Terrestrial Photogrammetric Reconstruction of Black Spruce (Picea mariana) Tree

This photogrammetric point cloud was constructed using a set of 12 spherical images taken at different heights at two locations around the tree. The tree is a black spruce (Picea mariana), with a diameter at breast height of 20.7 cm. The tree is located within Forest Management Unit S20 near the towns of Slave Lake and Swan Hills in Alberta, Canada. This research was funded by the AWARE (Assessment of Wood Attributes using Remote sEnsing) Natural Sciences and Engineering Research Council of Canada Collaborative Research and Development grant to a team led by Dr. Nicholas Coops with support from West Fraser and Alberta Agriculture and Forestry

Terrestrial Photogrammetric Reconstruction of Black Spruce (Picea mariana) Tree

This photogrammetric point cloud was constructed using a set of 12 spherical images taken at different heights at two locations around the tree. The tree is a black spruce (Picea mariana), with a diameter at breast height of 20.7 cm. The tree is located within Forest Management Unit S20 near the towns of Slave Lake and Swan Hills in Alberta, Canada. This research was funded by the AWARE (Assessment of Wood Attributes using Remote sEnsing) Natural Sciences and Engineering Research Council of Canada Collaborative Research and Development grant to a team led by Dr. Nicholas Coops with support from West Fraser and Alberta Agriculture and Forestry.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Automated Forest Harvest Detection With a Normalized PlanetScope Imagery Time Series

The advent of CubeSat constellations is revolutionizing the ability to observe Earth systems through time. The improved spatial and temporal resolutions from these data could assist in tracking forest harvesting by forest management companies or government organizations interested in monitoring the sustainable management of forest resources. However, differing characteristics of individual satellites in each constellation requires study into geometric and radiometric normalization of the imagery and tuning parameters for change detection algorithms. In this study, a method for the spatial and temporal detection of forest harvest operations using images from the PlanetScope constellation was developed and implemented for a managed forest in Ontario, Canada. Temporal smoothing was applied on Landsat-normalized PlanetScope values of the Normalized Differential Vegetation Index (NDVI), and change points were detected based on the first derivative of the NDVI trend. Detected changes were compared to known locations of harvesting machines. Results indicate that 80–90% of harvested areas were detected, with temporal errors of approximately 9–10 days for two sites. Overall, this study demonstrated that forest harvesting can be detected with relative accuracy, deriving previously unavailable levels of spatial and temporal detail and enhancing the ability of forest stakeholders to monitor the sustainable use of forest resources

Digital Terrestrial Photogrammetry to Enhance Field-Based Forest Inventory across Stand Conditions

Forest inventories in uncertain future economic and environmental conditions require the development of cost-effective measurement techniques to provide robust and accurate information on forests across regional and global scales. Digital terrestrial photogrammetry (DTP) can be used to detect and measure trees on sample plots. In this study, a method was developed which used spherical images taken strategically within plots, and under varying acquisition conditions, to derive forest inventory attributes. Using a set of 102 photos on 400 m2 circular plots achieved a mean detection rate of 72.3% and estimated diameter to an RMSE of 19.0%. This study also explored the sensitivity of detection and estimation accuracy to different field and acquisition conditions. Detection of individual trees was significantly influenced by the tree size and species (p < 0.05 in a regression analysis), while plot-level detection was influenced by size and stem density. Tree size and the distance to the camera significantly influenced the accuracy of estimated attributes. These results are comparable to those of other DTP and terrestrial laser scanning studies in similar forest types while using fewer photos and less time, demonstrating the value of cost-effective methods for DTP estimation of forest attributes

Framework for near real-time forest inventory using multi source remote sensing data

Forestry inventory update is a critical component of sustainable forest management, requiring both the spatially explicit identification of forest cover change and integration of sampled or modelled components like growth and regeneration. Contemporary inventory data demands are shifting, with an increased focus on accurate attribute estimation via the integration of advanced remote sensing data such as airborne laser scanning (ALS). Key challenges remain, however, on how to maintain and update these next-generation inventories as they age. Of particular interest is the identification of remotely sensed data that can be applied cost effectively, as well as establishing frameworks to integrate these data to update information on forest condition, predict future growth and yield, and integrate information that can guide forest management or silvicultural decisions such as thinning and harvesting prescriptions. The purpose of this article is to develop a conceptual framework for forestry inventory update, which is also known as the establishment of a ‘living inventory’. The proposed framework contains the critical components of an inventory update including inventory and growth monitoring, change detection and error propagation. In the framework, we build on existing applications of ALS-derived enhanced inventories and integrate them with data from satellite constellations of free and open, analysis-ready moderate spatial resolution imagery. Based on a review of the current literature, our approach fits trajectories to chronosequences of pixel-level spectral index values to detect change. When stand-replacing change is detected, corresponding values of cell-level inventory attributes are reset and re-established based on an assigned growth curve. In the case of non–stand-replacing disturbances, cell estimates are modified based on predictive models developed between the degree of observed spectral change and relative changes in the inventory attributes. We propose that additional fine-scale data can be collected over the disturbed area, from sources such as CubeSats or remotely piloted airborne systems, and attributes updated based on these data sources. Cells not identified as undergoing change are assumed unchanged with cell-level growth curves used to increment inventory attributes. We conclude by discussing the impact of error propagation on the prediction of forest inventory attributes through the proposed near real-time framework, computing needs and integration of other available remote sensing data

