About the benefits of poststratification in forest inventories

A large virtual population is created based on the GIS data base of a forest district and inventory data. It serves as a population where large scale inventories with systematic and simple random poststratified estimators can be simulated and the gains in precision studied. Despite their selfweighting property, systematic samples combined with poststratification can still be clearly more efficient than unstratified systematic samples, the gain in precision being close to that resulting from poststratified over simple random samples. The poststratified variance estimator for the conditional variance given the within strata sample sizes served as a satisfying estimator in the case of systematic sampling. The differences between conditional and unconditional variance were negligible for all sample sizes analyzed.

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Assessing Forest Production Using Terrestrial Monitoring Data

International Journal of Forestry Research ◽

10.1155/2012/961576 ◽

2012 ◽

Vol 2012 ◽

pp. 1-8 ◽

Cited By ~ 5

Author(s):

Hubert Hasenauer ◽

Chris S. Eastaugh

Keyword(s):

Large Scale ◽

Forest Biomass ◽

Forest Sector ◽

Inventory Data ◽

Carbon Sequestration Potential ◽

Forest Inventories ◽

Needed Information ◽

Sequestration Potential ◽

National Forest Inventories ◽

Interpretation Method

Accurate assessments of forest biomass are becoming an increasingly important aspect of natural resource management. Besides their use in sustainable resource usage decisions, a growing focus on the carbon sequestration potential of forests means that assessment issues are becoming important beyond the forest sector. Broad scale inventories provide much-needed information, but interpretation of growth from successive measurements is not trivial. Even using the same data, various interpretation methods are available. The mission of this paper is to compare the results of fixed-plot inventory designs and angle-count inventories with different interpretation methods. The inventory estimators that we compare are in common use in National Forest Inventories. No method should be described as “right” or “wrong”, but users of large-scale inventory data should be aware of the possible errors and biases that may be either compensated for or magnified by their choice of interpretation method. Wherever possible, several interpretation methods should be applied to the same dataset to assess the possibility of error.

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Occurrence of the Green Shield-Moss Buxbaumia viridis (Moug.) Brid. in the Bieszczady Mountains of Poland

Forests ◽

10.3390/f12030374 ◽

2021 ◽

Vol 12 (3) ◽

pp. 374

Author(s):

Piotr Brewczyński ◽

Kamil Grałek ◽

Piotr Bilański

Keyword(s):

National Park ◽

Large Scale ◽

Conservation Status ◽

Natura 2000 ◽

Field Work ◽

Managed Forests ◽

Moss Species ◽

Microhabitat Preferences ◽

Natura 2000 Site ◽

Forest District

The small-sized gametophytes and sporophytes of the green shield-moss Buxbaumia viridis (Moug.) Brid. make it difficult to study. However, in Europe, there has been increasing interest in this species in the past few years, mostly as a result of the implementation of the Natura 2000 network. In Poland, B. viridis has only been reported in isolated studies that have been limited in terms of area and the number of participating workers. One of the Polish regions where B. viridis was recently recorded is the Bieszczady Mountains, but there have been no large-scale surveys of that region to date. The objective of the current work was to describe the B. viridis population in the Bieszczady Mountains in terms of its spatial distribution and abundance, investigate its selected microhabitat preferences, and evaluate the conservation status of this moss species within the Natura 2000 site Bieszczady PLC180001. The studied region encompassed 93,490.44 ha, including 69,056.23 ha of managed forests and 24,434.21 ha of forests belonging to the Bieszczady National Park. A preliminary survey was conducted in the Cisna Forest District (forest area of 19,555.82 ha) on 15–17 November 2017, while the main survey was performed in selected forest subcompartments of four forest districts—Baligród, Komańcza, Lutowiska, and Stuposiany—as well as the Bieszczady National Park from 5 to 16 November 2018. The field work consisted of searching for B. viridis sporophytes and setae and recording selected population and locality characteristics. The study led to the discovery of 353 new B. viridis localities in 202 study areas, with 9197 diploid individuals (sporophytes or setae only) growing in 545 microhabitats. The number of B. viridis localities discovered in the Bieszczady Mountains during 17 days of survey in 2017 and 2018 was two times higher than the combined number of localities previously found in Poland over more than 150 years (159 localities). Additionally, the number of sporophytes and setae identified was two times greater than their overall number in previous records. In addition, this study provides information about selected microhabitat preferences and the conservation status of this moss in the Bieszczady Natura 2000 site.

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Modelling Tree Growth in Monospecific Forests from Forest Inventory Data

Forests ◽

10.3390/f12060753 ◽

2021 ◽

Vol 12 (6) ◽

pp. 753

Author(s):

Guadalupe Sáez-Cano ◽

Marcos Marvá ◽

Paloma Ruiz-Benito ◽

Miguel A. Zavala

Keyword(s):

Tree Growth ◽

Large Scale ◽

Temporal Dynamics ◽

Carbon Sink ◽

Size Variation ◽

Biotic Interactions ◽

Richards Equation ◽

Tree Level ◽

Forest Inventories ◽

Large Scale Data

The prediction of tree growth is key to further understand the carbon sink role of forests and the short-term forest capacity on climate change mitigation. In this work, we used large-scale data available from three consecutive forest inventories in a Euro-Mediterranean region and the Bertalanffy–Chapman–Richards equation to model up to a decade’s tree size variation in monospecific forests in the growing stages. We showed that a tree-level fitting with ordinary differential equations can be used to forecast tree diameter growth across time and space as function of environmental characteristics and initial size. This modelling approximation was applied at different aggregation levels to monospecific regions with forest inventories to predict trends in aboveground tree biomass stocks. Furthermore, we showed that this model accurately forecasts tree growth temporal dynamics as a function of size and environmental conditions. Further research to provide longer term prediction forest stock dynamics in a wide variety of forests should model regeneration and mortality processes and biotic interactions.

