Mary C. Henry

Factors influencing wildfire occurrence and distribution in eastern Kentucky, USA

Most wildfires in Kentucky occur in the heavily forested Appalachian counties in the eastern portion of the state. In the present study, we reconstructed a brief fire history of eastern Kentucky using Landsat Thematic Mapper and Enhanced Thematic Mapper Plus images acquired between 1985 and 2002. We then examined relationships between fire occurrence and area burned, and abiotic and human factors. Abiotic factors included Palmer Drought Severity Index, slope, aspect, and elevation, and human factors included county unemployment rates, distance to roads, and distance to populated places. Approximately 83% of the total burned area burned only once, 14% twice, and 3% thrice. More fires burned in the winter compared with the fall, but the latter fires were larger on average and accounted for ~71% of the total area burned. Fire size was negatively correlated with Palmer Drought Severity Index for certain times of the year. There were significant relationships between elevation and slope and fire occurrence, but not between aspect and fire occurrence. We found links between fire location and proximity to roads and settlements, but we found no correlations between monthly unemployment rates and arson-caused fires.

Monitoring post-burn recovery of chaparral vegetation in southern California using multi-temporal satellite data

Monitoring the post-burn recovery condition of chaparral vegetation in southern California is important for managers to determine the appropriate time to conduct controlled burns. Due to the difficulty of monitoring post-fire recovery over large areas and the absence of detailed fire records in many areas, we examined the possibility of using satellite observations to establish the postfire recovery stage of chamise chaparral stands in this region. SPOT XS data collected on three dates between 1986 and 1992 were analysed to determine if temporal changes in a spectral vegetation index tracked the expected post-fire recovery trajectory of the above-ground biomass of chamise chaparral stands of varying post-fire ages. Results of the study indicated that neither the normalized difference vegetation index nor the soil adjusted vegetation index followed the expected post-fire recovery patterns in these stands. These findings are explained by interannual variations in precipitation having a larger than expected ...

Detecting an Invasive Shrub in Deciduous Forest Understories Using Remote Sensing

Abstract Remote sensing has been used to directly detect and map invasive plants, but has not been used for forest understory invaders because they are obscured by a canopy. However, if the invasive species has a leaf phenology distinct from native forest species, then temporal opportunities exist to detect the invasive. Amur honeysuckle, an Asian shrub that invades North American forests, expands leaves earlier and retains leaves later than native woody species. This research project explored whether Landsat 5 TM and Landsat 7 ETM+ imagery could predict Amur honeysuckle cover in woodlots across Darke and Preble Counties in southwestern Ohio and Wayne County in adjacent eastern Indiana. The predictive abilities of six spectral vegetation indices and six reflectance bands were evaluated to determine the best predictor or predictors of Amur honeysuckle cover. The use of image differencing in which a January 2001 image was subtracted from a November 2005 image provided better prediction of Amur honeysuckle cover than the use of the single November 2005 image. The Normalized Difference Vegetation Index (NDVI) was the best-performing predictor variable, compared to other spectral indices, with a quadratic function providing a better fit (R2  =  0.75) than a linear function (R2  =  0.65). This predictive model was verified with 15 other woodlots (R2  =  0.77). With refinement, this approach could map current and past understory invasion by Amur honeysuckle. Nomenclature: Amur honeysuckle, Lonicera maackii (Rupr.) Herder.

Detecting an invasive shrub in a deciduous forest understory using late-fall Landsat sensor imagery

Landsat TM and ETM+ imagery was used to distinguish areas of high vs. low cover of Amur honeysuckle (Lonicera maackii), taking advantage of the late leaf retention of this invasive shrub. L. maackii cover was measured in eight stands and compared to 15 Landsat 5 TM and Landsat 7 ETM+ images from spring and autumn dates from 1999 to 2006. Jeffries–Matusita (JM) distance calculations showed potential separability between high vs. low/zero cover classes of L. maackii on some late fall images. The Soil Adjusted Atmospheric Resistant Vegetation Index (SARVI2) revealed higher levels of green biomass in high L. maackii cover plots than low/zero cover plots for November images only. These findings justify further investigation of the effectiveness of late fall images to map the historical spread of L. maackii and other forest understory invasives with similar phenology.

Comparison of Single- and Multi-date Landsat Data for Mapping Wildfire Scars in Ocala National Forest, Florida

Remote sensing techniques have been widely used to map fire scars in the western United States, but have not been thoroughly tested in the eastern portion of the country. In this study, a 1998 Landsat Thematic Mapper (TM) image and a 1999 Enhanced Thematic Mapper (ETM1) image were used to test different image enhancements and classification algorithms for mapping wildfire scars in Ocala National Forest, Florida. Single-date analysis was conducted using the 1999 image, while both images were used to complete multi-temporal analysis. Both single- and multi-date datasets were classified using a traditional method (maximum likelihood classification: MLC) and a non-parametric technique (classification and regression trees: CART). Comparison of all techniques showed that MLC of a single image (1999) resulted in high accuracy compared to the other methods and that principal components analysis (PCA) and multitemporal PCA provided the best spectral separability between burned and unburned areas.

Using Advanced Land Imager (ALI) and Landsat Thematic Mapper (TM) for the Detection of the Invasive Shrub Lonicera maackii in Southwestern Ohio Forests

We tested how accurately image data from the Advanced Land Imager (ALI) sensor vs. the Landsat Thematic Mapper (TM) predict the land cover of Lonicera maackii in the forest understory, taking advantage of this invasive shrubs extended leaf retention in the fall when the canopy is leafless. Percent cover of L. maackii in 20 woodlots in southwestern Ohio was regressed on values for spectral vegetation indices (SVIs) derived for each image. The land cover of L. maackii was best explained by the Simple Ratio (SR) using TM data (R 2 = 0.537). The regression results for SVIs from TM vs. ALI suggest that the ALI image was acquired too late in the fall to accurately detect this invasive shrub.

