Quantitative detection of change in grass cover from multi-temporal TM satellite data

In this paper, change in grassland cover near Lake Qinghai, west China was quantitatively detected from satellite remote-sensing data. Two Thematic Mapper images recorded in 1987 and 2000 were radiometrically corrected and used to derive the Normalized Difference Vegetation Index (NDVI). The NDVI image in 2000, after standardization via in situ measured spectra, was converted to a map of grass cover with the aid of in situ grass-cover samples. Another map was produced from the 1987 image after it was radiometrically benchmarked to the 2000 image using the calibration to like-values method. Comparison of these two maps revealed that a total of 36.28 km² of grassland had a higher cover, versus 44.72 km² that experienced grassland degradation in the study area. The absolute cover changed by a net value of??1.27%. The magnitude of change is related inversely to the value of the cover. The large majority of the area (82.6%), however, had a small change that was within ±20%. With this proposed method, it is possible to quantify changes in grassland cover from multi-temporal satellite data if one set of ground samples are concurrently collected with one of the satellite images.     

Quantification of grassland properties: how it can benefit from geoinformatic technologies?

The emergence of advanced geoinformatic techniques raises the feasibility of successfully quantifying grassland properties. Accurate quantification of these parameters faces opportunities and challenges, both of which are reviewed critically in this paper. The principle of quantifying grassland properties is presented first, together with the requirements, followed by a review of the grassland properties (percentage grass cover, grassland biomass and grassland degradation) that have been quantified. Assessment of quantification accuracy has evolved from reliance on the R ² value of regression analysis to comparison against independent samples, with the highest accuracy being 89%. Achievement of higher accuracy is hindered by three obstacles, namely positional uncertainty of in situ samples, differential ground and image sampling intervals, and temporal irreversibility of historical satellite images. It is proposed that the global positioning system (GPS) be used to handle the first challenge, and hyperspatial resolution images to minimize disparity in the sampling intervals. The third challenge should be tackled through radiometric calibration of historic images based on invariant ground targets. With the emergence of hyperspectral imagery (e.g. AVIRIS and CASI), more grassland features (e.g. grassland productivity and carrying capacity) can be quantified in the future in a geographic information system (GIS). It is concluded that advances in the geoinformatic technology will enable more grassland properties to be quantified more accurately.     

Monitoring northern mixed prairie health using broadband satellite imagery

The mixed prairie in Canada is characterized by its low to medium green vegetation cover, high amount of non‐photosynthetic materials, and ground level biological crust. It has proven to be a challenge for the application of remotely sensed data in extracting biophysical variables for the purpose of monitoring grassland health. Therefore, this study was conducted to evaluate the efficiency of broadband‐based reflectance and vegetation indices in extracting ground canopy information. The study area was Grasslands National Park (GNP) Canada and the surrounding pastures, which represent the northern mixed prairie. Fieldwork was conducted from late June to early July 2005. Biophysical variables—canopy height, cover, biomass, and species composition—were collected for 31 sites. Two satellite images, one SPOT 4 image on 22 June 2005, and one Landsat 5 TM image on 14 July 2005, were collected for the corresponding time period. Results show that the spectral curve of the grass canopy was similar to that of the bare soil with lower reflectance at each band. Consequently, commonly used vegetation indices were not necessarily better than reflectance when it comes to single wavelength regions at extracting biophysical information. Reflectance, NDVI, ATSAVI, and two new coined cover indices were good at extracting biophysical information.     

Investigating the spectral and ecological characteristics of grassland communities across an ecological gradient of the Inner Mongolian grasslands with in situ hyperspectral data

