Skip to main content

Advertisement

Log in

Accuracy assessment of a small-area method for estimating the spatial distribution of the degree of tree damage

  • Published:
Environmental Monitoring and Assessment Aims and scope Submit manuscript

Abstract

Tree damage, gauged by the amount of defoliation, is one of the basic criteria used to determine treatments for protected and economic forests. Monitoring should include an assessment of the degree of tree damage in different spatial scales. Therefore, in addition to the commonly applied large-area methods, small-area methods should be used. The aim of the paper is to present the results of the accuracy assessment of a small-area method, proposed by Podlaski (2005) [Podlaski, R. (2005). Inventory of the degree of tree defoliation in small areas. Forest Ecology and Management, 215, 361–377], for monitoring the degree of tree damage. The degree of tree damage was shown in sub-blocks P3 of the system of information on natural environment (SINUS). To estimate the spatial distribution of the degree of tree defoliation, survey sampling, based on simple random sampling with replacement (SRSWR), was used. The degree of damage to fir (Abies alba Mill.) and beech (Fagus sylvatica L.) was analysed in the Święty Krzyż forest section in the Świętokrzyski National Park. The maximum total estimation errors for the proportion of trees with a degree zero of damage, and with second and third degrees of damage together (for α = 0.05) were at most 30.8% for fir and 24.3% for beech trees. For standard, small-area evaluations, these are satisfactory values. In the Święty Krzyż forest section, the number of P3 sub-blocks with 0.00–5.00% of undamaged trees and with 80.01–100.00% of moderately- or severely-damaged trees was significantly greater for fir than for beech. These results indicate that the fir population was unhealthier than the beech group in the study area. P3 sub-blocks of the SINUS system, in which the proportion of the healthiest trees was highest, were situated at the forest margin, bordering on meadows and arable fields (in the case of fir) and forming dense patches consisting of several sub-blocks, or occurring singly in the whole study area (in the case of beech). The results show the significant differentiation of forest tree health in small areas.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Borecki, T., & Keczyński, A. (1992). Atlas ubytku aparatu asymilacyjnego drzew leśnych. Warszawa: Agencja ATUT.

    Google Scholar 

  • Ciołkosz, A. (1991) SINUS – system informacji o środowisku przyrodniczym. In S. Mazur (Ed.), Ekologiczne Podstawy Gospodarowania Środowiskiem Przyrodniczym. Wizje – problemy – trudności (pp. 317–328). Warszawa: Wyd. SGGW-AR.

    Google Scholar 

  • Cochran, W. G. (1977). Sampling techniques. New York: Wiley.

    Google Scholar 

  • Conover, W. J. (1980). Practical nonparametric statistics. New York: Wiley.

    Google Scholar 

  • FAO ISRIC ISSS (1998). World reference base for soil resources, 84 World Soil Resources Reports. Rome: FAO.

  • Ghosh, S., Innes, J. L., & Hoffmann, C. (1995). Observer variation as a source of error in assessments of crown condition through time. Forest Science, 41, 235–254.

    Google Scholar 

  • Goreaud, F., & Pélissier, R. (1999). On explicit formulas of edge effect correction for Ripley’s K-function. Journal of Vegetation Science, 10, 433–438.

    Article  Google Scholar 

  • Jaszczak, R. (1999). Historia monitoringu kondycji lasów w Polsce. Sylwan, 143(2), 5–25.

    Google Scholar 

  • Jaworski, A. (1982). Fir regression in Polish mountain areas. European Journal of Forest Pathology, 12, 143–149.

    Article  Google Scholar 

  • Jaworski, A., Karczmarski, J., Pach, M., Skrzyszewski, J., & Szar, J. (1995). Ocena żywotności drzewostanów jodłowych w oparciu o cechy biomorfologiczne koron i przyrost promienia pierśnicy. Acta Agraria et Silvestria, Series silvestris, 33, 115–131.

    Google Scholar 

  • Koronacki, J., & Mielniczuk, J. (2001). Statystyka. Warszawa: Wydawnictwa Naukowo-Techniczne.

