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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 delineati...

An important property of wildfire behaviour is rate of spread (ROS). The objectives of this study are to evaluate the uncertainty of landscape-scale ROS estimates derived from repetitive airborne thermal infrared (ATIR) georeferenced imagery and the utility of such estimates for understanding fire behaviour and controls on spread rates. Time-sequential ATIR image data were collected for the Cedar, Detwiler, and Rey Fires, which burned in California during summers of 2016 and 2017. We analyse error, uncertainty, and precision of ROS estimates associated with co-location accuracy, delineation of active fire front positions, and generation of fire spread vectors. The major sources of uncertainty influencing accuracy of ROS estimates are co-registration accuracy of sequential image pairs and procedures for delineating active fire front locations and spread vectors between them; none of these were found to be substantial. Median ROS estimates are 11 m min−1 for the Cedar Fire and 8 m min−1 for the Detwiler Fire, both of which burned through mixed shrub and tree areas of the Sierra Nevada foothills and were estimated for downslope spread events. Of the three study fires, the fastest spread rates (average spread of 25 m min−1 with maximum of 39 m min−1) are estimated for the Rey Fire, which burned on variable directional slopes through chaparral shrubland vegetation.

Focusing on the semi-arid and highly disturbed landscape of San Clemente Island (SCI), California, we test the effectiveness of incorporating a hierarchical object-based image analysis (OBIA) appro...

Remote sensing scientists are increasingly adopting machine learning classifiers for land cover and land use (LCLU) mapping, but model selection, a critical step of the machine learning classificat...

The objective of this article is to synthesize research findings and requirements pertaining to development of time-sensitive remote-sensing systems (TSRSS) that support decision-making pertaining ...

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 wi...

Multi-temporal satellite imagery can provide valuable information on the patterns of vegetation growth over large spatial extents and long time periods, but corresponding ground-referenced biomass information is often difficult to acquire, especially at an annual scale. In this study, we test the relationship between annual biomass estimated using shrub growth rings and metrics of seasonal growth derived from Moderate Resolution Imaging Spectroradiometer (MODIS) spectral vegetation indices (SVIs) for a small area of southern California chaparral to evaluate the potential for mapping biomass at larger spatial extents. These SVIs are related to the fraction of photosynthetically active radiation absorbed by the plant canopy, which varies throughout the growing season and is correlated with net primary productivity. The site had most recently burned in 2002, and annual biomass accumulation measurements were available from years 5 to 11 post-fire. We tested the metrics of seasonal growth using six SV...

Wildfires are a common occurrence in California shrublands, maintaining ecosystem functions with the regeneration of key shrub species. The Cedar Fire of 2003 in southern California was unique in that a portion of it burned with wildfire accelerated by dry, strong northeasterly Santa Ana winds that later subsided, while the remaining area burned under an onshore, westerly wind of lower velocity and higher humidity. These nearby areas, having similar terrain, fuel type, and environments, burned under these different conditions. Our goal is to understand the connection between vegetation response to extreme fire events by analysing life-form regrowth in chaparral from the Santa Ana wind driven, Santa Ana backing, and non-Santa Ana fire types. Study sites representing these three fire conditions were based on fire progression maps generated from Moderate Resolution Imaging Spectroradiometer MODIS hotspot data. Shrub cover before and six years after the fire were mapped based on a spatial contextual classifier applied to colour infrared orthoimagery, and analysed per slope aspect and angle, elevation, and fire characteristic categories to isolate shrub regrowth patterns. Six years after the fire, shrub cover in the Santa Ana wind driven site was substantially lower than in the other two sites. Such differences in shrub cover at the landscape scale may have resulted from different wind speed, direction, and humidity during the fire, coupled with terrain differences on wildfire behaviour and different rates of recovery associated primarily with moisture availability to plants. The information gathered from this study can help land managers assess shrub regrowth and possibility of vegetation type change after extreme fire events in southern California shrubland ecosystems.

