Compact Airborne Spectrographic Imager Research Articles

Identification of the spectral characteristics of British semi-natural upland vegetation using direct ordination: a case study from Dartmoor, UK

The direct ordination technique Canonical Correspondence Analysis (CCA) is applied to the examination of the relationships between the floristic composition of semi-natural vegetation and its spectral reflectance. Paired measurements of floristic and spectral characteristics, the latter being measured using a portable field spectrometer, were collected at ground level for a range of upland semi-natural vegetation within two study sites on Dartmoor, south-west England. The spectral data were converted to simulate a Compact Airborne Spectrographic Imager (CASI) bandset. Both floristic and spectral data sets were then jointly ordinated using CCA. A sequence of individual species changes along the first ordination axis was identified that showed a strong correspondence with variation in the simulated CASI wavebands covering the 736 nm to 870 nm wavelengths. A significant relationship between first axis CCA scores for quadrats and their estimated total percentage vegetation cover was also identified. Although UK National Vegetation Classification (NVC) categories corresponded to a general sequence of plant community types along the first CCA axis, with the possible exception of the U20 Pteridium aquilinum-dominated community, it proved impossible to demonstrate any close link between any specific plant community type and a distinct set of spectral characteristics because of the continuum nature of the vegetation. The second axis of the CCA showed no interpretable relationship with variation in floristic/spectral data and this was confirmed further by use of Detrended Canonical Correspondence Analysis (DCCA). Possibilities for further research into floristic/spectral data using CCA/DCCA are discussed.

Remote sensing of water cloud droplet size distributions using the backscatter glory: a case study

Abstract. Cloud single scattering properties are mainly determined by the effective radius of the droplet size distribution. There are only few exceptions where the shape of the size distribution affects the optical properties, in particular the rainbow and the glory directions of the scattering phase function. Using observations by the Compact Airborne Spectrographic Imager (CASI) in 180° backscatter geometry, we found that high angular resolution aircraft observations of the glory provide unique new information which is not available from traditional remote sensing techniques: Using only one single wavelength, 753nm, we were able to determine not only optical thickness and effective radius, but also the width of the size distribution at cloud top. Applying this novel technique to the ACE-2 CLOUDYCOLUMN experiment, we found that the size distributions were much narrower than usually assumed in radiation calculations which is in agreement with in-situ observations during this campaign. While the shape of the size distribution has only little relevance for the radiative properties of clouds, it is extremely important for understanding their formation and evolution.

Retrieval of crop chlorophyll content and leaf area index from decompressed hyperspectral data: the effects of data compression

The objective of this study is to evaluate whether the retrieval of the leaf chlorophyll content and leaf area index (LAI) for precision agriculture application from hyperspectral data is significantly affected by data compression. This analysis was carried out using the hyperspectral data sets acquired by Compact Airborne Spectrographic Imager (CASI) over corn fields at L'Acadie experimental farm (Agriculture and Agri-Food Canada) during the summer of 2000 and over corn, soybean and wheat fields at the former Greenbelt farm (Agriculture and Agri-Food Canada) in three intensive field campaigns during the summer of 2001. Leaf chlorophyll content and LAI were retrieved from the original data and the reconstructed data compressed/decompressed by the compression algorithm called Successive approximation multi-stage vector quantization (SAMVQ) at compression ratios of 20:1, 30:1, and 50:1. The retrieved products were evaluated against the ground-truth. In the retrieval of leaf chlorophyll content (the first data set), the spatial patterns were examined in all of the images created from the original and reconstructed data and were proven to be visually unchanged, as expected. The data measures R 2, absolute RMSE, and relative RMSE between the leaf chlorophyll content derived from the original and reconstructed data cubes, and the laboratory-measured values were calculated as well. The results show the retrieval accuracy of crop chlorophyll content is not significantly affected by SAMVQ at the compression ratios of 20:1, 30:1, and 50:1, relative to the observed uncertainties in ground truth values. In the retrieval of LAI (the second data set), qualitative and quantitative analyses were performed. The results show that the spatial and temporal patterns of the LAI images are not significantly affected by SAMVQ and the retrieval accuracies measured by the R 2, absolute RMSE, and relative RMSE between the ground-measured LAI and the estimated LAI are not significantly affected by the data compression either.

