scholarly journals An Application of LiDAR in a Double-Sample Forest Inventory

2004 ◽  
Vol 19 (2) ◽  
pp. 95-101 ◽  
Author(s):  
Robert C. Parker ◽  
David L. Evans
Keyword(s):  
Forest Inventory ◽  
Sampling Error ◽  
Phase 1 ◽  
Basal Area ◽  
Phase 2 ◽  
Volume Estimate ◽  
Regression Estimate ◽  
Regression Estimators ◽  
Mean Differences ◽  
Area And Volume

Abstract Multireturn LiDAR data (2-m posting) were used in a double-sample forest inventory in central Idaho. Twenty-four 15-plot (0.2 ac) strips were established with a real-time Differential Global Positioning System. Tree dbh and height were measured on every 5th plot. Volume and basal area were computed for eight encountered species. LiDAR trees were selected with a focal max filter and height computed as the z-difference between interpolated canopy and DEM surfaces. LiDAR-derived trees/ac, height, and dbh had mean differences of −4.4 trees, −10.7 ft, and −1.01 in. from ground values. Four dbh-height models were fitted. Predicted dbh was used to compute LiDAR estimates of basal area and volume on 360 Phase 1 plots. Phase 2 LiDAR estimates on 60 plots were computed by randomly assigning heights to species classes using a 500-iteration Monte Carlo simulation. Regression estimators for Phase 2 ground and LiDAR ft3 and ft2 were computed by single and composite species. Phase 1 estimates were partitioned to obtain species volumes. The regression estimate of composite volume was partitioned by percent species distribution of trees, basal area, and volume. There was no statistical difference between individual and partitioned composite species estimates. Sampling error was ±11.5% on a mean volume estimate of 1,246 ft3/ac with standard error ±72.98 ft3/ac. West. J. Appl. For. 19(2):95–101.

scholarly journals Stratified Light Detection and Ranging Double-Sample Forest Inventory

2007 ◽  
Vol 31 (2) ◽  
pp. 66-72 ◽  
Author(s):  
Robert C. Parker ◽  
David L. Evans
Keyword(s):  
Forest Inventory ◽  
Sampling Error ◽  
Phase 1 ◽  
Sample Volume ◽  
Light Detection And Ranging ◽  
Phase 2 ◽  
Light Detection ◽  
Volume Estimate ◽  
Individual Tree ◽  
Volume Estimates

Abstract An industrial application of a light detection and ranging (LiDAR) individual-tree, stratified double-sample forest inventory of approximately 18,000 ha of southeastern pine plantations was accomplished with an 9:1 ratio of 0.02-ha phase 1 LiDAR and phase 2 ground plots in ages 6 to 28 years. Phase 2 ground inventory data of tree dbh and sample tree heights for 2 trees per plot were used to obtain dbh-height relationships and volumes of standing trees. Phase 1 LiDAR data with 1.9 points per m2 were used to obtain ground–LiDAR height relationships for phase 2 matching LiDAR trees and phase 1 estimates of basal area and volume. A conventional ground inventory of 971 ground plots by private contractors applying standard company field specifications resulted in an overall sampling error of ±2.7% (α = 0.05) for a single-phase volume estimate and ±2.2% for the double-sample volume estimate. Sampling error was defined as one-half the 1-α confidence interval expressed as a percentage of the mean. Reducing the phase 2 ground sample to 15 plots per age class stratum achieved sampling errors of approximately ±15% for half the strata, with a combined error of ±3.9%. Adjusting the LiDAR-ground height bias of approximately 1.8 m resulted in more realistic volume estimates compared with the industry's continuing forest inventory volumes. The double-sample volume estimates were obtained at a cost of approximately $3.88/ha of timberland inventoried as compared with $1.67/ha for the conventional inventory.

scholarly journals Smoothed Versus Unsmoothed LiDAR in a Double-Sample Forest Inventory

2005 ◽  
Vol 29 (1) ◽  
pp. 40-47 ◽  
Author(s):  
Robert C. Parker ◽  
A. Lee Mitchel
Keyword(s):  
Forest Inventory ◽  
Phase 1 ◽  
Basal Area ◽  
Product Distribution ◽  
Sample Volume ◽  
Species Group ◽  
Low Density ◽  
Phase 2 ◽  
Sampling Errors ◽  
Hardwood Species

