Sample Plot Size Research Articles

Potentials and limitations of using large-scale forest inventory data for evaluating forest succession models

Forest gap models have been applied widely to examine forest development under natural conditions and to investigate the effect of climate change on forest succession. Due to the complexity and parameter requirements of such models a rigorous evaluation is required to build confidence in the simulation results. However, appropriate data for model assessment are scarce at the large spatial and temporal scales of successional dynamics. In this study, we explore a data source for the evaluation of forest gap models that has been used only little in the past, i.e., large-scale National Forest Inventory data. The key objectives of this study were (a) to examine the potentials and limitations of using large-scale forest inventory data for evaluating the performance of forest gap models and (b) to test two particular models as case studies to derive recommendations for their future improvement. We used data from the first Swiss National Forest Inventory to examine the species basal area and tree numbers in different diameter classes simulated by the gap models F orC lim (version 2.9.3) and PICUS (version 1.4) for forest types that are typical of mountain forests in Switzerland. The results showed the potential of data from large-scale forest inventories for evaluating model performance. Since this type of data is typically based on a large number of samples across environmental gradients, they are particularly suited for investigations at the general level of the dominant species based on stand basal area. However, the surprisingly small variability of juvenile trees (trees <12 cm diameter at breast height; dbh) indicated limitations of the data used. Insufficient representativeness due to small sample plot size and uncertainty regarding past management limit an evaluation of structural forest aspects such as species diversity, and number of small trees (dbh < 12 cm). The examined models reproduced the observed species composition satisfactorily. However, there were clear model deficiencies in the simulation of successional patterns and of juvenile tree numbers. We identified priorities for future model development. We conclude that large-scale forest inventory data can be valuable for model evaluation, particularly when they cover large environmental gradients and do not come from intensively managed forests. Due to their limitations, they must, however, be complemented by other data such as from a full cruise.

Assessing effects of laser point density, ground sampling intensity, and field sample plot size on biophysical stand properties derived from airborne laser scanner data

Canopy height distributions were created from small-footprint airborne laser scanner data with an average sampling density of 1.13 points·m–2 collected over 132 sample plots and 61 forest stands. Field measurements of each plot were carried out within two concentric circles corresponding to fixed areas of 200 m2 and 300 or 400 m2. The laser point clouds were thinned to approximately 0.25, 0.13, and 0.06 point·m–2. For all comparisons, the maximum values of the first as well as last return canopy height distributions differed significantly between the full density and the thinned data. The combined effects of number of field plots, field plot sizes, and point densities on the accuracy of mean tree height, stand basal area, and stand volume predicted at stand level using a two-stage procedure combining field training data and laser data, were assessed using Monte Carlo simulation randomly selecting 75% and 50% of the field plots. The average standard deviation showed only a minor increase by decreasing point density and increased when the number of sample plots was reduced. The effects of field plot size varied with canopy structure and stem density.

Airborne Laser Scanning of Forest Stem Volume in a Mountainous Environment

Airborne laser scanning (ALS) is an active remote sensing technique that uses the time-of-flight measurement principle to capture the three-dimensional structure of the earth’s surface with pulsed lasers that transmit nanosecond-long laser pulses with a high pulse repetition frequency. Over forested areas most of the laser pulses are reflected by the leaves and branches of the trees, but a certain fraction of the laser pulses reaches the forest floor through small gaps in the canopy. Thus it is possible to reconstruct both the three-dimensional structure of the forest canopy and the terrain surface. For the retrieval of quantitative forest parameters such as stem volume or biomass it is necessary to use models that combine ALS with inventory data. One approach is to use multiplicative regression models that are trained with local inventory data. This method has been widely applied over boreal forest regions, but so far little experience exists with applying this method for mapping alpine forest. In this study the transferability of this approach to a 128 km2 large mountainous region in Vorarlberg, Austria, was evaluated. For the calibration of the model, inventory data as operationally collected by Austrian foresters were used. Despite these inventory data are based on variable sample plot sizes, they could be used for mapping stem volume for the entire alpine study area. The coefficient of determination R2 was 0.85 and the root mean square error (RMSE) 90.9 m3ha-1 (relative error of 21.4%) which is comparable to results of ALS studies conducted over topographically less complex environments. Due to the increasing availability, ALS data could become an operational part of Austrian’s forest inventories.