Remote sensing for hazard response requires a priori identification of sensor, transmission, processing, and distribution methods to permit the extraction of relevant information in timescales sufficient to allow managers to make a given time-sensitive decision. This study applies and demonstrates the utility of the Remote Sensing Communication Model RSCM to improve a tactical wildfire remote-sensing system to better meet the time-sensitive information requirements of emergency response managers in San Diego County, USA. A thermal infrared airborne remote-sensing system designed and operated by the United States Forest Service Pacific Southwest Research Station for active wildfire monitoring is documented and updated based on the RSCM. Analysis of the thermal infrared remote-sensing system in the context of the RSCM identified three configuration changes that can improve the effectiveness of the information produced when employed by wildfire incident commanders for suppression prioritization: 1 limit spectral sampling collection to a single waveband; 2 complete image processing steps on-board the aircraft; and 3 provide information on wildfire locations to incident commanders in the form of a static map.

Remote sensing employs a range of conceptual and mechanistic models. Several conceptual models have been proposed to explain remote sensing systems (RSSs) and either directly prescribe or elucidate the configuration of such systems based on scene conditions and information requirements. This study interrogates the utility of these models for the design and practical implementation of RSSs to address time-sensitive information requirements and proposes a novel conceptual model, the remote sensing communication model, that places remote sensing within a decision support context.Three pertinent remote sensing conceptual models are critically assessed based on their satisfaction of three basic requirements: (1) prescribe or explicate RSS configuration based on user information requirements, (2) elucidate dependencies between information type, accuracy, and timeliness, and (3) describe the effect of RSS configuration on the effectiveness of users’ decisions. The conceptual remote sensing models that are evalua...

A novel approach to image radiometric normalization for change detection is presented. The approach referred to as stratified relative radiometric normalization SRRN uses a time-series of imagery to stratify the landscape for localized radiometric normalization. The goal is to improve the detection accuracy of abrupt land cover changes human-induced, natural disaster, etc. while decreasing false detection of natural vegetation changes that are not of interest. These vegetation changes may be associated with such phenomena as phenology, growth and stress e.g. drought, which occur at varying spatial and temporal scales, depending on landscape position, vegetation type, season, precipitation history and historic episodes of local disturbance. The SRRN approach was tested for a study area on the Californian border between the USA and Mexico using Landsat Thematic Mapper and Enhanced Thematic Mapper Plus satellite imagery. Change products were generated from imagery radiometrically normalized using the SRRN procedure and with imagery normalized using a traditional empirical line technique. Reference data derived from high spatial resolution airborne imagery were utilized to validate the two change products. The SRRN procedure provided several benefits and was found to improve the overall accuracy of detecting abrupt land cover changes by nearly 20%.

The primary objective of this study was to compare the sensitivity of two different normalized difference vegetation index (NDVI) time series derived from Local Area Coverage (LAC) and Global Areal Coverage (GAC) data sets of the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) satellite system. This comparison was conducted in the context of analysing spatiotemporal patterns of Arctic tundra vegetation greenness change in the 1990s within the North Slope of Alaska. A second objective was to examine patterns of greenness change with respect to the distribution of vegetation association types. An 8 km spatial resolution NDVI series was produced by the Global Inventory Modeling and Mapping Studies (GIMMS) group at the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center based on a GAC data set and corrected for stratospheric aerosol effects from the eruption of Mt Pinatubo. The LAC (1 km spatial resolution) NDVI time series was generated through recalibration and fine-tuning of image registration of a twice-monthly time series produced by the US Geological Survey, and was cross-calibrated with the GIMMS data set to reduce stratospheric aerosol effects from the Mt Pinatubo eruption. While the general patterns of pixels exhibiting significant increase in seasonally integrated NDVI over the 1990s were similar from both data sets, many of the more localized areas of more rapidly increasing greenness (i.e. 'hotspots') between 1990 and 1999 were lost with the product from the GIMMS data set. The majority of the 'hotspots' of greenness increase within the North Slope region are located in the southern portions of the foothills physiographic province and within vegetation units composed primarily of prostrate or dwarf shrubs with a mixture of graminoid species. Notably fewer hotspots of greenness increase were detected in Arctic tundra areas of the Seward Peninsula and none in the Chukotka Peninsula of the Russian Far East, an area that had not experienced the same warming trend in the 1990s and preceding decades as the Alaskan Arctic.

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.

