Scott L. Powell

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.

Bringing an ecological view of change to Landsat-based remote sensing

When characterizing the processes that shape ecosystems, ecologists increasingly use the unique perspective offered by repeat observations of remotely sensed imagery. However, the concept of change embodied in much of the traditional remote-sensing literature was primarily limited to capturing large or extreme changes occurring in natural systems, omitting many more subtle processes of interest to ecologists. Recent technical advances have led to a fundamental shift toward an ecological view of change. Although this conceptual shift began with coarser-scale global imagery, it has now reached users of Landsat imagery, since these datasets have temporal and spatial characteristics appropriate to many ecological questions. We argue that this ecologically relevant perspective of change allows the novel characterization of important dynamic processes, including disturbances, longterm trends, cyclical functions, and feedbacks, and that these improvements are already facilitating our understanding of critical driving forces, such as climate change, ecological interactions, and economic pressures.

Effect of Alternative Splitting Rules on Image Processing Using Classification Tree Analysis

Rule-based classification using classification tree analysis (CTA) is increasingly applied to remotely sensed data. CTA employs splitting rules to construct decision trees using training data as input. Results are then used for image classification. Software implementations of CTA offer different splitting rules and provide practitioners little guidance for their selection. We evaluated classification accuracy from four commonly used splitting rules and three types of imagery. Overall accuracies within data types varied less than 6 percent. Pairwise comparisons of kappa statistics indicated no significant differences (p-value � 0.05). Individual class accuracies, measured by user’s and producer’s accuracy, however, varied among methods. The entropy and twoing splitting rules most often accounted for the poorest performing classes. Based on analysis of the structure of the rules and the results from our three data sets, when the software provides the option, we recommend the gini and class probability rules for classification of remotely sensed data.

Moderate resolution remote sensing alternatives: a review of Landsat-like sensors and their applications

Earth observation with Landsat and other moderate resolution sensors is a vital component of a wide variety of applications across disciplines. Despite the widespread success of the Landsat program, recent problems with Landsat 5 and Landsat 7 create uncertainty about the future of moderate resolution remote sensing. Several other Landsat-like sensors have demonstrated applicability in key fields of earth observation research and could potentially complement or replace Landsat. The objective of this paper is to review the range of applications of 5 satellite suites and their Landsat-like sensors: SPOT, IRS, CBERS, ASTER, and ALI. We give a brief overview of each sensor, and review the documented applications in several earth observation domains, including land cover classification, forests and woodlands, agriculture and rangelands, and urban. We conclude with suggestions for further research into the fields of cross-sensor comparison and multi-sensor fusion. This paper is significant because it provides the remote sensing community a concise synthesis of Landsat-like sensors and research demonstrating their capabilities. It is also timely because it provides a framework for evaluating the range of Landsat alternatives, and strategies for minimizing the impact of a possible Landsat data gap.

Review of Alternative Methods for Estimating Terrestrial Emittance and Geothermal Heat Flux for Yellowstone National Park Using Landsat Imagery

Yellowstone National Park (YNP) is legally mandated to monitor geothermal features for their future preservation, and remote sensing is a component of the current monitoring plan. Landsat imagery was explored as a tool for mapping terrestrial emittance and geothermal heat flux for this purpose. Several methods were compared to estimate terrestrial emittance and geothermal heat flux (GHF) using images from 2007 (Landsat Thematic Mapper) and 2002 (Landsat Thematic Mapper Plus). Accurate estimations were reasonable when compared to previously established values and known patterns but were likely limited due to inherent properties of Landsat data, the effects of solar radiation, and variation among geothermal areas. Landsat data can be valuable for calculation of GHF in YNP. The method suggested in this paper is not highly parameterized. Landsat data provide the means to calculate GHF for all of YNP and have the potential to enable scientists to identify locations for in-depth study.

Conifer Cover Increase in the Greater Yellowstone Ecosystem: Frequency, Rates, and Spatial Variation

Extensive fires in recent decades in the Greater Yellowstone Ecosystem (GYE) garnered much attention for causing a significant decrease in the extent of conifer forest cover. Meanwhile, conifer forests in unburned parts of the GYE have continued to increase in extent and density. Conifer cover increase has been well documented by repeat historical photography, but the average rate of increase and the spatial variation remain unquantified. We examined changes in conifer cover across biophysical gradients in the GYE based on stratified random samples from aerial photographs. The percent conifer cover for samples in 1971 and 1999 was quantified to determine the frequency and rate of conifer cover change. A slight majority of samples (56%) showed no change, whereas increases (22%) were balanced by decreases (22%). However, among samples that were not recently burned or logged, or already closed-canopy, nearly 40% increased in conifer cover, at an average annual rate of 0.22%. We quantified significant variability in the frequency and rate of conifer cover increase across gradients of elevation, aspect, vegetation type, and proximity to nearby conifer forest. The most dynamic locations were low density conifer woodlands on northerly aspects at lower elevations, with average annual rates of increase up to 0.51%. This study is significant because it demonstrates that rates of conifer cover increase vary across biophysical gradients, an important consideration for management of dynamic forest ecosystems. Improved understanding of this variability helps us to better understand what factors ultimately cause conifer cover increase. It is also a critical step towards accurate quantification of the magnitude of carbon uptake by conifer cover increase.

Effect of thematic map misclassification on landscape multi-metric assessment

Advancements in remote sensing and computational tools have increased our awareness of large-scale environmental problems, thereby creating a need for monitoring, assessment, and management at these scales. Over the last decade, several watershed and regional multi-metric indices have been developed to assist decision-makers with planning actions of these scales. However, these tools use remote-sensing products that are subject to land-cover misclassification, and these errors are rarely incorporated in the assessment results. Here, we examined the sensitivity of a landscape-scale multi-metric index (MMI) to error from thematic land-cover misclassification and the implications of this uncertainty for resource management decisions. Through a case study, we used a simplified floodplain MMI assessment tool, whose metrics were derived from Landsat thematic maps, to initially provide results that were naive to thematic misclassification error. Using a Monte Carlo simulation model, we then incorporated map misclassification error into our MMI, resulting in four important conclusions: (1) each metric had a different sensitivity to error; (2) within each metric, the bias between the error-naive metric scores and simulated scores that incorporate potential error varied in magnitude and direction depending on the underlying land cover at each assessment site; (3) collectively, when the metrics were combined into a multi-metric index, the effects were attenuated; and (4) the index bias indicated that our naive assessment model may overestimate floodplain condition of sites with limited human impacts and, to a lesser extent, either over- or underestimated floodplain condition of sites with mixed land use.

Resampling-based multiple comparison procedure with application to point-wise testing with functional data

In this paper we describe a coherent multiple testing procedure for correlated test statistics such as are encountered in functional linear models. The procedure makes use of two different

Hyperspectral Detection of a Subsurface CO2 Leak in the Presence of Water Stressed Vegetation

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