Publications by T.H. Hilker

Augmenting forest inventory attributes with geometric optical modelling in support of regional susceptibility assessments to bark beetle infestations

Wed, 28 Nov 2012

Assessment of the susceptibility of forests to mountain pine beetle (Dendroctonus ponderosae Hopkins) infestation is based upon an understanding of the characteristics that predispose the stands to attack. These assessments are typically derived from conventional forest inventory data; however, this information often represents only managed forest areas. It does not cover areas such as forest parks or conservation regions and is often not regularly updated resulting in an inability to assess forest susceptibility. To address these shortcomings, we demonstrate how a geometric optical model (GOM) can be applied to Landsat-5 Thematic Mapper (TM) imagery (30 m spatial resolution) to estimate stand-level susceptibility to mountain pine beetle attack. Spectral mixture analysis was used to determine the proportion of sunlit canopy and background, and shadow of each Landsat pixel enabling per pixel estimates of attributes required for model inversion. Stand structural attributes were then derived from inversion of the geometric optical model and used as basis for susceptibility mapping. Mean stand density estimated by the geometric optical model was 2753 (standard deviation ± 308) stems per hectare and mean horizontal crown radius was 2.09 (standard deviation ± 0.11) metres. When compared to equivalent forest inventory attributes, model predictions of stems per hectare and crown radius were shown to be reasonably estimated using a Kruskal–Wallis ANOVA (p < 0.001). These predictions were then used to create a large area map that provided an assessment of the forest area susceptible to mountain pine beetle damage.

Prediction of Wood Fiber Attributes from LiDAR-Derived Forest Canopy Indicators.

Wed, 01 Aug 2012

We investigated the potential use of airborne light detection and ranging (LiDAR) data to predict key wood fiber properties from extrinsic indicators in lodgepole pine leading forest stands located in the foothills of central Alberta, Canada. Six wood fiber attributes (wood density, cell perimeter, cell coarseness, mature fiber length, microfibril angle, and modulus of elasticity) were measured at 21 plots, and with use of data reduction techniques, two components of wood properties were derived: wood strength, stiffness, and fiber yield and fiber strength and smoothness. These wood fiber components were then compared with extrinsic indicators of wood characteristic-derived LiDAR-estimated topographic morphology, tree height, and canopy light metrics. The first principal component indicating wood strength and stiffness was significantly correlated to the depth of different canopy zones (or light regimes) (r² = 0.55, P < 0.05). The second component, related to fiber strength and smoothness, was significantly correlated to the height of the canopy and canopy thickness (r² = 0.65, P < 0.05). The results indicate that airborne LiDAR attributes can explain about half of the observed variance in intrinsic wood fiber attributes, which is approximately 5–10% less than that explained by growth-related field-measured variables such as diameter increment and height. This reduction in explained variance can be balanced by the opportunities for much broader spatial characterizations of wood quantity and quality at the stand and landscape levels.

Lidar calibration and validation for geometric-optical modeling with Landsat imagery

Tue, 05 Jun 2012

There is a paucity of detailed and timely forest inventory information available for Canada’s large, remote northern boreal forests. The Canadian National Forest Inventory program has derived a limited set of attributes from a Landsat-based land cover product representing circa year 2000 conditions. Of the required inventory attributes, forest vertical structure (e.g., tree height) is critical for terrestrial biomass and carbon modeling and to date, is unavailable for these remote areas. In this study, we develop a large-area, fine-scale (25 m) mapping solution to estimate tree height (mean, dominant, and Lorey’s height) across Canada’s northern forests by integrating lidar data (representing 0.27% of the study area), and Landsat imagery (representing 100% of the study area), using a geometric-optical modeling technique. First, spectral mixture analysis (SMA) was used to extract image endmembers and generate fraction images. Second, lidar data were used to calibrate the inverted geometric-optical model by adjusting the model’s three key fractional inputs: sunlit crown, sunlit background, and shade fraction, based upon the SMA derived images. The heterogeneity of the study area, spanning 2.16 million ha, made it challenging to directly and accurately decompose mixed Landsat image pixels into the canopy and background fractions used for the Li-Strahler geometric-optical model inversion. As a result we developed a novel method to use the lidar plot data to facilitate the calculation of these fractions in an accurate and automated manner. The average estimation errors for mean, dominant, and Lorey’s height were 4.9 m, 4.1 m, and 4.7 m, respectively when compared to the lidar data, with the best result achieved using dominant tree height, where the average error was 3.5 m for over 80% of the forested area. Using this approach of optical remotely sensed data calibrated and validated with lidar height estimates we generate and evaluate wall-to-wall estimates of tree height that can subsequently be used as inputs for biomass and carbon modeling.

Lidar sampling for large-area forest characterization: A review

Wed, 07 Mar 2012

The ability to use digital remotely sensed data for forest inventory is often limited by the nature of the measures, which, with the exception of multi-angular or stereo observations, are largely insensitive to vertically distributed attributes. As a result, empirical estimates are typically made to characterize attributes such as height, volume, or biomass, with known asymptotic relationships as signal saturation occurs. Lidar (light detection and ranging) has emerged as a robust means to collect and subsequently characterize vertically distributed attributes. Lidar has been established as an appropriate data source for forest inventory purposes; however, large area monitoring and mapping activities with lidar remain challenging due to the logistics, costs, and data volumes involved.

