-
Remote Sensing of Environment 01/2012; 122:2-10. · 4.57 Impact Factor
-
MCFNS. 01/2011; 3:102-113.
-
Thomas A Spies,
Jay D Miller,
Joseph B Buchanan,
John F Lehmkuhl,
Jerry F Franklin,
Sean P Healey,
Paul F Hessburg,
Hugh D Safford, Warren B Cohen,
Rebecca S H Kennedy,
Eric E Knapp,
James K Agee,
Melinda Moeur
Conservation Biology 02/2010; 24(1):330-3; discussion 334-7. · 4.69 Impact Factor
-
BioScience 09/2009; · 4.62 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Interest in preserving older forests at the landscape level has increased in many regions, including the Pacific Northwest
of the United States. The Northwest Forest Plan (NWFP) of 1994 initiated a significant reduction in the harvesting of older
forests on federal land. We used historical satellite imagery to assess the effect of this reduction in relation to: past
harvest rates, management of non-federal forests, and the growing role of fire. Harvest rates in non-federal large-diameter
forests (LDF) either decreased or remained stable at relatively high rates following the NWFP, meaning that harvest reductions
on federal forests, which cover half of the region, resulted in a significant regional drop in the loss of LDF to harvest.
However, increased losses of LDF to fire outweighed reductions in LDF harvest across large areas of the region. Elevated fire
levels in the western United States have been correlated to changing climatic conditions, and if recent fire patterns persist,
preservation of older forests in dry ecosystems will depend upon practical and coordinated fire management across the landscape.
Ecosystems 10/2008; 11(7):1106-1119. · 3.49 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Initiated in 1972, the Landsat program has provided a continuous record of earth observation for 35 years. The assemblage of Landsat spatial, spectral, and temporal resolutions, over a reasonably sized image extent, results in imagery that can be processed to represent land cover over large areas with an amount of spatial detail that is absolutely unique and indispensable for monitoring, management, and scientific activities. Recent technical problems with the two existing Landsat satellites, and delays in the development and launch of a successor, increase the likelihood that a gap in Landsat continuity may occur. In this communication, we identify the key features of the Landsat program that have resulted in the extensive use of Landsat data for large area land cover mapping and monitoring. We then augment this list of key features by examining the data needs of existing large area land cover monitoring programs. Subsequently, we use this list as a basis for reviewing the current constellation of earth observation satellites to identify potential alternative data sources for large area land cover applications. Notions of a virtual constellation of satellites to meet large area land cover mapping and monitoring needs are also presented. Finally, research priorities that would facilitate the integration of these alternative data sources into existing large area land cover monitoring programs are identified. Continuity of the Landsat program and the measurements provided are critical for scientific, environmental, economic, and social purposes. It is difficult to overstate the importance of Landsat; there are no other systems in orbit, or planned for launch in the short-term, that can duplicate or approach replication, of the measurements and information conferred by Landsat. While technical and political options are being pursued, there is no satellite image data stream poised to enter the National Satellite Land Remote Sensing Data Archive should system failures occur to Landsat-5 and -7.
Remote Sensing of Environment 04/2008; 112:955-969. · 4.57 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: A bstr act The objective of this study was to quantify the extent and distribution of conifer cover increase in the Greater Yellowstone Ecosystem (GYE) between 1985 and 1999. In many locations across the GYE, conifer forests have increased in density and expanded into grasslands and shrublands, but the actual extent of change is unknown. Accurate quantification is critical for assessment of ecological consequences, including biogeochemical cycling. The 67,156 km 2 study area was located in the GYE, encompassing parts of Montana, Wyoming, and Idaho, U.S.A. We used a combination of aerial photos, Landsat satellite imagery, and statistical methods to quantify percent conifer cover and change across the study area between 1985 and 1999. We used aerial photos to characterize the rates of conifer cover increase, and satellite imagery to estimate the spatial extent of conifer cover increase. We analyzed the spatial distribution of conifer cover increase with respect to biophysical gradients. We estimated that the area impacted by conifer cover increase between 1985 and 1999 was 685,075 ha (~10% of the study area). The majority (87%) of that area was existing conifer forest that increased in density, while the remainder was grassland and shrubland that was encroached upon by conifer forest. Conifer cover increase was more common at lower elevations and on northerly aspects. The significance of this study is that it demonstrates an integrated approach to answering critical questions about the extent and rate of conifer cover increase across a large region. This study represents an important step towards improved understanding of the scope of conifer cover increase, potential drivers, and consequences for carbon dynamics.