    Google Scholar 

  • Law, A. M., & Kelton, W. D. (2000). Simulation modelling and analysis. New York: McGraw-Hill.

    Google Scholar 

  • Lorenz, M., Mues, V., Becher, G., Seidling, W., Fischer, R., Langouche, D., et al. (2002). Forest condition in Europe. Results of the 2001 large-scale survey. Geneva, Brussels: United Nations Economic Commission for Europe, European Commission.

    Google Scholar 

  • Matuszkiewicz, J. M. (2002). Zespoły leśne Polski. Warszawa: PWN.

    Google Scholar 

  • Palenka, J., Konôpka, B., & Bucha, T. (1996). Poškodenie lesov v oblasti Spiša. Zvolen: LVÚ.

    Google Scholar 

  • Penttinen, A. (2000). Small-area statistics in mapping of geo-referenced data. In The Yearbook of The Finnish Statistical Society 1999–2000 (pp. 39–48). Finnish Statistical Society.

  • Podlaski, R. (2004). Validation of a small-area method for estimating the spatial distribution of the degree of tree damage. European Journal of Forest Research, 123, 229–237.

    Article  Google Scholar 

  • Podlaski, R. (2005). Inventory of the degree of tree defoliation in small areas. Forest Ecology and Management, 215, 361–377.

    Article  Google Scholar 

  • Regulation (EC) no 2152/2003 of the European Parliament and of the Council of 17 November 2003 concerning monitoring of forests and environmental interactions in the Community (Forest Focus), Official Journal of the European Union, L324/1, 11.12.2003.

  • Ripley, B. D. (1981). Spatial statistics. New York: Wiley.

    Google Scholar 

  • Roesch Jr., F. A. (1993). Adaptive cluster sampling for forest inventories. Forest Science, 39, 655–669.

    Google Scholar 

  • Solberg, S., & Strand, L. (1999). Crown density assessments, control surveys and reproducibility. Environmental Monitoring and Assessment, 56, 75–86.

    Article  Google Scholar 

  • Spiecker, H., Mielikäinen, K., Köhl, M., & Skovsgaard, J. P. (1996a). Conclusions and summary. In H. Spiecker, K. Mielikäinen, M. Köhl, & J. P. Skovsgaard (Eds.), Growth trends in European forests (pp. 369–372). Berlin, Heidelberg, New York: Springer.

    Google Scholar 

  • Spiecker, H., Mielikäinen, K., Köhl, M., & Skovsgaard, J. P. (1996b). Discussion. In H. Spiecker, K. Mielikäinen, M. Köhl, & J. P. Skovsgaard (Eds), Growth trends in European forests (pp. 355–367). Berlin, Heidelberg, New York: Springer.

    Google Scholar 

  • Talvitie, M., Leino, O., & Holopainen, M. (2006). Inventory of sparse forest populations using adaptive cluster sampling. Silva Fennica, 40, 101–108.

    Google Scholar 

  • Thompson, S. K. (1990). Adaptive cluster sampling. Journal of the American Statistical Association, 85, 1050–1059.

    Article  Google Scholar 

  • Thompson, S. K. (1991). Stratified adaptive cluster sampling. Biometrika, 78, 389–397.

    Article  Google Scholar 

  • Wawrzoniak, J., & Małachowska, J. (2004). Dynamika poziomu uszkodzeń drzewostanów w latach 1999–2003. In J. Wawrzoniak, & J. Małachowska (Eds.), Stan uszkodzenia lasów w Polsce w 2003 roku na podstawie badań monitoringowych (pp. 19–21). Warszawa: Biblioteka Monitoringu Środowiska, Inspekcja Ochrony Środowiska.

    Google Scholar 

  • Zieliński, R. (1972). Tablice statystyczne. Warszawa: PWN.

    Google Scholar 

  • Zieliński, R., & Zieliński, W. (1990). Tablice statystyczne. Warszawa: PWN.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Rafał Podlaski.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Podlaski, R. Accuracy assessment of a small-area method for estimating the spatial distribution of the degree of tree damage. Environ Monit Assess 135, 339–351 (2007). https://doi.org/10.1007/s10661-007-9654-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10661-007-9654-x

Keywords

Navigation