Stephen R. Yool

Mapping forest post-fire canopy consumption in several overstory types using multi-temporal Landsat TM and ETM data

To facilitate the identification of appropriate post-fire watershed treatments and minimize erosion effects after socio-economically important fires, Interagency Burned Area Emergency Rehabilitation (BAER) teams produce initial timely estimates of the fire perimeter and classifications of burn severity, forest mortality, and vegetation mortality. Accurate, cost-effective, and minimal time-consuming methods of mapping fire are desirable to assist rehabilitation efforts immediately after containment of the fire. BAER teams often derive their products by manually interpreting color infrared aerial photos and/or field analysis. Automated classification of multispectral satellite data are examined to determine whether they can provide improved accuracy over manually digitized aerial photographs. In addition, pre-fire vegetation data are incorporated to determine whether further gains in accuracy of mapped canopy consumption can be made. BAER team classifications from the Cerro Grande Fire were compared to estimates of overstory consumption produced using a pre-fire vegetation classification, and a change detection algorithm using bands 4 and 7 from July 1997 pre-fire Landsat Thematic Mapper (TM) and July 2000 post-fire Enhanced Thematic Mapper (ETM) data. BAER team classifications are highly correlated to overstory consumption and should produce high Kappa statistics when verified using the same dataset. Our three-class supervised classification of the change image incorporating a pre-fire vegetation classification yielded the highest Kappa at 0.86. A three-class unsupervised classification of the change image yielded a lower Kappa of 0.72. BAER team classifications yielded Kappas ranging from 0.38 to 0.63 using the same verification dataset.

Mapping Fire-Induced Vegetation Mortality Using Landsat Thematic Mapper Data: A Comparison of Linear Transformation Techniques

Abstract Forests in the U.S. southwest experience large, intense wildfires. Fire severity maps can assist management of such fire-scarred landscapes. Remote sensing appears suitable for wildfire mapping, provided data have sufficient spatial, radiometric, and spectral resolutions. Using a 1995 Thematic Mapper (TM) post-fire scene of the 8900 ha Rattlesnake Fire in southeastern Arizona as a case study, two linear transformation techniques, the Kauth–Thomas (KT) and principal components analysis (PC) transforms were invoked to enhance Thematic Mapper data prior to supervised classification. The KT and PC transformations were selected to enhance fire-related brightness, greenness, and wetness variations in the image, detecting the extent of different fire severities. The KT transform produced 17% higher overall classification accuracies than the PC transform. The higher accuracy recorded by the KT transform results from brightness, greenness, and wetness variations which, in this case, are associated with fire severity.

REGRESSION-TREE MODELING OF DESERT TORTOISE HABITAT IN THE CENTRAL MOJAVE DESERT

This paper describes an interdisciplinary study of the habitat requirements of threatened desert tortoises (Gopherus agassizii) on eight 225-ha study plots in a 14 000 ha study area near the southern boundary of the U.S. Armys National Training Center at Fort Irwin in the central Mojave Desert of southern California. The objective of the study was to produce an empirical, statistical, GIS-based model of desert tortoise habitat use based on a combination of field data and data derived from various spatial databases, including satellite imagery. A total of 11 primary and secondary data layers constitute the spatial database used for this project. Vegetation and tortoise relative density data were obtained from field surveys. Regression-tree methods were used to develop the statistical model. The tree has 11 terminal nodes and a residual mean deviance of 1.985. Out of 73 potential predictors in the model specification, only eight were selected by the algorithm to be used in construction of the tree. The mod...

Spatial and temporal corroboration of a fire-scar-based fire history in a frequently burned ponderosa pine forest.

