David P. Turner

A global terrestrial monitoring network integrating tower fluxes, flask sampling, ecosystem modeling and EOS satellite data

Abstract Accurate monitoring of global scale changes in the terrestrial biosphere has become acutely important as the scope of human impacts on biological systems and atmospheric chemistry grows. For example, the Kyoto Protocol of 1997 signals some of the dramatic socioeconomic and political decisions that may lie ahead concerning CO2 emissions and global carbon cycle impacts. These decisions will rely heavily on accurate measures of global biospheric changes Schimel 1998 , IGBP TCWG 1998 . An array of national and international programs have inaugurated global satellite observations, critical field measurements of carbon and water fluxes, and global model development for the purposes of beginning to monitor the biosphere. The detection by these programs of interannual variability of ecosystem fluxes and of longer term trends will permit early indication of fundamental biospheric changes which might otherwise go undetected until major biome conversion begins. This article describes a blueprint for more comprehensive coordination of the various flux measurement and modeling activities into a global terrestrial monitoring network that will have direct relevance to the political decision making of global change.

Relationships between Leaf Area Index and Landsat TM Spectral Vegetation Indices across Three Temperate Zone Sites

Abstract Mapping and monitoring of leaf area index (LAI) is important for spatially distributed modeling of vegetation productivity, evapotranspiration, and surface energy balance. Global LAI surfaces will be an early product of the MODIS Land Science Team, and the requirements for LAI validation at selected sites have prompted interest in accurate LAI mapping at a more local scale. While spectral vegetation indices (SVIs) derived from satellite remote sensing have been used to map LAI, vegetation type, and related optical properties, and effects of Sun–surface–sensor geometry, background reflectance, and atmospheric quality can limit the strength and generality of empirical LAI–SVI relationships. In the interest of a preliminary assessment of the variability in LAI–SVI relationships across vegetation types, we compared Landsat 5 Thematic Mapper imagery from three temperate zone sites with on-site LAI measurements. The sites differed widely in location, vegetation physiognomy (grass, shrubs, hardwood forest, and conifer forest), and topographic complexity. Comparisons were made using three different red and near-infrared-based SVIs (NDVI, SR, SAVI). Several derivations of the SVIs were examined, including those based on raw digital numbers (DN), radiance, top of the atmosphere reflectance, and atmospherically corrected reflectance. For one of the sites, which had extreme topographic complexity, additional corrections were made for Sun–surface–sensor geometry. Across all sites, a strong general relationship was preserved, with SVIs increasing up to LAI values of 3 to 5. For all but the coniferous forest site, sensitivity of the SVIs was low at LAI values above 5. In coniferous forests, the SVIs decreased at the highest LAI values because of decreasing near-infrared reflectance associated with the complex canopy in these mature to old-growth stands. The cross-site LAI–SVI relationships based on atmospherically corrected imagery were stronger than those based on DN, radiance, or top of atmosphere reflectance. Topographic corrections at the conifer site altered the SVIs in some cases but had little effect on the LAI–SVI relationships. Significant effects of vegetation properties on SVIs, which were independent of LAI, were evident. The variability between and around the best fit LAI–SVI relationships for this dataset suggests that for local accuracy in development of LAI surfaces it will be desirable to stratify by land cover classes (e.g., physiognomic type and successional stage) and to vary the SVI.

A Carbon Budget for Forests of the Conterminous United States

The potential need for national-level comparisons of greenhouse gas emis- sions, and the desirability of understanding terrestrial sources and sinks of carbon, has prompted interest in quantifying national forest carbon budgets. In this study, we link a forest inventory database, a set of stand-level carbon budgets, and information on harvest levels in order to estimate the current pools and flux of carbon in forests of the conterminous United States. The forest inventory specifies the region, forest type, age class, productivity class, management intensity, and ownership of all timberland. The stand-level carbon bud- gets are based on growth and yield tables, in combination with additional information on carbon in soils, the forest floor, woody debris, and the understory. Total carbon in forests of the conterminous U.S. is estimated at 36.7 Pg, with half of that in the soil compartment. Tree carbon represents 33% of the total, followed by woody debris (10%), the forest floor (6%), and the understory (1%). The carbon uptake associated with net annual growth is 331 Tg, however, much of that is balanced by harvest-related mortality (266 Tg) and decomposition of woody debris. The forest land base at the national level is accumulating 79 Tg/yr, with the largest carbon gain in the Northeast region. The similarity in the mag- nitude of the biologically driven flux and the harvest-related flux indicates the importance of employing an age-class-based inventory, and of including effects associated with forest harvest and harvest residue, when modeling national carbon budgets in the temperate zone.

