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Geovisualization tools can supplement the statistical analyses of landscape-level wildfire behavior by enabling the discovery of nuanced information regarding the relationships between fire spread, topography, fuels, and weather. The objectives of this study were to develop and evaluate the effectiveness of geovisualization tools for analyzing wildfire behavior and specifically to apply those tools to study portions of the Thomas and Detwiler wildfire events that occurred in California in 2017. Fire features such as active fire fronts and rate of spread (ROS) vectors derived from repetitive airborne thermal infrared (ATIR) imagery sequences were incorporated into geovisualization tools hosted in a web geographic information systems application. This geovisualization application included ATIR imagery, fire features derived from ATIR imagery (rate of spread vectors and fire front delineations), growth form maps derived from NAIP imagery, and enhanced topographic rasters for visualizing changes in local topography. These tools aided in visualizing and analyzing landscape-level wildfire behavior for study portions of the Thomas and Detwiler fires. The primary components or processes of fire behavior analyzed in this study were ROS, spotting, fire spread impedance, and fire spread over multidirectional slopes. Professionals and researchers specializing in wildfire-related topics provided feedback on the effectiveness and utility of the geovisualization tools. The geovisualization tools were generally effective for visualizing and analyzing (1) fire spread over multidirectional slopes; (2) differences in spread magnitudes within and between sequences over time; and (3) the relative contributions of fuels, slope, and weather at any given point within the sequences. Survey respondents found the tools to be moderately effective, with an average effectiveness score of 6.6 (n = 5) for the visualization tools on a scale of 1 (ineffective) to 10 (effective) for postfire spread analysis and visualizing fire spread over multidirectional slopes. The results of the descriptive analysis indicate that medium- and fine-scale topographic features, roads, and riparian fuels coincided with cases of fire spread impedance and exerted control over fire behavior. Major topographic features such as ridges and valleys slowed, or halted, fire spread consistently between study areas. The relationships between spotting, fuels, and topography were inconclusive. [ABSTRACT FROM AUTHOR]

Annual burned area has increased in California over the past three decades as a result of rising temperatures and a greater atmospheric demand for moisture, a trend that is projected to continue throughout the 21st century as a result of climate change. Here, we implement a bias-correction and statistical downscaling technique to obtain high resolution, daily meteorological conditions for input into two fire weather indices: vapor pressure deficit (VPD) and the Canadian Fire Weather Index System (FWI). We focus our analysis on 10 ecoregions that together account for the diverse range of climates, ecosystems, topographies, and vegetation types found across the state of California. Our results provide evidence that fire weather conditions will become more extreme and extend into the spring and fall seasons in most areas of California by 2100, extending the amount of time vegetation is exposed to increased atmospheric demand for moisture, and heightening the overall risk for the ignition and spread of large wildfire. The ecoregion-level spatial scale adopted for this study increases the spatial specificity of fire weather information, as well as the resolution with which fire and land managers can implement strategies and counter-measures when addressing issues related to climate change. [ABSTRACT FROM AUTHOR]

The objectives of this study were to evaluate spatial sampling and statistical aspects of landscape-level wildfire rate of spread (ROS) estimates derived from airborne thermal infrared imagery (ATIR). Wildfire progression maps and ROS estimates were derived from repetitive ATIR image sequences collected during the 2017 Thomas and Detwiler wildfire events in California. Three separate landscape sampling unit (LSU) sizes were used to extract remotely sensed environmental covariates known to influence fire behavior. Statistical relationships between fire spread rates and landscape covariates were analyzed using (1) bivariate regression, (2) multiple stepwise regression, (3) geographically weighted regression (GWR), (4) eigenvector spatial filtering (ESF) regression, (5) regression trees (RT), and (6) and random forest (RF) regression. GWR and ESF regressions reveal that relationships between covariates and ROS estimates are substantially non-stationary and suggest that the global association of fire spread controls are locally differentiated on landscape scales. Directional slope is by far the most strongly associated covariate of ROS for the imaging sequences analyzed and the size of LSUs has little influence on any of the covariate relationships. [ABSTRACT FROM AUTHOR]

