Prediction of forest canopy fuel parameters in managed boreal forests using multispectral and unispectral airborne laser scanning data and aerial images

Maltamo, Matti; Räty, Janne; Korhonen, Lauri; Kotivuori, Eetu; Kukkonen, Mikko; Peltola, Heli; Kangas, Jyrki; Packalén, Petteri

doi:10.1080/22797254.2020.1816142

Cited by 13 publications

(6 citation statements)

References 50 publications

(73 reference statements)

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“…Intensity measures were derived from the raw discrete-return LiDAR data (provided separately), but we did not have access to the proprietary software employed for correcting atmospheric and noise effects; thus, we could not regenerate the single-channel point clouds. Therefore, the contribution of each channel to the final models cannot be proved experimentally at the present time in our case, but there is at least a strong evidence from the results of Reference [73] mentioned in Section 1. Nevertheless, the existence of a second channel effectively doubles the point cloud density (reaching to an average of 82.99 points/m 2 ), which is highly expected to increase the surface fuel estimation potential, even if that cannot be proven experimentally.…”

Section: Discussionmentioning

confidence: 65%

“…The results suggested that multispectral intensity information is useful in the examination of forest understory. The estimation of canopy fuel parameters was addressed in Reference [73], where a comparative analysis of unispectral and multispectral LiDAR data was performed for the prediction of canopy fuel weight, canopy base height, vegetation height and biomass through regression analysis. The results of this research highlighted the usefulness of multispectral LiDAR compared to the unispectral LiDAR data in the examination of forest fuel parameters.…”

Section: Introductionmentioning

confidence: 99%

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Multispectral LiDAR-Based Estimation of Surface Fuel Load in a Dense Coniferous Forest

et al. 2020

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Surface fuel load (SFL) constitutes one of the most significant fuel components and is used as an input variable in most fire behavior prediction systems. The aim of the present study was to investigate the potential of discrete-return multispectral Light Detection and Ranging (LiDAR) data to reliably predict SFL in a coniferous forest characterized by dense overstory and complex terrain. In particular, a linear regression analysis workflow was employed with the separate and combined use of LiDAR-derived structural and pulse intensity information for the load estimation of the total surface fuels and individual surface fuel types. Following a leave-one-out cross-validation (LOOCV) approach, the models developed from the different sets of predictor variables were compared in terms of their estimation accuracy. LOOCV indicated that the predictive models produced by the combined use of structural and intensity metrics significantly outperformed the models constructed with the individual sets of metrics, exhibiting an explained variance (R2) between 0.59 and 0.71 (relative Root Mean Square Error (RMSE) 19.3–37.6%). Overall, the results of this research showcase that both structural and intensity variables provided by multispectral LiDAR data are significant for surface fuel load estimation and can successfully contribute to effective pre-fire management, including fire risk assessment and behavior prediction in case of a fire event.

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Section: Discussionmentioning

confidence: 65%

Section: Introductionmentioning

confidence: 99%

Multispectral LiDAR-Based Estimation of Surface Fuel Load in a Dense Coniferous Forest

et al. 2020

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“…Their findings revealed that although the incorporation of intensity metrics yielded a modest enhancement in accuracy, the significance of these metrics becomes particularly pronounced when dealing with lower-resolution data in the context of 1 m and 2 m voxel models. The results of Maltamo et al [ 113 ] substantiated the better efficiency of MSL in the prediction of forest canopy fuel parameters, including canopy fuel weight, canopy base height, biomass of living and dead trees, and height and biomass of the understory tree layer and site fertility. In 2023, Rana et al [ 114 ] showed that MSL is superior to the combination of traditional monochromatic LiDAR and color–infrared image in monitoring seedling stands.…”

Section: Multispectral Lidar Applicationsmentioning

confidence: 93%

Multispectral Light Detection and Ranging Technology and Applications: A Review

Takhtkeshha,

Mandlburger,

Remondino

et al. 2024

Sensors

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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.

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“…In fact, multispectral LiDAR systems have the ability to provide information on the vertical distribution of physiological processes in different spectral channels [64]. In recent years, the multispectral LiDAR point clouds have been widely used for land cover classification [65], tree species composition [66], surface [37] and forest canopy fuel load estimation [67]. Additionally, the multispectral LiDAR data provided more accurate AGB estimations [68] and individual tree detection compared with the monospectral [69].…”

Section: Introductionmentioning

confidence: 99%

Estimating Crown Biomass in a Multilayered Fir Forest Using Airborne LiDAR Data

Georgopoulos,

Gitas,

Korhonen

et al. 2023

Remote Sensing

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The estimation of individual biomass components within tree crowns, such as dead branches (DB), needles (NB), and branch biomass (BB), has received limited attention in the scientific literature despite their significant contribution to forest biomass. This study aimed to assess the potential of multispectral LiDAR data for estimating these biomass components in a multi-layered Abies borissi-regis forest. Destructive (i.e., 13) and non-destructive (i.e., 156) field measurements were collected from Abies borisii-regis trees to develop allometric equations for each crown biomass component and enrich the reference data with the non-destructively sampled trees. A set of machine learning regression algorithms, including random forest (RF), support vector regression (SVR) and Gaussian process (GP), were tested for individual-tree-level DB, NB and BB estimation using LiDAR-derived height and intensity metrics for different spectral channels (i.e., green, NIR and merged) as predictors. The results demonstrated that the RF algorithm achieved the best overall predictive performance for DB (RMSE% = 17.45% and R2 = 0.89), NB (RMSE% = 17.31% and R2 = 0.93) and BB (RMSE% = 24.09% and R2 = 0.85) using the green LiDAR channel. This study showed that the tested algorithms, particularly when utilizing the green channel, accurately estimated the crown biomass components of conifer trees, specifically fir. Overall, LiDAR data can provide accurate estimates of crown biomass in coniferous forests, and further exploration of this method’s applicability in diverse forest structures and biomes is warranted.

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Prediction of forest canopy fuel parameters in managed boreal forests using multispectral and unispectral airborne laser scanning data and aerial images

Cited by 13 publications

References 50 publications

Multispectral LiDAR-Based Estimation of Surface Fuel Load in a Dense Coniferous Forest

Multispectral LiDAR-Based Estimation of Surface Fuel Load in a Dense Coniferous Forest

Multispectral Light Detection and Ranging Technology and Applications: A Review

Estimating Crown Biomass in a Multilayered Fir Forest Using Airborne LiDAR Data

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