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.     

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.     

Remote detection of Trichodesmium blooms in optically complex coastal waters: Examples with MODIS full-spectral data

Remote detection of the Trichodesmium spp. cyanobacteria blooms on the west Florida shelf (WFS) has been problematic due to optical complexity caused by sediment resuspension, coastal runoff, and bottom interference. By combining MODIS data measured by the ocean bands and land bands, an approach was developed to identify surface mats of Trichodesmium on the WFS. The approach first identifies possible bloom patches in MODIS FAI (floating algae index) 250 m resolution imagery derived from the Rayleigh-corrected reflectance at 667, 859, and 1240 nm. Then, spectral analysis examines the unique reflectance characteristics of Trichodesmium at 469, 488, 531, 551, and 555 nm due to specific optical properties (absorption, backscattering, and fluorescence) of the unusual pigments in Trichodesmium. These spectral characteristics (i.e., high-low-high-low-high reflectance at 469-488-531-551-555 nm, respectively) differentiate Trichodesmium mats unambiguously from other features observed in the FAI imagery, such as Sargassum spp. Tests in other coastal locations show that the approach is robust and applicable to other optically complex waters. Results shown here can help study Trichodesmium bloom dynamics (e.g., initiation and bloom formation) and may also help design future sensors to better detect and quantify Trichodesmium, an important N₂ fixer in the global oceans.     

Regional aboveground live carbon losses due to drought-induced tree dieback in piñon-juniper ecosystems

Recent large-scale dieback of piñon-juniper (P-J) woodlands and forests across the western US occurred as a result of multi-year drought and subsequent insect and disease outbreaks. P-J vegetation is spatially extensive, thus large-scale mortality events such as the one that has occurred over the past several years could significantly alter regional carbon (C) budgets. Our objective was to use a remote sensing technique coupled with field-based data to estimate changes in aboveground live C stocks across a 4100 km² region of Colorado caused by P-J tree mortality. We hypothesized that dieback would amplify the phenological dynamics of P-J vegetation, and these variations would be related to drought-induced losses of live P-J aboveground biomass (AGB) that are discernible using time-series remote sensing vegetation data. Here, we assess live P-J AGB loss using dry season fractional photosynthetic vegetation cover (PV) derived from multi-year Landsat images. Our results showed a strong linear positive relationship between the maximum decline in PV and field-measured losses of live P-J AGB during the period 2000-05 (r² = 0.64, p = 0.002). These results were then used to map AGB losses throughout the study region. Mean live aboveground C loss (± sd) was 10.0 (± 3.4) Mg C ha^− 1. Total aboveground live P-J C loss was 4.6 Tg C, which was approximately 39 times higher than the concurrent C loss attributed to wildfire and management treatments within or near to the national forests of the study region. Our results suggest that spatially extensive mortality events such as the one observed in P-J woodlands across the western US in the past decade may significantly alter the ecosystem C balance for decades to come. Remote sensing techniques to monitor changes in aboveground C stocks, such as the one developed in our study, may support regional and global C monitoring in the future.     

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).     

Broadband, red-edge information from satellites improves early stress detection in a New Mexico conifer woodland

Multiple plant stresses can affect the health, esthetic condition, and timber harvest value of conifer forests. To monitor spatial and temporal dynamic forest stress conditions, timely, accurate, and cost-effective information is needed that could be provided by remote sensing. Recently, satellite imagery has become available via the RapidEye satellite constellation to provide spectral information in five broad bands, including the red-edge region (690-730 nm) of the electromagnetic spectrum. We tested the hypothesis that broadband, red-edge satellite information improves early detection of stress (as manifest by shifts in foliar chlorophyll a + b) in a woodland ecosystem relative to other more commonly utilized band combinations of red, green, blue, and near infrared band reflectance spectra. We analyzed a temporally dense time series of 22 RapidEye scenes of a piñon-juniper woodland in central New Mexico acquired before and after stress was induced by girdling. We found that the Normalized Difference Red-Edge index (NDRE) allowed stress to be detected 13 days after girdling — between and 16 days earlier than broadband spectral indices such as the Normalized Difference Vegetation Index (NDVI) and Green NDVI traditionally used for satellite based forest health monitoring. We conclude that red-edge information has the potential to considerably improve forest stress monitoring from satellites and warrants further investigation in other forested ecosystems.     