01/2008;
-
[show abstract]
[hide abstract]
ABSTRACT: Biogeochemical models offer an important means of understanding carbon dynamics, but the computational complexity of many
models means that modeling all grid cells on a large landscape is computationally burdensome. Because most biogeochemical
models ignore adjacency effects between cells, however, a more efficient approach is possible. Recognizing that spatial variation
in model outputs is solely a function of spatial variation in input driver variables such as climate, we developed a method
to sample the model outputs in input variable space rather than geographic space, and to then use simple interpolation in
input variable space to estimate values for the remainder of the landscape. We tested the method in a 100km×260km area of
western Oregon, U.S.A. , comparing interpolated maps of net primary production (NPP) and net ecosystem production (NEP) with
maps from an exhaustive, wall-to-wall run of the model. The interpolation method can match spatial patterns of model behavior
well (correlations>0.8) using samples of only 5 t o 15% of the landscape. Compression of temporal variation in input drivers
is a key step in the process, with choice of input variables for compression largely determining the upper bounds on the degree
of match between interpolated and original maps. The method is applicable to any model that does not consider adjacency effects,
and could free up computational expense for a variety of other computational burdens, including spatial sensitivity analyses,
alternative scenario testing, or finer grain-size mapping.
Landscape Ecology 01/2006; 21(2):213-224. · 3.06 Impact Factor
-
IEEE T. Geoscience and Remote Sensing. 01/2006; 44:1843-1857.
-
Journal of Geophysical Research-Biogeosciences. 01/2006; 111(G4).
-
David P. Turner,
William D. Ritts, Warren B. Cohen,
Thomas K. Maeirsperger,
Stith T. Gower,
Al A. Kirschbaum,
Steve W. Running,
Maosheng Zhao,
Steven C. Wofsy,
Allison L. Dunn,
Beverly E. Law,
John L. Campbell,
Walter C. Oechel,
Hyo Jung Kwon,
Tilden P. Meyers,
Eric E. Small,
Shirley A. Kurc,
John A. Gamon
[show abstract]
[hide abstract]
ABSTRACT: Operational monitoring of global terrestrial gross primary production (GPP) and net primary production (NPP) is now underway using imagery from the satellite-borne Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Evaluation of MODIS GPP and NPP products will require site-level studies across a range of biomes, with close attention to numerous scaling issues that must be addressed to link ground measurements to the satellite-based carbon flux estimates. Here, we report results of a study aimed at evaluating MODIS NPP/GPP products at six sites varying widely in climate, land use, and vegetation physiognomy. Comparisons were made for twenty-five 1 km2 cells at each site, with 8-day averages for GPP and an annual value for NPP. The validation data layers were made with a combination of ground measurements, relatively high resolution satellite data (Landsat Enhanced Thematic Mapper Plus at ∼30 m resolution), and process-based modeling. There was strong seasonality in the MODIS GPP at all sites, and mean NPP ranged from 80 g C m−2 yr−1 at an arctic tundra site to 550 g C m−2 yr−1 at a temperate deciduous forest site. There was not a consistent over- or underprediction of NPP across sites relative to the validation estimates. The closest agreements in NPP and GPP were at the temperate deciduous forest, arctic tundra, and boreal forest sites. There was moderate underestimation in the MODIS products at the agricultural field site, and strong overestimation at the desert grassland and at the dry coniferous forest sites. Analyses of specific inputs to the MODIS NPP/GPP algorithm – notably the fraction of photosynthetically active radiation absorbed by the vegetation canopy, the maximum light use efficiency (LUE), and the climate data – revealed the causes of the over- and underestimates. Suggestions for algorithm improvement include selectively altering values for maximum LUE (based on observations at eddy covariance flux towers) and parameters regulating autotrophic respiration.
Global Change Biology 03/2005; 11(4):666 - 684. · 6.86 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Forest inventories and remote sensing are the two principal data sources used to estimate carbon (C) stocks and fluxes for large forest regions. National governments have historically relied on forest inventories for assessments but developments in remote sensing technology provide additional opportunities for operational C monitoring. The estimate of total C stock in live forest biomass modeled from Landsat imagery for the St. Petersburg region was consistent with estimates derived from forest inventory data for the early 1990s (272 and 269 TgC, respectively). The estimates of mean C sink in live forest biomass also agreed well (0.36 and 0.34 Mg C ha–1 yr–1). Virtually all forest lands were accumulating C in live biomass, however when the net change in total ecosystem C stock was considered, 19% of the forest area were a net source of C. The average net C sink in total ecosystem biomass is quite weak (0.08 MgC ha–1 yr–1 and could be reversed by minor increases in harvest rates or a small decline in biomass growth rates.
