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According to the current forest management manual, deadwood volume should be evaluated on 10% of sampling plots, located in different species−age layers, which are used for determining stand volume in a given forest unit. Sampling plot size differs depending on tree stand age and ranges from 0.005 to 0.05 ha. The results are reported for the entire forest district and by forest site type. The objective of the study was to analyze the accuracy of deadwood volume estimations in the light of the guidelines stipulated in the forest management manual and to find the ways to improve the obtained results. Deadwood volume was measured on 2752 sample plots used to determine stand volume and the mean value calculated on that basis was 5.4 m³/ha. Subsequently, 30 random draws of sampling plots were performed. Estimates based on randomly selected pools consisting of 10% of sampling plots ranged from 3.5 to 8.6 m³/ha. Subsequently, another 10% of sampling plots were randomly drawn and added to the previous ones. The results for 20% of sampling plots were 4.5−7.0 m³/ha, for 30% – 4.3−6.4 m³/ha, for 40% – 4.6−6.4 m³/ha and for 50% – 4.7−6.0 m³/ha. In the next step, 225 sampling plots located in reserves and special zones around the nests of protected species were discarded. The mean volume of deadwood computed for the managed forest areas alone (2527 sample plots) was 4.7 m³/ha. The random drawing procedure was repeated to give the following results: 3.6−6.8 m³/ha for 10% of sampling plots; 3.8−5.8 m³/ha for 20%; 3.9−5.3 m³/ha for 30%; 4.2−5.3 m³/ha for 40%; and 4.2−5.1 m³/ha for 50% of sampling plots. The categorization of the randomly selected sampling plots by forest site type in most cases yielded results significantly differed from the values computed based on all sampling plots. It was found that estimates based on 10% of sampling plots may diverge considerably from true values due to the uneven distribution of deadwood. In particular, managed and unmanaged forest areas should not be combined due to the high differences in the volume of deadwood between them. If a relatively low number of sampling plots is used, it seems advisable to report results only for the forest division as a whole, without a breakdown into site types. Satisfactory estimates for the different forest sites types would require much more work. The use of a greater number of sampling plots than specified in the forest management manual seems to be a fundamental prerequisite for improving the accuracy of deadwood volume estimates.
Rosnące zapotrzebowanie społeczne na pozagospodarcze funkcje lasu zmusza leśników do poszukiwania metod równoważenia znaczenia wszystkich funkcji. Do realizacji nowych zadań leśnictwu niezbędne są dane, które pozwolą określić zdolność lasu do spełniania wielostronnych funkcji i aktualny stopień ich realizacji. Jednym ze sposobów pozyskania takich informacji może być opracowanie punktowej metody oceny funkcji lasu. Prezentowana praca jest próbą weryfikacji opracowanej metody waloryzacji funkcji lasu, polegającej na analizie funkcji lasu w zależności od takich cech jak typ siedliskowy lasu, gatunek panujący oraz wiek gatunku panującego.
In most of European countries, basic information about forests that is used for its monitoring and formulation of national policies comes from the National Forest Inventories (NFIs). Assessment of forest resources at the national level was initiated in Poland in 2005. In 2014, the second cycle of NFI was completed. Since 2010, results of NFI are the main source of information about the amount of forest resources in Poland, their structure and condition. In the paper, we analyse the indicators of forest structure. Classification of forest categories (forested, temporary non−forested, related to forestry), vertical stand structure, species composition and age structure determined directly from NFI's sample plots were compared with descriptions of stands in which plots are located. Data from more than 29,000 NFI sample plots measured in 2010−2014 were analysed. Our results demonstrate that the share of temporary non−forested areas (resulting from management practices) assessed from NFI plots is higher than this based upon the stands description (3.9% and 1.8% respectively). We also observed that results of NFI show the huge discrepancy in percentage of land related to forestry (forest roads generally) in forests of private property comparing to cadastral data (1,2% and 0,04% respectively). Most of forests in Poland (92% based on NFI) are single−layer stands. But it should be emphasised that NFI indicate more than twice higher share of two−layer stands (4.8%) than that resulting from description of stands in which samples are located (2.2%). However, only one third of NFI plots located in two−generation and uneven−aged stands was assessed in the same way. On 80% of sample plots dominant species was in accordance with stand description. On 85% plots there were compatibility of the age of dominant species with age recorded in forest management plans. The results of our research confirm that description of the entire diversity of forests by any of the indicators is not practically possible. Simplifying the forests description occurs during NFI measurements as well as within forest management planning. However, some issues need clarification and additional analysis when NFI is used as a source of forest structure. NFI plots with maximal size of a few hundred square meters seem to be too small to observe vertical structure of stands. On the other hand, it should be recognized whether division of the sample plots into smaller sections does not cause overestimation of the area of temporary non−forested land.
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