Analysis of normalized difference and surface temperature observations over southeastern Australia

Abstract

Relations between radiative surface temperature (T_R) and visible and near-infrared reflectances expressed as the normalized difference (ND) from a Landsat Thematic Mapper scene were analysed to study the heat balance of agriculture and native evergreen forests in southeastern Australia. The scene was at 0922 h (local time) during late spring (15 October 1986) with phenology of winter annual species between flowering and grain filling. Factors determining the slope of, and residual scatter about, the T_R)/ND relationships were analysed using a coupled two-layer soil-vegetation model of the surface heat balance. Inverse linear relationships were found between T_R), and ND for agriculture, but not for forests. This was a result of a wide range of ND and T_R) values in agricultural areas caused by wide variations in fractional vegetation cover. The relationships between ND and fractional vegetation cover was not general, thus, when forest and agricultural data were combined, the lower near-infrared reflectance of forests (16-9 per cent) compared with agricultural crops (29-0 per cent) resulted in forest data falling below the regression line for agriculture. From the agricultural relations, 60 to 70 per cent of the variation in T_R) was explained by ND, indicating that fractional vegetation cover was the dominant factor determining T_R). Residual variability of T_R) was attributed to spatial variability in ambient temperature, rates of soil evaporation and variations in stage of phenological development affecting stoma-tal resistance. Energy-balance analysis of the effect of soil water availability on the slope of the _R)‘ND relationship indicated opposite effects depending on whether the reduction in evaporation was from soil or from vegetation. Thus, the generality of using the slope of the T_R)/ND relationship to predict surface resistance to evaporation may be limited. It was concluded that extraction of information contained in T_R) and ND data for regional estimation of evaporation requires the separation of T_R) into soil and vegetation temperatures and an alternative to ND that relates more generally to fractional vegetation cover.     

A Review of: “Environmental Information Management and Analysis: Ecosystem 10 global scales”. Edited by W. K. MICHENER,J. W. BRUNT and S. G. STAFFORD (London: Taylor &: Francis Ltd, 1994). [Pp. 535.] Price £55,00.

Abstract

Satellite indices of vegetation from the Australian continent were calculated from May 1986 to April 1987 from NOAA-9 AVHRR (Advanced Very High Resolution Radiometer) and Nimbus-7 SMMR (Scanning Multichannel Microwave Radiometer) satellite data. The visible (VIS) and near infrared (N1R) reflectances and their combination, the Normalized Difference (ND) Vegetation Index were calculated from the AVHRR sensor. From the SMMR, the microwave Polarization Difference (PD) was calculated as the difference between the vertically and horizontally polarized brightness temperatures at 37 GHz. The AVHRR data were gridded to match the 25 km spatial resolution of the SMMR 37 GHz data and both data sets were analysed to provide a temporal resolution of one month. Using a one month lag, the ND, PD, VIS and NIR, indices were plotted against rainfall and water balance estimates of evaporation, calculated using the monthly rainfall data and long term averages of pan evaporation from 74 locations covering a range of vegetation types. The monthly plots had wide scatter. This scatter was reduced markedly by aggregating the data over twelve months, leading to the conclusion that direct satellite monitoring of annual evaporation across the Australian continent using PD or VIS is feasible for areas with evaporation less than 600 mm y^?1. The ND relationship was limited by scatter and the PD and VIS relationships by their saturation above 600 mm y^?1, which spanned about two-thirds of the continental range studied. Scatter was reduced and ND had a predictive range above 600 mm y^?1 if evaporation was normalized by evaporative demand. But prior knowledge of potential evaporation is needed in this approach. The NIR reflectance of forests were consistently lower than neighbouring areas of agriculture, thus ND may underpredict the evaporation of forests relative to agriculture. Temporal resolution of the satellite indices over periods of one month could not be evaluated due to spatial and temporal variability of climatic and biological factors not accounted for in the water balance estimates of evaporation.     