Relationship between airborne multispectral image texture and aspen defoliation

A Compact Airborne Spectrographic Imager (CASI) multiresolution dataset, comprised of imagery with spatial resolutions of 60 cm, 1 m and 2 m, was used to asses the relationship between defoliation severity of aspen (Populus tremuloides Michx.) stands infested with the Bruce spanworm (Operophtera bruceata), the Leaf Area Index (LAI) of these stands and the CASI image components comprising of Normalized Difference Vegetation Index (NDVI) and image texture. The study area was located in the foothills of the Alberta Canadian Rockies. Defoliation severity, LAI, and crown closure of the stands were measured on the ground. Multiple stepwise regression methods were used to develop relationships between the field and imagery data. Image texture derived from the grey-level co-occurrence matrix of the first principle component, or ‘brightness' image, was incorporated into the discriminant analysis of defoliation severity classes. The highest spatial resolution imagery outperformed the coarser image resolutions. The characteristics of the defoliation changed the spectral response of the moderately and severely defoliated stands considerably when compared to healthy stands. The following paper demonstrates that aspen defoliation severity can be detected with CASI image data, specifically through the incorporation of imagery spatial component captured by image texture.

The cover of living and dead corals from airborne remote sensing

Trends in coral cover are widely used to indi- cate the health of coral reefs but are costly to obtain from field survey over large areas. In situ studies of reflected spectra at the coral surface show that living and recently dead colonies can be distinguished. Here, we investigate whether such spectral differences can be detected using an airborne remote sensing instrument. The Compact Airborne Spectrographic Imager (Itres Research Ltd, Canada) was flown in two configurations: 10 spectral bands with 1-m 2 pixels and 6 spectral bands with 0.25-m 2 pixels. First, we show that an instrument with 10 spectral bands possesses adequate spectral res- olution to distinguish living Porites, living Pocillopora spp., partially dead Porites, recently dead Porites (total colony mortality within 6 months), old dead (>6 months) Porites, Halimeda spp., and coralline red algae when there is no water column to confuse spectra. All substrata were distinguished using fourth-order spectral derivatives around 538 nm and 562 nm. Then, at a shallow site (Tivaru) at Rangiroa Atoll, Tuamotu Archipelago (French Polynesia), we show that live and dead coral can be distinguished from the air to a depth of at least 4 m using first- and fourth-order spectral derivatives between 562-580 nm. However, partially dead and recently dead Poritescolonies could not be distinguished from an airborne platform. Spectral dif- ferences among substrata are then exploited to predict the cover of reef substrata in ten 25-m 2 plots at nearby Motu Nuhi (max depth 8 m). The actual cover in these plots was determined in situ using quadrats with a 0.01- m 2 grid. Considerable disparity occurred between field and image-based measures of substrate cover within individual 25-m 2 quadrats. At this small scale, disparity, measured as the absolute difference in cover between field and remote-sensing methods, reached 25% in some substrata but was always less than 10% for living coral (99% of which consisted of Porites spp.). At the scale of the reef (all ten 25-m 2 quadrats), however, disparities in percent cover between imagery and field data were less than 10% for all substrata and extremely low for some classes (e.g. <3% for living Porites, recently dead Porites and Halimeda). The least accurately estimated substrata were sand and coralline red algae, which were overestimated by absolute values 7.9% and 6.6%, respectively. The precision of sampling was similar for field and remote-sensing methods: field methods re- quired 19 plots to detect a 10% difference in coral cover among three reefs with a statistical power of 95%. Re- mote-sensing methods required 21 plots. However, it took 1 h to acquire imagery over 92,500 m 2 of reef, which represents 3,700 plots of 25 m 2 each, compared with 3 days to survey 10 such plots underwater. There were no significant differences in accuracy between 1-m 2 and 0.25-m 2 image resolutions, suggesting that the advantage of using smaller pixels is offset by reduced spectral information and an increase in noise (noise was observed to be 1.6-1.8 times greater in 0.25-m 2 pixels). We show that airborne remote sensing can be used to monitor coral and algal cover over large areas, provid- ing that water is shallow and clear, and that brown fleshy macroalgae are scarce, that depth is known independently (e.g. from sonar survey).

