2014-05-25 NRMI: NATURAL RESOURCE MONITORING ITEMS OF INTEREST
This Issue: Lodgepole Pine (Pinus contorta) Stand Density and Some Other Good Things.
YELLOWSTONE LODGEPOLE PINE (Pinus contorta) STAND DENSITY- Recently a member of one of the lists soought information on Lodgepole pine (Pinus contorta) density in Yellowstone National Park. Here are some sites that may be of interest.
Arcano, Rick; Tinker, Dan.2006. Allometric model development, and biomass allocation patterns of lodgepole pine in the greater Yellowstone ecosystem.Poster
Chen, Gang et al. 2012.The influence of sampling density on geographically weighted regression: a case study using forest canopy height and optical data, International Journal of Remote Sensing, 33:9, 2909-2924
Coops, Nicholas C. et al.2009. Prediction and assessment of bark beetle-induced mortality of lodgepole pine using estimates of stand vigor derived from remotely sensed data. Remote Sensing of Environment. 113: 1058-1066
Cornforth, William et al. 2012. Remote sensing for practical forestry: Arboriculture to UAVs. 27 slides.
Crabb, Benjamin A.; et al. 2012. Development and assessment of 30-meter pine density maps for landscape-level modeling of mountain pine beetle dynamics. Res. Pap. RMRS-RP-93WWW. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 43 p.
Erdody, T.L.,Moskal, L. M., 2008. LiDAR derived canopy fuel metrics for wildfire modeling. Factsheet # 4. Remote Sensing and Geospatial Application Laboratory, University of Washington, Seattle, WA..
Framls. Sjammpm/ et al. 2013. Monitoring forest regrowth following large scale fire using satellite data - A case study of Yellowstone National Park, USA. European Journal of Remote Sensing -46 ; 561-569
Franks, Shannon. et al. 2013. Monitoring forest regrowth following large scale fire using satellite data - A case study of Yellowstone National Park, USA. European Journal of Remote Sensing 46: 561-569
Groot, Art. 2013. Using Remotely Sensed Data to Increase Knowledge of Wood Fibre Resources. 22 slides.
Harrison, W. Doug; Potter, Tom. 2001. A Satellite-Based Land Cover Map for the Upper Yellowstone River Watershed, Montana and Wyoming. 23 p.
Jakubauskas, Mark E.; Price Kevin. 2000. Regression-Based Estimation of Lodgepole Pine Forest Age from Landsat Thematic Mapper Data. Geocarto International, 15(1): 21- 26,
Jakubauskas, Mark E.; Price, Kevin P. 1997. Empirical Relationships between Structural and Spectral Factors of Yellowstone Lodgepole Pine Forests. Photogrammetric Engineering & Remote Sensing,63(12): 1375-1381.
Johnson, Erik Wayne.2010. A landscape-scale investigation into the risk of lodgepole pine mortality caused by mountain pine beetle dendroctonus ponderosae (Coleoptera: Curculioidae: Scolytinae).Thesis. 159 p.
Kashian, Daniel M. et al. 2004. Spatial heterogeneity of lodgepole pine sapling densities following the 1988 fires in Yellowstone National Park, Wyoming, USA. Can. J. For. Res. 34: 2263–2276
Kashian, Daniel M. et al. 2006. Carbon Storage on Landscapes with Stand-replacing Fires. Bioscience 56(7): 598-606.
Kaufmann, Merrill R. et al. 2009? The Status of Our Scientific Understanding of Lodgepole Pine and Mountain Pine Beetles – A Focus on Forest Ecology and Fire Behavior. TNC. 16 p.
Landram. Michael. 2004. The Influence of Stand Density on Mortality in California’s Forests - An Analysis of Inventory Data. 50 slides.
Littron, Creighton. et al..2004. Effects of tree density and stand age on carbonallocation patterns in postfire lodgepole pine. Ecological Applications, 14(2): 460–475.
McGregor, M.; Cole, D. 1985. Integrating management strategies for the mountain pine beetle with multiple-resource management of lodgepole pine forests. USDA Forest Service, Intermountain Forest and Range Experiment Station, GTR INT-174, 67 p.From George Wooten, Appeal list.
Moskal, L. Monika; Jakubauskas, Mark E. 2013. Monitoring Post Disturbance Forest Regeneration with Hierarchical Object-Based Image Analysis. Forests, 4, 808-829;
Nolan, Ann W. 2004. Towards retrieval of forest cover density over snow from the Multi-angle Imaging ; SpectroRadiometer (MISR). Hydrol. Process. 18, 3623–3636
Ryan, Michael G. et al. 2008. Carbon cycling at the landscape scale: the effect of changes in climate and fire frequency on age distribution, stand structure, and net ecosystem production. Final Report JFSP Project Number: 03-1-1-06. 26 p.
Schoennagel, T. et al. 2009. The effect of fire interval on post-fire understorey communities in Yellowstone National Park. Journal of Vegetation Science 15(6) Abstract.
