scholarly journals Geostatistics as a tool to reduce the sampling effort in forest inventories

2021 ◽  
pp. 174-179
Author(s):  
Myrcia Minatti ◽  
Carlos Roberto Sanquetta ◽  
Sylvio Péllico Neto ◽  
Ana Paula Dalla Corte ◽  
Vinicius Costa Cysneiros
Keyword(s):  
Forest Inventory ◽  
Ordinary Kriging ◽  
Simple Random Sampling ◽  
Exponential Model ◽  
Indicator Kriging ◽  
Sampling Effort ◽  
Thematic Maps ◽  
Visual Interpretation ◽  
Forest Inventories ◽  
Geostatistical Techniques

Geostatistics is one of the tools applied to investigate the spatial variability of forests to reduce costs and recognize the best productivity areas for planning. This study aimed to test the performance of geostatistical techniques in reducing the sampling effort in forest inventories. For this purpose, we used the height of dominant trees as a discriminator of the homogeneous strata to obtain a better representation of the productivity within the forest stands. We carried out the study in Pinus taeda L. stands in the Center-South of Paraná, Brazil, by using plots from a forest inventory allocated with the systematic process. Then, we tested three models to determine the site curves (Schumacher, Chapman-Richards 2, and 3 coefficients) with the thirty-seventh year being the reference age. To model the spatial patterns of the dominant height, we used the ordinary kriging, and, after that, we generated the thematic maps of the site classes. Similarly, we used the indicator kriging which allowed obtaining the probabilities of high, medium, and low productivity sites. The processing of the stratified sampling, with the support of the visual interpretation of the images, allowed us to define five strata according to productivity. Results showed that ordinary kriging is effective in defining the productivity classes. Along with geostatistical techniques, it produces more homogeneous strata and reduces the errors of the forest inventory. Moreover, the best-selected model was the Chapman-Richards (3 coefficients) for the site curves. The exponential model was the best model to identify the best areas of the probability of occurrence of sites with higher productivity. The efficiency of indicative kriging generated thematic maps to delimit the likely locations of the most promising sites. Overall, geostatistics proved to be efficient concerning error when compared to simple random sampling.

scholarly journals Application of kriging techniques for assessing the salinity of irrigated soils: the case of El Ghrous perimeter, Biskra, Algeria

2019 ◽  
Vol 9 ◽  
Author(s):  
Mohamed Amine Abdennour ◽  
Abdelkader Douaoui ◽  
Abdelhamid Bradai ◽  
Amel Bennacer ◽  
Manuel Pulido Fernández
Keyword(s):  
Soil Salinity ◽  
Ordinary Kriging ◽  
Indicator Kriging ◽  
Spatial Trend ◽  
Risk Areas ◽  
Geostatistical Techniques ◽  
Anthropogenic Processes ◽  
Irrigated Perimeter ◽  
Made In ◽  
Irrigated Soils

In semi-arid and arid areas, soil salinity has adverse effects both on the environment and agricultural production. The main causes of this salinization come from natural or anthropogenic processes, which is certainly an environmental problem that affects more than 20% of the world's land. This study was made in order to map the spatial distribution of soil salinity of the irrigated perimeter of El Ghrous in southeastern Algeria. These maps were performed based on data collected from 190 soil samples from 0 to 15 cm deep. We used ordinary kriging (OK) to analyze the spatial variability of soil salinity, while indicator kriging (IK) was used to analyze salinity versus threshold values. The salinity map predicted by the electrical conductivity (EC) values using the ordinary kriging (OK) method showed the different classes of salinity according to Durand's classification with moderately saline 3rd order dominance, while the unsalted soil (EC &lt; 0.6 dS m<sup>-1</sup>) represents a very low percentage (1.5%). The indicator kriging (IK) was carried out by four thresholds which correspond to the salinity class limits: EC &gt; 0.6, EC &gt; 1, EC &gt; 2, EC &gt; 3, and EC &gt; 4 dS m<sup>-1</sup>, for developing probability maps to determine risk areas. This study has shown the spatial trend of soil salinity by geolocation of different classes, and to carry out risk maps using geostatistical techniques.