Maps of burned area derived from Land Remote-Sensing Satellite (LANDSAT) system imagery may be more reliable than fire perimeter records based on ground observation or manual cartographic delineation. This study evaluates accuracy of LANDSAT-derived burned area maps associated with 19 fires (65 to 86,776 ha) within shrublands of southern California in the period 1996–2018. High spatial resolution aerial images collected soon after these 19 fires were used to verify burned fractions within LANDSAT ground resolution elements. Validated burned fractions were used to optimize classification thresholds applied to burn severity metrics based on LANDSAT Normalized Burn Ratio (NBR), Normalized Burn Ratio 2 (NBR2), and Normalized Difference Vegetation Index (NDVI). The burn severity metrics included post-fire spectral index data (e.g., NBR), pre-fire to post-fire differences (e.g., dNBR), and relative differences (e.g., RdNBR). Optimized classifications of dNBR had the greatest overall accuracy (94%) compared to dNBR2 (93%) and dNDVI (92%). Classifications based on standard difference metrics yielded burn area maps that were 1% more accurate than those based on relative-difference metrics for each spectral index. Classification products based solely on post-fire imagery were 85% to 90% accurate, depending on the spectral index utilized. In comparison, perimeter data from the California Fire and Resource Assessment Programme (FRAP) and Monitoring Trends in Burn Severity (MTBS) substantially overestimated burned extent (by about 44%). The MTBS severity maps excluding the 'unburned/low severity' class slightly overestimated burned extent (11%). Commission error in the FRAP data set was attributed to low cartographic detail and inclusion of internal unburned patches. Site-specific differences in unburned soil and vegetation fractions (within partly-burned areas) correlated strongly with overestimation of burned area in the optimized LANDSAT-derived maps; areal overestimates of 10%–15% resulted from data grain size rather than map commission error. The main innovation presented in this study is an empirical method to predict dNBR thresholds based on local (pre-fire) NDVI mean and variance, which produced burned area maps of 90% median accuracy. This LANDSAT-based method could support an efficient reconstruction of fire history in recent decades, which would be more comprehensive and accurate than available fire records for southern California. [ABSTRACT FROM AUTHOR]

Mirror self‐recognition, as an index of self‐awareness, has been proposed as a precursor for more complex social cognitive abilities, such as prosocial reasoning and cooperative decision‐making. Indeed, evidence for mirror self‐recognition has been shown for animals possessing complex social cognitive abilities such as great apes, dolphins, elephants and corvids. California scrub jays (Aphelocoma californica) have provided strong evidence that non‐human animals are capable of mental state attribution. For instance, scrub jays are reported to use their experience stealing the food of others to infer that other birds may similarly intend to steal from them. If a concept of "self" is required for such complex social cognitive abilities, then scrub jays might be expected to show mirror self‐recognition. Thus, we examined whether California scrub jays are capable of mirror self‐recognition using two experimental contexts: a caching task and the mark test. During the caching task, we compared the extent to which scrub jays protected their food after caching alone, in the presence of a conspecific and in the presence of a mirror. The birds did not engage in more cache protection behaviours with a mirror present than when caching alone, suggesting scrub jays may have recognized their reflection and so did not expect cache theft. Alternative explanations for this behaviour are also discussed. During the mark test, the scrub jays were surreptitiously marked with a red or plumage‐coloured control sticker. The scrub jays showed no evidence of mirror self‐recognition during the mark test, as the birds did not preferentially attempt to remove the red mark in the presence of a mirror. Together, the results provide mixed evidence of the mirror self‐recognition abilities of California scrub jays. We highlight the need to develop alternative approaches for evaluating mirror self‐recognition in non‐human animals to better understand its relationship with complex social cognition. [ABSTRACT FROM AUTHOR]

Identifying habitats that should be protected from further disturbance or conversion and isolating high-risk areas is a focus of community habitat plans in southern California shrublands. Larger wildfires are occurring at shorter intervals in recent decades, contributing to degradation and conversion of shrubland vegetation. Multitemporal remote-sensing approaches can bridge the gap between vegetation mapping and field sampling in habitats where frequent quantification and mapping of vegetation growth forms over large extents is required. The objective of this study is to examine the reliability and stability of a multiple endmember spectral mixture analysis (MESMA) approach with moderate spatial resolution imagery for monitoring changes in growth form fractional cover in shrubland habitats. Estimates from visual interpretation of high spatial resolution image were used as reference data for validating MESMA-derived maps and as basis for providing complementary monitoring protocols that may be accurate and cost-effective across multiple scales. Growth form proportions modelled in burned and unburned management areas compare well with expected fractional cover in mature and regenerating shrublands. In the management areas recovering from fire, herbaceous cover fraction exceeded 0.40 for all three study dates, suggesting that large portions of those management areas may already be invaded. From 2008 to 2011 overall herbaceous cover fraction in shrubland area increased by 2%. Herbaceous cover fraction was modelled with an overall mean absolute error (MAE) of 0.08, a smaller percentage than the percentage of herbaceous cover change recorded in areas recovering from fire (increase in herbaceous cover fraction from 0.09 to 0.13). This MESMA approach would be effective for quantifying changes in fractional cover that exceed 0.10, providing a way to delineate and quantify herbaceous invasions and expansions following disturbance or succession. [ABSTRACT FROM AUTHOR]