The use of lidar as a sampling tool for large-area estimation may mitigate some or all of these problems. A number of factors drive, and are common to, the use of airborne profiling, airborne scanning, and spaceborne lidar systems as sampling tools for measuring and monitoring forest resources across areas that range in size from tens of thousands to millions of square kilometers. In this communication, we present the case for lidar sampling as a means to enable timely and robust large-area characterizations. We briefly outline the nature of different lidar systems and data, followed by the theoretical and statistical underpinnings for lidar sampling. Current applications are presented and the future potential of using lidar in an integrated sampling framework for large area ecosystem characterization and monitoring is presented. We also include recommendations regarding statistics, lidar sampling schemes, applications (including data integration and stratification), and subsequent information generation.

Linking ground-based to satellite-derived phenological metrics in support of habitat assessment

Tue, 20 Sep 2011

Changes in the timing of plant phenology are important indicators of inter-annual climatic variations and are a critical driver of food availability and habitat use for a range of species. A number of remote sensing techniques have recently been developed to observe vegetation cycles throughout the year, including the use of inexpensive visible spectrum digital cameras at the stand level and the use of high temporal frequency Advanced Very High Resolution Radiometer National Oceanic and Atmospheric Administration (AVHRR NOAA) and MODerate resolution Imaging Spectroradiometer (MODIS) imagery at a satellite scale. A fundamental challenge with using satellite data to track plant phenology, however, is the trade-off between the level of spatial detail and the revisit time provided by the sensor, and the ability to verify the interpretation of phenological activity. One way to address this challenge is to integrate remotely sensed observations obtained at different spatial and temporal scales to provide information that contains both high temporal density and fine spatial resolution observations. In this article, we compare measures of vegetation phenology observed from a network of ground-based cameras with satellite-derived measures of greenness derived from a fused broad (MODIS) and fine spatial (Landsat) scale satellite data set. We derive and compare three key indicators of phenological activity including the start date of green-up, start date of senescence and length of growing season from both a ground-based camera network and 30 m spatial resolution synthetic Landsat scenes. Results indicate that although field-based estimates, generally, predicted an earlier start and end of the vegetation season than the fused satellite observations, highly significant relationships were found for the prediction of the start (R 2 = 0.65), end (R 2 = 0.72) and length (R 2 = 0.70) of the growing season across all sites. We conclude that some predictable bias exists however unlike visual field measures of the collected data represent both a spectral and a visual archive for later use.

A simple technique for co-registration of terrestrial LiDAR observations for forestry applications.

Thu, 15 Sep 2011

Light detection and ranging (LiDAR) from terrestrial platforms provides unprecedented detail about the three-dimensional structure of forest canopies. Although airborne laser scanning is designed to yield a relatively homogeneous distribution of returns, the radial perspective of terrestrial laser scanning (TLS) results in a rapid decrease of number of returns with increasing distance from the instrument. Additionally, when used in forested environments, significant parts of the area under investigation may be obscured by tree trunks and understorey. A possible approach to mitigate this effect is to combine TLS observations acquired at different locations to obtain multiple perspectives of an area under investigation. The denser and more evenly distributed observations then allow a spatially explicit and more comprehensive study of forest characteristics. This study demonstrates a simple approach to combine TLS observations made at multiple locations using bright reference targets as tie-points. Results show this technique was able to accurately combine the different TLS data sets (root mean square error (RMSE): 0.04–0.7 m, coefficient of determination (R²): 0.70–0.99). Terrain elevations from TLS system were highly correlated with field-measured terrain heights (R²: 0.70–0.98).

Assessing the impact of N-fertilization on biochemical composition and biomass of a Douglas-fir canopy—A remote sensing approach

Wed, 27 Apr 2011

Vegetation biochemistry is a critical driver of the forest carbon and water cycle and the fluxes between the land surface and the atmosphere. As result, monitoring biochemistry is a key to improving our estimates of the terrestrial carbon and energy budget. While destructive sampling techniques have been widely applied to determine nutrient content in foliage, scaling of these measurements to the stand and landscape is challenging. As an alternative to traditional field-based approaches, optical remote sensing is a powerful technique for sampling biochemical constituents in a spatially continuous fashion. Remote sensing of biochemical constituents is based on the understanding that leaf biochemistry is closely linked to absorption and reflectance properties in characteristic, often spectrally narrow, wavebands. Spectral absorption features can be identified to characterize and quantify biochemical properties at the leaf, stand and landscape level. At the same time, Light Detection and Ranging (LiDAR) remote sensing can allow inference about the impact of leaf biochemistry on tree growth and canopy structure. In this study, we report the effect of nitrogen-fertilization of a Douglas-fir dominated forest on Vancouver Island, British Columbia, Canada using active and passive remote sensing techniques. Leaf pigment concentrations were estimated from inversion of a canopy reflectance model (PROSAIL) and canopy nitrogen (N) was inferred from an airborne imaging spectrometer (AVIRIS). The impact of leaf biochemistry on canopy structure and tree growth was then investigated using a temporal sequence of LiDAR data acquired two years before, and after, the fertilization treatment. Results indicate that while fertilization had a significant impact on canopy pigment concentrations, it did not impact canopy nitrogen. A notable increase in tree growth was found for younger stands of less than 15 m of height, but not for more mature stand with taller trees. Fertilization had no immediate impact on canopy density measured from LiDAR derived leaf area and canopy volume. The use of advanced remote sensing tools and techniques such as those demonstrated in this study can be a powerful addition to ongoing efforts to model carbon and water fluxes throughout the landscape.

