Sciences in Cold and Arid Regions ›› 2015, Vol. 7 ›› Issue (3): 245-256.doi: 10.3724/SP.J.1226.2015.00245

• ARTICLES • Previous Articles    

Characterizing stand structure in a spruce forests: effects of sampling protocols

Jun Du1, WeiJun Zhao2, ZhiBin He1, JunJun Yang1, LongFei Chen1, Xi Zhu1   

  1. 1. Linze Inland River Basin Research Station, Chinese Ecosystem Research Network, Heihe Key Laboratory of Ecohydrology and Integrated River Basin Science, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China;
    2. Gansu Province Qilian Water Resource Conservation Forest Research Institute, Zhangye, Gansu 734000, China
  • Received:2014-12-13 Revised:2015-02-11 Published:2018-11-23
  • Contact: ZhiBin He,
  • Supported by:
    This work was supported by the Hundred Talents Program of the Chinese Academy of Sciences (No. 29Y127D11), the National Natural Science Foundation of China (No. 41271524), Natural Science Foundation of Gansu Province (No. 1210RJDA015), and Forestry Industry Research Special Funds for Public Welfare Projects (No. 201104009-08).

Abstract: Spatial heterogeneity is an inherent characteristic of natural forest landscapes, therefore estimation of structural variability, including the collection and analyzing of field measurements, is a growing challenge for monitoring wildlife habitat diversity and ecosystem sustainability. In this study, we investigated the combined influence of plot shape and size on the accuracy of assessment of conventional and rare structural features in two young-growth spruce-dominated forests in northwestern China. We used a series of inventory schemes and analytical approaches. Our data showed that options for sampling protocols, especially the selection of plot size considered in structural attributes measurement, dramatically affect the minimum number of plots required to meet a certain accuracy criteria. The degree of influence of plot shape is related to survey objectives; thus, effects of plot shape differ for evaluations of the "mean" or "representative" stand structural conditions from that for the range of habitat (in extreme values). Results of Monte Carlo simulations suggested that plot sizes <0.1 ha could be the most efficient way to sample for conventional characteristics (features with relative constancy within a site, such as stem density). Also, 0.25 ha or even larger plots may have a greater likelihood of capturing rare structural attributes (features possessing high randomness and spatial heterogeneity, such as volume of coarse woody debris) in our forest type. These findings have important implications for advisable sampling protocol (plot size and shape) to adequately capture information on forest habitat structure and diversity; such efforts must be based on a clear definition of which types are structural attributes to measure.

Key words: forest structure, sampling protocol, Monte Carlo method, spatial pattern, spruce forest

