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.     

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.     

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.     

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.     

Mapping insect defoliation in Scots pine with MODIS time-series data

Insect damage is a general problem that disturbs the growth of forests, causing economic losses and affecting carbon sequestration. Coarse-resolution data from satellites are potentially useful for national and regional mapping of forest damage, but the accuracy of these methods has not been fully examined. In this study, a method was tested for the mapping of defoliation in Scots pine [Pinus silvestris] forests in southeast Norway caused by the pine sawfly [Neodiprion sertifer], with the use of multi-temporal MODIS 16-day composite vegetation index data and the TIMESAT processing method. The damage mapping method used differences in summer mean values and angles of the seasonal profiles, indicating decreasing foliage density, to identify pixels that represent areas containing forest damage. In addition to 16-day NDVI the Wide Dynamic Range Vegetation Index (WDRVI) was tested. Damage areas were identified by classifying data into pixels representing damaged versus undamaged forest areas using a boolean combination of thresholded parameters. Classification results were evaluated against the change in LAI estimated from airplane LIDAR measurements, as an indicator of defoliation. The damage classifications detected 71% to 82% of the pixels with damage, and had kappa coefficients varying between 0.48 and 0.63, indicating some overestimation. This was due e.g. to failure to include clear-cut areas in the evaluation data. Damage classification with WDRVI only resulted in slight improvement compared to the NDVI. Only weak relationships were found between the LIDAR-estimated defoliation and the change parameters obtained from MODIS. Consequently, mapping of the degree of defoliation from MODIS was abandoned. In conclusion, the damage detection method based on MODIS data was found to be useful for locating insect damage, but not for estimating its intensity. Control of the detected damage areas using high-resolution remote sensing data, aerial survey, or fieldwork is recommended for accurate delineation in operational applications.     

The k-MSN method for the prediction of species-specific stand attributes using airborne laser scanning and aerial photographs

Various studies have been presented within the last 10 years on the possibilities for predicting forest variables such as stand volume and mean height by means of airborne laser scanning (ALS) data. These have usually considered tree stock as a whole, even though it is tree species-specific forest information that is of primary interest in Finland, for example. We will therefore concentrate here on prediction of the species-specific forest variables volume, stem number, basal area, basal area median diameter and tree height, applying the non-parametric k-MSN method to a combination of ALS data and aerial photographs in order to predict these stand attributes simultaneously for Scots pine, Norway spruce and deciduous trees as well as total characteristics as sums of the species-specific estimates. The predictor variables derived from the ALS data were based on the height distribution of vegetation hits, whereas spectral values and texture features were employed in the case of the aerial photographs. The data covered 463 sample plots in 67 stands in eastern Finland, and the results showed that this approach can be used to predict species-specific forest variables at least as accurately as from the current stand-level field inventory for Finland. The characteristics of Scots pine and Norway spruce were predicted more accurately than those of deciduous trees.     

Estimating aboveground biomass using Landsat 7 ETM+ data across a managed landscape in northern Wisconsin, USA

Aboveground biomass (AGB; Mg/ha) is defined in this study as a biomass of growing stock trees greater than 2.5 cm in diameter at breast height (dbh) for stands >5 years and all trees taller than 1.3 m for stands <5 years. Although AGB is an important variable for evaluating ecosystem function and structure across the landscape, such estimates are difficult to generate without high-resolution satellite data. This study bridges the application of remote sensing techniques with various forest management practices in Chequamegon National Forest (CNF), Wisconsin, USA by producing a high-resolution stand age map and a spatially explicit AGB map. We coupled AGB values, calculated from field measurements of tree dbh, with various vegetation indices derived from Landsat 7 ETM+ data through multiple regression analyses to produce an initial biomass map. The initial biomass map was overlaid with a land-cover map to generate a stand age map. Biomass threshold values for each age category (e.g., young, intermediate, and mature) were determined through field observations and frequency analysis of initial biomass estimates by major cover types. We found that AGB estimates for hardwood forests were strongly related to stand age and near-infrared reflectance (r²=0.95) while the AGB for pine forests was strongly related to the corrected normalized difference vegetation index (NDVIc; r²=0.86). Separating hardwoods from pine forests improved the AGB estimates in the area substantially, compared to overall regression (r²=0.82). Our AGB results are comparable to previously reported values in the area. The total amount of AGB in the study area for 2001 was estimated as 3.3 million metric tons (dry weight), 76.5% of which was in hardwood and mixed hardwood/pine forests. AGB ranged from 1 to 358 Mg/ha with an average of 70 and a standard deviation of 54 Mg/ha. The AGB class with the highest percentage (16.1%) was between 81 and 100 Mg/ha. Forests with biomass values >200 Mg/ha accounted for less than 3% of the study area and were usually associated with mature hardwood forests. Estimated AGB was validated using independent field measurements (R²=0.67, p<0.001). The AGB and age maps can be used as baseline information for future landscape level studies such as quantifying the regional carbon budget, accumulating fuel, or monitoring management practices.     

