides the possibility of realizing full automation in stereo-image tasks. This paper investigated disparity map generation from aerial images with different methods: unsupervised method and supervised ... ...

Abstract	Recent advancement in hardware and software provides the possibility of realizing full automation in stereo-image tasks. This paper investigated disparity map generation from aerial images with different methods: unsupervised method and supervised methods. The datasets were from aerial stereo dense matching benchmark dataset for deep learning in ISPRS 2021: Vaihingen dataset and the WHU MVS/Stereo Dataset released in the CVPR 2020. Two neural networks: GC-net and PSMnet have been trained with the Vaihingen dataset and the WHU MVS/Stereo Dataset. With unsupervised methods, stereo block matching(StereoBM) and Stereo Semi-Global Matching (StereoSGM) methods from the OpenCV were studied. We selected seven image pairs from the Vaihingen dataset and six image pairs from the WHU dataset for testing and evaluation. Difficulty scenes such as textureless areas, reflective surfaces, and repetitive patterns were also included in our study. The performance from different methods was compared by both visualization and quantitative means. The advantages and disadvantages are presented.
Subject code	710
Language	English
Publishing date	2022-08-09
Publishing country	de
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Characterizing ecosystem phenological diversity and its macroecology with snow cover phenology.

Lin, Yi / Hyyppä, Juha

Scientific reports

2019 Volume 9, Issue 1, Page(s) 15074

Abstract: One critical challenge of exploring flora phenology is on characterizing ecosystem phenological diversity (EPD), and thus how EPD's performance is influenced by climate changes has also been an open macro-ecological question. To fill these two gaps, we ... ...

Abstract	One critical challenge of exploring flora phenology is on characterizing ecosystem phenological diversity (EPD), and thus how EPD's performance is influenced by climate changes has also been an open macro-ecological question. To fill these two gaps, we proposed an innovative method for reflecting EPD, by taking the advantage of the often-classified inverse factor of spatial resolution discrepancy between the used remote sensing datasets of vegetation phenological dates (green-up and brown-up) and snow cover phenological dates (SPDs) (onset and end) around the Arctic, and further, we examined the cross response/feedbacks of the two kinds of EPDs to the two categories of SPDs. We found that the circumpolar green-up and brown-up EPDs both were shrinking, driven more by the delaying of the onset SPDs than the advancing of the end SPDs; North America and North Eurasia performed with inconsistent EPD response/feedbacks to the related SPD anomalies; and further, the EPD-SPD response/feedbacks in some locations exhibited the time-lag effect, e.g., the green-up EPDs made the strongest response to the onset SPDs of two years earlier. Overall, the validated method and the new findings are of implications for improving the phenology modules in Earth system models, and the contributions of the present study have enlightening significance for kicking off the new EPD branch in macrosystem phenological ecology.
Language	English
Publishing date	2019-10-21
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-019-51602-1
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Article: Close-range hyperspectral spectroscopy reveals leaf water content dynamics

Junttila, S. / Hölttä, T. / Saarinen, N. / Kankare, V. / Yrttimaa, T. / Hyyppä, J. / Vastaranta, M.

Remote sensing of environment. 2022 Aug., v. 277

2022

Abstract: Water plays a crucial role in maintaining plant functionality and drives many ecophysiological processes. The distribution of water resources is in a continuous change due to global warming affecting the productivity of ecosystems around the globe, but ... ...

Abstract	Water plays a crucial role in maintaining plant functionality and drives many ecophysiological processes. The distribution of water resources is in a continuous change due to global warming affecting the productivity of ecosystems around the globe, but there is a lack of non-destructive methods capable of continuous monitoring of plant and leaf water content that would help us in understanding the consequences of the redistribution of water. We studied the utilization of novel small hyperspectral sensors in the 1350–1650 nm and 2000–2450 nm spectral ranges in non-destructive estimation of leaf water content in laboratory and field conditions. We found that the sensors captured up to 96% of the variation in equivalent water thickness (EWT, g/m²) and up to 90% of the variation in relative water content (RWC). Further tests were done with an indoor plant (Dracaena marginate Lem.) by continuously measuring leaf spectra while drought conditions developed, which revealed detailed diurnal dynamics of leaf water content. The laboratory findings were supported by field measurements, where repeated leaf spectra measurements were in fair agreement (R² = 0.70) with RWC and showed similar diurnal dynamics. The estimation of leaf mass per area (LMA) using leaf spectra was investigated as a pathway to improved RWC estimation, but no significant improvement was found. We conclude that close-range hyperspectral spectroscopy can provide a novel tool for continuous measurement of leaf water content at the single leaf level and help us to better understand plant responses to varying environmental conditions.
Keywords	Dracaena ; drought ; ecophysiology ; leaves ; specific leaf weight ; spectroscopy ; water content
Language	English
Dates of publication	2022-08
Publishing place	Elsevier Inc.
Document type	Article
ZDB-ID	431483-9
ISSN	0034-4257
ISSN	0034-4257
DOI	10.1016/j.rse.2022.113071
Database	NAL-Catalogue (AGRICOLA)