Biweekly disturbance capture and attribution: case study in western Alberta grizzly bear habitat

Tue, 06 Dec 2011

An increasing number of studies have demonstrated the impact of landscape disturbance on ecosystems. Satellite remote sensing can be used for mapping disturbances, and fusion techniques of sensors with complimentary characteristics can help to improve the spatial and temporal resolution of satellite-based mapping techniques. Classification of different disturbance types from satellite observations is difficult, yet important, especially in an ecological context as different disturbance types might have different impacts on vegetation recovery, wildlife habitats, and food resources. We demonstrate a possible approach for classifying common disturbance types by means of their spatial characteristics. First, landscape level change is characterized on a near biweekly basis through application of a data fusion model (spatial temporal adaptive algorithm for mapping reflectance change) and a number of spatial and temporal characteristics of the predicted disturbance patches are inferred. A regression tree approach is then used to classify disturbance events. Our results show that spatial and temporal disturbance characteristics can be used to classify disturbance events with an overall accuracy of 86% of the disturbed area observed. The date of disturbance was identified as the most powerful predictor of the disturbance type, together with the patch core area, patch size, and contiguity.

Inferring terrestrial photosynthetic light use efficiency of temperate ecosystems from space

Wed, 27 Jul 2011

Terrestrial ecosystems absorb about 2.8 Gt C yr^‐1, which is estimated to be about a quarter of the carbon emitted from fossil fuel combustion. However, the uncertainties of this sink are large, in the order of ± 40%, with spatial and temporal variations largely unknown. One of the largest factors contributing to the uncertainty is photosynthesis, the process by which plants absorb carbon from the atmosphere. Currently, photosynthesis, or gross ecosystem productivity (GEP), can only be inferred from flux‐towers by measuring the exchange of CO₂ in the surrounding air column. Consequently, carbon models suffer from a lack of spatial coverage of accurate GEP observations. Here, we show that photosynthetic lightuse efficiency (ε), hence photosynthesis can be directly inferred from spaceborne measurements of reflectance. We demonstrate that the differential between reflectance measurements in bands associated with the vegetation xanthophyll cycle and estimates of canopy shading obtained from multiangular satellite observations (using the CHRIS/Proba sensor) permits us to infer plant photosynthetic efficiency, independently of vegetation type and structure (r²=0.68, compared to flux measurements). This is a significant advance over previous approaches seeking to model global scale photosynthesis indirectly from a combination of growth limiting factors, most notably pressure deficit and temperature. When combined with modeled global scale photosynthesis, satellite inferred ε can improve model estimates through data assimilation. We anticipate that our findings will guide the development of new spaceborne approaches to observe vegetation carbon uptake and improve current predictions of global CO₂ budgets and future climate scenarios by providing regularly timed calibration points for modeling plant photosynthesis consistently at a global scale.

Stability of sample-based scanning LiDAR-derived vegetation metrics for forest monitoring

Thu, 02 Jun 2011

The objective of this paper is to gain insights into the reproducibility of light detection and ranging (LiDAR)-derived vegetation metrics for multiple acquisitions carried out on the same day, where we can assume that forest and terrain conditions at a given location have not changed. Four overlapping lines were flown over a forested area in Vancouver Island, British Columbia, Canada. Forty-six 0.04-ha plots were systematically established, and commonly derived variables were extracted from first and last returns, including height-related metrics, cover estimates, return intensities, and absolute scan angles. Plot-level metrics from each LiDAR pass were then compared using multivariate repeated-measures analysis-of-variance tests. Results indicate that, while the number of returns was significantly different between the four overlapping flight lines, most LiDAR-derived first return vegetation height metrics were not. First return maximum height and overstory cover, however, were significantly different and varied between flight lines by an average of approximately 2% and 4%, respectively. First return intensities differed significantly between overpasses where sudden changes in the metric occurred without any apparent explanation; intensity should only be used following calibration. With the exception of the standard deviation of height, all second return metrics were significantly different between flight lines. Despite these minor differences, the study demonstrates that, when the LiDAR sensor, settings, and data acquisition flight parameters remain constant, and time-related forest dynamics are not factors, LiDAR-derived metrics of the same location provide stable and repeatable measures of the forest structure, confirming the suitability of LiDAR for forest monitoring.

Assessment of standing wood and fiber quality using ground and airborne laser scanning: A review

Mon, 04 Apr 2011

Accurate information on the wood-quality characteristics of standing timber and logs is needed to optimize the forest production value chain and to assess the potential of forest resources to meet other services. Physical and chemical characteristics of wood vary with both tree and site characteristics. At the tree scale, crown development, stem shape and taper, branch size and branch location, knot size, type and placement, and age all influence wood properties. More broadly, at the stand level, stocking density, moisture, nutrient availability, climate, competition, disturbance, and stand age have also been identified as key determinants of wood quality. Such information is often captured in polygon based forest inventory data. Other terrain-related spatial information, such as elevation, slope and aspect, can improve assessments of site conditions and limitations upon plant growth which impact wood quality. Light Detection And Ranging (LiDAR) is an emerging technology, which directly measures the three-dimensional structure of forest canopies using ground or airborne laser instruments, and can provide highly accurate information on individual-tree and stand-level forest structure. In this paper, we explore the potential of LiDAR and other geospatial information sources to model and predict wood quality based on individual-tree and stand structural metrics. We identify a number of key wood quality attributes (i.e., basic wood density, cell perimeter, cell coarseness, fiber length, and microfibril angle) and demonstrate links between these properties and forest structure and site attributes. Finally, the potential for using LiDAR in combination with other geospatial information sources to predict wood quality in standing timber is discussed.

