Sample Plot Size Research Articles

Investigating the Feasibility of Multi-Scan Terrestrial Laser Scanning to Characterize Tree Communities in Southern Boreal Forests

Terrestrial laser scanning (TLS) has proven to accurately represent individual trees, while the use of TLS for plot-level forest characterization has been studied less. We used 91 sample plots to assess the feasibility of TLS in estimating plot-level forest inventory attributes, namely the stem number (N), basal area (G), and volume (V) as well as the basal area weighed mean diameter (Dg) and height (Hg). The effect of the sample plot size was investigated by using different-sized sample plots with a fixed scan set-up to also observe possible differences in the quality of point clouds. The Gini coefficient was used to measure the variation in tree size distribution at the plot-level to investigate the relationship between stand heterogeneity and the performance of the TLS-based method. Higher performances in tree detection and forest attribute estimation were recorded for sample plots with a low degree of tree size variation. The TLS-based approach captured 95% of the variation in Hg and V, 85% of the variation in Dg and G, and 67% of the variation in N. By increasing the sample plot size, the tree detection rate was decreased, and the accuracy of the estimates, especially G and N, decreased. This study emphasizes the feasibility of TLS-based approaches in plot-level forest inventories in varying southern boreal forest conditions.

Predicting Forest Inventory Attributes Using Airborne Laser Scanning, Aerial Imagery, and Harvester Data

The aim of the study was to develop a new method to use tree stem information recorded by harvesters along operative logging in remote sensing-based prediction of forest inventory attributes in mature stands. The reference sample plots were formed from harvester data, using two different tree positions: harvester positions (XYH) in global satellite navigation system and computationally improved harvester head positions (XYHH). Study materials consisted of 158 mature Norway-spruce-dominated stands located in Southern Finland that were clear-cut during 2015–16. Tree attributes were derived from the stem dimensions recorded by the harvester. The forest inventory attributes were compiled for both stands and sample plots generated for stands for four different sample plot sizes (254, 509, 761, and 1018 m2). Prediction models between the harvester-based forest inventory attributes and remote sensing features of sample plots were developed. The stand-level predictions were obtained, and basal-area weighted mean diameter (Dg) and basal-area weighted mean height (Hg) were nearly constant for all model alternatives with relative root-mean-square errors (RMSE) roughly 10–11% and 6–8%, respectively, and minor biases. For basal area (G) and volume (V), using either of the position methods, resulted in roughly similar predictions at best, with approximately 25% relative RMSE and 15% bias. With XYHH positions, the predictions of G and V were nearly independent of the sample plot size within 254–761 m2. Therefore, the harvester-based data can be used as ground truth for remote sensing forest inventory methods. In predicting the forest inventory attributes, it is advisable to utilize harvester head positions (XYHH) and a smallest plot size of 254 m2. Instead, if only harvester positions (XYH) are available, expanding the sample plot size to 761 m2 reaches a similar accuracy to that obtained using XYHH positions, as the larger sample plot moderates the uncertainties when determining the individual tree position.

The effect of spatial structure of forests on the precision and costs of plot-level forest resource estimation

BackgroundWe investigated how the precision and costs of forest resource estimates for sample plots of different type and size depend on the spatial structure of forests and jointly studied the effects of tree density and size distribution. Statistically thinking, the trees in a forest can be regarded as a point pattern. Based on the spatial properties of the point pattern, we classified the forests into clustered, random, and regular. We used empirical data from 396 mapped forest plots from Finland. The variance of the unbiased Horvitz-Thompson estimator and expected costs of the basal area and tree density estimation were calculated for 99 different sample plots of different type and size in each of the 396 forest plots. Further, we considered the estimation of the change between two time points for a subset of the data.ResultsThe precision and expected cost depended on the tree size distribution and spatial pattern of trees. While large sample plots are advisable for clustered forests or the monitoring of young forests with small trees, we see potential for measuring smaller sample plots in regular forests. The choice of sample plot was more important in clustered forests, where also the variability of the expected costs was higher.ConclusionsIf the spatial structure of forests could be predicted accurately and precisely prior to field measurements, for instance from remote sensing data, the precision of forest inventories could potentially be improved or costs decreased by allowing the sample plot size and type to vary from one forest stand to another. When using a compromise sample plot over a large region and a long inventory rotation, optimizing the sample plot for one time point ignores possible changes in forest structures caused by changes in forest management practices.