Optimal spatial resolution for collection of ground data and multi-sensor image mapping of a soil erosion cover factor

Military training activities disturb ground and vegetation cover of landscapes and increases potential soil erosion. To monitor the dynamics of soil erosion, there is an important need for an optimal sampling design in which determining the optimal spatial resolutions in terms of size of sample plots used for the collection of ground data and the size of pixels for mapping. Given a sample size, an optimal spatial resolution should be cost-efficient in both sampling costs and map accuracy. This study presents a spatial variability-based method for that purpose and compared it with the traditional methods in a study area in which a soil erosion cover factor was sampled and mapped with multiple plot sizes and multi-sensor images. The results showed that the optimal spatial resolutions obtained using the spatial variability-based method were 12 and 20 m for years 1999 and 2000, respectively, and were consistent with those using the traditional methods. Moreover, the most appropriate spatial resolutions using the high-resolution images were also consistent with those using ground sample data, which provides a potential to use the high-resolution images instead of ground data to determine the optimal spatial resolutions before sampling. The most appropriate spatial resolutions above were then verified in terms of cost-efficiency which was defined as the product of sampling cost and map error using ordinary kriging without images and sequential Gaussian co-simulation with images to generate maps.

Species richness correlations among primary producers in boreal forests

ABSTRACTClose correlations in species numbers may make it possible to indirectly assess the species richness of difficult taxonomic groups by investigating indicator groups, for which data are more easily collected. We asked if species numbers correlate among the four dominating groups of primary producers in boreal forests (liverworts, macrolichens, mosses, and vascular plants) and if substrate affiliation of species (ground vs. other substrates), sample plot size (0.01–1000 m2), and stand age (young vs. old) influence correlation strength. We used three sets of study plots from northern Sweden each including wide ranges of species richness. Although there are large differences in the ecophysiology and substrate use of vascular plants and the two bryophyte groups (mosses and liverworts), we found strong positive correlations among them not previously reported from boreal forests. In contrast, no correlation in total species richness was found between macrolichens and the two bryophyte groups, despite large overlaps in their ecology. We suggest that the positive correlations among land plants (liverworts, mosses, and vascular plants) are linked to positive relationships between site moisture and species number for all three groups. In contrast, total species number of macrolichens has not been shown to be strongly associated with moisture. However, ground‐living lichens and mosses correlated negatively in old forests. This may relate to the inability of macrolichens to exploit shaded and wet old forest ground, a habitat that is used by many moss species. Furthermore, lichens and mosses of ‘other substrates’ correlated positively in old forests, probably because the amount of boulders was positively related to species richness in both groups. Generally, correlations became stronger with increasing plot size, whereas stand age had relatively little influence. We conclude that vascular plants could be used as an indicator group for species richness of mosses and liverworts in boreal landscapes.

Integrating vegetation field surveys with remotely sensed data

Summary This paper explores data compatibility issues arising from the assessment of remnant native vegetation condition using satellite remote sensing and field‐based data. Space‐borne passive remote sensing is increasingly used as a way of providing a total sample and synoptic overview of the spectral and spatial characteristics of native vegetation canopies at a regional scale. However, integrating field‐collected data often not designed for integration with remotely sensed data can lead to data compatibility issues. Subsequent problems associated with the integration of unsuited datasets can contribute to data uncertainty and result in inconclusive findings. It is these types of problems (and potential solutions) that form the basis of this paper. In other words, how can field surveys be designed to support and improve compatibility with remotely sensed total surveys? Key criteria were identified for consideration when designing field‐based surveys of native vegetation condition (and other similar applications) with the intent to incorporate remotely sensed data. The criteria include recommendations for the siting of plots, the need for reference location plots, the number of sample sites and plot size and distribution, within a study area. The difficulties associated with successfully integrating these data are illustrated using real examples taken from a study of the vegetation in the Little River Catchment, New South Wales, Australia.