Schoennagel, T., et al. 2003. Influence of fire interval and serotiny on postfire lodgepole pine density in Yellowstone National Park. Ecology, 84(11): 2967—2978.
Schoennagel, Tania et al. 2006. Influence of fire regimes on lodgepole pine stand age and density across the Yellowstone National Park (USA) landscape. Landscape Ecol (2006) 21:1281–1296
Turner, Moncia. 2009. Ecological Effects of the ’88 Yellowstone Fires - A Story of Surprise, Constancy, and Change. 27 p. Yellowstone Science 17(2) 24-49.
Turner, Monica G. et al. 2007. Cone production in young post-fire stands in Greater Yellowstone (USA). Forest Ecology and Management 242 (2007) 119–126
Turner, Monica. N.d. Fire, vegetation and ecosystem processes in Yellowstone National Park.
Vernier, Matt. 2004.- Making Timber Cruising More Efficient - Automated Timber Stand Delineation and Density Estimation from Aerial Imagery
SOME PUBLICATIONS/URLS OF INTEREST
FAO. 2014. Forestry Communication Toolkit
Medeiros, A.C., et al. 2014. Dry forest restoration and unassisted native tree seedling recruitment at Auwahi, Maui. Pacific Sci. 68(1):33-45.
Mellone, U., et al. 2013. Summer pre-breeding movements of Eleonora's Falcon Falco eleonorae revealed by satellite telemetry: implications for conservation. Bird Conserv. Int. 23(4):487-494.
Mendoza, S.V., et al. 2014. Consistency in bird use of tree cover across tropical agricultural landscapes. Ecol. Appl. 24(1):158-168. Abstract.
Mikusiński, G., et al. 2014. Biodiversity priority areas and religions-a global analysis of spatial overlap. Oryx 48(1):17-22.
Mohan, Vishwa. 2014. New forest survey mooted to address sustainable development. The Times of India.
Monahan, W.B., et al. 2013. Forecasting distributional responses of limber pine to climate change at management-relevant scales in Rocky Mountain National Park. PLoS ONE 8(12):e83163.
Morgenroth, Justin; Visse, Rien, 2013. Uptake and barriers to the use of geospatial technologies in forest management New Zealand Journal of Forestry Science 2013, 43:16
Moritz, C.C.; Potter, S. 2013. The importance of an evolutionary perspective in conservation policy planning. Mol. Ecol. 22(24):5969-5971.
Moskal, Monika L.; Jakubauskas, Mark E.2013. Monitoring Post Disturbance Forest Regeneration with Hierarchical Object-Based Image Analysis. Forests 4, 808-829.
Nagendra, Harini; Gadil, Madhav. 1999. Biodiversity assessment at multiple scales: Linking remotely sensed data with field information. Proc. Natl. Acad. Sci. USA 96. 5 p.
Nan, Z.B. 2001. Remote sensing application to grassland monitoring. 17 p.
Nichole, Wendee. 2014. Seeing the Forest for the Trees: How 'One Health' Connects Humans, Animals, and Ecosystems. Mongabay.com
Norman, James. 2014. Drones striking a high-tech blow for conservation and the environment. Theguardian
Oslisly, R., et al. 2013. Climatic and cultural changes in the west Congo Basin forests over the past 5000 years. Philos. Trans. R. Soc. London. [Biol.] 368(1625):20120304.
Pan, Y.D., et al. 2013. The structure, distribution, and biomass of the world's forests. Annu. Rev. Ecol. Evol. S. 44:593-622.
Peng, Jing et al. 2013. Estimate of extended long-term LAI data set derived from AVHRR and MODIS based on the correlations between LAI and key variables of the climate system from 1982 to 2009. International Journal of Remote Sensing 34(21): 7761-7778.
Pettorelli, Nathalie et al. 2014. Satellite remote sensing for biodiversity research and conservation applications. Published online: April 2014 Published in print: May 2014 Special offer price for print issue: £35.00 (usual price: £59.50). Details at http://www.isprs.org/news/newsletter/2014-02/34_Satellite_remote_sensing-TB_1643-April_2014.pdf
Potter, K.M.; Woodall, C.W. 2014. Does biodiversity make a difference? Relationships between species richness, evolutionary diversity, and aboveground live tree biomass across U.S. forests. Forest Ecology and Management. 321:117-129. Abstract. From Barbara Conkling, USFS.
Pretorius, E. 2013. Image Classification towards Mapping of Vegetation Structure: - A practical approach. 12 p.
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NEXT ISSUE: World Pine Plantations
-- H. Gyde Lund Forest Information Services 6238 Settlers Trail Place Gainesville, VA 20155-1374 USA Tel: +1-703-743-1755 Email: gyde<at>comcast.net CV: http://home.comcast.net/~gyde/cv.html. Publications: http://home.comcast.net/~gyde/lundpub.htm. Skype: forestgyde
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