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.     

Monitoring canopy biophysical and biochemical parameters in ecosystem scale using satellite hyperspectral imagery: An application on a Phlomis fruticosa Mediterranean ecosystem using multiangular CHRIS/PROBA observations

This study focuses on the potential of satellite hyperspectral imagery to monitor vegetation biophysical and biochemical characteristics through narrow-band indices and different viewing angles. Hyperspectral images of the CHRIS/PROBA sensor in imaging mode 1 (5 observation angles, 62 bands, 410-1005 nm) were acquired throughout a two-year period for a Mediterranean ecosystem fully covered by the semi-deciduous shrub Phlomis fruticosa. During each acquisition, coincident ecophysiological field measurements were conducted. Leaf area index (LAI), leaf biochemical content (chlorophyll a, chlorophyll b, carotenoids) and leaf water potential were measured. The hyperspectral images were corrected for coherent noises, cloud and atmosphere, in order to produce ground reflectance images. The reflectance spectrum of each image was used to calculate a variety of vegetation indices (VIs) that are already published in relevant literature. Additionally, all combinations of the 62 bands were used in order to calculate Normalized Difference Spectral Indices (NDSI_(x,y)) and Simple Subtraction Indices (SSI_(x,y)). The above indices along with raw reflectance and reflectance derivatives were examined for linear relationship with the ground-measured variables and the strongest relationships were determined. It is concluded that higher observation angles are better for the extraction of biochemical indices. The first derivative of the reflectance spectra proved to be very useful in the prediction of all measured variables. In many cases, complex and improved spectral indices that are proposed in the literature do not seem to be more accurate than simple NDSIs such as NDVI. Even traditional broadband NDVI is proved to be adequate in LAI prediction, while green bands seem also very useful. However, in biochemical estimation narrow bands are necessary. Indices that incorporate red, blue and IR bands, such as PSRI, SIPI and mNDVI presented good performance in chlorophyll estimation, while CRI did not show any relevance to carotenoids and WI was poorly correlated to water potential. Moreover, analyses indicated that it is very important to use a near red-edge band (701 nm) for effective chlorophyll index design. SSIs that incorporate 701 nm with 511 or 605 nm showed best performance in chlorophyll determination. For carotenoid estimation, a band on the edge of carotenoid absorption (511 nm) combined with a red band performed best, while a normalized index of two water absorption bands (945, 971 nm) proved to be an effective water index. Finally, the attempt to investigate stress conditions through pigment ratios resulted in the use of the band centred at 701 nm.     

Imaging chlorophyll fluorescence with an airborne narrow-band multispectral camera for vegetation stress detection

Progress in assessing the feasibility for imaging fluorescence using the O₂-A band with 1 nm full-width half-maximum (FWHM) bands centered at 757.5 and 760.5 nm is reported in this paper. Multispectral airborne data was acquired at 150 m above ground level in the thermal, visible and near infrared regions yielding imagery at 15 cm spatial resolution. Simultaneous field experiments conducted in olive, peach, and orange orchards (water stress trials), and an olive orchard (variety trial) enabled the detected variability in fluorescence emission to be examined as function of stress status. In a parallel modelling activity the coupled leaf-canopy reflectance-fluorescence model, FluorMOD, was used to assess fluorescence retrieval capability by the in-filling method, as well as by fluorescence indices from the published literature. Fluorescence retrievals using the in-filling method, the derivative index D702/D680 and reflectance indices R690/R630, R761-R757, and R761/R757 yielded the best results in the simulation study, while demonstrating insensitivity to leaf area index (LAI) variation. The fluorescence in-filling method, derivative index D702/D680, and R761-R757 were the indices least affected by chlorophyll a + b (Cab) variation. On the other hand, other published indices for fluorescence detection at leaf and canopy levels exhibited high sensitivity to variations in Cab and LAI, and therefore were considered less suitable for in-field fluorescence detection. The fluorescence signal extraction from airborne imagery using the in-filling method was validated through comparisons with field-measured steady-state fluorescence (Fs) using the PAM-2100 and GFS-3000 instruments, confirming simulation predictions. The water stress experiments conducted on olive and peach orchards demonstrated the feasibility of chlorophyll fluorescence (F) extraction at the tree level from the airborne imagery, yielding determination coefficients r² = 0.57 (olive), and r² = 0.54 (peach). Consistent results were obtained between airborne F and ground truth assimilation (A) measured in the olive variety field experiment under no water stress levels, yielding r² = 0.71.     