Grassland degradation is serious in the Mongolian plateau, especially in Inner Mongolia, China. Accurate monitoring of grassland types and qualities is increasingly important for the purposes of grassland conservation and restoration. Using in situ hyperspectral reflectance data and ground-based ecological measurements, we explored the potential for large-scale monitoring grassland communities using imaging spectroradiometers. We compared the spectral reflectance of the major types of grasslands and field plots with/without livestock grazing. We also did statistical analysis about the relationship between hyperspectral indices and aboveground biomass (AGB) of the surveyed grassland communities. The results showed that: (1) the dominant plant species varied across meadow, typical, and desert steppe, and they also varied between fenced and grazed plots; (2) in situ hyperspectral data are useful for differentiating grassland communities of meadow, typical, and desert steppe and grassland communities with and without livestock grazing; and (3) the prediction accuracies of vegetation indices for AGB decreased from desert to typical and meadow steppe, and the results were contrary for the prediction accuracies of red edge inflection point (REIP). REIP may not be suitable for estimating AGB of the low-density grassland communities. The above results implied that care must be taken while using statistical models to link spectral and ecological measurements in large geographical scales since there is lack of portability over different types of grassland communities. This study provides foundations for future large-scale efforts of monitoring grassland communities in Inner Mongolia using imaging spectroradiometers.     

Spectral unmixing of vegetation,soil and dry carbon cover in arid regions: Comparing multispectral and hyperspectral observations

Remote measurements of the fractional cover of photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV) and bare soil are critical to understanding climate and land-use controls over the functional properties of arid and semi-arid ecosystems. Spectral mixture analysis is a method employed to estimate PV, NPV and bare soil extent from multispectral and hyperspectral imagery. To date, no studies have systematically compared multispectral and hyperspectral sampling schemes for quantifying PV, NPV and bare soil covers using spectral mixture models. We tested the accuracy and precision of spectral mixture analysis in arid shrubland and grassland sites of the Chihuahuan Desert, New Mexico, USA using the NASA Airborne Visible and Infrared Imaging Spectrometer (AVIRIS). A general, probabilistic spectral mixture model, Auto-MCU, was developed that allows for automated sub-pixel cover analysis using any number or combination of optical wavelength samples. The model was tested with five different hyperspectral sampling schemes available from the AVIRIS data as well as with data convolved to Landsat TM, Terra MODIS, and Terra ASTER optical channels. Full-range (0.4-2.5 w m) sampling strategies using the most common hyperspectral or multispectral channels consistently over-estimated bare soil extent and under-estimated PV cover in our shrubland and grassland sites. This was due to bright soil reflectance relative to PV reflectance in visible, near-IR, and shortwave-IR channels. However, by utilizing the shortwave-IR2 region (SWIR2; 2.0-2.3 w m) with a procedure that normalizes all reflectance values to 2.03 w m, the sub-pixel fractional covers of PV, NPV and bare soil constituents were accurately estimated. AVIRIS is one of the few sensors that can provide the spectral coverage and signal-to-noise ratio in the SWIR2 to carry out this particular analysis. ASTER, with its 5-channel SWIR2 sampling, provides some means for isolating bare soil fractional cover within image pixels, but additional studies are needed to verify the results.     

Using hyperspectral remote sensing to estimate chlorophyll‐a and phycocyanin in a mesotrophic reservoir

This research estimates phytoplankton pigment concentrations (chlorophyll‐a (chl‐a) and phycocyanin (PC)) from hyperspectral Airborne Imaging Spectrometer for Applications (AISA) imagery. AISA images were acquired for a meso‐eutrophic reservoir in Central Indiana, USA. Concurrent with the airborne image acquisition, in situ water samples and reflectances were collected. The water samples were subsequently analysed for pigment concentrations, and in situ measured reflectance spectra were used for calibrating the AISA images. Spectral indices, derived from the AISA reflectance spectra, were regressed against the measured pigment concentrations to derive algorithms for estimating chl‐a and PC. The relationship between the pigment concentrations and the spectral indices were analysed and evaluated. The results indicate that the highest correlation occurred between chl‐a and a near‐infrared to red ratio (coefficient of determination R ²?=?0.78) and between PC and the reflectance trough at 628 nm (R ²?=?0.80). The relationship between PC and the reflectance at 628 nm provides an approach to the estimation of cyanobacteria concentration from hyperspectral imagery, which facilitates water‐quality authorities or management agencies in making well‐informed management decisions.     