Characterizing stand replacing disturbance in western Alberta grizzly bear habitat, using a satellite-derived high temporal and spatial resolution change sequence

Thu, 27 Jan 2011

Timely and accurate mapping of anthropogenic and natural disturbance patterns can be used to better understand the nature of wildlife habitats, distributions and movements. One common approach to map forest disturbance is by using high spatial resolution satellite imagery, such as Landsat 5 Thematic Mapper (TM) or Landsat 7 Enhanced Thematic Mapper plus (ETM+) imagery acquired at a 30 m spatial resolution. However, the low revisit times of these sensors acts to limit the capability to accurately determine dates for a sequence of disturbance events, especially in regions where cloud contamination is a frequent occurrence. As wildlife habitat use can vary significantly seasonally, annual patterns of disturbance are often insufficient in assessing relationships between disturbance and foraging behaviour or movement patterns.

The Spatial Temporal Adaptive Algorithm for mapping Reflectance Change (STAARCH) allows the generation of high-spatial (30 m) and -temporal (weekly or bi-weekly) resolution disturbance sequences using fusion of Landsat TM or ETM+ and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. The STAARCH algorithm is applied here to generate a disturbance sequence representing stand-replacing events (disturbances over 1 ha in area) for the period 2001–2008, over almost 6 million ha of grizzly bear habitat along the eastern slopes of the Rocky Mountains in Alberta. The STAARCH algorithm incorporates pairs of Landsat images to detect the spatial extent of disturbances; information from the bi-weekly MODIS composites is used in this study to assign a date of disturbance (DoD) to each detected disturbed area. Dates of estimated disturbances with areas over 5 ha are validated by comparison with a yearly Landsat-based change sequence, with producer's accuracies ranging between 15 and 85% (average overall accuracy 62%, kappa statistic of 0.54) depending on the size of the disturbance event. The spatial and temporal patterns of disturbances within the entire region and in smaller subsets, representative of the size of a grizzly bear annual home range, are then explored. Disturbance levels are shown to increase later in the growing season, with most disturbances occurring in late August and September. Individual events are generally small in area (<10 ha) except in the case of wildfires, with, on average, 0.4% of the total area disturbed each year. The application of STAARCH provides unique high temporal and spatial resolution disturbance information over an extensive area, with significant potential for improving understanding of wildlife habitat use.

Using digital time-lapse cameras to monitor species-specific understorey and overstorey phenology in support of wildlife habitat assessment.

Fri, 07 Jan 2011

Critical to habitat management is the understanding of not only the location of animal food resources, but also the timing of their availability. Grizzly bear (Ursus arctos) diets, for example, shift seasonally as different vegetation species enter key phenological phases. In this paper, we describe the use of a network of seven ground-based digital camera systems to monitor understorey and overstorey vegetation within species-specific regions of interest. Established across an elevation gradient in western Alberta, Canada, the cameras collected true-colour (RGB) images daily from 13 April 2009 to 27 October 2009. Fourth-order polynomials were fit to an RGB-derived index, which was then compared to field-based observations of phenological phases. Using linear regression to statistically relate the camera and field data, results indicated that 61% (r2 = 0.61, df = 1, F = 14.3, p = 0.0043) of the variance observed in the field phenological phase data is captured by the cameras for the start of the growing season and 72% (r2 = 0.72, df = 1, F = 23.09, p = 0.0009) of the variance in length of growing season. Based on the linear regression models, the mean absolute differences in residuals between predicted and observed start of growing season and length of growing season were 4 and 6 days, respectively. This work extends upon previous research by demonstrating that specific understorey and overstorey species can be targeted for phenological monitoring in a forested environment, using readily available digital camera technology and RGB-based vegetation indices.

Remote sensing of photosynthetic light-use efficiency across two forested biomes: Spatial scaling

