The role of LiDAR in sustainable forest management

Wulder, Michael A.; Bater, Christopher W.; Coops, Nicholas C.; Hilker, Thomas; White, Joanne C.

doi:10.5558/tfc84807-6

Cited by 318 publications

(196 citation statements)

References 76 publications

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“…Laser pulse densities of 0.5Á1.0 pulses m (2 are typically used for areabased inventories and provide stable and accurate results, at least in conifer forests (Maltamo et al, 2006a;Gobakken & Naesset, 2009;Magnussen et al, 2010). Wulder et al (2008a) assert that lidar now has moved from research and development to operational implementation over a broad range of forest inventory attributes. Tests are also being conducted to incorporate ALS as an operational tool to facilitate forest management and inventories in other parts of the world: Australia (Rombouts et al, 2010), Austria (Hollaus et al, 2007), Canada (Thomas et al, 2006), Germany (Breidenbach et al, 2008) and the United States of America (Jensen et al, 2006).…”

Section: Lidar Applications For Forest Management Inventoriesmentioning

confidence: 99%

“…However, when sensors or acquisition parameters differ between AOIs, models should be constructed using locally acquired data to reduce the risk of bias in the estimators as a result of model misspecification (Naesset, 2009). Wulder et al (2008a) identifies factors that should be considered when planning acquisition of ALS data for forest inventories with specific focus on Canadian conditions.…”

Section: Additional Considerationsmentioning

confidence: 99%

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Using remotely sensed data to construct and assess forest attribute maps and related spatial products

McRoberts

Cohen

Næsset

et al. 2010

Scandinavian Journal of Forest Research

111

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Tremendous advances in the construction and assessment of forest attribute maps and related spatial products have been realized in recent years, partly as a result of the use of remotely sensed data as an information source. This review focuses on the current state of techniques for the construction and assessment of remote sensing-based maps and addresses five topic areas: statistical classification and prediction techniques used to construct maps and related spatial products, accuracy assessment methods, map-based statistical inference, and two emerging topics, change detection and use of lidar data. Multiple general conclusions were drawn from the review: (1) remotely sensed data greatly contribute to the construction of forest attribute maps and related spatial products and to the reduction of inventory costs; (2) parametric prediction techniques, accuracy assessment methods and probability-based (design-based) inferential methods are generally familiar and mature, although inference is surprisingly seldom addressed; (3) non-parametric prediction techniques and modelbased inferential methods lack maturity and merit additional research; (4) change detection methods, with their great potential for adding a spatial component to change estimates, will mature rapidly; and (5) lidar applications, although currently immature, add an entirely new dimension to remote sensing research and will also mature rapidly. Crucial forest sustainability and climate change applications will continue to push all aspects of remote sensing to the forefront of forest research and operations.

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Section: Lidar Applications For Forest Management Inventoriesmentioning

confidence: 99%

Section: Additional Considerationsmentioning

confidence: 99%

Using remotely sensed data to construct and assess forest attribute maps and related spatial products

McRoberts

Cohen

Næsset

et al. 2010

Scandinavian Journal of Forest Research

111

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“…Ground truthing LiDAR data consist of randomly locating plots across the landscape, measuring the trees on the plot, and georeferencing the trees so that they can be located in the LidAR data set for crown delineation (Wulder et al, 2008). LiDAR datasets can be used to describe large areas of forested landscape at one time.…”

Section: Airborne Lidar Scanner (Als)mentioning

confidence: 99%

A Comparison of Selected Parametric and Non-Parametric Imputation Methods for Estimating Forest Biomass and Basal Area

Gagliasso¹,

Hummel²,

Temesgen³

2014

OJF

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Various methods have been used to estimate the amount of above ground forest biomass across landscapes and to create biomass maps for specific stands or pixels across ownership or project areas. Without an accurate estimation method, land managers might end up with incorrect biomass estimate maps, which could lead them to make poorer decisions in their future management plans. The goal of this study was to compare various imputation methods to predict forest biomass and basal area, at a project planning scale (<20,000 acres) on the Malheur National Forest, located in eastern Oregon, USA. We examined the predictive performance of linear regression, geographic weighted regression (GWR), gradient nearest neighbor (GNN), most similar neighbor (MSN), random forest imputation, and k-nearest neighbor (k-nn) to estimate biomass (tons/acre) and basal area (sq. feet per acre) across 19,000 acres on the Malheur National Forest. To test the different methods, a combination of ground inventory plots, light detection and ranging (LiDAR) data, satellite imagery, and climate data was analyzed, and their root mean square error (RMSE) and bias were calculated. Results indicate that for biomass prediction, the k-nn (k = 5) had the lowest RMSE and least amount of bias. The second most accurate method consisted of the k-nn (k = 3), followed by the GWR model, and the random forest imputation. For basal area prediction, the GWR model had the lowest RMSE and least amount of bias. The second most accurate method was k-nn (k = 5), followed by k-nn (k = 3), and the random forest method. For both metrics, the GNN method was the least accurate based on the ranking of RMSE and bias.

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“…Two published studies present actual costs: Wulder et al (2008) estimated a cost of ~ $1 per acre ($3 per hectare) for mapping with low-density (0.1 pulse per square foot, or 1 pulse per square meter) discrete Lidar data. Renslow et al (2000) outlined a forest management scenario for a typical even-aged, managed forest of 500,000 acres in which 2% of 10,000 acres Lidar costs are declining, and there are potential savings for forest managers who wish to use Lidar data as long as they can focus on plot-level measurements .…”

Section: Future Of Lidarmentioning

confidence: 99%