Fire scars are used widely to reconstruct historical fire regime parameters in forests around the world. Because fire scars provide incomplete records of past fire occurrence at discrete points in space, inferences must be made to reconstruct fire frequency and extent across landscapes using spatial networks of fire-scar samples. Assessing the relative accuracy of fire-scar fire history reconstructions has been hampered due to a lack of empirical comparisons with independent fire history data sources. We carried out such a comparison in a 2780-ha ponderosa pine forest on Mica Mountain in southern Arizona (USA) for the time period 1937-2000. Using documentary records of fire perimeter maps and ignition locations, we compared reconstructions of key spatial and temporal fire regime parameters developed from documentary fire maps and independently collected fire-scar data (n = 60 plots). We found that fire-scar data provided spatially representative and complete inventories of all major fire years (> 100 ha) in the study area but failed to detect most small fires. There was a strong linear relationship between the percentage of samples recording fire scars in a given year (i.e., fire-scar synchrony) and total area burned for that year (y = 0.0003x + 0.0087, r2 = 0.96). There was also strong spatial coherence between cumulative fire frequency maps interpolated from fire-scar data and ground-mapped fire perimeters. Widely reported fire frequency summary statistics varied little between fire history data sets: fire-scar natural fire rotations (NFR) differed by < 3 yr from documentary records (29.6 yr); mean fire return intervals (MFI) for large-fire years (i.e., > or = 25% of study area burned) were identical between data sets (25.5 yr); fire-scar MFIs for all fire years differed by 1.2 yr from documentary records. The known seasonal timing of past fires based on documentary records was furthermore reconstructed accurately by observing intra-annual ring position of fire scars and using knowledge of tree-ring growth phenology in the Southwest. Our results demonstrate clearly that representative landscape-scale fire histories can be reconstructed accurately from spatially distributed fire-scar samples.

Modeling potential erosion due to the Cerro Grande Fire with a GIS-based implementation of the Revised Universal Soil Loss Equation

Erosional processes directly influenced by wildland fire include reduction or elimination of above- ground biomass, reduction of soil organic matter, and hydrophobicity. High fuel loads promoted by decades of fire suppression in the U.S. increase the duration and intensity of burning, amplifying these effects. The Cerro Grande fire (6-31 May 2000) consumed approximately 15 000 hectares around and within the town of Los Alamos, New Mexico, USA. Private and public infrastructure including Los Alamos National Laboratory are at continuing risk due to increased threats of upstream erosion. We use a geographic information system (GIS) based implementation of the Revised Universal Soil Loss Equation (RUSLE) to model pre- and post-fire soil loss conditions and aid erosion risk analysis. Pre- and post-fire vegetation cover data layers were generated from Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM) data. Based upon annual average rainfall amounts we estimate that subwatershed average pre-fire erosion rates range from 0.45 to 9.22 tonnes ha −1 yr −1 while post-fire erosion rates before watershed treatments range from 1.72 to 113.26 tonnes ha −1 yr −1 . Rates are approximately 3.7 times larger for 50 year return interval rainfall amounts. It is estimated that watershed treatments including reseeding will decrease soil loss between 0.34 and 25.98% in the first year on treated subwatersheds. Immediately after the fire an interagency Burned Area Emergency Rehabilitation (BAER) team produced initial estimates of soil erosion. Our estimates of average erosion rates by subwatershed were in general larger than those initial estimates.

Modeling fire in semi-desert grassland/oak woodland: the spatial implications

Fire-evolved forests that historically had high fire return intervals (<100 years) in the western United States are currently overstocked with fuels due to a century or more of fire suppression and anthropogenic modification. Additionally, some western rangelands have changed composition from fire maintained grasslands to grazed shrublands. Land managers are beginning to reintroduce fire to these ecosystems as a functional component. Estimating fire behavior through the use of computer simulations is one tool to assist in planning management-ignited fire. We evaluated the sensitivity of the fire model FARSITE to the level of detail in the fuels data, both spatially and quantitatively, to better understand requirements for mapping fuels to produce accurate fire simulations. Simulated fires generated using site specific fuel models mapped at 30 m and degraded to 210 m were compared to fires simulated using standard generic Northern Forest Fire Laboratory (NFFL) fuel types. Eight classes of surface fuels were mapped by classification of satellite imagery with an overall accuracy of 0.78. A percent tree canopy cover map was created from digital orthophotos using a linear regression model with an Radj2=0.93 of field sampled percent canopy cover data to a tree canopy shadow model. The dominant site specific fuel model (63% cover) was found to agree with the most suitable NFFL fuel model. Site specific fuel models mapped at fine resolution were found to produce statistically smaller fire areas than those produced with generic fuel models mapped at a fine scale and site specific fuels mapped at a coarse scale. In the worst case scenario (low fuel moistures and high wind speeds) the average fire size was about 20% larger with the fuel map using NFFL fuel models than with the fine scale map using site specific fuel models.

