LiDAR remote sensing of forest structure

Lim, Kevin; Treitz, Paul; Wulder, Michael A.; St-Onge, Benoît; Flood, Martin

doi:10.1191/0309133303pp360ra

Cited by 919 publications

(589 citation statements)

References 39 publications

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“…These emitted laser pulses are reflected from the terrain or from objects on the terrain, such as vegetation or buildings. The reflected pulses can be categorized as either first or last pulse returns (Lim et al 2003). Although LIDAR pulses interact with the terrain and all materials above the ground to produce a complex cloud of points, increasingly reliable post-processing procedures for distinguishing between ground and object points are being implemented (e.g., Roggero 2001; Sithole and Vosselman 2004;Chen et al 2007).…”

Section: Lidarmentioning

confidence: 99%

“…LIDAR data (either as a direct measure or as a data input for modeling) have been used to characterize forest attributes such as stand height (Naesset et al 2005), canopy structure (Ni-Meister et al 2001), crown closure (Lim et al 2003), biomass (Popescu 2007), and canopy volume (Lefsky et al 2005;Coops et al 2007;Heo et al 2008). Although LIDAR has largely been used for collecting highresolution geospatial data over vegetated areas, only in the last few years have natural resource scientists begun to use LIDAR for forest structure mapping and inventory purposes (Reutebuch et al 2003).…”

Section: Lidarmentioning

confidence: 99%

“…The estimation of tree height and stand height from LIDAR data has been the subject of extensive research (for summary, see Lim et al 2003). When mean stand height is computed from field measurements, the heights of individual trees are typically weighted according to their basal area (Lorey's mean height); therefore, larger trees have a greater impact on the estimation of mean stand height.…”

Section: Stand Height Estimationmentioning

confidence: 99%

“…LIDAR data are used to produce accurate depictions of tree height and structure (Lim et al 2003) and, when collected with an appropriate density of hits on the ground (Lovell et al 2005), may be used to generate accurate models of stand height (Naesset and Økland 2002).…”

Section: Introductionmentioning

confidence: 99%

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Implications of differing input data sources and approaches upon forest carbon stock estimation

Wulder

White

Stinson

et al. 2009

Environ Monit Assess

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Site index is an important forest inventory attribute that relates productivity and growth expectation of forests over time. In forest inventory programs, site index is used in conjunction with other forest inventory attributes (i.e., height, age) for the estimation of stand volume. In turn, stand volumes are used to estimate biomass (and biomass components) and enable conversion to carbon. In this research, we explore the implications and consequences of different estimates of site index on carbon stock characterization for a 2,500-ha Douglas-fir-dominated landscape located on Eastern Vancouver Island, British Columbia, Canada. We compared site index estimates from an existing forest inventory to estimates generated from a combination of forest inventory and light detection and ranging (LIDAR)-derived attributes and then examined the resultant differences in biomass estimates generated from a carbon budget model (Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3)). Significant differences were found between the original and LIDAR-derived site indices for all species types and for the resulting 5-m site classes (p < 0.001). The LIDAR-derived site class was greater than the original site class for 42% of stands; however, 77% of stands were within ±1 site class of the original class. Differences in biomass estimates between the model scenarios were significant for both total stand biomass and biomass per hectare ( p < 0.001); differences for Douglas-fir-dominated stands (representing 85% of all stands) were not significant ( p = 0.288). Overall, the relationship between the two biomass estimates was strong (R 2 = 0.92, p < 0.001), suggesting that in certain circumstances, LIDAR may have a role to play in site index estimation and biomass mapping.

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Section: Lidarmentioning

confidence: 99%

Section: Lidarmentioning

confidence: 99%

Section: Stand Height Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Implications of differing input data sources and approaches upon forest carbon stock estimation

Wulder

White

Stinson

et al. 2009

Environ Monit Assess

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“…For example, rmse of mean volume at the stand or plot level of area-based ALS inventory has been found to ranging between 10% and 27% (e.g., [4,5,7,41,51,52]). …”

Section: Forest Property Dmentioning

confidence: 99%

Uncertainty in Forest Net Present Value Estimations

Holopainen

Mäkinen

Rasinmäki³

et al. 2010

Forests

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Uncertainty related to inventory data, growth models and timber price fluctuation was investigated in the assessment of forest property net present value (NPV). The degree of uncertainty associated with inventory data was obtained from previous area-based airborne laser scanning (ALS) inventory studies. The study was performed, applying the Monte Carlo simulation, using stand-level growth and yield projection models and three alternative rates of interest (3, 4 and 5%). Timber price fluctuation was portrayed with geometric mean-reverting (GMR) price models. The analysis was conducted for four alternative forest properties having varying compartment structures: (A) a property having an even development class distribution, (B) sapling stands, (C) young thinning stands, and (D) mature stands. Simulations resulted in predicted yield value (predicted NPV) distributions at both stand and property levels. Our results showed that ALS inventory errors were the most prominent source of uncertainty, leading to a 5.1-7.5% relative deviation of property-level NPV when an interest rate of 3% was applied. Interestingly, ALS inventory led to significant biases at the property level, ranging from 8.9% to 14.1% (3% interest rate). ALS inventory-based bias was the most significant in mature stand properties. Errors related to the growth predictions led to a relative standard deviation in NPV, varying from 1.5% to 4.1%. Growth model-related uncertainty was most significant OPEN ACCESSForests 2010, 1 178 in sapling stand properties. Timber price fluctuation caused the relative standard deviations ranged from 3.4% to 6.4% (3% interest rate). The combined relative variation caused by inventory errors, growth model errors and timber price fluctuation varied, depending on the property type and applied rates of interest, from 6.4% to 12.6%. By applying the methodology described here, one may take into account the effects of various uncertainty factors in the prediction of forest yield value and to supply the output results with levels of confidence.

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A review of stemflow generation dynamics and stemflow‐environment interactions in forests and shrublands

Levia

Germer

2015

Reviews of Geophysics

289

271

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Many geoscientists now recognize stemflow as an important phenomenon which can exert considerable effects on the hydrology, biogeochemistry, and ecology of wooded ecosystems and shrublands. Despite the explosive growth of stemflow research, until this review there has been no comprehensive attempt to summarize and synthesize this literature since 2003. Topical areas of substantive new knowledge in stemflow research include the following: (1) the interrelationships among stemflow and meteorological conditions, especially within individual rain events; (2) the dynamic interplay between stemflow and canopy structure; (3) stemflow and the cycling of solutes and transport of particulate matter; (4) stemflow and its interactions with canopy fungi and corticolous lichens; and (5) stemflow-soil interactions. Each of these five topical areas of substantive new stemflow research is summarized and synthesized, with areas of future research opportunities discussed. In addition, we have reviewed the parameters which can be used to describe stemflow and critically evaluate their utility for different purposes. This review makes a call for scientists studying stemflow to utilize common metrics in an effort to increase the cross-site comparability of stemflow studies. Capitalizing on the insights of prior research, exciting research opportunities await hydrologists, biogeoscientists, and forest ecologists who will conduct studies to deepen our knowledge of stemflow which will enable a better and more accurate framing of stemflow in the larger context of watershed hydrology and biogeochemistry.

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LiDAR remote sensing of forest structure

Cited by 919 publications

References 39 publications

Implications of differing input data sources and approaches upon forest carbon stock estimation

Implications of differing input data sources and approaches upon forest carbon stock estimation

Uncertainty in Forest Net Present Value Estimations

A review of stemflow generation dynamics and stemflow‐environment interactions in forests and shrublands

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