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Book ; Online: Leveraging Road Area Semantic Segmentation with Auxiliary Steering Task

Maanpää, Jyri / Melekhov, Iaroslav / Taher, Josef / Manninen, Petri / Hyyppä, Juha

2022

Abstract: Robustness of different pattern recognition methods is one of the key challenges in autonomous driving, especially when driving in the high variety of road environments and weather conditions, such as gravel roads and snowfall. Although one can collect ... ...

Abstract	Robustness of different pattern recognition methods is one of the key challenges in autonomous driving, especially when driving in the high variety of road environments and weather conditions, such as gravel roads and snowfall. Although one can collect data from these adverse conditions using cars equipped with sensors, it is quite tedious to annotate the data for training. In this work, we address this limitation and propose a CNN-based method that can leverage the steering wheel angle information to improve the road area semantic segmentation. As the steering wheel angle data can be easily acquired with the associated images, one could improve the accuracy of road area semantic segmentation by collecting data in new road environments without manual data annotation. We demonstrate the effectiveness of the proposed approach on two challenging data sets for autonomous driving and show that when the steering task is used in our segmentation model training, it leads to a 0.1-2.9% gain in the road area mIoU (mean Intersection over Union) compared to the corresponding reference transfer learning model. Comment: 11 pages, 4 figures (Supplementary material 6 pages, 3 figures). Author's accepted version of the contribution included in proceedings of the 21st International Conference on Image Analysis and Processing (ICIAP), 2022
Keywords	Computer Science - Computer Vision and Pattern Recognition
Subject code	380
Publishing date	2022-12-19
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Adding single tree features and correcting edge tree effects enhance the characterization of seedling stands with single-photon airborne laser scanning

Imangholiloo, M. / Yrttimaa, T. / Mattsson, T. / Junttila, S. / Holopainen, M. / Saarinen, N. / Savolainen, P. / Hyyppä, J. / Vastaranta, M.

ISPRS journal of photogrammetry and remote sensing. 2022 Sept., v. 191

2022

Abstract: Silvicultural tending of seedling stands is important to producing quality timber. However, it is challenging to allocate where and when to apply these silvicultural tending actions. Here, we tested and evaluated two methodological modifications of the ... ...

Abstract	Silvicultural tending of seedling stands is important to producing quality timber. However, it is challenging to allocate where and when to apply these silvicultural tending actions. Here, we tested and evaluated two methodological modifications of the ordinary area-based approach (ABAOᵣdᵢₙₐᵣy) that could be utilized in the airborne laser scanning-based forest inventories and especially seedling stand characterization. We hypothesize that ABA with added individual tree detection-derived features (ABAITD) or correcting edge-tree effects (ABAEdgₑ) would display improved performance in estimating the tree density and mean tree height of seedling stands. We tested this hypothesis using single-photon laser (SPL) and linear-mode laser (LML) scanning data covering 89 sample plots. The obtained results supported the hypothesis as the methodological modifications improved seedling stand characterization. Compared to the performance of ABAₒᵣdᵢₙₐᵣy, relative bias in tree density estimation decreased from 17.2% to 10.1% when we applied ABAITD. In the case of mean height estimation, the relative root mean square error decreased from 19.5% to 16.3% when we applied ABAEdgₑITD. The SPL technology provided practically comparable or, in some cases, enhanced performance in seedling stand characterization when compared to conventional LML technology. Based on the obtained findings, it seems that the tested methodological improvements should be carefully considered when ALS-based inventories supporting forest management and silvicultural decision-making are developed further.
Keywords	decision making ; forest management ; forests ; photogrammetry ; seedlings ; tree height ; trees
Language	English
Dates of publication	2022-09
Size	p. 129-142.
Publishing place	Elsevier B.V.
Document type	Article
ZDB-ID	1007774-1
ISSN	0924-2716
ISSN	0924-2716
DOI	10.1016/j.isprsjprs.2022.07.005
Database	NAL-Catalogue (AGRICOLA)