An evaluation of satellite-based drought indices on a regional scale

We compared conventional and satellite-based drought indices from drought vulnerable sites in South Korea during 2004–2013. Satellite-based drought indices, the energy-based water deficit index (EWDI), and the standalone Moderate Resolution Imaging Spectroradiometer (MODIS)-based evaporative stress index (stMOD_ESI) were evaluated using MODIS imagery to assess its capability to analyse the complex topography of the Korean peninsula. Of the drought indices examined, the EWDI and stMOD_ESI were accurate when capturing moderate drought conditions, compared to the observed precipitation-based conventional drought indices (standardized precipitation index (SPI-3) and Palmer drought severity index (PDSI)). In addition, the satellite-basedsoil moisture index (SSMI) developed from the Advanced Microwave Scanning Radiometer (AMSR-E) and Advanced Scatterometer (ASCAT) soil moisture products were reasonably correlated with the EWDI and stMOD_ESI. These results suggest that the satellite-based drought indices (EWDI and stMOD_ESI) may be applicable on a regional scale.     

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.     

Comparison of three individual tree crown detection methods

Three image processing methods for single tree crown detection in high spatial resolution aerial images are presented and compared using the same image material and reference data. The first method uses templates to find the tree crowns. The other two methods uses region growing. One of them is supported by fuzzy rules while the other uses an image produced by Brownian motion. All three methods detect around 80%, or more, of the visible sunlit trees in two pine Pinus Sylvestris L.) and two spruce stands Picea abies Karst.) in a boreal forest. For all methods, large tree crowns are easier to detect than small ones.     

Comparison of airborne lidar,aerial photography,and field surveys to model the habitat suitability of a cryptic forest species – the hazel grouse

Light detection and ranging (lidar) is a useful tool for measuring three-dimensional habitat structure; hence, its use in habitat suitability models has been explored, both as a single resource and in combination with other remote-sensing techniques. Here, we evaluated the suitability of airborne lidar data in comparison with aerial photographs and field surveys for modelling the distribution of an endangered and cryptic forest species, the hazel grouse (Bonasa bonasia). The study was conducted in the Bavarian Forest National Park of southeast Germany. Subsequently, a prediction map for conservation planning was generated for a large area, which encompassed the National Park. We examined the utility of lidar data for generating a hazel grouse distribution model by using machine learning (boosted regression trees), and then compared the results to variables derived from field surveys and aerial photographs, both separately and in combination. The cross-validated discrimination ability of the model was slightly higher when using lidar data (area under the receiver operator characteristic curve (AUC), 0.79) compared to models using aerial photographs (AUC, 0.75) or field survey data (AUC, 0.78). The predictive performance consistently increased when combining the predictors from different sources, with an AUC of 0.86 being produced in the model combining all three data sources. The three data sources complemented one another, with each data source probably having an advantage at deriving one of three key aspects of the hazel grouse habitat, namely, vertically well-structured forest stands, horizontally mixed successional vegetation stages, and certain deciduous trees as food resources such as mountain ash (Sorbus aucuparia). In addition, the diverse lidar metrics might be applied to simultaneously characterize vertically and horizontally well-structured forest stands. We conclude that public available airborne lidar data are a viable source for creating habitat suitability maps for large areas and may have increased utility for detecting forest characteristics and valuable wildlife habitats.     