Tue, 12 Oct 2010

Eddy covariance (EC) measurements have greatly advanced our knowledge of carbon exchange in terrestrial ecosystems. However, appropriate techniques are required to upscale these spatially discrete findings globally. Satellite remote sensing provides unique opportunities in this respect, but remote sensing of the photosynthetic light-use efficiency (E), one of the key components of Gross Primary Production, is challenging. Some progress has been made in recent years using the photochemical reflectance index, a narrow waveband index centered at 531 and 570 nm. The high sensitivity of this index to various extraneous effects such as canopy structure, and the view observer geometry has so far prevented its use at landscape and global scales. One critical aspect of upscaling PRI is the development of generic algorithms to account for structural differences in vegetation. Building on previous work, this study compares the differences in the PRI: relationship between a coastal Douglas-fir forest located on Vancouver Island, British Columbia, and a mature Aspen stand located in central Saskatchewan, Canada. Using continuous, tower-based observations acquired from an automated multi-angular spectro-radiometer (AMSPEC II) installed at each site, we demonstrate that PRI can be used to measure throughout the vegetation season at the DF-49 stand (r2 = 0.91, p < 0.00) as well as the deciduous site (r2 = 0.88, p < 0.00). It is further shown that this PRI signal can be also observed from space at both sites using daily observations from the Moderate Resolution Imaging Spectro-radiometer (MODIS) and a multi-angular implementation of atmospheric correction (MAIAC) (r2 = 0.54 DF-49; r2 = 0.63 SOA; p < 0.00). By implementing a simple hillshade model derived from airborne light detection and ranging (LiDAR) to approximate canopy shadow fractions (as), it is further demonstrated that the differences observed in the relationship between PRI and e at DF-49 and SOA can be attributed largely to differences in as. The findings of this study suggest that algorithms used to separate physiological from extraneous effects in PRI reflectance may be more broadly applicable and portable across these two climatically and structurally different biome types, when the differences in canopy structure are known.

Comparing canopy metrics derived from terrestrial and airborne laser scanning in a Douglas-fir dominated forest stand

Fri, 18 Jun 2010

Accurate estimates of vegetation structure are important for a large number of applications including ecological modeling and carbon budgets. Light detection and ranging (LiDAR) measures the three-dimensional structure of vegetation using laser beams. Most LiDAR applications today rely on airborne platforms for data acquisitions, which typically record between 1 and 5 “discrete” returns for each outgoing laser pulse. Although airborne LiDAR allows sampling of canopy characteristics at stand and landscape level scales, this method is largely insensitive to below canopy biomass, such as understorey and trunk volumes, as these elements are often occluded by the upper parts of the crown, especially in denser canopies. As a supplement to airborne laser scanning (ALS), a number of recent studies used terrestrial laser scanning (TLS) for the biomass estimation in spatially confined areas. One such instrument is the Echidna® Validation Instrument (EVI), which is configured to fully digitize the returned energy of an emitted laser pulse to establish a complete profile of the observed vegetation elements. In this study we assess and compare a number of canopy metrics derived from airborne and TLS. Three different experiments were conducted using discrete return ALS data and discrete and full waveform observations derived from the EVI. Although considerable differences were found in the return distribution of both systems, ALS and TLS were both able to accurately determine canopy height (? height < 2.5 m) and the vertical distribution of foliage and leaf area (0.86 > r 2 > 0.90, p < 0.01). When using more spatially explicit approaches for modeling the biomass and volume throughout the stands, the differences between ALS and TLS observations were more distinct; however, predictable patterns exist based on sensor position and configuration.

Implications of differing input data sources and approaches upon forest carbon stock estimation

Mon, 29 Mar 2010

Site index is an important forest inventory attribute that relates productivity and growth expectation of forests over time. In forest inventory programs, site index is used in conjunction with other forest inventory attributes (i.e., height, age) for the estimation of stand volume. In turn, stand volumes are used to estimate biomass (and biomass components) and enable conversion to carbon. In this research, we explore the implications and consequences of different estimates of site index on carbon stock characterization for a 2,500-ha Douglas-fir-dominated landscape located on Eastern Vancouver Island, British Columbia, Canada. We compared site index estimates from an existing forest inventory to estimates generated from a combination of forest inventory and light detection and ranging (LIDAR)-derived attributes and then examined the resultant differences in biomass estimates generated from a carbon budget model (Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3)). Significant differences were found between the original and LIDAR-derived site indices for all species types and for the resulting 5-m site classes (p<0.001). The LIDAR-derived site class was greater than the original site class for 42% of stands; however, 77% of stands were within ±1 site class of the original class. Differences in biomass estimates between the model scenarios were significant for both total stand biomass and biomass per hectare (p<0.001); differences for Douglas-fir-dominated stands (representing 85% of all stands) were not significant (p=0.288). Overall, the relationship between the two biomass estimates was strong (R 2=0.92, p<0.001), suggesting that in certain circumstances, LIDAR may have a role to play in site index estimation and biomass mapping.

Generation of dense time series synthetic Landsat data through data blending with MODIS using a spatial and temporal adaptive reflectance fusion model

