Matthew J. Gregory

Effects of spatial variability in light use efficiency on satellite-based NPP monitoring

Light use efficiency (LUE) algorithms are a potentially effective approach to monitoring global net primary production (NPP) using satellite-borne sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS). However, these algorithms are applied at relatively coarse spatial resolutions ( � 1 km), which may subsume significant heterogeneity in vegetation LUE (en, gM J � 1 ) and, hence, introduce error. To examine the effects of spatial heterogeneity on a LUE algorithm, imagery from the Advanced Very High Resolution Radiometer (AVHRR) at � 1-km resolution was used to implement a LUE approach for NPP estimation over a 25-km 2 area of corn (Zea mays L.) and soybean (Glycine max Merr.) in central Illinois, USA. Results from several en formulations were compared with a NPP reference surface based on measured NPPs and a high spatial resolution land cover surface derived from Landsat ETM+. Determination of en based on measurements of biomass production and monitoring of absorbed photosynthetically active radiation (APAR) revealed that en of soybean was 68% of that for corn. When a LUE algorithm for estimating NPP was implemented in the study area using the assumption of homogeneous cropland and the en for corn, the estimate for total biomass production was 126% of that from the NPP reference surface. Because of counteracting errors, total biomass production using the soybean en was closer (86%) to that from the NPP reference surface. Retention of high spatial resolution land cover to assign en resulted in a total NPP very similar to the reference NPP because differences in leaf phenology between the crop types were small except early in the growing season. These results suggest several alternative approaches to accounting for land cover heterogeneity in en when implementing LUE algorithms at coarse resolution. D 2002 Elsevier Science Inc. All rights reserved.

Mapping wildland fuels and forest structure for land management: a comparison of nearest neighbor imputation and other methods

Land managers need consistent information about the geographic distribution of wildland fuels and forest struc- ture over large areas to evaluate fire risk and plan fuel treatments. We compared spatial predictions for 12 fuel and forest structure variables across three regions in the western United States using gradient nearest neighbor (GNN) imputation, lin- ear models (LMs), classification and regression trees (CART), and geostatistical methods (kriging and universal kriging (UK)). Local-scale map accuracy varied considerably across sites, variables, and methods. GNN performed best for forest structure variables in Oregon, but LMs and UK were better for canopy variables and for forest structure variables in Wash- ington and California. Kriging performed poorly throughout, and kriging did not improve prediction accuracy when added to LMs as UK. GNN outperformed CART in predicting vegetation classes and fuel models, complex variables defined by multiple attributes. Regional distributions of vegetation classes and fuel models were accurately represented by GNN and very poorly by CART and LMs. Despite their often limited accuracy at the local scale, GNN maps are useful when infor- mation on a wide range of forest attributes is needed for analysis and forest management at intermediate, i.e., landscape to ecoregional, scales.

INFLUENCE OF ENVIRONMENT, DISTURBANCE, AND OWNERSHIP ON FOREST VEGETATION OF COASTAL OREGON

Information about how vegetation composition and structure vary quantitatively and spatially with physical environment, disturbance history, and land ownership is fundamental to regional conservation planning. However, current knowledge about patterns of vegetation variability across large regions that is spatially explicit (i.e., mapped) tends to be general and qualitative. We used spatial predictions from gradient models to examine the influence of environment, disturbance, and ownership on patterns of forest vegetation biodiversity across a large forested region, the 3-million-ha Oregon Coast Range (USA). Gradients in tree species composition were strongly associated with environment, especially climate, and insensitive to disturbance, probably because many dominant tree species are long-lived and persist throughout forest succession. In contrast, forest structure was strongly correlated with disturbance and only weakly with environmental gradients. Although forest structure differed among ownerships, differences were blurred by the presence of legacy trees that originated prior to current forest management regimes. Our multi-ownership perspective revealed biodiversity concerns and benefits not readily visible in single-ownership analyses, and all ownerships contributed to regional biodiversity values. Federal lands provided most of the late-successional and old-growth forest. State lands contained a range of forest ages and structures, including diverse young forest, abundant legacy dead wood, and much of the high-elevation true fir forest. Nonindustrial private lands provided diverse young forest and the greatest abundance of hardwood trees, including almost all of the foothill oak woodlands. Forest industry lands encompassed much early-successional forest, most of the mixed hardwood-conifer forest, and large amounts of legacy down wood. The detailed tree- and species-level data in the maps revealed regional trends that would be masked in traditional coarse-filter assessment. Although abundant, most early-successional forests originated after timber harvest and lacked legacy live and dead trees important as habitat and for other ecological functions. Many large-conifer forests that might be classified as old growth using a generalized forest cover map lacked structural features of old growth such as multilayered canopies or dead wood. Our findings suggest that regional conservation planning include all ownerships and land allocations, as well as fine-scale elements of vegetation composition and structure.