Time series of normalized difference indices (NDIs) derived from MODIS surface reflectance data provide potentially useful information for monitoring fuel moisture content (FMC) for fire risk assessment. The visible atmospherically resistant index (VARI) and normalized difference water index (NDWI) were compared for monitoring live FMC of chaparral shrublands. Regression coefficients are encouraging given disparate spatial resolutions of ground‐based FMC measurements and MODIS‐derived NDIs. VARI exhibited greater temporal co‐variability (0.79>r 2

A negative, linear relationship between thermal emissions and a spectral vegetation index has been demonstrated for numerous mid‐latitude ecosystems. In this study, it is hypothesized that the relationship between surface temperature and the normalized difference vegetation index (NDVI) will be linear, but positive in Arctic tundra ecosystems due to the contrast between warm vegetation and the cold soil/moss background. This hypothesis is tested using Advanced Very High Resolution Radiometer (AVHRR) data collected over the North Slope of Alaska on three days during the summer of 1999. Results of the study generally provide support for this hypothesis. However, a consistent relationship observed across two contrasting physiographic provinces on one study day was shown to change the following day and could not be readily explained by differences in satellite zenith angle or observed air temperature. Surface temperatures are shown to respond directly to spatial and temporal variations in air temperature.

This paper reports on preliminary results from a study applying the technique of spectral mixture analysis (SMA) to the measurement of temporal changes in the composition of urban morphology in the metropolitan area of Greater Cairo, Egypt, between 1987 and 1998. Although several remote sensing techniques have been used successfully for urban change analysis, most of these focus on change ‘between’ classes measured in a discrete, crisp way through which each pixel is assigned to a label indicating either a change or no change in the class to which the pixel originally belonged. In many major cities, such as Cairo, change also occurs within classes (e.g. vertical growth of buildings, increase in housing density, decrease in open spaces) and is reflected by an aggregation of land cover and urban materials. None of these materials may seem important in isolation. Rather, the significance of these urban land covers arises from the way they interweave with each other to structure the morphology of the urban pl...

The relationship between normalized difference vegetation index (NDVI) patterns obtained from high spatial resolution aircraft and low spatial resolution satellite data (Advanced Very High Resolution Radiometer (AVHRR)) was investigated with the intent of using multilevel data to scale carbon flux models in Arctic tundra ecosystems. Despite variable illumination conditions during the aircraft missions and maximum value compositing of the AVHRR data, the difference between 3 km average aircraft and AVHRR NDVI values was generally constant along each flight transect. However, the magnitude of the offset differed between flight dates and small lakes had a greater effect on area averaged aircraft NDVI values than on the satellite values. A cloud index was calculated using incident solar radiation measured by the aircraft and this index was used to identify periods when the aircraft NDVI values may have been biased by cloud cover. Removal of NDVI values based on a cloud index threshold did not appear to be jus...

Coastal sage scrub is one of the most endangered habitat types in the continental United States. Airborne multi-spectral imagery having very high spatial resolution (1 m) was analysed to determine its utility for monitoring impacts on coastal sage scrub habitat from recreation activities. Field-based measurements of bare ground fraction and image metrics were investigated to establish which variables had the strongest relationship. Regression results show that bare ground fraction and red band reflectance had the highest agreement (r {\rm ^{2}} =0.55). An inverted regression model of per cent bare ground and red band data was applied to airborne multi-spectral imagery to create a map of bare ground fraction. Analysis of the map reveals higher bare ground fractions in high intensity recreation areas compared to areas utilized less frequently. Trail feature extraction methods were also explored, where a series of spectral and spatial transforms were tested and the epsilon (ϵ) error was utilized to quantify ...

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.

Interannual above-ground production patterns are characterized for three tundra ecosystems in the Kuparuk River watershed of Alaska using NOAA-AVHRR Normalized Difference Vegetation Index (NDVI) data. NDVI values integrated over each growing season (SINDVI) were used to represent seasonal production patterns between 1989 and 1996. Spatial differences in ecosystem production were expected to follow north-south climatic and soil gradients, while interannual differences in production were expected to vary with variations in seasonal precipitation and temperature. It was hypothesized that the increased vegetation growth in high latitudes between 1981 and 1991 previously reported would continue through the period of investigation for the study watershed. Zonal differences in vegetation production were confirmed but interannual variations did not covary with seasonal precipitation or temperature totals. A sharp reduction in the SINDVI in 1992 followed by a consistent increase up to 1996 led to a further hypothe...