Fri, 14 Aug 2009

Landsat imagery with a 30 m spatial resolution is well suited for characterizing landscape-level forest structure and dynamics. While Landsat images have advantageous spatial and spectral characteristics for describing vegetation properties, the Landsat sensor's revisit rate, or the temporal resolution of the data, is 16 days. When considering that cloud cover may impact any given acquisition, this lengthy revisit rate often results in a dearth of imagery for a desired time interval (e.g., month, growing season, or year) especially for areas at higher latitudes with shorter growing seasons. In contrast, MODIS (MODerate-resolution Imaging Spectroradiometer) has a high temporal resolution, covering the Earth up to multiple times per day, and depending on the spectral characteristics of interest, MODIS data have spatial resolutions of 250 m, 500 m, and 1000 m. By combining Landsat and MODIS data, we are able to capitalize on the spatial detail of Landsat and the temporal regularity of MODIS acquisitions. In this research, we apply and demonstrate a data fusion approach (Spatial and Temporal Adaptive Reflectance Fusion Model, STARFM) at a mainly coniferous study area in central British Columbia, Canada. Reflectance data for selected MODIS channels, all of which were resampled to 500 m, and Landsat (at 30 m) were combined to produce 18 synthetic Landsat images encompassing the 2001 growing season (May to October). We compared, on a channel-by-channel basis, the surface reflectance values (stratified by broad land cover types) of four real Landsat images with the corresponding closest date of synthetic Landsat imagery, and found no significant difference between real (observed) and synthetic (predicted) reflectance values (mean difference in reflectance: mixed forest x¯ = 0.086, s = 0.088, broadleaf x¯ = 0.019, s = 0.079, coniferous x¯ = 0.039, s = 0.093). Similarly, a pixel based analysis shows that predicted and observed reflectance values for the four Landsat dates were closely related (mean r2 = 0.76 for the NIR band; r2 = 0.54 for the red band; p < 0.01). Investigating the trend in NDVI values in synthetic Landsat values over a growing season revealed that phenological patterns were well captured; however, when seasonal differences lead to a change in land cover (i.e., disturbance, snow cover), the algorithm used to generate the synthetic Landsat images was, as expected, less effective at predicting reflectance.

A new data fusion model for high spatial- and temporal-resolution mapping of forest disturbance based on Landsat and MODIS

Fri, 14 Aug 2009

Investigating the temporal and spatial pattern of landscape disturbances is an important requirement for modeling ecosystem characteristics, including understanding changes in the terrestrial carbon cycle or mapping the quality and abundance of wildlife habitats. Data from the Landsat series of satellites have been successfully applied to map a range of biophysical vegetation parameters at a 30 m spatial resolution; the Landsat 16 day revisit cycle, however, which is often extended due to cloud cover, can be a major obstacle for monitoring short term disturbances and changes in vegetation characteristics through time.

The development of data fusion techniques has helped to improve the temporal resolution of fine spatial resolution data by blending observations from sensors with differing spatial and temporal characteristics. This study introduces a new data fusion model for producing synthetic imagery and the detection of changes termed Spatial Temporal Adaptive Algorithm for mapping Reflectance Change (STAARCH). The algorithm is designed to detect changes in reflectance, denoting disturbance, using Tasseled Cap transformations of both Landsat TM/ETM and MODIS reflectance data. The algorithm has been tested over a 185 × 185 km study area in west-central Alberta, Canada. Results show that STAARCH was able to identify spatial and temporal changes in the landscape with a high level of detail. The spatial accuracy of the disturbed area was 93% when compared to the validation data set, while temporal changes in the landscape were correctly estimated for 87% to 89% of instances for the total disturbed area. The change sequence derived from STAARCH was also used to produce synthetic Landsat images for the study period for each available date of MODIS imagery. Comparison to existing Landsat observations showed that the change sequence derived from STAARCH helped to improve the prediction results when compared to previously published data fusion techniques.

Assessing tower flux footprint climatology and scaling between remotely sensed and eddy covariance measurements

Mon, 15 Jun 2009

We describe pragmatic and reliable methods to examine the influence of patch-scale heterogeneities on the uncertainty in long-term eddy-covariance (EC) carbon flux data and to scale between the carbon flux estimates derived from land surface optical remote sensing and directly derived from EC flux measurements on the basis of the assessment of footprint climatology. Three different aged Douglas-fir stands with EC flux towers located on Vancouver Island and part of the Fluxnet Canada Research Network were selected. Monthly, annual and interannual footprint climatologies, unweighted or weighted by carbon fluxes, were produced by a simple model based on an analytical solution of the Eulerian advection-diffusion equation. The dimensions and orientation of the flux footprint depended on the height of the measurement, surface roughness length, wind speed and direction, and atmospheric stability. The weighted footprint climatology varied with the different carbon flux components and was asymmetrically distributed around the tower, and its size and spatial structure significantly varied monthly, seasonally and inter-annually. Gross primary productivity (GPP) maps at 10-m resolution were produced using a tower-mounted multi-angular spectroradiometer, combined with the canopy structural information derived from airborne laser scanning (Lidar) data. The horizontal arrays of footprint climatology were superimposed on the 10-m-resolution GPP maps. Monthly and annual uncertainties in EC flux caused by variations in footprint climatology of the 59-year-old Douglas-fir stand were estimated to be approximately 15–20% based on a comparison of GPP estimates derived from EC and remote sensing measurements, and on sensor location bias analysis. The footprint-variation-induced uncertainty in long-term EC flux measurements was mainly dependent on the site spatial heterogeneity. The bias in carbon flux estimates using spatially-explicit ecological models or tower-based remote sensing at finer scales can be estimated by comparing the footprint-weighted and EC-derived flux estimates. This bias is useful for model parameter optimizing. The optimization of parameters in remote-sensing algorithms or ecosystem models using satellite data will, in turn, increase the accuracy in the upscaled regional carbon flux estimation.

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

Mon, 12 Jan 2009

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, r2NDVI= 0.90; r2CHL3 = 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.

A modeling approach for upscaling gross ecosystem production to the landscape scale using remote sensing data.