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Article: Exploring tree growth allometry using two-date terrestrial laser scanning

Yrttimaa, T. / Luoma, V. / Saarinen, N. / Kankare, V. / Junttila, S. / Holopainen, M. / Hyyppä, J. / Vastaranta, M.

Forest ecology and management. 2022 Aug. 15, v. 518

2022

Abstract: Tree growth is a physio-ecological phenomena of high interest among researchers across disciplines. Observing changes in tree characteristics has conventionally required either repeated measurements of the characteristics of living trees, retrospective ... ...

Abstract	Tree growth is a physio-ecological phenomena of high interest among researchers across disciplines. Observing changes in tree characteristics has conventionally required either repeated measurements of the characteristics of living trees, retrospective measurements of destructively sampled trees, or modelling. The use of close-range sensing techniques such as terrestrial laser scanning (TLS) has enabled non-destructive approaches to reconstruct the three-dimensional (3D) structure of trees and tree communities in space and time. This study aims at improving the understanding of tree allometry in general and interactions between tree growth and its neighbourhood in particular by using two-date point clouds. We investigated how variation in the increments in basal area at the breast height (Δg₁.₃), basal area at height corresponding to 60% of tree height (Δg₀₆ₕ), and volume of the stem section below 50% of tree height (Δv₀₅ₕ) can be explained with TLS point cloud-based attributes characterizing the spatiotemporal structure of a tree crown and crown neighbourhood, entailing the competitive status of a tree. The analyses were based on 218 trees on 16 sample plots whose 3D characteristics were obtained at the beginning (2014, T1) and at the end of the monitoring period (2019, T2) from multi-scan TLS point clouds using automatic point cloud processing methods. The results of this study showed that, within certain tree communities, strong relationships (\|r\| > 0.8) were observed between increments in the stem dimensions and the attributes characterizing crown structure and competition. Most often, attributes characterizing the competitive status of a tree, and the crown structure at T1, were the most important attributes to explain variation in the increments of stem dimensions. Linear mixed-effect modelling showed that single attributes could explain up to 35–60% of the observed variation in Δg₁.₃, Δg₀₆ₕ and Δv₀₅ₕ, depending on the tree species. This tree-level evidence of the allometric relationship between stem growth and crown dynamics can further be used to justify landscape-level analyses based on airborne remote sensing technologies to monitor stem growth through the structure and development of crown structure. This study contributes to the existing knowledge by showing that laser-based close-range sensing is a feasible technology to provide 3D characterization of stem and crown structure, enabling one to quantify structural changes and the competitive status of trees for improved understanding of the underlying growth processes.
Keywords	administrative management ; allometry ; data collection ; forest ecology ; space and time ; stem elongation ; tree crown ; tree growth ; tree height ; trees
Language	English
Dates of publication	2022-0815
Publishing place	Elsevier B.V.
Document type	Article
ZDB-ID	751138-3
ISSN	0378-1127
ISSN	0378-1127
DOI	10.1016/j.foreco.2022.120303
Database	NAL-Catalogue (AGRICOLA)

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Book ; Online: Towards High-Definition Maps

Manninen, Petri / Hyyti, Heikki / Kyrki, Ville / Maanpää, Jyri / Taher, Josef / Hyyppä, Juha

a Framework Leveraging Semantic Segmentation to Improve NDT Map Compression and Descriptivity

2023

Abstract: High-Definition (HD) maps are needed for robust navigation of autonomous vehicles, limited by the on-board storage capacity. To solve this, we propose a novel framework, Environment-Aware Normal Distributions Transform (EA-NDT), that significantly ... ...

