Assessment of QuickBird high spatial resolution imagery to detect red attack damage due to mountain pine beetle infestation

High spatial resolution remotely sensed data has the potential to complement existing forest health programs for both strategic planning over large areas, as well as for detailed and precise identification of tree crowns subject to stress and infestation. The area impacted by the current mountain pine beetle (Dendroctonus ponderosae Hopkins) outbreak in British Columbia, Canada, has increased 40-fold over the previous 5 years, with approximately 8.5 million ha of forest infested in 2005. As a result of the spatial extent and intensity of the outbreak, new technologies are being assessed to help detect, map, and monitor the damage caused by the beetle, and to inform mitigation of future beetle outbreaks. In this paper, we evaluate the capacity of high spatial resolution QuickBird multi-spectral imagery to detect mountain pine beetle red attack damage. ANOVA testing of individual spectral bands, as well as the Normalized Difference Vegetation Index (NDVI) and a ratio of red to green reflectance (Red-Green Index or RGI), indicated that the RGI was the most successful (p < 0.001) at separating non-attack crowns from red attack crowns. Based on this result, the RGI was subsequently used to develop a binary classification of red attack and non-attack pixels. The total number of QuickBird pixels classified as having red attack damage within a 50 m buffer of a known forest health survey point were compared to the number of red attack trees recorded at the time of the forest health survey. The relationship between the number of red attack pixels and observed red attack crowns was assessed using independent validation data and was found to be significant (r² = 0.48, p < 0.001, standard error = 2.8 crowns). A comparison of the number of QuickBird pixels classified as red attack, and a broader scale index of mountain pine beetle red attack damage (Enhanced Wetness Difference Index, calculated from a time series of Landsat imagery), was significant (r² = 0.61, p < 0.001, standard error = 1.3 crowns). These results suggest that high spatial resolution imagery, in particular QuickBird satellite imagery, has a valuable role to play in identifying tree crowns with red attack damage. This information could subsequently be used to augment existing detailed forest health surveys, calibrate synoptic estimates of red attack damage generated from overview surveys and/or coarse scale remotely sensed data, and facilitate the generation of value-added information products, such as estimates of timber volume impacts at the forest stand level.     

Assessing canopy mortality during a mountain pine beetle outbreak using GeoEye-1 high spatial resolution satellite data

Continuing, severe outbreaks of mountain pine beetle (Dendroctonus ponderosae) across western North America have resulted in widespread mortality of lodgepole pine (Pinus contorta). Multiple studies have used high spatial resolution satellite data to map areas of beetle kill; these studies have largely focused on mapping red canopy cover associated with recent tree mortality and have not examined mapping gray canopy cover that occurs after red needles have dropped. The work presented here examines the use of newly available GeoEye-1 data for mapping both red and gray canopy area in southeastern Wyoming lodgepole pine forest. A 0.5 m spatial resolution, pan-sharpened GeoEye-1 image was used to classify areas of green, red, and gray canopy cover. Reference data were collected at twelve 500 m² field plots. Shadow-normalized green, red, and gray canopy area from classified GeoEye-1 data closely agreed with field-estimated green, red, and gray canopy area. Mean absolute error in canopy cover for the twelve sample plots was 8.3% for the green class, 5.4% for the red class, and 7.2% for the gray class. When all twelve plots were aggregated, remotely sensed estimates of green, red, and gray cover were within 1.7% of the field-estimated cover. Our results demonstrate that high spatial resolution spaceborne multispectral data are a promising tool for mapping canopy mortality caused by mountain pine beetle outbreaks.     

Prediction and assessment of bark beetle-induced mortality of lodgepole pine using estimates of stand vigor derived from remotely sensed data