Die antizipierte Varianz: ein Werkzeug für die Optimierung von Waldinventuren | The anticipated variance: a tool for the optimization of forest inventory

2003 ◽  
Vol 154 (3-4) ◽  
pp. 117-121 ◽  
Author(s):  
Daniel Mandallaz
Keyword(s):  
Spatial Distribution ◽  
Forest Inventory ◽  
Random Sampling ◽  
Poisson Model ◽  
Simple Algebra ◽  
Simple Random Sampling ◽  
Two Stage ◽  
Two Phase ◽  
Forest Inventories ◽  
Mathematical Review

This paper gives a non-mathematical review of the concept of anticipated variance which allows to solve entirely the optimisation problem for two-phase two-stage forest inventories with cluster or simple random sampling, in the sense that the anticipated variance is minimised for given costs. The anticipated variance is the average of the design-based variance under a local Poisson-model for the spatial distribution of the trees. The resulting sampling rules have a clear intuitive background and require only simple algebra to be implemented. The required parameters can be estimated from any pre-existing two-phase inventory. An example based on the Swiss National Inventory illustrates the method.

On the error of estimates from successive forest inventories with SPR

1987 ◽  
Vol 17 (5) ◽  
pp. 442-447
Author(s):  
Tiberius Cunia
Keyword(s):  
Forest Inventory ◽  
Random Sampling ◽  
Regression Coefficients ◽  
Partial Replacement ◽  
Forest Inventories ◽  
Continuous Forest ◽  
Regression Functions ◽  
The Given ◽  
Linear Regressions ◽  
Continuous Forest Inventory

The approach used by Cunia to combine the error from sample plots with the error from volume or biomass tables when Continuous Forest Inventory (CFI) estimates of current values and growth are calculated is extended to the CFI systems using Sampling with Partial Replacement (SPR). The formulae are derived for the case of SPR on two measurement occasions when (i) volume or biomass tables are constructed from linear regressions for which an estimate of the covariance matrix of the regression coefficients is known, and (ii) the sample plots or points are selected by random sampling independently of the given volume or biomass regression functions.

scholarly journals Improvement of Spatial Modeling of Cr, Pb, Cd, As and Ni in Soil Based on Portable X-ray Fluorescence (PXRF) and Geostatistics: A Case Study in East China

2019 ◽  
Vol 16 (15) ◽  
pp. 2694 ◽  
Author(s):  
Xia ◽  
Hu ◽  
Shao ◽  
Xu ◽  
Zhou ◽  
...  
Keyword(s):  
Heavy Metals ◽  
Hot Spots ◽  
Ordinary Kriging ◽  
Eastern China ◽  
Indicator Kriging ◽  
Polluted Water ◽  
X Ray ◽  
Soil Heavy Metals ◽  
Metals Pollution ◽  
Heavy Metals Pollution

To verify the feasibility of portable X-ray fluorescence (PXRF) for rapidly analyzing, assessing and improving soil heavy metals mapping, 351 samples were collected from Fuyang District, Hangzhou City, in eastern China. Ordinary kriging (OK) and co-ordinary kriging (COK) combined with PXRF measurements were used to explore spatial patterns of heavy metals content in the soil. The Getis-Ord index was calculated to discern hot spots of heavy metals. Finally, multi-variable indicator kriging was conducted to obtain a map of multi-heavy metals pollution. The results indicated Cd is the primary pollution element in Fuyang, followed by As and Pb. Application of PXRF measurements as covariates in COK improved model accuracy, especially for Pb and Cd. Heavy metals pollution hot spots were mainly detected in northern Fuyang and plains along the Fuchun River in southern Fuyang because of mining, industrial and traffic activities, and irrigation with polluted water. Area with high risk of multi-heavy metals pollution mainly distributed in plain along the Fuchun River and the eastern Fuyang. These findings certified the feasibility of using PXRF as an efficient and reliable method for soil heavy metals pollution assessment and mapping, which could contribute to reduce the cost of surveys and pollution remediation.

scholarly journals Review of the Kriging Technique Applications to Groundwater Quality

2021 ◽  
Vol 27 (12) ◽  
pp. 23-32
Author(s):  
Hayat Azawi ◽  
May Samir Saleh
Keyword(s):  
Groundwater Quality ◽  
Quality Indicator ◽  
Ordinary Kriging ◽  
Indicator Kriging ◽  
Disjunctive Kriging ◽  
Kriging Method ◽  
Lognormal Kriging ◽  
Groundwater Contaminants ◽  
Simple Kriging