Predicting C3 and C4 grass nutrient variability using in situ canopy reflectance and partial least squares regression

The use of hyperspectral data to estimate forage nutrient content can be a challenging task, considering the multicollinearity problem, which is often caused by high data dimensionality. We predicted some variability in the concentration of limiting nutrients such as nitrogen (N), crude protein (CP), moisture, and non-digestible fibres that constrain the intake rate of herbivores. In situ hyperspectral reflectance measurements were performed at full canopy cover for C3 and C4 grass species in a montane grassland environment. The recorded spectra were resampled to 13 selected band centres of known absorption and/or reflectance features, WorldView-2 band settings, and to 10 nm-wide bandwidths across the 400–2500 nm optical region. The predictive accuracy of the resultant wavebands was assessed using partial least squares regression (PLSR) and an accompanying variable importance (VIP) projection. The results indicated that prediction accuracies ranging from 66% to 32% of the variance in N, CP, moisture, and fibre concentrations can be achieved using the spectral-only information. The red, red-edge, and shortwave infrared (SWIR) wavelength regions were the most sensitive to all nutrient variables, with higher VIP values. Moreover, the PLSR model constructed based on spectra resampled around the 13 preselected band centres yielded the highest sensitivity to the predicted nutrient variables. The results of this study thus suggest that the use of the spectral resampling technique that uses only a few but strategically selected band centres of known absorption or reflectance features is sufficient for forage nutrient estimation.     

Leaf area index mapping in northern Canada

Leaf area index (LAI) is an important structural vegetation parameter that is commonly derived from remotely sensed data. It has been used as a reliable indicator for vegetation's cover, status, health and productivity. In the past two decades, various Canada-wide LAI maps have been generated by the Canada Centre for Remote Sensing (CCRS). These products have been produced using a variety of very coarse satellite data such as those from SPOT VGT and NOAA AVHRR satellite data. However, in these LAI products, the mapping of the Canadian northern vegetation has not been performed with field LAI measurements due in large part to scarce in situ measurements over northern biomes. The coarse resolution maps have been extensively used in Canada, but finer resolution LAI maps are needed over the northern Canadian ecozones, in particular for studying caribou habitats and feeding grounds.

In this study, a new LAI algorithm was developed with particular emphasis over northern Canada using a much finer resolution of remotely sensed data and in situ measurements collected over a wide range of northern arctic vegetation. A statistical relationship was developed between the in situ LAI measurements collected over vegetation plots in northern Canada and their corresponding pixel spectral information from Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data. Furthermore, all Landsat TM and ETM+ data have been pre-normalized to NOAA AVHRR and SPOT VGT data from the growing season of 2005 to reduce any seasonal or temporal variations. Various spectral vegetation indices developed from the Landsat TM and ETM?+?data were analysed in this study. The reduced simple ratio index (RSR) was found to be the most robust and an accurate estimator of LAI for northern arctic vegetation. An exponential relationship developed using the Theil–Sen regression technique showed an R ² of 0.51 between field LAI measurement and the RSR. The developed statistical relationship was applied to a pre-existing Landsat TM 250 m resolution mosaic for northern Canada to produce the final LAI map for northern Canada ecological zones. Furthermore, the 250 m resolution LAI estimates, per ecological zone, were almost generally lower than those of the CCRS Canada-wide VGT LAI maps for the same ecozones. Validation of the map with LAI field data from the 2008 season, not used in the derivation of the algorithm, shows strong agreement between the in situ LAI measurement values and the map-estimated LAI values.     

Mapping of native plant species and noxious weeds to investigate grassland degradation in the Three-River Headwaters region using HJ-1A/HSI imagery

ABSTRACT

Characterized by an alpine meadow, the ecological environment system in the ‘Three-River Headwaters’ region (TRHR) is considered to be a typical fragile ecological system. Numerous observations and research results have indicated that grassland degradation has occurred in the TRHR in recent years. However, research related to utilize the species information of grass communities to monitor grassland degradation remains rare. Therefore, the aim of this study is to produce the distribution maps of native plant species and noxious weeds to investigate grassland degradation for livestock farming perspective. In this study, the fused HJ-1A/HSI data was combined with field investigation samples to define the coverage of native plant species and noxious weeds at different coverage levels. Then, coverage distribution maps of native plant species and noxious weeds were produced by using support vector machine (SVM) classification and random forests (RF) regression methods. Meanwhile, the overall accuracy (OA) and root-mean-square error (RMSE) of each coverage map were assessed. Finally, a grassland degradation map was derived according to the native plant species and noxious weeds cover information. The experimental results show that (1) the spectral feature of native plant species and noxious weeds can be distinguished based on field measurement spectra in the TRHR; (2) the fractional coverage of native plant species and noxious weeds can be relatively accurately estimated when coverage is divided into nine levels; (3) the grass coverage estimation accuracies of SVM classification are similar with these of RF regression method. The OAs of SVM classification are 69.7% at nine grassland coverage levels for native plant species and noxious weeds, and corresponding RMSEs are 8.2% and 8.0%, respectively; and (4) the coverage of native plant species is generally higher than that of the noxious weeds in the study area.     