The current outbreak of mountain pine beetle (Dendroctonus ponderosae Hopkins) in British Columbia (BC), Canada, has led forest managers to consider thinning as a means of decreasing residual tree susceptibility to attack and subsequent mortality. Previous research indicates that susceptibility to mountain pine beetle is a function of a tree's physiological vigor and the intensity of attack. Trees able to produce ≥ 80 g (g) of wood per m² of projected leaf area annually are highly resistant, because they are able to shift resource allocation locally from wood to resin production to isolate blue-stain fungi introduced by attacking beetles. Typically, the leaf area of susceptible stands must be reduced by two-thirds to permit most residual trees to increase their vigor to a safe level. We evaluate whether Landsat Thematic Mapper (TM) imagery (30 × 30 m) provides a means to assess the maximum leaf area index (LAI) of unthinned stands and the extent that thinning reduces LAI. The extent that residual trees in thinned stands may have increased their resistance to attack from mountain pine beetle is predicted from a non-linear relationship between % maximum LAI and mean tree vigor.We investigated the merits of this approach in the vicinity of Parson, British Columbia using four stands of lodgepole pine (Pinus contorta Dougl.), two of which were heavily thinned (stands were spaced to 4 and 5 m, approximately 70% reduction in stand density). An analysis of archived Landsat TM imagery indicated that prior to thinning in 1993, all four stands had full canopy, which, for mature stands, would translate to mean tree vigor between 40 and 70 g of annual wood production per m² of foliage. By 1995, based on estimated changes in LAI derived from a second data of Landsat TM imagery, stand vigor in the unthinned stands had not changed; however, in the thinned stands, a nearly two third reduction in LAI resulted in a predicted increase in vigor to between 100 and 160 g wood m^− 2 of leaf area. A subsequent assessment in 2001 indicated that stand vigor remained higher in the thinned stands relative to the control stands. Following an infestation of mountain pine beetle in the study area in 2002, mortality data indicated that the thinned stands experienced no mortality relative to the unthinned stands which experienced 5.5% mortality in the initial years of the attack. In the larger area surrounding the study site, a general relationship was found between predicted stand vigor and mountain pine beetle-induced mortality as estimated from aerial overview survey data (r² = 0.43, p < 0.01).     

Evaluating the potential of multispectral imagery to map multiple stages of tree mortality

Insect outbreaks are major forest disturbances, causing tree mortality across millions of ha in North America. Resultant spatial and temporal patterns of tree mortality can profoundly affect ecosystem structure and function. In this study, we evaluated the classification accuracy of multispectral imagery at different spatial resolutions. We used four-band digital aerial imagery (30-cm spatial resolution and aggregated to coarser resolutions) acquired over lodgepole pine-dominated stands in central Colorado recently attacked by mountain pine beetle. Classes of interest included green trees and multiple stages of post-insect attack tree mortality, including dead trees with red needles (“red-attack”), dead trees without needles (“gray-attack”), and non-forest. The 30-cm resolution image facilitated delineation of trees located in the field, which were used in image classification. We employed a maximum likelihood classifier using the green band, Red-Green Index (RGI), and Normalized Difference Vegetation Index (NDVI). Pixel-level classification accuracies using this imagery were good (overall accuracy of 87%, kappa = 0.84), although misclassification occurred between a) sunlit crowns of live (green) trees and herbaceous vegetation, and b) sunlit crowns of gray- and red-attack trees and bare soil. We explored the capability of coarser resolution imagery, aggregated from the 30-cm resolution to 1.2, 2.4, and 4.2 m, to improve classification accuracy. We found the highest accuracy at the 2.4-m resolution, where reduction in omission and commission errors and increases in overall accuracy (90%) and kappa (0.88) were achieved, and visual inspection indicated improved mapping. Pixels at this resolution included more shadow in forested regions than pixels in finer resolution imagery, thereby reducing forest canopy reflectance and allowing improved separation between forest and non-forest classes, yet were fine enough to resolve individual tree crowns better than the 4.2-m imagery. Our results illustrate that a classification of an image with a spatial resolution similar to the area of a tree crown outperforms that of finer and coarser resolution imagery for mapping tree mortality and non-forest classes. We also demonstrate that multispectral imagery can be used to separate multiple postoutbreak attack stages (i.e., red-attack and gray-attack) from other classes in the image.     

Detection of foliage conditions and disturbance from multi-angular high spectral resolution remote sensing

Disturbance of forest ecosystems, an important component of the terrestrial carbon cycle, has become a focus of research over recent years, as global warming is about to increase the frequency and severity of natural disturbance events. Remote sensing offers unique opportunities for detection of forest disturbance at multiple scales; however, spatially and temporally continuous mapping of non-stand replacing disturbance remains challenging. First, most high spatial resolution satellite sensors have relatively broad spectral ranges with bandwidths unsuitable for detection of subtle, stress induced, features in canopy reflectance. Second, directional and background reflectance effects, induced by the interactions between the sun-sensor geometry and the observed canopy surface, make up-scaling of empirically derived relationships between changes in spectral reflectance and vegetation conditions difficult. Using an automated tower based spectroradiometer, we analyse the interactions between canopy level reflectance and different stages of disturbance occurring in a mountain pine beetle infested lodgepole pine stand in northern interior British Columbia, Canada, during the 2007 growing season. Directional reflectance effects were modelled using a bidirectional reflectance distribution function (BRDF) acquired from high frequency multi-angular spectral observations. Key wavebands for observing changes in directionally corrected canopy spectra were identified using discriminant analysis and highly significant correlations between canopy reflectance and field measured disturbance levels were found for several broad and narrow waveband vegetation indices (for instance, r²_NDVI = 0.90; r²_CHL3 = 0.85; p < 0.05). Results indicate that multi-angular observations are useful for extraction of disturbance related changes in canopy reflectance, in particular the temporally and spectrally dense data detected changes in chlorophyll content well. This study will help guide and inform future efforts to map forest health conditions at landscape and over increasingly coarse scales.     

