Nicholas Goodwin

Characterizing Urban Surface Cover and Structure with Airborne Lidar Technology

Urban and landscape planners are becoming increasingly aware of the potential of light detection and ranging (lidar) technology to produce height and structural information over large geographic areas in both an economic and time-efficient fashion. In urban environments where the structural complexity is high, for example, lidar is seen as a critical and innovative dataset to improve the characterization of both vegetation and building attributes. Using a small-footprint, first- and last-return lidar dataset of Vancouver, Canada, we demonstrate the potential to derive a suite of attributes important for describing the interactions of the urban surface and atmosphere in weather forecasting, air pollution, and urban dispersion modelling. Two levels of attributes were defined. First, primary attributes such as building shape, size, and location and tree classification were calculated. Building extent and size were computed using an object-based approach based on connectivity and height rules. The classification of tree crown areas was derived from the location of last-return data, filtered to remove the incidence of last returns caused by the interaction of the lidar beam with building edges, and height rules. Validation showed that building areas derived from lidar compared well with aerial photography estimates (r2 = 0.96, p < 0.001, n = 98). The percentage difference between estimates was equal to 16% (n = 83) when buildings were discriminated from the surrounding features. However, the percentage difference between estimates increased to 35% (n = 98) when commission errors were considered, as lidar often overestimated building areas due to closely spaced buildings (gaps less than 1–2 m) not being separated. Similarly, the height and area of lidar-extracted trees were highly correlated with field-based measurements (r2 = 0.84 and 0.76, respectively, p < 0.001, n = 50). Once these primary attributes were derived, we demonstrate the extraction of a number of secondary attributes including building mean height, normalized building volume, building wall surface area, and interelement spacing. Of significance, this research has shown that lidar can provide spatially detailed estimates of urban structure and cover which characterize the aerodynamic and energetic properties of urban areas.

Classifying Eucalyptus forests with high spatial and spectral resolution imagery: an investigation of individual species and vegetation communities

Mapping the spatial distribution of individual species is an important ecological and forestry issue that requires continued research to coincide with advances in remote-sensing technologies. In this study, we investigated the application of high spatial resolution (80 cm) Compact Airborne Spectrographic Imager 2 (CASI-2) data for mapping both spectrally complex species and species groups (subgenus grouping) in an Australian eucalypt forest. The relationships between spectral reflectance curves of individual tree species and identified statistical differences among species were analysed with ANOVA. Supervised maximum likelihood classifications were then performed to assess tree species separability in CASI-2 imagery. Results indicated that turpentine (Syncarpia glomulifera Smith), mesic vegetation (primarily rainforest species), and an amalgamated group of eucalypts could be readily distinguished. The discrimination of S. glomulifera was particularly robust, with consistently high classification accuracies. Eucalypt classification as a broader species group, rather than individual species, greatly improved classification performance. However, separating sunlit and shaded aspects of tree crowns did not increase classification accuracy.

Curve fitting of time-series Landsat imagery for characterizing a mountain pine beetle infestation

In this technical note we present a new technique using mixed linear models for characterizing a mountain pine beetle (Dendroctonus ponderosae Hopkins) infestation from multiyear satellite imagery. The main benefit of our approach is an ability to determine the statistical significance of each annual spectral change. Knowledge of the annual spectral change characteristics can then be used to statistically determine if a disturbance event has occurred, the timing of a given disturbance event, as well as to provide information for clustering fitted multitemporal reflectance curves (i.e. spectral trajectories) with a common shape. The spatial clustering of spectral trajectories provides insights into the nature of the disturbance and recovery imposed by infestation over a 14-year period.

Assessing differences in tree and stand structure following beetle infestation using lidar data

The current mountain pine beetle (Dendroctonus ponderosae Hopkins) infestation in British Columbia is the largest in recorded history and has caused unprecedented damage to the lodgepole pine (Pinus contorta Dougl. var. latifolia Engelm.) forests of the interior of the province. During the early years after attack, changes to overall crown structure are relatively minor due to low needle loss; within several years, however, needle loss can be substantial, even at the stand level. Needle loss can affect snow hydrology due to the role of the canopy in interception and accumulation and its impacts on radiation transmission, wind speed, and the overall snowmelt energy balance. In addition, the infestation is impacting other forest attributes such as wildlife habitat, forest fire risk and behaviour, and biogeochemistry. In this paper we investigate variations in light detection and ranging (lidar) return hit densities and distributions, analyzed with high spatial resolution digital camera imagery, in response to changes in forest cover and structure due to beetle infestation at both the individual tree level and the stand level. Results indicate that the density of lidar returns from tree crowns is impacted by the later health status of the tree, with a larger number of returns from green and early attack phases and a significantly smaller number of returns from grey-attack crowns. At the stand level, there are a number of significant relationships between plot-level indicators of infestation and lidar-derived structural metrics, in particular with vegetation cover (r2  = 0.76, p < 0.001). The total number and vertical distribution of returns from vegetation in green, red-attacked, and grey-attacked pine stands were distinct. We conclude that the potential to combine the structure information derived from lidar technology with assessment of heath status from aerial imagery provides unique quantitative data that may be used to map lodgepole pine stands according to structural attributes relevant to both silviculturalists and hydrologists.

