An object-oriented approach to assessing changes in tree cover in the Colorado Front Range 1938-1999

Forest Ecology and Management

Volumn 258, Issue 7, 2009, Pages 1342-1349

  Platt, R V ^a   Schoennagel, T ^b  

^a GETTYSBURG COLLEGE   (United States)

^b UNIVERSITY OF COLORADO   (United States)

Author keywords

Aerial photographs; Fire regime condition class; Forest cover; Ponderosa pine; Wildfire

Indexed keywords

AERIAL PHOTOGRAPHS; FIRE REGIME CONDITION CLASS; FOREST COVER; PONDEROSA PINE; WILDFIRE;

AERIAL PHOTOGRAPHY; FIRE HAZARDS; FIRES; FORESTRY; GRAPH THEORY; IMAGE ANALYSIS; MEMBERSHIP FUNCTIONS; PHOTOGRAPHIC EQUIPMENT; REMOTE SENSING; RISK PERCEPTION;

CONSERVATION;

AERIAL PHOTOGRAPHY; CATASTROPHIC EVENT; ENVIRONMENTAL RESTORATION; FIRE MANAGEMENT; FOREST COVER; FOREST FIRE; FOREST MANAGEMENT; FORESTRY POLICY; IMAGE ANALYSIS; NATIONAL PLANNING; SPATIAL ANALYSIS; TREE;

AIR CRAFT; CONSERVATION; FOREST FIRES; HAZARDS; IMAGE ANALYSIS; PHOTOGRAPHS; PINUS PONDEROSA; REMOTE SENSING; TREES;

COLORADO; FRONT RANGE; NORTH AMERICA; ROCKY MOUNTAINS; UNITED STATES;

CONIFEROPHYTA; PINUS CONTORTA;

EID: 68949164747     PISSN: 03781127     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.foreco.2009.06.039     Document Type: Article

References (29)

