Derivation of a threshold function for the Advanced Very High Resolution Radiometer 3.75 µm channel and its application in automatic cloud discrimination over snow/ice surfaces

The distinct contrast between the reflectance of solar radiation in Advanced Very High Resolution Radiometer (AVHRR) channel 3 (3.75?µm) by clouds and by bright surfaces provides an effective means of cloud discrimination over snow/ice surfaces. A threshold function for the top-of-atmosphere (TOA) albedo in channel 3 (r ₃) is derived and used to develop an improved method for cloud discrimination over snow/ice surfaces that makes explicit use of TOA r ₃. Corrections for radiance anisotropy and temperature effects are required to derive accurate values of r ₃ from satellite measurements and to utilize the threshold function. It has been used to retrieve cloud cover fractions from National Oceanic and Atmospheric Administration (NOAA)-14 AVHRR data over the Arctic Ocean and over the North Slope of Alaska (NSA) Atmospheric Radiation Measurement (ARM) site in Barrow, Alaska. The retrieved cloud fractions are in good agreement with SHEBA (Surface HEat Budget of the Arctic Ocean) surface visual observations and with NSA cloud radar and lidar observations, respectively. This method can be utilized to improve cloud discrimination over snow/ice surfaces for any satellite sensor with a channel near 3.7?µm.     

Estimating fractional cover of plant functional types in African savannah from harmonic analysis of MODIS time-series data

Assessments of tree/grass fractional cover in savannahs using remote sensing are challenging due to the heterogeneous mixture of the two plant functional types. Time-series decomposition models can be used to characterize vegetation phenology from satellite data, but have rarely been used for attributing phenological signal components to different plant functional types. Here, tree/grass dynamics are assessed in savannah ecosystems using time-series decomposition of 14 years of Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index data acquired from 2002 to 2015. The decomposition method uses harmonic analysis and tests the individual harmonic terms for statistical significance. Field data of fractional cover of trees and grasses were collected for 28 plots in Kruger National Park, South Africa. Matching MODIS pixels were analysed for their tree/grass phenological signals. Tree/grass annual and interannual variability were then assessed based on the harmonic models. In most harmonic cycles, grass-dominated sites had higher amplitudes than tree-dominated sites, while the tree green-up started earlier than grasses, before the start of the wet season. While changes in tree phenology are gradual, grasses present higher variability over time. Tree cover showed a significant correlation with the amplitude (r (correlation coefficient) = ?0.59, p = 0.001) and phase of the first harmonic term (r = ?0.73, p = 0.0001) and the number of cycles of the second harmonic term (r = 0. 56, p = 0.002). Grass cover was also significantly correlated with the amplitude (r = 0. 51, p = 0.005) and phase of the first harmonic term (r = 0.55, p = 0.002) and the number of cycles of the second harmonic term (r = ?0.52, p = 0.005). The positive correlation of grass cover with phase and negative correlation with number of cycles is indicating a late greening period and higher variability, respectively. Tree cover estimated from the phase of the strongest harmonic term showed a positive correlation with field-measured tree cover (R² (coefficient of determination) = 0.55, p < 0.01, slope = 0.93, root mean square error = 13.26%). The estimated tree cover also had a strong correlation with the woody cover map (r = 0.78, p < 0.01) produced by Bucini. The results show that MODIS time-series data can be used to estimate the fractional tree cover in heterogeneous savannahs from the phase of the plant functional type’s phenological behaviour. This study shows that harmonic analysis is able to discriminate between fractional cover by trees and grasses in savannahs. The quantitative analysis of tree/grass phenology from satellite time-series data enables a better understanding of the dynamics of the tree/grass competition and coexistence.     

The integrated radiometric correction of optical remote sensing imageries

Abstract Environmental analysis, management and modelling require detailed and precise land‐use/land‐cover discrimination as initial conditions of land surface characteristics. With the ultimate goal of accurate land surface classification analysis, we devised a fully image‐based and physically based correction method (the Integrated Radiometric Correction (IRC) method) considering both the atmospheric and the topographic effects simultaneously, using the information deduced from the satellite images and 5 m resolution DEM data. The overall process is carried out in four steps: (i) calculation of the radiance/irradiance relational expression for horizontal surfaces, (ii) devising the radiance/irradiance relational expression for inclined surfaces, (iii) derivation of solar and land geometric parameters from DEM data, as well as the calculation of the topographic correction factor (A‐factor) and the atmospheric transmittance functions, and (iv) retrieval of the corrected surface reflectance and radiance. Using Landsat/ETM+ satellite data, the performance of the formulated IRC method is evaluated visually and statistically. Visual evaluation of radiometrically corrected images shows significant improvements for each band as well as for various bands composites, while the independence between the corrected surface reflectance and radiance, and the topography (incidence angle (i) or solar illumination (cos i)) is shown by very weak correlation coefficients as compared with non‐corrected data.     