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Implications of Small Samples for Generalization: Adjustments and Rules of Thumb

Evaluation Review ◽

10.1177/0193841x16655665 ◽

2016 ◽

Vol 41 (5) ◽

pp. 472-505 ◽

Cited By ~ 16

Author(s):

Elizabeth Tipton ◽

Kelly Hallberg ◽

Larry V. Hedges ◽

Wendy Chan

Keyword(s):

Observational Studies ◽

Small Sample ◽

Average Treatment Effect ◽

Small Samples ◽

Sample Sizes ◽

Random Samples ◽

Rules Of Thumb ◽

Large Populations ◽

Small Sample Sizes ◽

Combine Information

Background: Policy makers and researchers are frequently interested in understanding how effective a particular intervention may be for a specific population. One approach is to assess the degree of similarity between the sample in an experiment and the population. Another approach is to combine information from the experiment and the population to estimate the population average treatment effect (PATE). Method: Several methods for assessing the similarity between a sample and population currently exist as well as methods estimating the PATE. In this article, we investigate properties of six of these methods and statistics in the small sample sizes common in education research (i.e., 10–70 sites), evaluating the utility of rules of thumb developed from observational studies in the generalization case. Result: In small random samples, large differences between the sample and population can arise simply by chance and many of the statistics commonly used in generalization are a function of both sample size and the number of covariates being compared. The rules of thumb developed in observational studies (which are commonly applied in generalization) are much too conservative given the small sample sizes found in generalization. Conclusion: This article implies that sharp inferences to large populations from small experiments are difficult even with probability sampling. Features of random samples should be kept in mind when evaluating the extent to which results from experiments conducted on nonrandom samples might generalize.

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Evaluating the Condition of Selectively Logged Production Forests in Myanmar: An Analysis Using Large-scale Forest Inventory Data for Yedashe Township

Journal of Forest Planning ◽

10.20659/jfp.23.1_1 ◽

2018 ◽

Vol 23 (1) ◽

pp. 1-8 ◽

Cited By ~ 1

Author(s):

Zar Chi Win ◽

Nobuya Mizoue ◽

Tetsuji Ota ◽

Tsuyoshi Kajisa ◽

Shigejiro Yoshida ◽

...

Keyword(s):

Forest Inventory ◽

Large Scale ◽

Inventory Data ◽

Forest Inventory Data ◽

Production Forests

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Determination of the influence of dispersion pattern of pesticide-resistant individuals on the reliability of resistance estimates using different sampling plans

Bulletin of Entomological Research ◽

10.1017/s0007485312000065 ◽

2012 ◽

Vol 102 (5) ◽

pp. 531-538

Author(s):

R. Shah ◽

S.P. Worner ◽

R.B. Chapman

Keyword(s):

Random Sampling ◽

Management Strategies ◽

Small Sample ◽

Systematic Sampling ◽

Computer Simulation Model ◽

Dispersion Pattern ◽

Sample Sizes ◽

Sampling Plans ◽

Resistant Individual ◽

Resistance Detection

AbstractPesticide resistance monitoring includes resistance detection and subsequent documentation/ measurement. Resistance detection would require at least one (≥1) resistant individual(s) to be present in a sample to initiate management strategies. Resistance documentation, on the other hand, would attempt to get an estimate of the entire population (≥90%) of the resistant individuals. A computer simulation model was used to compare the efficiency of simple random and systematic sampling plans to detect resistant individuals and to document their frequencies when the resistant individuals were randomly or patchily distributed. A patchy dispersion pattern of resistant individuals influenced the sampling efficiency of systematic sampling plans while the efficiency of random sampling was independent of such patchiness. When resistant individuals were randomly distributed, sample sizes required to detect at least one resistant individual (resistance detection) with a probability of 0.95 were 300 (1%) and 50 (10% and 20%); whereas, when resistant individuals were patchily distributed, using systematic sampling, sample sizes required for such detection were 6000 (1%), 600 (10%) and 300 (20%). Sample sizes of 900 and 400 would be required to detect ≥90% of resistant individuals (resistance documentation) with a probability of 0.95 when resistant individuals were randomly dispersed and present at a frequency of 10% and 20%, respectively; whereas, when resistant individuals were patchily distributed, using systematic sampling, a sample size of 3000 and 1500, respectively, was necessary. Small sample sizes either underestimated or overestimated the resistance frequency. A simple random sampling plan is, therefore, recommended for insecticide resistance detection and subsequent documentation.

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Reconstruction of Kyoto of the Edo era based on arts and historical documents: 3D urban model based on historical GIS data

International Journal of Humanities and Arts Computing ◽

10.3366/ijhac.2009.0007 ◽

2009 ◽

Vol 3 (1-2) ◽

pp. 21-38 ◽

Cited By ~ 10

Author(s):

Yuzuru Isoda ◽

Akihiro Tsukamoto ◽

Yoshihiro Kosaka ◽

Takuya Okumura ◽

Masakazu Sawai ◽

...

Keyword(s):

Large Scale ◽

Low Cost ◽

3D Models ◽

Historical Documents ◽

Historical Gis ◽

Model Based ◽

Urban Model ◽

Gis Data ◽

Point Data

This paper explores a method for creating large-scale urban 3D models using Historical GIS data. The method is capable of automatically generating realistic VR models based on GIS data at a low cost. 3D models of houses are created from polygon data, fences from line data, and pedestrians and trees from point data. The method is applied to the Virtual Kyoto Project in which the landscape of the whole city of Kyoto of the early Edo era (ca 17C) is reconstructed.

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