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.     

Continuous wavelet analysis for the detection of green attack damage due to mountain pine beetle infestation

Mountain pine beetle (Dendroctonus ponderosae Hopkins) is the most destructive insect infesting mature pine forests in North America and has devastated millions of hectares of forest in western Canada. Past studies have demonstrated the use of multispectral imagery for remote identification and mapping of visible or red attack damage in forests. This study aims to detect pre-visual or green attack damage in lodgepole pine needles by means of hyperspectral measurements, particularly via continuous wavelet analysis. Field measurements of lodgepole pine stands were conducted at two sites located northwest of Edmonton, Alberta, Canada. In June and August of 2007, reflectance spectra (350-2500 nm) were collected for 16 pairs of trees. Each of the 16 tree pairs included one control tree (healthy), and one stressed tree (girdled to simulate the effects of beetle infestation). In addition, during the period of June through October 2008, spectra were collected from 15 pairs of control- and beetle-infested trees. Spectra derived from these 31 tree pairs were subjected to a continuous wavelet transform, generating a scalogram that compiles the wavelet power as a function of wavelength location and scale of decomposition. Linear relationships were then explored between the wavelet scalograms and chemical properties or class labels (control and non-control) of the sample populations in order to isolate the most useful distinguishing spectral features that related to infested or girdled trees vs. control trees.A deficit in water content is observed in infested trees while an additional deficit in chlorophyll content is seen for girdled trees. The measurable water deficit of infested and girdled tree samples was detectable from the wavelet analysis of the reflectance spectra providing a novel method for the detection of green attack. The spectral features distinguishing control and infested trees are predominantly located between 950 and 1390 nm from scales 1 to 8. Of those, five features between 1318 to 1322 nm at scale 7 are consistently found in the July and August 2008 datasets. These features are located at longer wavelengths than those investigated in previous studies (below 1100 nm) and provide new insights into the potential remote detection of green attack. Spectral features that distinguish control and girdled trees were mostly observed between 1550 and 2370 nm from scales 1 to 5. The differing response of girdled and infested trees appears to indicate that the girdling process does not provide a perfect simulation of the effects caused by beetle infestation.It remains to be determined if the location of the 1318-1322 nm features, near the edge of a strong atmospheric water absorption band, will be sufficiently separable for use in airborne detection of green attack. A plot comparing needle water content and wavelet power at 1320 nm reveals considerable overlap between data derived from both infested and control samples, though the groups are statistically separable. This obstacle may preclude a high accuracy separation of healthy and infected single individuals, but establishing threshold identification levels may provide an economical, efficient and expeditious method for discriminating between healthy and infested tree populations.     

NOAA/AVHRR satellite detection of Siberian silkmoth outbreaks in eastern Siberia

During 1993-1996, in central Siberia, a silkmoth (Dendrolimus superans sibiricus Tschetw.) infestation damaged approximately 700 000 ha of fir, Siberian pine and spruce stands. Temporal (1995-1997) Advanced Very High Resolution Radiometer (AVHRR) images were used for pest outbreak monitoring of this event. Damaged stands were detected, with heavy (50-75% dead and dying trees) plus very heavy (>75%) levels of damage classified. Summer and winter images were used for delineation of the northern border of the region of pest outbreaks. The Siberian taiga insects were classified with respect to their harmfulness to forests, based on the frequency of outbreaks, the size of the damaged territory, and the available food sources based on forest type.     

