Environmental Monitoring and Assessment

, Volume 131, Issue 1–3, pp 221–230 | Cite as

Using Spatial Pattern to Quantify Relationship Between Samples, Surroundings, and Populations

  • Michael A. WulderEmail author
  • Trisalyn A. Nelson
  • David Seemann


The need for accurate carbon budgeting, climate change modelling, and sustainable resource management has lead to an increase in the number of large area forest monitoring programs. Large area forest monitoring programs often utilize field and remotely sensed data sources. Sampling, via field or photo plots, enables the collection of data with the desired level of categorical detail in a timely and efficient manner. When sampling, the aim is to collect representative detailed data enabling the statistical reporting upon the characteristics of larger areas. As a consequence, approaches for investigating how well sample data represent larger areas (i.e., the sample neighbourhood and the population) are desired. Presented in this communication is a quantitative approach for assessing the nature of sampled areas in relation to surrounding areas and the overall population of interest. Classified Landsat data is converted to forest/non-forest categories to provide a consistent and uniform data set over a 130,000 km2 study region in central British Columbia, Canada. From this larger study area 322 2 × 2 km photo plots on a 20 × 20 km systematic grid are populated with composition and configuration information for comparison to non-sampled areas. Results indicate that typically, within the study area, the spatial pattern of forest within a photo plot is representative of the forest patterns found within primary and secondary neighbourhoods and over the entire population of the study. These methods have implications for understanding the nature of data used in monitoring programs worldwide. The ability to audit photo and field plot information promotes an increased understanding of the results developed from sampling and provides tools identifying locations of possible bias.