We examine spatial patterns of conifer tree mortality and their changes over time for the montane mixed-conifer forests of San Diego County. These forest areas have recently experienced extensive tree mortality due to multiple factors. A spatial contextual image processing approach was utilized with high spatial resolution digital airborne imagery to map dead trees for the years 1997, 2000, 2002, and 2005 for three study areas: Palomar, Volcan, and Laguna mountains. Plot-based fieldwork was conducted to further assess mortality patterns. Mean mortality remained static from 1997 to 2002 (4, 2.2, and 4.2 trees/ha for Palomar, Volcan, and Laguna) and then increased by 2005 to 10.3, 9.7, and 5.2 trees/ha, respectively. The increase in mortality between 2002 and 2005 represents the temporal pattern of a discrete disturbance event, attributable to the 2002-2003 drought. Dead trees are significantly clustered for all dates, based on spatial cluster analysis, indicating that they form distinct groups, as opposed to spatially random single dead trees. Other tests indicate no directional shift or spread of mortality over time, but rather an increase in density. While general temporal and spatial mortality processes are uniform across all study areas, the plot-based species and quantity distribution of mortality, and diameter distributions of dead vs. living trees, vary by study area. The results of this study improve our understanding of stand- to landscape-level forest structure and dynamics, particularly by examining them from the multiple perspectives of field and remotely sensed data. [ABSTRACT FROM AUTHOR]

The classification of image-objects is usually done using parametric statistical measures of central tendency and/or dispersion (e.g., mean or standard deviation). The objectives of this study were to analyze digital number histograms of image objects and evaluate classifications measures exploiting characteristic signatures of such histograms. Two histograms matching classifiers were evaluated and compared to the standard nearest neighbor to mean classifier. An ADS40 airborne multispectral image of San Diego, California was used for assessing the utility of curve matching classifiers in a geographic object-based image analysis (GEOBIA) approach. The classifications were performed with data sets having 0.5 m, 2.5m, and 5 m spatial resolutions. Results show that histograms are reliable features for characterizing classes. Also, both histogram matching classifiers consistently performed better than the one based on the standard nearest neighbor to mean rule. The highest classification accuracies were produced with images having 2.5m spatial resolution. [ABSTRACT FROM AUTHOR]

In recent decades, fire frequency has increased with population growth at the wildland-urban interface in southern California, USA. Short intervals (-5 years) between successive fires can be detrimental to plant species that require longer intervals between fires to reach reproductive maturity. The resilience of vegetation to changes in fire regimes is of particular interest for shrublands in Mediterranean climates, which in general are susceptible to high frequencies of wildfire. Research on how fire has shaped the life histories of shrubs in Mediterranean climates is central to understanding how to protect sensitive habitat while allowing for the natural occurrence of wildfire in these regions. Frequently burned chamise chaparral shrublands in San Diego County, California, were mapped in the field with the aid of satellite imagery and analysed to investigate changes in vegetation condition and composition associated with short intervals between fires. Fire history, terrain and land-cover characteristics of mapped stands were tested to determine the factors associated with disturbed and converted vegetation. Results indicate that numberofburns and mean fire interval are contributing factors in post-fire change in chaparral stands. Chamise chaparral is vulnerable to alteration and type conversion when fire return intervals are 4-5 years or less. [ABSTRACT FROM AUTHOR]

