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Book ; Online: Remote Sensing Technology Applications in Forestry and REDD+

Calders, Kim / Jonckheere, Inge / Vastaranta, Mikko / Nightingale, Joanne

2020

Abstract: Advances in close-range and remote sensing technologies are driving innovations in forest resource assessments and monitoring on varying scales. Data acquired with airborne and spaceborne platforms provide high(er) spatial resolution, more frequent ... ...

Abstract	Advances in close-range and remote sensing technologies are driving innovations in forest resource assessments and monitoring on varying scales. Data acquired with airborne and spaceborne platforms provide high(er) spatial resolution, more frequent coverage, and more spectral information. Recent developments in ground-based sensors have advanced 3D measurements, low-cost permanent systems, and community-based monitoring of forests. The UNFCCC REDD+ mechanism has advanced the remote sensing community and the development of forest geospatial products that can be used by countries for the international reporting and national forest monitoring. However, an urgent need remains to better understand the options and limitations of remote and close-range sensing techniques in the field of forest degradation and forest change. Therefore, we invite scientists working on remote sensing technologies, close-range sensing, and field data to contribute to this Special Issue. Topics of interest include: (1) novel remote sensing applications that can meet the needs of forest resource information and REDD+ MRV, (2) case studies of applying remote sensing data for REDD+ MRV, (3) timeseries algorithms and methodologies for forest resource assessment on different spatial scales varying from the tree to the national level, and (4) novel close-range sensing applications that can support sustainable forestry and REDD+ MRV. We particularly welcome submissions on data fusion
Keywords	Technology (General) ; Environmental technology. Sanitary engineering
Size	1 electronic resource (244 pages)
Publisher	MDPI - Multidisciplinary Digital Publishing Institute
Document type	Book ; Online
Note	English ; Open Access
HBZ-ID	HT020480948
ISBN	9783039284702 ; 9783039284719 ; 3039284703 ; 3039284711
DOI	10.3390/books978-3-03928-471-9
Database	ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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Article: Impact of rising temperatures on the biomass of humid old-growth forests of the world

Larjavaara, Markku / Lu, Xiancheng / Chen, Xia / Vastaranta, Mikko

Carbon balance and management. 2021 Dec., v. 16, no. 1

2021

Abstract: BACKGROUND: Understanding how warming influence above-ground biomass in the world’s forests is necessary for quantifying future global carbon budgets. A climate-driven decrease in future carbon stocks could dangerously strengthen climate change. ... ...

Abstract	BACKGROUND: Understanding how warming influence above-ground biomass in the world’s forests is necessary for quantifying future global carbon budgets. A climate-driven decrease in future carbon stocks could dangerously strengthen climate change. Empirical methods for studying the temperature response of forests have important limitations, and modelling is needed to provide another perspective. Here we evaluate the impact of rising air temperature on the future above-ground biomass of old-growth forests using a model that explains well the observed current variation in the above-ground biomass over the humid lowland areas of the world based on monthly air temperature. RESULTS: Applying this model to the monthly air temperature data for 1970–2000 and monthly air temperature projections for 2081–2100, we found that the above-ground biomass of old-growth forests is expected to decrease everywhere in the humid lowland areas except boreal regions. The temperature-driven decrease is estimated at 41% in the tropics and at 29% globally. CONCLUSIONS: Our findings suggest that rising temperatures impact the above-ground biomass of old-growth forests dramatically. However, this impact could be mitigated by fertilization effects of increasing carbon dioxide concentration in the atmosphere and nitrogen deposition.
Keywords	aboveground biomass ; administrative management ; air temperature ; carbon ; carbon dioxide ; climate change ; models ; nitrogen
Language	English
Dates of publication	2021-12
Size	p. 31.
Publishing place	Springer International Publishing
Document type	Article
ZDB-ID	2243512-8
ISSN	1750-0680
ISSN	1750-0680
DOI	10.1186/s13021-021-00194-3
Database	NAL-Catalogue (AGRICOLA)

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Article: Impact of rising temperatures on the biomass of humid old-growth forests of the world.

