Rutherford V. Platt

An Evaluation of an Object-Oriented Paradigm for Land Use/Land Cover Classification

Object-oriented image classification has tremendous potential to improve classification accuracies of land use and land cover (LULC), yet its benefits have only been minimally tested in peer-reviewed studies. We aim to quantify the benefits of an object-oriented method over a traditional pixel-based method for the mixed urban–suburban–agricultural landscape surrounding Gettysburg, Pennsylvania. To do so, we compared a traditional pixel-based classification using maximum likelihood to the object-oriented image classification paradigm embedded in eCognition Professional 4.0 software. This object-oriented paradigm has at least four components not typically used in pixel-based classification: (1) the segmentation procedure, (2) nearest neighbor classifier, (3) the integration of expert knowledge, and (4) feature space optimization. We evaluated each of these components individually to determine the source of any improvement in classification accuracy. We found that the combination of segmentation into image objects, the nearest neighbor classifier, and integration of expert knowledge yields substantially improved classification accuracy for the scene compared to a traditional pixel-based method. However, with the exception of feature space optimization, little or no improvement in classification accuracy is achieved by each of these strategies individually.

A Comparison of AVIRIS and Landsat for Land Use Classification at the Urban Fringe

In this study we tested whether AVIRIS data allowed for improved land use classification over synthetic Landsat ETM+ data for a location on the urban-rural fringe of Colorado. After processing the AVIRIS image and creating a synthetic Landsat image, we used standard classification and post-classification procedures to compare the data sources for land use mapping. We found that, for this location, AVIRIS holds modest, but real, advantages over Landsat for the classification of heterogeneous and vegetated land uses. Furthermore, this advantage comes almost entirely from the large number of sensor spectral bands rather than the high Signal-to-Noise Ratio (SNR).

Historical, observed, and modeled wildfire severity in montane forests of the Colorado Front Range.

Large recent fires in the western U.S. have contributed to a perception that fire exclusion has caused an unprecedented occurrence of uncharacteristically severe fires, particularly in lower elevation dry pine forests. In the absence of long-term fire severity records, it is unknown how short-term trends compare to fire severity prior to 20th century fire exclusion. This study compares historical (i.e. pre-1920) fire severity with observed modern fire severity and modeled potential fire behavior across 564,413 ha of montane forests of the Colorado Front Range. We used forest structure and tree-ring fire history to characterize fire severity at 232 sites and then modeled historical fire-severity across the entire study area using biophysical variables. Eighteen (7.8%) sites were characterized by low-severity fires and 214 (92.2%) by mixed-severity fires (i.e. including moderate- or high-severity fires). Difference in area of historical versus observed low-severity fire within nine recent (post-1999) large fire perimeters was greatest in lower montane forests. Only 16% of the study area recorded a shift from historical low severity to a higher potential for crown fire today. An historical fire regime of more frequent and low-severity fires at low elevations (<2260 m) supports a convergence of management goals of ecological restoration and fire hazard mitigation in those habitats. In contrast, at higher elevations mixed-severity fires were predominant historically and continue to be so today. Thinning treatments at higher elevations of the montane zone will not return the fire regime to an historic low-severity regime, and are of questionable effectiveness in preventing severe wildfires. Based on present-day fuels, predicted fire behavior under extreme fire weather continues to indicate a mixed-severity fire regime throughout most of the montane forest zone. Recent large wildfires in the Front Range are not fundamentally different from similar events that occurred historically under extreme weather conditions.

Recruitment facilitation and spatial pattern formation in soft-bottom mussel beds

Mussels (Mytilus edulis) build massive, spatially complex, biogenic structures that alter the biotic and abiotic environment and provide a variety of ecosystem services. Unlike rocky shores, where mussels can attach to the primary substrate, soft sediments are unsuitable for mussel attachment. We used a simple lattice model, field sampling, and field and laboratory experiments to examine facilitation of recruitment (i.e., preferential larval, juvenile, and adult attachment to mussel biogenic structure) and its role in the development of power-law spatial patterns observed in Maine, USA, soft-bottom mussel beds. The model demonstrated that recruitment facilitation produces power-law spatial structure similar to that in natural beds. Field results provided strong evidence for facilitation of recruitment to other mussels—they do not simply map onto a hard-substrate template of gravel and shell hash. Mussels were spatially decoupled from non-mussel hard substrates to which they can potentially recruit. Recent...

