P.V. Bolstad | Geology Reports

USGS2015

Growth-climate relationships across topographic gradients in the northern Great Lakes

Climatic conditions exert important control over the growth, productivity, and distribution of forests, and characterizing these relationships is essential for understanding how forest ecosystems will respond to climate change. We used dendrochronological methods to develop climate–growth relationships for two dominant species, Populus tremuloides (quaking aspen) and Pinus resinosa (red pine), in the upper Great Lakes region to understand how climate and water availability influence annual forest productivity. Trees were sampled along a topographic gradient at the Marcell Experimental Forest (Minnesota, USA) to assess growth response to variations in temperature and different water availability metrics (precipitation, potential evapotranspiration (PET), cumulative moisture index (CMI), and soil water storage). Climatic variables were able to explain 33–58% of the variation in annual growth (as measured by ring-width increment) for quaking aspen and 37–74% of the variation for red pine. Climate–growth relationships were influenced by topography for quaking aspen but not for red pine. Annual ring growth for quaking aspen decreased with June CMI on ridges, decreased with temperature in the November prior to the growing season on sideslopes, and decreased with June PET on toeslopes. Red pine growth increased with increasing July PET across all topographic positions. These results indicate the sensitivity of both quaking aspen and red pine to local climate and show several vulnerabilities of these species to shifts in water supply and temperature because of climate change.

Minnesota↗

USGS2009

Using LiDAR and quickbird data to model plant production and quantify uncertainties associated with wetland detection and land cover generalizations

Spatiotemporal data from satellite remote sensing and surface meteorology networks have made it possible to continuously monitor global plant production, and to identify global trends associated with land cover/use and climate change. Gross primary production (GPP) and net primary production (NPP) are routinely derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard satellites Terra and Aqua, and estimates generally agree with independent measurements at validation sites across the globe. However, the accuracy of GPP and NPP estimates in some regions may be limited by the quality of model input variables and heterogeneity at fine spatial scales. We developed new methods for deriving model inputs (i.e., land cover, leaf area, and photosynthetically active radiation absorbed by plant canopies) from airborne laser altimetry (LiDAR) and Quickbird multispectral data at resolutions ranging from about 30??m to 1??km. In addition, LiDAR-derived biomass was used as a means for computing carbon-use efficiency. Spatial variables were used with temporal data from ground-based monitoring stations to compute a six-year GPP and NPP time series for a 3600??ha study site in the Great Lakes region of North America. Model results compared favorably with independent observations from a 400??m flux tower and a process-based ecosystem model (BIOME-BGC), but only after removing vapor pressure deficit as a constraint on photosynthesis from the MODIS global algorithm. Fine-resolution inputs captured more of the spatial variability, but estimates were similar to coarse-resolution data when integrated across the entire landscape. Failure to account for wetlands had little impact on landscape-scale estimates, because vegetation structure, composition, and conversion efficiencies were similar to upland plant communities. Plant productivity estimates were noticeably improved using LiDAR-derived variables, while uncertainties associated with land cover generalizations and wetlands in this largely forested landscape were considered less important.

Remote Sensing of Environment↗