Robert J. McGaughey

Accuracy of a high-resolution lidar terrain model under a conifer forest canopy

Airborne laser scanning systems (commonly referred to as light detection and ranging or lidar systems) can provide terrain elevation data for open areas with a vertical accuracy of 15 cm. Accuracy in heavily forested areas has not been thoroughly tested. In this study, a high-resolution digital terrain model (DTM) was produced from high-density lidar data. Vegetation in the 500-ha mountainous study area varied from bare ground to dense 70-year-old conifer forest. Conventional ground survey methods were used to collect coordinates and near-ground vegetation heights at 347 ground checkpoints distributed under a range of canopy covers. These points were used to check the DTM accuracy. The mean DTM error was 0.22 ± 0.24 m (mean ± SD). DTM elevation errors for four tree canopy cover classes were: clearcut 0.16 ± 0.23 m, heavily thinned 0.18 ± 0.14 m, lightly thinned 0.18 ± 0.18 m, and uncut 0.31 ± 0.29 m. These DTM errors show a slight increase with canopy density but the differences are strikingly small. In general, the lidar DTM was found to be extremely accurate and potentially very useful in forestry.

A rigorous assessment of tree height measurements obtained using airborne lidar and conventional field methods

Tree height is an important variable in forest inventory programs but is typically time-consuming and costly to measure in the field using conventional techniques. Airborne light detection and ranging (lidar) provides individual tree height measurements that are highly correlated with field-derived measurements, but the imprecision of conventional field techniques does not allow for definitive assessments regarding the absolute accuracy of lidar tree height measurements and the relative influence of beam divergence setting (i.e., laser footprint size), species type, and digital terrain model (DTM) error on the accuracy of height measurements. In this study, we developed a methodology for acquiring accurate individual tree height measurements (<2 cm error) using a total station survey and used these measurements to establish the expected accuracy of lidar- and field-derived tree height measurements for two of the most ecologically and commercially significant species in western North America, Douglas-fir (Pseudotsuga menziesii) and ponderosa pine (Pinus ponderosa). Tree height measurements obtained from narrow-beam (0.33 m), high-density (6 points/m2) lidar were more accurate (mean error ± SD = –0.73 ± 0.43 m) than those obtained from wide-beam (0.8 m) lidar (–1.12 ± 0.56 m). Lidar-derived height measurements were more accurate for ponderosa pine (–0.43 ± 0.13 m) than for Douglas-fir (–1.05 ± 0.41 m) at the narrow beam setting. Although tree heights acquired using conventional field techniques (–0.27 ± 0.27 m) were more accurate than those obtained using lidar (–0.73 ± 0.43 m for narrow beam setting), this difference will likely be offset by the wider coverage and cost efficiencies afforded by lidar-based forest survey.

Examining conifer canopy structural complexity across forest ages and elevations with LiDAR data

LiDAR measurements of canopy structure can be used to classify forest stands into structural stages to study spatial patterns of canopy structure, identify habitat, or plan management actions. A key assumption in this process is that differences in canopy structure based on forest age and elevation are consistent with predictions from models of stand de- velopment. Three LiDAR metrics (95th percentile height, rumple, and canopy density) were computed for 59 secondary and 35 primary forest plots in the Pacific Northwest, USA. Hierarchical clustering identified two precanopy closure classes, two low-complexity postcanopy closure classes, and four high-complexity postcanopy closure classes. Forest de- velopment models suggest that secondary plots should be characterized by low-complexity classes and primary plots char- acterized by high-complexity classes. While the most and least complex classes largely confirmed this relationship, intermediate-complexity classes were unexpectedly composed of both secondary and primary forest types. Complexity classes were not associated with elevation, except that primary Tsuga mertensiana (Bong.) Carriere (mountain hemlock) plots were complex. These results suggest that canopy structure does not develop in a linear fashion and emphasize the im- portance of measuring structural conditions rather than relying on development models to estimate structural complexity across forested landscapes.

Data-driven simulation, dimensional accuracy and realism in a landscape visualization tool

Computer-based landscape simulation tools are used for assessing the visual impact of land-use decisions. Many systems rely on the artistic manipulation of two-dimensional scanned photographic or videotape images. The specific manipulation of an image is often not directly driven by data describing the proposed landscape modification. Also, it is difficult to move from a modified two-dimensional image to a three-dimensional real world design. This paper discusses how issues of data-driven simulation and dimensional accuracy are addressed in the Vantage Point landscape visualization tool. We describe features of the tool that contribute to image realism. We also propose how a tool with Vantage Points capabilities would be useful for research in visual quality management and psychophysics.

Predicting live and dead tree basal area of bark beetle affected forests from discrete-return lidar

Bark beetle outbreaks have killed large numbers of trees across North America in recent years. Lidar remote sensing can be used to effectively estimate forest biomass, but prediction of both live and dead standing biomass in beetle-affected forests using lidar alone has not been demonstrated. We developed Random Forest (RF) models predicting total, live, dead, and percent dead basal area (BA) from lidar metrics in five different beetle-affected coniferous forests across western North America. Study areas included the Kenai Peninsula of Alaska, southeastern Arizona, north-central Colorado, central Idaho, and central Oregon, U.S.A. We created RF models with and without intensity metrics as predictor variables and investigated how intensity normalization affected RF models in Idaho. RF models predicting total BA explained the most variation, whereas RF models predicting dead BA explained the least variation, with live and percent dead BA models explaining intermediate levels of variation. Important metrics varied between models depending on the type of BA being predicted. Generally, height and density metrics were important in predicting total BA, intensity and density metrics were important in predicting live BA, and intensity metrics were important in predicting dead and percent dead BA. Several lidar metrics were important across all study areas. Whether needles were on or off beetle-killed trees at the time of lidar acquisition could not be ascertained. Future work, where needle conditions at the time of lidar acquisition are known, could improve upon our analysis and results. Although RF models predicting live, dead, and percent dead BA did not perform as well as models predicting total BA, we concluded that discrete-return lidar can be used to provide reasonable estimations of live and dead BA. Our results also showed which lidar metrics have general utility across different coniferous forest types.