Relationship between plot size and the variance of the density estimator in West African savannas

The relationship between the variance of the density estimator and the plot size in forest inventories based on fixed area plots was characterized in West African savannas. Nine sites ranging from dry to moist savanna were surveyed, and the variance of the density estimator was assessed for varying sample plot sizes. An approximate theoretical expression of the variance was derived, taking into account the uncertainty on plot limits. In six sites, a Matérn process was fitted to the spatial pattern of trees. The point process was used to generate spatial patterns, yielding simulated values of the variance. A power function was also fitted to observed and simulated data. The theoretical expression and simulations showed that the contribution of uncertain plot limits to the variability of the density estimator was negligible with respect to the contribution of the spatial pattern of trees. The theoretical expression matched the data for small areas, but was inaccurate for large areas. The power function provided better fits. Nevertheless, the theoretical expression did not require any statistical fit to data and established a clear link between the spatial pattern of trees and the variance of the density estimator, with interpretable parameters.

N-Tree distance sampling for per-tree estimates with application to unequal-sized cluster sampling of increment core data

Samples from the n trees nearest to a point or plot center are sometimes used to estimate per-tree values such as age or growth from increment cores. Clutter et al. (J.L. Clutter, J.C. Fortson, L.V. Pienaar, G.H. Brister, and R.L. Bailey. 1983. Timber management: a quantitative approach. John Wiley &amp; Sons, New York) indicated that this procedure can be biased because it is more likely to sample large trees occupying large amounts of space. This sampling procedure falls into the category of n-tree distance sampling in which the nth closest tree to a point defines a plot radius that can be used to estimate number of trees or amount of volume per hectare. When a ratio of n-tree per-hectare estimates is used to estimate per-tree attributes, the resulting estimator is a weighted average in which weights are the inverse of the n-tree sampling plot size. Since this ratio estimator essentially weights observations inversely with plot size, it is not subject to the objections of Clutter et al. (1983). This estimator is used to estimate age by diameter at breast height class for eastern cottonwood (Populus deltoides Bartr. ex Marsh.) on the Cimarron National Grassland.

Weiterentwicklung der Kontrollstichproben am Beispiel des Kantons Glarus | Further development of permanent forest inventory; the example of Canton Glarus

In accordance with the revision of the forest law, a planning concept and new regulations for forestry planning have been drawn up for Canton Glarus. Owing to the size of Canton Glarus forest inventory is established at a cantonal level so that it can be related to other planning issues. The cantonal forest inventory is drawn up on the basis of previous inventories. From 1975 to 1993 a total of 3500 permanent sample plots were set up over three acres. In the follow-up inventory at a cantonal level the goal was to gather and analyse 30 important characteristics (growing stock, increment) of a sufficient degree of exactitude. In this way it was possible to reduce the size of the inventory by two-thirds, namely to 1135 sample plots. In addition, plots were rearranged in clusters with six sample plots in each because individual sample plots were too far apart. This makes it possible to change from one sample plot to another with shorter walking distances in between, an important consideration in sometimes difficult mountainous terrain. In smaller clusters, numerous individual sample plots still exist. Boundary conditions (10 500 ha of community forests),the measure of girth limit (16 cm) and sample plot size (3 ares)remained unchanged allowing the measurements to be used to calculate changes in increment, mortality and growing stock. The catalogue of inventoried criteria was supplemented with stability assessment and young forest surveys in order to be able to address new questions, such as the sustainability of the protective function of the forest. The work was put out to tender and awarded to qualified enterprises companies on the basis of fixed, net payment. In order to monitor and ensure quality control samplings were taken of 7% of the sample plots in the years 2001 and 2002. A changeover in the inventory system from the level of the enterprises to the level of the canton is indeed possible without having to invent a new system.

Impact of plot size on individual-tree competition measures for growth and yield simulators

The study addresses the effect of sample plot size on the bias related to measured stand density. We analyzed the effect of plot size on model coefficients and model performance in the simulation. Alternative growth models were developed for Norway spruce (Picea abies (L.) Karst.) on the basis of data obtained from permanent inventory sample plots of varying size. The competition measures were estimated from small plots with an average radius of 6 m, large plots with an average radius of 10 m, a cluster of three small plots within a stand, and a cluster of three large plots within a stand. The response of the models to competition varied depending on the plot size. Increasing the plot size increased the sensitivity of the models to the variation of overall stand density and the competitive status of a tree. The development of repeatedly measured, unthinned and thinned Norway spruce sample plots was simulated with the models, and the predictions were compared with the observed development. In the unthinned stand, the model with competition measures based on small plots resulted in a higher and more biased prediction of growth and mortality than the models based on larger plots. In the thinned stand, the differences between the models were negligible.