Systematic corrections of AVHRR image composites for temporal studies

For quantitative studies of vegetation dynamics, satellite data need to be corrected for spurious effects. In this study, we have applied several changes to an earlier advanced very high resolution radiometer (AVHRR) processing methodology (ABC3; [Remote Sens. Environ. 60 (1997) 35; J. Geophys. Res.-Atmos. 102 (1997) 29625; Can. J. Remote Sens. 23 (1997) 163]), to better represent the various physical processes causing contamination of the AVHRR measurements. These included published recent estimates of the NOAA-11 and NOAA-14 AVHRR calibration trajectories for channels 1 and 2; the best available estimates for the water vapour, aerosol and ozone amounts at the time of AVHRR data acquisition; an improved bidirectional reflectance algorithm that also takes into consideration surface topography; and an improved image screening algorithm for contaminated pixels. Unlike the previous study that compared the composite images to a single-date AVHRR image, we employed coincident TM images to approximate the AVHRR pixel field of view during the data acquisition. Compared to ABC3, the modified procedure ABC3V2 was found to improve the accuracy of AVHRR pixel reflectance estimates, both in the sensitivity (slope) of the regression and in r². The improvements were especially significant in AVHRR channel 1. In comparison with reference values derived from two full TM scenes, the corrected AVHRR surface reflectance estimates had average standard errors values of ±0.009 for AVHRR C1, ±0.019 for C2, and ±0.04 for NDVI; the corresponding r² values were 0.55, 0.80, and 0.50, respectively. The changes in ABC3V2 were not able to completely remove interannual variability for land cover types with little or no vegetation cover, which would be expected to remain stable over time, and they increased the interannual variability of mixed forest and grassland. These results are attributed to a combination of increased sensitivity to interannual dynamics on one hand, and the inability to remove all sources of noise for barren or sparsely vegetated northern land cover types on the other.     

Mapping leaf area index over a mixed natural forest area in the flooding season using ground-based measurements and Landsat TM imagery

Leaf area index (LAI) is an important structural parameter in terrestrial ecosystem modelling and management. Therefore, it is necessary to conduct an investigation on using moderate-resolution satellite imagery to estimate and map LAI in mixed natural forests in southeastern USA. In this study, along with ground-measured LAI and Landsat TM imagery, the potential of Landsat 5 TM data for estimating LAI in a mixed natural forest ecosystem in southeastern USA was investigated and a modelling method for mapping LAI in a flooding season was developed. To do so, first, 70 ground-based LAI measurements were collected on 8 April 2008 and again on 1 August 2008 and 30 July 2009; TM data were calibrated to ground surface reflectance. Then univariate correlation and multivariate regression analyses were conducted between the LAI measurement and 13 spectral variables, including seven spectral vegetation indices (VIs) and six single TM bands. Finally, April 08 and August 08 LAI maps were made by using TM image data, a multivariate regression model and relationships between April 08 and August 08 LAI measurements. The experimental results indicate that Landsat TM imagery could be used for mapping LAI in a mixed natural forest ecosystem in southeastern USA. Furthermore, TM4 and TM3 single bands (R ² > 0.45) and the soil adjusted vegetation index, transformed soil adjusted vegetation index and non-linear vegetation index (R ² > 0.64) have produced the highest and second highest correlation with ground-measured LAI. A better modelling result (R ²?=?0.78, accuracy?=?73%, root mean square error (RMSE)?=?0.66) of the 10-predictor multiple regression model was obtained for estimating and mapping April 08 LAI from TM data. With a linear model and a power model, August 08 LAI maps were successfully produced from the April 08 LAI map (accuracy?=?79%, RMSE?=?0.57), although only 58–65% of total variance could be accounted for by the linear and non-linear models.     

Determining the rate of forest conversion in Mato Grosso,Brazil, using Landsat MSS and AVHRR data

Abstract

AVHRR-LAC thermal data and Landsat MSS and TM spectral data were used to estimate the rate of forest clearing in Mato Grosso, Brazil, between 1981 and 1984. The Brazilian state was stratified into forest and non-forest. A list sampling procedure was used in the forest stratum to select Landsat MSS scenes for processing based on estimates of fire activity in the scenes. Fire activity in 1984 was estimated using AVHRR-LAC thermal data. Slate-wide estimates of forest conversion indicate that between 1981 and 1984, 353966 ha ±77 000 ha (0·4 percent of the state area) were converted per year. No evidence of reforestation was found in this digital sample. The relationship between forest clearing rate (based on MSS-TM analysis)and fire activity (estimated using AVHRR data)was noisy (R²= 0·41). The results suggest that AVHRR data may be put to better use as a stratification tool rather than as a subsidiary variable in list sampling.     

