scholarly journals Systematic Sampling for Estimating Harvest-Induced Changes of a Forest Stand

2009 ◽  
Vol 9 (1) ◽  
pp. 39-55
Author(s):  
Dennis Peque ◽  
Keyword(s):  
Sample Size ◽  
Standard Error ◽  
Basal Area ◽  
Systematic Sampling ◽  
Relative Standard Error ◽  
Stem Density ◽  
Relative Standard ◽  
The Individual ◽  
Induced Changes ◽  
Forest District

This study was conducted in Compartment 2012a in Bosinghausen Forest District in Germany covering an area of 5 hectared. Twenty two sampling plots were laid out in the field following systematic sampling design. Results showed that all estimates for all variables (e.g. tree heights, DBH, stem density, basal area and volume) under trees that were marked for cutting have higher relative standard error. This was due to higher dispersion of individual estimates in each plot. On the other hand, the simulation study shows that sampling efficiency can be acheived by increasing the sample size. When more samples are included, the relative standard error becomes low. From this study, it can be concluded that the variability of the estimates were affected by sample size and the variability of individual units in the population or the individual esitmates (in this case, estimates in each plot).

scholarly journals Investigating the Effects of k and Area Size on Variance Estimation of Multiple Pixel Areas Using a k-NN Technique for Forest Parameters

2021 ◽  
Vol 13 (22) ◽  
pp. 4688
Author(s):  
Dylan Walshe ◽  
Daniel McInerney ◽  
João Paulo Pereira ◽  
Kenneth A. Byrne
Keyword(s):  
Variance Estimation ◽  
Basal Area ◽  
Picea Sitchensis ◽  
Stem Density ◽  
Estimation Technique ◽  
Auxiliary Variables ◽  
Small Areas ◽  
Area Of Interest ◽  
Relative Standard ◽  
Forest Managers

Combining auxiliary variables and field inventory data of forest parameters using the model-based approach is frequently used to produce synthetic estimates for small areas. These small areas arise when it may not be financially feasible to take ground measurements or when such areas are inaccessible. Until recently, these estimates have been calculated without providing a measure of the variance when aggregating multiple pixel areas. This paper uses a Random Forest algorithm to produce estimates of quadratic mean diameter at breast height (QMDBH) (cm), basal area (m2 ha−1), stem density (n/ha−1), and volume (m3 ha−1), and subsequently estimates the variance of multiple pixel areas using a k-NN technique. The area of interest (AOI) is the state owned commercial forests in the Slieve Bloom mountains in the Republic of Ireland, where the main species are Sitka spruce (Picea sitchensis (Bong.) Carr.) and Lodgepole pine (Pinus contorta Dougl.). Field plots were measured in summer 2018 during which a lidar campaign was flown and Sentinel 2 satellite imagery captured, both of which were used as auxiliary variables. Root mean squared error (RMSE%) and R2 values for the modelled estimates of QMDBH, basal area, stem density, and volume were 19% (0.70), 22% (0.67), 28% (0.62), and 26% (0.77), respectively. An independent dataset of pre-harvest forest stands was used to validate the modelled estimates. A comparison of measured values versus modelled estimates was carried out for a range of area sizes with results showing that estimated values in areas less than 10–15 ha in size exhibit greater uncertainty. However, as the size of the area increased, the estimated values became increasingly analogous to the measured values for all parameters. The results of the variance estimation highlighted: (i) a greater value of k was needed for small areas compared to larger areas in order to obtain a similar relative standard deviation (RSD) and (ii) as the area increased in size, the RSD decreased, albeit not indefinitely. These results will allow forest managers to better understand how aspects of this variance estimation technique affect the accuracy of the uncertainty associated with parameter estimates. Utilising this information can provide forest managers with inventories of greater accuracy, therefore ensuring a more informed management decision. These results also add further weight to the applicability of the k-NN variance estimation technique in a range of forests landscapes.

A Counting Method for Measuring the Volumes of Tissue Components in Microscopical Sections

1964 ◽  
Vol s3-105 (72) ◽  
pp. 503-517
Author(s):  
A. DOUGLAS HALLY
Keyword(s):  
Standard Error ◽  
Relative Volume ◽  
Square Lattice ◽  
Relative Standard Error ◽  
Consistent Estimate ◽  
Relative Area ◽  
Counting Method ◽  
Tissue Sections ◽  
Relative Standard ◽  
Random Sections

Several methods are available for estimating the relative volume of a tissue component from a study of tissue sections. These methods are all based on the fact that the mean relative area of a component in a series of random sections through a tissue is a consistent estimate of its relative volume in the whole tissue. Thus the problem is basically one of measuring area in a section, which can be done by the following simple counting method. The method consists of placing a regular pattern of points in the form of a square lattice upon the section image, and counting the number of points over the section N, and over the component n Relative area of component ≑n/N The method also measures absolute ares, and where d is the distance between adjacent points, absolute area of component ≑nd2 This capacity to measure absolute area means that the method is particularly suitable for determining a component which has a low relative volume. The accuracy of the method is influenced by several factors including the size of the grid mesh, and the relative area, shape, and spatial arrangment of the component. With reasonable care the error will not be larger than that of a truly randon system of points, as expressed by the following: relative standard error ≑√(1-p)/√n, where p is the relative area of the component, and the relative standard error (R.S.E.) is S.E./relative area of component. The method is equallyapplicable to either light or electron microscopy. A series of measurements on electron micrographs of rat cardiac muscle revealed a close agreement between the counting method and planimetry. The method is rapid, simple and accurate, and requres no complex apparatus.