Assessing the influence of flight parameters, interferometric processing, slope and canopy density on the accuracy of X-band IFSAR-derived forest canopy height models

High resolution, active remote sensing technologies, such as interferometric synthetic aperture radar (IFSAR) and airborne laser scanning (lidar) have the capability to provide forest managers with direct measurements of 3‐dimensional forest canopy surface structure. While lidar systems can provide highly accurate measurements of canopy and terrain surfaces, high resolution (X‐band) IFSAR systems provide slightly less accurate measurements of canopy surface elevation over very large areas with a much higher data collection rate, leading to a lower cost per unit area. In addition, canopy height can be measured by taking the difference between the IFSAR‐derived canopy surface elevation and a lidar‐derived terrain surface elevation. Therefore, in areas where high‐accuracy terrain models are available, IFSAR may be used to economically monitor changes in forest structure and height over large areas on a relatively frequent basis. However, IFSAR flight parameters and processing techniques are not currently optimized for the forest canopy mapping application. In order to determine optimal flight parameters for IFSAR forest canopy measurement, we evaluated the accuracy of high resolution, X‐band canopy surface models obtained over a mountainous forested area in central Washington state (USA) from two different flying heights (6000 m and 4500 m), from different look directions, and with different interferometric processing. In addition, we assessed the influence of terrain slope and canopy density on the accuracy of IFSAR canopy height models. High‐accuracy lidar‐derived canopy height models were used as a basis for comparison. Results indicate that sensing geometry is the single most important factor influencing the accuracy of IFSAR canopy height measurements, therefore acquiring IFSAR from multiple look directions can be critically important when using IFSAR for forest canopy measurement applications, especially in mountainous areas.

Accuracy of an IFSAR-derived digital terrain model under a conifer forest canopy

Accurate digital terrain models (DTMs) are necessary for a variety of forest resource management applications, including watershed management, timber harvest planning, and fire management. Traditional methods for acquiring topographic data typically rely on aerial photogrammetry, where measurement of the terrain surface below forest canopy is difficult and error prone. The recent emergence of airborne P-band interferometric synthetic aperture radar (IFSAR), a high-resolution, microwave remote sensing technology, has the potential to provide significantly more accurate terrain models in forested areas. Low-frequency, P-band radar energy physically penetrates through the vegetation canopy and reflects from the underlying terrain surface, allowing for accurate measurement of the terrain surface elevation even in areas with dense forest cover. In this study, the accuracy of a high-resolution DTM derived from P-band IFSAR data collected over a mountainous forest area in western Washington State was rigorously evaluated through a comparison with 347 topographic checkpoints measured with total station survey equipment and collected under a variety of canopy densities. The mean DTM error was –0.28 ± 2.59 m (mean ± standard deviation), and the root mean squared error (RMSE) was 2.6 m. DTM elevation errors for four canopy cover classes were –0.67 ± 1.20 m (RMSE = 1.38 m) for clearcut, –0.62 ± 1.00 m (RMSE = 1.18 m) for heavily thinned, –0.41 ± 2.32 m (RMSE = 2.36 m) for lightly thinned, and 0.20 ± 3.31 m (RMSE = 3.32 m) for uncut.

Assessing the influence of return density on estimation of lidar-based aboveground biomass in tropical peat swamp forests of Kalimantan, Indonesia

The airborne lidar system (ALS) provides a means to efficiently monitor the status of remote tropical forests and continues to be the subject of intense evaluation. However, the cost of ALS acquisition can vary significantly depending on the acquisition parameters, particularly the return density (i.e., spatial resolution) of the lidar point cloud. This study assessed the effect of lidar return density on the accuracy of lidar metrics and regression models for estimating aboveground biomass (AGB) and basal area (BA) in tropical peat swamp forests (PSF) in Kalimantan, Indonesia. A large dataset of ALS covering an area of 123,000 ha was used in this study. This study found that cumulative return proportion (CRP) variables represent a better accumulation of AGB over tree heights than height-related variables. The CRP variables in power models explained 80.9% and 90.9% of the BA and AGB variations, respectively. Further, it was found that low-density (and low-cost) lidar should be considered as a feasible option for assessing AGB and BA in vast areas of flat, lowland PSF. The performance of the models generated using reduced return densities as low as 1/9 returns per m2 also yielded strong agreement with the original high-density data. The use model-based statistical inferences enabled relatively precise estimates of the mean AGB at the landscape scale to be obtained with a fairly low-density of 1/4 returns per m2, with less than 10% standard error (SE). Further, even when very low-density lidar data was used (i.e., 1/49 returns per m2) the bias of the mean AGB estimates were still less than 10% with a SE of approximately 15%. This study also investigated the influence of different DTM resolutions for normalizing the elevation during the generation of forest-related lidar metrics using various return densities point cloud. We found that the high-resolution digital terrain model (DTM) had little effect on the accuracy of lidar metrics calculation in PSF. The accuracy of low-density lidar metrics in PSF was more influenced by the density of aboveground returns, rather than the last return. This is due to the flat topography of the study area. The results of this study will be valuable for future economical and feasible assessments of forest metrics over large areas of tropical peat swamp ecosystems.