A comparison of NOAA–AVHRR fire data with three Landsat data sets in arid and semi-arid Australia

Burnt area data, derived from the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) imagery, are validated in 11 regions of arid and semi-arid Australia, using three separate Landsat-derived burnt area data sets. Mapping accuracy of burnt extent is highly variable between areas and from year to year within the same area. Where there are corresponding patches in the AVHRR and Landsat data sets, the fit is good. However, the AVHRR data set misses some large patches. Overall, 63% of the Landsat burnt area is also mapped in the AVHRR data set, but this varies from 0% to 89% at different sites. In total, 81% of the AVHRR burnt area data are matched in the Landsat data set, but range from 0% to 94%. The lower match rates (<50%) are generally when little area has burnt (0–500 km²), with figures generally better in the more northerly sites. Results of regressions analysis based on 10 km?×?10 km cells are also variable, with R ² values ranging from 0.37 (n?=?116) to 0.94 (n?=?85). For the Tanami Desert scene, R ² varies from 0.41 to 0.61 (n?=?368) over three separate years. Combining the data results in an R ² of 0.60 (n?=?1315) (or 0.56 with the intercept set to 0). The slopes of the regressions indicate that mapping the burnt area from AVHRR imagery underestimates the ‘true’ extent of burning for all scenes and years. Differences in mapping accuracy between low and high fire years are examined, as well as the influence of soil, vegetation, land use and tenure on mapping accuracy. Issues which are relevant to mapping fire in arid and semi-arid environments and discontinuous fuels are highlighted.     

Correction of the effect of Sun-sensor-target geometry in NOAA AVHRR data

Models of determining the effects of the bidirectional reflectance distribution function (BRDF) of different surfaces and of eliminating the effect of Sun-sensor-target geometry from the remotely sensed satellite data are actual. The objective of this study is to develop a simple relation between the Sun-sensor-target geometry and the remotely sensed vegetation index. In this investigation 238 National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) images were used over Hungary during the period 1996-98. The greenness vegetation index (the difference between the reflectance values of near-infrared and visible channels) was used between days of the year 140-200, because the greenness values can be considered as constant in this period over the agricultural areas. The so-called 'hot spot effect' can be observed in the variation of reflectance values with different viewing zenith angles of the sensor. A simple quadratic relation was found between the raw AVHRR greenness values and the angle enclosed by the Sun-target and target-sensor directions over the agricultural areas, forests and grasslands. A correction method was developed to eliminate the effect of the Sun-sensor-target geometry, which it is hoped will improve the accuracy of yield forecasting and estimation procedures using NOAA AVHRR data.     

Reflectance characteristics of major land surfaces in slash‐and‐burn ecosystems in Laos

The in situ reflectance spectra in the 400–2500 nm wavelength region were obtained using a portable radiometer over a range of land surfaces including burnt fields, crop canopies, and fallow vegetation at different community ages in slash‐and‐burn ecosystems in Laos. Normalized difference spectral indices (NDSI[i,j] = [R_j ?R_i ]/[R_j +R_i ]) were derived using reflectance R_i and R_j at i and j nm wavelengths for a thorough combination (14 706 pairs) of 172 wavebands (10‐nm resolution). The separability of burnt fields from dry/senescent vegetation was highest at NDSI[1090, 2390], whereas it was highly discriminated from fallow and crop vegetation by NDSI[760, 1970]. NDSIs using 730–760 nm with 1970–1990 nm showed the largest differences between dry/senescent vegetation and fallow or crop vegetation. None of the NDSIs was useful in discriminating between fallow and crop vegetations or between slashed/senescent vegetation and crop residue/abandoned field. Community age and biomass of fallow vegetation could not be inferred directly from spectral information, since no NDSIs showed any significant differences among crop and fallow vegetation that had a large variability in the amount of green vegetation. Results would provide useful information for various applications of optical satellite sensor images especially in assessments of land use or post‐fire regeneration of vegetation.     