Resolution dependence in an area-based approach to forest inventory with airborne laser scanning

In an Area Based Approach (ABA) to forest inventories using Airborne Laser Scanning (ALS) data, the sample plot size may vary or the cell size may differ from the plot size. Although this resolution mismatch may cause bias and increase in prediction error, it has not been thoroughly studied. The aim of this study was to clarify the meaning of resolution dependence in ABA, and to further identify its causal factors and quantify their effects. In general, a number of factors contribute to resolution dependence in ABA forest inventories, including the varying point density of the ALS data, the type of response variable, how the predictor variables are computed, and the properties of the prediction model. For quantification, we used field plots with mapped tree locations, which enabled the generation of different sized sample plots inside a larger plot. Plot level above ground biomass (AGB) was the response variable employed in all the models. The error rate seemed to increase when the prediction plots were larger than the fitting plots, and vice versa. The maximum BIAS was 1.50% and the maximum change of RMSE compared to its value in native resolution was 0.97% when there was a 4-fold difference in resolution. This indicates that the resolution effect is small in most real-world use cases, however, resolution effect should be carefully considered in ALS-assisted large area inventories that target unbiased estimates of forest parameters.

English

Woody species are the major components of traditional agroforestry systems. These species vary across the different types of agroforestry practices in different parts of Ethiopia. This study was conducted to assess woody species across land use in three different sites around Jimma town, Southwest Ethiopia. A total of 100 plots were laid out on six transect lines with sample plot size of 25 m × 25 m for 20 plots of homegarden, 19 plots of coffee farm and 20 plot of grazing land, 40 m × 40 m for 24 plots of crop field and 10 m × 10 m for 17 plots of woodlots and were calculated in hectares. Species diversity, richness, evenness, density and frequency were analyzed between land uses types and sites. The result showed that a total of 60 woody species belonging to 34 families and 54 genera were identified from these three sites. Fabaceae was the most dominant family with 11 (18.3%) species followed by Euphorbiaceae family with 7 (11.7%) species. Out of the identified species, 53.3% were trees, 45% shrubs and 1.67% climbers. In terms of species diversity, grazing lands (3.1) were more diversified than homegardens (2.87), crop field (2.555), coffee farm (0.667) and woodlots (0.643) in the overall study sites. In terms of tree and shrubs density, woodlots were the most dense (9495 stems/ha). Generally, agroforestry systems are conserving several woody species diversity in their systems and woody species varied among land use types in the study area. Practices which aimed at maintaining these woody species should be encouraged and applied to support the conservation of these species in agroforestry systems.   Key words: Agroforestry system, woody species, homegardens, crop field, grazing land, coffee farm, Jimma.

Effects of Sample Plot Size and GPS Location Errors on Aboveground Biomass Estimates from LiDAR in Tropical Dry Forests

