Lars L. Pierce

Remote sensing of temperate coniferous forest leaf area index. The influence of canopy closure, understory vegetation and background reflectance.

Abstract The relationship between the leaf area index (LAI) of temperate coniferous forests in the western United States and Thematic Mapper (TM) data corrected for atmospheric effects and Sun-surface-sensor geometry was influenced by canopy closure, understory vegetation and background reflectance. Strong inverse curvilinear relationships were observed between coniferous forest LAI and both TM bands 3 (0-63-0-69μm) and 5 (1-55-1-75μm). The inverse relationships are explained by increased reflectance of understory vegetation and background in open stands of lower LAI and decreased reflectance of the overstory in closed canopy stands with higher LAI. A strong positive relationship was observed between LAI and TM band 4 (0-76-0-90μm) radiance in stands with greater than 89 per cent canopy closure. Open stands with low overstory LAI had elevated band 4 radiances caused by understory vegetation and/or a highly reflective granite background. Old growth stands with incomplete overstories had low band 4 radiance...

Rapid Estimation of Coniferous Forest Leaf Area Index Using a Portable Integrating Radiometer

Canopy transmittance was measured at 1200 and 1400 local solar time using an integrating radiometer on seven coniferous forest stands in western Montana, ranging in projected leaf area index (LAI) from 1.7—5.3 m2/m2. Transmittance of each 1—ha stand was measured at 96,000 points, yet measurement required <1 h because the instrument instantaneously integrates 80 radiometer measurements at once. The Beer—Lambert Law was inverted to estimate LAI using measured transmittance and an extinction coefficient of 0.52. LAI estimated by transmittance was highly correlated with LAI measured by sapwood—based allometric equations at both the 1200 (R2 = 0.97) and 1400 (R2 = 0.94) measurement times. The results suggest that the technique has a wide applicability given the range of LAIs, stand densities (450—4140 trees/ha) and illumination angles (32°—57°) under which it was tested. See full-text article at JSTOR

A Remote Sensing Based Vegetation Classification Logic for Global Land Cover Analysis

Abstract This article proposes a simple new logic for classifying global vegetation. The critical features of this classification are that 1) it is based on simple, observable, unambiguous characteristics of vegetation structure that are important to ecosystem biogeochemistry and can be measured in the field for validation, 2) the structural characteristics are remotely sensible so that repeatable and efficient global reclassifications of existing vegetation will be possible, and 3) the defined vegetation classes directly translate into the biophysical parameters of interest by global climate and biogeochemical models. A first test of this logic for the continental United States is presented based on an existing 1 km AVHRR normalized difference vegetation index database. Procedures for solving critical remote sensing problems needed to implement the classification are discussed. Also, some inferences from this classification to advanced vegetation biophysical variables such as specific leaf area and photosynthetic capacity useful to global biogeochemical modeling are suggested.

The seasonality of AVHRR data of temperate coniferous forests: relationship with leaf area index.

Abstract Advanced Very High Resolution Radiometer (AVHRR) data from the National Oceanic and Atmospheric Administration (NOAA)-9 satellite were acquired of the western United States from March 1986 to November 1987. Monthly maximum value composites of AVHRR normalized difference vegetation index (NDVI) [(near infrared — visible)/(near infrared + visible)] were calculated for 19 coniferous forest stands in Oregon, Washington, Montana, and California. The leaf area index (LAI) of the conifer forests explained 70% and 79% of the variation of the summer maximum AVHRR NDVI in July 1986 and July 1987, respectively. The seasonal variation of NDVI was related to phenological changes in LAI, as well as the proportion of surface cover types contributing to the overall reflectance. The varying solar zenith angles in the summer and winter months complicated analyses of the seasonal differences in LAI of the forest stands by reducing NDVI values in the winter months. It is concluded that AVHRR NDVI data from July were related to the seasonal maximum leaf area index of coniferous forests of the western United States, and that seasonal differences in the AVHRR NDVI were related to: a) phenological changes in LAI caused by climate, b) the proportions of surface cover types contributing to the overall reflectance, and c) large variations in the solar zenith angle.