Climatic Change 11/2004; 67(2):257-272. · 3.38 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Boreal forest is the major type of land cover in Northern Eurasia. It forms one of the world’s largest forest tracts and amounts
to some 25% of the world’s forest cover. Boreal forest ecosystems developed under the influence of an active natural disturbance
regime that created a complex and dynamic pattern of land cover. Species mix, patterns of succession, and the role of specific
disturbance factors vary from region to region. Fire and insect damage represent two major natural disturbance factors, although
the extent and patterns of these disturbances may partly reflect human influences.
12/2003: pages 123-138;
-
[show abstract]
[hide abstract]
ABSTRACT: Spatially-distributed estimates of biologically-driven CO2 flux are of interest in relation to understanding the global carbon cycle. Global coverage by satellite sensors offers an opportunity to assess terrestrial carbon (C) flux using a variety of approaches and corresponding spatial resolutions. An important consideration in evaluating the approaches concerns the scale of the spatial heterogeneity in land cover over the domain being studied. In the Pacific Northwest region of the United States, forests are highly fragmented with respect to stand age class and hence C flux. In this study, the effects of spatial resolution on estimates of total annual net primary production (NPP) and net ecosystem production (NEP) for a 96km2 area in the central Cascades Mountains of western Oregon were examined. The scaling approach was a simple `measure and multiply' algorithm. At the highest spatial resolution (25m), a stand age map derived from Landsat Thematic Mapper imagery provided the area for each of six forest age classes. The products of area for each age class and its respective NPP or NEP were summed for the area wide estimates. In order to evaluate potential errors at coarser resolutions, the stand age map was resampled to grain sizes of 100, 250, 500 and 1000m using a majority filter reclassification. Local variance in near-infrared (NIR) band digital number at successively coarser grain sizes was also examined to characterize the scale of the heterogeneity in the scene. For this managed forest landscape, proportional estimation error in land cover classification at the coarsest resolution varied from –1.0 to +0.6 depending on the initial representation and the spatial distribution of the age class. The overall accuracy of the 1000m resolution map was 42% with respect to the 25m map. Analysis of local variance in NIR digital number suggested a patch size on the order of 100–500m on a side. Total estimated NPP was 12% lower and total estimated NEP was 4% lower at 1000m compared to 25m. Carbon flux estimates based on quantifying differences in total biomass stored on the landscape at two points in time might be affected more strongly by a coarse resolution analysis because the differences among classes in biomass are more extreme than the differences in C flux and because the additional steps in the flux algorithm would contribute to error propagation. Scaling exercises involving reclassification of fine scale imagery over a range of grain sizes may be a useful screening tool for stratifying regions of the terrestrial surface relative to optimizing the spatial resolution for C flux estimation purposes.
Landscape Ecology 01/2000; 15(5):441-452. · 3.06 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: To assist in validating future MODIS land cover, LAI, IPAR, and NPP products, this project conducted a series of prototyping exercises that resulted in enhanced understanding of the issues regarding such validation. As a result, we have several papers to appear as a special issue of Remote Sensing of Environment in 1999. Also, we have been successful at obtaining a follow-on grant to pursue actual validation of these products over the next several years. This document consists of a delivery letter, including a listing of published papers.
12/1998;
-
[show abstract]
[hide abstract]
ABSTRACT: To help determine the utility of satellite data for analysis and inventory of Douglas-fir/western hemlock forests west of the Cascade Mountains crest in Oregon and Washington, USA, we evaluate relationships between spectral and texture variables derived from SPOT HRV 10 m panchromatic and LANDSAT TM 30 m multispectral data and 16 forest stand structural attributes. Texture of the HRV data was strongly related to many of the stand attributes evaluated, whereas TM texture was weakly related to all attributes. Wetness, a feature of the TM Tasseled Cap, was the spectral variable most highly correlated to all stand attributes. Wetness appears to respond to the degree of maturity in a forest stand. One of the primary reasons HRV texture and TM wetness exhibited strong relationships with stand attributes is their relative insensitivity to topographically induced illumination angle. Although TM texture also was insensitive to topography, the spatial resolution of TM data is too coarse to detect the spatial variability within the forest stands evaluated. Regression models used to estimate values for the stand attributes from the satellite data indicate that both TM and HRV imagery should yield equally accurate estimates of forest age class and stand structure. Of all stand attributes evaluated, the standard deviation of tree sizes, mean size and density of trees in the upper canopy layers, a structural complexity index, and stand age can be most reliably estimated using the satellite data.
Remote Sensing of Environment.
