Ellsworth LeDrew

Spectral Mixture Analysis and Geometric-Optical Reflectance Modeling of Boreal Forest Biophysical Structure

Biophysical structural information such as biomass, LAI, and NPP are important inputs to regional scale models of ecosystem processes and photosynthetic activity within boreal forests. However, traditional methods such as NDVI for deriving these variables from remotely sensed data have been inconsistent and unsatisfactory due to factors such as the confounding influence of background reflectance and canopy geometry on the overall pixel signal. To address this problem, we present new results which use spectral mixture analysis to determine areal fractions of sunlit canopy, sunlit background, and shadow at subpixel scales for predicting these biophysical variables. Geometric-optical reflectance models are used to estimate sunlit canopy component reflectance for input to the analysis together with field measures of background and shadow reflectance. In this article, we compare cylinder, cone, and spheroid models of canopy geometry and evaluate the importance of solar zenith angle variations in reflectance estimates for mixture fractions. These are computed from helicopter MMR radiometer data for 31 stands of black spruce along a gradient of stand densities near the southern fringe of the North American boreal forest. Component fractions are evaluated against ground data derived from dense-grid point analyses of coincident high resolution color photography and also for predicting biophysical variables. In general, the Li–Strahler spheroid model was better than the cone and cylinder models and the importance of correcting for solar zenith angle (SZA) was illustrated, with significant improvements noted for higher SZA as a result of corrections for canopy mutual shadowing. The best overall results were obtained from the shadow fraction using a spheroid model of canopy geometry at SZA 45°. Linear regression analyses showed biomass could be estimated with r2 values of 0.83 and a standard error (S.E.) of 1.7 kg/m2; LAI: r2=0.82, S.E.=0.46; and NPP: r2=0.86, S.E.=0.05 kg/m2/yr. These results were significantly higher than with NDVI for estimating biomass (r2=0.44), LAI (r2=0.60), and NPP (r2=0.56). Current and future areas of research are outlined towards improving our understanding of carbon cycling in large forested ecosystems as a function of variability in the physical climate system and environmental change.

Aerial image texture information in the estimation of northern deciduous and mixed wood forest leaf area index (LAI)

Abstract Leaf area index (LAI) currently may be derived from remotely sensed data with limited accuracy. This research addresses the need for increased accuracy in the estimation of LAI through integration of texture to the relationship between LAI and vegetation indices. The inclusion of texture, which acts as a surrogate for forest structure, to the relationship between LAI and the normalized difference vegetation index (NDVI) increased the accuracy of modeled LAI estimates. First-order, second-order, and a newly developed semivariance moment texture are assessed in the relationship with LAI. The ability to increase the accuracy of LAI estimates was demonstrated over a range of forest species, densities, closures, tolerances, and successional regimes. Initial assessment of LAI from spectral response over the full range of stand types demonstrated the need for stratification by stand type prior to analysis. Stratification of the stands based upon species types yields an improvement in the regression relationships. For example, deciduous hardwood stands, spanning an LAI range from ≈1.5 to 7, have a moderate initial bivariate relationship between LAI and NDVI at an r 2 of 0.42. Inclusion of additional texture statistics to the multivariate relationship between LAI and NDVI further increases the amount of variation accounted for, to an R 2 of 0.61, which represents an increase in ability to estimate hardwood forest LAI from remotely sensed imagery by approximately 20% with the inclusion of texture. Mixed forest stands, which are spectrally diverse, had an insignificant initial r 2 of 0.01 between LAI and NDVI, which improved to a significant R 2 of 0.44 with the inclusion of semivariance moment texture.

