Rick L. Lawrence

Ecological Causes and Consequences of Demographic Change in the New West

R areas in the American West are undergoing a dra m a tic tra n s i ti on in dem ogra phy, econ om i c s ,a n d eco s ys tem s . Long known as the “Wi l d ” We s t , the regi on has been ch a racteri zed by low human pop u l a ti on den s i ties and vast tracts of u n s et t l ed or undevel oped land (Wi l k i n s on 1993, Power 1998). For most of the 1900s, the pop u l a ti on of m a ny ru ral areas in the West grew very slowly or even dec re a s ed . Because local econ omies were based on natu ral re s o u rce indu s tries su ch as mining, l oggi n g, f a rm i n g, and ra n ch i n g, m a ny re s i dents of the regi on con s i dered con s erva ti on stra tegies on public lands detri m ental to local econ omic devel opm en t . E f forts to establish natu re re s erves and to pre s erve p u blic lands from com m ercial devel opm ent were seen as res tri cting the use of vital natu ral re s o u rce s . In recent dec ade s ,p a rts of that Wild West have given way to the “ New ” West (Ri ebsame et al. 1 9 9 7 ) . People from t h ro u gh o ut the Un i ted States have been migra ting to the Rocky Mountains and the inland We s t . With a pop u l a ti on growth ra te of 2 5 . 4 % , the mountain West was the faste s t growing regi on of the co u n try du ring the 1990s. Su rpri si n gly, rapid pop u l a ti on increases are occ u rring not on ly in urban areas su ch as Denver and Salt Lake Ci ty but also in ru ra l co u n ti e s ,m a ny of wh i ch are gaining pop u l a ti on even faster than urban areas (Th eobald 2000). Some 67% of the co u nties in the Rocky Mountains grew faster than the nati onal avera ge du ring the 1990s (Beyers and Nel s on 2000). Con s equ en t ly, s m a ll cities su ch as Bozem a n , Mon t a n a , and Moa b, Ut a h , a re beginning to ex peri en ce traffic con ge s ti on and s prawl . Some of the ru ral pop u l a ti on growth in the New We s t repre s ents an intra regi onal red i s tri buti on of people from the h i gh plains, wh i ch con ti nue to lose pop u l a ti on (Jo h n s on 1 9 9 8 ) , to more mountainous are a s . Ma ny of the new re s i den t s , h owever, a re in-migrants from other regi ons thro u gh o ut the Un i ted States (Ri ebsame et al. 1 9 9 7 ) . The re s i dents of a ru ra l su b d ivi s i on in a boom co u n ty in Montana might inclu de recent arrivals from big East Coast citi e s ,m i dwe s tern farm s ,a n d the nearest small town . Am ong the in-migrants are reti ree s , we a l t hy young adu l t s , and profe s s i onals in com p uter techn o l ogy, real estate , and other servi ce indu s tries (Nel s on 1999).

Mapping wetlands and riparian areas using Landsat ETM+ imagery and decision-tree-based models

The location and distribution of wetlands and riparian zones influence the ecological functions present on a landscape. Accurate and easily reproducible land-cover maps enable monitoring of land-management decisions and ultimately a greater understanding of landscape ecology. Multi-season Landsat ETM+ imagery from 2001 combined with ancillary topographic and soils data were used to map wetland and riparian systems in the Gallatin Valley of Southwest Montana, USA. Classification Tree Analysis (CTA) and Stochastic Gradient Boosting (SGB) decision-tree-based classification algorithms were used to distinguish wetlands and riparian areas from the rest of the landscape. CTA creates a single classification tree using a one-step-look-ahead procedure to reduce variance. SGB uses classification errors to refine tree development and incorporates multiple tree results into a single best classification. The SGB classification (86.0% overall accuracy) was more effective than CTA (73.1% overall accuracy) at detecting a variety of wetlands and riparian zones present on this landscape.

Wheat yield estimates using multi-temporal NDVI satellite imagery

We examined seasonal growth profiles developed from AVHRR-NDVI for estimating wheat yield at regional and farm scales in Montana for the years 1989-1997. Both regions and farms showed strong relationships between wheat yields and integrated NDVI over the entire growing season, and with late-season NDVI parameters. The use of AVHRR-NDVI growth profiles at the regional level provided the strongest yield estimates. At the farm scale, the spatial resolution (1 km 2 ) limited the certainty for accurate portrayal of field locations. However, our models provide a basis for further examination of time-series satellite data.

Comparisons among vegetation indices and bandwise regression in a highly disturbed, heterogeneous landscape : Mount St. Helens, Washington

Abstract Spectral vegetation indices have been used extensively to predict ecological variables, such as percent vegetation cover, above-ground biomass, and leaf-area index. We examined the use of various vegetation indices and multiple linear regression using raw spectral bands for predicting vegetation cover in a landscape characterized by high variability in vegetation cover and soil properties. We were able to improve the explanatory value of several vegetation indices by using regression fitting techniques including log transformations and polynomial regressions. We expected soil-adjusted indices to perform better than nonadjusted indices. However, soil-adjusted vegetation indices based on a ratio of red and near-infrared bands explained 55–65% of the variability in vegetation cover, while two nonadjusted indices each explained 70%. An index using six spectral bands explained 40%. The best multiple regression model used the red and near-infrared bands and explained 75% of the variability in vegetation cover. Among the soil-adjusted indices, an index which used a computed soil line performed best. Ratio-based vegetation indices were less sensitive to shadow influences, but this influence was outweighed by the advantages of multiple regression against original bands.