Accurate estimates of above ground biomass (AGB) are needed for monitoring carbon in tropical forests. LiDAR data can provide precise AGB estimations because it can capture the horizontal and vertical structure of vegetation. However, the accuracy of AGB estimations from LiDAR is affected by a co-registration error between LiDAR data and field plots resulting in spatial discrepancies between LiDAR and field plot data. Here, we evaluated the impacts of plot location error and plot size on the accuracy of AGB estimations predicted from LiDAR data in two types of tropical dry forests in Yucatán, México. We sampled woody plants of three size classes in 29 nested plots (80 m2, 400 m2 and 1000 m2) in a semi-deciduous forest (Kiuic) and 28 plots in a semi-evergreen forest (FCP) and estimated AGB using local allometric equations. We calculated several LiDAR metrics from airborne data and used a Monte Carlo simulation approach to assess the influence of plot location errors (2 to 10 m) and plot size on ABG estimations from LiDAR using regression analysis. Our results showed that the precision of AGB estimations improved as plot size increased from 80 m2 to 1000 m2 (R2 = 0.33 to 0.75 and 0.23 to 0.67 for Kiuic and FCP respectively). We also found that increasing GPS location errors resulted in higher AGB estimation errors, especially in the smallest sample plots. In contrast, the largest plots showed consistently lower estimation errors that varied little with plot location error. We conclude that larger plots are less affected by co-registration error and vegetation conditions, highlighting the importance of selecting an appropriate plot size for field forest inventories used for estimating biomass.

Assessing methods for comparing species diversity from disparate data sources: the case of urban and peri‐urban forests

AbstractMulti‐scale forest inventory and monitoring data are increasingly being used in studies assessing forest diversity, structure, disturbance, and carbon dynamics. Also, local‐level urban forest inventories are providing plot data and protocols to study tree diversity and ecosystem services in urban forests worldwide. But, differences in the sampling methods underlying these disparate protocols and data sources is a non‐trivial concern in formulating comparative analyses. We assess commonly used methods for comparing tree diversity in peri‐urban and urban forests when available data have different sample sizes, plot sizes, and sampling intensities. We present methods for appropriately evaluating species richness, as well as methods for comparing species distributions via community data matrices. Using permanent plot data from the southeastern United States, we present a case study comparing urban and peri‐urban forests along a north–south gradient, and assessing species richness and the ecological homogenization hypothesis. Our findings indicate that comparisons of tree species richness among communities, or forest types, are often inconclusive since commonly used sample sizes do not provide precise estimates of the number of species present. While the ecological homogenization hypotheses can be tested under conditions of unequal sampling effort, we suggest robust methods such as PERMANOVA and the Raup‐Crick dissimilarity index. A framework for selecting appropriate methods is also discussed. As forests are increasingly being altered by anthropogenic drivers, future studies using disparate data sources must account for differences in measurements and sampling protocols in order to produce results that are both statistically defensible and useful for science‐based management.

Appropriate Sample Sizes for Monitoring Burned Pastures in Sagebrush Steppe: How Many Plots are Enough, and Can One Size Fit All?

Statistically defensible information on vegetation conditions is needed to guide rangeland management decisions following disturbances such as wildfire, often for heterogeneous pastures. Here we evaluate sampling effort needed to achieve a robust statistical threshold using >2 000 plots sampled on the 2015 Soda Fire that burned across 75 pastures and 113 000 ha in Idaho and Oregon. We predicted that the number of plots required to generate a threshold of standard error/mean ≤ 0.2 (TSR, threshold sampling requirement) for plant cover within pasture units would vary between sampling methods (rapid ocular versus grid-point intercept) and among plot sizes (1, 6, or 531 m2), as well as relative to topography, elevation, pasture size, spatial complexity of soils, vegetation treatments (herbicide or seeding), and dominance by exotic annual or perennial grasses. Sampling was adequate for determining exotic annual and perennial grass cover in about half of the pastures. A tradeoff in number versus size of plots sampled was apparent, whereby TSR was attainable with less area searched using smaller plot sizes (1 compared with 531 m2) in spite of less variability between larger plots. TSR for both grass types decreased as their dominance increased (0.5–1.5 plots per % cover increment). TSR decreased for perennial grass but increased for exotic annual grass with higher elevations. TSR increased with standard deviation of elevation for perennial grass sampled with grid-point intercept. Sampling effort could be more reliably predicted from landscape variables for the grid-point compared with the ocular sampling method. These findings suggest that adjusting the number and size of sample plots within a pasture or burn area using easily determined landscape variables could increase monitoring efficiency and effectiveness.

