Spatial Variation in Distribution and Growth Patterns of Old Growth Strip-Bark Pines

Postindustrial rises in CO2 have the potential to confound the interpretation of climatically sensitive tree-ring chronologies. Increased growth rates observed during the 20th century in strip-bark trees have been attributed to CO2 fertilization. Absent in the debate of CO2 effects on tree growth are spatially explicit analyses that examine the proximate mechanisms that lead to changes in rates of tree growth. Twenty-seven pairs of strip-bark and companion entire-bark trees were analyzed in a spatially explicit framework for abiotic environmental correlates. The strip-bark tree locations were not random but correlated to an abiotic proxy for soil moisture. The strip-bark trees showed a characteristic increase in growth rates after about 1875. Furthermore, the difference in growth rates between the strip-bark trees and entire-bark companions increased with increasing soil moisture. A possible mechanism for these findings is that CO2 is affecting water-use efficiency, which in turn affects tree-ring growth. These results point to the importance of accounting for microsite variability in analyzing the potential role of CO2 in governing growth responses

Hyperspectral detection of a subsurface CO2 leak in the presence of water stressed vegetation.

Remote sensing of vegetation stress has been posed as a possible large area monitoring tool for surface CO2 leakage from geologic carbon sequestration (GCS) sites since vegetation is adversely affected by elevated CO2 levels in soil. However, the extent to which remote sensing could be used for CO2 leak detection depends on the spectral separability of the plant stress signal caused by various factors, including elevated soil CO2 and water stress. This distinction is crucial to determining the seasonality and appropriateness of remote GCS site monitoring. A greenhouse experiment tested the degree to which plants stressed by elevated soil CO2 could be distinguished from plants that were water stressed. A randomized block design assigned Alfalfa plants (Medicago sativa) to one of four possible treatment groups: 1) a CO2 injection group; 2) a water stress group; 3) an interaction group that was subjected to both water stress and CO2 injection; or 4) a group that received adequate water and no CO2 injection. Single date classification trees were developed to identify individual spectral bands that were significant in distinguishing between CO2 and water stress agents, in addition to a random forest classifier that was used to further understand and validate predictive accuracies. Overall peak classification accuracy was 90% (Kappa of 0.87) for the classification tree analysis and 83% (Kappa of 0.77) for the random forest classifier, demonstrating that vegetation stressed from an underground CO2 leak could be accurately discerned from healthy vegetation and areas of co-occurring water stressed vegetation at certain times. Plants appear to hit a stress threshold, however, that would render detection of a CO2 leak unlikely during severe drought conditions. Our findings suggest that early detection of a CO2 leak with an aerial or ground-based hyperspectral imaging system is possible and could be an important GCS monitoring tool

Variable importance plots for the single date random forest models.

<p>Note that the leaf position categorical variable is not included.</p

Out-of-bag accuracies and Kappa statistics for each single date random forest model.

<p>Treatment group columns represent the reference data while the rows represent the classified data.</p><p>Out-of-bag accuracies and Kappa statistics for each single date random forest model.</p

Sample reference spectra for each of the four treatment groups.

<p>Spectral signatures for February 07 (before treatment application) and February 21 (when spectral distinction was greatest between treatment groups).</p

Sampling method.

<p>Two dissected alfalfa leaves on the spectralon target (left). The ASD fiber optic and plant probe assembly (right).</p

Pruned single date classification trees.

<p>Each tree shows the utilized spectral bands (by central band wavelength) and reflectance levels for each splitting rule. Splitting rules apply to the left branches of the tree. C = control class; I = CO₂ injection class; WS = water stress class; and WSI = water stress and CO₂ injection interaction class.</p

Internal classification accuracies and Kappa statistics for each single date classification tree model containing at least two terminal nodes.

<p>Treatment group columns represent the reference data while the rows represent the classified data.</p><p>Internal classification accuracies and Kappa statistics for each single date classification tree model containing at least two terminal nodes.</p

Overall internal accuracies for each random forest and classification tree model over the course of the experiment.

<p>Note that the February 7–11 classification trees did not contain ≥ two terminal nodes.</p

Treatment block design.

<p>Single treatment block shown with plumbing and labeled treatments shown in white for each plant (left). Four of five treatment blocks are shown within the greenhouse (right).</p