-
[show abstract]
[hide abstract]
ABSTRACT: Peatlands play a major role in the global carbon cycle but are largely overlooked in current large-scale vegetation mapping efforts. In this study, we investigated the potential of the Moderate Resolution Imaging Spectroradiometer (MODIS) to capture the extent and distribution of peatlands in the St. Petersburg region of Russia by analyzing the relationships between peatland cover fractions derived from reference maps and ∼ 1-km resolution MODIS Nadir BRDF-Adjusted Reflectance (NBAR) data from year 2002.First, we characterized and mapped 50 peatlands from forest inventory and peat deposit inventory data. The peatlands represent three major nutritional types (oligotrophic, mesotrophic, eutrophic) and different sizes (0.6–7800 ha). In addition, parts of 6 peatlands were mined for peat and these were mapped separately. The reference maps provided information on peatland cover for 1105 MODIS pixels. We performed regression analysis on 50% of the pixels and reserved the remainder for model validation. Canonical correlation analysis on the MODIS reflectance bands and the peatland cover fractions produced a multi-spectral peatland cover index (PCI), which served as the predictor in a reduced major axis (RMA) regression model. The results suggest a high potential for mapping peatlands with MODIS. The RMA regression models explained much of the variance in the PCI (r2 = 0.74 for mined and r2 = 0.81 for unmined peatlands). Model validation showed high correlation between observed versus predicted peatland cover (mined: r = 0.87; unmined: r = 0.92). We used the models to derive peatland cover estimates for the St. Petersburg region and compared the results to current MODIS land cover maps.
Global and Planetary Change.
-
[show abstract]
[hide abstract]
ABSTRACT: 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 (ϵn, g MJ−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-km2 area of corn (Zea mays L.) and soybean (Glycine max Merr.) in central Illinois, USA. Results from several ϵn 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 ϵn based on measurements of biomass production and monitoring of absorbed photosynthetically active radiation (APAR) revealed that ϵn 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 ϵn 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 ϵn was closer (86%) to that from the NPP reference surface. Retention of high spatial resolution land cover to assign ϵn 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 ϵn when implementing LUE algorithms at coarse resolution.
Remote Sensing of Environment.
-
[show abstract]
[hide abstract]
ABSTRACT: This study compared aspatial and spatial methods of using remote sensing and field data to predict maximum growing season leaf area index (LAI) maps in a boreal forest in Manitoba, Canada. The methods tested were orthogonal regression analysis (reduced major axis, RMA) and two geostatistical techniques: kriging with an external drift (KED) and sequential Gaussian conditional simulation (SGCS). Deterministic methods such as RMA and KED provide a single predicted map with either aspatial (e.g., standard error, in regression techniques) or limited spatial (e.g., KED variance) assessments of errors, respectively. In contrast, SGCS takes a probabilistic approach, where simulated values are conditional on the sample values and preserve the sample statistics. In this application, canonical indices were used to maximize the ability of Landsat ETM+ spectral data to account for LAI variability measured in the field through a spatially nested sampling design. As expected based on theory, SGCS did the best job preserving the distribution of measured LAI values. In terms of spatial pattern, SGCS preserved the anisotropy observed in semivariograms of measured LAI, while KED reduced anisotropy and lowered global variance (i.e., lower sill), also consistent with theory. The conditional variance of multiple SGCS realizations provided a useful visual and quantitative measure of spatial uncertainty. For applications requiring spatial prediction methods, we concluded KED is more useful if local accuracy is important, but SGCS is better for indicating global pattern. Predicting LAI from satellite data using geostatistical methods requires a distribution and density of primary, reference LAI measurements that are impractical to obtain. For regional NPP modeling with coarse resolution inputs, the aspatial RMA regression method is the most practical option.
Remote Sensing of Environment.
-
[show abstract]
[hide abstract]
ABSTRACT: A scanning lidar, a relatively new type of sensor which explicitly measures canopy height, was used to measure structure of conifer forests in the Pacific Northwest. SLICER (Scanning Lidar Imager of Canopies by Echo Recovery), an airborne pulsed laser developed by NASA which scans a swath of five 10-m diameter footprints along the aircraft’s flightpath, captures the power of the reflected laser pulse as a function of height from the top of the canopy to the ground. Ground measurements of forest stand structure were collected on 26 plots with coincident SLICER data. Height, basal area, total biomass, and leaf biomass as estimated from field data could be predicted from SLICER-derived metrics with r2 values of 0.95, 0.96, 0.96, and 0.84, respectively. These relationships were strong up to a height of 52 m, basal area of 132 m2/ha and total biomass of 1300 Mg/ha. In light of these strong relationships, large-footprint, airborne scanning lidar shows promise for characterizing stand structure for management and research purposes.
Remote Sensing of Environment.