1. Arroyo L.A., Healey S.P., Cohen W.B., Cocero D., and Manzanera J.A. Using object-oriented classification and high-resolution imagery to map fuel types in a Mediterranean region. Journal of Geophysics Research 111 (2006) G04S04
2. Baatz M., Benz U., Dehghani S., Heynen M., Holtje A., Hofmann P., Lingenfelder I., et al. eCognition Professional User Guide, version 4.0. Definiens Imaging GmbH (2004), Definiens, München, Germany
3. Benz U.C., Hofmann P., Willhauck G., Lingenfelder I., and Heyen M. Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS Journal of Photogrammetry & Remote Sensing 58 (2004) 239-258
4. Browning D.M., Archer S.R., and Byrne A.T. Field validation of 1930s aerial photography: what are we missing?. Journal of Arid Environments 73 9 (2009) 844-853
5. Chobey M.S., Franklin S.E., and Wulder M.A. Object-based analysis of Ikonos-2 imagery for extraction of forest inventory parameters. Photogrammetric Engineering & Remote Sensing 72 4 (2006) 383-394
6. Cleve C., Kelly M., Kearns F.R., and Moritz M. Classification of the wildland-urban interface: a comparison of pixel- and object-based classifications using high-resolution aerial photography. Computers, Environment and Urban Systems 32 (2008) 317-326
7. Covington W.W., and Moore M. Southwestern ponderosa forest structure. Journal of Forestry 92 (1994) 39-47
8. Ehle D.S., and Baker W.L. Disturbance and stand dynamics in ponderosa pine forests in Rocky Mountain National Park, USA. Ecological Monographs 73 4 (2003) 543-566
9. Fule P.Z., Covington W.W., and Moore M.M. Determining reference conditions for ecosystem management of southwestern ponderosa pine forests. Ecological Applications 7 3 (1997) 895-908
10. Hann, W., Shlisky, A., Havlina, D., Schon, K., Barrett, S., DeMeo, T., Pohl, K., Menakis, J., Hamilton, D., Jones, J., Levesque, M., 2004. Interagency Fire Regime Condition Class Guidebook. Interagency and The Nature Conservancy Fire Regime Condition Class website. USDA Forest Service, U.S. Department of the Interior, The Nature Conservancy, and Systems for Environmental Management. Available online: www.frcc.gov.
11. Im J., Jensen J.R., and Tullis J.A. Object-based change detection using correlation image analysis and image segmentation. International Journal of Remote Sensing 29 2 (2007) 399-423
12. Kaufmann M.R., Regan C.M., and Brown P.M. Heterogeneity in ponderosa pine/Douglas-fir forests: age and size structure in unlogged and logged landscapes of central Colorado. Canadian Journal of Forest Research 30 (2000) 698-711
13. Laliberte A.S., Rango A., Havstad K.M., Paris J.F., Beck R.F., McNeely R., and Gonzalez A.L. Object-oriented image analysis for mapping shrub encroachment from 1937 to 2003 in southern New Mexico. Remote Sensing of Environment 93 (2004) 198-210
14. Mast J.N., Veblen T.T., and Hodgson M.E. Tree invasion within a pine/grassland ecotone: an approach with historical aerial photography and GIS modeling. Forest Ecology and Management 93 (1997) 181-194
15. Mallinis G., Koutsias N., Tsakiri-Strati M., and Karteris M. Object-based classification using Quickbird imagery for delineating forest vegetation polygons in a Mediterranean test site. ISPRS Journal of Photogrammetry & Remote Sensing 63 (2008) 237-250
16. Ozdemir I., Norton D.A., Ozkan U.Y., Mert A., and Senturk O. Estimation of tree size diversity using object oriented texture analysis and aster imagery. Sensors 8 (2008) 4709-4724
17. Ozdemir I. Estimating stem volume by tree crown area and tree shadow area extracted from pan-sharpened Quickbird imagery in open Crimean juniper forests. International Journal of Remote Sensing 29 19 (2008) 5643-5655
18. Pillai R.B., Weisberg P.J., and Lingua E. Object-oriented classification of repeat aerial photography for quantifying woodland expansion in central Nevada. 20th Biennial Workshop on Aerial Photography, Videography, and High Resolution Digital Imagery for Resource Assessment. Waslaco, TX, October 2-6 (2005)
19. Platt R.V., and Rapoza L.M. An evaluation of an object-oriented paradigm for land use/land cover classification. Professional Geographer 60 1 (2008) 87-100
20. Sherriff R.L., and Veblen T.T. A spatially-explicit reconstruction of fire regime types in ponderosa pine forests of the Colorado Front Range. Ecosystems 10 (2007) 311-323
21. Sherriff R.L., and Veblen T.T. Variability in fire-climate relationships in ponderosa pine forests of the Colorado Front Range. International Journal of Wildland Fire 17 (2008) 50-59
22. The National Map LANDFIRE: LANDFIRE National Existing Vegetation Type layer, 2006. U.S. Department of Interior, Geological Survey. http://gisdata.usgs.net/website/landfire (accessed 15 May 2008).
23. US Government Accountability Office, 2007. Wildland fire management: a cohesive strategy and clear cost-containment goals are needed for federal agencies to manage wildland fire activities effectively. GAO-07-1017T. http://www.gao.gov/new.items/d071017t.pdf (accessed May 15, 2008).
24. US Department of the Interior (DOI) and Department of Agriculture (USDA), 2007. Healthy Forests Report: FY 2007 Accomplishments. http://www.forestsandrangelands.gov/reports/documents/healthyforests/2007/fy2007_final_healthy_forests_report_12112007.pdf (accessed May 15, 2008).
25. USDA Forest Service, 2003. Arapaho Roosevelt National Forest, Common Vegetation Unit: USDA Forest Service, Fort Collins, Colorado.
26. Veblen T.T., Kitzberger T., and Donnegan J. Climatic and human influences on fire regimes in ponderosa pine forests in the Colorado Front Range. Ecological Applications 10 4 (2000) 1178-1195
27. Veblen T.T., and Lorenz D.C. Anthropogenic disturbance and recovery patterns in montane forests, Colorado front range. Physical Geography 7 (1986) 1-24
28. Waser L.T., Baltsavias E., Ecker K., Eisenbeiss H., Feldmeyer-Christe E., Ginzler C., Kuchler M., and Zhang L. Assessing changes of forest area and shrub encroachment in a mire ecosystem using digital surface models and CIR aerial images. Remote Sensing of Environment 112 (2008) 1956-1968
29. White House, 2003. Reducing the threat of catastrophic wildfires and improving forest health. http://www.whitehouse.gov/ceq/hfi_12-02_wh_fact_sheet.pdf (accessed May 15, 2008).