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

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.

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Smoothed Versus Unsmoothed LiDAR in a Double-Sample Forest Inventory

Southern Journal of Applied Forestry ◽

10.1093/sjaf/29.1.40 ◽

2005 ◽

Vol 29 (1) ◽

pp. 40-47 ◽

Cited By ~ 5

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.

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

International Journal of Forestry Research ◽

10.1155/2009/864108 ◽

2009 ◽

Vol 2009 ◽

pp. 1-6 ◽

Cited By ~ 8

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%.

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Stratified Light Detection and Ranging Double-Sample Forest Inventory

Southern Journal of Applied Forestry ◽

10.1093/sjaf/31.2.66 ◽

2007 ◽

Vol 31 (2) ◽

pp. 66-72 ◽

Cited By ~ 12

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.

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An Application of LiDAR in a Double-Sample Forest Inventory

Western Journal of Applied Forestry ◽

10.1093/wjaf/19.2.95 ◽

2004 ◽

Vol 19 (2) ◽

pp. 95-101 ◽

Cited By ~ 30

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.

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Effects of Basal Area Factor and Plot Size on Precision and Accuracy of Forest Inventory Estimates

Northern Journal of Applied Forestry ◽

10.1093/njaf/28.3.152 ◽

2011 ◽

Vol 28 (3) ◽

pp. 152-156 ◽

Cited By ~ 1

Author(s):

Peter Becker ◽

Tom Nichols

Keyword(s):

Forest Inventory ◽

Basal Area ◽

Cost Effective ◽

Sampling Errors ◽

Plot Size ◽

Accuracy And Precision ◽

Coefficients Of Variation ◽

Fixed Radius ◽

Tree Number ◽

Biased Estimates

Abstract We tested the effects of plot size (0.05-0.30 ac) and basal area factor (BAF) (5-30) on the accuracy and precision of per-acre estimates of tree number, basal area, biomass (all for trees ≥4.5 in. dbh), and sawtimber volume (for trees ≥11.6 in. dbh). Field sampling errors, such as missing in-trees, did not affect our tests. Virtual variable- and fixed-radius plots were randomly located within an artificial matrix of 130 real plots in well-stocked upland hardwood forests of sawtimber-sized trees in the Missouri Ozarks. Inventory parameters were essentially independent of plot size and BAF, whereas their coefficients of variation decreased with plot size and increased with BAF. Thus, our results for random plots agreed with sampling theory, unlike a previous study using concentric virtual plots in West Virginia forests. A very concentrated zone of high tree density around some plot centers apparently caused the biased estimates by concentric plots. Compared with the entire composite forest, inventory means were accurately estimated (to within 5%) and size class distributions were well represented for plots ≥0.1 ac or ≤15 BAF. Our procedures provide a basis for selecting an efficient and cost-effective sampling design suited to forest characteristics and the inventory's purpose.

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Optimization of a Density Gradient Centrifugation Protocol for Isolation of Peripheral Blood Mononuclear Cells

Acta Medica Marisiensis ◽

10.2478/amma-2018-0011 ◽

2018 ◽

Vol 64 (2) ◽

pp. 83-90 ◽

Cited By ~ 2

Author(s):

Georgiana Mihaela Şerban ◽

Ion Bogdan Mănescu ◽

Doina Ramona Manu ◽

Minodora Dobreanu

Keyword(s):

Mononuclear Cells ◽

Density Gradient Centrifugation ◽

Gradient Centrifugation ◽

Phase 1 ◽

Sample Volume ◽

Mean Value ◽

Phase 2 ◽

Cell Recovery ◽

Peripheral Blood Mononuclear ◽

Mean Values

Abstract Objective: Peripheral blood mononuclear cells (PBMC) are extremely important in the body’s immune response. Their isolation represents a major step in many immunological experiments. In this two phase study, we aimed to establish an optimum protocol for PBMC isolation by density-gradient centrifugation. Methods: During Phase-1, we compared two commercially available PBMC isolation protocols, Stemcell Technologies (ST) and Miltenyi Biotec (MB), in terms of PBMC recovery and purity. Twelve blood samples were assigned to each protocol. Each sample was divided in three subsamples of 1ml, 2ml and 3ml in order to assess the influence of blood sample volume on isolation performance. During Phase-2, a hybrid protocol was similarly tested, processing six blood samples. Additionally, we performed a flow cytometric analysis using an Annexin-V/Propidium-Iodide viability staining protocol. Results: Phase-1 results showed that, for all subsample volumes, ST had superior PBMC recovery (mean values: 56%, 80% and 87%, respectively) compared to MB (mean values: 39%, 54% and 43%, respectively). However, platelet removal was significantly higher for MB (mean value of 96.8%) than for ST (mean value of 75.2%). Regarding granulocyte/erythrocyte contamination, both protocols performed similarly, yielding high purity PBMC (mean values: 97.3% for ST and 95.8% for MB). During Phase-2, our hybrid protocol yielded comparable results to MB, with an average viability of 89.4% for lymphocytes and 16.9% for monocytes. Conclusions: ST yields higher cell recovery rates and MB excels at platelet removal, while the hybrid protocol is highly similar to MB. Both cell recovery and viability increase with blood sample volume.

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Rationalization and internalization

Swiss Journal of Psychology ◽

10.1024//1421-0185.60.4.215 ◽

2001 ◽

Vol 60 (4) ◽

pp. 215-230 ◽

Cited By ~ 6

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.

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

Jurnal Teknologi Pendidikan (JTP) ◽

10.24114/jtp.v5i1.509 ◽

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.

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