Inverting a forest canopy reflectance model to retrieve the overstorey and understorey leaf area index for forest stands

In this article, the Kuusk–Nilson forest reflectance and transmittance (FRT) model was inverted to retrieve the overstorey and understorey leaf area index (OU-LAI) of forest stands in the Longmenhe forest nature reserve in China. Data from detailed sample sites were collected in 30 forest stands representing the typical vegetation community in the study area. An uncertainty and sensitivity matrix (USM) was used to analyse the sensitivity of the FRT model parameters based on these data. The results indicated that overstorey LAI strongly influenced stand reflectance, whereas understorey LAI had a much lower impact. To predict OU-LAI in forest stands, FRT model inversion is carried out by minimizing a merit function that provides a measure of the difference between the reflectance simulated by the FRT model and the reflectance originating from optimal band selection of Hyperion data. Various combinations of Hyperion bands were tested to evaluate the most effective wavelengths for the inversion of OU-LAI. The best estimates from 17 Hyperion bands (5 VIS, 8 NIR, 4 SWIR) by the FRT model inversion showed an R ²?=?0.41 and RMSE/mean?=?0.21 for overstorey LAI and R ²?=?0.49 and RMSE/mean?=?0.91 for understorey LAI. Advantages and disadvantages of FRT inversion for retrieval OU-LAI combined with Hyperion data are discussed.     

Modelling forest cover attributes as continuous variables in a regional context with Thematic Mapper data

We modelled forest vegetation attributes as continuous variables across western Oregon using a multi-image mosaic of Thematic Mapper (TM) data. Four specific attributes were modelled using regression analysis: percent green vegetation cover, percent conifer cover, conifer crown diameter, and conifer stand age. Reference data for the cover and diameter attributes were derived from airphotos, and existing agency polygon databases were used for stand age. We developed and applied a new method for regional mapping called applied radiometric normalization. The method involved development of a set of models for a centrally located 'source' scene which were then extended to 'destination' scenes (neighboring scenes in the TM mosaic). Use of airphotos and existing digital databases in combination with applied radiometric normalization translates to a cost-effective procedure for regional mapping with TM data. Modelling forest attributes as continuous variables enables creation of a flexible forest cover information base, containing important fundamental building blocks for a variety of related classification schemes.     

A robust new method for the remote estimation of LAI in montane and boreal forests

Two promising techniques for estimating Leaf Area Index (LAI) using remote sensing are Linear Spectral Mixture Analysis (LSMA) and Modification of Spectral Vegetation Indices (MSVI). The Normalized Distance Method (ND), which uses principles employed by the LSMA and MSVI techniques, is introduced in this study. These three methods are applied to a region of montane forest in Kananaskis Country, Alberta, Canada, in order to estimate LAI. In situ measurements of LAI in 10 deciduous and 10 coniferous plots, and a SPOT‐4 image taken at the height of the growing season, provided test data that produced relationships for LAI in pure stands of either coniferous or deciduous vegetation using each of the three methods. All methods exhibited varying degrees of performance and demonstrated significant dependence on vegetation type. The ND method produced relationships with coefficients of determination (R ²) of 0.86 and 0.65 for coniferous and deciduous vegetation, respectively; the MSVI method (when using the adjusted Normalized Difference Vegetation Index) produced relationships with R ² values of 0.79 and 0.59 for coniferous and deciduous vegetation, respectively; and the LSMA technique produced relationships with R ² values of 0.83 and 0.0 for coniferous and deciduous vegetation, respectively.     

Subpixel forest cover in central Africa from multisensor, multitemporal data

Seven Landsat Multispectral Scanner (MSS) scenes in central Africa were coregistered with 8 km resolution data from the 1987 AVHRR Pathfinder Land data set. Percent forest cover in each 8 km grid cell was derived from the classified MSS scenes. Linear relationships between percent forest cover and 30 multitemporal metrics derived from all AVHRR optical and thermal channels were determined. Correlations were strongest for the mean annual normalized difference vegetation index (NDVI) and mean annual brightness temperature (AVHRR Channel 3) and weakest for those metrics, besides NDVI, based on near-infrared reflectances (AVHRR Channel 2). The relationships were used to estimate percent forest cover in various locations in the study area using multiple linear regression and regression trees. Overall, the multiple linear regression provided more accurate results. Predicted percent forest cover estimates were within 20% of the “actual” percent forest cover (derived from the MSS data) for approximately 90% of the grid cells. The RMS error for the prediction was 12% forest cover. RMS errors above 18% forest cover were obtained when using AVHRR data from a single month to derive predictive relationships. The results demonstrate that multitemporal data reflecting vegetation phenology can be used to estimate subpixel forest cover at coarse spatial resolutions.     