Forest monitoring Land cover mapping Large area mapping Photo plots Spatial pattern Landsat Large area Forest inventory 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Boots, B. (2003). Developing local measure of spatial association for categorical data. Journal of Geographical Systems, 5, 139–160.CrossRefGoogle Scholar
  2. Cliff, A., & Ord, J. (1981). Spatial processes models and applications. London: Pion Limited.Google Scholar
  3. Franklin, S. E., & Wulder, M. A. (2002). Remote sensing methods in medium spatial resolution satellite data land cover classification of large areas. Progress in Physical Geography, 26, 173–205.CrossRefGoogle Scholar
  4. Gillis, M. (2001). Canada’s national forest inventory. Environmental Monitoring and Assessment, 67, 121–129.CrossRefGoogle Scholar
  5. Gillis, M., Omule, A. Y., & Brierley, T. (2005). Monitoring Canada’s forests: The national forest inventory. The Forest Chronicle, 81, 214–221.Google Scholar
  6. Griffith, D. (1992). What is spatial autocorrelation? Reflections on the past 25 years of spatial statistics. L’Espace geographique, 3, 265–280.Google Scholar
  7. Gustafson, E. (1998). Quantifying landscape spatial pattern: what is the state of the art? Ecosystems, 1, 143–156.CrossRefGoogle Scholar
  8. Hoshizaki, K., Niiyama, K., Kimura, K., Yamashita, T., Bekku, Y., Okuda, T., et al. (2004). Temporal and spatial variation of forest biomass in relation to stand dynamics in mature, lowland tropical rainforest, Malaysia. Ecological Research, 19, 357–363.CrossRefGoogle Scholar
  9. Huang, C., Yang, L., Homer, C., Coan, M., Rykhus, R., Zhang, Z., et al. (2001). Synergistic use of FIA plot data and Landsat 7 ETM+ images for large area forest mapping. In Proceedings of the Third Annual Forest Inventory and Analysis Symposium, Traverse City, MI, October 17–19, 2001 (pp. 50–55). U.S. Department of Agriculture, Forest Service GTR NC-230.Google Scholar
  10. Kangis, A. (2006). Assessing the world’s forests. In A. Kangis & M. Maltamo (Eds.), Forest inventory: Methodology and applications (pp. 279–366). Dordrecht, The Netherlands: Springer.CrossRefGoogle Scholar
  11. Kleinn, C., Ramirez, C., Holmgren, P., Valverde, S. L., & Chavez, G. (2005). A national forest resources assessment for Costa Rica based on low intensity sampling. Forest Ecology and Management, 210, 9–23.CrossRefGoogle Scholar
  12. Li, H., & Reynolds, J. F. (1993). A new contagion index to quantify spatial patterns of landscapes. Landscape Ecology, 8, 155–162.CrossRefGoogle Scholar
  13. Li, H., & Reynolds, J. F. (1994). A simulation experiment to quantify spatial heterogeneity in categorical maps. Ecology, 75, 2446–2455.CrossRefGoogle Scholar
  14. Li, H., & Reynolds, J. F. (1995). On definition and quantification of heterogeneity. Okios, 73, 280–284.CrossRefGoogle Scholar
  15. Mayaux, P., Homgren, P., Achard, F., Eva, H., Stibig H., & Branthomme, A. (2005). Tropical forest cover change in the 1990s and options for future monitoring. Philosophical Transactions of the Royal Society of London Series B Biological Sciences, 360, 373–384.CrossRefGoogle Scholar
  16. Meidinger, D., & Pojar, J. (Eds.) (1991). Ecosystems of British Columbia. British Columbia Ministry of Forests Special Report series no. 6 (330 pp.).Google Scholar
  17. Nonomura, A., Songa-Ngoie, K., & Fukuyama, K. (2003). Devising a new digital vegetation model for eco-climatic analysis in Africa using GIS and NOAA AVHRR data. International Journal of Remote Sensing, 24, 3611–3633.Google Scholar
  18. Remmel, T. K., Csillag, F., Mitchell, S., & Wulder, M. A. (2005). Integration of forest inventory and satellite imagery: A Canadian status assessment and research issues. Forest Ecology and Management, 207, 405–428.CrossRefGoogle Scholar
  19. Roberts, D. A., Keller, M., & Soares, J. V. (2003). Studies of land-cover, land-use, and biophysical properties of vegetation in the large scale biosphere atmosphere experiment in Amazonia. Remote Sensing of Environment, 84, 377–388.CrossRefGoogle Scholar
  20. Smith, W. B. (2002). Forest inventory and analysis: A national inventory and monitoring program. Environmental Pollution, 116, S233–S242.CrossRefGoogle Scholar
  21. Tokola, T. (2006). Europe. In A. Kangis & M. Maltamo (Eds.), Forest inventory: Methodology and applications (pp. 295–366). Dordrecht, The Netherlands: Springer.CrossRefGoogle Scholar
  22. Tomppo, E. (1996). Multi-source national forest inventory of Finland. In Proceedings of the Subject Group S4.02-00 Forest Resource Inventory and Monitoring and Subject Group S4.12-00 Remote Sensing Technology; Tampere, Finland. IUFRO World Congress (pp. 27–49)Google Scholar
  23. Wulder, M. A., Dechka, J. A., Gillis, M., Luther, J., Hall, R. J., Beaudoin J., et al. (2003). Operational mapping of the land cover of the forested area of Canada with Landsat data: EOSD land cover program. Forestry Chronicle, 79, 1075–1083.Google Scholar
  24. Wulder, M. A., Kurz, W., & Gillis, M. (2004). National level forest monitoring and modeling in Canada. Progress in Planning, 61, 365–381.CrossRefGoogle Scholar
  25. Wulder, M., Loubier, E., & Richardson, D. (2002). Landsat-7 ETM+ orthoimage coverage of Canada. Canadian Journal of Remote Sensing, 28, 667–671.Google Scholar
  26. Wulder, M. A. & Nelson, T. (2003). EOSD legend: characteristics, suitability, and compatibility. Technical report, Canadian Forest Service, Pacific Forestry Centre, Victoria, British Columbia, Canada. Retrieved from

Copyright information

© Springer Science+Business Media B.V. 2007

Authors and Affiliations

  • Michael A. Wulder
    • 1
    Email author
  • Trisalyn A. Nelson
    • 2
  • David Seemann
    • 1
  1. 1.Canadian Forest Service (Pacific Forestry Centre)Natural Resources CanadaVictoriaCanada
  2. 2.Department of GeographyUniversity of VictoriaVictoriaCanada

Personalised recommendations