Larjavaara, Markku / Lu, Xiancheng / Chen, Xia / Vastaranta, Mikko

Carbon balance and management

2021 Volume 16, Issue 1, Page(s) 31

Abstract: Background: Understanding how warming influence above-ground biomass in the world's forests is necessary for quantifying future global carbon budgets. A climate-driven decrease in future carbon stocks could dangerously strengthen climate change. ... ...

Abstract	Background: Understanding how warming influence above-ground biomass in the world's forests is necessary for quantifying future global carbon budgets. A climate-driven decrease in future carbon stocks could dangerously strengthen climate change. Empirical methods for studying the temperature response of forests have important limitations, and modelling is needed to provide another perspective. Here we evaluate the impact of rising air temperature on the future above-ground biomass of old-growth forests using a model that explains well the observed current variation in the above-ground biomass over the humid lowland areas of the world based on monthly air temperature. Results: Applying this model to the monthly air temperature data for 1970-2000 and monthly air temperature projections for 2081-2100, we found that the above-ground biomass of old-growth forests is expected to decrease everywhere in the humid lowland areas except boreal regions. The temperature-driven decrease is estimated at 41% in the tropics and at 29% globally. Conclusions: Our findings suggest that rising temperatures impact the above-ground biomass of old-growth forests dramatically. However, this impact could be mitigated by fertilization effects of increasing carbon dioxide concentration in the atmosphere and nitrogen deposition.
Language	English
Publishing date	2021-10-12
Publishing country	England
Document type	Journal Article
ZDB-ID	2243512-8
ISSN	1750-0680
ISSN	1750-0680
DOI	10.1186/s13021-021-00194-3
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Remote Sensing Technology Applications in Forestry and REDD+

Calders, Kim / Jonckheere, Inge / Nightingale, Joanne / Vastaranta, Mikko

Forests. 2020 Feb. 07, v. 11, no. 2

2020

Abstract: Advances in close-range and remote sensing technologies drive innovations in forest resource assessments and monitoring at varying scales. Data acquired with airborne and spaceborne platforms provide us with higher spatial resolution, more frequent ... ...

Abstract	Advances in close-range and remote sensing technologies drive innovations in forest resource assessments and monitoring at varying scales. Data acquired with airborne and spaceborne platforms provide us with higher spatial resolution, more frequent coverage and increased spectral information. Recent developments in ground-based sensors have advanced three dimensional (3D) measurements, low-cost permanent systems and community-based monitoring of forests. The REDD+ mechanism has moved the remote sensing community in advancing and developing forest geospatial products which can be used by countries for the international reporting and national forest monitoring. However, there still is an urgent need to better understand the options and limitations of remote and close-range sensing techniques in the field of degradation and forest change assessment. This Special Issue contains 12 studies that provided insight into new advances in the field of remote sensing for forest management and REDD+. This includes developments into algorithm development using satellite data; synthetic aperture radar (SAR); airborne and terrestrial LiDAR; as well as forest reference emissions level (FREL) frameworks.
Keywords	algorithms ; data collection ; emissions ; forest management ; forests ; lidar ; monitoring ; national forests ; reducing emissions from deforestation and forest degradation ; remote sensing ; synthetic aperture radar
Language	English
Dates of publication	2020-0207
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article
ZDB-ID	2527081-3
ISSN	1999-4907
ISSN	1999-4907
DOI	10.3390/f11020188
Database	NAL-Catalogue (AGRICOLA)

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Article: Assessing the Dependencies of Scots Pine (Pinus sylvestris L.) Structural Characteristics and Internal Wood Property Variation

Kankare, Ville / Saarinen, Ninni / Pyörälä, Jiri / Yrttimaa, Tuomas / Hynynen, Jari / Huuskonen, Saija / Hyyppä, Juha / Vastaranta, Mikko

Forests. 2022 Feb. 28, v. 13, no. 3

2022

Abstract: Wood density is well known to vary between tree species as well as within and between trees of a certain species depending on the growing environment causing uncertainties in forest biomass and carbon storage estimation. This has created a need to ... ...