What Is the Observed Relationship between Species Richness and Productivity?

Understanding the relationship between species richness and productivity is fundamental to the management and preservation of biodiversity. Yet despite years of study and intense theoretical interest, this relationship remains controversial. Here, we present the results of a literature survey in which we examined the relationship between species richness and productivity in 171 published studies. We extracted the raw data from published tables and graphs and subjected these data to a standardized analysis, using ordinary least-squares (OLS) regression and generalized linear-model (GLIM) regression to test for significant positive, negative, or curvilinear relationships between productivity and species diversity. If the relationship was curvilinear, we tested whether the maximum (or minimum) of the curve occurred within the range of productivity values observed (i.e., was there evidence of a hump?). A meta-analysis conducted on the distribution of standardized quadratic regression coefficients showed that the average quadratic coefficient was negative (i.e., the average species richness–productivity relationship was curvilinear and decelerating), and that the distribution of standardized quadratic regression coefficients was significantly heterogeneous (i.e., the studies did not sample the same underlying species richness–productivity relationship). Looking more closely at the patterns of productivity–diversity relationships, we found that, for vascular plants at geographical scales smaller than continents, hump-shaped relationships occurred most frequently (41–45% of all studies). A positive relationship between productivity and species richness was the next most common pattern, and positive and hump-shaped relationships co-dominated at the continental scale. For animals, positive, negative, and hump-shaped patterns were common at most geographical scales, and no one pattern predominated. For both plants and animals, hump-shaped curves were relatively more common in studies that crossed community boundaries compared to studies conducted within a community type, and plant studies that crossed community types tended to span a greater range of productivity compared to studies within community types. Sample size and plot size did not affect the probability of finding a particular productivity–diversity relationship (e.g., positive, hump-shaped, etc.). However, hump-shaped curves were especially common (65%) in studies of plant diversity that used plant biomass as a measure of productivity, and in studies conducted in aquatic systems.

Bryophytes, lichens and phanerogams in an alvar grassland: relationships at different scales and contributions to plant community pattern

The relationship between bryophytes and lichens versus phanerogams has been investigated in three stands of a limestone grassland community, along three transects of 500 10 X 10 cm plots. Ordination axes resulting from Detrended Correspondence Analysis for bryophytes and lichens versus phanerogams were correlated using Spearman rank correlation coefficient. The effect of grain (sample plot) size on relationships between species along the gradient and on the correlation between the layers formed by bryophytes and lichens (cryptogams) and phanerogams, and with environmental variables has been as well examined. Values for light, moisture, pH and nitrogen have been derived from the vegetation itself with the help of Ellenberg indicator values. The relative position of species in the ordination space is more or less the same until grain size 3 (10 x 40 cm) for cryptogams and phanerogams and until grain size 2 (10 × 20 cm) for all‐species together. Therefore, it is suggested that sample plot sizes of 10 × 10 cm to 10×20 cm are appropriate as units for field experiments testing interactions between cryptogams and phanerogams. The respective layers were weakly correlated and the correlation between them increased with increase in grain size. The correlation of DCA axis 1 from the ordination of cryptogams, phanerogams and all‐species with the environmental factors was weak and similar in order of magnitude. Only the environmental variables which were strongly correlated with the DCA axis 1 increased in correlation at larger grain sizes. The ordinations of cryptogams, phanerogams and all‐species were correlated along DCA axis 1 with pH in all investigated stands and at almost all grain sizes. Multi‐species patches have been detected by pattern analysis (PASFRAN) on DCA sample scores from ordinations of cryptogams, phanerogams and combined matrices. Multi‐species patterns with sizes between 20‐240 cm. composed by bryophytes and lichens and by phanerogams have been found. Complex patterns formed by cryptogams and phanerogams together, which are different in size than those in the separate layers, suggest that bryophytes + lichens and phanerogams may interact with each other.