Arevalo JR, Fernadez-Palacios JM, 1998. Treefall gap characteristics and regeneration in the laurel forest of Tenerife. Journal of Vegetation Science, 9: 297-306. DOI: 10.2307/3237094.
Besag JE, 1977. Comments on Ripley's paper. Journal of the Royal Statistical Society (Series B: Statistical Methodology), 39: 193-195.
Bilek L, Remes J, Zahradnik D, 2011. Managed vs. unmanaged. Structure of beech forest stands (Fagus sylvatica L.) after 50 years of development, Central Bohemia. Forest Systems, 20 (1): 122-138.
Bradford JB, Birdsey RA, Joyce LA, et al., 2008. Tree age, disturbance history, and carbon stocks and fluxes in subalpine Rocky Mountain forests. Global Change Biology, 14: 2882-2897. DOI: 10.1111/j.1365-2486.2008.01686.x.
Bradford JB, Weishampel P, Smith ML, et al., 2010. Carbon pools and fluxes in small temperate forest landscapes: variability and implications for sampling design. Forest Ecology and Management, 259: 1245-1254. DOI: 10.1016/j.foreco.2009.04.009.
Chang XX, Zhao WZ, Liu H, et al., 2014. Qinghai spruce (Picea crassifolia) forest transpiration and canopy conductance in the upper Heihe River Basin of arid northwestern China. Agricultural and Forest Meteorology, 198-199: 209-220. DOI: 10.1016/j.agrformet.2014.08.015.
Chen G, Hay GJ, Castilla G, et al., 2011. A multiscale geographic object-based image analysis to estimate lidar-measured forest canopy height using Quickbird imagery. International Journal of Geographical Information Science, 25: 877-893. DOI: 10.1080/13658816.2010.496729.
Du J, He ZB, Yang JJ, et al., 2014. Detecting the effects of climate change on canopy phenology in coniferous forests in semi-arid mountain regions of China. International Journal of Remote Sensing, 35(17): 6490-6507. DOI: 10.1080/01431161.2014. 955146.
Dutilleul P, 1998. Incorporating scale in ecological experiments: study design. In: Peterson DL, Parker VT (eds.). Ecological Scale—Theory and Applications. New York: Columbia University Press, pp. 369-386.
Evans TD, Viengkham OV, 2001. Inventory time-cost and statistical power: a case study of a Lao rattan. Forest Ecology and Management, 150: 313-322. DOI: 10.1016/S0378-1127(00) 00589-2.
Gray A, 2003. Monitoring stand structure in mature coastal Douglas-fir forest: effect of plot size. Forest Ecology and Management, 175: 1-16. DOI: 10.1016/S0378-1127(02)00078-6.
Gray L, He FL, 2009. Spatial point-pattern analysis for detecting density-dependent competition in a boreal chronosequence of Alberta. Forest Ecology and Management, 259: 98-106. DOI: 10.1016/j.foreco.2009.09.048.
He ZB, Zhao WZ, Liu H, et al., 2010. Successional process of Picea crassifolia forest after logging disturbance in semiarid mountains: a case study in the Qilian Mountains, northwestern China. Forest Ecology and Management, 260: 396-402. DOI: 10.1016/j.foreco.2009.09.048.
He ZB, Zhao WZ, Liu H, et al., 2012. Effect of forest on annual water yield in the mountains of an arid inland river basin: a case study in the Pailougou catchment on northwestern China's Qilian Mountains. Hydrological Processes, 26: 613-621. DOI: 10.1002/hyp.8162.
Holeksa J, Saniga M, Szwagrzyk J, et al., 2007. Altitudinal variability of stand structure and regeneration in the subalpine spruce forests of the Pol'ana biosphere reserve Central Slovakia. European Journal of Forest Research, 126: 303-313. DOI: 10.1007/s10342-006-0149-z.
Holeksa J, Saniga M, Szwagrzyk J, et al., 2009. A giant tree stand in the West Carpathians—an exception or a relic of formerly widespread mountain European forests? Forest Ecology and Management, 257: 1577-1585. DOI: 10.1016/j.foreco.2009. 01.008.
Hyde P, Dubayah R, Walker W, et al., 2006. Mapping forest structure for wildlife habitat analysis using multi-sensor (LiDAR, SAR/InSAR, ETM+, Quickbird) synergy. Remote Sensing of Environment, 102: 63-73. DOI: 10.1016/j.rse.2006.01.021.
Jung J, Kim S, Hong S, et al., 2013a. Effects of national forest inventory plot location error on forest carbon stock estimation using k-nearest neighbor algorithm. ISPRS Journal of Phtogrammetry and Remote Sensing, 81: 82-92. DOI: 10.1016/ j.isprsjprs.2013.04.008.
Jung M, Tautenhahn S, Wirth C, et al., 2013b. Estimating basal area of spruce and fir in post-fire residual stands in Central Siberia using Quickbird, feature selection, and Random Forests. Procedia Computer Science, 18: 2386-2395. DOI: 10.1016/ j.procs.2013.05.410.
Jyrki J, Vanha I, Tina T, 1998. Optimal sample and plot size for inventory of field and ground layer vegetation in a mature Myrtillus-type boreal spruce forest. Annales Botanici Fennici, 35: 191-196.
Kadavul K, Parthasarathy N, 1999. Plant biodiversity and conservation of tropical semi-evergreen forest in the Shervarayan hills of Eastern Ghats, India. Biodiversity and Conservation, 8: 421-439.
Kangas A, Maltamo M, 2007. Forest inventory: methodology and applications. Dordrecht: Springer.
Keller M, Palace M, Hurtt G, 2001. Biomass estimation in the Tapajos National Forest, Brazil Examination of sampling and allometric uncertainties. Forest Ecology and Management, 154: 371-382. DOI: 10.1016/j.foreco.2010.09.020.