Abstract	Wood density is well known to vary between tree species as well as within and between trees of a certain species depending on the growing environment causing uncertainties in forest biomass and carbon storage estimation. This has created a need to develop novel methodologies to obtain wood density information over multiple tree communities, landscapes, and ecoregions. Therefore, the aim of this study was to evaluate the dependencies between structural characteristics of Scots pine (Pinus sylvestris L.) tree communities and internal wood property (i.e., mean wood density and ring width) variations at breast height. Terrestrial laser scanning was used to derive the structural characteristics of even-aged Scots pine dominated forests with varying silvicultural treatments. Pearson’s correlations and linear mixed effect models were used to evaluate the interactions. The results show that varying silvicultural treatments did not have a statistically significant effect on the mean wood density. A notably stronger effect was observed between the structural characteristics and the mean ring width within varying treatments. It can be concluded that single time terrestrial laser scanning is capable of capturing the variability of structural characteristics and their interactions with mean ring width within different silvicultural treatments but not the variation of mean wood density.
Keywords	Pinus sylvestris ; biomass ; carbon sequestration ; forests ; trees ; wood ; wood density
Language	English
Dates of publication	2022-0228
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article
ZDB-ID	2527081-3
ISSN	1999-4907
ISSN	1999-4907
DOI	10.3390/f13030397
Database	NAL-Catalogue (AGRICOLA)

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Article: Airborne laser scanning reveals large tree trunks on forest floor

Heinaro, Einari / Tanhuanpää, Topi / Yrttimaa, Tuomas / Holopainen, Markus / Vastaranta, Mikko

Forest ecology and management. 2021 July 01, v. 491

2021

Abstract: Fallen trees decompose on the forest floor and create habitats for many species. Thus, mapping fallen trees allows identifying the most valuable areas regarding biodiversity, especially in boreal forests, enabling well-focused conservation and ... ...

Abstract	Fallen trees decompose on the forest floor and create habitats for many species. Thus, mapping fallen trees allows identifying the most valuable areas regarding biodiversity, especially in boreal forests, enabling well-focused conservation and restoration actions. Airborne laser scanning (ALS) is capable of characterizing forests and the underlying topography. However, its potential for detecting and characterizing fallen trees under varying boreal forest conditions is not yet well understood. ALS-based fallen tree detection methods could improve our understanding regarding the spatiotemporal characteristics of dead wood over large landscapes. We developed and tested an automatic method for mapping individual fallen trees from an ALS point cloud with a point density of 15 points/m². The presented method detects fallen trees using iterative Hough line detection and delineates the trees around the detected lines using region growing. Furthermore, we conducted a detailed evaluation of how the performance of ALS-based fallen tree detection is impacted by characteristics of fallen trees and the structure of vegetation around them. The results of this study showed that large fallen trees can be detected with a high accuracy in old-growth forests. In contrast, the detection of fallen trees in young managed stands proved challenging. The presented method was able to detect 78% of the largest fallen trees (diameter at breast height, DBH > 300 mm), whereas 30% of all trees with a DBH over 100 mm were detected. The performance of the detection method was positively correlated with both the size of fallen trees and the size of living trees surrounding them. In contrast, the performance was negatively correlated with the amount of undergrowth, ground vegetation, and the state of decay of fallen trees. Especially undergrowth and ground vegetation impacted the performance negatively, as they covered some of the fallen trees and lead to false fallen tree detections. Based on the results of this study, ALS-based collection of fallen tree information should be focused on old-growth forests and mature managed forests, at least with the current operative point densities.
Keywords	administrative management ; biodiversity ; boreal forests ; data collection ; dead wood ; forest litter ; ground vegetation ; topography ; tree and stand measurements ; trees
Language	English
Dates of publication	2021-0701
Publishing place	Elsevier B.V.
Document type	Article
Note	NAL-AP-2-clean
ZDB-ID	751138-3
ISSN	0378-1127
ISSN	0378-1127
DOI	10.1016/j.foreco.2021.119225
Database	NAL-Catalogue (AGRICOLA)