Combining canopy height and tree species map information for large-scale timber volume estimations under strong heterogeneity of auxiliary data and variable sample plot sizes

A timber volume regression model applicable to the state and communal forest area of the federal German state of Rhineland-Palatinate is identified using a combination of airborne laser scanning (ALS)-derived metrics and information from a satellite-based tree species classification map available on the federal state level. As is common in many forest inventory datasets, strong heterogeneity in the ALS data due to different acquisition dates and misclassifications in the tree species classification map had noticeable effects on the regression model’s performance. This article specifically addresses techniques that improve the performance of ordinary least square regression models under such restricting conditions. We introduce a calibration technique to neutralize the effect of misclassifications in the tree species variable that originally caused a residual inflation of 0.05 in adjusted R^2. Incorporating the calibrated tree species information improved the model accuracy by up to 0.07 in adjusted R^2 and suggests the use of such information in forthcoming inventories. We also found that including ALS quality information as categorical variables within the regression model considerably mitigates issues with time lags between the ALS and terrestrial data acquisition and ALS quality variations (increase of 0.09 in adjusted R^2). The model achieved an adjusted R^2 of 0.48 and a cross-validated root-mean-square error (RMSE_{mathrm{cv}}) of 46.7% under incorporation of the tree species and ALS quality information and was thus improved by 0.12 in adjusted R^2 (5% in RMSE_{mathrm{cv}}) compared to the simple model only containing ALS height metrics (adjusted R^2=0.36, RMSE_{mathrm{cv}}=51.7%).

Recovery of Baikiaea forest of southwestern Zambia from shifting cultivation and its implications for sustainable management

We studied the dominant species by age category of re-growth, and how subsistence forest use through shifting cultivation impacted on the floristic composition of Baikiaea forest. For re-growth stands, variable sampling method was adopted in data collection while in mature stands, a sample plot size of 50 m × 50 m was used at each of three study sites. Distribution of species by age categories showed that shrub, herb, and grass species were abundant in young re-growth stands while woody vegetation became common with increasing age of the re-growth stand. Hierarchical clustering for common woody species showed several species associations with age categories and also with relatively undisturbed woodland. Common regeneration mechanisms of key woody species were mainly by seed (48%), coppicing (40%) and root suckers (12%). The young re-growth stands of 6–10 and 11–15 years were characterized by B. petersiana, B. massiensis, Combretum zeyheri, and Guibourtia coleosperma. Older stands (16–20 years after abandonment) were characterized by B. plurijuga, Pterocarpus antunesii, and Baphia massiensis, thus closely mimicking the relatively undisturbed forest whose dominant key woody species were B. plurijuga, P. antunesii, and G. coloesperma. There were many stems in younger stands (2–5 years) and fewer stems in older re-growth stands of 16–20 years after abandonment following shifting cultivation. Older re-growth stands (16–20 years) were similar to undisturbed stands (P < 0.005). The similarity between re-growth stands of 16–20 years and control stands demonstrates the recovery of Baikiaea forest over time after cessation of shifting agriculture. Baikiaea forest requires some disturbance to perpetuate the dominant species and begins to show signs of recovery from shifting cultivation within 16–20 years after abandonment. This is contrary to assertions that this ecosystem could take up to about 100 years for abandoned fields to begin showing signs of recovery

Sample size, plot size and number of replications for trials with Solanum melongena L.

Abstract Due to the increasing consumption of eggplant (Solanum melongena L.), research should be carried out with this horticultural species aiming at providing technical recommendations for increasing production and quality of fruits. The aim of this study was to estimate the plot and sample size, as well the number of replications suitable for eggplant trials. Two uniformity trials were performed with eggplant in a plastic tunnel. The fresh fruit weight and the total number of fruits were assessed in each basic unit and in each harvest. The harvests were analyzed individually and grouped. Heterogeneity was found among crop rows and individual harvests within the same row. However, when the harvests were grouped, this fact was not observed. When the harvests are grouped, there was a reduction in plot size and sample needed the grouping of six harvests presented the smallest sample size and plot size. The plot size is five plants, the sample is nine plants in the direction of the crop row with a half-range of the 20% confidence interval. Aiming at achieving a minimum-significant difference in the Tukey’s test of 30% of treatment mean, three replications with five plants each plot is needed.

