Steven R. Garrity

A remotely sensed pigment index reveals photosynthetic phenology in evergreen conifers

Significance Evergreen photosynthetic activity has been difficult to determine from remote sensing, causing errors in terrestrial photosynthetic carbon uptake models. Using a reflectance chlorophyll/carotenoid index (CCI) sensitive to seasonally changing chlorophyll/carotenoid pigment ratios, we demonstrate a method of tracking photosynthetic phenology in evergreen conifers. The CCI reveals seasonally changing photosynthetic rates and detects the onset of the growing season in evergreen foliage. This method could improve our understanding of changing photosynthetic activity in a warming climate, and could improve assessment of the evergreen component of the terrestrial carbon budget, which has been elusive. In evergreen conifers, where the foliage amount changes little with season, accurate detection of the underlying “photosynthetic phenology” from satellite remote sensing has been difficult, presenting challenges for global models of ecosystem carbon uptake. Here, we report a close correspondence between seasonally changing foliar pigment levels, expressed as chlorophyll/carotenoid ratios, and evergreen photosynthetic activity, leading to a “chlorophyll/carotenoid index” (CCI) that tracks evergreen photosynthesis at multiple spatial scales. When calculated from NASA’s Moderate Resolution Imaging Spectroradiometer satellite sensor, the CCI closely follows the seasonal patterns of daily gross primary productivity of evergreen conifer stands measured by eddy covariance. This discovery provides a way of monitoring evergreen photosynthetic activity from optical remote sensing, and indicates an important regulatory role for carotenoid pigments in evergreen photosynthesis. Improved methods of monitoring photosynthesis from space can improve our understanding of the global carbon budget in a warming world of changing vegetation phenology.

Observations of stem water storage in trees of opposing hydraulic strategies

Hydraulic capacitance and water storage form a critical buffer against cavitation and loss of conductivity within the xylem system. Withdrawal from water storage in leaves, branches, stems, and roots significantly impacts sap flow, stomatal conductance, and transpiration. Storage quantities differ based on soil water availability, tree size, wood anatomy and density, drought tolerance, and hydraulic strategy (anisohydric or isohydric). However, the majority of studies focus on the measurement of storage in conifers or tropical tree species. We demonstrate a novel methodology using frequency domain reflectometry (FDR) to make continuous, direct measurements of wood water content in two hardwood species in a forest in Michigan. We present results of a two month study comparing the water storage dynamics between a mature red oak and red maple, two species with differing wood densities, hydraulic architecture, and hydraulic strategy. We also include results pertaining to the use of different probe lengths to ...

Production of vegetation spatial-structure maps by per-object analysis of juniper encroachment in multitemporal aerial photographs

The remote sensing of vegetation, which has predominantly applied methods that analyze each image pixel as independent observations, has recently seen the development of several methods that identify groups of pixels that share similar spectral or structural properties as objects. The outputs of “per-object” rather than “per-pixel” methods represent characteristics of vegetation objects, such as location, size, and volume, in a spatially explicit manner. Before decisions can be influenced by data products derived from per-object remote sensing methods, it is first necessary to adopt methodologies that can quantify the spatial and temporal trends in vegetation structure in a quantitative manner. In this study, we present one such methodological framework where (i) marked point patterns of vegetation structure are produced from two per-object methods, (ii) new spatial-structural data layers are developed via moving-window statistics applied to the point patterns, (iii) the layers are differenced to highlight spatial-structural change over a 60 year period, and (iv) the resulting difference layers are evaluated within an ecological context to describe landscape-scale changes in vegetation structure. Results show that this framework potentially provides information on the population, growth, size association (nonspatial distribution of large and small objects), and dispersion. We present an objective methodological comparison of two common per-object approaches, namely image segmentation and classification using Definiens software and two-dimensional wavelet transformations.

Estimating plot-level tree structure in a deciduous forest by combining allometric equations, spatial wavelet analysis and airborne LiDAR

Object-oriented classification methods are increasingly used to derive plant-level structural information from high-resolution remotely sensed data from plant canopies. However, many automated, object-based classification approaches perform poorly in deciduous forests compared with coniferous forests. Here, we test the performance of the automated spatial wavelet analysis (SWA) algorithm for estimating plot-level canopy structure characteristics from a light detection and ranging (LiDAR) data set obtained from a northern mixed deciduous forest. Plot-level SWA-derived and co-located ground-based measurements of tree diameter at breast height (DBH) were linearly correlated when canopy cover was low (correlation coefficient (r) = 0.80) or moderate (r = 0.68), but were statistically unrelated when canopy cover was high. SWA-estimated crown diameters were not significantly correlated with allometrically based estimates of crown diameter. Our results show that, when combined with allometric equations, SWA can be useful for estimating deciduous forest structure information from LiDAR in forests with low to moderate (<175% projected canopy area/ground area) levels of canopy cover.