Assessment of crown condition in eucalypt vegetation by remotely sensed optical indices.

Leaf and crown damage and discoloration characteristics are important variables when defining the health of eucalypt tree species and have been used as key indicators of environmental quality. These indicators can vary significantly over a few hectares, especially in mixed-species forests, making field-based environmental surveillance of crown condition an extremely expensive and logistically impractical task. Reflectance in narrow spectral wavelengths obtained from a field-based spectroradiometer and a Compact Airborne Spectrographic Imager 2 (CASI-2) were collected over eucalypt vegetation of varying condition in southeastern Australia and compared with leaf- and crown-based attributes including percent red foliage discoloration, percent leaf damage, and crown density and crown foliage condition. Of the leaf attributes sampled, percent leaf damage was well correlated with a red-green spectral index (r = 0.68, p < 0.01), and percent red discoloration was well correlated with the slope of the red-edge for selected species (r = 0.89, p < 0.001). Within-tree crown density was well correlated with the slope of the red-edge (r = 0.77, p < 0.001) and a previously published index of plant stress with crown foliage condition (r = 0.88, p < 0.01) for selected species. Despite evidence of strong interspecific variability, a set of narrow spectral wavelengths in the visible and near-infrared regions of the spectrum have been identified that will be useful in the development of forest ecosystem environmental quality indicators.

Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture

A growing number of studies have focused on evaluating spectral indices in terms of their sensitivity to vegetation biophysical parameters, as well as to external factors affecting canopy reflectance. In this context, leaf and canopy radiative transfer models are valuable for modeling and understanding the behavior of such indices. In the present work, PROSPECT and SAILH models have been used to simulate a wide range of crop canopy reflectances in an attempt to study the sensitivity of a set of vegetation indices to green leaf area index (LAI), and to modify some of them in order to enhance their responsivity to LAI variations. The aim of the paper was to present a method for minimizing the effect of leaf chlorophyll content on the prediction of green LAI, and to develop new algorithms that adequately predict the LAI of crop canopies. Analyses based on both simulated and real hyperspectral data were carried out to compare performances of existing vegetation indices (Normalized Difference Vegetation Index [NDVI], Renormalized Difference Vegetation Index [RDVI], Modified Simple Ratio [MSR], Soil-Adjusted Vegetation Index [SAVI], Soil and Atmospherically Resistant Vegetation Index [SARVI], MSAVI, Triangular Vegetation Index [TVI], and Modified Chlorophyll Absorption Ratio Index [MCARI]) and to design new ones (MTVI1, MCARI1, MTVI2, and MCARI2) that are both less sensitive to chlorophyll content variations and linearly related to green LAI. Thorough analyses showed that the above existing vegetation indices were either sensitive to chlorophyll concentration changes or affected by saturation at high LAI levels. Conversely, two of the spectral indices developed as a part of this study, a modified triangular vegetation index (MTVI2) and a modified chlorophyll absorption ratio index (MCARI2), proved to be the best predictors of green LAI. Related predictive algorithms were tested on CASI (Compact Airborne Spectrographic Imager) hyperspectral images and, then, validated using ground truth measurements. The latter were collected simultaneously with image acquisition for different crop types (soybean, corn, and wheat), at different growth stages, and under various fertilization treatments. Prediction power analysis of proposed algorithms based on MCARI2 and MTVI2 resulted in agreements between modeled and ground measurement of non-destructive LAI, with coefficients of determination ( r 2) being 0.98 for soybean, 0.89 for corn, and 0.74 for wheat. The corresponding RMSE for LAI were estimated at 0.28, 0.46, and 0.85, respectively.