GIS Pedagogy, Web-based Learning and Student Achievement

The authors evaluate impacts of web-based learning (WBL) for a geographic information system (GIS) course in which self-paced interactive learning modules replaced lectures to prepare students for GIS laboratory activities. They compare student laboratory, mid-term, final exam and overall scores before and after introduction of WBL, analyzing for gender differences in performance. Laboratory scores improved significantly for both males and females, though course grades did not change significantly overall for either group. Gender results show females performing better than males with either method. Most students were receptive to WBL. Future research is needed to understand what combination of graphics and text is most useful to students.

Improving thematic mapper based classification of wildfire induced vegetation mortality

Abstract In many areas suppression of wildfire has produced fuel accumulations that pre‐dispose forests to undesirable fire behavior. Image processing techniques can be used to combine different elements of terrain data into a single composite image. This composite terrain image is used to improve the accuracy of a supervised classification of expected vegetation mortality in a 20, 000 acre forest fire in the Cibola National Forest in New Mexico. Error matrices are produced that indicate that combining TM and terrain data provides a 40% improvement in accuracy compared to TM data alone. Computer‐assisted mapping of observed and potential patterns of wildfire can provide forest managers cost‐effective tools for wildfire planning and ecosystem management.

Enhancing fire scar anomalies in AVHRR NDVI time-series data

Abstract Fire mapping science can benefit from standard techniques for analyzing time‐series data. The Z transform produces the z‐score, termed a standardized variable because the units are dimensionless standard deviations. The Z transform represents a simple, older way to characterize non‐image data, but is presented here as a new way to enhance fire scar anomalies in image time‐series data. The transform is invertible, and can be applied to any continuous, gridded data. Time series data from the Advanced Very High Resolution Radiometer (AVHRR) are featured. When applied in the x, y (i.e., spatial) plane of AVHRR Normalized Difference Vegetation Index (NDVI) data, the Z transform places each NDVI value in the statistical context at the time of image formation, producing Z‐standardized NDVI (ZNDVI) values. Invoking the Z transform across the z (time series) plane of ZNDVI images produces a multitemporal Z (MTZ) score for each pixel in a ZNDVI target image for a selected time step. The MTZ image thus depicts the deviation or departure of a given pixel, for a specific step in the series, relative to the mean for that pixel across the time series. A case study demonstrates an MTZ enhancement of AVHRR NDVI data that enhances in the Rincon Mountains east of Tucson, Arizona unusually low NDVI values associated with a 1994 wildfire scar. Enhancements are confirmed using higher‐resolution remote sensor data from the Landsat Thematic Mapper (TM).

A country bug in the city: urban infestation by the Chagas disease vector Triatoma infestans in Arequipa, Peru

BackgroundInterruption of vector-borne transmission of Trypanosoma cruzi remains an unrealized objective in many Latin American countries. The task of vector control is complicated by the emergence of vector insects in urban areas.MethodsUtilizing data from a large-scale vector control program in Arequipa, Peru, we explored the spatial patterns of infestation by Triatoma infestans in an urban and peri-urban landscape. Multilevel logistic regression was utilized to assess the associations between household infestation and household- and locality-level socio-environmental measures.ResultsOf 37,229 households inspected for infestation, 6,982 (18.8%; 95% CI: 18.4 – 19.2%) were infested by T. infestans. Eighty clusters of infestation were identified, ranging in area from 0.1 to 68.7 hectares and containing as few as one and as many as 1,139 infested households. Spatial dependence between infested households was significant at distances up to 2,000 meters. Household T. infestans infestation was associated with household- and locality-level factors, including housing density, elevation, land surface temperature, and locality type.ConclusionsHigh levels of T. infestans infestation, characterized by spatial heterogeneity, were found across extensive urban and peri-urban areas prior to vector control. Several environmental and social factors, which may directly or indirectly influence the biology and behavior of T. infestans, were associated with infestation. Spatial clustering of infestation in the urban context may both challenge and inform surveillance and control of vector reemergence after insecticide intervention.