Settlement location and population density estimation in rugged terrain using information derived from Landsat ETM and SRTM data

It is useful to have a disaggregated population database at uniform grid units in disaster situations. This study presents a method for settlement location probability and population density estimations at a 90 m resolution for northern Iraq using the Shuttle Radar Topographic Mission (SRTM) digital terrain model and Landsat Enhanced Thematic Mapper satellite imagery. A spatial model each for calculating the probability of settlement location and for estimating population density is described. A randomly selected subset of field data (equivalent to 50%) is first analysed for statistical links between settlement location probability and population density; and various biophysical features which are extracted from Landsat or SRTM data. The model is calibrated using this subset. Settlement location probability is attributed to the distance from roads and water bodies and land cover. Population density can be estimated based upon land cover and topographic features. The Landsat data are processed using a segmentation and subsequent feature–based classification approach making this method robust to seasonal variations in imagery and therefore applicable to a time series of images regardless of acquisition date. The second half of the field data is used to validate the model. Results show a reasonable estimate of population numbers (r = 0.205, p<0.001) for both rural and urban settlements. Although there is a strong overall correlation between the results of this and the LandScan model (r = 0.464, p<0.001), this method performs better than the 1 km resolution LandScan grid for settlements with fewer than 1000 people, but is less accurate for estimating population numbers in urban areas (LandScan rural r = 0.181, p<0.001; LandScan urban r = 0.303, p<0.001). The correlation between true urban population numbers is superior to that of LandScan however when the 90 m grid values are summed using a filter which corresponds to the LandScan spatial resolution (r = 0.318, p<0.001).     

Influence of clouds on OMI satellite total daily UVA exposure over a 12-year period at a southern hemisphere site

ABSTRACT

This research investigated and evaluated the influence of clouds on the total daily UVA (320–400 nm) exposures calculated from the three Ozone Monitoring Instrument (OMI) UV spectral irradiances at solar noon. These evaluated satellite total daily UVA exposure data were compared to the total daily UVA exposures of a ground-based instrument over the period of October 2004 to December 2014 at a sub-tropical Australian site (27.5°S, 151.9°E) under all cloud cover conditions including sun obscured and not obscured conditions. The aim was to evaluate the influence of clouds on the total daily UVA. When the sun was not obscured by clouds, there was good agreement between satellite and ground-based daily UVA exposure measurements with coefficient of determination (R²) between 0.80 and 0.84, for the cloud conditions 0 to 2, >2 to 4, >4 to 6 and >6 to 8 oktas. For sun obscured by clouds, the R² was 0.71, 0.64 and 0.75, respectively, for >2 to 4, >4 to 6 and >6 to 8 oktas. The method was validated using total daily UVA exposures from ground measurements taken in 2015 and 2016 giving a mean absolute error of 84.2 kJ m^?2 (10%) and 138.8 kJ m^?2 (30%) respectively, for the cases of sun not obscured cloudy days and sun obscured by cloud cover. Total daily UVA exposures were able to be calculated from the OMI satellite spectral irradiance for all cloud conditions, including cases where the sun was obscured, demonstrating the potential application of the technique to be applied in locations that do not record surface UVA measurements directly.     