Mapping the invasive species,Chinese tallow,with EO1 satellite Hyperion hyperspectral image data and relating tallow occurrences to a classified Landsat Thematic Mapper land cover map

Our objective was to provide a realistic and accurate representation of the spatial distribution of Chinese tallow (Triadica sebifera) in the Earth Observing 1 (EO1) Hyperion hyperspectral image coverage by using methods designed and tested in previous studies. We transformed, corrected, and normalized Hyperion reflectance image data into composition images with a subpixel extraction model. Composition images were related to green vegetation, senescent foliage and senescing cypress‐tupelo forest, senescing Chinese tallow with red leaves (‘red tallow’), and a composition image that only corresponded slightly to yellowing vegetation. These statistical and visual comparisons confirmed a successful portrayal of landscape features at the time of the Hyperion image collection. These landscape features were amalgamated in the Landsat Thematic Mapper (TM) pixel, thereby preventing the detection of Chinese tallow occurrences in the Landsat TM classification. With the occurrence in percentage of red tallow (as a surrogate for Chinese tallow) per pixel mapped, we were able to link dominant land covers generated with Landsat TM image data to Chinese tallow occurrences as a first step toward determining the sensitivity and susceptibility of various land covers to tallow establishment. Results suggested that the highest occurrences and widest distribution of red tallow were (1) apparent in disturbed or more open canopy woody wetland deciduous forests (including cypress‐tupelo forests), upland woody land evergreen forests (dominantly pines and seedling plantations), and upland woody land deciduous and mixed forests; (2) scattered throughout the fallow fields or located along fence rows separating active and non‐active cultivated and grazing fields, (3) found along levees lining the ubiquitous canals within the marsh and on the cheniers near the coastline; and (4) present within the coastal marsh located on the numerous topographic highs.     

Estimating the leaf area index in Indian tropical forests using Landsat-8 OLI data

Leaf area index (LAI) is a key vegetation biophysical parameter and is extensively used in modelling of phenology, primary production, light interception, evapotranspiration, carbon, and nitrogen dynamics. In the present study, we attempt to spatially characterize LAI for natural forests of Western Ghats India, using ground based and Landsat-8 Operational Land Imager (OLI) sensor satellite data. For this, 41 ground-based LAI measurements were carried out across a gradient of tropical forest types, viz. dry, moist, and evergreen forests using LAI-2200 plant canopy analyser, during the month of March 2015. Initially, measured LAI values were regressed with 15 spectral variables, including nine spectral vegetation indices (SVIs) and six Landsat-8 surface reflectance (ρ) variables using univariate correlation analysis. Results showed that the red (ρ_red), near-infrared (ρ_NIR), shortwave infrared (ρ_SWIR1, ρ_SWIR2) reflectance bands (R² > 0.6), and all SVIs (R² > 0.7) except simple ratio (SR) have the highest and second highest coefficient of determination with ground-measured LAI. In the second step, to select significant (high R², low root mean square error (RMSE), and p-level < 0.05) SVIs to determine the best representative model, stepwise multiple linear regression (SMLR) was implemented. The results indicate that the SMLR model predicted LAI with better coefficient of determination (R² = 0.83, RMSE = 0.78) using normalized difference vegetation index, enhanced vegetation index, and soil-adjusted vegetation index variables compared to the univariate approach. The predicted SMLR model was used to estimate a spatial map of LAI. It is desirable to evaluate the stability and potentiality of regional LAI models in natural forest ecosystems against the operationally accepted Moderate Resolution Imaging Spectroradiometer (MODIS) global LAI product. To do this, the Landsat-8 pixel-based LAI map was resampled to 1 km resolution and compared with the MODIS derived LAI map. Results suggested that Landsat-8 OLI-based VIs provide significant LAI maps at moderate resolution (30 m) as well as coarse resolution (1 km) for regional climate models.     