An improved strategy for regression of biophysical variables and Landsat ETM+ data

Empirical models are important tools for relating field-measured biophysical variables to remote sensing data. Regression analysis has been a popular empirical method of linking these two types of data to provide continuous estimates for variables such as biomass, percent woody canopy cover, and leaf area index (LAI). Traditional methods of regression are not sufficient when resulting biophysical surfaces derived from remote sensing are subsequently used to drive ecosystem process models. Most regression analyses in remote sensing rely on a single spectral vegetation index (SVI) based on red and near-infrared reflectance from a single date of imagery. There are compelling reasons for utilizing greater spectral dimensionality, and for including SVIs from multiple dates in a regression analysis. Moreover, when including multiple SVIs and/or dates, it is useful to integrate these into a single index for regression modeling. Selection of an appropriate regression model, use of multiple SVIs from multiple dates of imagery as predictor variables, and employment of canonical correlation analysis (CCA) to integrate these multiple indices into a single index represent a significant strategic improvement over existing uses of regression analysis in remote sensing. To demonstrate this improved strategy, we compared three different types of regression models to predict LAI for an agro-ecosystem and live tree canopy cover for a needleleaf evergreen boreal forest: traditional (Yon X) ordinary least squares (OLS) regression, inverse (X on Y) OLS regression, and an orthogonal regression method called reduced major axis (RMA). Each model incorporated multiple SVIs from multiple dates and CCA was used to integrate these. For a given dataset, the three regression-modeling approaches produced identical coefficients of determination and intercepts, but different slopes, giving rise to divergent predictive characteristics. The traditional approach yielded the lowest root mean square error (RMSE), but the variance in the predictions was lower than the variance in the observed dataset. The inverse method had the highest RMSE and the variance was inflated relative to the variance of the observed dataset. RMA provided an intermediate set of predictions in terms of the RMSE, and the variance in the observations was preserved in the predictions. These results are predictable from regression theory, but that theory has been essentially ignored within the discipline of remote sensing. D 2002 Elsevier Science Inc. All rights reserved.

Carbon dynamics of Oregon and Northern California forests and potential land‐based carbon storage

Net uptake of carbon from the atmosphere (net ecosystem production, NEP) is dependent on climate, disturbance history, management practices, forest age, and forest type. To improve understanding of the influence of these factors on forest carbon stocks and flux in the western United States, federal inventory data and supplemental field measurements at additional plots were used to estimate several important components of the carbon balance in forests in Oregon and Northern California during the 1990s. Species- and ecoregion-specific allometric equations were used to estimate live and dead biomass stores, net primary productivity (NPP), and mortality. In the semiarid East Cascades and mesic Coast Range, mean total biomass was 8 and 24 kg C/m2, and mean NPP was 0.30 and 0.78 kg C.m(-2).yr(-1), respectively. Maximum NPP and dead biomass stores were most influenced by climate, whereas maximum live biomass stores and mortality were most influenced by forest type. Within ecoregions, mean live and dead biomass were usually higher on public lands, primarily because of the younger age class distribution on private lands. Decrease in NPP with age was not general across ecoregions, with no marked decline in old stands (>200 years old) in some ecoregions. In the absence of stand-replacing disturbance, total landscape carbon stocks could theoretically increase from 3.2 +/- 0.34 Pg C to 5.9 +/- 1.34 Pg C (a 46% increase) if forests were managed for maximum carbon storage. Although the theoretical limit is probably unattainable, given the timber-based economy and fire regimes in some ecoregions, there is still potential to significantly increase the land-based carbon storage by increasing rotation age and reducing harvest rates.

Integrating Remote Sensing and Ecosystem Process Models for Landscape- to Regional-Scale Analysis of the Carbon Cycle

Abstract A growing body of research has demonstrated the complementary nature of remote sensing and ecosystem modeling in studies of terrestrial carbon cycling. Whereas remote sensing instruments are designed to capture spatially continuous information on the reflectance properties of landscape and vegetation, models focus on the underlying biogeochemical processes that regulate carbon transformation, often over longer temporal scales. Remote sensing capabilities, developed over the past several decades, now provide regular, high-resolution (10-meter to 1-kilometer) mapping and monitoring of land surface characteristics relevant to modeling, including vegetation type, biomass, stand age class, phenology, leaf area index, and tree height. Integration of these data sets with ecosystem process models and distributed climate data provides a means for regional assessment of carbon fluxes and analysis of the underlying processes affecting them. Applications include monitoring of carbon pools and flux in response to the United Nations Framework Convention on Climate Change.

