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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.

Forest depletions caused by re can be detected by remote sensors if the change event causes a change in surface re ective or thermal properties. Pre and post-re TM imagery of a semi-arid region was used to map the burn scar of a management-ignited re into three classes of re severity. The multitemporal imagery was enhanced using several brightness, greenness, and wetness indices. The wetness indices were most accurate at delineating re severity because re severity appears related to changes in plant and soil moisture content. Overall kappa for Kauth Thomas D wetness, the TM 7/4 index, and the second standardised Principal Component were 0.62, 0.59, and 0.61 respectively. The highest overall kappa of 0.66 was achieved using combined Kauth Thomas D brightness, D greenness and D wetness indices.

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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.

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. Army's 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 model suggests that tortoises tend to occur on southwest exposures and loamy soils, and that they avoid stony soils, north exposure, and areas of very low plant cover. Our results imply that soil composition and parent materials can be important determinants of habitat suitability for desert tortoises, and for burrowing animals in general. Our study also provides an example of how the interdisciplinary integration of biology, earth sciences, GIS, and statistical modeling can reveal important aspects of the habitat requirements of endangered species.

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