Abstract: The California sage scrub (CSS) community type in California''s Mediterranean-type ecosystems is known for its high biodiversity and is home to a large number of rare, threatened, and endangered species. Because of extensive urban development in the past fifty years, this ecologically significant community type is highly degraded and fragmented. To conserve endangered CSS communities, monitoring internal conditions of communities is as crucial as monitoring distributions of the community type in the region. Vegetation type mapping and field sampling of individual plants provide ecologically meaningful information about CSS communities such as spatial distribution and species compositions, respectively. However, both approaches only provide spatially comprehensive information but no information about internal conditions or vice versa. Therefore, there is a need for monitoring variables which fill the information gap between vegetation type maps and field-based data. A number of field-based studies indicate that life-form fractional cover is an effective indicator of CSS community health and habitat quality for CSS-obligated species. This study investigates the effectiveness of remote sensing approaches for estimating fractional cover of true shrub, subshrub, herb, and bare ground in CSS communities of southern California. Combinations of four types of multispectral imagery ranging from 0.15m resolution scanned color infrared aerial photography to 10m resolution SPOT 5 multispectral imagery and three image processing models – per-pixel, object-based, and spectral mixture models – were tested. An object-based image analysis (OBIA) routine consistently yielded higher accuracy than other image processing methods for estimating all cover types. Life-form cover was reliably predicted, with error magnitudes as low as 2%. Subshrub and herb cover types required finer spatial resolution imagery for more accurate predictions than true shrub and bare ground types. Positioning of sampling grids had a substantial impact on the reliability of accuracy assessment, particularly for cover estimates predicted using multiple endmember spectral mixture analysis (MESMA) applied to SPOT imagery. Of the approaches tested in this study, OBIA using pansharpened QuickBird imagery is one of the most promising approaches because of its high accuracy and processing efficiency and should be tested for more heterogeneous CSS landscapes. MESMA applied to SPOT imagery should also be examined for effectiveness in estimating factional cover over more extensive habitat areas because of its low data cost and potential for conducting retrospective studies of vegetation community conditions. [Copyright &y& Elsevier]

Abstract: Vegetation characteristics of a watershed can be important in estimating hydrological response variables (HRVs) such as streamflow (Q), evapotranspiration (ET), and river yield (Q/P). Quantifying the relationship between satellite-derived vegetation metrics and hydrological response to precipitation (P) has the potential to facilitate prediction of HRVs for ungauged watersheds, and/or aid in the assessment of watershed similarity as an initial phase of hydrological regionalization. The utility of Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data to estimate HRVs of watersheds at the regional scale (southern and central California) is tested in this study. An exhaustive statistical regression analysis was conducted to quantify the relationships between MODIS vegetation metrics and HRVs using both ordinary least squares and spatially varying parameter models. Additionally a confirmatory analysis was conducted to test the effect precipitation and potential evaporation have on the exploratory regression results. Results from both the exploratory and confirmatory analyses suggest that (1) while there are limitations in the water balance approach to estimating ET (errors associated with changes in storage and meteorological data are unknown), moderate statistical relationships exist between MODIS vegetation metrics and HRVs; (2) these relationships are heavily influenced by vegetation–precipitation relationships and general precipitation magnitudes; (3) relationships between MODIS metrics and precipitation/HRVs are strongest when drought conditions prevail; and (4) LAI has the strongest relationship with precipitation and HRVs compared to other MODIS vegetation metrics. [ABSTRACT FROM AUTHOR]

Abstract. Large fire events in southern California have burned thousands of hectares over the past decade. Landscape pattern and natural system processes are shaped by these large conflagrations, thereby influencing the ecological structure and functioning of the region. Unburned vegetation remnants can be used to assess general fuel consumption and to provide valuable information regarding fire behaviour, weather effects and post-fire regeneration. For this study, post-fire unburned vegetation was mapped at a very fine spatial resolution based on semi-automatic classification of airborne large- format multispectral image data and compared across different fire environment zones within the 2003 Cedar Fire burn perimeter. Landscape metrics were used to characterise unburned vegetation patches for a section of the Cedar Fire affected by Santa Ana weather conditions and for a section of the fire that burned under non-Santa Ana weather conditions. Maps of remnant vegetation and associated landscape metrics were compared across these two sections and within shrubland community type, topography and age-class strata using inferential statistics. Key findings reveal more unburned vegetation in larger, rounder patches in the non-Santa Ana section. Pre-fire stand age greater than 6 years showed little effect on the amount or pattern of unburned vegetation within the Santa Ana section. [ABSTRACT FROM AUTHOR]