In situ reflectance measurements and TM data of some sedimentary rocks with emphasis on white sandstone,southwestern Sinai,Egypt

This work presents the results of in situ reflectance measurements and Landsat-TM data analysis for some sedimentary rocks exposed in southwestern Sinai. Particular emphasis was given to white sandstone. In situ reflectance measurements were carried out in four selected sites (Musaba Salama, Abu Natash, El Dehisa and Abu Qafas areas) to define the distinctive surface reflectance patterns caused by the abundance of silicates, carbonates, clay minerals and iron oxides in white sandstone and other sedimentary rock types (clay, kaolin, limestone and dolomite). Moreover, in situ measured radiometric data were used to establish the theoretical basis for lithological discrimination.

Enhanced band ratios (2/3, 3/4, 4/5 and 5/7) and colour stretched ratio composite (2/3, 3/4 and 5/7) of the TM data 25 January 1984 of the Musaba Salama area were utilized to identify rocks rich in silicates and thereby to distinguish the white sandstone. The results demonstrated that the processed TM data can be used reliably in arid regions to distinguish white sandstone from other sandstone varieties as well as other rock types (limestone, dolomite, kaolin and shale).     

Discriminating between cool season and warm season grassland cover types in northeastern Kansas

This study assesses the ability of multitemporal Landsat Thematic Mapper (TM) data and the normalized difference vegetation index (NDVI) to spectrally separate grazed cool season and warm season grassland cover types in Douglas County, Kansas. Biophysical data collected during the summer of 1997 suggest that differences in the per cent of total living vegetation cover, per cent of senescent vegetation, and proportion of forb cover between the two grassland cover types could make cool season and warm season grassland cover types spectrally distinct. The results show that the two grassland cover types were spectrally different in several spring (May) and mid-summer (July) bands, but not in any fall (September) bands. Furthermore, the two grassland cover types could be discriminated with a high level of accuracy. Accuracy assessments of the three single dates showed that the mid-summer (July) image and NDVI discriminated between the grassland cover types most accurately (81.8%). The multitemporal TM and NDVI data did not improve the spectral discrimination of the two grassland cover types over the mid-summer image or NDVI and had classification accuracy levels of 63.6% and 68.2%, respectively.     

Using remote sensing to estimate sea ice thickness in the Bohai Sea,China based on ice type

It is challenging to use traditional remote sensing techniques to accurately determine the extent and thickness of ice in the Bohai Sea, on account of the presence of sea impurities (i.e. mud, salt bubbles and sand) and shape irregularities. Accordingly, we performed a series of reflectance spectra experiments to empirically link remote measurements of surface reflectance with in situ sea ice thickness measurements in the Bohai Sea. Two years of Thematic Mapper (TM) band 2 and Moderate Resolution Imaging Spectroradiometer (MODIS) band 4 data were used to distinguish between the following sea ice types, using spectral reflectance thresholds of 6.4, 9.6, 10.3 and 12.1%: (a) clean nilas ice (a thin elastic crust of ice up to 10 cm thick that, under pressure, may deform by finger rafting; (b) nilas ice and pancake ice (roughly circular accumulations of frazil ice, usually less than about 3 m in diameter, with raised rims caused by collisions); (c) grey and grey–white ice; and (d) cumulative ice (<30 cm). By establishing a relationship between sea ice type and ice thickness, a novel, practical and low-cost remote sensing technique is introduced to estimate the extent and distribution of sea ice thickness over a large spatial scale. The results obtained by remote sensing are validated with in situ ice shape measurements. The MODIS and TM data are used to distinguish between three ice thickness grades (6–9, 10–20 and 20–30 cm).     