Automatic detection of shrub location, crown area, and cover using spatial wavelet analysis and aerial photography

Characterizing shrub-steppe rangeland condition often requires fine-scale measurement of individual plants across broad areas. Advances in remote sensing to develop improved algorithms to census and monitor individual rangeland plants using image data are important for improving the efficiency with which these critical areas are monitored. Here, we performed and evaluated the first test of spatial wavelet analysis (SWA) to automatically detect the location and crown diameter of individuals of two species of shrubs (Artemisia tridentata and Purshia tridentata). Additionally, we quantified the aggregated cover of these shrubs at the plot scale. High spatial resolution (0.25 and 1 m) multispectral aerial imagery and field-based vegetation measurements were collected in both spring and fall 2005. We found that image- and field-based measures of individual shrubs and their crown areas were highly correlated in the fall imagery (r = 0.89). Image-based SWA prediction of shrub cover at the plot level correlated better with field-based measures (r = 0.91) than did a traditional, image texture-based measure (r = 0.71). Analyses of imagery acquired in spring resulted in poorer relationships due to the decreased phenological contrast between shrubs and understory grasses in spring relative to fall. Statistical equivalence tests demonstrated that individual shrub crown areas derived from field data and SWA were statistically equivalent and not biased, but the SWA- and field-based assessments of plot-level cover were not statistically equivalent. These results represent progress towards developing automatic methods to analyze shrubs at the landscape scale using remotely sensed imagery.

Modeling of particulate matter dispersion from a poultry facility using AERMOD

This study evaluates the performance of AERMOD, the current U.S. Environmental Protection Agency (EPA) regulatory model, in simulating particulate matter (PM10 and PM2.5) dispersion from a poultry pullet facility. At the source, the daily mean PM10 and PM2.5 concentrations with strong diurnal patterns were estimated to be 436.01 ± 166.77 μg m−3 and 291.09 ± 105.81 μg m−3, respectively. This corresponded to daily mean emission rates of PM10 and PM2.5 as 0.067–0.073 g sec−1 and 0.044–0.047 g sec−1, respectively. The modeled hourly PM concentration showed acceptable accuracy relative to the measured PM concentrations downwind of the source. Increasing the averaging period from hourly to daily resulted in improved prediction. The simulations revealed that PM concentrations at and beyond the property line of the poultry facility were within the National Ambient Air Quality Standards. This study suggested that AERMOD is effective in predicting and assessing the impacts of PM downwind of poultry facilities. Implications: Sampling and monitoring of PM emission around concentrated animal feeding operations (CAFOs) can be both time-consuming and costly. This makes dispersion modeling an alternative method for air quality assessment around CAFOs. The acceptable performance of the current EPA regulatory model, AERMOD, in modeling PM10 and PM2.5 dispersion around the pullet poultry facility suggested that this model can effectively predict PM downwind concentrations at and around CAFOs and can be used as a tool to assess the impacts of PM at downwind locations.

The Calibration and Use of Capacitance Sensors to Monitor Stem Water Content in Trees

Water transport and storage through the soil-plant-atmosphere continuum is critical to the terrestrial water cycle, and has become a major research focus area. Biomass capacitance plays an integral role in the avoidance of hydraulic impairment to transpiration. However, high temporal resolution measurements of dynamic changes in the hydraulic capacitance of large trees are rare. Here, we present procedures for the calibration and use of capacitance sensors, typically used to monitor soil water content, to measure the volumetric water content in trees in the field. Frequency domain reflectometry-style observations are sensitive to the density of the media being studied. Therefore, it is necessary to perform species-specific calibrations to convert from the sensor-reported values of dielectric permittivity to volumetric water content. Calibration is performed on a harvested branch or stem cut into segments that are dried or re-hydrated to produce a full range of water contents used to generate a best-fit regression with sensor observations. Sensors are inserted into calibration segments or installed in trees after pre-drilling holes to a tolerance fit using a fabricated template to ensure proper drill alignment. Special care is taken to ensure that sensor tines make good contact with the surrounding media, while allowing them to be inserted without excessive force. Volumetric water content dynamics observed via the presented methodology align with sap flow measurements recorded using thermal dissipation techniques and environmental forcing data. Biomass water content data can be used to observe the onset of water stress, drought response and recovery, and has the potential to be applied to the calibration and evaluation of new plant-level hydrodynamics models, as well as to the partitioning of remotely sensed moisture products into above- and belowground components.