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Book ; Online: Remote Sensing Technology Applications in Forestry and REDD+

Calders, Kim / Jonckheere, Inge / Vastaranta, Mikko / Nightingale, Joanne

2020

Abstract	Advances in close-range and remote sensing technologies are driving innovations in forest resource assessments and monitoring on varying scales. Data acquired with airborne and spaceborne platforms provide high(er) spatial resolution, more frequent coverage, and more spectral information. Recent developments in ground-based sensors have advanced 3D measurements, low-cost permanent systems, and community-based monitoring of forests. The UNFCCC REDD+ mechanism has advanced the remote sensing community and the development of forest geospatial products that can be used by countries for the international reporting and national forest monitoring. However, an urgent need remains to better understand the options and limitations of remote and close-range sensing techniques in the field of forest degradation and forest change. Therefore, we invite scientists working on remote sensing technologies, close-range sensing, and field data to contribute to this Special Issue. Topics of interest include: (1) novel remote sensing applications that can meet the needs of forest resource information and REDD+ MRV, (2) case studies of applying remote sensing data for REDD+ MRV, (3) timeseries algorithms and methodologies for forest resource assessment on different spatial scales varying from the tree to the national level, and (4) novel close-range sensing applications that can support sustainable forestry and REDD+ MRV. We particularly welcome submissions on data fusion.
Keywords	sentinel imagery ; above-ground biomass ; predictive mapping ; machine learning ; geographically weighted regression ; canopy cover (CC) ; spectral ; texture ; digital hemispherical photograph (DHP) ; random forest (RF) ; gray level co-occurrence matrix (GLCM) ; forest inventory ; LiDAR ; tall trees ; overstory trees ; tree mapping ; crown delineation ; aboveground biomass ; Landsat ; random forest ; topography ; human activity ; aboveground biomass estimation ; remote sensing ; crown density ; low-accuracy estimation ; model comparison ; old-growth forest ; multispectral satellite imagery ; forest classification ; forestry ; phenology ; silviculture ; forest growing stock volume (GSV) ; full polarimetric SAR ; subtropical forest ; topographic effects ; environment effects ; geographic information system ; support vector machine ; ensemble model ; hazard mapping ; 3D tree modelling ; destructive sampling ; Guyana ; local tree allometry ; model evaluation ; quantitative structural model ; Pinus massoniana ; specific leaf area ; leaf area ; terrestrial laser scanning ; voxelization ; forest canopy ; REDD+ ; Cameroon ; reference level ; deforestation ; agriculture ; forest baseline ; airborne laser scanning
Language	English
Publisher	MDPI - Multidisciplinary Digital Publishing Institute
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Using Leaf-Off and Leaf-On Multispectral Airborne Laser Scanning Data to Characterize Seedling Stands

Imangholiloo, Mohammad / Saarinen, Ninni / Holopainen, Markus / Yu, Xiaowei / Hyyppä, Juha / Vastaranta, Mikko

Remote Sensing. 2020 Oct. 13, v. 12, no. 20

2020

Abstract: Information from seedling stands in time and space is essential for sustainable forest management. To fulfil these informational needs with limited resources, remote sensing is seen as an intriguing alternative for forest inventorying. The structure and ... ...