A multispectral remote sensing study of coastal waters off Vancouver Island

In March 1996, a multispectral aircraft survey of the coastal waters off Vancouver Island was carried out using a Compact Airborne Spectrographic Imager (CASI). This survey was combined with in situ measurements of water properties (phytoplankton composition, phytoplankton pigments, absorption spectra of phytoplankton, and concentration of dissolved organic carbon, or DOC). Comparison of the phytoplankton absorption data from this experiment with similar data from other regions shows that phytoplankton community has a significant impact on the spectral form and magnitude of absorption spectra, when normalized to unit chlorophyll-a. Concurrent measurements of in situ properties and aircraft data were obtained at eight stations. The in situ measurements of phytoplankton absorption and estimates of downwelling irradiance based on a clear-sky atmospheric-transmission model are used as inputs to a model of water-leaving irradiance. The modelled irradiances are compared with the remotely sensed values of water-leaving radiances. The observed differences between model and observation are used to evaluate the potential influence of DOC on water-leaving radiance. Practical difficulties of separating the phytoplankton signal from that of the coloured component of DOC (also known as yellow substance) are examined. Algorithms for estimation of the concentration of chlorophyll-a (the major phytoplankton pigment) can be based on their absorption or fluorescence properties. The distribution of chlorophyll-a in the study area is estimated using both these approaches, and possible causes for the observed discrepancies are discussed.

Detection and assessment of trees with Phellinus weirii (laminated root rot) using high resolution multi-spectral imagery

The forests of western North America are affected by root diseases caused by several endemic fungi. These have both important economical and ecological impacts. Phellinus weirii (laminated root rot) is particularly important in coastal Douglas fir forests. Forest managers would like to know the location of pockets of Phellinus weirii infected trees for the purpose of salvage, remedial activities and inventory. Airborne multi-spectral imagers, coupled with automated detection of damaged trees have potential to provide a cost-effective survey method. Two sets of Compact Airborne Spectrographic Imager (CASI) airborne multi-spectral imagery were acquired at 60 cm resolution over the same Douglas fir dominated site in coastal British Columbia, Canada. They were acquired in successive years and radiometric corrections for the effects of illumination and view angle applied. Trees of varying levels of root rot symptoms were assessed in the field and manually delineated on the imagery. Spectral properties of these trees were related to levels of damage symptoms. There was considerable overlap of the spectral signatures of the different damage levels, especially healthy through moderate. The range of reflectances for healthy trees was large. The near-infrared and red bands and band ratio involving those two bands proved most related to root rot damage. A blue band was also useful, as were ratios of the near-infrared or red bands to the blue band. Classification of these trees using the best combination of four spectral bands indicated average class accuracies in the order of 55–60% for healthy, light-healthy, light, moderate, severe, 100% needle loss, snag and shadowed snag classes. There was important confusion among the moderate through to healthy class. However, these classes are a finer categorization than is necessary for most applications. Accuracy for broader classes was much better (e.g. average class accuracies were 82% if a tolerance of ±1 class was permitted, ranging from 50–100% for individual classes). An automated tree isolation method was applied to the data. This automated tree isolation was good for the 1995 data but suffered from splitting of large trees into several segments on the 1996 data. All but one of the ground reference trees had associated automatically isolated tree crowns. Classification of the isolations corresponding well to ground reference trees was similar to accuracies for the manually delineated trees, but poorer if ground reference trees without a good matched isolation are considered an error (42% and in the order of 60% with a ±1 class tolerance). The overall distribution of root rot damaged trees as indicated by the automated tree isolation and classification was spot checked throughout the site. There was a generally good correspondence, with concentrations of moderate and severe damage trees being associated with areas of root disease. Concentrations of predominantly light damage trees were not a reliable indication of root disease, and forest regions where the main symptoms of root disease are light will be difficult to survey. Some damage zones occurred that seemed to be related to poor health but not specifically related to root disease. As well, isolated trees with similar characteristics as laminated root rot infected trees do appear on the imagery in scattered locations unrelated to root disease activity. It is felt that these false alarms can be largely mitigated by identifying the characteristic pattern of root damaged trees (i.e. stressed trees around a centre, the centre often being a hole or gap in the canopy). High resolution multi-spectral imagery combined with automated procedures seems viable for detecting laminated root rot centres when severe symptoms are present.