Multi-temporal analysis of high spatial resolution imagery for disturbance monitoring

Mountain pine beetle red attack damage has been successfully detected and mapped using single-date high spatial resolution (< 4 m) satellite multi-spectral data. Forest managers; however, need to monitor locations for changes in beetle populations over time. Specifically, counts of individual trees attacked in successive years provide an indication of beetle population growth and dynamics. Surveys are typically used to estimate the ratio of green (current) attack trees to red (previous) attack trees or G:R. In this study, we estimate average stand-level G:R using a time-series of QuickBird multi-spectral and panchromatic satellite data, combined with field data for three forested stands near Merritt, British Columbia, Canada. Using a ratio of QuickBird red to green wavelengths (Red-Green Index or RGI), the change in RGI (ΔRGI) in successive image pairs is used to estimate red attack damage in 2004, 2005, and 2006, with true positive accuracies ranging from 89 to 93%. To overcome issues associated with differing viewing geometry and illumination angles that impair tracking of individual trees through time, segments are generated from the QuickBird multi-spectral data to identify small groups of trees. These segments then serve as the vehicle for monitoring changes in red attack damage over time. A local maxima filter is applied to the panchromatic data to estimate stem counts, thereby allowing an indication of the total stand population at risk of attack. By combining the red attack damage estimates with the local maxima stem counts, predictions are made of the number of attacked trees in a given year. Backcasting the current year's red attack damaged trees as the previous year's green attack facilitates the estimation of an average stand G:R. In this study area, these retrospective G:R values closely match those generated from field surveys. The results of this study indicate that a monitoring program using a time series of high spatial resolution remotely sensed data (multi-spectral and panchromatic) over select sample locations, could be used to estimate G:R over large areas, facilitating landscape level management strategies and/or providing a mechanism for assessing the efficacy of previously implemented strategies.     

A Landsat time series approach to characterize bark beetle and defoliator impacts on tree mortality and surface fuels in conifer forests

Insects are important forest disturbance agents, and mapping their effects on tree mortality and surface fuels represents a critical research challenge. Although various remote sensing approaches have been developed to monitor insect impacts, most studies have focused on single insect agents or single locations and have not related observed changes to ground-based measurements. This study presents a remote sensing framework to (1) characterize spectral trajectories associated with insect activity of varying duration and severity and (2) relate those trajectories to ground-based measurements of tree mortality and surface fuels in the Cascade Range, Oregon, USA. We leverage a Landsat time series change detection algorithm (LandTrendr), annual forest health aerial detection surveys (ADS), and field measurements to investigate two study landscapes broadly applicable to conifer forests and dominant insect agents of western North America. We distributed 38 plots across multiple forest types (ranging from mesic mixed-conifer to xeric lodgepole pine) and insect agents (defoliator [western spruce budworm] and bark beetle [mountain pine beetle]). Insect effects were evident in the Landsat time series as combinations of both short- and long-duration changes in the Normalized Burn Ratio spectral index. Western spruce budworm trajectories appeared to show a consistent temporal evolution of long-duration spectral decline (loss of vegetation) followed by recovery, whereas mountain pine beetle plots exhibited both short- and long-duration spectral declines and variable recovery rates. Although temporally variable, insect-affected stands generally conformed to four spectral trajectories: short-duration decline then recovery, short- then long-duration decline, long-duration decline, long-duration decline then recovery. When comparing remote sensing data with field measurements of insect impacts, we found that spectral changes were related to cover-based estimates (tree basal area mortality [R²_adj = 0.40, F_1,34 = 24.76, P < 0.0001] and down coarse woody detritus [R²_adj = 0.29, F_1,32 = 14.72, P = 0.0006]). In contrast, ADS changes were related to count-based estimates (e.g., ADS mortality from mountain pine beetle positively correlated with ground-based counts [R²_adj = 0.37, F_1,22 = 14.71, P = 0.0009]). Fine woody detritus and forest floor depth were not well correlated with Landsat- or aerial survey-based change metrics. By characterizing several distinct temporal manifestations of insect activity in conifer forests, this study demonstrates the utility of insect mapping methods that capture a wide range of spectral trajectories. This study also confirms the key role that satellite imagery can play in understanding the interactions among insects, fuels, and wildfire.     