This research tested the ability of a multiple endmember (EM) spectral mixture analysis (SMA) approach, applied to multi-temporal Landsat Thematic Mapper (TM) data, to produce realistic and meaningful EM fractions for the study of post-fire regrowth in a southern California chaparral landscape. Eight different image EMs were used, two types for each EM class of interest (green vegetation (GV), non-photosynthetic vegetation (NPV), soil, and shade); the best EM combination was selected for each pixel. These EM fractions were validated with fractions derived from 1 m Airborne Data Acquisition and Registration multi-spectral image data. The EM fractions from the two datasets were similar (r=0.873, 0.776, 0.790 for GV, NPV, and soil, respectively). Chaparral stands were delineated using vegetation type, fire history and slope aspect GIS layers. Mean EM fractions were calculated for each stand, and analysis of variance was performed to determine if EM fractions were different for stands of different age. Short-...

A frame centre (FC) matching approach to the acquisition and geometric processing of multi-temporal datasets derived from airborne digital frame imagery was tested to determine if registration errors could be reduced relative to more ad hoc acquisition and registration approaches. The root mean square error (RMSE) of test points from image sets derived with the FC approach was at least 50% less than when frames for the second date of imagery were not centred on frames from the first date. The registration RMSE for FC images approached 2 pixels (2 m) for the central portion of image frames that is normally used to generate an image mosaic. The FC approach yielded visual and quantitative improvements in multi-temporal difference images that are commonly utilized for change detection analyses.

The interannual variability and trend of above-ground photosynthetic activity of Arctic tundra vegetation in the 1990s is examined for the north slope region of Alaska, based on the seasonally integrated normalized difference vegetation index (SINDVI) derived from local area coverage (LAC) National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) data. Smaller SINDVI values occurred during the three years (1992-1994) following the volcanic eruption of Mt Pinatubo. Even after implementing corrections for this stratospheric aerosol effect and adjusting for changes in radiometric calibration coefficients, an apparent increasing trend of SINDVI in the 1990s is evident for the entire north slope. The most pronounced increase was observed for the foothills physiographical province.

Maps of functional vegetation types are often used to extrapolate in situ measurements of biogeochemical fluxes to make regional-scale or biome-scale estimates. The objective of this study was to determine the optimal spectral-radiometric and temporal features derived from single-date and seasonal time series NOAA AVHRR imagery for classifying three Arctic tundra functional types. A single-date, three-band (VIS, NIR and NDVI) input yielded a map with the highest agreement (86.1% for supervised and 87.8% for unsupervised approaches) compared to a reference map. The map generated with the conventional maximum value NDVI for 10 semi-monthly periods through the growing season was in 82.7% agreement.

Australian Acacia plant species invade the fynbos biome of southern Africa and threaten the exceptionally high plant diversity in the Cape Floristic Region. We examine the utility of very-high spatial resolution (0.5 m) colour infrared (CIR) digital image data for discriminating Acacia species from native fynbos vegetation, other alien vegetation and bare ground. Image data were acquired at a very low cost with a single-chip, digital CIR camera mounted on a light aircraft. Shrub and tree features were uniquely identified using visual or computer-assisted interpretation. However, increases in dynamic range and accuracy of interpolation schemes for the single chip sensor will be required if semi-automatic and accurate mapping of invasive plants is to be achieved.

A model that compensates for misregistration effects on change detection results shows promise for reducing artefacts and enhancing land change features at or near the pixel scale and for reducing noise caused by misregistered muti-temporal images. Sparse estimates of misregistration across the scene are combinedwithcalculations of spatial brightness gradients toadjust the magnitude of multi-temporal image differences. The model is tested on a multi-temporal Landsat Thematic Mapper image data set for a rapidly urbanizing landscape in southern California.

The aim is to provide accurate data on elevation change due to mining activity, via the application of interferometry with ERS SAR data. Ultimately, these elevation changes can then be fed into subsidence models. Interferometry results are presented showing initial evidence of subsidence occurring within the 35 day repeat orbit period of the ERS satellites. The result of extending the monitoring period beyond 35 days is also shown.