Using high spatial resolution hyperspectral imagery to map intertidal habitat structure in Hood Canal, Washington, U.S.A.

An understanding of the distribution and structure of discontinuous nearshore habitats is critical to effectively manage estuarine resources, especially migratory and mobile nekton that may depend on the integrity of shallow-water environments. We used 19-band compact airborne spectrographic imager (CASI) imagery, collected at 1.5 m spatial resolution, to map two cover classes of eelgrass (Zostera marina) and six other estuarine habitat classes along 64 km of the intertidal shoreline of Hood Canal in the Pacific Northwest, United States. We used control points derived from digital orthoquads (DOQ) and a differential global positioning system (GPS) to geometrically correct CASI flight lines to within 4.3–23.5 m root mean square error (RMSE). After unsupervised and supervised classification, we found good correspondence between classified eelgrass polygons and field training and ground-truthing data. Although this was not the focus of our habitat mapping, the same was true for most of the other habitat classes, except for oyster beds, which were difficult to separate from the wet sand–gravel–cobble class. We are encouraged by the ability of CASI to produce spatially accurate, high-resolution descriptions of intertidal habitat structure. Results from this study will become the foundation of a broader study to develop a quantitative index of essential habitat quality for migrating juvenile summer chum salmon (Oncorhynchus keta) in Hood Canal. When CASI image processing is complete, fisheries scientists–managers will be able to effectively evaluate estuarine landscape patterns at a spatial scale appropriate for migrating juvenile summer chum salmon.

SYNOPTIC MAPPING OF SHALLOW, MARINE, COASTAL COMMUNITIES USING AIRBORNE HYPERSPECTRAL SENSORS: TECHNICAL ASPECTS, OPERATIONAL APPROACHES, AND PROVEN APPLICATIONS

The integrity of littoral ecosystems is seriously threatened by the sustained and evergrowing exploitation of coastal resources by humankind, the introduction of non indigenous species, and the ongoing climate change. The concurrent exploitation and preservation of coastal ecosystems is a major challenge, which can only be effectively addressed using synoptic tools and approaches for detailed mapping and monitoring of subtidal communities. One of the most promising technologies to this end is the use of airborne hyperspectral remote sensing technologies that feature both high spectral and spatial resolutions. This paper provides an overview of the technical aspects and operational approaches to map the extent of shallow, marine, coastal habitats over large scales using the compact airborne spectrographic imager (casi). The applicability of this approach is demonstrated through a review of recent subtidal mapping projects conducted by Hyperspectral Data International in tropical and temperate zones

Retrieval of the canopy leaf area index in the BOREAS flux tower sites using linear spectral mixture analysis

This paper reports on the use of linear spectral mixture analysis for the retrieval of canopy leaf area index (LAI) in three flux tower sites in the Boreal Ecosystem-Atmosphere Study (BOREAS) southern study area: Old Black Spruce, Old Jack Pine, and Young Jack Pine (SOBS, SOJP, and SYJP). The data used were obtained by the Compact Airborne Spectrographic Imager (CASI) with a spatial resolution of 2 m in the winter of 1994. The convex geometry method was used to select the endmembers: sunlit crown, sunlit snow, and shadow. Along transects for these flux tower sites, the fraction of sunlit snow was found to have a higher correlation with the field-measured canopy LAI than the fraction of sunlit crown or the fraction of shadow. An empirical equation was obtained to describe the relation between canopy LAI and the fraction of sunlit snow. There is a strong correlation between the estimated LAI and the field-measured LAI along transects (with R 2 values of 0.54, 0.71, and 0.60 obtained for the SOBS, SYJP, and SOJP sites, respectively). The estimated LAI for the whole tower site is consistent with that obtained by the inversion of a canopy model in our previous study where values of 0.94, 0.92, and 0.63 were obtained for R 2 for the SOBS, SYJP and SOJP sites, respectively. The CASI 2-m summer data over the SOBS site was also employed to investigate the possibility of deriving canopy LAI from the summer data using linear mixture analysis. At a spatial resolution of 10 m, the correlation between the field-measured LAI and the estimated LAI along transects is small at R 2 less than 0.3, while R 2 increases to 0.6 at a spatial resolution of 30 m. The difficulty in canopy LAI retrieval from the summer data at a spatial resolution of 10 m is likely due to the variation of the understory reflectance across the scene, although spatial misregistration of the CASI data used may also be a possible contributing factor.