Predicting Sphaeropsis sapinea Damage in Pinus radiata Canopies Using Spectral Indices and Spectral Mixture Analysis

Maintaining the health and condition of the forest plantation estate is critical to ensuring there are no adverse losses in productivity. Within Australian Pinus radiata plantations a diverse range of damaging agents are present. One significant agent is a fungal pathogen Sphaeropsis sapinea. In this research, we detail the development of relationships between a range of individual crown health attributes representing symptoms of Sphaeropsis sapinea infection and high spatial and spectral resolution remotely sensed imagery characteristics. To do this, two methods were used; the first utilized vegetation spectral indices including simple and normalized difference ratios, and the second, linear spectral mixture analysis. Results indicate that spectral indices that utilize either chlorophyll absorption wavelengths at 680 nm with a non-chlorophyll region of the spectrum (such as 710 or 750 nm) or the slope of the upper red-edge between 710 and 740 nm were most significantly related to individual crown damage attributes. Linear unmixing analysis consistently extracted four fraction endmember images (sunlit canopy, soil, shadow, and non-photosynthetic vegetation (NPV)) from the 12 channel imagery. Multiple linear stepwise regression models developed using mixed fractional abundances provided similar results to those derived using spectral indices. The NPV and shadow endmembers, in order, were consistently identified as the most significant in these developed models.

Application of narrow-band digital camera imagery to plantation canopy condition assessment

Ensuring forest plantations remain in optimum health and condition is critical to minimizing adverse losses in productivity. A health monitoring program capable of accurately assessing the extent and severity of symptoms of canopy strain could permit forest managers to take a proactive course of action to minimize losses in productivity and tree mortality. Across a range of factors associated with tree stress and defoliation (a fungal pathogen Sphaeropsis sapinea, low soil nitrogen (N) availability, and an aphid Essigella californica), we compared field-based observations of canopy condition with coincident imagery obtained in September 2002 and 2003 from digital camera technology fitted with selected narrow-band (10 nm) spectral interference filters. From these wavelengths a number of chlorophyll and red-edge spectral indices were derived at 50 cm spatial resolution. In the case of S. sapinea where infection is significant and results in necrotic breakdown of needle tissue, the slope of the upper red-edge was the variable most highly correlated with crown attributes (r2 = 0.76 and 0.88 for the 2 years), with an independent classification accuracy of over 90%. Feeding by E. californica is commonly associated with needle chlorosis and defoliation and was predicted at a lower level of accuracy with a simple chlorophyll index (67% overall accuracy). The results indicate that narrow-band digital camera imagery can be used to derive indices of chlorophyll sensitivity and red-edge wavelengths. Comparison of predictions over a 2 year period indicate that the red-edge-based indicators can detect differences in canopy condition and that these relationships appear robust. The results indicate the chlorophyll-based indices were less robust through time, possibly due to interactions with needle defoliation.

An Accuracy Assessment of Derived Digital Elevation Models from Terrestrial Laser Scanning in a Sub-Tropical Forested Environment

Forest structure attributes produced from terrestrial laser scanning (TLS) rely on normalisation of the point cloud values from sensor coordinates to height above ground. One method to do this is through the derivation of an accurate and repeatable digital elevation model (DEM) from the TLS point cloud that is used to adjust the height. The primary aim of this paper was to test a number of TLS scan configurations, filtering options and output DEM grid resolutions (from 0.02 m to 1.0 m) to define a best practice method for DEM generation in sub-tropical forest environments. The generated DEMs were compared to both total station (TS) spot heights and a 1-m DEM generated from airborne laser scanning (ALS) to assess accuracy. The comparison to TS spot heights found that a DEM produced using the minimum elevation (minimum Z value) from a point cloud derived from a single scan had mean errors >1 m for DEM grid resolutions <0.2 m at a 25-m plot radius. At a 1-m grid resolution, the mean error was 0.19 m. The addition of a filtering approach that combined a median filter with a progressive morphological filter and a global percentile filter was able to reduce mean error of the 0.02-m grid resolution DEM to 0.31 m at a 25-m plot radius using all returns. Using multiple scan positions to derive the DEM reduced the mean error for all DEM methods. Our results suggest that a simple minimum Z filtering DEM method using a single scan at the grid resolution of 1 m can produce mean errors <0.2 m, but for a small grid resolution, such as 0.02 m, a more complex filtering approach and multiple scan positions are required to reduced mean errors. The additional validation data provided by the 1-m ALS DEM showed that when using the combined filtering method on a point cloud derived from a single scan at the plot centre, errors between 0.1 and 0.5 m occurred in the TLS DEM for all tested grid resolutions at a plot radius of 25 m. These findings present a protocol for DEM production from TLS data at a range of grid resolutions and provide an overview of factors affecting DEMs produced from single and multiple TLS scan positions.

Predicting Sphaeropsis sapinea damage on Pinus radiata stands from CASI-2 using spectral mixture analysis

Within Australian Pinus radiata plantations a diverse range of damaging agents are present. A significant issue is the presence of a fungal pathogen Sphaeropsis sapinea which is present in many softwood plantations. In this research we investigate the use of CASI-2 imagery to detect Sphaeropsis sapinea infestation using linear spectral mixture analysis approaches. Results indicate that four fraction endmember images could be reliably extracted from the 12 channel CASI-2 imagery with sunlit canopy, soil, shadow, and nonphotosynthetic vegetation (NPV) all well estimated. Using multiple linear stepwise regression, models were developed using mixed fractional abundances with model predictions found to be highly significant. The NPV and shadow endmembers, in order, were consistently identified as important in the regression models and confirm their importance in crown condition modelling.