Influence of Plot Size on Efficiency of Biomass Estimates in Inventories of Dry Tropical Forests Assisted by Photogrammetric Data from an Unmanned Aircraft System

Applications of unmanned aircraft systems (UASs) to assist in forest inventories have provided promising results in biomass estimation for different forest types. Recent studies demonstrating use of different types of remotely sensed data to assist in biomass estimation have shown that accuracy and precision of estimates are influenced by the size of field sample plots used to obtain reference values for biomass. The objective of this case study was to assess the influence of sample plot size on efficiency of UAS-assisted biomass estimates in the dry tropical miombo woodlands of Malawi. The results of a design-based field sample inventory assisted by three-dimensional point clouds obtained from aerial imagery acquired with a UAS showed that the root mean square errors as well as the standard error estimates of mean biomass decreased as sample plot sizes increased. Furthermore, relative efficiency values over different sample plot sizes were above 1.0 in a design-based and model-assisted inferential framework, indicating that UAS-assisted inventories were more efficient than purely field-based inventories. The results on relative costs for UAS-assisted and pure field-based sample plot inventories revealed that there is a trade-off between inventory costs and required precision. For example, in our study if a standard error of less than approximately 3 Mg ha−1 was targeted, then a UAS-assisted forest inventory should be applied to ensure more cost effective and precise estimates. Future studies should therefore focus on finding optimum plot sizes for particular applications, like for example in projects under the Reducing Emissions from Deforestation and Forest Degradation, plus forest conservation, sustainable management of forest and enhancement of carbon stocks (REDD+) mechanism with different geographical scales.

Optimal sample size and plot size or point sampling factor based on cost-plus-loss using the Fairfield Smith relationship for plot size

Optimal sample size and plot size or point sampling factor based on cost-plus-loss using the Fairfield Smith relationship for plot size

The effect of the calculation method, plot size, and stand density on the accuracy of top height estimation in Norway spruce stands

Abstract: The aim of this study was to evaluate top height (TH) estimates for Norway spruce stands calculated according to different computational methods, and to assess the effects of stand density and plot size on TH estimation accuracy. Field data were collected from twelve 1 ha research plots located in even-aged spruce stands. Conventional estimates were found to generally overstate TH. The accuracy of TH estimation was dependent on sample plot size. TH estimation error decreased rapidly with increasing sample plot area, but only up to a certain cut-off point. Errors in TH estimation were also related to local stand density, with low and very high density levels leading to decreased accuracy. The most reliable TH estimates were obtained using the U-estimator method, which is resistant to changes in sample plot size.

Characterization of Brazilian forest types utilizing canopy height profiles derived from airborne laser scanning

AbstractQuestionsThis study explores how airborne laser scanning surveys can contribute to characterizing and discriminating different Brazilian forest types based on canopy height profiles, and how sampling intensity and plot size affect the results.LocationSeven different vegetation types in Brazil – open ombrophilous Amazonian forest type in the state of Pará State, dense ombrophilous Atlantic forest in the state of São Paulo, semi‐deciduous and rupestrian field in the state of Minas Gerais, and three fast‐growing eucalyptus plantations in the state of São Paulo.MethodsCanopy height profiles were modelled for each forest type using a two‐parameter Weibull distribution. Differences in the values of the Weibull cumulative distribution parameters were studied to evaluate their discriminative capacity. Simulation was subsequently used to determine the number of cells needed to define a reliable sampling intensity for each cell size tested. Eight grid cell sizes were tested against six sampling intensities using 1000 iterations for all seven forest types.ResultsEach forest presented distinct characteristics regarding the canopy height profile. The higher presence of ground returns was remarkable in less layered structured forests (i.e. rupestrian field and eucalyptus plantations). Depending on the forest type, monotonic and sigmoidal shapes were observed for the cumulative distribution function of the canopy height profile. A sigmoidal cumulative distribution function was typical of a more layered structure (dense and open ombrophilous, and semi‐deciduous), whereas less structured typologies (i.e. Rupestrian field and forest plantation) were associated with monotonically shaped functions. Modelling convergence for the canopy height profile was achieved in most cases with data representing at least 2% cover.ConclusionCanopy height profiles generated fromALSdata proved different among five different forest types: open ombrophilous, dense ombrophilous, semi‐deciduous, rupestrian field and eucalyptus plantations. Furthermore, large‐scale forest inventories can benefit from the results observed in this study, as it provides recommendations on grid cell size and sampling intensity levels.