Abstract	Information from seedling stands in time and space is essential for sustainable forest management. To fulfil these informational needs with limited resources, remote sensing is seen as an intriguing alternative for forest inventorying. The structure and tree species composition in seedling stands have created challenges for capturing this information using sensors providing sparse point densities that do not have the ability to penetrate canopy gaps or provide spectral information. Therefore, multispectral airborne laser scanning (mALS) systems providing dense point clouds coupled with multispectral intensity data theoretically offer advantages for the characterization of seedling stands. The aim of this study was to investigate the capability of Optech Titan mALS data to characterize seedling stands in leaf-off and leaf-on conditions, as well as to retrieve the most important forest inventory attributes, such as distinguishing deciduous from coniferous trees, and estimating tree density and height. First, single-tree detection approaches were used to derive crown boundaries and tree heights from which forest structural attributes were aggregated for sample plots. To predict tree species, a random forests classifier was trained using features from two single-channel intensities (SCIs) with wavelengths of 1550 (SCI-Ch1) and 1064 nm (SCI-Ch2), and multichannel intensity (MCI) data composed of three mALS channels. The most important and uncorrelated features were analyzed and selected from 208 features. The highest overall accuracies in classification of Norway spruce, birch, and nontree class in leaf-off and leaf-on conditions obtained using SCI-Ch1 and SCI-Ch2 were 87.36% and 69.47%, respectively. The use of MCI data improved classification by up to 96.55% and 92.54% in leaf-off and leaf-on conditions, respectively. Overall, leaf-off data were favorable for distinguishing deciduous from coniferous trees and tree density estimation with a relative root mean square error (RMSE) of 37.9%, whereas leaf-on data provided more accurate height estimations, with a relative RMSE of 10.76%. Determining the canopy threshold for separating ground returns from vegetation returns was found to be critical, as mapped trees might have a height below one meter. The results showed that mALS data provided benefits for characterizing seedling stands compared to single-channel ALS systems.
Keywords	Betula ; Picea abies ; canopy gaps ; classification ; conifers ; data collection ; decision support systems ; density ; detection ; estimation ; forest inventory ; forests ; height ; information ; lidar ; remote sensing ; seedlings ; species diversity ; sustainable forestry ; trees ; wavelengths
Language	English
Dates of publication	2020-1013
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article
Note	NAL-light
ZDB-ID	2513863-7
ISSN	2072-4292
ISSN	2072-4292
DOI	10.3390/rs12203328
Database	NAL-Catalogue (AGRICOLA)

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Article: Comparison of Backpack, Handheld, Under-Canopy UAV, and Above-Canopy UAV Laser Scanning for Field Reference Data Collection in Boreal Forests

Hyyppä, Eric / Yu, Xiaowei / Kaartinen, Harri / Hakala, Teemu / Kukko, Antero / Vastaranta, Mikko / Hyyppä, Juha

Remote Sensing. 2020 Oct. 13, v. 12, no. 20

2020

Abstract: In this work, we compared six emerging mobile laser scanning (MLS) technologies for field reference data collection at the individual tree level in boreal forest conditions. The systems under study were an in-house developed AKHKA-R3 backpack laser ... ...