SpaRef: a clustering algorithm for multispectral images

Multispectral images such as multispectral chemical images or multispectral satellite images provide detailed data with information in both the spatial and spectral domains. Many segmentation methods for multispectral images are based on a per-pixel classification, which uses only spectral information and ignores spatial information. A clustering algorithm based on both spectral and spatial information would produce better results. In this work, spatial refinement clustering (SpaRef), a new clustering algorithm for multispectral images is presented. Spatial information is integrated with partitional and agglomeration clustering processes. The number of clusters is automatically identified. SpaRef is compared with a set of well-known clustering methods on compact airborne spectrographic imager (CASI) over an area in the Klompenwaard, The Netherlands. The clusters obtained show improved results. Applying SpaRef to multispectral chemical images would be a straight-forward step.

Hyperspectral Remote Sensing of Forest Condition: Estimating Chlorophyll Content in Tolerant Hardwoods

Abstract To develop practical and objective measures of forest condition, the Bioindicators of Forest Sustainability Project has used a physiological, remote sensing approach that emphasizes identifying early warning measures of stress effects in forests. While stress indicators exist at the leaf level (e.g., chlorophyll fluorescence, pigment levels), developing reliable indicators at the canopy level is a challenge. Hyperspectral sensors, such as the Compact Airborne Spectrographic Imager (CASI), may be useful in remotely detecting vegetation stress effects. In this study, an inverse modeling approach demonstrated that the CASI could be used to map chlorophyll content (root mean square errors ranging from 12.6 to 13.0 mg/cm2) following different silvicultural treatments in a tolerant hardwood (sugar maple [Acer saccharum M.]) forest. This capability could be readily applied to operationally assessing forest physiological strain and in classifying forest condition based on chlorophyll content. A change analysis study was also conducted to evaluate chlorophyll estimation across seasons for a range of sites. The implications of these findings and recommendations for a prototype system to monitor forest condition are presented. FOR. SCI. 49(3):381–391.

Classification of hyperspectral data by decision trees and artificial neural networks to identify weed stress and nitrogen status of corn

This study evaluates the potential of decision tree classification algorithms for the classification of hyperspectral data, with the goal of discriminating between different growth scenarios in a cornfield. A comparison was also made between decision tree and artificial neural networks (ANNs) classification accuracies. In the summer of the year 2000, a two-factor field experiment representing different crop conditions was carried out. Corn was grown under four weed management strategies: no weed control, control of grasses, control of broadleaf weeds, and full weed control with nitrogen levels of 60, 120, and 250 N kg/ha. Hyperspectral data using a Compact Airborne Spectrographic Imager were acquired three times during the entire growing season. Decision tree technology was applied to classify different treatments based on the hyperspectral data. Various tree-growing mechanisms were used to improve the accuracy of classification. Misclassification rates of detecting all the combinations of different nitrogen and weed categories were 43, 32, and 40% for hyperspectral data sets obtained at the initial growth, the tasseling and the full maturity stages, respectively. However, satisfactory classification results were obtained when one factor (nitrogen or weed) was considered at a time. In this case, misclassification rates were only 22 and 18% for nitrogen and weeds, respectively, for the data obtained at the tasseling stage. Slightly better results were obtained by following the ANN approach. However, the advantage with the decision tree was the formulation of simple and clear classification rules. The highest accuracy was obtained for the data acquired at tasseling stage. The results indicate the potential of decision tree classification algorithms and ANN usage in the classification of hyperspectral data for crop condition assessment.