Mapping inland water carbon content with Landsat 8 data

Landsat 8 is the first Earth observation satellite with sufficient radiometric and spatial resolution to allow global mapping of lake CDOM and DOC (coloured dissolved organic matter and dissolved organic carbon, respectively) content. Landsat 8 is a multispectral sensor however, the number of potentially usable band ratios, or more sophisticated indices, is limited. In order to test the suitability of the ratio most commonly used in lake carbon content mapping, the green–red band ratio, we carried out fieldwork in Estonian and Brazilian lakes. Several atmospheric correction methods were also tested in order to use image data where the image-to-image variability due to illumination conditions would be minimal. None of the four atmospheric correction methods tested, produced reflectance spectra that matched well with in situ measured reflectance. Nevertheless, the green–red band ratio calculated from the reflectance data was in correlation with measured CDOM values. In situ data show that there is a strong correlation between CDOM and DOC concentrations in Estonian and Brazilian lakes. Thus, mapping the global CDOM and DOC content from Landsat 8 is plausible but more data from different parts of the world are needed before decisions can be made about the accuracy of such global estimation.     

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.     

Atmospheric correction of Advanced Very High Resolution Radiometer imagery

The present paper proposes an automated approach to estimate the aerosol reflectance at the Advanced Very High Resolution Radiometer (AVHRR) red channel. The aerosol dominant pixels were separated through two orthogonal transforms. The aerosol reflectance ratio at these pixels was estimated through regression. The results are validated with in situ measurements. The retrieved water-leaving reflectance matched the modelled values with a relative error below 45%. The smallest error values were at the stations with the closest sampling time to image acquisition. However, a weak correlation of 16% was found between water-leaving reflectance and aerosol signals. This suggested that these errors could be attributed to the spatial and temporal variability between the two sampling methods (ship measurement and pixel reflectance).     

Development of topsoil grain size index for monitoring desertification in arid land using remote sensing

The grain size composition of topsoil characterizes the soil texture and other physical properties. The coarsening of topsoil grain size is a visible symbol of land degradation; thereby the change in topsoil grain size can be potentially used to monitor desertification using remote sensing. This study proposes a new index for detecting topsoil grain size composition through ground in situ soil spectral reflectance measurements and soil physical analysis in the laboratory. The proposed topsoil grain size index (GSI), which has a positive correlation with fine sand content, was then applied to detect desertification in Siziwang Banner, Inner Mongolia, China, using a Landsat TM (1993) image and a Landsat ETM+ image (2000). The result shows the fine sand content of topsoil increased in most places, indicating a coarsening process of the topsoil in the study area. The fast soil coarsening of degradation is largely caused by human activities.     

Retrieval of subpixel Tamarix canopy cover from Landsat data along the Forgotten River using linear and nonlinear spectral mixture models

Repeatable approaches for mapping saltcedar (Tamarix spp.) at regional scales, with the ability to detect low density stands, is crucial for the species' effective control and management, as well as for an improved understanding of its current and potential future dynamics. This study had the objective of testing subpixel classification techniques based on linear and nonlinear spectral mixture models in order to identify the best possible classification technique for repeatable mapping of saltcedar canopy cover along the Forgotten River reach of the Rio Grande. The suite of methods tested were meant to represent various levels of constraints imposed in the solution as well as varying levels of classification details (species level and landscape level), sources for endmembers (space-borne multispectral image, airborne hyperspectral image and in situ spectra measurements) and mixture modes (linear and nonlinear). A multiple scattering approximation (MSA) model was proposed as a means to represent canopy (image) reflectance spectra as a nonlinear combination of subcanopy (field) reflectance spectra. The accuracy of subpixel canopy cover was assessed through a 1-m spatial-resolution hyperspectral image and field measurements. Results indicated that: 1) When saltcedar was represented by one single image spectrum (endmember), the unconstrained linear spectral unmixing with post-classification normalization produced comparable accuracy (OA = 72%) to those delivered by partially and fully constrained linear spectral unmixing (63-72%) and even by nonlinear spectral unmixing (73%). 2) The accuracy of the fully constrained linear spectral unmixing method increased (from 67% to 77%) when the classes were represented with several image spectra. 3) Saltcedar canopy reflectance showed the strongest nonlinear relationship with respect to subcanopy reflectance, as indicated through a range of estimated canopy recollision probabilities. 4) Despite the considerations of these effects on canopy reflectance, the inversion of the nonlinear spectral mixing model with subcanopy reflectance (field) measurements yielded slightly lower accuracy (73%) than the linear counterpart (77%). Implications of these results for region-wide monitoring of saltcedar invasion are also discussed.     