Invasion of an Exotic Shrub into Forested Stands in an Agricultural Matrix

Abstract We investigated the relative importance of stand and landscape characteristics in the invasion of a nonnative shrub, Amur honeysuckle, in 40 woodlots in an agricultural matrix in southwest Ohio. We quantified stand characteristics that could influence invasibility, the intrinsic susceptibility of an area to invasion, including woodlot size, perimeter-to-area ratio, tree basal area, and stand age. At the landscape scale we included factors that potentially influence propagule rain (the contribution of seeds from individuals established outside the focal area), including the land cover and road density in a 1,500-m buffer around each woodlot, as well as the extent to which the perimeter was forested at two points in the past, and latitude (based on an apparent south-to-north invasion in this region). Based on stepwise regression, we determined that honeysuckle cover was determined primarily by landscape parameters, particularly the percent of the buffer comprised of cropland. Woodlots surrounded by more cropland had less honeysuckle cover, which we attribute to paucity of nearby seed sources and/or minimal movement of seed-dispersing animals. From these findings, we argue that impediments to propagule rain are more important in shaping the invasion of this exotic shrub than are characteristics of the woodlots themselves, i.e., community invasibility. Nomenclature: Amur honeysuckle, Lonicera maackii (Rupr.) Herder. Management Implications: Understanding the factors that make forests susceptible to invasion can inform management strategies, including identifying forest stands at greatest risk of invasion and formulating steps that can be taken to minimize invasion risk. We investigated what stand and landscape characteristics best explained the cover of the invasive shrub Lonicera maackii (Amur honeysuckle), in a landscape it recently invaded, consisting of woodlots in an agricultural matrix in southwest Ohio. We found that cover of this shrub was best explained by landscape characteristics, rather than by stand characteristics, such as age or basal area. Specifically, the percentage of the 1,500-m buffer around the woodlot that was comprised of cropland, as opposed to pasture, forest, and other land-cover types, was the best predictor of honeysuckle cover. Woodlots surrounded by more cropland had lower cover, which we think indicates more recent colonization. Thus cropland impedes honeysuckle invasion, either by providing a buffer free of seed sources (fruiting shrubs), or a land cover that is unlikely to be crossed by animals dispersing seeds from more distant sources. These findings suggest that woodlots surrounded by cropland, and perhaps by other shrub-free land covers, are at lower risk of invasion by animal-dispersed nonnative plants, and that active management of buffers around forest stands will reduce invasion risk.

Map Interpretation Instruction in Introductory Textbooks: A Preliminary Investigation

ABSTRACT Maps are often understood as the primary tool in geography; however, recent research indicates that the number of students taking map interpretation courses has declined. As geography students are expected to master the uses of maps, this study investigates the materials available in introductory collegiate textbooks that promote the development of those skills. Seventeen widely used introductory geography texts are analyzed for the following: the presence of text material dedicated to map interpretation; content related to map interpretation concepts; and additional resources such as study boxes designed to enhance student map interpretation abilities. After taking an inventory of introductory geography textbooks, findings indicate broad inclusion of map interpretation concepts in physical, human, world regional, and general geography textbooks, although physical and general geography textbooks include more substantive map interpretation explanation and tools than their human and world regional counterparts. This study is intended to be useful for instructors evaluating the breadth and depth of map interpretation concepts and tools in introductory textbooks, as well as those looking for specific terms or exercises to use in classroom instruction.

Assessing Relationships between Forest Spatial Patterns and Fire History with Fusion of Optical and Microwave Remote Sensing

Abstract In this paper, we tested the use of active and passive sensor fusion for relating forest fire history to landscape spatial patterns. Principal Components Analysis (PCA) was implemented to combine Landsat Thematic Mapper (TM) and Shuttle Imaging Radar (SIR-C) data from October 1994. Resulting PCs were converted to landscape patch maps. Plots with known fire history were delineated using a fire atlas of the study area. These plots came from four fire history categories: unburned (nine plots), once burned (three plots), twice burned (three plots), and multiple burned (three plots). Landscape metrics were calculated for each plot, including a shape index, mean patch size, Shannons Diversity Index, and Shannons Evenness Index. Spearmans Rank Correlation Analysis was used to compare the patch map‐derived landscape metrics to fire history characteristics, such as average fire‐free interval and number of fire‐free years in different time periods. Results showed that landscape patterns derived from fused data were significantly (p < 0.05) related to fire history and typically performed better than SIR-C data (a greater number of significant correlations), but not as well as TM data.

The sensitivity of SIR-C backscatter to fire-related forest spatial patterns

This research investigates the use of Shuttle Imaging Radar (SIR-C) data to analyze spatial patterns in forests experiencing different fire histories between 1943 and 1994. C-HH, C-HV, L-HH, and L-HV band data were used to calculate mean backscatter (σ°) and several landscape metrics (e.g., largest patch index, mean patch size, edge density, and patch richness). We assessed the relationship between σ° and fire history, as well as spatial patterns and fire history. Significant (p < 0.05) results were obtained for landscape metrics derived from σ° and several fire history variables, although we did not find significant relationships (p < 0.05) between mean backscatter and fire occurrence.