The topographic normalization of hyperspectral data: implications for the selection of spectral end members and lithologic mapping

Compact Airborne Spectrographic Imager (CASI) hyperspectral data is used to investigate the effects of topography on the selection of spectral end members, and to assess whether the topographic correction improves the discrimination of rock units for lithologic mapping. A publicly available Digital Elevation Model (DEM), at a scale of 1:50,000, is used to model the radiance variation of the scene as a function of topography, assuming a Lambertian surface. Skylight is estimated and removed from the airborne data using a dark object correction. The CASI data is corrected on a pixel-by-pixel basis to normalize the scene to a uniform solar illumination and viewing geometry. The results show that topography has the effect of expanding end member clusters at times resulting in the overlap of clusters and that the correction process can effectively reduce the variation in detected radiance due to changes in local illumination. When topographic effects are embedded in the hyperspectral data, methods typically used for the selection of end members, such as the convex hull method, can miss end members or result in the selection of nonrepresentative pixels as end members. Thus, end members selected by some conventional methods are very likely “incomplete” or “nonrepresentative” if the topographic effect is embedded in the data. As shown in this study, the topographic correction can reveal hidden end members and achieve a better representation of end members via the statistical center of isolated clusters.

Potential of airborne hyperspectral remote sensing to detect nitrogen deficiency and weed infestation in corn

A compact airborne spectrographic imager (CASI) was used to obtain images over a field that had been set up to study the effects of various nitrogen application rates and weed control on corn ( Zea mays). The objective was to determine to what extent the reflectances obtained in the 72 visible and near-infrared (NIR) wavebands (from 409 to 947 nm) might be related to differences associated with combinations of weed control (none, full, grasses only or broadleafs only) and nitrogen application rate (60, 120 or 250 kg/ha). Plots were arranged in split-plot experiment in completely randomized design at the McGill University Research Farm on Macdonald Campus, Ste Anne de Bellevue, Que., Canada. Weeding treatments were assigned to the main-plot units, and nitrogen rates to the sub-plot units. Three flights were made during the growing season. Data were analyzed for each flight and each band separately, then regrouped into series of neighboring bands yielding identical analyses with respect to the significance of the main effects and interactions on reflectance. The results indicate that the reflectance of corn is significantly influenced ( α=0.05) at certain wavelengths by the presence of weeds, the nitrogen rates and their interaction. The influence of weeds was most easily observed in the data from the second flight (August 5, 2000), about 9 weeks after planting. The nitrogen effect was detectable in all the three flights. Differences in response due to nitrogen stress were most evident at 498 nm and in the band at 671 nm. In these bands, differences due to nitrogen levels were observed at all growth stages, and the presence of weeds had no interactive effect. Differences in other regions, whether related to nitrogen, weeds or the combination of the two, appeared to be dependent on the growth stage. Furthermore, results comparable to those of the hyperspectral sensor were obtained when a multispectral sensor was simulated, indicating little advantage of using the former.

High resolution mapping of tropical mangrove ecosystems using hyperspectral and radar remote sensing