Enhancing the Estimation of Stem-Size Distributions for Unimodal and Bimodal Stands in a Boreal Mixedwood Forest with Airborne Laser Scanning Data

Stem size distribution (SSD), which describes tree frequencies in diameter classes within an area, has a variety of direct and indirect applications that are critical for forest management. In this study, we evaluated which structural characteristics derived from Airborne Laser Scanning (ALS) data were best able to differentiate between unimodal and bimodal stands in a managed boreal mixedwood forest in Alberta, Canada. We then used wall-to-wall ALS data to predict (for 20 m-by-20 m grid cells) the parameters of a Weibull SSD in unimodal cells, and a Finite Mixture Model (FMM) in bimodal cells. The resulting SSDs were evaluated for their fit to ground plot-measured SSDs using an Error Index (EI). We found that the variance of ALS return heights was the best metric for differentiating between unimodal and bimodal stands, with a classification accuracy of 77%. Parameters of both the Weibull and FMM distributions were accurately predicted (r2 ~ 0.5, Root Mean Square Error (RMSE) ~ 30%), and that differentiating for modality prior to estimating SSD improved the accuracy of estimates (EI of 49.13 with differentiation versus 51.31 without differentiation). Unique to our presented approach is the stratification by SSD modality prior to the modelling of distributions. To achieve this, we apply a threshold to an ALS metric that allows SSD modality to be distinguished for each cell at the landscape level, and this a priori information is then used to ensure that the appropriate distribution is modelled. Our approach is parsimonious and efficient, enabling improved accuracy in SSD estimation across diverse landscapes when ALS data is the sole data source

Enhancing the Estimation of Stem-Size Distributions for Unimodal and Bimodal Stands in a Boreal Mixedwood Forest with Airborne Laser Scanning Data

Stem size distribution (SSD), which describes tree frequencies in diameter classes within an area, has a variety of direct and indirect applications that are critical for forest management. In this study, we evaluated which structural characteristics derived from Airborne Laser Scanning (ALS) data were best able to differentiate between unimodal and bimodal stands in a managed boreal mixedwood forest in Alberta, Canada. We then used wall-to-wall ALS data to predict (for 20 m-by-20 m grid cells) the parameters of a Weibull SSD in unimodal cells, and a Finite Mixture Model (FMM) in bimodal cells. The resulting SSDs were evaluated for their fit to ground plot-measured SSDs using an Error Index (EI). We found that the variance of ALS return heights was the best metric for differentiating between unimodal and bimodal stands, with a classification accuracy of 77%. Parameters of both the Weibull and FMM distributions were accurately predicted (r2 ~ 0.5, Root Mean Square Error (RMSE) ~ 30%), and that differentiating for modality prior to estimating SSD improved the accuracy of estimates (EI of 49.13 with differentiation versus 51.31 without differentiation). Unique to our presented approach is the stratification by SSD modality prior to the modelling of distributions. To achieve this, we apply a threshold to an ALS metric that allows SSD modality to be distinguished for each cell at the landscape level, and this a priori information is then used to ensure that the appropriate distribution is modelled. Our approach is parsimonious and efficient, enabling improved accuracy in SSD estimation across diverse landscapes when ALS data is the sole data source.Forestry, Faculty ofNon UBCReviewedFacult

Small Field Plots Can Cause Substantial Uncertainty in Gridded Aboveground Biomass Products from Airborne Lidar Data

Emerging satellite radar and lidar platforms are being developed to produce gridded aboveground biomass (AGB) predictions that are poised to expand our understanding of global carbon stocks and changes. However, the spatial resolution of AGB map products from these platforms is often larger than the available field plot data underpinning model calibration and validation efforts. Intermediate-resolution/extent remotely sensed data, like airborne lidar, can serve as a bridge between small plots and map resolution, but methods are needed to estimate and propagate uncertainties with multiple layers of data. Here, we introduce a workflow to estimate the pixel-level mean and variance in AGB maps by propagating uncertainty from a lidar-based model using small plots, taking into account prediction uncertainty, residual uncertainty, and residual spatial autocorrelation. We apply this workflow to estimate AGB uncertainty at a 100 m map resolution (1 ha pixels) using 0.04 ha field plots from 11 sites across four ecoregions. We compare uncertainty estimates using site-specific models, ecoregion-specific models, and a general model using all sites. The estimated AGB uncertainty for 1 ha pixels increased with mean AGB, reaching 7.8–33.3 Mg ha−1 for site-specific models (one standard deviation), 11.1–28.2 Mg ha−1 for ecoregion-specific models, and 21.1–22.1 Mg ha−1 for the general model for pixels in the AGB range of 80–100 Mg ha−1. Only 3 of 11 site-specific models had a total uncertainty ofForestry, Faculty ofNon UBCForest Resources Management, Department ofReviewedFacultyResearche