Abstract	In this work, we compared six emerging mobile laser scanning (MLS) technologies for field reference data collection at the individual tree level in boreal forest conditions. The systems under study were an in-house developed AKHKA-R3 backpack laser scanner, a handheld Zeb-Horizon laser scanner, an under-canopy UAV (Unmanned Aircraft Vehicle) laser scanning system, and three above-canopy UAV laser scanning systems providing point clouds with varying point densities. To assess the performance of the methods for automated measurements of diameter at breast height (DBH), stem curve, tree height and stem volume, we utilized all of the six systems to collect point cloud data on two 32 m-by-32 m test sites classified as sparse (n = 42 trees) and obstructed (n = 43 trees). To analyze the data collected with the two ground-based MLS systems and the under-canopy UAV system, we used a workflow based on our recent work featuring simultaneous localization and mapping (SLAM) technology, a stem arc detection algorithm, and an iterative arc matching algorithm. This workflow enabled us to obtain accurate stem diameter estimates from the point cloud data despite a small but relevant time-dependent drift in the SLAM-corrected trajectory of the scanner. We found out that the ground-based MLS systems and the under-canopy UAV system could be used to measure the stem diameter (DBH) with a root mean square error (RMSE) of 2–8%, whereas the stem curve measurements had an RMSE of 2–15% that depended on the system and the measurement height. Furthermore, the backpack and handheld scanners could be employed for sufficiently accurate tree height measurements (RMSE = 2–10%) in order to estimate the stem volumes of individual trees with an RMSE of approximately 10%. A similar accuracy was obtained when combining stem curves estimated with the under-canopy UAV system and tree heights extracted with an above-canopy flying laser scanning unit. Importantly, the volume estimation error of these three MLS systems was found to be of the same level as the error corresponding to manual field measurements on the two test sites. To analyze point cloud data collected with the three above-canopy flying UAV systems, we used a random forest model trained on field reference data collected from nearby plots. Using the random forest model, we were able to estimate the DBH of individual trees with an RMSE of 10–20%, the tree height with an RMSE of 2–8%, and the stem volume with an RMSE of 20–50%. Our results indicate that ground-based and under-canopy MLS systems provide a promising approach for field reference data collection at the individual tree level, whereas the accuracy of above-canopy UAV laser scanning systems is not yet sufficient for predicting stem attributes of individual trees for field reference data with a high accuracy.
Keywords	accuracy ; algorithms ; automation ; boreal forests ; data collection ; detection ; diameter ; estimation ; flight ; measurement ; prediction ; remote sensing ; scanners ; trajectories ; tree and stand measurements ; tree height ; trees ; unmanned aerial vehicles ; volume
Language	English
Dates of publication	2020-1013
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article
Note	NAL-light
ZDB-ID	2513863-7
ISSN	2072-4292
ISSN	2072-4292
DOI	10.3390/rs12203327
Database	NAL-Catalogue (AGRICOLA)

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Article: Multispectral Imagery Provides Benefits for Mapping Spruce Tree Decline Due to Bark Beetle Infestation When Acquired Late in the Season

Junttila, Samuli / Näsi, Roope / Koivumäki, Niko / Imangholiloo, Mohammad / Saarinen, Ninni / Raisio, Juha / Holopainen, Markus / Hyyppä, Hannu / Hyyppä, Juha / Lyytikäinen-Saarenmaa, Päivi / Vastaranta, Mikko / Honkavaara, Eija

Remote Sensing. 2022 Feb. 14, v. 14, no. 4

2022

Abstract: Climate change is increasing pest insects’ ability to reproduce as temperatures rise, resulting in vast tree mortality globally. Early information on pest infestation is urgently needed for timely decisions to mitigate the damage. We investigated the ... ...

Abstract	Climate change is increasing pest insects’ ability to reproduce as temperatures rise, resulting in vast tree mortality globally. Early information on pest infestation is urgently needed for timely decisions to mitigate the damage. We investigated the mapping of trees that were in decline due to European spruce bark beetle infestation using multispectral unmanned aerial vehicles (UAV)-based imagery collected in spring and fall in four study areas in Helsinki, Finland. We used the Random Forest machine learning to classify trees based on their symptoms during both occasions. Our approach achieved an overall classification accuracy of 78.2% and 84.5% for healthy, declined and dead trees for spring and fall datasets, respectively. The results suggest that fall or the end of summer provides the most accurate tree vitality classification results. We also investigated the transferability of Random Forest classifiers between different areas, resulting in overall classification accuracies ranging from 59.3% to 84.7%. The findings of this study indicate that multispectral UAV-based imagery is capable of classifying tree decline in Norway spruce trees during a bark beetle infestation.
Keywords	Picea abies ; Scolytidae ; bark beetle infestations ; climate change ; data collection ; decline ; forestry equipment ; multispectral imagery ; spring ; summer ; tree mortality ; trees ; unmanned aerial vehicles ; Finland
Language	English
Dates of publication	2022-0214
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article
ZDB-ID	2513863-7
ISSN	2072-4292
ISSN	2072-4292
DOI	10.3390/rs14040909
Database	NAL-Catalogue (AGRICOLA)

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