Mangrove ecosystems are in serious decline around the world and various initiatives are underway to assess their current coverage and loss in cover. These ecosystems occur as thin strips along coastlines or rivers and, due to the strong environmental gradients present, mangroves show high spatial variability along short transects. Remote sensing tools that offer high spatial resolution mapping and high information content are needed to provide good differentiation of the various mangrove zones and types. The added complexities of tropical atmospheric conditions provide further challenges in terms of the selection of sensors and image analysis methodologies. This paper explores the possibility of combining a high spatial/spectral resolution scanner, 'Compact Airborne Spectrographic Imager' (CASI), with the airborne National Aeronautics @ Space Administration's polarimetric radar, 'AIRSAR', for mapping and monitoring of mangrove estuaries. The Daintree River estuary in far North Queensland, Australia was chosen for this study due to its diversity of mangrove species. Imagery acquired by both the CASI airborne scanner (14 bands, 2.5 m pixel) and the AIRSAR (L- and P-band polarimetry, C-band interferometry, 10 m pixel) has been used to produce detailed maps of the mangrove zones in the estuary. The advantages and difficulties associated with multi-source data integration are investigated in this paper. While radar provides general structural information in relation to mangrove zonation, high-resolution hyperspectral scanners allow for finer-detailed analysis and green-biomass information. Classifications (maximum likelihood) of both the individual and integrated datasets are performed, with the latter producing more accurate results. Application of a hierarchical neural network classification is also explored, where the more general mangrove zones are separated first based on structural information, then species or specie-complexes are extracted in subsequent levels using spectral differences.

Chlorophyll content in eucalypt vegetation at the leaf and canopy scales as derived from high resolution spectral data.

The physiological status of forest canopy foliage is influenced by a range of factors that affect leaf pigment content and function. Recently, several indices have been developed from remotely sensed data that attempt to provide robust estimates of leaf chlorophyll content. These indices have been developed from either hand-held spectroradiometer spectra or high spectral resolution (or hyperspectral) imagery. We determined if two previously published indices (Datt 1999), which were specifically developed to predict chlorophyll content in eucalypt vegetation by remote sensing at the leaf scale, can be extrapolated accurately to the canopy. We derived the two indices from hand-held spectroradiometer data of eucalypt leaves exhibiting a range of insect damage symptoms. We also derived the indices from spectra obtained from high spectral and spatial resolution Compact Airborne Spectrographic Imager 2 (CASI-2) imagery to determine if reasonable estimates at a scale of < 1 m can be achieved. One of the indices (R 850/R 710 index, where R is reflectance) derived from hand-held spectroradiometer data showed a moderate correlation with relative leaf chlorophyll content (r = 0.59, P < 0.05) for all dominant eucalypt species in the study area. The R (850)/R (710) index derived from CASI-2 imagery yielded slightly lower correlations over the entire data set (r = 0.42, P < 0.05), but correlations for individual species were high (r = 0.77, P < 0.05). A scaling analysis indicated that the R (850)/R (710) index was strongly affected by soil and water cover types when pixels were mixed, but appeared to be invariant to changes in proportions of understory, which may limit its application.

Multi-scale remote sensing of microbial mats in an atoll environment

Microbial mats are encountered in many coastal environments. They are generally constituted by stratified layers produced by the development of various micro-organisms. A current research programme aims to assess the biotechnological potential of the microbial communities in South Pacific atolls. As part of this project, the characterisation of mats was examined in Rangiroa atoll (French Polynesia) using high resolution (20-30 m) multi-spectral images (SPOT HRV and Landsat ETM+), hyperspectral imagery (CASI (Compact Airborne Spectrographic Imager), 27 bands, 5.5 2 1 m) and in situ reflectance spectra. At atoll scale, mats are successfully inventoried among different geomorphological environments by fuzzy classification/segmentation of the 20-30 m resolution multi-spectral data. Laboratory biochemical analysis (not described here) highlighted and identified the most promising mats for their biotechnological potential. The spectral signatures of these remarkable mats are described at two scales. Major reflectance features of the microbial community were identified by peaks in fourth-derivatives analysis, discriminating the surface layers dominated by the cyanobacteria Schizothrix sp. (orange mats), Scytonema sp. (grey-black mats) and Phormidium sp. (green mats). At mat scale, CASI-derived spectral signatures from heterogeneous 5.5m 2 pixels including various microbial communities, vegetation, sand and water did not provide fine optical distinctions between mats because of mixing effects. This multi-scale analysis provides optical and geomorphological criteria to locate interesting stratified mats in other atolls.