Reliability of fetal–infant mortality rates in perinatal periods of risk (PPOR) analysis

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Vito Di Bona
Keyword(s):  
Infant Mortality ◽  
Standard Error ◽  
Infant Mortality Rate ◽  
Relative Standard Error ◽  
Statistical Reliability ◽  
Relative Standard ◽  
Vital Records ◽  
Large Populations ◽  
Perinatal Periods Of Risk ◽  
Interval Estimators

Abstract The Fetal–Infant mortality rate (FIMR) is the basic surveillance statistic in perinatal periods of risk (PPOR) analyses. This paper presents a model for the FIMR as the ratio of two Poisson random variables. From this model, expressions for estimators of variance, standard error, and relative standard error are developed. The coverage properties of interval estimators for the FIMR are investigated in a simulation study for both small and large populations and FIMR rates. Results from these studies are applied to a PPOR analysis of NC vital records. Results suggest that the sample size guidance provided in the literature to ensure statistical reliability is overly conservative and interval construction methodology should be selected based on population size.

scholarly journals Improving precision of field inventory estimation of above ground biomass through an alternative view on plot biomass

2020 ◽  
Author(s):  
Christoph Kleinn ◽  
Steen Magnussen ◽  
Nils Noelke ◽  
Paul Magdon ◽  
Juan Gabriel Álvarez-González ◽  
...  
Keyword(s):  
Sample Size ◽  
Remote Sensing Data ◽  
Current Approach ◽  
Tree Level ◽  
Alternative View ◽  
Relative Standard Error ◽  
Model Errors ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
Relative Standard

Abstract We contrast a new continuous approach (CA) for estimating plot-level above-ground biomass (AGB) in forest inventories with the current approach of estimating AGB exclusively from the tree-level AGB predicted for each tree in a plot; henceforth called DA (discrete approach). With the CA, the AGB in a forest is modelled as a continuous surface and the AGB estimate for a fixed-area plot is computed as the integral of the AGB surface taken over the plot area. Hence with the CA, the portion of the biomass of in-plot-trees that extends across the plot perimeter is ignored while the biomass from trees outside of the plot reaching inside the plot is added. We use a sampling simulation with data from a fully mapped two hectare area to illustrate that important differences in plot-level AGB estimates can emerge. Ideally CA-based estimates of mean AGB should be less variable than those derived from the DA. If realized, this difference translates to a higher precision from field sampling, or a lower required sample size. In our case study with a target precision of 5 % (i.e. relative standard error of the estimated mean AGB), the CA required a 27.1 % lower sample size for small plots of 100m² and a 10.4 % lower sample size for larger plots of 1700 m², where we examined sampling induced errors only and did not yet consider model errors. We discuss practical issues in implementing the CA in field inventories and the potential in applications that model biomass with remote sensing data. The CA is a variation on a plot design for above-ground forest biomass; as such it can be applied in combination with any forest inventory sampling design.

Kinetic Method for the Determination of 2,4-Dinitrophenol

2008 ◽  
Vol 59 (7) ◽  
Author(s):  
S. S. Mitic ◽  
V. V. Zivanovic ◽  
G. Z. Miletic ◽  
D. A. Kostic ◽  
I. D. Rasic
Keyword(s):  
Detection Limit ◽  
River Water ◽  
Standard Error ◽  
Kinetic Method ◽  
Relative Standard Error ◽  
Potassium Periodate ◽  
Experimental Conditions ◽  
Inhibiting Effect ◽  
Relative Standard

A kinetic method for the determination of dinitrophenol is proposed. The method is based on the inhibiting effect of 2,4-dinitrophenol on the Mn(II) catalysis of the oxidation of malachite green with potassium periodate. The reaction was monitored spectrophotometrically at 615 nm. Kinetic expressions for the reaction are postulated. The optimal experimental conditions for the determination of 2,4-dinitrophenol were established and 2,4-dinitrophenol was determined in concentrations from 0.092-0.92 mg . mL-1 with relative standard error of 5.9 %. Detection limit is 0.014 mg . mL-1. The selectivity of the method is appropriate. The method was applied for the determination of dinitrophenol in urine and river water.

scholarly journals Improving precision of field inventory estimation of aboveground biomass through an alternative view on plot biomass