Time series of spectral vegetation indices (SVIs) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and live fuel moisture (LFM) data for chaparral vegetation of southern California were extended to include one of the driest (2004) and one of the wettest (2005) years on record. Independent, spatially varying field-based estimates of LFM enabled accuracy of MODIS-derived estimates to be quantified. Pixel-based scaling of SVI values based on maximum and minimum values of the time series reduced effects of varying vegetation cover and substantially reduced root mean square errors for two of the three SVIs tested. [ABSTRACT FROM AUTHOR]

Exotic plant invasion is a major environmental and ecological concern and is a particular issue for Mediterranean-type ecosystems. Early detection of invasive plants is crucial for effective weed management. Several studies have explored hyperspectral imagery for mapping invasive plants with promising results. However, only a few extensive or comparative studies about image processing techniques for invasive plant detection have been reported, and even fewer studies have involved very high spatial and spectral resolution imagery. The primary goal of this study was to investigate the utility of very high spatial (0.5 m) and spectral (4 nm) resolution imagery and several classification approaches for detecting tamarisk (Tamarix spp.) infestations, the most problematic invasive plant species in the riparian habitats of southern California. Hierarchical clustering was a particularly effective and efficient statistical method for identifying wavebands and spectral transforms having the greatest discriminatory power. Products resulting from the classification of airborne hyperspectral image data varied by scene, input data type, classifier, and minimum patch size. Overall accuracy of image classification accuracy of products co-varied with commission error rates, such that products having strong agreement with reference data also had a high number of false detections. Integrating the findings from qualitative map analysis, areal proportion statistics, and object-based accuracy assessment indicates that the parallelepiped classifier with several narrow wavebands selected through hierarchical clustering yielded the most accurate and reliable tamarisk classification products. [Copyright &y& Elsevier]

The objective of this study was to evaluate image-based procedures for monitoring cross-border foot trails in the US - Mexico border zone in eastern San Diego County using airborne remote sensing techniques. Specifically, digital multi-spectral and multi-temporal imagery from an airborne digital multi-spectral imaging system, digital image processing, and visual image analysis techniques were explored in the context of detecting and delineating new trail features and updating trail GIS layers. Three trail updating approaches: map-to-image (M-I) overlay, map and image-to-image (M/I-I) differencing, map and image-to-image (M/I-I) swiping and two types of spectral transform, PCA and NDVI, were tested and compared. The M-I overlay was found to be the most reliable trail updating approach. The optimal image enhancement method for the M-I overlay approach varied with vegetation structure. PCA imagery yielded better results than NDVI imagery in a highly disturbed area and NDVI imagery performed better in a densely vegetated area. The M/I-I swiping approach was useful for distinguishing misregistered extant trails from new trail features. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to evaluate spatial resolution effects on image classification. Classification maps were generated with a maximum likelihood (ML) classifier applied to three multi-spectral bands and variance texture images. A total of eight urban land use/cover classes were obtained at six spatial resolution levels based on a series of aggregated Colour Infrared Digital Orthophoto Quarter Quadrangle (DOQQ) subsets in urban and rural fringe areas of the San Diego metropolitan area. The classification results were compared using overall and individual classification accuracies. Classification accuracies were shown to be influenced by image spatial resolution, window size used in texture extraction and differences in spatial structure within and between categories. The more heterogeneous are the land use/cover units and the more fragmented are the landscapes, the finer the resolution required. Texture was more effective for improving the classification accuracy of land use classes at finer resolution levels. For spectrally homogeneous classes, a small window is preferable. But for spectrally heterogeneous classes, a large window size is required. [ABSTRACT FROM AUTHOR]

We monitored land-cover change in San Diego County (1990-2996) using multitemporal Landsat TM data. Change vectors of Kauth Thomas features were combined with stable multitemporal Kauth Thomas features and a suite of ancillary variables within a classification tree classifier. A combination of aerial photointerpretation and field measurements yielded training and validation data. Maps of land-cover change were generated for three hierarchical levels of change classification of increasing detail: change vs. no-change; four classes representing broad increase and decrease classes; and nine classes distinguishing increases or decreases in tree canopy cover, shrub cover, and urban change. The multitemporal Kauth Thomas (both stable and change features representing brightness, greenness, and wetness) provided information for magnitude and direction of land-cover change. Overall accuracies of the land-cover change maps were high (72 to 92 percent). Anciliary variables representing elevation, fire history, and slope were most significant in mapping the most complicated level of land-cover change, contributing 15 percent to overall accuracy. Classification trees hove not previously been used operationally with remotely sensed and ancillary data to map land-cover change at this level of thematic detail. [ABSTRACT FROM AUTHOR]