Spectral Discrimination of Healthy and Non-Healthy Corals Based on Cluster Analysis, Principal Components Analysis, and Derivative Spectroscopy

Abstract Reports of global mass coral bleaching are of major concern, but the scientific basis of these reports is questionable. There exists no objective measure of coral health, so that individual perceptions of the paleness of an individual coral head, or an entire coral reef, are the foundation of coral bleaching reports. It is understood that coral bleaching results from an expulsion or reduction of the algae housed in the individual polyp, which causes the coral to lose its color. Satellite or airborne remote sensing may be a feasible means of mapping and monitoring coral reefs over large geographic areas if a quantitative means of remotely determining coral health can be developed. In an effort to remotely detect coral stress, in situ spectral reflectance of healthy and bleached Fijian scleractinian corals was measured with a hand-held spectroradiometer. Principal components and cluster analysis revealed that there is a spectral distinction between healthy and bleached coral based largely on magnitude of reflectance. Spectral derivative analysis was used to determine the specific wavelength regions ideal for remote identification of substrate type. These results are encouraging with respect to using an airborne spectroradiometer to identify areas of bleached corals thus enabling accurate monitoring over time.

Reflectance processing of remote sensing spectroradiometer data

Abstract Spectral reflectance is the ratio of incident-to-reflected radiant flux measured from an object or area over specified wavelengths. Unlike radiance and irradiance values, reflectance is an inherent property of an object and is independent of time, location, illumination intensity, atmospheric conditions and weather. Although reflectance is a key unit of measure in remote sensing, it is not measured directly and instead must be derived. Accordingly, the conversion of field and laboratory measurements of spectral radiance into reflectance values is a frequent requirement with ground data in support of airborne and satellite remote sensing applications in the environmental and earth sciences. In this paper, laboratory and computer methods for processing field spectroradiometer measurements of spectral radiance into calibrated absolute reflectance values are described. Target radiance measures are obtained under direct and diffuse illumination using a portable field spectroradiometer, with irradiance spectra captured by near simultaneous acquisition of reflected radiation from a reference panel. The approach for converting raw target and panel radiance spectra to calibrated reflectance involves five major processing stages: (i) panel calibration, (ii) solar zenith angle computations, (iii) spectral and angular interpolation, (iv) computation of reflectance, and (v) automated batch mode execution of stages (ii)–(iv) for processing large data volumes. Equipment, methods, and computer programs for achieving these stages are described. Example forestry ground spectra acquired in the Boreal Ecosystem Atmosphere Study (BOREAS) are presented to illustrate raw field measurements and final reflectance products. These methods would also be useful in other applications such as agriculture, water resources, oceanic studies, rangeland management, and geological exploration and mineral identification.

A conceptual framework for managing very diverse data for complex, interdisciplinary science

Much attention has been given to the challenges of handling massive data volumes in modern data-intensive science. This paper examines an equally daunting challenge – the diversity of interdisciplinary data, notably research data, and the need to interrelate these data to understand complex systemic problems such as environmental change and its impact. We use the experience of the International Polar Year 2007–8 (IPY) as a case study to examine data management approaches seeking to address issues around complex interdisciplinary science. We find that, while technology is a critical factor in addressing the interdisciplinary dimension of the data intensive science, the technologies developing for exa-scale data volumes differ from those that are needed for extremely distributed and heterogeneous data. Research data will continue to be highly heterogeneous and distributed and will require technologies to be much simpler and more flexible. More importantly, there is a need for both technical and cultural adaptation. We describe a vision of discoverable, open, linked, useful, and safe collections of data, organized and curated using the best principles and practices of information and library science. This vision provides a framework for our discussion and leads us to suggest several short- and long-term strategies to facilitate a socio-technical evolution in the overall science data ecosystem.

Measuring and modeling water column effects on hyperspectral reflectance in a coral reef environment

Abstract Much attention has been given to hyperspectral remote sensing of benthic habitat recently to quantify spectral signatures, examine linear mixing, map geomorphic zonation, or identify temporal change with varying degrees of confidence and success. Relatively less attention has been given to the effects of the water column on the hyperspectral signal given various water depths and bottom types. Hyperspectral in situ reflectance was measured at both the top and bottom of the water column to examine the effects of the intervening water layer. A radiative transfer model was used to predict the top-of-the-water column reflectance from a large number of close-range measured bottom spectra. The measured and modeled hyperspectral reflectance spectra were examined separately to compare the degree to which different substrate types can be discriminated once the water column is “added” to the spectra. The classification accuracy assessment indicated that the ability to discriminate benthic habitat based on hyperspectral characteristics is limited when the effects of the water column are included as the kappa statistic drops from 0.70 to 0.49.