Using hyperspectral remote sensing to detect and quantify southeastern pine senescence effects in red-cockaded woodpecker (Picoides borealis) habitat

Conservation of threatened and endangered species requires maintenance of critical habitat. The red-cockaded woodpecker Picoides borealis (RCW) is a threatened bird species, endemic to the mixed conifer forests of the southeastern United States. RCW nests and forages preferentially in mature longleaf pine Pinus palustris, but also uses mature loblolly pine Pinus taeda and shortleaf pine Pinus echinata forests. In the last century, the extent of mature pine forests has been greatly reduced by logging. The RCW, in contrast to other woodpeckers, excavates nest cavities in living trees and senescence symptoms (year round leaf chlorosis and leaf mortality) have been observed in mature pine stands across the southeast. Widespread mortality of the mature pine forests would threaten the long-term survival of the RCW. We used airborne hyperspectral data across a portion of Ft. Benning Military Installation, Georgia, U.S.A., to determine if senescent trees can be identified and mapped and assess the likely persistence of mature pines in the RCW habitat. Univariate analysis of variance showed good separation between asymptomatic, senesced and dead physiological conditions with asymptomatic trees having significantly higher reflectance for all bands in the wavelength range between 0.719 and 1.1676 µm, senescent trees having significantly lower reflectance for bands in the range between 1.1927 and 1.3122 µm, and dead trees having significantly higher reflectance for bands in the range between 1.8151 and 1.9471 µm. Classification and Regression Tree (CART) models achieved correct classification rates and kappa statistics above 70%. CART models selected information from wavelength regions similar to those identified from the ANOVA, which likely explains their performance. Our aggregated CART model of tree senescence estimated that 141.4 ha (3%) of the study area is affected. RCW nests occurred in areas with significantly higher tree cover, and trees within foraging and home ranges did not have significantly more senescence than areas without RCW. These results indicate that although tree senescence is widespread, mortality is yet to significantly affect RCW habitat. Results and analysis of critical habitat similar to those exemplified in this study can extend our knowledge about the stressors of these important and imperiled components of biodiversity.     

The effects of bark beetle stress on the foliar spectral reflectance of lodgepole pine

Abstract

Abstract. This paper reports the results of a study of the foliar reflectance properties of lodgepole pine trees under mountain pine beetle attack which aimed to determine the sequence of changes which occur. The motivation for this study was to identify spectral regions showing the earliest signs of attack in order to choose spectral bands for airborne linear array sensors. Normal illumination, diffuse hemispherical reflectance spectra were obtained of needles from trees showing varying degrees of stress from beetle attack and for needles from unattacked trees for comparison. The more pronounced changes in the spectra were interpreted visually and compared to changes reported by other authors. The more subtle changes were studied by analysing variance methods. Three spectral bands (the green peak, red edge and near-infrared (NIR) shoulder regions) have been identified as most promising for detecting early effects of bark beetle attack. Three additional bands (the blue, red and NIR plateau regions) are identified as reference bands for calibration and comparison purposes. The difference between foliar reflectance of attacked and unattacked trees is found to be most significant in the visible and red edge regions for current foliage and in the NIR for previous foliage. A red edge red shift was observed in the spectra of current foliage from attacked trees, in contrast to red edge blue shifts associated with stress in other studies. The observed sequence of subtle to more pronounced changes cannot be explained qualitatively using current knowledge of the plant pigment and anatomical changes which occur at the cellular and needle levels in stressed conifers. Further study of the detailed changes in pigments anel cellular and foliar anatomy is recommended, both to elucidate on the cause-and-effect relationships which occur, and to indicate the extent to which this paper's findings can be generalized. The findings suggest that multispectral linear array airborne scanners may be able to detect stressed conifers long before the red attack stage, but further investigation is required to determine whether the differences at foliar level between attacked and unattacked trees can also be detected at the whole-tree level, and whether confusion with other ground cover types can be avoided.     

Integrating remotely sensed and ancillary data sources to characterize a mountain pine beetle infestation

The ongoing mountain pine beetle (Dendroctonus ponderosae Hopkins) outbreak in British Columbia, Canada, has reached epidemic proportions, with the beetle expanding into geographic areas outside its known biological range. In this study, estimates of red attack damage were derived from a logistic regression model using multi-date Landsat imagery, and ancillary information including terrain attributes and solar radiation. The model estimates were found to be approximately 70% accurate using an independent set of beetle survey data as validation. This probability surface of red attack damage, along with forest inventory and terrain attributes, were used as inputs to decision tree analyses, in order to identify which forest attributes were associated with stands that had a greater likelihood of mountain pine beetle red attack damage. Three distinct decision tree models were developed, with each having a different set of input variables. The results of the analyses indicated that site index (an indicator of the quality of a forest site) and slope were the principal discriminators of the current mountain pine beetle attack, followed by basal area of pine dominated stands, and to a lesser extent, crown closure and stem density. The results suggest that indicators of site quality, particularly site index, could be a complementary addition to existing stand susceptibility rating models.     

