Subpixel canopy cover estimation of coniferous forests in Oregon using SWIR imaging spectrometry

Abstract

Abstract. The percent cover of vegetation canopies is an important variable for many land-surface biophysical and biogeochemical models and serves as a useful measure of land cover change. Remote sensing methods to estimate the subpixel fraction of vegetation canopies with spectral mixture analysis (SMA) require knowledge of the reflectance properties of major land cover units, called endmembers. However, variability in endmember reflectance across space and time has 14mited the interpretation and general applicability of SMA approaches. In this study, a subpixel vegetation cover of coniferous forests in Oregon, United States, was successfully estimated by employing shortwave infrared reflectance measurements (SWIR2 region, 2080-2280 nm) collected by the NASA Airborne Visible Infrared Imaging Spectrometer (AVIRIS). The approach presented here, referred to as AutoSWIR [Asner and Lobell, 2000], was originally developed for semiarid and arid environments and exploits the low SWIR2 variability of materials found in most ecosystems. SWIR2 field spectra from Oregon were compared with spectra from an arid systems database, revealing significant differences only for soil reflectance. However, SWIR2 variability remained low, as indicated by field spectra and principal component analysis, and AutoSWIR was then modified to use coniferous forest spectra collected in Oregon. Subsequent high spatial resolution estimates of forest canopy cover agreed well with estimates from low-altitude air photos (rms = 3%), demonstrating the successful extension of AutoSWIR to a coniferous forest ecosystem. The generality of AutoSWIR facilitates accurate estimates of vegetation cover that can be automatically retrieved from SWIR2 spectral measurements collected by forthcoming spaceborne imaging spectrometers such as NASA's New Millenium Program EO-1 Hyperion. These estimates can then be used to characterize landscape heterogeneity important for landsurface, atmospheric, and biogeochemical research. . However, unresolved heterogeneity in vegetation properties in these ecosystems can be significant; Kimball et al. [1999], for instance, found that subpixel-scale land cover complexity in boreal forests could lead to annual net primary production (NPP) errors of more than 14% using an ecosystem process model. In another study, Bonan et al. [1993] showed that landscape heterogeneity in coniferous forests had a highly nonlinear effect on sensible heat and evapotranspiration calculations, with errors as high as 46% and 15%, respectively. The ability to accurately estimate vegetation cover in coniferous forests, and thereby constrain models used to understand land-surface, atmospheric, and biogeochemical processes, is of great importance in these regions. Remote sensing is a relatively cheap and fast method for estimating showed that the reflectance variability of major land cover types (e.g., green vegetation, senescent vegetation, and soil) was dominated by variation in overall albedo of the shortwave region from 400 to 2500 nm. They also showed ...

Enhanced Automated Canopy Characterization from Hyperspectral Data by a Novel Two Step Radiative Transfer Model Inversion Approach

“…Nevertheless, the automated CRASh approach as presented in this study showed the potential of a fully automated, image based retrieval of vital agroecosystem variables, and could be an important contribution towards the incorporation of land surface products in operational chains for upcoming high resolution airborne and spaceborne imaging spectrometers such as APEX [64], ARES [65,66], and EnMap [67]. [85] L = 0.5 [14]; a = 1.2 [14,55]; b = 0.04 [14]; X=0.08 [14]; β = 1 [86] …”

Relationships Between Errors Propagated in Fraction of Vegetation Cover by Algorithms Based on a Two-Endmember Linear Mixture Model

“…One such parameter is the fraction of vegetation cover (FVC), which is a measure of the horizontal vegetation distribution, usually provided by satellite remote sensing data [4,5]. Information regarding the FVC is also considered to be an important indicator of forest degradation [6], land-use, land-cover change [7], and management or policy development [8]. Hence, accurate estimation of the FVC from remotely sensed data would contribute to a variety of environmental studies.…”

Inter-Algorithm Relationships for the Estimation of the Fraction of Vegetation Cover Based on a Two Endmember Linear Mixture Model with the VI Constraint

“…Vegetation quantity is often parameterized by the fractional area covered by green vegetation (horizontal density) and the leaf area index (vertical density) estimated from spectral reflectance measurements of satellite sensors [7]. In this study, we focus on a parameter that represents the horizontal density, called the fraction of vegetation cover (FVC) [8], which has been widely used for global and local analysis [8][9][10][11][12].…”

“…Variations in the algorithm arise from the choice of variable used to measure the similarity between the model and the measurement data. For example, when reflectance is directly used as a variable, the minimization process considers the difference (often defined as the root mean square error, RMSE) between the modeled and measured spectrum [8][9][10][11][12][13][14]21,22,24,28,[31][32][33][34][35]. Altering the reflectance into a spectral vegetation index (VI) produces another group of algorithms [1,4,7,25,26,[36][37][38][39].…”