Abstract	High-Definition (HD) maps are needed for robust navigation of autonomous vehicles, limited by the on-board storage capacity. To solve this, we propose a novel framework, Environment-Aware Normal Distributions Transform (EA-NDT), that significantly improves compression of standard NDT map representation. The compressed representation of EA-NDT is based on semantic-aided clustering of point clouds resulting in more optimal cells compared to grid cells of standard NDT. To evaluate EA-NDT, we present an open-source implementation that extracts planar and cylindrical primitive features from a point cloud and further divides them into smaller cells to represent the data as an EA-NDT HD map. We collected an open suburban environment dataset and evaluated EA-NDT HD map representation against the standard NDT representation. Compared to the standard NDT, EA-NDT achieved consistently at least 1.5x higher map compression while maintaining the same descriptive capability. Moreover, we showed that EA-NDT is capable of producing maps with significantly higher descriptivity score when using the same number of cells than the standard NDT. Comment: 8 pages, 8 figures, IROS22
Keywords	Computer Science - Robotics
Publishing date	2023-01-10
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Capturing seasonal radial growth of boreal trees with terrestrial laser scanning

Yrttimaa, T. / Junttila, S. / Luoma, V. / Calders, K. / Kankare, V. / Saarinen, N. / Kukko, A. / Holopainen, M. / Hyyppä, J. / Vastaranta, M.

Forest Ecology and Management. 2023 Feb., v. 529 p.120733-

2023

Abstract: Detailed observation techniques are needed to reveal the underlying eco-physiological mechanisms driving tree growth processes. Terrestrial laser scanning (TLS) has proven to be a feasible technique for characterizing trees, but it has still remained ... ...

Abstract	Detailed observation techniques are needed to reveal the underlying eco-physiological mechanisms driving tree growth processes. Terrestrial laser scanning (TLS) has proven to be a feasible technique for characterizing trees, but it has still remained unclear whether TLS point clouds and the existing point cloud processing methods can be used for capturing even the smallest signs of the growth process of individual trees. The aim of this study was to investigate the capacity of TLS in observing seasonal radial growth of boreal trees. The experimental setup included 91 sample trees from 20 sample plots characterized with multi-scan TLS point clouds pre- and post-growing season. The sample trees were equipped with dendrometers that provided reference measurements for the increment in diameter at the breast height (Δdbh) that varied from −1.4 mm to 4.0 mm with a mean of 1.0 mm. The experiment confirmed challenges related to quantification of millimeter-level increments in dbh using TLS but cautiously highlighted its feasibility for radial tree growth monitoring when the magnitude of Δdbh exceeds several millimeters and when the aim is to characterize sample plot mean rather than individual tree growth. While the capacity of TLS to characterize Δdbh of individual trees remained rather low (r = 0.17, p = 0.07), the TLS-based estimates for sample plot mean Δdbh were slightly better in line with dendrometer measurements (r = 0.46, p = 0.04). At an individual tree level, the capacity of TLS to determine the occurrence of radial tree growth seemed to be dependent on the magnitude of observed Δdbh and benefit from the analysis of paired diameter measurements along the stem for determining individual tree growth. The results showed overall classification accuracies of a) 60.7 % and b) 70.6 % for the use of TLS in determining whether radial growth had occurred or not when the analysis was based on a) Δdbh measurements only or b) statistically significant mean increment in paired diameter measurements along the stem, respectively. Using the Δdbh-based method, the overall accuracy improved from 56.3 % to 73.0 % when the magnitude of observed Δdbh increased from ≤ 1 mm to > 1 mm, as was expected. Altogether, this study contributes by demonstrating that with TLS data acquisition and existing point cloud processing methods, it is possible to observe seasonal increments in tree structures, which emphasizes the feasibility of TLS in regular monitoring of structural changes even in boreal forest ecosystems.
Keywords	administrative management ; boreal forests ; data collection ; dendrometers ; ecophysiology ; forest ecology ; tree growth ; trees ; Close-range sensing ; Time-series analyses ; Carbon allocation ; Growth and yield ; Terrestrial LiDAR
Language	English
Dates of publication	2023-02
Publishing place	Elsevier B.V.
Document type	Article ; Online
Note	Use and reproduction
ZDB-ID	751138-3
ISSN	0378-1127
ISSN	0378-1127
DOI	10.1016/j.foreco.2022.120733
Database	NAL-Catalogue (AGRICOLA)

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