Detection of mountain pine beetle-killed ponderosa pine in a heterogeneous landscape using high-resolution aerial imagery

Previous studies have used remote-sensing images to map tree mortality caused by mountain pine beetle (Dendroctonus ponderosae; MPB) in relatively homogeneous lodgepole pine (Pinus contorta) forests; however, classification methods have not been tested for the patchy landscape of ponderosa pine-dominated (Pinus ponderosae) montane forests characterized by highly variable tree density. This study explores two supervised classification methods to identify MPB-caused mortality (red attack) in heterogeneous montane forests of the Colorado Front Range using 1 m-resolution 2011 imagery of the National Agriculture Imagery Program (NAIP): maximum likelihood using the red, green, and blue bands, and the red-green index (RGI), and a thresholding technique using the RGI. Two variations of the RGI threshold method were also explored: the addition of a green-band threshold and the incorporation of a focal analysis. Evaluation pixels were used to assess the accuracy of the classification methods. The maximum likelihood (97 Percentage Correctly Classified (PCC); 11% error of commission for red attack) and RGI threshold (85 PCC; 46% error of commission for red attack) classification methods overestimated the red attack. The RGI and green band threshold classification reduced the error of commission (5%) and had high overall accuracy (97 PCC). In a comparison of classification methods across tree-density sites, we found the maximum likelihood classification had a very high accuracy in the high-density site (95 PCC), but substantially lower accuracy in the low-density site (85 PCC) due to the presence of more visible cover types. The RGI threshold classification with the green band constraint produced more consistent PCCs across tree densities: high (93.7 PCC), moderate (95.2 PCC), and low (92.0 PCC). Our results indicate forest structure may affect the classification accuracy and should be considered when selecting a classification method for a landscape.     

Mapping whitebark pine mortality caused by a mountain pine beetle outbreak with high spatial resolution satellite imagery

Insect outbreaks cause significant tree mortality across western North America, including in high-elevation whitebark pine forests. These forests are under several threats, which include attack by insects and white pine blister rust, as well as conversion to other tree species as a result of fire suppression. Mapping tree mortality is critical to determining the status of whitebark pine as a species. Satellite remote sensing builds upon existing aerial surveys by using objective, repeatable methods that can result in high spatial resolution monitoring. Past studies concentrated on level terrain and only forest vegetation type. The objective of this study was to develop a means of classifying whitebark pine mortality caused by a mountain pine beetle infestation in rugged, remote terrain using high spatial resolution satellite imagery. We overcame three challenges of mapping mortality in this mountainous region: (1) separating non-vegetated cover types, green and brown herbaceous cover, green (live) tree cover, and red-attack (dead) tree cover; (2) variations in illumination as a result of variations in slope and aspect related to the mountainous terrain of the study site; and (3) the difficulty of georegistering the imagery for use in comparing field measurements. Quickbird multi-spectral imagery (2.4 m spatial resolution) was used, together with a maximum likelihood classification method, to classify vegetation cover types over a 6400 ha area. To train the classifier, we selected pixels in each cover class from the imagery guided by our knowledge of the study site. Variables used in the maximum likelihood classifier included the ratio of red reflectance to green reflectance as well as green reflectance. These variables were stratified by solar incidence angle to account for illumination variability. We evaluated the results of the classified image using a reserved set of image-derived class members and field measurements of live and dead trees. Classification results yielded high overall accuracy (86% and 91% using image-derived class members and field measurements respectively) and kappa statistics (0.82 and 0.82) and low commission (0.9% and 1.5%) and omission (6.5% and 15.9%) errors for the red-attack tree class. Across the scene, 700 ha or 31% of the forest was identified as in the red-attack stage. Severity (percent mortality by canopy cover) varied from nearly 100% for some areas to regions with little mortality. These results suggest that high spatial resolution satellite imagery can provide valuable information for mapping and monitoring tree mortality even in rugged, mountainous terrain.     

The uncertainty in conifer plantation growth prediction from multi-temporal lidar datasets