AN EXERCISE IN THE DETERMINATION OF OPTIMAL PLOT SHAPE AND SIZE FOR ESTIMATING STEM BIOMASS IN PLANTATIONS OFBAMBUSA VULGARISSCHARADER EX WENDLAND VAR.VULGARISIN THE STATE OF PARAIBA, BRAZIL

ABSTRACT For any sampling method adopted, the optimal plot shape and size have considerable influence in cost and precision. The method of relative efficiency in percent (RE%) was used to compare and determine optimal sample plot shape and size for estimating stem biomass in plantations of Bambusa vulgaris Scharader ex Wendland var. vulgaris, in the State of Paraiba, Brazil. The bamboo was planted at a spacing of 4.0 m between double rows 1.4 m apart. A square sample plot of 15 m × 15 m was found to be most efficient in terms of precision and cost of plot establishment.

Vegetation‐environment relationships of old‐growth spruce forest vegetation in Østmarka Nature Reserve, SE Norway, and comparison of three ordination methods

The relationship between vegetation and environmental variables has been studied in 100 sample plots, each 0.25 m2, in old‐growth spruce forest at Høgkollen, ØOstmarka Nature Reserve, SE Norway. Each sample plot was supplied with measurements of 13 environmental and 5 biotic variables. Parallel application of three ordination techniques, PCA, DCA and LNMDS, resulted in different sample plot configurations. PCA performed poorest due to strong influence of outliers and circumstantial evidence indicated better performance of LNMDS than DCA. Statistical analyses of the relationships between vegetation and ecological data revealed a parallel gradient in soil moisture (decreasing) and canopy closure (increasing) as the most important for differentiation of the vegetation. Species number and field layer cover decreased, while bottom layer cover increased, due to increasing cover of Dicranum majus, with decreasing soil moisture and increasing canopy closure. Constrained canonical correspondence analysis (CCA) was used to partition the variation of the species‐sample plot matrix into spatial, environmental and unexplained variation, and combinations. The fraction of unexplained variation was high (80.9 %), most likely due to small sample plot size and short gradient lengths. Most of the explained variation was attributable to environmental factors alone (54.5%). Only 6.3% was shared between environmental and spatial variation, which indicated minor importance of broad‐scale and geographically structured environmental variation. Strictly spatial variation constituted 39.3%. However, the spatially structured environmental variation was low, so the causes of spatial variation were likely not to be found among the measured environmental variables.

Patterns of bryophyte associations at different scales in a Norwegian boreal spruce forest

Abstract. Patterns of associations between 36 bryophytes and their relationships with trends in α‐ and β‐diversity were studied at five spatial scales (from 1 m2 to 1/256 m2) in a Norwegian boreal spruce forest. The range and dispersion of α‐diversity values in the data were significantly higher than in comparable model data sets, indicating varation from favourable to unfavourable conditions for bryophytes at all scales, particularly with increasing cover of the upper layers. The number of positive associations was significantly higher than predicted from a random distribution, for all sample plot sizes except the smallest. For the most frequent species, this number decreased with decreasing sample plot size. The excess of positive associations was due to the presence of (1) α‐diversity trends, as demonstrated by a stochastic simulation, and (2) β‐diversity (variation along environmental gradients). A microscale coenocline from dominance of large forest floor mosses to small liverworts, is added to two broad‐scale coenoclines demonstrated earlier. Facilitation ‐ positive density‐dependence caused by more favourable moisture conditions within dense stands ‐ is discussed as a possible third cause of excess positive associations.The number of significant negative associations was generally low, and deviated neither from theoretical values assuming random distribution of species, nor from predicted values accounting for α‐diversity trends. The low proportion of negative associations, even in the presence of β‐diversity trends, suggests that interspecific competition is not important in this vegetation. Several alternatives to competition are discussed; (1) static mechanisms for avoidance of competition, (2) mobility, and (3) the importance of density‐independent mortality, in particular due to fine‐scale disturbance.Simulation studies for assessing the effects of α‐diversity trends on species associations are emphasized.