Appropriate sample plot size for planted forest cruising

Appropriate sample plot size for planted forest cruising

Effect of sample plot size and shape on estimates of structural indices: A case study in mature silver birch (Betula pendula Roth) dominating stand in Järvselja

Abstract Structural indices and characteristics have often been employed for assessing different aspects of forest stands. In order to assess the performance of such measures in forest sample plots of different size and shape, the current study is based on a data set for a relatively large (1 ha) silver birch dominated stand, and five simulated planar point patterns (a Poisson process, two clustered and two regular patterns) with similar intensity of points distributed on an area equal to the silver birch stand as reference stands. The analyses was based on repeated samplings of the stands, with randomly placed circular or square shaped plots of different sizes, with area ranging from 0.007 to 0.636 ha. Similar centre positions were considered for plots of different shapes, but the locations of the plot centres differed with size, so that plots of different sizes were able to cover the maximum possible area of reference stands. Some structural indices accounting for one or four nearest neighbours, and also the Point-pair correlation function was then quantified for each random sample plot. We used standard deviation and the differences of the mean estimates from the expected values to assess the sensitivity of the structural measures to the size and shape of sampling plots. The results indicated the significant effect of plot size and shape on structural indices. The correlation between plot size and estimates precision was positive, and small plots, regardless of their shapes, showed big variation among them while circular plots often provided more accurate estimates. Even though, as expected, expanding the plots increased the precision of the neighbourhood-based indices’ estimates (spatial measures), and also resulted in inaccurate estimates for some reference stands, depending on the arrangement of trees/points within that stand.

Influence of topographic aspect on floristic diversity, structure and treeline of afromontane cloud forests in the Bale Mountains, Ethiopia

We studied the influence of east and west aspects on floristic composition, diversity, structure and treeline of afromontane cloud forests at Rira in the Bale Mountains, southeast Ethiopia. In addition, we studied how aspect relates to and/or interacts with other topographic and edaphic factors in influencing vegetation diversity. Stratified systematic plot sampling was used to survey the floristic composition, diversity and structure of forests on east- and west-facing slopes. The sample plot size was 20 × 20 m and a total of 36 plots were inventoried. A total of 72 composite soil samples were collected and analysed. Woody species richness of the forest on the east-facing slope was 1.7 times higher than on the west-facing slope. Shannon, Simpson and Log-series alpha diversity indices and evenness of forests on the east-facing slope were significantly higher than on the west-facing slope. NMDS ordination indicated that the east- and west-facing slopes formed two clusters of species and aspect explained 55.2 % and 10.4 % of the variation in species richness and abundance, respectively. There was no significant difference between aspects in stand structure except in dominant height, which was higher on the east-facing slopes. There was significant interaction between aspect and elevation in influencing woody species diversity. The four plant community groups, which were identified using cluster and indicator species analysis were represented differently on the east and west aspects. The treeline on the east-facing slope (3352 m) was located about 110 m higher than on the west-facing slope (3240 m). Soil moisture deficiency was unlikely to be a limiting factor on either site. Near the equator, east–west aspect was shown to have considerable impact on floristic composition, diversity, structure, and treeline position of montane forests. Diurnal cloud movement patterns and its impact on microclimate of slope aspect should be taken into account in future studies of cloud forest diversity, structure, and treeline position.

