Kenneth C. McGwire

Hyperspectral mixture modeling for quantifying sparse vegetation cover in arid environments.

Abstract A linear mixture model based on calibrated, atmospherically corrected Probe-1 hyperspectral imagery was compared with three vegetation indices to test its relative ability to measure small differences in percent green vegetative cover for areas of sparse vegetation in arid environments. The goal of this research was to compare multispectral and hyperspectral remote sensing approaches for detecting human disturbance of arid environments. The normalized difference vegetation index (NDVI) was tested using both narrow and broad bandwidths. Broadband NDVI provided results r 2 =0.63 similar to NDVI derived from individual hyperspectral channels r 2 =0.60 . While the soil-adjusted vegetation index (SAVI) was designed as an improvement to NDVI for sparse vegetation, in this study SAVI performed significantly worse than NDVI r 2 =0.51 . The modified soil-adjusted vegetation index (MSAVI) provided an insignificant improvement over NDVI r 2 =0.64 . Linear mixture modeling provided significantly better results, r2 of 0.74. Cross-validation was used to test the significance of differences between the various methods and to determine the standard error associated with each method. Results suggest that any improvements provided by adjusted vegetation indices over NDVI may be strongly dependent on those adjustments being derived from local conditions. The use of a linear mixture model with multiple soil endmembers appears to provide the best method for quantifying sparse vegetative cover. Though present in small amounts, a single plant species, Krameria erecta, was strongly correlated with residuals of the mixture model. Inclusion of a spectral endmember for this species increased the r2 of the fit with percent green cover to 0.86. However, it is not clear if the explained variation was actually due to K. erecta or a correlated phenomena. Problems were also identified with the use of multiple vegetation endmembers.

Onset of deglacial warming in West Antarctica driven by local orbital forcing

The cause of warming in the Southern Hemisphere during the most recent deglaciation remains a matter of debate. Hypotheses for a Northern Hemisphere trigger, through oceanic redistributions of heat, are based in part on the abrupt onset of warming seen in East Antarctic ice cores and dated to 18,000 years ago, which is several thousand years after high-latitude Northern Hemisphere summer insolation intensity began increasing from its minimum, approximately 24,000 years ago. An alternative explanation is that local solar insolation changes cause the Southern Hemisphere to warm independently. Here we present results from a new, annually resolved ice-core record from West Antarctica that reconciles these two views. The records show that 18,000 years ago snow accumulation in West Antarctica began increasing, coincident with increasing carbon dioxide concentrations, warming in East Antarctica and cooling in the Northern Hemisphere associated with an abrupt decrease in Atlantic meridional overturning circulation. However, significant warming in West Antarctica began at least 2,000 years earlier. Circum-Antarctic sea-ice decline, driven by increasing local insolation, is the likely cause of this warming. The marine-influenced West Antarctic records suggest a more active role for the Southern Ocean in the onset of deglaciation than is inferred from ice cores in the East Antarctic interior, which are largely isolated from sea-ice changes.

A new bipolar ice core record of volcanism from WAIS Divide and NEEM and implications for climate forcing of the last 2000 years

Volcanism is a natural climate forcing causing short-term variations in temperatures. Histories of volcanic eruptions are needed to quantify their role in climate variability and assess human impacts. We present two new seasonally resolved, annually dated non-sea-salt sulfur records from polar ice cores - WAIS Divide (WDC06A) from West Antarctica spanning 408 B.C.E. to 2003 C.E. and NEEM (NEEM-2011-S1) from Greenland spanning 78 to 1997 C.E. - both analyzed using high-resolution continuous flow analysis coupled to two mass spectrometers. The high dating accuracy allowed placing the large bi-hemispheric deposition event ascribed to the eruption of Kuwae in Vanuatu (previously thought to be 1452/1453 C.E. and used as a tie-point in ice core dating) into the year 1458/1459 C.E. This new age is consistent with an independent ice core timescale from Law Dome and explains an apparent delayed response in tree rings to this volcanic event. A second volcanic event is detected in 1453 C.E. in both ice cores. We show for the first time ice core signals in Greenland and Antarctica from the strong eruption of Taupo in New Zealand in 232 C.E. In total, 133 volcanic events were extracted from WDC06A and 138 from NEEM-2011-S1, with 50 ice core signals - predominantly from tropical source volcanoes - identified simultaneously in both records. We assess the effect of large bipolar events on temperature-sensitive tree ring proxies. These two new volcanic records, synchronized with available ice core records to account for spatial variability in sulfate deposition, provide a basis for improving existing time series of volcanic forcing.

Precise interpolar phasing of abrupt climate change during the last ice age

The last glacial period exhibited abrupt Dansgaard–Oeschger climatic oscillations, evidence of which is preserved in a variety of Northern Hemisphere palaeoclimate archives. Ice cores show that Antarctica cooled during the warm phases of the Greenland Dansgaard–Oeschger cycle and vice versa, suggesting an interhemispheric redistribution of heat through a mechanism called the bipolar seesaw. Variations in the Atlantic meridional overturning circulation (AMOC) strength are thought to have been important, but much uncertainty remains regarding the dynamics and trigger of these abrupt events. Key information is contained in the relative phasing of hemispheric climate variations, yet the large, poorly constrained difference between gas age and ice age and the relatively low resolution of methane records from Antarctic ice cores have so far precluded methane-based synchronization at the required sub-centennial precision. Here we use a recently drilled high-accumulation Antarctic ice core to show that, on average, abrupt Greenland warming leads the corresponding Antarctic cooling onset by 218 ± 92 years (2σ) for Dansgaard–Oeschger events, including the Bølling event; Greenland cooling leads the corresponding onset of Antarctic warming by 208 ± 96 years. Our results demonstrate a north-to-south directionality of the abrupt climatic signal, which is propagated to the Southern Hemisphere high latitudes by oceanic rather than atmospheric processes. The similar interpolar phasing of warming and cooling transitions suggests that the transfer time of the climatic signal is independent of the AMOC background state. Our findings confirm a central role for ocean circulation in the bipolar seesaw and provide clear criteria for assessing hypotheses and model simulations of Dansgaard–Oeschger dynamics.

