Benoit Rivard

Iterative Spectral Unmixing for Optimizing Per-Pixel Endmember Sets

Fractional abundances predicted for a given pixel using spectral mixture analysis (SMA) are most accurate when only the endmembers that comprise it are used, with larger errors occurring if inappropriate endmembers are included in the unmixing process. This paper presents an iterative implementation of SMA (ISMA) to determine optimal per-pixel endmember sets from the image endmember set using two steps: 1) an iterative unconstrained unmixing, which removes one endmember per iteration based on minimum abundance and 2) analysis of the root-mean-square error as a function of iteration to locate the critical iteration defining the optimal endmember set. The ISMA was tested using simulated data at various signal-to-noise ratios (SNRs), and the results were compared with those of published unmixing methods. The ISMA method correctly selected the optimal endmember set 96% of the time for SNR of 100 : 1. As a result, per-pixel errors in fractional abundances were lower than for unmixing each pixel using the full endmember set. ISMA was also applied to Airborne Visible/Infrared Imaging Spectrometer hyperspectral data of Cuprite, NV. Results show that the ISMA is effective in obtaining abundance fractions that are physically realistic (sum close to one and nonnegative) and is more effective at selecting endmembers that occur within a pixel as opposed to those that are simply used to improve the goodness of fit of the model but not part of the mixture

Monitoring secondary tropical forests using space-borne data: implications for Central America

Tropical secondary forests, which play an important role in carbon sequestration, may be monitored using space-borne sensors. Secondary forest biomass or age estimation from space-borne data may be used to quantify the carbon sink these forests represent. At current capabilities, roughly three successional stages up to 15 years of age may be identified from Landsat TM data. Using synthetic aperture radar, reliable biomass estimates may be made up to approximately 60 tons/ha. The potential for overcoming these limitations is reviewed, including the synergy of radar and optical imagery and the unprecedented spatial and spectral resolutions of new sensors. Most of the available literature to date is from the Amazon; in this paper, applicability to Central America is considered, which has a much more heterogeneous landscape and the dynamics of secondary growth have a special significance in the framework of conservation biology and carbon sequestration. We conclude that critical issues in this region will be topographical correction and stratification according to ecological and site quality variables.

Dynamics of Tropical Deforestation Around National Parks: Remote Sensing of Forest Change on the Osa Peninsula of Costa Rica

Abstract National parks and biological reserves play an important role in counteracting the effects of tropical deforestation in mountainous environments, a leading cause of biodiversity loss worldwide. Unfortunately, information is sparse on the nature, dynamics, and spatial dimension of land use and land cover change processes that contribute to park vulnerability. This article assesses the current state of landscape fragmentation and structure on the Osa Peninsula, Costa Rica, using Landsat Multispectral Scanner and Thematic Mapper satellite scenes between 1979 and 1997. The Osa Peninsula hosts the Corcovado National Park, which contains the only protected region of Tropical Wet forest on the Pacific slopes of Mesoamerica, including a significant number of species that are endemic, threatened, or new to science. The level of isolation of the Corcovado National Park is based on the degree of ecosystem degradation produced by frontal deforestation processes. Our results indicate that the proportion of the Osa Peninsula covered by forest declined from 97% in 1979 to 91% in 1987 and to 89% by 1997. Total forest area declined from 977 km2 in 1979 to 896 km2 by 1997. These results pose significant questions regarding the effectiveness of current conservation efforts in this mountain biodiversity-rich area of Mesoamerica.

Derivative spectral unmixing of hyperspectral data applied to mixtures of lichen and rock

Spectral mixture analysis (SMA) has been used extensively in the hyperspectral remote sensing community for the subpixel abundance estimation of targets. However, the task of defining every endmember can be difficult, as evident from the importance attributed to the topic in the recent literature. The effectiveness of SMA can be compromised when the required spectral endmembers are not well constrained in terms of their spectral magnitude and shape. The spectral magnitude of the endmembers is more difficult to obtain than their spectral shape, in part because the effects of the atmosphere and topography are difficult to constrain. This paper presents a derivative spectral unmixing (DSU) model, which is an extension of the spectral mixture analysis and derivative analysis. Using a DSU approach, it is possible to estimate the fraction of an endmember characterized by one or more diagnostic absorption features despite having only a general knowledge of the spectral shapes of the remaining endmembers. The DSU is assessed using spectral data acquired for a lichen-covered rock sample, and the estimated fractions of lichen and rock are assessed against that obtained from a high spatial resolution digital photograph. The results of the laboratory experiments suggests that the DSU is a promising algorithm for the quantitative analysis of hyperspectral data, but experiments on airborne/spaceborne imagery are now required to assess its value for geological mapping.

Estimating leaf area index from satellite imagery using Bayesian networks

In this study, we investigated the use of Bayesian networks for inferring tropical dry forest leaf area index (LAI) from satellite imagery in dry and wet seasons. LAI was chosen as the variable of interest because leaf area is the exchange surface between the photosynthetically active component of the canopy and the atmosphere. Initial network estimates were obtained from ground truth plot data with known forest structure, LAI, and satellite reflectance in the red and near-infrared bands (as observed by the Landsat 7 Enhanced Thematic Mapper Plus sensor). We tested the performance of the Bayesian networks with scoring rules and also with confidence and surprise scores. We evaluated the networks on a per-pixel basis and created both LAI maps of the study area as well predicted the probability maps for the highest LAI states. Results not only demonstrate the predictive power of a Bayesian network but also its explanatory power which is far beyond what is typically available with current pixel classifier approaches such as spectral vegetation indices or other approaches such as neural networks.