Regional‐Scale Relationships of Leaf Area Index to Specific Leaf Area and Leaf Nitrogen Content

Specific leaf area (SLA) is an important link between vegetation water and carbon cycles because it describes the allocation of leaf biomass per unit of leaf area. Several studies in many vegetation types have shown that canopy SLA is closely related to canopy leaf nitrogen (N) content and photosynthetic capacity. SLA increases as light is attenuated by leaf area down through a plant canopy. It therefore follows that across an individual biome the spatial patterns in canopy-average SLA and leaf N content should be significantly correlated with the spatial patterns in leaf area index (LAI) and canopy transmittance. In this paper, we show that the LAI across the Oregon transect is closely related to canopy- average SLA (R2 = 0.82) and leaf N content on a mass basis (R2 = 0.80). Canopy-average leaf N per unit area is highly correlated to canopy transmittance (R2 = 0.94) across the transect. At any given site, canopy-average SLA and leaf N per unit area do not vary significantly, either seasonally or between different codominant species occupying the same site. The results of this study suggest that the spatial distribution of canopy-average SLA and leaf nitrogen content (and perhaps canopy photosynthetic capacity) can be predicted across biomes from satellite estimates of LAI.

The effects of aggregating sub-grid land surface variation on large-scale estimates of net primary production

The use of large grid cell databases (1/2° to 5°) to drive nonlinear ecosystem process models may create an incompatibility of scales which can often lead to biased outputs. Global simulations of net primary production (NPP) often assume that bias due to averaging of sub-grid variations in climate, topography, soils, and vegetation is minimal, yet the magnitude and behavior of this bias on estimates of NPP are largely unknown. The effects of averaging sub-grid land surface variations on NPP estimates were evaluated by simulating a 1° × 1° land surface area as represented by four successive levels of landscape complexity, ranging from a single computation to 8,456 computations of NPP for the study area. Averaging sub-grid cell landscape variations typical of the northern US Rocky Mountains can result in overestimates of NPP as large as 30 %. Aggregating climate within the 1° cell contributed up to 50 % of the bias to NPP estimates, while aggregating topography, soils, and vegetation was of secondary importance. Careful partitioning of complex landscapes can efficiently reduce the magnitude of this overestimation.

Ground-based canopy transmittance and satellite remotely sensed measurements for estimation of coniferous forest canopy structure☆

Abstract Ground-based measurements of forest canopy transmittance provide a ready means of estimating intercepted photosynthetically active radiation (IPAR) for use in calibrating satellite remotely sensed estimates of forest canopy structure. The relationship between canopy transmittance and Landsat Thematic Mapper (TM) near IR/red radiance ratio data was examined for a temperature coniferous forest study site in northwestern Montana. Semivariogram analysis showed that the canopy transmittance and the TM near IR/red ratio had a similar spatial autocorrelation structure. Due to the fine scale patchiness of the forest canopy, the canopy transmittance and TM data were averaged at the coarser scale of the hillslope terrain units for regression analysis. These hillslope averaged data sets showed a strong linear relationship (R2 = 0.66). The transmittance measurements were converted to leaf area index (LAI) but comparison with previous results obtained for coniferous forests in Oregon (Peterson et al., 1987) shows some differences in the relationship between LAI and TM near IR/red ratio.

Analysis Of Forest Disturbance Using TM and AVHRR Data

A methodology that will be used to determine the proportions of undisturbed, successional vegetation and recently disturbed land cover within coniferous forests using remotely sensed data from the advanced very high resolution radiometer (AVHRR) is presented. The method uses thematic mapper (TM) data to determine the proportions of the three stages of forest disturbance and regrowth for each AVHRR pixel in the sample areas, and is then applied to interpret all AVHRR imagery. Preliminary results indicate that there are predictable relationships between TM spectral response and the disturbance classes. Analysis of ellipse plots from a TM classification of the disturbed forested landscape indicates that the forest classes are separable in the red (0.63-0.69 micron) and near-infrared (0.76-0.90 micron) bands, providing evidence that the proportion of disturbance classes may be determined from AVHRR data.

Image-Based Decision Tools for Vineyard Management

Vineyard managers in California’s premium wine industry are concerned with canopy development, field uniformity, relative amounts of leaf and fruit production, and irrigation management strategy. The application of high-resolution satellite imagery to viticultural management in Napa Valley was examined with respect to each of these issues. Ikonos multispectral data were transformed to a spectral vegetation index and combined with ground measurements to map vineyard canopy density, expressed both as leaf area index (LAI) and leaf area per vine. Within-field variance was used to quantify field uniformity. Leaf area and yield data were combined to map end-of-season vine balance (leaf area to fruit weight ratio). A water balance model was developed to assist with irrigation planning. The model combines leaf area with weather and soils databases to predict soil moisture, vine stress, and water replacement needs. The simulation operates on a 24 hour timestep, and results can be temporally aggregated as needed. It is concluded that remote sensing can provide a basis for decision support in vineyard management.