Estimating individual tree heights of sugi (<i>Cryptomeria japonica</i> D. Don) plantations in mountainous areas using small-footprint airborne LiDAR

Takahashi, Takehiko; Yamamoto, Katsumi; Senda, Yoshimichi; Tsuzuku, Masashi

doi:10.1007/s10310-004-0125-8

Cited by 68 publications

(58 citation statements)

References 26 publications

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“…Plot slope, in general, has been found to be important previously too: For individual tree heights, it has been reported that underestimates of tree height were larger on lower elevations (−0.85 m) than on higher elevations (0.17 m) [38]. The importance of slope for the accuracy of Lidar-derived tree detection has also been studied [39]. It was reported that the accuracy of tree detection is 74% for steep slopes but is 86% for gentle slopes.…”

Section: Variable Importance From the Random Forest Modelmentioning

confidence: 99%

Prediction of Canopy Heights over a Large Region Using Heterogeneous Lidar Datasets: Efficacy and Challenges

et al. 2015

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Generating accurate and unbiased wall-to-wall canopy height maps from airborne lidar data for large regions is useful to forest scientists and natural resource managers. However, mapping large areas often involves using lidar data from different projects, with varying acquisition parameters. In this work, we address the important question of whether one can accurately model canopy heights over large areas of the Southeastern US using a very heterogeneous dataset of small-footprint, discrete-return airborne lidar data (with 76 separate lidar projects). A unique aspect of this effort is the use of nationally uniform and extensive field data (~1800 forested plots) from the Forest Inventory and Analysis (FIA) program of the US Forest Service. Preliminary results are quite promising: Over all lidar projects, we observe a good correlation between the 85th percentile of lidar heights and field-measured height (r = 0.85). We construct a linear regression model to predict subplot-level dominant tree heights from distributional lidar metrics (R 2 = 0.74, RMSE = 3.0 m, n = 1755). We also identify and quantify the importance of several factors (like heterogeneity of vegetation, point density, the predominance of hardwoods or softwoods, the average height of the forest stand, slope of the plot, and average scan angle of lidar acquisition) that influence the efficacy of predicting canopy heights from lidar data. For example, a subset of plots (coefficient of variation of vegetation heights <0.2) significantly reduces the RMSE of our model from 3.0-2.4 m (~20% reduction). We conclude that when all these elements are factored OPEN ACCESSRemote Sens. 2015, 7 11037 into consideration, combining data from disparate lidar projects does not preclude robust estimation of canopy heights.

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Section: Variable Importance From the Random Forest Modelmentioning

confidence: 99%

Prediction of Canopy Heights over a Large Region Using Heterogeneous Lidar Datasets: Efficacy and Challenges

et al. 2015

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“…A LiDAR point density of 10 points∕m 2 or more would significantly improve the ability to accurately map PPC and vegetation ground cover and detect detailed slope variation of stream banks at higher spatial resolution and enable a reduction of the plot section length. 32,67,68 For assessment of the mapping results and for comparison of different stream sections, it is recommended that sections covering at least one meander wavelength be used, as the outside of meander bends are often exposed to erosive processes and hence appear with low bank condition scores, whereas point bars are generally more stable. 40 Using at least one meander wavelength for assessment of bank condition mapping results will ensure a less biased comparison of different stream sections.…”

Section: Spatial Scaling Considerationsmentioning

confidence: 99%

Assessing stream bank condition using airborne LiDAR and high spatial resolution image data in temperate semirural areas in Victoria, Australia

Johansen

Grove

Denham

2013

J. Appl. Remote Sens

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Abstract. Stream bank condition is an important physical form indicator for streams related to the environmental condition of riparian corridors. This research developed and applied an approach for mapping bank condition from airborne light detection and ranging (LiDAR) and high-spatial resolution optical image data in a temperate forest/woodland/urban environment. Field observations of bank condition were related to LiDAR and optical image-derived variables, including bank slope, plant projective cover, bank-full width, valley confinement, bank height, bank top crenulation, and ground vegetation cover. Image-based variables, showing correlation with the field measurements of stream bank condition, were used as input to a cumulative logistic regression model to estimate and map bank condition. The highest correlation was achieved between field-assessed bank condition and image-derived average bank slope (R 2 ¼ 0.60, n ¼ 41), ground vegetation cover (R 2 ¼ 0.43, n ¼ 41), bank width/height ratio (R 2 ¼ 0.41, n ¼ 41), and valley confinement (producer's accuracy ¼ 100%, n ¼ 9). Crossvalidation showed an average misclassification error of 0.95 from an ordinal scale from 0 to 4 using the developed model. This approach was developed to support the remotely sensed mapping of stream bank condition for 26,000 km of streams in Victoria, Australia, from 2010 to 2012.

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“…Similarly Naesset and Økland (2002) found an average point spacing of 1 m was insufficient to accurately estimate individual crown attributes (height to green canopy and crown length) in a Norway spruce (Picea abies (L.) Karst.) and Scots pine (Pinus sylvestris L.) forest, whilst Takahashi et al (2005) required data as dense as 8.8 LiDAR returns per m 2 to successfully estimate tree heights to within 1 m of field estimates in sugi (Cryptomeria japonica D. Don) plantation forests.…”

Section: Individual Tree-based Assessmentmentioning

confidence: 99%

The role of LiDAR in sustainable forest management

Wulder¹,

Bater²,

Coops³

et al. 2008

The Forestry Chronicle

307

183

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Forest characterization with light detection and ranging (LiDAR) data has recently garnered much scientific and operational attention. The number of forest inventory attributes that may be directly measured with LiDAR is limited; however, when considered within the context of all the measured and derived attributes required to complete a forest inventory, LiDAR can be a valuable tool in the inventory process. In this paper, we present the status of LiDAR remote sensing of forests, including issues related to instrumentation, data collection, data processing, costs, and attribute estimation. The information needs of sustainable forest management provide the context within which we consider future opportunities for LiDAR and automated data processing.Key words: LiDAR, airborne laser altimetry, forest inventory, height, volume, biomass, update, remote sensing RÉSUMÉ La représentation des forêts à partir de données LiDAR (détection de la lumière et calcul de la distance) a attiré dernière-ment beaucoup d'attention tant scientifique qu' opérationnelle. Le nombre de variables d'inventaire forestier qui peuvent être mesurées directement par LiDAR est limité, mais lorsqu' on considère le contexte de toutes les variables mesurées et dérivées requises pour compléter un inventaire forestier, le LiDAR peut constituer un outil précieux du processus d'inventaire. Nous présentons dans cet article un portrait de la télédétection des forêts par LiDAR, ainsi que les questions portant sur l'appareillage, la collecte des données, les coûts et l' estimation des variables. Les besoins d'information en matière d'aménagement forestier durable constituent le contexte que nous retenons pour les possibilités d'application future du LiDAR et du traitement automatisé des données.

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Estimating individual tree heights of sugi (Cryptomeria japonica D. Don) plantations in mountainous areas using small-footprint airborne LiDAR

Cited by 68 publications

References 26 publications

Prediction of Canopy Heights over a Large Region Using Heterogeneous Lidar Datasets: Efficacy and Challenges

Prediction of Canopy Heights over a Large Region Using Heterogeneous Lidar Datasets: Efficacy and Challenges

Assessing stream bank condition using airborne LiDAR and high spatial resolution image data in temperate semirural areas in Victoria, Australia

The role of LiDAR in sustainable forest management

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