Using lidar and effective LAI data to evaluate IKONOS and Landsat 7 ETM+ vegetation cover estimates in a ponderosa pine forest

Structural and functional analyses of ecosystems benefit when high accuracy vegetation coverages can be derived over large areas. In this study, we utilize IKONOS, Landsat 7 ETM+, and airborne scanning light detection and ranging (lidar) to quantify coniferous forest and understory grass coverages in a ponderosa pine (Pinus ponderosa) dominated ecosystem in the Black Hills of South Dakota. Linear spectral mixture analyses of IKONOS and ETM+ data were used to isolate spectral endmembers (bare soil, understory grass, and tree/shade) and calculate their subpixel fractional coverages. We then compared these endmember cover estimates to similar cover estimates derived from lidar data and field measures. The IKONOS-derived tree/shade fraction was significantly correlated with the field-measured canopy effective leaf area index (LAI_e) (r²=0.55, p<0.001) and with the lidar-derived estimate of tree occurrence (r²=0.79, p<0.001). The enhanced vegetation index (EVI) calculated from IKONOS imagery showed a negative correlation with the field measured tree canopy effective LAI and lidar tree cover response (r²=0.30, r=−0.55 and r²=0.41, r=−0.64, respectively; p<0.001) and further analyses indicate a strong linear relationship between EVI and the IKONOS-derived grass fraction (r²=0.99, p<0.001). We also found that using EVI resulted in better agreement with the subpixel vegetation fractions in this ecosystem than using normalized difference of vegetation index (NDVI). Coarsening the IKONOS data to 30 m resolution imagery revealed a stronger relationship with lidar tree measures (r²=0.77, p<0.001) than at 4 m resolution (r²=0.58, p<0.001). Unmixed tree/shade fractions derived from 30 m resolution ETM+ imagery also showed a significant correlation with the lidar data (r²=0.66, p<0.001). These results demonstrate the power of using high resolution lidar data to validate spectral unmixing results of satellite imagery, and indicate that IKONOS data and Landsat 7 ETM+ data both can serve to make the important distinction between tree/shade coverage and exposed understory grass coverage during peak summertime greenness in a ponderosa pine forest ecosystem.     

Simultaneous determination of aerosol optical thickness and surface reflectance using ASTER visible to near-infrared data over land

An innovative method for the determination of aerosol optical thickness (AOT) and surface reflectance for operational use of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) visible to near-infrared data is presented. This method is designed to obtain the atmospheric parameters needed in the correction of the image. This method is based on a simplified radiative transfer equation describing the relation between the ground surface reflectance, AOT and top-of-atmosphere reflectance. By exploiting the ASTER dual-angle view capabilities in band 3N (Nadir) and band 3B (Backwards), surface reflectance and AOT can be retrieved synchronously. Thus, it solves the problem of separating atmospheric radiance from the transmitted radiance of the surface to some extent. After applying this new atmospheric correction method to three areas of ASTER images, Beijing urban city, the Heihe River Basin and Hong Kong of China, ASTER surface reflectance products (AST07) were obtained. AOT values from in situ measurements of CIMEL Electronique 318 Sun Photometers or AERONET (AErosol RObotic NETwork) and surface reflectance in situ measured using an Analytical Spectral Device (ASD) Field Spec spectral radiometer are used for validation. AOT derived from the new method is consistent with in situ station measurements from CIMEL Electronique 318 Sun Photometer and level 2.0 data from AERONET, with correlation coefficient (R ²) of 0.98 and root mean square error of 0.05, whereas Multi-angle Imaging Spectroradiometer AOT products underestimate AERONET AOT and Moderate-Resolution Imaging Spectroradiometer AOT products overestimate AERONET AOT in these regions. More encouraging is the comparison between the corrected surface reflectance, AST07 and ASD measurements. Root mean square error of AST07 and retrieved surface reflectance are as follows: band 1 (556 nm) = 0.04 and 0.05; band 2 (661 nm) = 0.036 and 0.035; band 3 (807 nm) = 0.056 and 0.038, which suggests that compared with AST07 in bands 2 and 3, retrieved surface reflectance has better agreement with measured reflectance from ASD.     

A MODIS-based perpendicular moisture index to retrieve leaf moisture content of forest canopies

