Douglas E. Ahl

Fire as the dominant driver of central Canadian boreal forest carbon balance

Changes in climate, atmospheric carbon dioxide concentration and fire regimes have been occurring for decades in the global boreal forest, with future climate change likely to increase fire frequency—the primary disturbance agent in most boreal forests. Previous attempts to assess quantitatively the effect of changing environmental conditions on the net boreal forest carbon balance have not taken into account the competition between different vegetation types on a large scale. Here we use a process model with three competing vascular and non-vascular vegetation types to examine the effects of climate, carbon dioxide concentrations and fire disturbance on net biome production, net primary production and vegetation dominance in 100 Mha of Canadian boreal forest. We find that the carbon balance of this region was driven by changes in fire disturbance from 1948 to 2005. Climate changes affected the variability, but not the mean, of the landscape carbon balance, with precipitation exerting a more significant effect than temperature. We show that more frequent and larger fires in the late twentieth century resulted in deciduous trees and mosses increasing production at the expense of coniferous trees. Our model did not however exhibit the increases in total forest net primary production that have been inferred from satellite data. We find that poor soil drainage decreased the variability of the landscape carbon balance, which suggests that increased climate and hydrological changes have the potential to affect disproportionately the carbon dynamics of these areas. Overall, we conclude that direct ecophysiological changes resulting from global climate change have not yet been felt in this large boreal region. Variations in the landscape carbon balance and vegetation dominance have so far been driven largely by increases in fire frequency.

MODIS leaf area index products: from validation to algorithm improvement

Global products of vegetation green Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) are being operationally produced from Terra and Aqua Moderate Resolution Imaging Spectroradiometers (MODIS) at 1-km resolution and eight-day frequency. This paper summarizes the experience of several collaborating investigators on validation of MODIS LAI products and demonstrates the close connection between product validation and algorithm refinement activities. The validation of moderate resolution LAI products includes three steps: 1) field sampling representative of LAI spatial distribution and dynamic range within each major land cover type at the validation site; 2) development of a transfer function between field LAI measurements and high resolution satellite data to generate a reference LAI map over an extended area; and 3) comparison of MODIS LAI with aggregated reference LAI map at patch (multipixel) scale in view of geo-location and pixel shift uncertainties. The MODIS LAI validation experiences, summarized here, suggest three key factors that influence the accuracy of LAI retrievals: 1) uncertainties in input land cover data, 2) uncertainties in input surface reflectances, and 3) uncertainties from the model used to build the look-up tables accompanying the algorithm. This strategy of validation efforts guiding algorithm refinements has led to progressively more accurate LAI products from the MODIS sensors aboard NASAs Terra and Aqua platforms

Analysis and optimization of the MODIS leaf area index algorithm retrievals over broadleaf forests

Broadleaf forest is a major type of Earths land cover with the highest observable vegetation density. Retrievals of biophysical parameters, such as leaf area index (LAI), of broadleaf forests at global scale constitute a major challenge to modern remote sensing techniques in view of low sensitivity (saturation) of surface reflectances to such parameters over dense vegetation. The goal of the performed research is to demonstrate physical principles of LAI retrievals over broadleaf forests with the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI algorithm and to establish a basis for algorithm refinement. To sample natural variability in biophysical parameters of broadleaf forests, we selected MODIS data subsets covering deciduous broadleaf forests of the eastern part of North America and evergreen broadleaf forests of Amazonia. The analysis of an annual course of the Terra MODIS Collection 4 LAI product over broadleaf forests indicated a low portion of best quality main radiative transfer-based algorithm retrievals and dominance of low-reliable backup algorithm retrievals during the growing season. We found that this retrieval anomaly was due to an inconsistency between simulated and MODIS surface reflectances. LAI retrievals over dense vegetation are mostly performed over a compact location in the spectral space of saturated surface reflectances, which need to be accurately modeled. New simulations were performed with the stochastic radiative transfer model, which poses high numerical accuracy at the condition of saturation. Separate sets of parameters of the LAI algorithm were generated for deciduous and evergreen broadleaf forests to account for the differences in the corresponding surface reflectance properties. The optimized algorithm closely captures physics of seasonal variations in surface reflectances and delivers a majority of LAI retrievals during a phenological cycle, consistent with field measurements. The analysis of the optimized retrievals indicates that the precision of MODIS surface reflectances, the natural variability, and mixture of species set a limit to improvements of the accuracy of LAI retrievals over broadleaf forests.

Application of Geostatistics to Characterize Leaf Area Index (LAI) from Flux Tower to Landscape Scales Using a Cyclic Sampling Design

AbstractAccurate characterization of leaf area index (LAI) is required to quantify the exchange of energy, water, and carbon between terrestrial ecosystems and the atmosphere. The objective of this study was to use a cyclic sampling design to compare the spatial patterns of LAI of the dominant terrestrial ecosystems that comprised the area around the 447-m WLEF television tower, equipped with an eddy flux system, near Park Falls, Wisconsin, USA. A second objective was to compare the efficiency of cyclic, random, and uniform sampling designs in terms of the precision of spatial information derived per unit sampling effort. The vegetation surrounding the tower was comprised (more than 80%) of four major forest cover types: forested wetlands, upland aspen forests, upland northern hardwood forests, and upland pine forests, and a fifth, nonforested cover type, grass (open meadow). LAI differed significantly among the five cover types and averaged 3.45, 3.57, 3.82, 3.99, and 1.14 for northern hardwoods, aspen, forested wetlands, upland conifers, and grass, respectively. The cyclic sampling design maximized information about the variance of vegetation characteristics of the heterogeneous landscape and decreased by 60% the number of plots needed to obtain the same confidence interval width using a random sampling design. The range of spatial autocorrelation for LAI was 147 m, but it was decreased to 117 m when vegetation cover was included as a covariate. The cyclic sampling design has several important advantages over other sampling designs. The cyclic sampling design increased the sampling efficiency by optimizing the placement of plots so they were distributed more efficiently for geostatistical analyses such as semi-variograms, correlograms, and spatial regression and can incorporate covariates (for example, vegetation cover, soil properties, and so on) that may explain the sources of spatial patterns. The cyclic sampling design was used to derive a spatial map of LAI and the average LAI for the 3 × 2 km area centered on the flux tower was 3.51 ± 0.89 (with a minimum of 0 and a maximum of 6.35). Airborne and satellite reflectance data have also been used to characterize LAI, but in this region, and many other forests of the world, remotely sensed vegetation indexes saturate in forests with an LAI greater than 3–5. The cyclic sampling design also provides a general ecological sampling approach that can be used at multiple scales.

Automated Parameterization of Land Surface Process Models Using Fuzzy Logic

All land surface process models require parameters that are proxies for spatial processes that are impractical or impossible to measure. Recent developments in model parameter estimation theory suggest that information obtained from calibrating such models is inherently uncertain in nature. As a consequence, identification of optimum parameter values is often highly non-specific. A calibration framework using fuzzy logic is presented to deal with such uncertain information. An application of this technique to calibrate the sub-canopy controls on transpiration in a land surface process model demonstrates that objective estimates of parameter values and expected ranges of predictions can be obtained with suitable choices for objective functions. An iterative refinement in parameter estimates was possible with conditional sampling techniques. The automated approach was able to correctly identify parameter tradeoffs such that two strongly different sets of parameters could