Evaluation of fraction of absorbed photosynthetically active radiation products for different canopy radiation transfer regimes: Methodology and results using Joint Research Center products derived from SeaWiFS against ground-based estimations

[1] This paper discusses the quality and the accuracy of the Joint Research Center (JRC) fraction of absorbed photosynthetically active radiation (FAPAR) products generated from an analysis of Sea-viewing Wide Field-of-view Sensor (SeaWiFS) data. The FAPAR value acts as an indicator of the presence and state of the vegetation and it can be estimated from remote sensing measurements using a physically based approach. The quality of the SeaWiFS FAPAR products assessed in this paper capitalizes on the availability of a 6-year FAPAR time series over the full globe. This evaluation exercise is performed in two phases involving, first, an analysis of the verisimilitude of the FAPAR products under documented environmental conditions and, second, a direct comparison of the FAPAR values with ground-based estimations where and when the latter are available. This second phase is conducted following a careful analysis of problems arising for performing such a comparison. This results in the grouping of available field information into broad categories representing different radiative transfer regimes. This strategy greatly helps the interpretation of the results since it recognizes the various levels of difficulty and sources of uncertainty associated with the radiative sampling of different types of vegetation canopies.

MODIS land cover and LAI collection 4 product quality across nine sites in the western hemisphere

Global maps of land cover and leaf area index (LAI) derived from the Moderate Resolution Imaging Spectrometer (MODIS) reflectance data are an important resource in studies of global change, but errors in these must be characterized and well understood. Product validation requires careful scaling from ground and related measurements to a grain commensurate with MODIS products. We present an updated BigFoot project protocol for developing 25-m validation data layers over 49-km2 study areas. Results from comparisons of MODIS and BigFoot land cover and LAI products at nine contrasting sites are reported. In terms of proportional coverage, MODIS and BigFoot land cover were in close agreement at six sites. The largest differences were at low tree cover evergreen needleleaf sites and at an Arctic tundra site where the MODIS product overestimated woody cover proportions. At low leaf biomass sites there was reasonable agreement between MODIS and BigFoot LAI products, but there was not a particular MODIS LAI algorithm pathway that consistently compared most favorably. At high leaf biomass sites, MODIS LAI was generally overpredicted by a significant amount. For evergreen needleleaf sites, LAI seasonality was exaggerated by MODIS. Our results suggest incremental improvement from Collection 3 to Collection 4 MODIS products, with some remaining problems that need to be addressed

Forest Disturbance and North American Carbon Flux

North Americas forests are thought to be a significant sink for atmospheric carbon. Currently, the rate of sequestration by forests on the continent has been estimated at 0.23 petagrams of carbon per year, though the uncertainty about this estimate is nearly 50%. This offsets about 13% of the fossil fuel emissions from the continent [Pacala et al., 2007]. However, the high level of uncertainty in this estimate and the scientific communitys limited ability to predict the future direction of the forest carbon flux reflect a lack of detailed knowledge about the effects of forest disturbance and recovery across the continent. The North American Carbon Program (NACP), an interagency initiative to better understand the distribution, origin, and fate of North American sources and sinks of carbon, has highlighted forest disturbance as a critical factor constraining carbon dynamics [Wofsy and Harris, 2002]. National forest inventory programs in Canada, the United States, and Mexico provide important information, but they lack the needed spatial and temporal detail to support annual estimation of carbon fluxes across the continent. To help with this, the NACP recommends that scientists use detailed remote sensing of the land surface to characterize disturbance.

An approach to spatially distributed modeling of net primary production (NPP) at the landscape scale and its application in validation of EOS NPP products

Substantial research seeks to improve estimates of ecosystem processes and fluxes at a range of scales, notably from the stand scale (<1 km2) using ecosystem physiology and eddy covariance techniques, to the landscape (∼102 km2) and global (108 km2) scales using a variety of modeling and data acquisition approaches. One approach uses remotely sensed ecosystem properties in the scaling process. This approach combines digital maps of key ecosystem properties such as land cover type, leaf area index, and/or canopy chemistry with quantitative models of biological processes based on these ecosystem properties. Constraints on parametrizing models for global scale applications mean that relatively simple algorithms must be used which are based almost exclusively on satellite-derived inputs, for example, the planned Earth Observation System (EOS)-MODIS Land Science Team model output. Presently, there are limited ways of validating these outputs. At the landscape scale, the opportunity exists to combine remote sensing data with spatially distributed, process-based biogeochemistry models to examine variation in ecosystem processes such as NPP as a function of land cover type, canopy attributes, and/or location along environmental gradients. These process models can be validated against direct measurements made with eddy covariance flux towers and ground-based NPP sampling. Once run and validated over local landscapes, these fine scale models may provide our best opportunity to provide meaningful evaluation (or “validation” in some sense) of simpler, globally applied models. In this article, we 1) provide a biological framework that links ecosystem attributes and ecosystem carbon flux processes at a variety of scales, and summarizes the state of knowledge and models in these areas, 2) describe the need for developing NPP surfaces at a local landscape scale as a means of validating global models, in particular the MODIS NPP product, 3) describe the approach of the BigFoot project to performing such a validation exercise for a series of sites in North America, and 4) present an example using one such model (PnET-II) across diverse vegetation types in a heterogeneous landscape in central North America.