Moisture dictates vegetation susceptibility to fire ignition and propagation. Various spectral indices have been proposed for the estimation of equivalent water thickness (EWT), which is defined as the mass of liquid water per unit of leaf surface. However, fire models use live fuel moisture content (LFMC) as a measure of vegetation moisture. LFMC is defined as the ratio of the mass of the liquid water in a leaf over the mass of dry matter, and traditional spectral indices are not as effective as with EWT in capturing LFMC variability. The aim of this research was to explore the potential of the Moderate Resolution Imaging Spectroradiometer (MODIS) on board Terra and Aqua satellites in retrieving LFMC from top of the canopy reflectance, and to develop a new spectral index sensitive to this parameter. All the analyses were based on synthetic canopy spectra constructed by coupling the PROSPECT (leaf optical properties model) and SAIL (Scattering by Arbitrarily Inclined Leaves) radiative transfer models. Simulated top of the canopy spectra were then convolved to MODIS ‘land’ channels 1–7 spectral response functions. All band pairs were evaluated to determine the subspace of MODIS measurements where the separability of points based on their value of LFMC was the highest. This led to the identification of isolines of LFMC in the plane defined by MODIS reflectance measurements in channels 2 and 5; the isolines are straight and parallel, and ordered from lower to higher values of LFMC. This observation allowed the construction of a novel spectral index that is directly related to LFMC – the perpendicular moisture index (PMI). This index measures the distance of a point in the plane spanned by reflectance measurements in MODIS channels 2 and 5 from a reference line, that of completely dry vegetation. Validation against simulated data showed that PMI exhibits a linear relationship with LFMC. When the vegetation cover is dense, the LFMC explains most of the variability in the PMI (R² = 0.70 when LAI > 2; R² = 0.87 when LAI > 4). When the LAI is lower, the contribution of soil background to the measured reflectance increases, and the index underestimates LFMC. The PMI was also validated against the LOPEX93 (Leaf Optical Properties Experiment 1993) data set of leaf optical and biophysical measurements, scaled to canopy reflectance with SAIL, showing acceptable results (R² = 0.56 when LAI > 2; R² = 0.63 when LAI > 4).     

Generation of dense time series synthetic Landsat data through data blending with MODIS using a spatial and temporal adaptive reflectance fusion model

Landsat imagery with a 30 m spatial resolution is well suited for characterizing landscape-level forest structure and dynamics. While Landsat images have advantageous spatial and spectral characteristics for describing vegetation properties, the Landsat sensor's revisit rate, or the temporal resolution of the data, is 16 days. When considering that cloud cover may impact any given acquisition, this lengthy revisit rate often results in a dearth of imagery for a desired time interval (e.g., month, growing season, or year) especially for areas at higher latitudes with shorter growing seasons. In contrast, MODIS (MODerate-resolution Imaging Spectroradiometer) has a high temporal resolution, covering the Earth up to multiple times per day, and depending on the spectral characteristics of interest, MODIS data have spatial resolutions of 250 m, 500 m, and 1000 m. By combining Landsat and MODIS data, we are able to capitalize on the spatial detail of Landsat and the temporal regularity of MODIS acquisitions. In this research, we apply and demonstrate a data fusion approach (Spatial and Temporal Adaptive Reflectance Fusion Model, STARFM) at a mainly coniferous study area in central British Columbia, Canada. Reflectance data for selected MODIS channels, all of which were resampled to 500 m, and Landsat (at 30 m) were combined to produce 18 synthetic Landsat images encompassing the 2001 growing season (May to October). We compared, on a channel-by-channel basis, the surface reflectance values (stratified by broad land cover types) of four real Landsat images with the corresponding closest date of synthetic Landsat imagery, and found no significant difference between real (observed) and synthetic (predicted) reflectance values (mean difference in reflectance: mixed forest x? = 0.086, σ = 0.088, broadleaf x? = 0.019, σ = 0.079, coniferous x? = 0.039, σ = 0.093). Similarly, a pixel based analysis shows that predicted and observed reflectance values for the four Landsat dates were closely related (mean r² = 0.76 for the NIR band; r² = 0.54 for the red band; p < 0.01). Investigating the trend in NDVI values in synthetic Landsat values over a growing season revealed that phenological patterns were well captured; however, when seasonal differences lead to a change in land cover (i.e., disturbance, snow cover), the algorithm used to generate the synthetic Landsat images was, as expected, less effective at predicting reflectance.     