Abstract Light detection and ranging (LiDAR) data at 0.5- and 1-m postings were used in a double-sample forest inventory on Louisiana State University's Lee Experimental Forest, Louisiana. Phase 2 plots were established with differential global positioning system (DGPS). Tree dbh (>4.5in.) and two sample heights were measured on every 10th plot of the Phase 1 sample. Volume was computed for natural and planted pine and mixed hardwood species. LiDAR trees were selected with focal filter procedures from smoothed and unsmoothed LiDAR canopy surfaces. Dbh-height and ground-LiDARheight models were used to predict dbh from LiDAR height and compute Phase 2 estimates of ft2 basal area and ft3 volume. Phase 1 LiDAR estimates were computed by randomly assigning heights to species classes using the probability distribution from ground plots in eachinventory strata. Phase 2 LiDAR estimates were computed by randomly assigning heights to species-product groups using a Monte Carlo simulation for each ground plot. Regression coefficients for Phase 2 estimates of ft2 and ft3 from the smoothed versus unsmoothed surfacesof high- and low-density LiDAR were computed by species group. Regression estimates for combined volume were partitioned by species-product distribution of Phase 2 volume. There was no statistical difference (α = 0.05) between smoothed versus unsmoothed for high- and low-density LiDAR on adjusted mean volume estimates (sampling errors of 9.52 versus 8.46% for high-density and 9.25 versus 7.65% for low-density LiDAR). South. J. Appl. For. 29(1):40–47.

scholarly journals High- Versus Low-Density LiDAR in a Double-Sample Forest Inventory

2004 ◽  
Vol 28 (4) ◽  
pp. 205-210 ◽  
Author(s):  
Robert C. Parker ◽  
Patrick A. Glass
Keyword(s):  
Forest Inventory ◽  
Phase 1 ◽  
Basal Area ◽  
Sample Volume ◽  
Low Density ◽  
Phase 2 ◽  
Sampling Errors ◽  
Height Difference ◽  
Hardwood Species ◽  
Volume Estimates

Abstract Light detection and ranging (LiDAR) data at 0.5- and 1-m postings were used in a double-sample forest inventory on Louisiana State University's Lee Experimental Forest, Louisiana. Phase 2 plots were established with DGPS. Tree dbh (>4.5 in.) and two sample heights (minimum and maximum dbh) were measured on every 10th plot of the phase 1 sample. Volume was computed for natural and planted pine and mixed hardwood species. LiDAR trees were selected with focal filter procedures and heights computed as the height difference between interpolated canopy and DEM surfaces. Dbh-height and ground-LiDAR height models were used to predict dbh from adjusted LiDAR height and compute ground and LiDAR estimates of ft2 basal area and ft3 volume. Phase 1 LiDAR estimates were computed by randomly assigning heights to species classes using the probability distribution from ground plots in each inventory strata. Phase 2 LiDAR estimates were computed by randomly assigning heights to species-product groups using a Monte Carlo simulation for each ground plot. There was no statistical difference between high-versus low-density LiDAR estimates on adjusted mean volume estimates (sampling errors of 8.16 versus 7.60% without height adjustment and 8.98 versus 8.63% with height adjustment). Low-density LiDAR surfaces without height adjustment produced the lowest sampling errors for stratified and nonstratified, double-sample volume estimates. South. J. Appl. For. 28(4):205–210.

scholarly journals LiDAR Forest Inventory with Single-Tree, Double-, and Single-Phase Procedures

2009 ◽  
Vol 2009 ◽  
pp. 1-6 ◽  
Author(s):  
Robert C. Parker ◽  
David L. Evans
Keyword(s):  
Single Phase ◽  
Phase 1 ◽  
Basal Area ◽  
Single Species ◽  
Phase 2 ◽  
Sampling Errors ◽  
Single Tree ◽  
Species Groups ◽  
Volume Estimates ◽  
Height Data

Light Detection and Ranging (LiDAR) data at 0.5–2 m postings were used with double-sample, stratified procedures involving single-tree relationships in mixed, and single species stands to yield sampling errors ranging from % to %. LiDAR samples were selected with focal filter procedures and heights computed from interpolated canopy and DEM surfaces. Tree dbh and height data were obtained at various ratios of LiDAR, ground samples for DGPS located ground plots. Dbh-height and ground-LiDAR height models were used to predict dbh and compute Phase 2 estimates of basal area and volume. Phase 1 estimates were computed using the species probability distribution from ground plots in each strata. Phase 2 estimates were computed by randomly assigning LiDAR heights to species groups using a Monte Carlo simulation for each ground plot. There was no statistical difference between volume estimates from 0.5 m and 1 m LiDAR densities. Volume estimates from single-phase LiDAR procedures utilizing existing tree attributes and height bias relationships were obtained with sampling errors of 1.8% to 5.5%.