Models of regional CO exchange processes in arctic tundra environ2 ments using landscape characteristics may need to incorporate spatial and temporal variations in the depth to the permafrost layer because it influences soil drainage, aeration, decomposition, and nutrient availability. However this depth, or the depth of the active layer (DAL), will have to be estimated indirectly since it is not practical to make a large number of ground measurements on a regular basis. Previous research has demonstrated that spatial variations in DAL are strongly related to aboveground vegetation production in sub-arctic tundra. Since aboveground vegetation production in Alaskan arctic tundra has been related to the Normalized Difference Vegetation Index (NDVI), it was hypothesized that spatial variations in NDVI would follow variations in DAL in these environments. Studies were conducted on the North Slope of Alaska during the summers of 1994 and 1995 to determine the feasibility of estimating DAL at multiple ...

We assessed the statistical relations between spectral vegetation indices (SVIs) derived from SPOT multi-spectral data and semi-arid shrub cover at the Jornada LTER site in New Mexico. Despite a limited range of shrub cover in the sample the analyses resulted in r2 values as high as 0.77. Greenness SVIs (e.g., Simple Ratio, NDVI, SAVI, PVI and an orthogonal Greenness index) were shown to be more sensitive to shrub type and phenology than brightness SVIs (e.g., green, red and near-infrared reflectances and a Brightness index). The results varied substantially with small-scale changes in plot size (60 m by 60 m to 100 m by 100 m) as a consequence of landscape heterogeneity. The results also indicated the potential for the spectral differentiation of shrub types, and shrubs from grass, using multi-temporal, multi-spectral analysis.

The objective was to quantify and analyse the spectral, spatial and temporal variability of solar radiation reflected from arctic tundra vegetation at a study site in the Brooks Range foothills of northern Alaska. Spectral radiance data from hand-held radiometers and the SPOT HRV sensor were sampled along hillslope transects (toposequences) and within four vegetation community types. The spatial trend of normalised difference vegetation index (NDVI) along the toposequences corresponded to variations in the abundance of green vegetation matter and in vegetation composition. A marked temporal increase in the NDVI occurred along the toposequences from the beginning of the growing season (mid-June) to peak green up (end of July). The spectral signatures of three tundra dominant vegetation communities, dry heath, moist tussock and wet sedge, were moderately separable, with dry heath being most separable. The overall separability of the major community types was similar at all times during the growing ...

Frequent cloud cover and logistical constraints hamper biophysical remote sensing studies in arctic locations, resulting in a general lack of information regarding relationships between biophysical quantities and the spectral reflectance of arctic vegetation communities. An experiment was conducted on the north slope of Alaska to characterize relationships between the spectral reflectance of three tussock tundra communities (moist tussock, dry heath and water track) and the above ground biomass and vegetation composition of each community. Hand-held radiometric and ground reference data were collected three times during the 1989 growing season. The normalized difference vegetation index (NDVI) was regressed on above ground photosynthetic and non-photosynthetic biomass quantities and individual blue, green red and near-infrared spectral reflectances and the NDVI were regressed on vegetation cover type fractions. Up to 51 per cent of the variance in the NDVI was explained by the amount of photosynt...

The objectives of this study were to (1) develop and test an airborne radiometer system for sampling spectral radiances over arctic tundra landscapes, (2) quantify variations in the Normalized Difference Vegetation Index (NDVI) as a function of tundra vegetation type and seasonal date, and (3) quantify variations of spectral radiance and NDVI in response to possible anthropogenic disturbance. Spectral radiometer data analyzed in this study were acquired over the northern foothills of the Brooks Range in Alaska. The radiometer was well suited to data acquisition in the arctic environment. Mean NDVI values for four major vegetation types during each of three seasonal dates were significantly different from each other. Seasonal trajectories of the NDVI for each vegetation type corresponded to general phenological characteristics of the vegetation. The spectral radiance in red and near infrared wavebands appears to respond to disturbance caused by dust deposition from the Dalton Highway.

Existing land use and cover maps and the aerial photographs they were derived from were studied for a number of different landscape settings using spatial analysis techniques. The purpose of this study was to (1) determine if spatial variations in land use and cover could be quantified and characterized, (2) investigate the spatial scale relationships between land use and cover parcels and their image brightness composition on remotely-sensed images and (3) assess the relevance of these findings to the selection of appropriate remote sensor spatial resolution for image classification of land use and cover types. Analysis of land use and cover parcel size data indicated that the mode and the ‘85 per cent cumulative frequency’ statistics were useful measures for characterizing and comparing surface cover variability for different landscape types. Results of Kolmogorov-Smirnoff tests showed that differences in parcel sizes between urban and rural landscapes could be quantified and that the size of u...