Estimation of vegetation cover at subpixel resolution using LANDSAT data

The present report summarizes the various approaches relevant to estimating canopy cover at subpixel resolution. The approaches are based on physical models of radiative transfer in non-homogeneous canopies and on empirical methods. The effects of vegetation shadows and topography are examined. Simple versions of the model are tested, using the Taos, New Mexico Study Area database. Emphasis has been placed on using relatively simple models requiring only one or two bands. Although most methods require some degree of ground truth, a two-band method is investigated whereby the percent cover can be estimated without ground truth by examining the limits of the data space. Future work is proposed which will incorporate additional surface parameters into the canopy cover algorithm, such as topography, leaf area, or shadows. The method involves deriving a probability density function for the percent canopy cover based on the joint probability density function of the observed radiances

The structure of red-infrared scattergrams of semivegetated landscapes

A physically based linear stochastic geometric canopy soil reflectance model is presented for characterizing spatial variability of semivegetated landscapes at subpixel and regional scales. Landscapes are conceptualized as stochastic geometric surfaces, incorporating not only the variability in geometric elements, but also the variability in vegetation and soil background reflectance which can be important in some scenes. The model is used to investigate several possible mechanisms which contribute to the often observed characteristic triangular shape of red-infrared scattergrams of semivegetated landscapes. Scattergrams of simulated and semivegetated scenes are analyzed with respect to the scales of the satellite pixel and subpixel components. Analysis of actual aerial radiometric data of a pecan orchard is presented in comparison with ground observations as preliminary confirmation of the theoretical results

Use of LANDSAT images of vegetation cover to estimate effective hydraulic properties of soils

The estimation of the spatially variable surface moisture and heat fluxes of natural, semivegetated landscapes is difficult due to the highly random nature of the vegetation (e.g., plant species, density, and stress) and the soil (e.g., moisture content, and soil hydraulic conductivity). The solution to that problem lies, in part, in the use of satellite remotely sensed data, and in the preparation of those data in terms of the physical properties of the plant and soil. The work was focused on the development and testing of a stochastic geometric canopy-soil reflectance model, which can be applied to the physically-based interpretation of LANDSAT images. The model conceptualizes the landscape as a stochastic surface with bulk plant and soil reflective properties. The model is particularly suited for regional scale investigations where the quantification of the bulk landscape properties, such as fractional vegetation cover, is important on a pixel by pixel basis. A summary of the theoretical analysis and the preliminary testing of the model with actual aerial radiometric data is provided

Combining MODIS LAI with ICESat-Based Canopy Heights Improves Spaceborne Estimates of Vegetation Roughness Length for Momentum

Most land-surface models require parameterization of vertical wind profiles within the atmospheric boundary layer. For vegetated surfaces, it is common to assume a logarithmic profile in the surface layer, which includes estimates of vegetation roughness length for momentum (z0) and zero-plane displacement height (d0). This study finds that remotely-sensed forest canopy heights improve estimates of aerodynamic roughness length for momentum using a previously-developed representation of the roughness sublayer (Raupach 1992; Jasinski et al. 2005). Resulting roughness products consist of two datasets: 1) 14 years of 8-day snapshots of the global land surface at a nominal spatial resolution of 500-meters for users who wish to retain full temporal resolution and interannual variability; and 2) multiyear averages of the 8-day snapshots, here referred to as "climatologies" of roughness, which retain underlying seasonality. Both products are suitable for use in data assimilation and reanalyses such as the National Climate Assessment Land Data Assimilation System (NCA-LDAS), for which these products were initially developed

Estimation of Vegetation Aerodynamic Roughness of Natural Regions Using Frontal Area Density Determined from Satellite Imagery

Parameterizations of the frontal area index and canopy area index of natural or randomly distributed plants are developed, and applied to the estimation of local aerodynamic roughness using satellite imagery. The formulas are expressed in terms of the subpixel fractional vegetation cover and one non-dimensional geometric parameter that characterizes the plant's shape. Geometrically similar plants and Poisson distributed plant centers are assumed. An appropriate averaging technique to extend satellite pixel-scale estimates to larger scales is provided. ne parameterization is applied to the estimation of aerodynamic roughness using satellite imagery for a 2.3 sq km coniferous portion of the Landes Forest near Lubbon, France, during the 1986 HAPEX-Mobilhy Experiment. The canopy area index is estimated first for each pixel in the scene based on previous estimates of fractional cover obtained using Landsat Thematic Mapper imagery. Next, the results are incorporated into Raupach's (1992, 1994) analytical formulas for momentum roughness and zero-plane displacement height. The estimates compare reasonably well to reference values determined from measurements taken during the experiment and to published literature values. The approach offers the potential for estimating regionally variable, vegetation aerodynamic roughness lengths over natural regions using satellite imagery when there exists only limited knowledge of the vegetated surface

Effective Interpolation of Incomplete Satellite-Derived Leaf-Area Index Time Series for the Continental United States