scholarly journals Precision and accuracy of sampling methods in thinned Pinus taeda L. stands

FLORESTA ◽  
2022 ◽  
Vol 52 (1) ◽  
pp. 045
Author(s):  
Rodrigo Otávio Veiga Miranda ◽  
Felipe De Antoni Zarpelon ◽  
Síntia Valério Kohler ◽  
Alvaro Augusto Vieira Soares ◽  
Izabele Domingues Soares Miranda ◽  
...  
Keyword(s):  
Pinus Taeda ◽  
Sampling Methods ◽  
Sampling Error ◽  
Basal Area ◽  
Population Means ◽  
Starting Point ◽  
Fixed Area ◽  
Precision And Accuracy ◽  
Pinus Taeda L ◽  
Area And Volume

Different sampling methods can be used in forest surveys. It is important to know the precision and accuracy of these sampling methods, and which one is the most appropriate in specific conditions of the forest population. The aim of this study was to compare estimates of a forest inventory performed by different sampling methods with forest census results. The sampling methods evaluated were the fixed-area method and the variable-area methods of Bitterlich, Prodan, and Strand. The data were obtained in a 15-year-old thinned stand of Pinus taeda L., located in the municipality of Teixeira Soares, southern Brazil, with a total area of 12.80 ha. Initially, the forest census was carried out, and subsequently, the sample units for each sampling method were distributed in the stand, with a common starting point. The variables used to compare the sample results with the census means were quadratic diameter, number of trees, basal area, and volume, per hectare. Precision and accuracy were evaluated by sampling error and whether the confidence intervals covered the population means, respectively. The fixed-area and Bitterlich methods stood out in precision for all variables analysed. The fixed-area, Bitterlich and Strand methods with proportion to height provided more accurate estimates. The Prodan method provided inaccurate and imprecise estimates for the variables under analysis, except for the quadratic diameter.

scholarly journals Variáveis dendrométricas e produção de Eucalyptus benthamii em diferentes espaçamentos

2020 ◽  
Vol 16 (6) ◽  
pp. 111-120
Author(s):  
Luciano Farinha Watzlawick ◽  
Cristiane Carla Benin
Keyword(s):  
Experimental Design ◽  
Forest Inventory ◽  
Basal Area ◽  
Forest Production ◽  
Individual Volume ◽  
The Individual ◽  
Area And Volume

This study aimed to evaluate the effect of planting spacing on production and dendrometric variables, in experimental planting with improved E. benthamiiseeds, at six years of age. The experimental design adopted was in randomized blocks, with four replications of twenty trees. The treatments were four planting spacing 3 x 2 m, 3 x 3 m, 3 x 4 m and 4 x 4 m. After the field procedures (forest inventory, sampling and cubage of 79 trees), the main dendrometric parameters were obtained in each spacing. The heights were determined by a hypsometric relationship and the volume ofother trees in the stand was estimated by the model of Schumacher and Hall (1933). The dendrometric variables showed a high correlation with the spacing. The wider spacing favored the diameter, height, transversal area and the individual volume, while the smaller vital spaces were responsible for the highest values of basal area and volume per hectare. Production ranged from 238.90 m³ ha-1in the largest spacing to 392.08 m³ ha-1in the smallest spacing. It was concluded that there was an effect of the planting spacing, and that with the trends observed regarding the higher production per hectare in the denser spacing, it is opportune to plan the forest production according to the planting spacing adopted.

scholarly journals Precision, efficiency and cost of shapes of fixed area plots for plantation ages of Pinus taeda L. in Santa Catarina State

2020 ◽  
Vol 19 (2) ◽  
pp. 124-131
Author(s):  
Alois Zator Filho ◽  
Myrcia Minatti ◽  
Antonio Pedro Fragoso Woycikievicz ◽  
Jonathan William Trautenmuller ◽  
Nelson Yoshihiro Nakajima
Keyword(s):  
Lower Cost ◽  
Sampling Error ◽  
Basal Area ◽  
Average Diameter ◽  
Measurement Time ◽  
Efficiency Cost ◽  
Fixed Area ◽  
Different Shapes ◽  
Pinus Taeda L ◽  
Area And Volume

