Linear Models for Airborne-Laser-Scanning-Based Operational Forest Inventory With Small Field Sample Size and Highly Correlated LiDAR Data

Junttila, Virpi; Kauranne, Tuomo; Finley, Andrew O.; Bradford, John B.

doi:10.1109/tgrs.2015.2425916

Cited by 27 publications

(29 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In a forest inventory context, recent research has focused on species characterization (Ørka et al 2013;Yu et al 2014, tree size or diameter distributions (Magnussen et al 2013;Saad et al 2015;Kankare et al 2015;Mehtätalo et al 2015;Tompalski et al 2015, Xu et al 2014, and exploration of issues that directly impact the cost and efficiency of the ABA (Fekety et al 2015;Junttila et al 2015;Keränen et al 2015;White, Arnett, et al 2015;Packalén et al 2015). Species composition information is required in order to inform a broad range of forest management information needs, including biodiversity, sustainable harvesting, and silvicultural prescriptions, to name but a few.…”

Section: Advanced Remote Sensing Technologies and Their Current Use Imentioning

confidence: 99%

Remote Sensing Technologies for Enhancing Forest Inventories: A Review

White

Coops

Wulder

et al. 2016

Canadian Journal of Remote Sensing

617

354

View full text Add to dashboard Cite

Abstract. Forest inventory and management requirements are changing rapidly in the context of an increasingly complex set of economic, environmental, and social policy objectives. Advanced remote sensing technologies provide data to assist in addressing these escalating information needs and to support the subsequent development and parameterization of models for an even broader range of information needs. This special issue contains papers that use a variety of remote sensing technologies to derive forest inventory or inventory-related information. Herein, we review the potential of 4 advanced remote sensing technologies, which we posit as having the greatest potential to influence forest inventories designed to characterize forest resource information for strategic, tactical, and operational planning: airborne laser scanning (ALS), terrestrial laser scanning (TLS), digital aerial photogrammetry (DAP), and high spatial resolution (HSR)/very high spatial resolution (VHSR) satellite optical imagery. ALS, in particular, has proven to be a transformative technology, offering forest inventories the required spatial detail and accuracy across large areas and a diverse range of forest types. The coupling of DAP with ALS technologies will likely have the greatest impact on forest inventory practices in the next decade, providing capacity for a broader suite of attributes, as well as for monitoring growth over time.

show abstract

Section: Advanced Remote Sensing Technologies and Their Current Use Imentioning

confidence: 99%

Remote Sensing Technologies for Enhancing Forest Inventories: A Review

White

Coops

Wulder

et al. 2016

Canadian Journal of Remote Sensing

617

354

View full text Add to dashboard Cite

show abstract

“…Many THMs are highly correlated with one another, and so care must be taken during model development to avoid issues of multicollinearity [15]. Furthermore, THMs generated from one LiDAR data set are sometimes not stable when applied to another due to variation in acquisition parameters such as laser penetration, pulse density, and scan angle [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

The Use of Three-Dimensional Convolutional Neural Networks to Interpret LiDAR for Forest Inventory

Ayrey

Hayes

2018

Remote Sensing

View full text Add to dashboard Cite

Abstract:As light detection and ranging (LiDAR) technology becomes more available, it has become common to use these datasets to generate remotely sensed forest inventories across landscapes. Traditional methods for generating these inventories employ the use of height and proportion metrics to measure LiDAR returns and relate these back to field data using predictive models. Here, we employ a three-dimensional convolutional neural network (CNN), a deep learning technique that scans the LiDAR data and automatically generates useful features for predicting forest attributes. We test the accuracy in estimating forest attributes using the three-dimensional implementations of different CNN models commonly used in the field of image recognition. Using the best performing model architecture, we compared CNN performance to models developed using traditional height metrics. The results of this comparison show that CNNs produced 12% less prediction error when estimating biomass, 6% less in estimating tree count, and 2% less when estimating the percentage of needleleaf trees. We conclude that using CNNs can be a more accurate means of interpreting LiDAR data for forest inventories compared to standard approaches.

show abstract

“…In practice, estimates calculated with SBR have very small systematic errors already with small sample sizes [6]. Further benefits in terms of efficient use of a small number of sample plots and faithful reproduction of forest properties can be attained by using refinements such as Bayesian Principal Component Regression (BPCR) [16,17] but these methods have not yet been incorporated into the ArboLiDAR tool kit.…”

Section: Introductionmentioning

confidence: 99%

Airborne Laser Scanning Based Forest Inventory: Comparison of Experimental Results for the Perm Region, Russia and Prior Results from Finland

Kauranne

Pyankov

Junttila

et al. 2017

Forests

Self Cite

View full text Add to dashboard Cite

Airborne laser scanning (ALS) based stand level forest inventory has been used in Finland and other Nordic countries for several years. In the Russian Federation, ALS is not extensively used for forest inventory purposes, despite a long history of research into the use of lasers for forest measurement that dates back to the 1970s. Furthermore, there is also no generally accepted ALS-based methodology that meets the official inventory requirements of the Russian Federation. In this paper, a method developed for Finnish forest conditions is applied to ALS-based forest inventory in the Perm region of Russia. Sparse Bayesian regression is used with ALS data, SPOT satellite images and field reference data to estimate five forest parameters for three species groups (pine, spruce, deciduous): total mean volume, basal area, mean tree diameter, mean tree height, and number of stems per hectare. Parameter estimates are validated at both the plot level and stand level, and the validation results are compared to results published for three Finnish test areas. Overall, relative root mean square errors (RMSE) were higher for forest parameters in the Perm region than for the Finnish sites at both the plot and stand level. At the stand level, relative RMSE generally decreased with increasing stand size and was lower when considered overall than for individual species groups.

show abstract

Linear Models for Airborne-Laser-Scanning-Based Operational Forest Inventory With Small Field Sample Size and Highly Correlated LiDAR Data

Cited by 27 publications

References 37 publications

Remote Sensing Technologies for Enhancing Forest Inventories: A Review

Remote Sensing Technologies for Enhancing Forest Inventories: A Review

The Use of Three-Dimensional Convolutional Neural Networks to Interpret LiDAR for Forest Inventory

Airborne Laser Scanning Based Forest Inventory: Comparison of Experimental Results for the Perm Region, Russia and Prior Results from Finland

Contact Info

Product

Resources

About