Using lidar and effective LAI data to evaluate IKONOS and Landsat 7 ETM+ vegetation cover estimates in a ponderosa pine forest

Structural and functional analyses of ecosystems benefit when high accuracy vegetation coverages can be derived over large areas. In this study, we utilize IKONOS, Landsat 7 ETM+, and airborne scanning light detection and ranging (lidar) to quantify coniferous forest and understory grass coverages in a ponderosa pine (Pinus ponderosa) dominated ecosystem in the Black Hills of South Dakota. Linear spectral mixture analyses of IKONOS and ETM+ data were used to isolate spectral endmembers (bare soil, understory grass, and tree/shade) and calculate their subpixel fractional coverages. We then compared these endmember cover estimates to similar cover estimates derived from lidar data and field measures. The IKONOS-derived tree/shade fraction was significantly correlated with the field-measured canopy effective leaf area index (LAI_e) (r²=0.55, p<0.001) and with the lidar-derived estimate of tree occurrence (r²=0.79, p<0.001). The enhanced vegetation index (EVI) calculated from IKONOS imagery showed a negative correlation with the field measured tree canopy effective LAI and lidar tree cover response (r²=0.30, r=−0.55 and r²=0.41, r=−0.64, respectively; p<0.001) and further analyses indicate a strong linear relationship between EVI and the IKONOS-derived grass fraction (r²=0.99, p<0.001). We also found that using EVI resulted in better agreement with the subpixel vegetation fractions in this ecosystem than using normalized difference of vegetation index (NDVI). Coarsening the IKONOS data to 30 m resolution imagery revealed a stronger relationship with lidar tree measures (r²=0.77, p<0.001) than at 4 m resolution (r²=0.58, p<0.001). Unmixed tree/shade fractions derived from 30 m resolution ETM+ imagery also showed a significant correlation with the lidar data (r²=0.66, p<0.001). These results demonstrate the power of using high resolution lidar data to validate spectral unmixing results of satellite imagery, and indicate that IKONOS data and Landsat 7 ETM+ data both can serve to make the important distinction between tree/shade coverage and exposed understory grass coverage during peak summertime greenness in a ponderosa pine forest ecosystem.     

Estimation of cyanobacterial pigments in a freshwater lake using OCM satellite data

Cyanobacteria represent a major harmful algal group in fresh to brackish water environments. Lac des Allemands, a freshwater lake of 49 km² southwest of New Orleans, Louisiana on the upper end of the Barataria Estuary, provides a natural laboratory for remote characterization of cyanobacterial blooms because of their seasonal occurrence. The Oceansat-1 satellite Ocean Colour Monitor (OCM) provides measurements similar to SeaWiFS but with higher spatial resolution, and this work is the first attempt to use OCM measurements to quantify cyanobacterial pigments. The satellite signal was first vicariously calibrated using SeaWiFS as a reference, and then corrected to remove the atmospheric effects using a customized atmospheric correction procedure. Then, empirical inversion algorithms were developed to convert the OCM remote sensing reflectance (R_rs) at bands 4 and 5 (centered at 510.6 and 556.4 nm, respectively) to concentrations of phycocyanin (PC), the primary cyanobacterial pigment. A holistic approach was used to minimize the influence of other optically active constituents on the PC algorithm. Similarly, empirical algorithms to estimate chlorophyll a (Chl a) concentrations were developed using OCM bands 5 and 6 (centered at 556.4 and 669 nm, respectively). The best PC algorithm (R² = 0.7450, p < 0.0001, n = 72) yielded a root mean square error (RMSE) of 36.92 μg/L with a relative RMSE of 10.27% (PC from 2.75 to 363.50 μg/L, n = 48). The best algorithm for Chl a (R² = 0.7510, p < 0.0001, n = 72) produced an RMSE of 31.19 μg/L with a relative RMSE of 16.56% (Chl a from 9.46 to 212.76 μg/L, n = 48). While more field data are required to further validate the long-term performance of these algorithms, currently they represent the best protocol for establishing a long time-series of cyanobacterial blooms in the Lac des Allemands using OCM data.     