Generally, the forest populations are extensive and frequently require to be inventoried in short term, where the implementation of forest inventory is closely linked to the sampling theory. With objective to compare three different shapes of fixed area plots to estimate parameters of the forest as average diameter, basal area, the number of trees and volume per hectare, evaluating the respective precision, relative efficiency, cost and measurement time in four different ages. The rectangular plot had presented better precision to estimate the average DBH, number of trees and basal area and the circular plot with better precision for volume per hectare. About efficiency, the square plot had presented the best efficiency for the variable average DBH, basal area and volume and the circular plot was the most efficient for the numbers of trees ha-1. The square plot also had shown the lower cost and measurement time to estimate the variables evaluated. The rectangular plot had presented the best precision in the estimative of the variables, as well as, the lower sampling error in the most of the cases evaluated in this study, following the circular plot, and the square plot with lower precision. In relation to the efficiency, the square plot had presented the best performance and the rectangular plot the worst performance in all age classes and evaluated variables. The square plots as the best plot shape to estimate the variables average DBH, number of trees, basal area and volume per hectare.

Rationalization and internalization

2001 ◽  
Vol 60 (4) ◽  
pp. 215-230 ◽  
Author(s):  
Jean-Léon Beauvois
Keyword(s):  
Phase 1 ◽  
Facilitatory Effect ◽  
Phase 2 ◽  
Causal Explanations ◽  
Reduction Effect ◽  
Dissonance Reduction

After having been told they were free to accept or refuse, pupils aged 6–7 and 10–11 (tested individually) were led to agree to taste a soup that looked disgusting (phase 1: initial counter-motivational obligation). Before tasting the soup, they had to state what they thought about it. A week later, they were asked whether they wanted to try out some new needles that had supposedly been invented to make vaccinations less painful. Agreement or refusal to try was noted, along with the size of the needle chosen in case of agreement (phase 2: act generalization). The main findings included (1) a strong dissonance reduction effect in phase 1, especially for the younger children (rationalization), (2) a generalization effect in phase 2 (foot-in-the-door effect), and (3) a facilitatory effect on generalization of internal causal explanations about the initial agreement. The results are discussed in relation to the distinction between rationalization and internalization.

PENERAPAN MODEL QUANTUM LEARNING UNTUK MENINGKATKAN HASIL BELAJAR AUTOCAD SISWA KELAS X TEKNIK PEMESINAN SMK NEGERI 1 STABAT

2013 ◽  
Vol 5 (1) ◽  
Author(s):  
Abdul Hasan Saragih
Keyword(s):  
Positive Response ◽  
Phase 1 ◽  
First Grade ◽  
Learning Model ◽  
Second Phase ◽  
Phase 2 ◽  
Phase 3 ◽  
The Third ◽  
Third Phase ◽  
Quantum Learning

This classroom research was conducted on the autocad instructions to the first grade of mechinary class of SMK Negeri 1 Stabat aiming at : (1) improving the student’ archievementon autocad instructional to the student of mechinary architecture class of SMK Negeri 1 Stabat, (2) applying Quantum Learning Model to the students of mechinary class of SMK Negeri 1 Stabat, arising the positive response to autocad subject by applying Quantum Learning Model of the students of mechinary class of SMK Negeri 1 Stabat. The result shows that (1) by applying quantum learning model, the students’ achievement improves significantly. The improvement ofthe achievement of the 34 students is very satisfactory; on the first phase, 27 students passed (70.59%), 10 students failed (29.41%). On the second phase 27 students (79.41%) passed and 7 students (20.59%) failed. On the third phase 30 students (88.24%) passed and 4 students (11.76%) failed. The application of quantum learning model in SMK Negeri 1 Stabat proved satisfying. This was visible from the activeness of the students from phase 1 to 3. The activeness average of the students was 74.31% on phase 1,81.35% on phase 2, and 83.63% on phase 3. (3) The application of the quantum learning model on teaching autocad was very positively welcome by the students of mechinary class of SMK Negeri 1 Stabat. On phase 1 the improvement was 81.53% . It improved to 86.15% on phase 3. Therefore, The improvement ofstudent’ response can be categorized good.

The Forest Inventory and Analysis Database: Database Description and User Guide for Phase 2 (version 6.1)

2016 ◽  
Author(s):  
Barbara M. O'Connell ◽  
Barbara L. Conkling ◽  
Andrea M. Wilson ◽  
Elizabeth A. Burrill ◽  
Jeffrey A. Turner ◽  
...  
Keyword(s):  
Forest Inventory ◽  
Phase 2 ◽  
Forest Inventory And Analysis
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