Towards the Remote Sensing of Matorral Vegetation Physiology: Relationships between Spectral Reflectance, Pigment, and Biophysical Characteristics of Semiarid Bushland Canopies

This study investigates the relationships between the spectral reflectance characteristics and the concentrations of photosynthetic pigments and biophysical attributes of a structurally complex, spatially heterogeneous vegetation canopy with varying background properties. A field experiment was performed in the Guadalentin basin, Spain using matorral vegetation canopies dominated by Rosmarinus officinalis, Cistus albidus, and Anthyllis cytosoides. A spectroradiometer was used to record the reflectance of a series of sites at which measurements were made of the concentrations per unit ground area and per unit leaf mass of chlorophyll a and b and the carotenoids, together with leaf area index and percent canopy cover. A range of spectral characteristics was examined which have been found previously to be related to pigment concentrations and biophysical properties of vegetation. For matorral vegetation many of these spectral characteristics were unrelated or only weakly related to canopy properties. However, it was found that pigment concentrations per unit ground area were related to ratios of reflectance in narrow spectral bands within the near-infrared region, ratios of bands within the red region, and characteristics of the amplitude of first derivative spectra in the red edge region. Pigment concentrations per unit leaf mass were correlated with ratios of bands around the nearinfrared “shoulder” and the amplitude of the first derivative in certain visible wavelengths. LAI and percent cover were related to ratios of reflectance in narrow bands on the near-infrared plateau and red edge features of canopy reflectance spectra, as well as with the amplitude of the first derivative in the red edge and visible regions respectively.     

Close-range remote sensing of aquatic macrophyte vegetation cover

This study used ground-based hyperspectral radiometry to examine variations in visible and near-infrared spectral reflectance of spatterdock (Nuphar polysepalum Engelm.) as a function of vegetation cover. Sites were sampled in Swan Lake in Grand Teton National Park, Wyoming, using a 512-band spectroradiometer to measure reflectance over the range 326.5-1055.3nm (visible-nearinfrared) and simultaneous estimates of spatterdock cover. Linear correlations between spatterdock cover and spectral reflectance were statistically significant at the 0.05 significance level in two specific ranges of the spectrum: 518-607 nm; and 697-900nm. Predictability of spatterdock cover using spectral variables was best using an NDVI transformation of the data in a non-linear equation (r ² = 0.95).     

Linear regression modelling for the estimation of oil palm age from Landsat TM

This paper investigates the accuracy with which the age since field planting of oil palm (Elaeis guineensis Jacq.) can be estimated from Landsat Thematic Mapper (TM) radiance at pixel and stand scales. The study site, a commercial plantation 30 km south-east of Kuala Lumpur in Selangor, Malaysia, consisted of even-aged blocks from 4 to 21 years old. Spectral data were the six reflective TM bands and three spectral indices. Nonlinear negative relationships between spectral variables and age are compared to published trends in leaf area, stem height and per cent canopy cover for oil palm and other tree plantations. Correlation coefficients between log age and log radiance are moderate and highly significant (p<0.01) for bands 2-5 and 7 (-0.214 to-0.776) at the pixel scale, and increase at the stand scale (r ²=0.985 for log band 5, p<0.01). Relationships are strongest for the mid-infrared bands, especially band 5 (r ²=0.585, p <0.01) and the infrared index (IRI), a normalized difference index of bands 4 and 5 (r ²= 0.48, p<0.01). Direct and inverse linear regression models for log age with log band and log age with IRI squared (IRIsq) were constructed at both scales. Equivalent age was estimated from the models using independent test sets for differing scales and degrees of aggregation of the age classes. Single age classes cannot be estimated accurately at the pixel or stand scales; the lowest RMS error was obtained from the direct model using all bands (RMS error=3.9 years at pixel scale, 2.7 at stand scale). A posteriori aggregation into generalized age classes (<5, 6-10, 11-15, 16-21 years) improved the RMS error but the results were still unacceptably high (2.2, 2.3, 2.7, 6.0 years respectively for direct model 3 using all bands). Acceptable RMS errors down to 0.58 years were obtained for models using IRIsq with generalized age classes developed and applied at the stand scale when variations in ground cover and other variables were averaged out. The spatial pattern of error in equivalent age deserves investigation for precision crop management.     