An evaluation of the use of airborne lidar for multi-temporal forest height growth assessment in a temperate mature red pine (Pinus resinosa Ait.) plantation over a five-year period is presented. The objective was to evaluate the level of uncertainty in lidar-based growth estimates through time so that the optimal repeat interval necessary for statistically meaningful growth measurements could be evaluated. Four airborne lidar datasets displaying similar survey configuration parameters were collected between 2000 and 2005. Coincident with the 2002 and 2005 acquisitions, field mensuration for 126 trees within 19 plots was carried out. Field measurements of stem height were compared to both coincident plot-level laser pulse return (LPR) height percentile metrics and stand level raster canopy height models (CHM).The average plot-level field heights were found to be 23.8 m (standard deviation (σ) = 0.4 m) for 2002 and 25.0 m (σ = 0.6 m) for 2005, with an approximate annual growth rate of 0.4 m/yr (σ = 0.5 m). The standard deviation uncertainty for field height growth estimates over the three year period was 41% at the plot-level (n = 19) and 92% at the individual tree level (n = 126). Of the lidar height percentile metrics tested, the 90th (L90), 95th (L95) and maximum (Lmax) LPR distribution heights demonstrated the highest overall correlations with field-measured tree height. While all lidar-based methods, including raster CHM comparison, tended to underestimate the field estimate of growth, Lmax provided the most robust overall direct estimate (0.32 m/yr, σ = 0.37 m). A single factor analysis of variance demonstrated that there was no statistically significant difference between all plot-level field and Lmax mean growth rate estimates (P = 0.38) and, further, that there was no difference in Lmax growth rate estimates across the examined time intervals (P = 0.59). A power function relationship between time interval and the standard deviation of error in growth estimate demonstrated that over a one-year period, the growth uncertainty was in the range of 0.3 m (∼ 100% of total growth) reducing to less than 0.1 m (∼ 6% of total growth) after 5 years. Assuming a 10% uncertainty is acceptable for operational or research-based conifer plantation growth estimates, this can be achieved at a three-year time interval.     

Estimation of insect infestation dynamics using a temporal sequence of Landsat data

The current outbreak of mountain pine beetle (Dendroctonus ponderosae) in western Canada has been increasing over the past decade and is currently estimated to be impacting 9.2 million hectares, with varying levels of severity. Large area insect monitoring is typically undertaken using manual aerial overview sketch mapping, whereby an interpreter depicts areas of homogenous insect attack conditions onto 1:250,000 or 1:100,000 scale maps. These surveys provide valuable strategic data for management at the provincial scale. The coarse spatial and attribute resolution of these data however, make them inappropriate for fine-scale monitoring and operational planning. For instance, it is not possible to estimate the initial timing of attack and year of stand death. In this study, we utilise eight Landsat scenes collected over a 14 year period in north-central British Columbia, Canada, where the infestation has gradually developed both spatially and temporally. After pre-processing and normalising the eight scenes using a relative normalisation procedure, decision tree analysis was applied to classify spectral trajectories of the Normalised Difference Moisture Index (NDMI). From the classified temporal sequence of images, key parameters were extracted including the presence of beetle disturbance and timing of stand decline. The accuracy of discriminating beetle attack from healthy forest stands was assessed both spatially and temporally using three years of aerial survey data (1996, 2003, and 2004) with results indicating overall classification accuracies varying between 71 and 86%. As expected, the earliest and least severe attack year (1996), recorded the lowest overall accuracy. The relationship between the timing of stand attack (i.e. moderate to severe beetle infestation) and NDMI (initial year of detected disturbance) was also explored. The results suggest that there is potential for deriving regional estimates of the year of stand death using Landsat data and decision tree analysis however, a higher temporal frequency of images is required to quantify the timing of mountain pine beetle attack.     

Spectroscopic determination of leaf water content using continuous wavelet analysis

The gravimetric water content (GWC, %), a commonly used measure of leaf water content, describes the ratio of water to dry matter for each individual leaf. To date, the relationship between spectral reflectance and GWC in leaves is poorly understood due to the confounding effects of unpredictably varying water and dry matter ratios on spectral response. Few studies have attempted to estimate GWC from leaf reflectance spectra, particularly for a variety of species. This paper investigates the spectroscopic estimation of leaf GWC using continuous wavelet analysis applied to the reflectance spectra (350-2500 nm) of 265 leaf samples from 47 species observed in tropical forests of Panama. A continuous wavelet transform was performed on each of the reflectance spectra to generate a wavelet power scalogram compiled as a function of wavelength and scale. Linear relationships were built between wavelet power and GWC expressed as a function of dry mass (LWC_D) and fresh mass (LWC_F) in order to identify wavelet features (coefficients) that are most sensitive to changes in GWC. The derived wavelet features were then compared to three established spectral indices used to estimate GWC across a wide range of species.Eight wavelet features observed between 1300 and 2500 nm provided strong correlations with LWC_D, though correlations between spectral indices and leaf GWC were poor. In particular, two features captured amplitude variations in the broad shape of the reflectance spectra and three features captured variations in the shape and depth of dry matter (e.g., protein, lignin, cellulose) absorptions centered near 1730 and 2100 nm. The eight wavelet features used to predict LWC_D and LWC_F were not significantly different; however, predictive models used to determine LWC_D and LWC_F differed. The most accurate estimates of LWC_D and LWC_F obtained from a single wavelet feature showed root mean square errors (RMSEs) of 28.34% (R² = 0.62) and 4.86% (R² = 0.69), respectively. Models using a combination of features resulted in a noticeable improvement predicting LWC_D and LWC_F with RMSEs of 26.04% (R² = 0.71) and 4.34% (R² = 0.75), respectively. These results provide new insights into the role of dry matter absorption features in the shortwave infrared (SWIR) spectral region for the accurate spectral estimation of LWC_D and LWC_F. This emerging spectral analytical approach can be applied to other complex datasets including a broad range of species, and may be adapted to estimate basic leaf biochemical elements such as nitrogen, chlorophyll, cellulose, and lignin.     