Many earth science modeling applications employ continuous input data fields derived from satellite data. Environmental factors, sensor limitations and algorithmic constraints lead to data products of inherently variable quality. This necessitates interpolation of one form or another in order to produce high quality input fields free of missing data. The present research tests several interpolation techniques as applied to satellite-derived leaf area index, an important quantity in many global climate and ecological models. The study evaluates and applies a variety of interpolation techniques for the Moderate Resolution Imaging Spectroradiometer (MODIS) Leaf-Area Index Product over the time period 2001-2006 for a region containing the conterminous United States. Results indicate that the accuracy of an individual interpolation technique depends upon the underlying land cover. Spatial interpolation provides better results in forested areas, while temporal interpolation performs more effectively over non-forest cover types. Combination of spatial and temporal approaches offers superior interpolative capabilities to any single method, and in fact, generation of continuous data fields requires a hybrid approach such as this

Sensitivity of Depth-Integrated Satellite Lidar to Subaqueous Scattering

A method is presented for estimating subaqueous integrated backscatter from the CALIOP lidar. The algorithm takes into account specular reflection of laser light, laser scattering by wind-generated foam as well as sun glint and solar scattering from the foam Analyses show that the estimated subaqueous integrated backscatter is most sensitive to the estimate of transmittance used in the atmospheric correction, and is very insensitive to the estimate of wind speed used. As a case study, CALIOP data over Tampa Bay were compared to MODIS 645 nm remote sensing reflectance, which previously has been shown to be nearly linearly related to turbidity. The results indicate good correlation on nearly all CALIOP clear-free dates during the period 2006 through 2007, particularly those with relatively high atmospheric transmittance. When data are composited over the entire period the correlation is reduced but still statistically significant, an indication of variability in the biogeochemical composition in the water. Overall, the favorable results show promise for the application of satellite lidar integrated backscatter in providing information about subsurface backscatter properties, which can be extracted using appropriate model

Physically-based parameterization of spatially variable soil and vegetation using satellite multispectral data

A stochastic-geometric landsurface reflectance model is formulated and tested for the parameterization of spatially variable vegetation and soil at subpixel scales using satellite multispectral images without ground truth. Landscapes are conceptualized as 3-D Lambertian reflecting surfaces consisting of plant canopies, represented by solid geometric figures, superposed on a flat soil background. A computer simulation program is developed to investigate image characteristics at various spatial aggregations representative of satellite observational scales, or pixels. The evolution of the shape and structure of the red-infrared space, or scattergram, of typical semivegetated scenes is investigated by sequentially introducing model variables into the simulation. The analytical moments of the total pixel reflectance, including the mean, variance, spatial covariance, and cross-spectral covariance, are derived in terms of the moments of the individual fractional cover and reflectance components. The moments are applied to the solution of the inverse problem: The estimation of subpixel landscape properties on a pixel-by-pixel basis, given only one multispectral image and limited assumptions on the structure of the landscape. The landsurface reflectance model and inversion technique are tested using actual aerial radiometric data collected over regularly spaced pecan trees, and using both aerial and LANDSAT Thematic Mapper data obtained over discontinuous, randomly spaced conifer canopies in a natural forested watershed. Different amounts of solar backscattered diffuse radiation are assumed and the sensitivity of the estimated landsurface parameters to those amounts is examined

Feasibility of Estimating Snow Depth in Complex Terrain Using Satellite Lidar Altimetry

Satellite retrievals of snow depth and water equivalent (SWE) are critical for monitoring watershed scale processes around the world. However, the problem is especially challenging in mountainous regions where complex heterogeneities limit the utility of low resolution satellite sensors. The Geoscience Laser Altimeter Sensor (GLAS) aboard the Ice, Cloud, and land Elevation Satellite (ICESat) collected surface elevation data along near-repeat reference transects over land areas from 2003-2009. Although intended for monitoring ice caps and sea ice, the seven year global GLAS data base has provided unprecedented opportunity to test the capability of satellite lidar technology for estimating snow depth over land. GLAS single track and low repeat frequency does not provide data sufficient for operational estimates. However, its comparatively small footprint size of -65 m and its database of seasonal repeat observations during both snow and no-snow conditions have been sufficient to evaluate the potential of spacebased lidar altimetry for estimating snow depth. Recent analysis of ICESat elevations in the Uinta Mountains in NE Utah provide encouraging results for watershed scale estimates of snow depth. Research reported here focuses on the sensitivity of several versions of an ICESat snow depth algorithm to a range of landscape types defined by vegetation cover, slope and roughness. Results are compared to available SNOTEL data

Copper (I) SNS Pincer Complexes: Impact of Ligand Design and Solvent Coordination on Conformer Interconversion from Spectroscopic and Computational Studies

The syntheses and detailed characterizations (X-ray crystallography, NMR spectroscopy, cyclic voltammetry, infrared spectroscopy, electrospray mass spectrometry, and elemental analyses) of two new Cu(I) pincer complexes are reported. The pincer ligand coordinates through one nitrogen and two sulfur donor atoms and is based on bis-imidazole or bis-triazole precursors. These tridentate SNS ligands incorporate pyridine and thione-substituted imidazole or triazole functionalities with connecting methylene units that provide flexibility to the ligand backbone and enable high bite-angle binding. Variable temperature 1H NMR analysis of these complexes and of a similar zinc(II) SNS system shows that all are fluxional in solution and permits the determination of ΔGexp‡ and ΔSexp‡. DFT calculations are used to model the fluxionality of these complexes and indicate that a coordinating solvent molecule can promote hemilability of the SNS ligand by lowering the energy barrier involved in the partial rotation of the methylene units