Quadrat Analysis of Wildland Fuel Spatial Variability

Quadrat analysis of two fuel properties (loading and depth) was used to assess the relation between variation and sample plot size. By this method, an optimum range of spatial resolutions was established for sampling Artemisia tridentata subsp. wyomingensis (Wyoming big sagebrush) and Pinus contorta (lodgepole pine) fuel types in Colorado. Results of the analysis demonstrated that quadrats provide homogeneous strata and precise measures of central tendency on both fuelbeds studied. Findings indicated that field inventories in which A. tridentata is viewed as a fire fuel could use sample spacings up to 60 m (i.e., reasonably small sample sizes). The optimum range of resolution for the P. contorta fuel type was down to 20-30 m so that larger sample sizes are required. Quadrat analysis shed light on more precise fuel sampling schemes by accounting for the microsite variation of fuel characteristics. Thus, analogous studies can reveal further the semi-stochastic phenomena that govern wildland fire behaviour and effects.

Aire minimum d'échantillonnage ou nombre de points de prisme nécessaires pour établir la structure d'un peuplement inéquienne

A complete forest survey was conducted on an 18-ha maple stand mixed with yellow birch and American beech. Diameter at breast height of all merchantable stems was recorded. The location of all those stems was reported on a grid system to allow subsequent simulation of sampling by varying the size of rectangular sample plots. Prism sampling was also carried out in the same area. Statistical testing (χ2) were used to verify if randomized sampling gave the same diameter distribution as the original stand. Both sampling methods were adequate. Rectangular sample plots, 2 m wide by 5000 m long, representing an area of 10 000 m2 (i.e., 5.6% of the original stand) or 36-point sample (2 points per hectare) gave a good representation of the entire population at a probability level of 90%, with a confidence level of α = 0.05. For both sampling methods, sampling units must be scattered as much as possible in the forest area to be surveyed.

On the relationship between sample plot size and beta diversity in boreal coniferous forests

Nested sample plots of three sizes (16, 1, and 1/16 sq. m) from three different studies of Norwegian coniferous forests have been subjected to DCA ordination using the same choice of options. At each sample plot size, species quantities are recorded as frequency in 16 subplots. Beta diversity, measured as length of the first DCA axis, invariably increased upon lowering of sample plot size. The same applied to the eigenvalues of the axes. This is explained as a consequence of the weakening of structure in the data matrices when the fine-grained patterns of the vegetation are emphasized.

Stocking Percent and Seedlings Per Acre in Naturally Established Longleaf Pine

Abstract A relationship between milacre stocking and number of longleaf pine seedlings (Pinus palustris Mill.) per acre was derived from observations of 128 populations naturally established under a wide range of site conditions. The nonlinear regression obtained from the data was Y = 100 [1-(0.561)X], in which Y is the percentage of milacres stocked and X the average number of seedlings per milacre plot (seedlings per acre in thousands). This regression accounted for 85 percent of the observed variation in milacre stocking. The coefficient decreased when sample plot size was reduced to 0.5 and 0.25 milacre.

Determination of appropriate grid dimension and sampling plot size for assessment of woody species diversity in Zagros Forest, Iran

Zohrevandi AA, Pourbabaei H, Akhavan R, Bonyad AE. 2015. Determination of appropriate grid dimension and sampling plotsize for assessment of woody species diversity in Zagros Forest, Iran. Biodiversitas 17: 24-30. This research was conducted to determinethe most suitable grid (dimension for sampling) and sampling plot size for assessment of woody species diversity in protected Zagros forests, west of Iran. Sampling was carried out using circular sample plots with areas of 1000 m2, 1500 m2 and 2000 m for 9 grid sizes in 200 ha forest area. Importance-value curve which fitted by a Lognormal Distribution Model was plotted using Species Importance Value Index (SVI). After determining the diversity indices such as Hills (N0, N1, N2) and Alatalo’s evenness (E5) in each plot and assigning each sampling time, E%2x T criterion was calculated in order to compare the methods. Results of hundred percent inventory and sampling in forests showed that the accuracy of sampling in 1000m2 plot size is higher than 2000m2 ones. According to the Importance-value curve, accuracy and E%2x T criterion, the 150 m x 400 m grid size and sampling plot size with an area of 1000 m2 was proposed as the most appropriate one to estimate woody species diversity in Iranian Zagros Forests.