Scaling net primary production to a MODIS footprint in support of Earth observing system product validation

Release of an annual global terrestrial net primary production (NPP) data layer has begun in association with the Moderate Imaging Spectroradiometer (MODIS) sensor, a component of the NASA Earth Observing System. The task of validating this product will be complicated by the mismatch in scale between ground-based NPP measurements and the coarse resolution (1 km) of the NPP product. In this paper we describe three relevant approaches to scaling NPP from the plot level to the approximately 25-km2 footprint of the sensor, and discuss issues associated with operational comparisons to the MODIS NPP product. All approaches revealed considerable spatial heterogeneity in NPP at scales less than the resolution of the MODIS NPP product. The effort to characterize uncertainty in the validation data layers indicated the importance of treating the combination of classification error, sampling error, and measurement error. Generally, the optimal procedure for scaling NPP to a MODIS footprint will depend on local vegetation type, the scale of spatial heterogeneity, and available resources. In all approaches, high resolution remote sensing can play a critical role in characterizing land cover and relevant biophysical variables.

Relationships of dead wood patterns with biophysical characteristics and ownership according to scale in Coastal Oregon, USA

Dead wood patterns and dynamics vary with biophysical factors, disturbance history, ownership, and management practices; the relative importance of these factors is poorly understood, especially at landscape to regional scales. This study examined current dead wood amounts in the Coastal Province of Oregon, USA, at multiple spatial scales. Objectives were to: (1) describe current regional amounts of several characteristics of dead wood; (2) compare dead wood amounts across ownerships; (3) determine the relative importance, according to spatial scale, of biophysical and ownership characteristics, to regional dead wood abundance. Dead wood plot data were evaluated with respect to explanatory variables at four spatial scales of resolution: plots, subwatersheds, watersheds and subbasins. The relationships of dead wood characteristics with biophysical attributes and ownership were diverse and scale-specific. Region-wide dead wood abundance and types varied among ownerships, with public lands typically having higher amounts of dead wood and more large dead wood than private lands. Regression analysis of total dead wood volume indicated that ownership was important at the subbasin scale. Growing season moisture stress was important at plot, subwatershed, and watershed scales. Topography was important at the two coarser scales. Multivariate analysis of dead wood gradients showed that ownership was important at all scales, topography at the subbasin scale, historical vegetation at watershed and subbasin scales, and current vegetation at plot and subwatershed scales. Management for dead wood and related biodiversity at watershed to landscape scales should consider the distinct dynamics of snags and logs, the importance of historical effects, and the relevance of ownership patterns.

A comparison of intercell metrics on discrete global grid systems

Abstract A discrete global grid system (DGGS) is a spatial data model that aids in global research by serving as a framework for environmental modeling, monitoring and sampling across the earth at multiple spatial scales. Topological and geometric criteria have been proposed to evaluate and compare DGGSs; two of which, intercell distance and the “cell wall midpoint” criterion, form the basis of this study. We propose evaluation metrics for these two criteria and present numerical results from these measures for several DGGSs. We also consider the impact of different design choices on these metrics, such as predominant tessellating shape, base modeling solid and partition density between recursive subdivisions. For the intercell distance metric, the Fuller–Gray DGGS performs best, while the Equal Angle DGGS performs substantially worse. For the cell wall midpoint metric, however, the Equal Angle DGGS has the lowest overall distortion with the Snyder and Fuller–Gray DGGSs also performing relatively well. Aggregation of triangles into hexagons has little impact on intercell distance measurements, although dual hexagon aggregation results in markedly different statistics and spatial patterns for the cell wall midpoint property. In all cases, partitions on the icosahedron outperform similar partitions on the octahedron. Partition density accounts for little variation.

Cumulative effects of wildfires on forest dynamics in the eastern Cascade Mountains, USA

Wildfires pose a unique challenge to conservation in fire-prone regions, yet few studies quantify the cumulative effects of wildfires on forest dynamics (i.e., changes in structural conditions) across landscape and regional scales. We assessed the contribution of wildfire to forest dynamics in the eastern Cascade Mountains, USA from 1985 to 2010 using imputed maps of forest structure (i.e., tree size and canopy cover) and remotely sensed burn severity maps. We addressed three questions: (1) How do dynamics differ between the region as a whole and the unburned portion of the region? (2) How do dynamics vary among vegetation zones differing in biophysical setting and historical fire frequency? (3) How have forest structural conditions changed in a network of late successional reserves (LSRs)? Wildfires affected 10% of forests in the region, but the cumulative effects at this scale were primarily slight losses of closed-canopy conditions and slight gains in open-canopy conditions. In the unburned portion of the region (the remaining 90%), closed-canopy conditions primarily increased despite other concurrent disturbances (e.g., harvest, insects). Although the effects of fire were largely dampened at the regional scale, landscape scale dynamics were far more variable. The warm ponderosa pine and cool mixed conifer zones experienced less fire than the region as a whole despite experiencing the most frequent fire historically. Open-canopy conditions increased slightly in the mixed conifer zone, but declined across the ponderosa pine zone even with wildfires. Wildfires burned 30% of the cold subalpine zone, which experienced the greatest increase in open-canopy conditions and losses of closed-canopy conditions. LSRs were more prone to wildfire than the region as a whole, and experienced slight declines in late seral conditions. Despite losses of late seral conditions, wildfires contributed to some conservation objectives by creating open habitats (e.g., sparse early seral and woodland conditions) that otherwise generally decreased in unburned landscapes despite management efforts to increase landscape diversity. This study demonstrates the potential for wildfires to contribute to regional scale conservation objectives, but implications for management and biodiversity at landscape scales vary geographically among biophysical settings, and are contingent upon historical dynamics and individual species habitat preferences.