Using airborne laser scanning to monitor tree migration in the boreal-alpine transition zone

The boreal tree line is expected to advance upwards into the mountains and northwards into the tundra due to global warming. The major objective of this study was to find out if it is possible to use high-resolution airborne laser scanner data to detect very small trees — the pioneers that are pushing the tree line up into the mountains and out onto the tundra. The study was conducted in a sub-alpine/alpine environment in southeast Norway. A total of 342 small trees of Norway spruce, Scots pine, and downy birch with tree heights ranging from 0.11 to 5.20 m were precisely georeferenced and measured in field. Laser data were collected with a pulse density of 7.7 m^− 2. Three different terrain models were used to process the airborne laser point cloud in order to assess the effects of different pre-processing parameters on small tree detection. Greater than 91% of all trees > 1 m tall registered positive laser height values regardless of terrain model. For smaller trees (< 1 m), positive height values were found in 5-73% of the cases, depending on the terrain model considered. For this group of trees, the highest rate of trees with positive height values was found for spruce. The more smoothed the terrain model was, the larger the portion of the trees that had positive laser height values. The accuracy of tree height derived from the laser data indicated a systematic underestimation of true tree height by 0.40 to 1.01 m. The standard deviation for the differences between laser-derived and field-measured tree heights was 0.11-0.73 m. Commission errors, i.e., the detection of terrain objects — rocks, hummocks — as trees, increased significantly as terrain smoothing increased. Thus, if no classification of objects into classes like small trees and terrain objects is possible, many non-tree objects with a positive height value cannot be separated from those actually being trees. In a monitoring context, i.e., repeated measurements over time, we argue that most other objects like terrain structures, rocks, and hummocks will remain stable over time while the trees will change as they grow and new trees are established. Thus, this study indicates that, given a high laser pulse density and a certain density of newly established trees, it would be possible to detect a sufficient portion of newly established trees over a 10 years period to claim that tree migration is taking place.     

Identifying species of individual trees using airborne laser scanner

Individual trees can be detected using high-density airborne laser scanner data. Also, variables characterizing the detected trees such as tree height, crown area, and crown base height can be measured. The Scandinavian boreal forest mainly consists of Norway spruce (Picea abies L. Karst.), Scots pine (Pinus sylvestris L.), and deciduous trees. It is possible to separate coniferous from deciduous trees using near-infrared images, but pine and spruce give similar spectral signals. Airborne laser scanning, measuring structure and shape of tree crowns could be used for discriminating between spruce and pine. The aim of this study was to test classification of Scots pine versus Norway spruce on an individual tree level using features extracted from airborne laser scanning data. Field measurements were used for training and validation of the classification. The position of all trees on 12 rectangular plots (50×20 m²) were measured in field and tree species was recorded. The dominating species (>80%) was Norway spruce for six of the plots and Scots pine for six plots. The field-measured trees were automatically linked to the laser-measured trees. The laser-detected trees on each plot were classified into species classes using all laser-detected trees on the other plots as training data. The portion correctly classified trees on all plots was 95%. Crown base height estimations of individual trees were also evaluated (r=0.84). The classification results in this study demonstrate the ability to discriminate between pine and spruce using laser data. This method could be applied in an operational context. In the first step, a segmentation of individual tree crowns is performed using laser data. In the second step, tree species classification is performed based on the segments. Methods could be developed in the future that combine laser data with digital near-infrared photographs for classification with the three classes: Norway spruce, Scots pine, and deciduous trees.     

Influence of terrain model smoothing and flight and sensor configurations on detection of small pioneer trees in the boreal-alpine transition zone utilizing height metrics derived from airborne scanning lasers