Subpixel measurement of tropical forest cover using AVHRR data

Abstract

Tropical forest assessment using data from the Advanced Very High Resolution Radiometer (AVHRR) may lead to inaccurate estimates of forest cover in regions of small subpixel forest or non-forest patches and in regions where the pattern of clearance is particularly convoluted. Test sites typifying these two patterns were chosen in Ghana and Rondonia, respectively. To capture the subpixel proportions of forest cover, a linear mixture model was applied to two AVHRR test images over the test sites. The model produced image outputs in which pixel intensities indicated the proporton of forest cover per km². For comparison, supervised maximum likelihood classifications were also performed. The outputs were assessed against classified Landsat TM scenes, converted to proportions maps and coregistered to the AVHRR images. An empirical method was applied for determining the critical forest cover per km² needed for an AVHRR pixel to be classified as forest. The critical values exceeded 50 per cent, indicating a tendency for AVHRR classification to underestimate forest cover. This was confirmed by comparing estimates of total forest cover obtained from the AVHRR and TM classifications. In the case of Ghana, a more accurate estimate of forest cover was obtained from the AVHRR mixture model than from the classification. Both mixture model outputs were found to be well correlated with those from Landsat TM. Further work should test the robustness of the approach adopted here when applied to much larger areas.     

Phenological differences in Tasseled Cap indices improve deciduous forest classification

Remote sensing needs to clarify the strengths of different methods so they can be consistently applied in forest management and ecology. Both the use of phenological information in satellite imagery and the use of vegetation indices have independently improved classifications of north temperate forests. Combining these sources of information in change detection has been effective for land cover classifications at the continental scale based on Advanced Very High Resolution Radiometer (AVHRR) imagery. Our objective is to test if using vegetation indices and change analysis of multiseasonal imagery can also improve the classification accuracy of deciduous forests at the landscape scale. We used Landsat Thematic Mapper (TM) scenes that corresponded to Populus spp. leaf-on and Quercus spp. leaf-off (May), peak summer (August), Acer spp. peak color (September), Acer spp. and Populus spp. leaf-off (October). Input data files derived from the imagery were: (1) TM Bands 3, 4, and 5 from all dates; (2) Normalized Difference Vegetation Index (NDVI) from all dates; (3) Tasseled Cap brightness, greenness, and wetness (BGW) from all dates; (4) difference in TM Bands 3, 4, and 5 from one date to the next; (5) difference in NDVI from one date to the next; and (6) difference in BGW from one date to the next. The overall kappa statistics (KHAT) for the aforementioned classifications of deciduous genera were 0.48, 0.36, 0.33, 0.38, 0.26, 0.43, respectively. The highest accuracies occurred from TM Bands 3, 4, and 5 (61.0% for deciduous genera, 67.8% for all classes) or from the difference in BGW (61.0% for deciduous genera, 67.8% for all classes). However, the difference in Tasseled Cap classification more accurately separated deciduous shrubs and harvested stands from closed canopy forest. Our results indicate that phenological change of forest is most accurately captured by combining image differencing and Tasseled Cap indices.     

Correcting the orbit drift effect on AVHRR land surface skin temperature measurements

The orbital drift of the National Oceanic and Atmospheric Administration (NOAA)-7, -9, -11, -14 series of satellites results in a significant cooling effect on their afternoon path Advanced Very High Resolution Radiometer (AVHRR) land surface skin temperature (T_s ) measurements. This effect mixes with the signal of true variations in the climate system, and thus prevents T_s from being directly used in climate change and global warming studies. This paper applies a physically based ‘typical pattern technique’ to remove the orbit drift effect from T_s . The technique utilizes a lookup table of representative land skin temperature diurnal cycles derived from the National Center for Atmospheric Research (NCAR) Climate Community Model (CCM3) coupled with the land surface model, Biosphere–Atmosphere Transfer Scheme (BATS). The generated typical patterns of T_s diurnal cycle are functions of vegetation type, season and latitude, and are combined with satellite observations to remove the cooling effect. Applying this methodology to eighteen years of AVHRR (1981–1998) T_s observations yields an improved skin temperature dataset. Analysis of the drift-corrected skin temperature illustrates a warming trend at the surface over the past two decades, a result which agrees well with the observed surface air temperature trend. A discussion of uncertainties in this technique is also presented.     