Tue, 28 Aug 2012

Gross ecosystem production (GEP) can be estimated at the global scale and in a spatially continuous mode using models driven by remote sensing. Multiple studies have demonstrated the capability of high resolution optical remote sensing to accurately measure GEP at the leaf and stand level, but upscaling this relationship using satellite data remains challenging. Canopy structure is one of the complicating factors as it not only alters the strength of a measured signal depending on integrated leaf-angle-distribution and sun-observer geometry, but also drives the photosynthetic output and light-use-efficiency (^ɛ) of individual leaves. This study introduces a new approach for upscaling multiangular canopy level reflectance measurements to satellite scales which takes account of canopy structure effects by using Light Detection and Ranging (LiDAR). A tower-based spectro-radiometer was used to observe canopy reflectances over an annual period under different look and solar angles. This information was then used to extract sunlit and shaded spectral end-members corresponding to minimum and maximum values of canopy-^ɛ over 8-d intervals using a bidirectional reflectance distribution model. Using three-dimensional information of the canopy structure obtained from LiDAR, the canopy light regime and leaf area was modeled over a 12 km² area and was combined with spectral end-members to derive high resolution maps of GEP. Comparison with eddy covariance data collected at the site shows that the spectrally driven model is able to accurately predict GEP (r ² between 0.75 and 0.91, p < 0.05).

The role of LiDAR in sustainable forest management

Mon, 01 Dec 2008

Forest characterization with light detection and ranging (LiDAR) data has recently garnered much scientific and operational attention. The number of forest inventory attributes that may be directly measured with LiDAR is limited; however, when considered within the context of all the measured and derived attributes required to complete a forest inventory, LiDAR can be a valuable tool in the inventory process. In this paper, we present the status of LiDAR remote sensing of forests, including issues related to instrumentation, data collection, data processing, costs, and attribute estimation. The information needs of sustainable forest management provide the context within which we consider future opportunities for LiDAR and automated data processing.

Update of forest inventory data with lidar and high spatial resolution satellite imagery

Mon, 28 Apr 2008

Most countries with significant forest resources have designed and implemented monitoring systems to inventory, at regular intervals, a range of forest stand attributes such as species composition, age, volume, biomass, and disturbance. These inventory systems are typically based upon the interpretation of air photos supplemented by ground measurements, with digital remotely sensed data often used to capture changes within inventory cycles. Light detection and ranging (lidar) and high spatial resolution digital satellite imagery (e.g., QuickBird) offer additional capacity and complementary data sources for inventory assessment, as demonstrated by this study over a 400 ha area on Vancouver Island, British Columbia, Canada. A range of lidar survey parameters were applied to update an existing forest inventory. Results indicate a strong relationship between the small-footprint lidar-derived heights and stand height as derived from aerial photographic interpretation (API) (R = 0.79, p < 0.05). In addition, there was no statistical difference (p < 0.05) between stand height as predicted from a complete lidar coverage or when sampled as a single 400 m wide transect (R = 0.89, p < 0.001). These results demonstrate the utility of lidar data, as a full coverage or sample, in combination with high spatial resolution imagery, as useful data sources for capturing forest inventory stand height and cover information.

Separating physiologically and directionally induced changes in PRI using BRDF models

Thu, 27 Mar 2008

Monitoring of photosynthetic efficiency (e) over space and time is a critical component of climate change research as it is a major determinant of the amount of carbon accumulated by terrestrial ecosystems. While the past decade has seen progress in the remote estimation of e at the leaf, canopy and stand level using the photochemical reflectance index PRI (based on the normalized difference of reflectance at 531 and 570 nm), little is known about the temporal and spatial requirements for up-scaling PRI to landscape and global levels using satellite observations. One potential way to investigate these requirements is using automated tower-based remote sensing platforms, which observe stand level reflectance with high spatial, temporal, and spectral resolution. Prediction of e from PRI diurnally or over a full year requires observations of canopy reflectance over multiple view and sun-angles. As a result, these observations are subject to directional reflectance effects which can be interpreted in terms of the bidirectional reflectance distribution function (BRDF) using semi-empirical kernel driven models. These semi-empirical models use a combination of physically based BRDF shapes and empirical observations to standardize multi-angular observations to a common viewing and illumination geometry. Directional reflectance effects are thereby modeled as a linear superposition of mathematical kernels, representing the bi-direction variation in reflectance from isotropic, geometric, and volumetric scattering components of the vegetation canopy. However, because variations in plant physiological conditions can also introduce bidirectional reflectance variations, we introduce an approach to separate bidirectional effects arising purely from plant physiological status from other effects by stratifying PRI observations into categories based on environmental conditions for which the expected physiological variability is low. Within each of these PRI strata, the derived physically based BRDF shapes were used to standardize multi-angular PRI measurements to a common viewing and illumination geometry. The method significantly enhanced the relationship found between PRI and e (from r2 = 0.38 for the directionally uncorrected case to r2 = 0.82 for the directionally corrected case) from data measured continuously over the course of 1 year over an evergreen conifer forest using an automated platform. Results show that isotropic PRI scattering is highly correlated to changes in e, while geometric scattering can be related to canopy level shading. Instrumentation and approaches such as the one demonstrated in this study may be integrated into current efforts aiming at predicting e at global scales using satellite observations.