Spatial structure, sampling design and scale in remotely-sensed imagery of a California savanna woodland

This article describes research related to sampling techniques for establishing linear relations between land surface parameters and remotely-sensed data. Predictive relations are estimated between percentage tree cover in a savanna environment and a normalized difference vegetation index (NDVI) derived from the Thematic Mapper sensor. Spatial autocorrelation in original measurements and regression residuals is examined using semi-variogram analysis at several spatial resolutions. Sampling schemes are then tested to examine the effects of autocorrelation on predictive linear models in cases of small sample sizes. Regression models between image and ground data are affected by the spatial resolution of analysis. Reducing the influence of spatial autocorrelation by enforcing minimum distances between samples may also improve empirical models which relate ground parameters to satellite data.

Coordinating hazardous waste management activities using geographical information systems

Abstract This paper describes a framework for the role of geographical information systems (GIS) in the monitoring and management of hazardous waste sites. Compilation of required information, incorporation of existing strategies for waste monitoring, analysis of these data in a GIS environment and the integration of computerized models for transport processes are discussed. Examples for the analysis of spatial data using techniques of cartographic overlay and the implementation of geo-statistical methods on monitoring data are provided from work in progress by the authors. These examples are set in the context of developing a fully integrated monitoring and management system utilizing GIS technology.

Spatially Variable Thematic Accuracy: Beyond the Confusion Matrix

An essential aspect of the increasing sophistication of ecological models is the use of spatially explicit inputs and outputs. Thus, the challenge of documenting the uncertainty of model parameters must expand to include the distribution of error across the surface of maps, satellite images, and other ecological data that are keyed by geographic location. It has become more common to report the overall accuracy of map data sets. Support for such accuracy statements is seen in the descriptive attributes that are defined in file format conventions (e.g., the spatial data transfer standard, SDTS; FGDC 1998). These attributes include documentation of the root mean square error for positional accuracy and error rates associated with the delineation of specific map features. The probability of mapping errors, however, is generally not consistent across the surface of a map data set (Congalton 1988a; Steele et al. 1998), and standard methods have not been adopted for presenting the spatial distribution of error in thematic maps. The confusion matrix is the most commonly accepted method for assessing the accuracy of thematic maps, but it is entirely devoid of spatial context. This chapter addresses shortfalls in various approaches to predicting the distribution of error in thematic maps derived from image data.

An Overview of Uncertainty in Optical Remotely Sensed Data for Ecological Applications

Remote sensing has become a widely used tool in ecology. Examples of ecological applications that use remote sensing include species conservation efforts such as GAP analysis Scott et al. 1993, land cover and land use change monitoring (Skole and Tucker 1993; DeFries and Townshend 1994), and estimation of ecosystem carbon assimilation rates and net primary production (Prince 1991). At biome to global scales, it has also been demonstrated that the utility of remote sensing for monitoring ecosystem dynamics at time scales is commensurate with global change processes (Braswell et al. 1997; Myneni et al. 1997). Developments in remote sensing technologies, including airborne radar, video imaging systems, and satellite instruments with high spatial and spectral resolution show substantial promise for ecological studies. Further, even though this chapter focuses on optical remote sensing, new technologies (e.g., radar, laser altimeter, and lidar systems, which provide detailed information regarding topography and vegetation structure in three dimensions) suggest that the use of remote sensing by ecologists is likely to increase in the future.

Crop area monitoring within an advanced agricultural information system

Abstract This paper describes a framework for an image processing procedure for operational agricultural crop area estimation. This operational framework has been conceived within the development of an Advanced Agricultural Information System (AAIS) for the “Regione del Veneto “ (RdV ‐ Veneto Region) in northeastern Italy. The objective of this program is to develop the ability to generating timely and accurate area estimates and production information for four major agricultural crops: soybeans, sugar beets, corn, and small grains. AAIS uses state of the art methods in remote sensing and geographic information systems (GIS) technology and integrates a variety of data types including satellite imagery. This paper describes the methodology developed for image and ancillary data processing for the production of crop area statistics. Using a combination of standard unsupervised classification and GIS operations that incorporate knowledge about the agricultural system, a “sequential masking” classification pr...

Dating annual layers of a shallow Antarctic ice core with an optical scanner

This study tests novel methods for automatically identifying annual layers in a shallow Antarctic ice core (WDC05Q) using images that were collected with an optical scanner at the US National Ice Core Laboratory. A new method of optimized variance maximization (OVM) modeled the density-related changes in annual layer thickness directly from image variance. This was done by using multi-objective complex (MOCOM) parameter optimization to drive a low-pass filtering scheme. The OVM-derived changes in annual layer thickness corresponded well with the results of an independent glaciochemical interpretation of the core. Individual annual cycles in image brightness were then identified by using OVM results to apply a depth-varying low-pass filter and fitting a second-order polynomial to a locally detrended neighborhood. The resulting map of annual cycles agreed to within 1% of the overall annual count of the glaciochemical interpretation. Agreement on the presence of specific annual layer features was 96%. It was also shown that the MOCOM parameter optimization could calibrate the image-based results to match directly the date of a specific volcanic marker.