Three different training strategies often used for supervised classification—single pixel, seed, and block or polygon training—are compared in this paper. The range parameter of semi-variograms obtained from sample image subsets of each land-use/land-cover class was used to measure the autocorrelation level during training set selection. Eight training sets with different sizes were generated and then applied to image subsets with three multispectral bands and variance texture images in the classification of six land-use classes. The classification results using these training sets were compared at five resolution levels an d were based on six Color Infrared Digital Orthophoto Quarter Quadrangle (DOQQ) subsets of different urban land types in urban and rural fringe areas of the San Diego metropolitan area. The performance of different training strategies is shown to be influenced by the training size, the image resolution, and the degree of autocorrelation inherent within each class. Training approaches had more impact on classification results at fine resolution levels than at coarse resolutions. For spectrally homogeneous classes, a spatially independent, single-pixel training approach is preferred. But for spatially heterogeneous classes, small block training has the advantage of readily capturing spectral and spatial information and reduces the amount of interaction time for the analyst. [ABSTRACT FROM AUTHOR]

The purpose of this study is to map shrub distributions and estimate shrub cover fractions based on the classification of high-spatial-resolution aerial orthoimagery and light detection and ranging (LiDAR) data for portions of the highly disturbed coastal sage scrub landscapes of San Clemente Island, California. We utilized nine multi-temporal aerial orthoimage sets for the 2010 to 2018 period to map shrub cover. Pixel-based and object-based image analysis (OBIA) approaches to image classification of growth forms were tested. Shrub fractional cover was estimated for 10, 20 and 40 m grid sizes and assessed for accuracy. The most accurate estimates of shrub cover were generated with the OBIA method with both multispectral brightness values and canopy height estimates from a normalized digital surface model (nDSM). Fractional cover products derived from 2015 and 2017 orthoimagery with nDSM data incorporated yielded the highest accuracies. Major factors that influenced the accuracy of shrub maps and fractional cover estimates include the time of year and spatial resolution of the imagery, the type of classifier, feature inputs to the classifier, and the grid size used for fractional cover estimation. While tracking actual changes in shrub cover over time was not the purpose, this study illustrates the importance of consistent mapping approaches and high-quality inputs, including very-high-spatial-resolution imagery and an nDSM. [ABSTRACT FROM AUTHOR]

Traditional vegetation mapping approaches require extensive field reconnaissance, and normally involve delineation of vegetation boundaries through interpretation of aerial photographs. In order to generatevegetation data layers within a geographic information system (GIS),boundaries must be digitized and georeferenced and vegetation attributes coded. A project --conducted through the National Aeronautics and Space Administration Affiliated Research Center at San Diego State University for Ogden Environmental and Energy Services, Inc.-- investigated the utility of very high spatial resolution (1-m) digital multispectral image data for generating GIS vegetation layers. Mapping and digital encoding of vegetation polygons was performed using USGS color-infrared (CIR) digital orthophotographic quarter quadrangle (DOQQ) and Airborne Data Acquisition and Registration (ADAR) 5500 imagery.The two data sources were compared in the context of a controlled experiment which tested the utility of the imagery under multiple mapping scenarios. The study area was a habitat reserve within Marine Corps Air Station Miramar near San Diego, California. This area primarilysupports shrubland vegetation types typical of the Mediterranean climate area of southern California. CIR image data derived directly from multispectral digital cameras (e.g., ADAR System 5500) enabled moreaccurate classification and mapping of vegetation than did digital imagery generated from scanned CIR aerial photographs. This result is largely attributed to the higher spectral and radiometric fidelity ofdirect digital capture, but may also be attributed to more optimal seasonality for the data of the ADAR acquisition. While the mapping was based upon interactive, visual image interpretation and on-screen digitizing, the following image processing techniques proved to be helpful for aiding interpretation: contrast enhancement prior to generating hardcopy prints for field analyses and during on-screen classification [ABSTRACT FROM AUTHOR]