Spectral properties of foliose and crustose lichens based on laboratory experiments

Reflectance spectra of rock encrusting lichens were acquired to determine the influence that this vegetation type may have on the reflectance properties of rock exposures located in high latitude and subarctic environments. The samples investigated consist of crustose and foliose lichen species collected from exposures of the Gog quartzite formation in Alberta, Canada. Lichen transmittance was estimated to be <3% throughout the 350–2500-nm spectral region, using spectra measured from the foliose lichen, Umbilicaria torrefacta, as a representative sample of a broader class of lichens. These findings suggest that lichen prevents the transmission of light to the underlying rock substrate. Therefore, the subpixel influence of lichen and rock within a scene can be considered linearly weighted. Discrimination of lichen species is made possible using ratios of reflectance at 400/685 and 773/685 nm. An index using the band ratios 2132/2198 and 2232/2198 nm shows the similarity of lichen spectra in the infrared and a distinguishing feature between rocks with OH bearing minerals and lichen. Thus, spectral unmixing of rock and crustose/foliose lichens may be successfully accomplished using a single lichen end-member for this spectral range.

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

Abstract 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.

Predicting leaf gravimetric water content from foliar reflectance across a range of plant species using continuous wavelet analysis

Leaf water content is an important variable for understanding plant physiological properties. This study evaluates a spectral analysis approach, continuous wavelet analysis (CWA), for the spectroscopic estimation of leaf gravimetric water content (GWC, %) and determines robust spectral indicators of GWC across a wide range of plant species from different ecosystems. CWA is both applied to the Leaf Optical Properties Experiment (LOPEX) data set and a synthetic data set consisting of leaf reflectance spectra simulated using the leaf optical properties spectra (PROSPECT) model. The results for the two data sets, including wavelet feature selection and GWC prediction derived using those features, are compared to the results obtained from a previous study for leaf samples collected in the Republic of Panamá (PANAMA), to assess the predictive capabilities and robustness of CWA across species. Furthermore, predictive models of GWC using wavelet features derived from PROSPECT simulations are examined to assess their applicability to measured data. The two measured data sets (LOPEX and PANAMA) reveal five common wavelet feature regions that correlate well with leaf GWC. All three data sets display common wavelet features in three wavelength regions that span 1732-1736 nm at scale 4, 1874-1878 nm at scale 6, and 1338-1341 nm at scale 7 and produce accurate estimates of leaf GWC. This confirms the applicability of the wavelet-based methodology for estimating leaf GWC for leaves representative of various ecosystems. The PROSPECT-derived predictive models perform well on the LOPEX data set but are less successful on the PANAMA data set. The selection of high-scale and low-scale features emphasizes significant changes in both overall amplitude over broad spectral regions and local spectral shape over narrower regions in response to changes in leaf GWC. The wavelet-based spectral analysis tool adds a new dimension to the modeling of plant physiological properties with spectroscopy data.

Estimation of the Distribution of Tabebuia guayacan (Bignoniaceae) Using High-Resolution Remote Sensing Imagery

Species identification and characterization in tropical environments is an emerging field in tropical remote sensing. Significant efforts are currently aimed at the detection of tree species, of levels of forest successional stages, and the extent of liana occurrence at the top of canopies. In this paper we describe our use of high resolution imagery from the Quickbird Satellite to estimate the flowering population of Tabebuia guayacan trees at Barro Colorado Island (BCI), in Panama. The imagery was acquired on 29 April 2002 and 21 March 2004. Spectral Angle Mapping via a One-Class Support Vector machine was used to detect the presence of 422 and 557 flowering tress in the April 2002 and March 2004 imagery. Of these, 273 flowering trees are common to both dates. This study presents a new perspective on the effectiveness of high resolution remote sensing for monitoring a phenological response and its use as a tool for potential conservation and management of natural resources in tropical environments.

Measured effects of desert varnish on the mid-infrared spectra of weathered rocks as an aid to TIMS imagery interpretation

The thermal infrared spectral properties (7-12 mu m) of natural rock surfaces from Silver Lake, CA, are discussed. Although the reflectance of weathered rocks is largely a function of the quartz content in rocks, the presence of desert varnish (clay coating) on rocks reduces the reflectance and spectral contrast with features unique to the rock spectra persisting if varnish is thin. Thick varnish has a spectrum with a reflectance peak near 9.6 mu m, due to clays, and resembles the spectra of clay-rich playa surfaces. Comparison of laboratory reflectance spectra for varnish and weathered rock samples with Thermal Infrared Multispectral Scanner (TIMS) emissivity spectra for Silver Lake suggests that TIMS signatures for felsic rocks are dominated by weathered rock and rock debris. In contrast, it is likely that varnish plays an important role in the TIMS signatures of mafic rocks. >