Defining the Landsat TM/ETM+ and CHRIS/PROBA spectral regions in which turbidity can be retrieved in inland waterbodies using field spectroscopy

This article describes the results obtained by an existing campaign in which in situ spectroradiometric measurements using a GER1500 field spectroradiometer, Secchi disk depth, and turbidity measurements (using a portable turbidity meter) were acquired at Asprokremmos Reservoir in Paphos District, Cyprus. Field spectroradiometric and water quality data span 18 sampling campaigns during the period May 2010–October 2010. By applying several regression analyses between ‘In-Band’ mean reflectance values against turbidity values for all spectral bands corresponding to Landsat TM/ETM+ (Bands 1 to 4) and CHRIS/PROBA (Bands A1 to A62), the highest correlation was found for Landsat TM/ETM+ Band 3 (R² = 0.85) and for CHRIS/PROBA Bands A30 to A32 (R² = 0.90).     

Reflectance relations in row crop scenes

Abstract

Models that relate composite reflectance to its components are useful for inferring crop growth information from measured scene reflectance. Radiation measurements in Thematic Mapper bands (TM1, TM2, TM3, and TM4) were made from cotton, soybean, sunflower and grain sorghum at three stages of growth and used to evaluate three reflectance models. Two models, AIRM1 and AIRM2, assumed that scene components contribute in an additive independent manner to composite reflectance. The third model, TRIM, assumes that radiation transmitted through the canopy interacts with bare soil in two scene components. The AIRM2 and TRIM models divide the composite reflectance into canopy, bare soil, and shadow components, but AIRM1 considers only canopy and bare soil. Ranking of models in order of decreasing accuracy for predicting composite reflectance in bands TM3 and TM4 was AIRM2, TRIM, and AIRM1. The AIRM1 and AIRM2 models estimated average TM3 reflectance at full plant cover between 1 and 4 per cent for all crops. Their estimations in band TM4 were 60 per cent for cotton, soybean, and sunflower with grain sorghum being 50 percent.

Measured canopy and composite reflectances were graphically compared at the lowest and highest levels of canopy cover observed in each crop. Measured band TM3 canopy reflectance did not change with solar zenith angle, composite reflectance decreased with increasing zenith angle at the lowest canopy cover levels but was invariant at the highest canopy cover levels. Measured band TM4 canopy reflectance increased linearly with increasing solar zenith angle in all crops, but for composite reflectance this pattern was observed only at the highest canopy cover levels of cotton and soybean. The absence of a uniform pattern between band TM4 composite reflectance and solar zenith angle in grain sorghum is presumably due to large horizontal leaf angles and in sunflower to long vertical spacings between leaves. Predicted compared to measured band TM3 and TM4 composite reflectances of the AIRM1 and AIRM2 models were insensitive but the TRIM model was overly sensitive to zenith angle.     

Spectral reflectance of coral reef benthos and substrate assemblages on Heron Reef,Australia

Studies investigating the spectral reflectance of coral reef benthos and substrates have focused on the measurement of pure endmembers, where the entire field of view (FOV) of a spectrometer is focused on a single benthos or substrate type. At the spatial scales of the current satellite sensors, the heterogeneity of coral reefs even at a sub-metre scale means that many individual image pixels will be made up of a mixture of benthos and substrate types. If pure endmember spectra are used as training data for image classification, there is a spatial discrepancy, because many pixels will have a mixed endmember spectral reflectance signature. This study investigated the spectral reflectance of coral reef benthos and substrates at a spatial scale directly linked to the pixel size of high spatial resolution imaging systems, by incorporating multiple benthos and substrate types into the spectrometer FOV in situ. A total of 334 spectral reflectance signatures were measured of 19 assemblages of the coral reef benthos and substrate types. The spectra were analysed for separability using first derivative values, and a discrimination decision tree was designed to identify the assemblages. Using the decision tree, it was possible to identify 15 assemblages with a mean overall classification accuracy of 62.6%.