Atmospheric correction of hyperspectral airborne GCAS measurements over the Louisiana Shelf using a cloud shadow approach

As an image-driven method to correct for atmospheric effects, the cloud shadow (CS) approach does not require accurate radiometric calibration of the sensor, making it feasible to process remotely sensed data when radiometric calibration may contain non-negligible uncertainties. Using measurements from the Geostationary Coastal and Air Pollution Events Airborne Simulator and from the Moderate Resolution Imaging Spectroradiometer over the Louisiana Shelf, we evaluate the CS approach to airplane measurements in turbid-water environments. The original CS approach somehow produced remote-sensing reflectance (R_rs, sr^?1) with an abnormal spectral shape, likely a result of the assumption of identical path radiance for the pair of pixels in and out of the shadow, which is not exactly valid for measurements made from a low-altitude airplane. To overcome this limitation, an empirical scheme using an effective wavelength-dependent radiance reflectance for the cloud (γ, sr^?1) was developed and reasonable GCAS R_rs retrievals are then generated, which were further validated against in situ R_rs. Issues and challenges in applying CS to measurements of low-altitude airplanes are discussed.     

MODIS‐derived visible atmospherically resistant index for monitoring chaparral moisture content

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<0.94) and spatial robustness with FMC than NDWI, even though the former is based solely on visible waveband reflectance data.     

Atmospheric effects on radiometry from zenith of a plane with dark vertical protrusions

Effects of an optically thin plane-parallel scattering atmosphere on radiometric imaging from the zenith of a specific surface type are analysed. The surface model was previously developed to describe arid steppe, where the sparse vegetation forms dark vertical protrusions from the bright soil plane. The analysis is in terms of the surface reflectivity to the zenith r_p for the direct beam, which is formulated as r_p = r_i exp(?s tan θ₀), where r_i is the Lambert-law reflectivity of the soil, the protrusion parameter 5 is the projection on a vertical plane of protrusions per unit area and θ⁰ is the zenith angle. The surface reflectivity r_P is approximately equal to that for the global irradiance (which is directly measured in the field) only for a narrow range of the solar zenith angles. The effects of the atmosphere when imaging large uniform areas of this type are comparable to those in imaging a Lambert surface with a reflectivity r_P . Thus, the effects can be approximated by those in the case of a dark Lambert surface (analysed previously), inasmuch as r_P is smalleSr than the soil reflectivity r_i for any off-zenith illumination. The surface becomes effectively darker with increasing solar zenith angle.

Adjacency effects of a reflection from one area and scattering in the instantaneous field of view (object pixel) are analysed as cross radiance and cross irradiance, The analysis is only for the case of a small object pixel embedded in a different terrain, extending to infinity as a uniform area. The effects of the cross radiance (which are dominant) are found to be smaller than those over a Lambert plane for the same surroundings-to-object-pixel contrast and atmospheric conditions. However, the adjacency effects are highly variable, because the effective contrast for our plane with dark protrusions is a function of not only the surface parameters but also of the solar zenith angle and the atmospheric conditions.     

Estimating aboveground biomass using Landsat 7 ETM+ data across a managed landscape in northern Wisconsin, USA

Aboveground biomass (AGB; Mg/ha) is defined in this study as a biomass of growing stock trees greater than 2.5 cm in diameter at breast height (dbh) for stands >5 years and all trees taller than 1.3 m for stands <5 years. Although AGB is an important variable for evaluating ecosystem function and structure across the landscape, such estimates are difficult to generate without high-resolution satellite data. This study bridges the application of remote sensing techniques with various forest management practices in Chequamegon National Forest (CNF), Wisconsin, USA by producing a high-resolution stand age map and a spatially explicit AGB map. We coupled AGB values, calculated from field measurements of tree dbh, with various vegetation indices derived from Landsat 7 ETM+ data through multiple regression analyses to produce an initial biomass map. The initial biomass map was overlaid with a land-cover map to generate a stand age map. Biomass threshold values for each age category (e.g., young, intermediate, and mature) were determined through field observations and frequency analysis of initial biomass estimates by major cover types. We found that AGB estimates for hardwood forests were strongly related to stand age and near-infrared reflectance (r²=0.95) while the AGB for pine forests was strongly related to the corrected normalized difference vegetation index (NDVIc; r²=0.86). Separating hardwoods from pine forests improved the AGB estimates in the area substantially, compared to overall regression (r²=0.82). Our AGB results are comparable to previously reported values in the area. The total amount of AGB in the study area for 2001 was estimated as 3.3 million metric tons (dry weight), 76.5% of which was in hardwood and mixed hardwood/pine forests. AGB ranged from 1 to 358 Mg/ha with an average of 70 and a standard deviation of 54 Mg/ha. The AGB class with the highest percentage (16.1%) was between 81 and 100 Mg/ha. Forests with biomass values >200 Mg/ha accounted for less than 3% of the study area and were usually associated with mature hardwood forests. Estimated AGB was validated using independent field measurements (R²=0.67, p<0.001). The AGB and age maps can be used as baseline information for future landscape level studies such as quantifying the regional carbon budget, accumulating fuel, or monitoring management practices.     

