Pigment-based Identification of Ozone-Damaged Pine Needles as a Basis for Spectral Segregation of Needle Conditions

Energy Biosciences Institute, Univ. of California, Berkeley, CA, USA.
Journal of Environmental Quality (Impact Factor: 2.65). 05/2009; 38(3):855-67. DOI: 10.2134/jeq2008.0260
Source: PubMed


Air pollution affects large areas of forest, and field assessment of these effects is a costly, site-specific process. This paper establishes a biochemical basis for identifying ozone-damaged pine trees to facilitate efficient remote sensing assessment of air pollution damage. Several thousand live needles were collected from ponderosa pine (Pinus ponderosa) and Jeffrey pine (P. jeffreyi) trees at three sites in Plumas National Forest and Sequoia-Kings Canyon National Park. These needles were assembled into 504 samples (based on the abaxial surface) and grouped according to five dominant needle conditions (green, winter fleck, sucking insect damage, scale insect damage, and ozone damage) and a random mixture of needles. Pigment concentrations per unit needle area of chlorophyll a, chlorophyll b, and total carotenoids were measured. The following pigment concentration ratios were calculated for all samples: chlorophyll a/total carotenoids, chlorophyll b/total carotenoids, total chlorophyll/carotenoids, chlorophyll a/chlorophyll b. The group of ozone-damaged needles had significantly lower mean pigment concentrations (family-wise p < 0.01) and significantly lower mean chlorophyll a/total carotenoid and total chlorophyll/total carotenoid ratios (family-wise p < 0.01) than all other groups of needles. Ozone-damaged needles had a significantly lower mean chlorophyll a/chlorophyll b ratio than all other groups except one (family-wise p < 0.01). Linear discriminant analysis with three factors (chlorophyll a concentration, the chlorophyll a/carotenoid ratio, and the chlorophyll a/chlorophyll b ratio) and subsequent maximum likelihood classification of damaged and non-damaged needles gave an overall cross-validated accuracy of 96%. These ozone-damaged needles are biochemically unique in relation to other needle conditions in this study, and further research is needed to generalize these results.

8 Reads
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The capacity to remotely identify impacts of ozone on conifers in California, USA and Catalonia, Spain was investigated using remote sensing and terrain-driven GIS analyses related to plant water relations and ozone uptake. The Ozone Injury Index (OII) field metric applied to Pinus ponderosa and Pinus jeffreyi in the USA and adapted to Pinus uncinata in Spain included visible chlorotic mottling, needle retention, needle length, and crown depth. Species classifications of AVIRIS and CASI hyperspectral imagery all approached 80% overall accuracy for the target bioindicator species. Remote sensing vegetation indices correlated best with longer-wavelength SWIR indices from the AVIRIS data in California, with the exception of the Photosynthetic Reflectance Index (PRI) correlation with the 011 Visual Component (OIIVI), which was also the highest direct correlation in Catalonia. In Catalonia, the OIIVI alone and its subparts correlated better with the CASI data than with the full OIL namely the PRI (R-2 = 0.28, p = 0.0044 for OIIVI-amount and R-2 = 0.33 and p = 0.0016 for OIIVI-severity). Stepwise regression models of ozone injury developed using remote sensing indices combined with terrain-derived GIS variables were significant for OII in California (R-2 = 0.59, p < 0.0001) and in Catalonia (R-2 = 0.68, p < 0.0001 for OIIVI). Multiple regression models of ozone injury including a three year average of O-3 exposure were significant both with imaging spectroscopy indices alone (R-2 = 0.56, p < 0.0001) and with topographic variables added (R-2 = 0.77, p < 0.0001) in Catalonia. Applying the multivariate models to image classifications could provide useful maps useful for ozone impact monitoring but requires further validation before being considered operational.
    Remote Sensing of Environment 12/2013; 139:138-148. DOI:10.1016/j.rse.2013.07.037 · 6.39 Impact Factor