Are Wildfire Mitigation and Restoration of Historic Forest Structure Compatible? A Spatial Modeling Assessment

Abstract In response to catastrophic wildfires, wide-reaching forest management policies have been enacted in recent years, most notably the Healthy Forests Restoration Act of 2003. A key premise underlying these policies is that fire suppression has resulted in denser forests than were present historically in some western forest types. Therefore, although reducing the threat of wildfire is the primary goal, forest managers commonly view fuel treatments as a means to restore historic forest structure in those forest types that are outside of their historic range of variation. This study evaluates where both wildfire mitigation and restoration of historic forest structure are potentially needed in the ponderosa pine–dominated montane forest zone of Boulder County, Colorado. Two spatial models were overlain: a model of potential fireline intensity and a model of historic fire frequency. The overlay was then aggregated by land management classes. Contrary to current assumptions, results of this study indicate that both wildfire mitigation and restoration of historic forest structure are needed in only a small part of the study area, primarily at low elevations. Furthermore, little of this land is located on Forest Service land where most of the current thinning projects are taking place. We question the validity of thinning as a means both to reduce the threat of wildfire and to restore historic forest structure in the absence of site-specific data collection on past and present landscape conditions.

A Model of Exurban Land-Use Change and Wildfire Mitigation

As exurban development spreads throughout fire-prone areas of the western United States, the threat of wildfire to life and property grows. To address this threat, wildfire mitigation, such as mechanical thinning, often takes place in areas close to exurban development. This study demonstrates the utility of spatially explicit dynamic models to understand better where the wildland–urban interface is, how it might change in the future, and how this might affect which land would be prioritized for mechanical thinning. Specifically, a model (WHAMED) is presented that forecasts the prioritized locations for future mechanical thinning as a function of projected exurban development in the montane zone of Boulder County, CO. To predict exurban development, WHAMED uses a cellular automata model with rules derived from statistical models of historical exurban development. The study tests two general sets of criteria for prioritizing mechanical thinning, that of the Community Protection Zone and the Healthy Forests Restoration Act of 2003. The study shows that under any forecast of exurban development, and under either set of criteria, prioritized land for mechanical thinning is set to expand primarily on US Forest Service land. Methodologically, the study illustrates the importance of making variability transparent and of providing multiple methods of model validation.

Mapping Settlements in the Wildland Urban Interface: A Decision Tree Approach

The wildland–urban interface (WUI) is the area where human-built structures intermingle or abut wildland vegetation. Maps of the WUI are important for resource management, particularly related to wildfire mitigation, but are often based on spatially coarse data such as housing counts from census blocks. Here, three decision tree models are used to create maps of human settlements for use in delineating the WUI. The first model uses statistics derived from image objects; the second model uses data related to topography, amenities, and accessibility; and the third model uses all available data. The accuracy of the models was evaluated in terms of the percentage of actual structures that fall within the area delineated as settlements. Overall, the three decision models performed similarly, although the third decision tree model was the best. For delineating settlements, all three decision tree models represent an improvement over a null model and the Radeloff et al. (2005) WUI mapping methodology and perform similar to the Wilmer and Aplet (2005) WUI mapping methodology. The models are also more flexible than many existing models, as they allow users to trade off accuracy and the size of the delineated settlement. The strategies described here can potentially yield improved maps of the WUI over larger areas.

Estimating the Creation and Removal Date of Fracking Ponds Using Trend Analysis of Landsat Imagery

Hydraulic fracturing, or fracking, is a process of introducing liquid at high pressure to create fractures in shale rock formations, thus releasing natural gas. Flowback and produced water from fracking operations is typically stored in temporary open-air earthen impoundments, or frack ponds. Unfortunately, in the United States there is no public record of the location of impoundments, or the dates that impoundments are created or removed. In this study we use a dataset of drilling-related impoundments in Pennsylvania identified through the FrackFinder project led by SkyTruth, an environmental non-profit. For each impoundment location, we compiled all low cloud Landsat imagery from 2000 to 2016 and created a monthly time series for three bands: red, near-infrared (NIR), and the Normalized Difference Vegetation Index (NDVI). We identified the approximate date of creation and removal of impoundments from sudden breaks in the time series. To verify our method, we compared the results to date ranges derived from photointerpretation of all available historical imagery on Google Earth for a subset of impoundments. Based on our analysis, we found that the number of impoundments built annually increased rapidly from 2006 to 2010, and then slowed from 2010 to 2013. Since newer impoundments tend to be larger, however, the total impoundment area has continued to increase. The methods described in this study would be appropriate for finding the creation and removal date of a variety of industrial land use changes at known locations.