Combining Multi-Date Airborne Laser Scanning and Digital Aerial Photogrammetric Data for Forest Growth and Yield Modelling

Abstract

The increasing availability of highly detailed three-dimensional remotely-sensed data depicting forests, including airborne laser scanning (ALS) and digital aerial photogrammetric (DAP) approaches, provides a means for improving stand dynamics information. The availability of data from ALS and DAP has stimulated attempts to link these datasets with conventional forestry growth and yield models. In this study, we demonstrated an approach whereby two three-dimensional point cloud datasets (one from ALS and one from DAP), acquired over the same forest stands, at two points in time (circa 2008 and 2015), were used to derive forest inventory information. The area-based approach (ABA) was used to predict top height (H), basal area (BA), total volume (V), and stem density (N) for Time 1 and Time 2 (T1, T2). We assigned individual yield curves to 20 × 20 m grid cells for two scenarios. The first scenario used T1 estimates only (approach 1, single date), while the second scenario combined T1 and T2 estimates (approach 2, multi-date). Yield curves were matched by comparing the predicted cell-level attributes with a yield curve template database generated using an existing growth simulator. Results indicated that the yield curves using the multi-date data of approach 2 were matched with slightly higher accuracy; however, projections derived using approach 1 and 2 were not significantly different. The accuracy of curve matching was dependent on the ABA prediction error. The relative root mean squared error of curve matching in approach 2 for H, BA, V, and N, was 18.4, 11.5, 25.6, and 27.53% for observed (plot) data, and 13.2, 44.6, 50.4 and 112.3% for predicted data, respectively. The approach presented in this study provides additional detail on sub-stand level growth projections that enhances the information available to inform long-term, sustainable forest planning and management. less detailed information than single-tree or size-class models [2]. The choice of growth simulator depends on its availability for the area of interest-e.g., in Canada growth simulators have been developed for each province separately, with whole-stand models being popular in British Columbia and Alberta. In the majority of cases, the core set of input attributes for stand-level growth simulators includes species composition, stand age, and top height or alternatively site index. Additional inputs can improve model accuracy and can include information on stand density, basal area, stocking, canopy cover, information on insect damage, and silvicultural practices such as thinning or fertilization [3,4]. Simulator output typically consists of a list of predicted stand attributes for a specified age sequence (e.g., 10, 20, 30 years in the future), stratified by species. Stand attributes include top height, basal area, merchantable and total volume, and stand density.Airborne laser scanning (ALS) has demonstrated capacity for characterizing a number of important forest stand attributes [5,6]. Three-dimensional point clouds acquired with ALS preci...

Forecasting Forest Inventory Using Imputed Tree Lists for LiDAR Grid Cells and a Tree-List Growth Model

“…While LiDAR-derived forest inventory can provide inventory data to make sound commercial thinning decisions, any forest inventory becomes less accurate and useful as the years since data acquisition increase. To increase the longevity of LiDAR-derived inventory and reduce the costs associated with LiDAR acquisition, processing, and inventory predictions, simplified process-based and size-class growth models have been used to forecast stand-level variables at grid-cell resolution [28][29][30]. In addition, tree-level variables have been forecasted using tree lists imputed for LiDAR grid cells and a tree-list growth model [31].…”

Challenges of Multi-Temporal and Multi-Sensor Forest Growth Analyses in a Highly Disturbed Boreal Mixedwood Forests

“…This effect increased with the decrease of accuracy of the T1 and T2 models, which was especially pronounced for the indirectly derived ∆BA and ∆V. Though the accuracy of the individual models for BA and V at T1 and T2 was on par with accuracies reported in the literature for similar forest stand conditions [24,50], the analyses show that this level of accuracy was insufficient for determining growth in these stand conditions.…”

“…For example, Lamb et al [23] demonstrated how ALS-predicted stand attributes can be matched with a library of plot-level measurements and used as inputs for a locally calibrate tree-list growth model. Forest stand attributes predicted with point cloud data can also be integrated with stand-level growth models by generating a database of growth curves and using cell-level predictions to identify the most suitable growth curve [24,52]. Though such approaches benefit from a bi-temporal dataset, they can be used with a single ALS acquisition.…”

“…As shown by Véga and St-Onge [22], longer time intervals between data acquisitions increase the accuracy of height growth despite errors in in height estimates at individual times. Estimates of stand growth can also be calculated by integrating exisitng growth simulators with ALS or DAP-based predictions of forest attributes [23,24]. Cell-level yield curves can be computed by assigning weights to the input attributes based on their prediction accuracy.…”

“…No patterns of successful outcomes to growth assessments were apparent regarding scale or sensor choice. Few studies that have evaluated height growth have validated their models against independent data sources, and only two have utilized a multi-sensor method [22,24]. A multi-sensor method can reduce the expense of obtaining multi-temporal point cloud data for stand-level growth evaluations.…”