Remote sensing of biophysical variables in boreal forest stands of Picea mariana

Using two hybrid radiative transfer models to represent conifer canopies and stands, algorithms to infer several important structural parameters of stands of black spruce (Picea mariana), the most common boreal forest dominant, were developed and evaluated. Spectral mixture analysis and multi-spectral reflectance data for 31 black spruce stands of varying density and structure were used to infer the values for the areal proportions of sunlit canopy, sunlit background and shadow fraction, which we call radiometric elements, and the areal proportions of these radiometric elements were strongly related to leaf area index, biomass density, and annual above ground net primary productivity. The best overall correspondence between the radiometric elements and biophysical variables was found from the shadow fraction obtained with the cone-based canopy reflectance model corrected for variations in solar zenith angle.

Progress in the use of remote sensing for coral reef biodiversity studies

Coral reefs are hotspots of marine biodiversity, and their global decline is a threat to our natural heritage. Conservation management of these precious ecosystems relies on accurate and up-to-date information about ecosystem health and the distribution of species and habitats, but such information can be costly to gather and interpret in the field. Remote sensing has proven capable of collecting information on geomorphologic zones and substrate types for coral reef environments, and is cost-effective when information is needed for large areas. Remote sensing-based mapping of coral habitat variables known to influence biodiversity has only recently been undertaken and new sensors and improved data processing show great potential in this area. This paper reviews coral reef biodiversity, the influence of habitat variables on its local spatial distribution, and the potential for remote sensing to produce maps of these habitat variables, thus indirectly mapping coral reef biodiversity and fulfilling information needs of coral reef managers.

TEMPORAL MIXTURE ANALYSIS OF ARCTIC SEA ICE IMAGERY: A NEW APPROACH FOR MONITORING ENVIRONMENTAL CHANGE

Abstract In this paper, we introduce the idea of temporal mixture analysis (TMA) for analyzing long sequences of hypertemporal remote sensing imagery. The basis of this approach is spectral mixture analysis, which we adapt from the spectral domain to the time domain. The TMA procedure is demonstrated by applying it to a 9-year record of scanning multichannel microwave radiometer sea ice concentrations in the Northern Hemisphere. We find that end-member fraction images provide a unique summary of spatial arrangements and temporal characteristics of the mapped phenomenon during a specific period and can be used to characterize climatic normals. A key distinction that differentiates temporal mixture imagery from similar images derived through more traditional means is that the data presented are derived from the temporal characteristics of the analyzed phenomenon and not the type of feature present.

ALBEDO AND DEPTH OF MELT PONDS ON SEA‐ICE

For a 1-month period during the spring–summer season, over 500 in situ measurements of the albedo, depth and selected properties of melt ponds were made at four sea-ice sites in the Canadian Arctic Archipelago. There are three aims of the investigation: to examine the variation in spectral reflectance of melt ponds in relation primarily to clouds, pond depth, and ice type; to compute and assess with reliability, and for the first time, the statistical properties of pond depth; and to derive pond albedo parameterizations for implementation in sea-ice model simulations. It was found that pond reflectance curves vary significantly with cloud cover, pond depth, and ice type. A simple univariate statistical analysis showed that surface ice morphology controls pond depth. Application of the Kruskal–Wallis test evidenced that differences between first-year ice, multi-year ice, and landfast ice-pond-depth distributions are statistically significant. Lastly, calibrated non-linear regression functions show that visible and near-infrared pond albedos decrease exponentially with depth until some critical depth value is exceeded. The relationship is weakest in the visible spectrum, particularly under cloudy sky conditions.