It has been suggested that airborne laser scanning (ALS) with high point densities could be used to monitor changes in the alpine tree line. The overall goal of this study was to assess the influence of ALS sensor and flight configurations on the ability to detect small trees in the alpine tree line and on the estimation of their heights. The study was conducted in a sub-alpine/alpine environment in southeast Norway. 342 small trees (0.11-5.20 m tall) of Norway spruce, Scots pine, and downy birch were precisely georeferenced and measured in field. ALS data acquired with two different instruments and at different flying altitudes (700-1130 m a.g.l.) with different pulse repetition frequencies (100, 125, and 166 kHz) were collected with a point density of all echoes of 7.7-11.0 m^− 2. For each acquisition, three different terrain models were used to process the ALS point clouds in order to assess the effects of different preprocessing parameters on the ability to detect small trees. Regardless of acquisition and terrain model, positive height values were found for 91% of the taller trees (> 1 m). For smaller trees (< 1 m), 29-61% of the trees displayed positive height values. For the lowest repetition frequencies (100 and 125 kHz) in particular, the portion of trees with positive laser height values increased significantly with increasing terrain smoothing. For the highest repetition frequency there were no differences between smoothing levels, likely because of large ALS measurement errors at low laser pulse energy levels causing a large portion of the laser echoes to be discarded during terrain modeling. Error analysis revealed large commission errors when detecting small trees. The commissions consisted of objects like terrain structures, rocks, and hummocks having positive height values. The magnitude of commissions ranged from 709 to 8948% of the true tree numbers and tended to increase with increasing levels of terrain smoothing and with acquisitions according to increasing point densities. The accuracy of tree height derived from the ALS data indicated a systematic underestimation of true tree height by 0.35 to 1.47 m, depending on acquisition, terrain model, and tree species. The underestimation also increased with increasing tree height. The standard deviation for the differences between laser-derived and field-measured tree heights was 0.16-0.57 m. Because there are significant effects of sensor and flight configurations on tree height estimation, field calibration of tree heights at each point of time is required when using airborne lasers for tree growth monitoring.     

Sensitivity of the thematic mapper enhanced wetness difference index to detect mountain pine beetle red-attack damage

Red-attack damage caused by mountain pine beetle (Dentroctonus ponderosa Hopkins) infestation in stands of lodgepole pine (Pinus contorta) in the Prince George Forest Region of British Columbia was examined using multitemporal Landsat-7 ETM+ imagery acquired in 1999, 2000, and 2001. The image data were geometrically and atmospherically corrected, and processed using the Tasseled Cap Transformation (TCT) to obtain wetness indices. The final steps included pixel subtraction, enhancement, and thresholding of the wetness index differences. The resulting enhanced wetness difference index (EWDI) was used to interpret spectral patterns in stands with confirmed (through aerial survey) red-attack damage in 2001, and these EWDI patterns were compared to the patterns of reflectance in normal-colour composites. We stratified the aerial survey dataset into two levels and used the EWDI to discriminate classes of 10-29 red-attack trees and 30-50 red-attack trees, and a sample of healthy forest collected from inventory data. Classification accuracy of red-attack damage based on the EWDI ranged from 67% to 78% correct.     

Capturing tree crown formation through implicit surface reconstruction using airborne lidar data

Forest structure data derived from lidar is being used in forest science and management for inventory analysis, biomass estimation, and wildlife habitat analysis. Regression analysis dominated previous approaches to the derivation of tree stem and crown parameters from lidar. The regression model for tree parameters is locally applied based on vertical lidar point density, the tree species involved, and stand structure in the specific research area. The results of this approach, therefore, are location-specific, limiting its applicability to other areas. For a more widely applicable approach to derive tree parameters, we developed an innovative method called ‘wrapped surface reconstruction’ that employs radial basis functions and an isosurface. Utilizing computer graphics, we capture the exact shape of an irregular tree crown of various tree species based on the lidar point cloud and visualize their exact crown formation in three-dimensional space. To validate the tree parameters given by our wrapped surface approach, survey-grade equipment (a total station) was used to measure the crown shape. Four vantage points were established for each of 55 trees to capture whole-tree crown profiles georeferenced with post-processed differential GPS points. The observed tree profiles were linearly interpolated to estimate crown volume. These fieldwork-generated profiles were compared with the wrapped surface to assess goodness of fit. For coniferous trees, the following tree crown parameters derived by the wrapped surface method were highly correlated (p < 0.05) with the total station-derived measurements: tree height (R² = 0.95), crown width (R² = 0.80), live crown base (R² = 0.92), height of the lowest branch (R² = 0.72), and crown volume (R² = 0.84). For deciduous trees, wrapped surface-derived parameters of tree height (R² = 0.96), crown width (R² = 0.75), live crown base (R² = 0.53), height of the lowest branch (R² = 0.51), and crown volume (R² = 0.89) were correlated with the total station-derived measurements. The wrapped surface technique is less susceptible to errors in estimation of tree parameters because of exact interpolation using the radial basis functions. The effect of diminished energy return causes the low correlation for lowest branches in deciduous trees (R² = 0.51), even though leaf-off lidar data was used. The wrapped surface provides fast and automated detection of micro-scale tree parameters for specific applications in areas such as tree physiology, fire modeling, and forest inventory.