Atmospheric optical depth effects on angular anisotropy of plant canopy reflectance

Abstract

A field experiment was conducted to determine whether changes in atmospheric aerosol optical depth would effect changes in bi-directional reflectance distributions of vegetation canopies. Measurements were made of the directionally reflected radiance distributions of two pasture grass canopies (same species, different growth forms) and one soya bean plant canopy under different sky irradiance distributions, which resulted from a variation in aerosol optical depth. The reflected radiance data were analysed in the solar principal plane in two narrow spectral bands, one visible (662 nm) and one infrared (826 nm). The observed changes in reflectance for both wavelengths from irradiance distribution variation is interpreted to be due largely to changes in the percentage of shadowed area viewed by the sensor for the incomplete canopies (pasture grass). For the complete coverage vegetation canopy (soya bean) studied, the effects of specular reflection and the increased diffuse irradiance penetration into the canopy are concluded to be primary physical mechanisms responsible for reflectance changes. Observed reflectivities were found to be lower on a hazy day (higher optical depth with a greater diffuse fraction) than on a clear day, with solar zenith angles at about 58^° on both days, for full-coverage soya bean canopies. The reduced reflectance most likely results from a diminished specular reflection and a greater diffuse radiation penetration into the canopy, which effects an increased energy absorption at large solar zenith angles. The opposite was true for fractional coverage grass canopies at solar zenith angles of about 56^° since the shadowing was less on the hazy day and, therefore, the soil/litter background was more fully illuminated. In the near-infrared waveband the changes in reflectance are much less than in the visible and, therefore, normalized difference vegetation index values differ substantially under clear and hazy sky conditions for the same vegetation canopy conditions. Thus, the influence of atmospheric optical depth must be considered for accurate remote sensing and in situ data interpretation.     

Testing the potential of multi-spectral remote sensing for retrospectively estimating fire severity in African Savannahs

The remote sensing of fire severity is a noted goal in studies of forest and grassland wildfires. Experiments were conducted to discover and evaluate potential relationships between the characteristics of African savannah fires and post-fire surface spectral reflectance in the visible to shortwave infrared spectral region. Nine instrumented experimental fires were conducted in semi-arid woodland savannah of Chobe National Park (Botswana), where fire temperature (T_max) and duration (dt) were recorded using thermocouples positioned at different heights and locations. These variables, along with measures of fireline intensity (FLI), integrated temperature with time (T_sum) and biomass (and carbon/nitrogen) volatilised were compared to post-fire surface spectral reflectance. Statistically significant relationships were observed between (i) the fireline intensity and total nitrogen volatilised (r² = 0.54, n = 36, p < 0.001), (ii) integrated temperature (T_sum−μ) and total biomass combusted (r² = 0.72, n = 32, p < 0.001), and (iii) fire duration as measured at the top-of-grass sward thermocouple (dt_T) and total biomass combusted (r² = 0.74, n = 34, p < 0.001) and total nitrogen volatilised (r² = 0.73, n = 34, p < 0.001). The post-fire surface spectral reflectance was found to be related to dt and T_sum via a quadratic relationship that varied with wavelength. The use of visible and shortwave infrared band ratios produced statistically significant linear relationships with fire duration as measured by the top thermocouple (dt_T) (r² = 0.76, n = 34, p < 0.001) and the mean of T_sum (r² = 0.82, n = 34, p < 0.001). The results identify fire duration as a versatile measure that relates directly to the fire severity, and also illustrate the potential of spectrally-based fire severity measures. However, the results also point to difficulties when applying such spectrally-based techniques to Earth Observation satellite imagery, due to the small-scale variability noted on the ground. Results also indicate the potential for surface spectral reflectance to increase following higher severity fires, due to the laying down of high albedo white mineral ash. Most current techniques for mapping burned area rely on the general assumption that surface albedo decreases following a fire, and so if the image spatial resolution was high enough such methods may fail. Determination of the effect of spatial resolution on a sensor's ability to detect white ash was investigated using a validated optical mixture modelling approach. The most appropriate mixing model to use (linear or non-linear) was assessed using laboratory experiments. A linear mixing model was shown most appropriate, with results suggesting that sensors having spatial resolutions significantly higher than those of Landsat ETM+ will be required if patches of white ash are to be used to provide EO-derived information on the spatial variation of fire severity.     