Kriging, a geostatistical technique, has been used for many years to evaluate groundwater quality. The best estimation data for unsampled points were determined by using this method depending on measured variables for an area. The groundwater contaminants assessment worldwide was found through many kriging methods. The present paper shows a review of the most known methods of kriging that were used in estimating and mapping the groundwater quality. Indicator kriging, simple kriging, cokriging, ordinary kriging, disjunctive kriging and lognormal kriging are the most used techniques. In addition, the concept of the disjunctive kriging method was explained in this work to be easily understood.

scholarly journals Zur Auswirkung der Hangneigungskorrektur auf Schätzwerte im Schweizerischen Landesforstinventar (LFI) | Investigation of the effect of the slope correction method as applied in the Swiss National Forest Inventory of estimates

2001 ◽  
Vol 152 (6) ◽  
pp. 215-225 ◽  
Author(s):  
Michael Köhl ◽  
Peter Brassel
Keyword(s):  
Forest Inventory ◽  
Basal Area ◽  
Correction Method ◽  
National Forest Inventory ◽  
National Forest ◽  
Average Deviation ◽  
Forest Inventories ◽  
Slope Correction ◽  
Number Of Stems ◽  
Elliptical Area

For forest inventories on slopes, it is necessary to correct the test areas, because the circular areas, when projected, become elliptical. Based on 93 samples from the Swiss National Forest Inventory (FNI), it was determined whether the simplified method, which increases the radius to match that of the elliptical area, leads to a distortion of the results. An average deviation of 2% was found between the FNI estimated values and the actual values for the basal area and the number of stems. For estimations of smaller units, greater distortions of the results are expected.

Presenting the GeForse approach to create synthetic LiDAR data from simulated forest stands to optimize forest inventories

2021 ◽  
Author(s):  
Fabian E. Fassnacht ◽  
Jannika Schäfer ◽  
Hannah Weiser ◽  
Lukas Winiwarter ◽  
Nina Krašovec ◽  
...  
Keyword(s):  
Forest Inventory ◽  
Forest Growth ◽  
Sensitivity Analyses ◽  
Future Application ◽  
Lidar Data ◽  
Real Point ◽  
Entire Area ◽  
Single Tree ◽  
Forest Inventories ◽  
Tree Models

&lt;p&gt;LiDAR-based forest inventories focusing on estimating and mapping structure-related forest inventory variables across large areas have reached operationality. In the commonly applied area-based approach, a set of field-measured inventory plots is combined with spatially co-located airborne laserscanning data to train empirical models that can then be used to predict the target metric over the entire area covered by LiDAR data.&lt;/p&gt;&lt;p&gt;The area-based approach was found to produce reliable estimates for structure-related forest inventory metrics such as wood volume and biomass across many forest types. However, the current workflows still leave space for improvement that may result in cost-reduction with respect to data acquisition or improved accuracies. This is particularly relevant as the area-based approach is increasingly used in operational forestry settings. To further optimize existing workflows, experiments are required that need large amounts of forest inventory data (e.g., to examine the effect of sample size or the field inventory design on the model performances) or multiple LiDAR acquisitions (e.g., to identify optimal/cost-efficient acquisition settings). The acquisition of these types of data is cost-intensive and is hence often limited to small extents within scientific experiments.&lt;/p&gt;&lt;p&gt;Here, we present the &amp;#8221;GeForse - Generating Synthetic Forest Remote Sensing Data&amp;#8221; approach to create synthetic LiDAR datasets suitable for such optimization studies. GeForse combines a database of single-tree models consisting of point clouds extracted from real LiDAR data with the outputs of a spatially explicit, single tree-based forest growth simulator (in this case SILVA). For each simulated tree, we insert a real point-cloud tree with properties (species, crown diameter, height) matching the properties of the simulated tree. This results in a synthetic 3D forest with a realistic 3D-structure where the inventory metrics of each tree are known. This 3D forest then serves as input to the &amp;#8220;Heidelberg LiDAR Operations Simulator&amp;#8221; (HELIOS++, https://github.com/3dgeo-heidelberg/helios) and thereby enables the simulation of LiDAR acquisition flights with varying acquisition settings and flight trajectories. In combination with the &amp;#8220;full inventory&amp;#8221; of all trees in the simulated forest, this enables a wide variety of sensitivity analyses.&lt;/p&gt;&lt;p&gt;In this contribution, we give an overview of the complete GeForse approach from extracting the tree models, to generating the 3D forest and simulating LiDAR flights over the 3D forest using HELIOS++. Further, we present a brief case-study where this approach was applied to optimize certain aspects of area-based forest inventory approaches using LiDAR data from a forest area in central Europe. Finally, we provide an outlook on future application fields of the GeForse approach.&lt;/p&gt;

scholarly journals Timber Inventory of Large Acreages Using Stratified Two-Stage List Sampling