Modeling mountain pine beetle infestation with an agent-based approach at two spatial scales

Extensive outbreaks of tree-killing insects have been occurring in many parts of North America, including the province of British Columbia, raising concerns about the health of pine forest ecosystems. The dynamic phenomenon of mountain pine beetle (MPB), Dendroctonus ponderosae Hopkins, infestation outbreaks is an inherent spatial and temporal complex process. Agent-based modeling (ABM) facilitates simulating spatial interactions that describe the ecological context in which insect populations spread. The main objective of this study was to develop a model of the MPB forest infestation dynamics. This spatially explicit model integrates geographic information systems (GISs) and ABM to simulate MPB outbreaks at the tree and landscape scales, providing spatiotemporal information of annual distribution and patterns of MPB outbreaks. This prototype was implemented with geographic data generated from aerial overview surveys carried out by the B.C. Ministry of Forests and Range, for the study site in Kamloops, Canada. Results show the direct influence that vigorous forest stands and trees have on higher breeding rates, and therefore in the MPB population increment at a tree scale, in a period of 5 years. The simulation results at the landscape level help to determine the most probable locations of future MPB infestations in a time frame of 10 years.     

A GIS-based decision support system for risk assessment of wind damage in forest management

In this study a GIS-based decision support system (DSS) was built for assessing the short- and long-term risk of wind damage in boreal forests. This was done by integrating a forest growth model SIMA and a mechanistic wind damage model HWIND into geographical information system software (ArcGIS 8.2) as a toolbar (DLL) using ArcObjects in ArcGIS and Visual Basic 6. In this DSS complex problems are solved within program so that forest gaps, edge stands and edges are automatically tracked when the forest structure changes over time as a result of forest growth dynamics and management. This DSS can be used to assess the risk of wind damage to Scots pine (Pinus sylvestris), Norway spruce (Picea abies) and birch (Betula spp.) stands, regarding the number of stands and area at risk and length of vulnerable edges of these risk stands at certain critical wind speed classes (i.e. corresponding the maximum wind speed a tree/stand can resist). This DSS can helps forest managers to analyse and visualise (charts, maps) the possible effects of forest management, such as clear-cuts, on both the immediate and long-term risks of wind damage at both stand and regional level.     

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.     

Extraction of damaged areas of windfall trees by typhoons using Landsat TM data

Two typhoons attacked, at an interval of about 2 weeks, the north part of Kyushu Island, Japan, in September 1991. Many trees, especially in artificial forests (main tree species are cedar and cypress) were felled by the typhoons. Landsat TM data taken before and after damage were collected and registered in order to extract the damaged areas. Typical damaged points were selected on the registered images, referring to aerial photographs taken immediately after damage, and the change characteristics of TM bands 1-5, 7 and Normalized Difference Vegetation Index (NDVI) due to the damage were examined. It showed that bands 5 and 7 of the middle-infrared increased more than other bands and that NDVI decreased. Bands 5, 7 and NDVI of each temporal TM data were merged into a single six-band dataset, and the damaged areas were extracted by a maximum likelihood classification using the merged dataset. The damaged areas extracted were evaluated using the aerial photographs. The damaged areas of windfall trees could be extracted with an accuracy of 90% using temporal Landsat TM data acquired before and after damage.     

Spectral identification of ozone‐damaged pine needles

Needles were collected from ponderosa and Jeffrey pine trees at three sites in the Sierra Nevada, and were assembled into 504 samples and grouped according to five dominant live needle conditions – green, winter fleck, sucking insect damage, scale insect damage, and ozone damage – and a random mixture. Reflectance and transmittance measurements of abaxial and adaxial surfaces were obtained at ca 0.3 nm spectral resolution from 400–800 nm, and binned to simulate Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) data. There were no significant differences in optical properties between the two surfaces. Ozone‐damaged needles were collected from Jeffrey pine trees at one site, and exhibited significantly different (family‐wise α = 0.01) reflectance and transmittance signatures – and significantly different signature slopes – at both spectral resolutions, from green and winter fleck needles from the same site. Ozone‐damaged needles had significantly different (family‐wise α = 0.01) abaxial surface reflectance and reflectance slope signatures from all other groups of needles, at both spectral resolutions. In comparison with three chlorophyll reflectance indices, a new red fall index (RFI) provides high classification accuracies for ozone‐damaged and non‐ozone‐damaged pine needles (overall acc. = 94%; κ = 59%). Thus, ozone‐damaged Jeffrey pine needles have a unique spectral signature in relation to dominant needle conditions of ponderosa and Jeffrey pine trees.