The use of remote sensing in light use efficiency based models of gross primary production: A review of current status and future requirements

Thu, 27 Mar 2008

Global estimation and monitoring of plant photosynthesis (known as Gross Primary Production — GPP) is a critical component of climate change research. Modeling of carbon cycling requires parameterization of the land surface, which, in a spatially continuous mode, is only possible using remote sensing. The increasing availability of high spectral resolution satellite observations with global coverage and high temporal frequency has allowed the scientific community to revisit a number of existing approaches for modeling GPP, and reassess the potential for using remotely sensed inputs. In this paper we examine the current status and future requirements of modeling global GPP thereby focusing on the light use efficiency approach which expresses GPP as product of the photosynthetically active radiation (PAR), the fraction of PAR being absorbed by the plant canopy (fPAR) and the efficiency with which this absorbed PAR can be converted into biomass. The capacity of remote sensing to provide the critical input variables for this approach is reviewed and key issues are identified and discussed for future research.

Estimating canopy structure of Douglas-fir forest stands from discrete-return LiDAR

Mon, 09 Jul 2007

Variations in vertical and horizontal forest structure are often difficult to quantify as field-based methods are labour intensive and passive optical remote sensing techniques are limited in their capacity to distinguish structural changes occurring below the top of the canopy. In this study the capacity of small footprint (0.19 cm), discrete return, densely spaced (0.7 hits/m-2), multiple return, Light Detection and Ranging (LiDAR) technology, to measure foliage height and to estimate several stand and canopy structure attributes is investigated. The study focused on six Douglas-fir [Pseudotsuga menziesii spp. menziesii (Mirb.) Franco] and western hemlock [Tsuga heterophylla (Raf.) Sarg.] stands located on the east coast of Vancouver Island, British Columbia, Canada, with each stand representing a different structural stage of stand development for forests within this biogeoclimatic zone. Tree height, crown dimensions, cover, and vertical foliage distributions were measured in 20 m × 20 m plots and correlated to the LiDAR data. Foliage profiles were then fitted, using the Weibull probability density function, to the field measured crown dimensions, vertical foliage density distributions and the LiDAR data at each plot. A modified canopy volume approach, based on methods developed for full waveform LiDAR observations, was developed and used to examine the vertical and horizontal variation in stand structure. Results indicate that measured stand attributes such as mean stand height, and basal area were significantly correlated with LiDAR estimates (r 2 = 0.85, P < 0.001, SE = 1.8 m and r 2 = 0.65, P < 0.05, SE = 14.8 m2 ha-1, respectively). Significant relationships were also found between the LiDAR data and the field estimated vertical foliage profiles indicating that models of vertical foliage distribution may be robust and transferable between both field and LiDAR datasets. This study demonstrates that small footprint, discrete return, LiDAR observations can provide quantitative information on stand and tree height, as well as information on foliage profiles, which can be successfully modelled, providing detailed descriptions of canopy structure.

Detecting mountain pine beetle red attack damage with EO-1 Hyperion moisture indices

Thu, 07 Jun 2007

The mountain pine beetle (Dendroctonus ponderosae) is the most destructive insect of mature pine forests in western North America. Time series of wetness transformations generated from Landsat imagery have been used to detect mountain pine beetle red attack damage over large areas. With the recent availability of high spatial (QuickBird) and high spectral (Hyperion) resolution satellite sensor imagery, the relationship between spectral moisture indices and levels of red attack damage may be investigated. Six moisture indices were generated from Hyperion data and were compared to the proportion of the Hyperion pixel having red attack damage. Results indicate the Hyperion moisture indices incorporating both the shortwave infrared (SWIR) and near infrared (NIR) regions of the electromagnetic spectrum concurrently, such as the Moisture Stress Index, were significantly correlated to levels of damage (r 2 = 0.51; p = 0.0001). The results corroborate the hypothesis that changes in foliage moisture resulting from mountain pine beetle attack are driving the broad-scale temporal variation in Landsat derived wetness indices. Furthermore, the results suggest that Hyperion data may be used to map low levels of mountain pine beetle red attack damage over large areas that are not consistently captured with Landsat data.

Instrumentation and approach for unattended year round tower based measurements of spectral reflectance

Wed, 14 Feb 2007

Phyto-pigments play an important role in plant physiological processes and primary production. Because different groups of pigments absorb light in distinctive ways, spectroscopy can be used as a tool for phyto-pigment quantification. While technical restrictions have prevented the use of remote sensing for pigment detection in the past, the advent of fine spectral resolution radiometers has offered opportunities to detect leaf-pigment concentrations at a range of scales from portable spectro-radiometers to airborne instruments. This paper describes the development of a fully automated spectral data collection system composed of a radiometer, a motor driven probe, and a datalogger mounted on a tower to measure year round spectral reflectance under different view and sun angles. The instrument features a motor driven probe pointing at the canopy which is able to sample spectra in a near 360-degree view around the tower by completing a full rotation every 15 min. The view zenith angle is adjustable and currently set to 62 degrees; one scan is completed within 3 s and the probe is forwarded about 11.5 degrees every 30 s. Simultaneous measurement of solar irradiance and reflectance facilitates sampling under various sky conditions. The height of the tower is 45 m, and the sensor is located about 10m above the canopy. Over a 6-month test period, nearly 2 million scans have been collected and compiled at a field site, where ongoing eddy covariance (EC) flux measurements are being undertaken as part of the Fluxnet-Canada research network.