Estimation of above-ground phytomass and carbon in tree resources outside the forest (TROF): A geo-spatial approach

This study aims to estimate above-ground phytomass and carbon of TROF ecosystem in part of Bijnor district in Uttar Pradesh state of India using IRS P6 LISS-IV satellite image by geo-spatial approach coupled with field sampling. Chacko’s formula was referred to compute number of samples in each TROF types and the sample plot size in each stratum was adopted from Vegetation Carbon Project (VCP) under National Carbon Project (NCP). With the help of field data consisting mainly the height and girth information, volume of each individual tree per plot was obtained using site and tree species-specific standard volumetric equations. The phytomass was calculated by multiplying volume with Biomass Expansion Factor (BEF) then with regional specific gravity of the individual species and summed up in each plot to get total phytomass per plot. The total phytomass per plot was reported to be maximum 544.00 t/ha for linear TROF followed by 121.89 t/ha for block TROF. The carbon from phytomass was obtained by multiplying the total phytomass by a conversion factor that represents the average carbon content in phytomass. Spectral modeling for phytomass with different bands and indices were established and the best fit curve (R2 = 0.552) with red band was applied to generate phytomass and carbon distribution map of the study area.Banko Janakari, Vol. 24, No. 1, pp. 34-40

The effects of field plot size on model-assisted estimation of aboveground biomass change using multitemporal interferometric SAR and airborne laser scanning data

Remotely sensed data from airborne laser scanning (ALS) and interferometric synthetic aperture radar (InSAR) can greatly improve the precision of estimates of forest resource parameters such as mean biomass and biomass change per unit area. Field plots are typically used to construct models that relate the variable of interest to explanatory variables derived from the remotely sensed data. The models may then be used in combination with the field plots to provide estimates for a geographical area of interest with corresponding estimates of precision using model-assisted estimators. Previous studies have shown that field plot sizes found suitable for pure field surveys may be sub-optimal for use in combination with remotely sensed data. Plot boundary effects, co-registration problems, and misalignment problems favor larger plots because the relative impact of these effects on the models of relationships may decline by increasing plot size. In a case study in a small boreal forest area in southeastern Norway (852.6ha) a probability sample of 145 field plots was measured twice over an 11year period (1998/1999 and 2010). For each plot, field measurements were recorded for two plot sizes (200m2 and 300/400m2). Corresponding multitemporal ALS (1999 and 2010) and InSAR data (2000 and 2011) were also available. Biomass for each of the two measurement dates as well as biomass change were modeled for all plot sizes separately using explanatory variables from the ALS and InSAR data, respectively. Biomass change was estimated using model-assisted estimators. Separate estimates were obtained for different methods for estimation of change, like the indirect method (difference between predictions of biomass for each of the two measurement dates) and the direct method (direct prediction of change). Relative efficiency (RE) was calculated by dividing the variance obtained for a pure field-based change estimate by the variance of a corresponding estimate using the model-assisted approach. For ALS, the RE values ranged between 7.5 and 15.0, indicating that approximately 7.5–15.0 as many field plots would be required for a pure field-based estimate to provide the same precision as an ALS-assisted estimate. For InSAR, RE ranged between 1.8 and 2.5. The direct estimation method showed greater REs than the indirect method for both remote sensing technologies. There was clearly a trend of improved RE of the model-assisted estimates by increasing plot size. For ALS and the direct estimation method RE increased from 9.8 for 200m2 plots to 15.0 for 400m2 plots. Similar trends of increasing RE with plot size were observed for InSAR. ALS showed on average 3.2–6.0 times greater RE values than InSAR. Because remote sensing can contribute to improved precision of estimates, sample plot size is a prominent design issue in future sample surveys which should be considered with due attention to the great benefits that can be achieved when using remote sensing if the plot size reflects the specific challenges arising from use of remote sensing in the estimation. That is especially the case in the tropics where field resources may be scarce and inaccessibility and poor infrastructure hamper field work.