Procedural justice and public involvement in natural resource decision making

The public involvement programs of natural resource agencies have been broadly criticized as unresponsive to public desires. Historically, improving natural resource decisions has been the primary conceptual basis for designing public participation programs. However, the social psychological field of procedural justice suggests a new conceptual basis for public involvement that recognizes the importance of procedures as well as outcomes. This theory is based on a balancing of the self‐interest and group‐value models of behavior. Issues that arise in the operationalization of this theory for natural resource decision making include (1) the impact on interest group, in addition to individual participants, (2) impacts on nonparticipants, (3) effects of historical mistrust, and (4) measures of procedural fairness.

Change detection of wetland ecosystems using Landsat imagery and change vector analysis

Accurate, efficient, and repeatable mapping of changes in wetlands and riparian areas (referred to collectively as wetlands) is critical for monitoring human, climatic, and other effects on these important systems. We used Landsat-based satellite imagery from 1988 and 2001 to map changes in wetland ecosystems in the Gallatin Valley of southwest Montana. Stochastic gradient boosting (SGB) was used to classify the 2001 image, and change vector analysis (CVA) was used to identify locations where wetland areas might have changed between 1988 and 2001. These potentially changed locations again were classified for the 1988 Landsat image using SGB. Areas of change constituted 3.4% of the study area, thus only this small percentage of the image was reclassified for the 1988 image. Overall change detection accuracy was 76%, although changes along the periphery of wetland boundaries and in areas of smaller upland inclusions were not distinguished as well as other changes. Overall accuracies of the SGB wetland classification maps were 81% for 1988 and 86% for 2001. CVA significantly reduced the number of pixels involved in the historical image classification compared to conducting independent classifications, thus reducing the potential for compounding classification errors in unchanged areas.

Choosing Public Participation Methods for Natural Resources: A Context-Specific Guide

Although public participation has been required of many public agencies and incorporated at all levels in natural resource decision making, little detailed, strategic guidance has been available to help managers understand when and how to involve the public. The Vroom-Yetton model, borrowed from the workplace arena, has shown considerable promise for this purpose. We found, however, that the model needs to be adapted to fit different criteria present in the public natural resource arena. We proposed several modifications to the model for this purpose and demonstrated the use of the revised model.Although public participation has been required of many public agencies and incorporated at all levels in natural resource decision making, little detailed, strategic guidance has been available to help managers understand when and how to involve the public. The Vroom-Yetton model, borrowed from the workplace arena, has shown considerable promise for this purpose. We found, however, that the model needs to be adapted to fit different criteria present in the public natural resource arena. We proposed several modifications to the model for this purpose and demonstrated the use of the revised model.

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.

Calculating Change Curves for Multitemporal Satellite Imagery: Mount St. Helens 1980–1995

Abstract We developed and tested a method for analyzing multitemporal satellite imagery using change curves. The method is flexible and allows an analyst to extract specific change parameters from the curves depending on the research question of interest. Eight Landsat TM images of the Mount St. Helens, Washington, blast zone from 1984 to 1995 were geometrically and radiometrically corrected. They were then transformed to estimates of green vegetation cover. Unsupervised clustering was performed on the set of eight transformed images and polynomial curves were fit to the cluster means. From these fitted curves, parameters of interest were extracted and returned to GIS layers, including number of years to reach 10% cover, the greatest rate of cover increase during the study period, and time-integrated cover. Statistical analysis indicated that the curves did a good job of representing the change trajectories of unclustered pixels. We demonstrated the use of change curve analysis by analyzing the importance in the revegetation of Mount St. Helens of the different types of disturbance resulting from the volcanic eruption. The change curve analysis is useful in a variety of applications where the data are continuous, more than two dates of data are available, and the underlying question of interest relates to trends in the data.

Fluvial response a decade after wildfire in the northern Yellowstone ecosystem: a spatially explicit analysis

Forest fire is a vital ecological process capable of inducing complex fluvial response, but the integration of these effects across entire watersheds remains poorly understood. We collected downstream cross-sectional and geomorphic data, acquired geographic information on land cover and forest fire, and performed spatially explicit statistical analyses to examine fire-related impacts in catchments burned to varying degrees. Generalized least squares (GLS) regression models suggested that channels with a greater percentage of burned drainage area were associated with markedly higher cross-sectional stream power, relatively smaller width/depth ratios, and lower bank failure rates 12 to 13 years after the fires. These results implied that streams became more powerful in the aftermath of forest fire and that net incision had been the primary response in second- to fourth-order channels since the 1988 Yellowstone fires. The extensive geographic coverage of our data, spanning multiple basins with measurements spaced every 100 m, allowed us to hypothesize a process–response model based on these results. We suggest that a wave of fire-related sediment propagates through burned catchments. High runoff events or even moderate flows provide sufficient energy to evacuate the finer-grained material delivered from burned hillslopes to the channel network over a period of 5–10 years. The combination of elevated post-fire discharges and decreased sediment supply then induces an episode of incision. Site-specific channel changes are highly variable because streams can accommodate post-fire increases in energy and sediment supply through multiple modes of adjustment. Characterizing the spatial distribution of stream power would provide a valuable management tool because this variable is strongly associated with percent-burned drainage area and integrates several elements of complex fluvial response. Future research focused on the channel substrate and its evolution through time is needed, but our results indicate a fundamental linkage between fire and fluvial processes. D 2002 Elsevier Science B.V. All rights reserved.