Neural network estimation of LAI, fAPAR, fCover and LAI×Cab, from top of canopy MERIS reflectance data: Principles and validation

A neural network is developed to operationally estimate biophysical variables over land surfaces from the observations of the ENVISAT-MERIS instrument: the leaf area index (LAI), the fraction of absorbed photosynthetically active radiation (fAPAR), the fraction of vegetation cover (fCover), and the canopy chlorophyll content (LAI×C_ab). The neural network requires as input the geometry of observation and the top of canopy reflectances, corrected from the atmospheric effects, in eleven spectral bands. It is trained on a reflectance database made of radiative transfer model simulations. The principles underlying the generation of the database and the design of the network are first presented. The estimated variables are then compared to other existing products, LAI- and fAPAR-MODIS and MGVI-MERIS, and validated against ground measurements performed in the framework of the VALERI project. Results show remarkable consistency of the temporal dynamics between the several products with however some differences in the range of variation. When compared to actual VALERI ground measurements, the proposed algorithm shows the best performances for LAI (RMSE = 0.47) and fAPAR (RMSE = 0.09).     

Wetland monitoring using classification trees and SPOT-5 seasonal time series

Multiseason reflectance data from radiometrically and geometrically corrected multispectral SPOT-5 images of 10-m resolution were combined with thorough field campaigns and land cover digitizing using a binary classification tree algorithm to estimate the area of marshes covered with common reeds (Phragmites australis) and submerged macrophytes (Potamogeton pectinatus, P. pusillus, Myriophyllum spicatum, Ruppia maritima, Chara sp.) over an area of 145,000 ha. Accuracy of these models was estimated by cross-validation and by calculating the percentage of correctly classified pixels on the resulting maps. Robustness of this approach was assessed by applying these models to an independent set of images using independent field data for validation. Biophysical parameters of both habitat types were used to interpret the misclassifications. The resulting trees provided a cross-validation accuracy of 98.7% for common reed and 97.4% for submerged macrophytes. Variables discriminating reed marshes from other land covers were the difference in the near-infrared band between March and June, the Optimized Soil Adjusted Vegetation Index of December, and the Normalized Difference Water Index (NDWI) of September. Submerged macrophyte beds were discriminated with the shortwave-infrared band of December, the NDWI of September, the red band of September and the Simple Ratio index of March. Mapping validations provided accuracies of 98.6% (2005) and 98.1% (2006) for common reed, and 86.7% (2005) and 85.9% (2006) for submerged macrophytes. The combination of multispectral and multiseasonal satellite data thus discriminated these wetland vegetation types efficiently. Misclassifications were partly explained by digitizing inaccuracies, and were not related to biophysical parameters for reedbeds. The classification accuracy of submerged macrophytes was influenced by the proportion of plants showing on the water surface, percent cover of submerged species, water turbidity, and salinity. Classification trees applied to time series of SPOT-5 images appear as a powerful and reliable tool for monitoring wetland vegetation experiencing different hydrological regimes even with a small training sample (N = 25) when initially combined with thorough field measurements.     

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.     