Král K, Janík D, Vrška T, et al., 2010a. Local variability of stand structural features in beech dominated natural forests of Central Europe: implications for sampling. Forest Ecology and Management, 260: 2196-2203. DOI: 10.1016/j.foreco. 2010.09.020.
Král K, Vrška T, Hort L, et al., 2010b. Developmental phases in a temperate natural spruce-fir-beech forest: determination by a supervised classification method. European Journal of Forest Research, 129: 339-351. DOI: 10.1007/s10342-009-0340-0.
Kupfer JA, Kirsch SW, 1998. Heterogeneity of forest characteristics in primary and secondary forest stands on the third Chickasaw loess bluff, Tennessee. Physical Geography, 19: 35-54.
Laumonier Y, Edin A, Kanninen M, et al., 2010. Landscape-scale variation in the structure and biomass of the hill dipterocarp forest of Sumatra: implications for carbon stock assessments. Forest Ecology and Management, 259: 505-513. DOI: 10.1016/j.foreco.2009.11.007.
Li LF, Wang JF, Cao ZD, et al., 2008. An information-fusion method to identify pattern of spatial heterogeneity for improving the accuracy of estimation. Stochastic Environmental Research and Risk Assessment, 22: 689-704. DOI: 10.1007/ s00477-007-0179-1.
Li XX, Liu XD, Zhao WJ, 2013. Community structure of a dynamical plot of Picea crassifolia forest in Qilian Mountains, China. Journal of Desert Research, 33(1): 94-100. DOI: 10.7522/j.issn.1000-694X.2013.00013.
Lindenmayer DB, Cunningham RB, Donnelly CF, et al., 2000. Structural features of old-growth Australian montane ash forests. Forest Ecology and Management, 134: 189-204. DOI: 10.1016/S0378-1127(99)00257-1.
Liu XM, 2012. Modeling potential distribution and spatial distribution biomass C stock of Qinghai spruce (Picea Crassifolia) in Qilian Mountains. Doctor thesis, Gansu Agricultural University, Lanzhou, Gansu, China.
Lõhmus A, Kraut A, 2010. Stand structure of hemiboreal old-growth forests: characteristic features, variation among site types, and a comparison with FSC-certified mature stands in Estonia. Forest Ecology and Management, 260: 155-165. DOI: 10.1016/j.foreco.2010.04.018.
Morio J, Pastel R, Le Gland F, 2010. An overview of importance splitting for rare event simulation. European Journal of Physics, 31: 1295-1303. DOI: 10.1088/0143-0807/31/5/028.
Rempel RS, Kushneriuk RS, 2003. The influence of sampling scheme and interpolation method on the power to detect spatial effects of forest birds in Ontario (Canada). Landscape ecology, 18: 741-757.
Ripley BD, 1977. Modeling spatial patterns. Journal of the Royal Statistical Society (Series B: Statistical Methodology), 39(2): 172-212.
Rozas V, 2006. Structural heterogeneity and tree spatial patterns in an old-growth deciduous lowland forest in Cantabria, northern Spain. Plant Ecology, 185(1): 57-72. DOI: 10.1007/ s11258-005-9084-1.
Salako VK, Kakaï RLG, Assogbadjo AE, et al., 2013. Efficiency of inventory plot patterns in quantitative analysis of vegetation: a case study of tropical woodland and dense forest in Benin. South Forests, 75: 137-143. DOI: 10.2989/20702620.2013. 816232.
Samonil P, Antolik L, Svoboda M, et al., 2009. Dynamics of windthrow events in a natural fir-beech forest in the Carpathian Mountains. Forest Ecology and Management, 257: 1148-1156. DOI: 10.1016/j.foreco.2008.11.024.
Stockdale MC, Wright HL, 1996. Rattan inventory: determining plot shape and size. In: Edwards DS, Booth WE, Choy SC (eds.). Tropical Rainforest Research—Current Issues. The Netherlands: Kluwer Academic Publishers, pp. 523-533.
Storck L, 2011. Partial collection of data on potato yield for experimental planning. Field Crop Research, 121: 286-290. DOI: 10.1016/j.fcr.2010.12.018.
Vedris M, Jazbec A, Frntic M, et al., 2009. Precision of structure elements' estimation in a Beech-Fir stand depending on circular sample plot size. Sumarski List, 133: 369-379.
Von Storch H, Zwiers FW, 1999. Statistical Analysis in Climate Research. Cambridge, UK: Cambridge University Press.
Wittmann F, Junk WJ, 2003. Sapling communities in Amazonian white-water forests. Journal of Biogeography, 30: 1533-1544. DOI: 10.1046/j.1365-2699.2003.00966.x.
Wulder M, 1998. Optical remote-sensing techniques for the assessment of forest inventory and biophysical parameters. Progress in Physical Geography, 22: 449-476. DOI: 10.1177/ 030913339802200402.
Xiao XY, Gertner G, Wang GX, et al., 2004. Optimal sampling scheme for estimation landscape mapping of vegetation cover. Landscape Ecology, 20: 375-387. DOI: 10.1007/s10980-00 4-3161-z.
Yamakura T, Hagihara A, Sukardjo S, et al., 1986. Aboveground biomass of tropical rain forest stands in Indonesian Borneo. Vegetatio, 68(2): 71-82.
Youngblood A, Max T, Coe K, 2004. Stand structure in eastside old-growth ponderosa pine forests of Oregon and northern California. Forest Ecology and Management, 199: 191-217. DOI: 10.1016/j.foreco.2004.05.056.
Zar JH, 1996. Biostatistical Analysis, 3rd Ed.. Prentice-Hall, Upper Saddle River, New Jersey.
Zenner EK, Peck JE, 2009. Characterizing structural conditions in mature managed red pine: spatial dependency of metrics and adequacy of plot size. Forest Ecology and Management, 257: 311-320. DOI: 10.1016/j.foreco.2008.09.006.
No related articles found!
Full text



No Suggested Reading articles found!