Evaluation of MODIS and NOAA AVHRR vegetation indices with in situ measurements in a semi‐arid environment

Much effort has been made in recent years to improve the spectral and spatial resolution of satellite sensors to develop improved vegetation indices reflecting surface conditions. In this study satellite vegetation indices from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Very High Resolution Radiometer (AVHRR) are evaluated against two years of in situ measurements of vegetation indices in Senegal. The in situ measurements are obtained using four masts equipped with self‐registrating multispectral radiometers designed for the same wavelengths as the satellite sensor channels. In situ measurements of the MODIS Normalized Difference Vegetation Index (NDVI) and AVHRR NDVI are equally sensitive to vegetation; however, the MODIS NDVI is consistently higher than the AVHRR NDVI. The MODIS Enhanced Vegetation Index (EVI) proved more sensitive to dense vegetation than both AVHRR NDVI and MODIS NDVI. EVI and NDVI based on the MODIS 16‐day constrained view angle maximum value composite (CV‐MVC) product captured the seasonal dynamics of the field observations satisfactorily but a standard 16‐day MVC product estimated from the daily MODIS surface reflectance data without view angle constraints yielded higher correlations between the satellite indices and field measurements (R ² values ranging from 0.74 to 0.98). The standard MVC regressions furthermore approach a 1?:?1 line with in situ measured values compared to the CV‐MVC regressions. The 16‐day MVC AVHRR data did not satisfactorily reflect the variation in the in situ data. Seasonal variation in the in situ measurements is captured reasonably with R ² values of 0.75 in 2001 and 0.64 in 2002, but the dynamic range of the AVHRR satellite data is very low—about a third to a half of the values from in situ measurements. Consequently the in situ vegetation indices were emulated much better by the MODIS indices than by the AVHRR NDVI.     

Estimation of solar-induced fluorescence using the canopy reflectance index

This article presents a methodology to quantitatively extract the solar-induced fluorescence (SIF) using the canopy reflectance index. The sensitivity analysis was conducted with a spectral vegetation Fluorescence Model (FluorMOD), and the results demonstrate that Sun zenith angle (θ), fluorescence quantum efficiency (Fi), leaf inclination distribution function (LIDF), leaf temperature (T), leaf area index, and leaf chlorophyll a + b content (chl-a+b) had large effects on the fluorescence radiance at 761 nm (LF,761). Based on the results of the sensitivity analysis, the input parameters θ, Fi, LIDF, T, and chl-a+b varied within a certain range during the generation of the simulated data. Based on the simulated data, R₇₄₀/R₆₃₀, R₆₈₅/R₈₅₀, and R₇₅₀/R₇₁₀ were thought to be the best candidates to extract the fluorescence radiation. The quantitative relationships between the fluorescence retrieved by R₇₄₀/R₆₃₀, R₆₈₅/R₈₅₀, and R₇₅₀/R₇₁₀ and LF,761 were analysed and expressed as functions of θ, Fi, T, and reflectance index. The correlation coefficients (r) between the fluorescence retrieved using R₆₈₅/R₈₅₀, R₇₄₀/R₆₃₀, and R₇₅₀/R₇₁₀ and LF,761 are 0.94, 0.95, and 0.95, respectively, and the root mean square errors (RMSEs) were 0.32, 0.29, and 0.30 W m^?2 μm^?1 sr^?1, respectively. Through comparison with FLD and 3FLD, the method presented in this article yielded better results, and could be used to estimate the fluorescence. This methodology provides new insights into the quantitative retrieval of SIF from the reflectance spectrum.     

A technique for extrapolating and validating forest cover across large regions Calibrating AVHRR data with TM data

Abstract

An approach to extending high-resolution forest cover information across large regions is presented and validated. Landsat Thematic Mapper (TM) data were classified into forest and nonforest for a portion of Jackson County, Illinois. The classified TM image was then used to determine the relationship between forest cover and the spectral signature of Advanced Very High Resolution Radiometer (AVHRR) pixels covering the same location. Regression analysis was used to develop an empirical relationship between AVHRR spectral signatures and forest cover. The regression equation developed from data from the single county calibration area in southern Illinois was then applied to the entire AVHRR scene, which covered all or parts of ten states, to produce a regional map of forest cover. This map was used to derive estimates of forest cover, within a geographical information system (GIS), for each of the 428 counties located within the boundaries of the original AVHRR scene. The validity of the overall regional map was tested by comparing the AVHRR/TM-derived estimates of county forest cover with independent estimates of county forest cover developed by the U.S. Forest Service (USFS). The overall correlation coefficient of the AVHRR/TM and USFS county forest cover estimates was r=0-89 (n=428 counties). Not surpris0ingly, some individual states and the areas nearer to the southern Illinois calibration centre had higher correlation coefficients. Absolute estimates of forest cover percentages were also significantly well predicted. With the future inclusion of multiple calibration centres representing a number of physiographic regions, the method shows promise for predicting continental and global estimates of forest cover.