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Christoph Kleinn ◽  
Steen Magnussen ◽  
Nils Nölke ◽  
Paul Magdon ◽  
Juan Gabriel Álvarez-González ◽  
...  
Keyword(s):  
Sample Size ◽  
Forest Biomass ◽  
Remote Sensing Data ◽  
Current Approach ◽  
Tree Level ◽  
Alternative View ◽  
Relative Standard Error ◽  
Model Errors ◽  
Forest Inventories ◽  
Relative Standard

Abstract We contrast a new continuous approach (CA) for estimating plot-level above-ground biomass (AGB) in forest inventories with the current approach of estimating AGB exclusively from the tree-level AGB predicted for each tree in a plot, henceforth called DA (discrete approach). With the CA, the AGB in a forest is modelled as a continuous surface and the AGB estimate for a fixed-area plot is computed as the integral of the AGB surface taken over the plot area. Hence with the CA, the portion of the biomass of in-plot trees that extends across the plot perimeter is ignored while the biomass from trees outside of the plot reaching inside the plot is added. We use a sampling simulation with data from a fully mapped two hectare area to illustrate that important differences in plot-level AGB estimates can emerge. Ideally CA-based estimates of mean AGB should be less variable than those derived from the DA. If realized, this difference translates to a higher precision from field sampling, or a lower required sample size. In our case study with a target precision of 5% (i.e. relative standard error of the estimated mean AGB), the CA required a 27.1% lower sample size for small plots of 100 m2 and a 10.4% lower sample size for larger plots of 1700 m2. We examined sampling induced errors only and did not yet consider model errors. We discuss practical issues in implementing the CA in field inventories and the potential in applications that model biomass with remote sensing data. The CA is a variation on a plot design for above-ground forest biomass; as such it can be applied in combination with any forest inventory sampling design.

scholarly journals GENERALIZED SYNTHETIC ESTIMATOR FOR DOMAIN MEAN IN TWO PHASE SAMPLING USING SINGLE AUXILIARY CHARACTER

2019 ◽  
pp. 139-151
Author(s):  
Ashutosh ◽  
B. B. Khare ◽  
S. Khare
Keyword(s):  
Simulation Study ◽  
Standard Error ◽  
Relative Bias ◽  
Relative Standard Error ◽  
Two Phase ◽  
Auxiliary Character ◽  
Relative Standard ◽  
Phase Sampling ◽  
Two Phase Sampling

In this paper, we have proposed a two phase sampling estimator for domain mean using auxiliary character with unknown X a domain mean. Also discussed properties of the proposed estimator for domain mean ps a T ,γ , using auxiliary character. Simulation study of the proposed estimator ps a T ,γ , has been made with conventional ratio synthetic estimator for domain mean ps a T ,−1, using auxiliary character in terms of simulated relative standard error (SRSE) and absolute relative bias (ARB). Simulation study shows that under synthetic assumption proposed estimator is more efficient than conventional ratio synthetic estimator for domain mean using auxiliary character.  

scholarly journals PENEREAPAN MODEL REGRESI THRESHOLD UNTUK PENINGKATAN EFISIENSI DALAM PENDUGAAN AREA KECIL

2020 ◽  
Vol 2019 (1) ◽  
pp. 84-92
Author(s):  
Azka Ubaidillah
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Standard Error ◽  
Linear Prediction ◽  
Relative Standard Error ◽  
Mean Square ◽  
Relative Standard

Pendugaan area kecil (SAE) dewasa ini berkembang cukup pesat seiring dengan meningkatnya kebutuhan atas penyediaan statistik yang terpercaya di area kecil, yaitu area dengan jumlah contoh (sampel) yang sedikit atau tidak mencukupi untuk dilakukan pendugaan secara langsung. Metode SAE dapat meningkatkan efektivitas contoh dengan “meminjam” kekuatan dari informasi area yang bertetanggaan dan pengaruh peubah penyertanya. Dalam aplikasinya, model Fay-Herriot menggunakan pendekatan Empirical Best Linear Prediction (EBLUP) banyak dilakukan karena sifat modelnya yang sederhana. Salah satu sifat sederhana dari model EBLUP adalah penggunaan hubungan linier antara peubah yang diamati dengan peubah penyertanya. Namun sering dijumpai bahwa hubungan linier tersebut belum cukup untuk meningkatkan efisiensi model SAE sebagai akibat pola yang terbentuk antara peubah amatan dan peubah penyertanya tidak linier. Paper ini menjelaskan salah satu alternatif cara untuk meningkatkan efisiensi model SAE dengan menerapkan model regresi threshold. Dari hasil simulasi dan aplikasi data pengeluaran perkapita makanan tingkat kabupaten/kota di Jawa Tengah tahun 2015 diperoleh keterangan bahwa model regresi threshold menghasilkan pendugaan dengan RMSE (root mean square error) dan RSE (relative standard error) yang lebih kecil dibandingkan model EBLUP. Hal ini menunjukkan bahwa penerapan model regresi threshold mampu untuk meningkatkan efisiensi dalam pendugaan area kecil.

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