INFLUENCES OF SECONDARY DISTURBANCES ON LODGEPOLE PINE STAND DEVELOPMENT IN ROCKY MOUNTAIN NATIONAL PARK

2007 ◽  
Vol 17 (6) ◽  
pp. 1638-1655 ◽  
Author(s):  
Jason S. Sibold ◽  
Thomas T. Veblen ◽  
Kathryn Chipko ◽  
Lauren Lawson ◽  
Emily Mathis ◽  
...  
Keyword(s):  
National Park ◽  
Lodgepole Pine ◽  
Rocky Mountain ◽  
Rocky Mountain National Park ◽  
Stand Development

Forest developmental trajectories in mountain pine beetle disturbed forests of Rocky Mountain National Park, Colorado

2011 ◽  
Vol 41 (4) ◽  
pp. 782-792 ◽  
Author(s):  
Matthew Diskin ◽  
Monique E. Rocca ◽  
Kellen N. Nelson ◽  
Carissa F. Aoki ◽  
W.H. Romme
Keyword(s):  
Forest Structure ◽  
National Park ◽  
Mountain Pine Beetle ◽  
Lodgepole Pine ◽  
Developmental Trajectories ◽  
Rocky Mountain ◽  
Rocky Mountain National Park ◽  
Forest Types ◽  
Mountain Pine ◽  
Pine Beetle

A mountain pine beetle (Dendroctonus ponderosae Hopkins) epidemic has caused widespread mortality of lodgepole pine (Pinus contorta Dougl. ex Loud. var. latifolia Engelm.) trees across western North America,. We characterized the initial effects of beetle-induced mortality on forest structure and composition in Rocky Mountain National Park, Colorado. In 2008, we surveyed stand structure and tree species composition across lodgepole pine dominated forests in the western portion of the Park. We defined five lodgepole pine forest types to describe variability in pre-epidemic forest conditions. This forested landscape appears to be resilient to the effects of the beetle. Surviving trees, including both canopy trees and saplings, were plentiful in most of the post-epidemic forests, even after accounting for anticipated future mortality. Subalpine fir (Abies lasiocarpa (Hook.) Nutt.), Engelmann spruce (Picea engelmannii Parry ex Engelm.), and aspen (Populus tremuloides Michx.) had modestly higher relative abundances after the epidemic. Lodgepole pine remained the dominant species on approximately 85% of the landscape. The impact of the outbreak on forest structure and composition varied considerably among the five forest types, suggesting that post-epidemic forest developmental trajectories will vary according to pre-outbreak stand characteristics. Active management efforts to regenerate lodgepole pine forests, e.g., tree planting, will likely not be necessary on this landscape.

scholarly journals Assessment of High Resolution Air Temperature Fields at Rocky Mountain National Park by Combining Scarce Point Measurements with Elevation and Remote Sensing Data

2020 ◽  
Vol 13 (1) ◽  
pp. 113
Author(s):  
Antonio-Juan Collados-Lara ◽  
Steven R. Fassnacht ◽  
Eulogio Pardo-Igúzquiza ◽  
David Pulido-Velazquez
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Air Temperature ◽  
Land Surface ◽  
National Park ◽  
Rocky Mountain ◽  
Rocky Mountain National Park ◽  
Temperature Fields ◽  
Lapse Rate ◽  
Validation Experiment

There is necessity of considering air temperature to simulate the hydrology and management within water resources systems. In many cases, a big issue is considering the scarcity of data due to poor accessibility and limited funds. This paper proposes a methodology to obtain high resolution air temperature fields by combining scarce point measurements with elevation data and land surface temperature (LST) data from remote sensing. The available station data (SNOTEL stations) are sparse at Rocky Mountain National Park, necessitating the inclusion of correlated and well-sampled variables to assess the spatial variability of air temperature. Different geostatistical approaches and weighted solutions thereof were employed to obtain air temperature fields. These estimates were compared with two relatively direct solutions, the LST (MODIS) and a lapse rate-based interpolation technique. The methodology was evaluated using data from different seasons. The performance of the techniques was assessed through a cross validation experiment. In both cases, the weighted kriging with external drift solution (considering LST and elevation) showed the best results, with a mean squared error of 3.7 and 3.6 °C2 for the application and validation, respectively.

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