Cloud parameter retrievals from Meteosat and their effects on the shortwave radiation at the surface

A method based on Spinning Enhanced Visible and Infrared Imager (SEVIRI) measured reflectance at 0.6 and 3.9 µm is used to retrieve the cloud optical thickness (COT) and cloud effective radius (r_e) over the Iberian Peninsula. A sensitivity analysis of simulated retrievals to the input parameters demonstrates that the cloud top height is an important factor in satellite retrievals of COT and r_e with uncertainties around 10% for small values of COT and r_e; for water clouds these uncertainties can be greater than 10% for small values of r_e. The uncertainties found related with geometries are around 3%. The COT and r_e are assessed using well-known satellite cloud products, showing that the method used characterize the cloud field with more than 80% (82%) of the absolute differences between COT (r_e) mean values of all clouds (water plus ice clouds) centred in the range from ±10 (±10 µm), with absolute bias lower than 2 (2 μm) for COT (r_e) and root mean square error values lower than 10 (8 μm) for COT (r_e). The cloud water path (CWP), derived from satellite retrievals, and the shortwave cloud radiative effect at the surface (CRE_SW) are related for high fractional sky covers (F_sc >0.8), showing that water clouds produce more negative CRE_SW than ice clouds. The COT retrieved was also related to the cloud modification factor, which exhibits reductions and enhancements of the surface SW radiation of the order of 80% and 30%, respectively, for COT values lower than 10. A selected case study shows, using a ground-based sky camera that some situations classified by the satellite with high F_sc values correspond to situations of broken clouds where the enhancements actually occur. For this case study, a closure between the liquid water path (LWP) obtained from the satellite retrievals and the same cloud quantity obtained from ground-based microwave measurements was performed showing a good agreement between both LWP data set values.     

Assessment of QuickBird high spatial resolution imagery to detect red attack damage due to mountain pine beetle infestation

High spatial resolution remotely sensed data has the potential to complement existing forest health programs for both strategic planning over large areas, as well as for detailed and precise identification of tree crowns subject to stress and infestation. The area impacted by the current mountain pine beetle (Dendroctonus ponderosae Hopkins) outbreak in British Columbia, Canada, has increased 40-fold over the previous 5 years, with approximately 8.5 million ha of forest infested in 2005. As a result of the spatial extent and intensity of the outbreak, new technologies are being assessed to help detect, map, and monitor the damage caused by the beetle, and to inform mitigation of future beetle outbreaks. In this paper, we evaluate the capacity of high spatial resolution QuickBird multi-spectral imagery to detect mountain pine beetle red attack damage. ANOVA testing of individual spectral bands, as well as the Normalized Difference Vegetation Index (NDVI) and a ratio of red to green reflectance (Red-Green Index or RGI), indicated that the RGI was the most successful (p < 0.001) at separating non-attack crowns from red attack crowns. Based on this result, the RGI was subsequently used to develop a binary classification of red attack and non-attack pixels. The total number of QuickBird pixels classified as having red attack damage within a 50 m buffer of a known forest health survey point were compared to the number of red attack trees recorded at the time of the forest health survey. The relationship between the number of red attack pixels and observed red attack crowns was assessed using independent validation data and was found to be significant (r² = 0.48, p < 0.001, standard error = 2.8 crowns). A comparison of the number of QuickBird pixels classified as red attack, and a broader scale index of mountain pine beetle red attack damage (Enhanced Wetness Difference Index, calculated from a time series of Landsat imagery), was significant (r² = 0.61, p < 0.001, standard error = 1.3 crowns). These results suggest that high spatial resolution imagery, in particular QuickBird satellite imagery, has a valuable role to play in identifying tree crowns with red attack damage. This information could subsequently be used to augment existing detailed forest health surveys, calibrate synoptic estimates of red attack damage generated from overview surveys and/or coarse scale remotely sensed data, and facilitate the generation of value-added information products, such as estimates of timber volume impacts at the forest stand level.