Enhancing a leaf radiative transfer model to estimate concentrations and in vivo specific absorption coefficients of total carotenoids and chlorophylls a and b from single-needle reflectance and transmittance

The relative concentrations of different pigments within a leaf have significant physiological and spectral consequences. Photosynthesis, light use efficiency, mass and energy exchange, and stress response are dependent on relationships among an ensemble of pigments. This ensemble also determines the visible characteristics of a leaf, which can be measured remotely and used to quantify leaf biochemistry and structure. But current remote sensing approaches are limited in their ability to resolve individual pigments. This paper focuses on the incorporation of three pigments—chlorophyll a, chlorophyll b, and total carotenoids—into the LIBERTY leaf radiative transfer model to better understand relationships between leaf biochemical, biophysical, and spectral properties.Pinus ponderosa and Pinus jeffreyi needles were collected from three sites in the California Sierra Nevada. Hemispheric single-leaf visible reflectance and transmittance and concentrations of chlorophylls a and b and total carotenoids of fresh needles were measured. These data were input to the enhanced LIBERTY model to estimate optical and biochemical properties of pine needles. The enhanced model successfully estimated reflectance (RMSE = 0.0255, BIAS = 0.00477, RMS%E = 16.7%), had variable success estimating transmittance (RMSE = 0.0442, BIAS = 0.0294, RMS%E = 181%), and generated very good estimates of carotenoid concentrations (RMSE = 2.48 µg/cm², BIAS = 0.143 µg/cm², RMS%E = 20.4%), good estimates of chlorophyll a concentrations (RMSE = 10.7 µg/cm², BIAS = − 0.992 µg/cm², RMS%E = 21.1%), and fair estimates of chlorophyll b concentrations (RMSE = 7.49 µg/cm², BIAS = − 2.12 µg/cm², RMS%E = 43.7%). Overall root mean squared errors of reflectance, transmittance, and pigment concentration estimates were lower for the three-pigment model than for the single-pigment model. The algorithm to estimate three in vivo specific absorption coefficients is robust, although estimated values are distorted by inconsistencies in model biophysics. The capacity to invert the model from single-leaf reflectance and transmittance was added to the model so it could be coupled with vegetation canopy models to estimate canopy biochemistry from remotely sensed data.     

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.     

Monitoring yield and fruit quality parameters in open-canopy tree crops under water stress. Implications for ASTER

Work on water stress detection at tree and orchard levels using a high-spatial airborne thermal sensor is presented, showing its connection with yield and some fruit quality indicators in olive and peach commercial orchards under different irrigation regimes. Two airborne campaigns were conducted with the Airborne Hyperspectral Scanner (AHS) over olive and peach orchards located in Córdoba, southern Spain. The AHS sensor was flown at three different times on 25 July 2004 and 16 July 2005, collecting 2 m spatial resolution imagery in 80 spectral bands in the 0.43-12.5 μm spectral range. Thermal bands were assessed for the retrieval of land surface temperature using the split-window algorithm and TES (Temperature-Emissivity-Separation) method, separating pure crowns from shadows and sunlit soil pixels using the reflectance bands. Stem water potential and stomatal conductance were measured on selected trees at the time of airborne flights over the orchards. Tree fruit yield and quality parameters such as oil, weight and water content (for the olive trees), and fruit volume and weight (for the peach trees) were obtained at harvest and through laboratory analysis. Relationships between airborne-estimated crown temperature minus air temperature and stem water potential yielded r² = 0.5 (12:30 GMT) at the olive tree level, and r² = 0.81 (9:00 GMT) at the treatment level in peach trees. These results demonstrate that water stress can be detected at the crown level even under the usual water management conditions of commercial orchards. Regressions of yield and fruit quality against remote sensing estimates of crown temperature as an indicator of water stress, yielded r² = 0.95 (olive fruit water content) and r² = 0.94 (peach fruit mean diameter). These results suggest that high-spatial remote sensing thermal imagery has potential as an indicator of some fruit quality parameters for crop field segmentation and irrigation management purposes. A simulation study using ASTER spectral bands and aggregated pixels for stress detection as a function of irrigation level was conducted in order to study the applicability of medium resolution thermal sensors for the global monitoring of open-canopy tree crops. The determination coefficients obtained between the ASTER-simulated canopy temperature minus air temperature and stem water potential yielded r² = 0.58 (12:30 GMT) for olive trees, suggesting the potential for extrapolating these methods to ASTER satellite for global monitoring of open tree canopies.     

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.     

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.     

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.