2005 ◽  
Vol 29 (3) ◽  
pp. 152-157 ◽  
Author(s):  
Bruce E. Borders ◽  
Barry D. Shiver ◽  
Michael L. Clutter
Keyword(s):  
Forest Inventory ◽  
Sampling Method ◽  
Sampling Effort ◽  
Field Sampling ◽  
Two Stage ◽  
Timber Inventory

Abstract We present two-stage list sampling estimators and methodology that are useful in a forest inventory context. The advantages of this sampling method are discussed and illustrated with an inventory of a 3,419-acre timber tract. In this example, two-stage list sampling resulted in strata level and tract level estimates that were very close to estimates from a more intensive cruise that used twice as much field sampling effort. South. J. Appl. For. 29(3):152–157.

scholarly journals SISTEMA DE INVENTÁRIO FLORESTAL PARA SERINGAL NATIVO

FLORESTA ◽  
2002 ◽  
Vol 32 (1) ◽  
Author(s):  
José de Arimatéa Silva ◽  
Sylvio Péllico Netto
Keyword(s):  
Forest Inventory ◽  
Random Sampling ◽  
Rubber Tree ◽  
Basal Area ◽  
Simple Random Sampling ◽  
Inventory System ◽  
Stratified Random Sampling ◽  
Stage Number ◽  
Second Stage ◽  
Two Stages

Este trabalho teve como objetivo desenvolver um Sistema de Inventário Florestal para seringal nativo. Aplicou-se a Amostragem Inteiramente Aleatória (AIA), em dois estágios: colocação de seringa, no primeiro, e estrada de seringa, no segundo. Foram estimados: número de seringueiras por estrada (N), área basal das seringueiras da estrada (G) e volume da porção explorada do fuste (V). Realizou-se uma pós-estratificação, considerando-se estradas de centro e de margem, aplicando-se a Amostragem Estratificada (AE). Comparou-se a AIA com a AE, com base na eficiência relativa. Os resultados revelaram as seguintes estimativas para as médias estratificadas: N=100; G=19,00 m², V= 62,8 m³. Concluiu-se que a AE revelou-se mais eficiente que a AIA para estimar as variáveis analisadas. Propõe-se que um sistema de inventário para seringal nativo deve combinar: informações de um censo das colocações; um processo de amostragem estratificada; e um método de amostragem cuja unidade de amostra é a estrada de seringa. Forest Inventory System for Rubber Trees Abstract Forest Inventory System for rubber trees. This work had as objective to develop an Inventory System for native rubber tree areas. The Simple Random Sampling (SRS) was applied in two stages: the setting, in the first, and the rubber trees tracks, in the second stage. Number of rubber trees per track (N), basal area of the rubber trees track (G) and volume of the stem portion explored (V) were the parameters estimated. A post-stratification was become fulfilled, considering itself center tracks and river side tracks, applying itself it Stratified Random Sampling (STRS). It was compared SRS with the STRS, on the basis of the relative efficiency. The results showed the following estimates for the stratified means: N=100; G=19,00 m², V = 62,8 m³. It was concluded that the STRS showed more efficient than the SRS to estimate the analyzed variables. It is considered that an Inventory System for native rubber tree areas must match: information of a census of the settings; a process of Stratified Random Sampling; and a sampling method whose unit of sample is the rubber tree track.

Forest inventory estimation with mapped plots

2004 ◽  
Vol 34 (2) ◽  
pp. 493-497 ◽  
Author(s):  
Paul C Van Deusen
Keyword(s):  
Forest Inventory ◽  
Random Sampling ◽  
Simple Random Sampling ◽  
Mean Squared Errors ◽  
Additional Work ◽  
Fixed Area ◽  
Plot Design

Procedures are developed for estimating means and variances with a mapped-plot design. The focus is on fixed-area plots, and simulations are used to validate the proposed estimators. The mapped-plot estimators for means and variances are compared with simple random sampling estimators that utilize only full plots. As expected, the mapped-plot estimates have smaller mean squared errors than the simple random sampling estimates. The theory for fixed-area plots is easy to apply, although additional work is required to map plots in the field. Corresponding theory for variable plots is developed but not tested with simulations. The difficulty of applying these methods to variable plots is greater, but not prohibitive.

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