Rethinking Productivity Evaluation in Precision Forestry through Dominant Height and Site Index Measurements Using Aerial Laser Scanning LiDAR Data
Abstract
:1. Introduction
1.1. Loblolly’s Pine Importance
1.2. Dominant Height as a Measure of Productivity
- Mean height for a given percentage of the tallest trees;
- Mean height for a given number of the tallest trees as defined by size/density;
- The tallest single tree height.
1.3. Stand Characterization with Remote Sensing Data
1.4. Use of Remote Sensing in Precision Forestry
- Assess how scale impacts these estimates for loblolly pine plantations;
- Compare different definitions of dominant height;
- Evaluate methods for stand stratification (e.g., soils, topography, and soils plus topography), relative to simulations of traditional field inventory sample plot data.
2. Materials and Methods
2.1. Site Locations
2.2. Soils and Geology
2.3. Data Collection and Processing
- Valleys—value of less than one negative standard deviation of the TPI;
- Flat—value greater than or equal to one negative standard deviation but less than one half standard deviation of the TPI;
- Ridge—value greater than one half standard deviation of the TP.
2.4. Data Analysis
2.4.1. Dominant Height Definitions
- Top HT 85—mean height of the 85th percentile of tallest trees;
- Top HT 100—mean height of the tallest 100 trees per hectare within the stand;
- Top HT 40—mean height of the tallest 40 trees per hectare within the stand.
Location | Stand | Area (ha) | Number of Trees | Mean Trees per Hectare | Mean Tree Height (m) | Number of Plots Simulated |
---|---|---|---|---|---|---|
AL | ALNorth1 | 49.1 | 35,537 | 724 | 17.3 (1.9) | 22 |
ALSouth1 | 20.4 | 19,154 | 938 | 17.2 (1.6) | 9 | |
NC | NCSouth1 | 51.5 | 15,136 | 294 | 21.2 (1.7) | 26 |
NCSouth2 | 13.0 | 5260 | 404 | 17.0 (1.4) | 9 | |
NCNorth1 | 16.7 | 7296 | 437 | 17.3 (1.5) | 9 | |
NCNorth2 | 56.5 | 22,723 | 400 | 17.2 (1.7) | 31 | |
TX | TXSouth1 | 52.3 | 19,061 | 365 | 17.7 (1.2) | 24 |
TXSouth2 | 35.8 | 18,585 | 519 | 22.0 (2.0) | 16 |
2.4.2. Scale
- Full stand: dominant trees are selected from the tree population for each stand without stratification.
- Soil classification: dominant trees are selected from the tree population for each soil type.
- Microtopography classification: dominant trees are selected from the tree population for each microtopography type.
- Combined soil and microtopography classification: dominant trees are selected from the tree population for each combination of soil and microtopography.
2.4.3. Site Index
3. Results
3.1. Dominant Height Definition
3.2. Scale
3.2.1. Soils
3.2.2. Microtopography
3.2.3. Soils and Microtopography
3.3. Site Index
4. Discussion
4.1. Productivity Definition
4.2. Dominant Height
4.3. Influence of Site Variability on Dominant Height
4.4. Precision Forestry Using Remote Sensing Data
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Location | Mean Annual Precipitation (mm) | Mean Annual Temperature (°C) | Mean Annual Maximum Temperature (°C) | Mean Annual Minimum Temperature (°C) |
---|---|---|---|---|
Alabama | 1298 | 17.3 | 33.8 | 0.2 |
North Carolina | 1154 | 15.6 | 33.1 | −2.1 |
Texas | 1241 | 20.1 | 35.6 | 3.4 |
State | Stand | Area (ha) | Year Planted | Age at Treatment (Years) | Tree Density after Thinning (Trees/ha) | Elevation Range above Sea Level (m) |
---|---|---|---|---|---|---|
AL | ALNorth1 | 49.1 | 2001 | 19 | 733 | 127.1–168.5 |
ALSouth1 | 20.4 | 2002 | 18 | 941 | 118.8–146.0 | |
NC | NCNorth1 | 16.7 | 2003 | 18 | 443 | 58.8–88.9 |
NCNorth2 | 56.5 | 2005 | 16 | 403 | 55.7–88.9 | |
NCSouth1 | 51.5 | 1994 | 27 | 298 | 52.0–78.9 | |
NCSouth2 | 13.0 | 2004 | 17 | 401 | 60.4–79.5 | |
TX | TXSouth1 | 52.3 | 2004 | 17 | 577 | 49.3–57.4 |
TXSouth2 | 35.8 | 1999 | 22 | 411 | 42.0–49.2 |
Site | Stand | Soil Type (FPC SPOT Codes) |
---|---|---|
AL | ALNorth1 | B4SyxfSrPD and C2WextScPD |
ALSouth1 | B1WeaoBgPD, B4SyxfBgPD, C2WextBgPD, and C2WextScPD | |
NC | NCNorth1 | B2WekoAuPD and E2WykoAuPD |
NCNorth2 | B2WekoAuPD | |
NCSouth1 | B2WexvAuPD, B3WekoAuPD, E2WykoAmPD, and E2WyxvAuPD | |
NCSouth2 | B2WexvAuPD, B3WekoAuPD, and E2WyxvAuPD | |
TX | TXSouth1 | B3WeioLbGF, C3MeioLbGF, C3PyaoLbGF, and C3WeioLbGF |
TXSouth2 | C2MeioLbGF, C3MeioLbGF, C3PyaoLbGF, C4WoioLbGF, C6MyioLbGF, and C6PyanLbGF |
Location | Dates of Collection | Sensor | Pulse Density | Flight Line Overlap |
---|---|---|---|---|
AL | January 2020 | Riegl 1560ii | 26 pulses/m2 | 60% |
NC | July 2021 | Riegl MiniVux1 | 20 pulses/m2 | 80% |
TX | January–March 2021 | Riegl VQ-480 II | 289 to 493 pulses/m2 | 55% |
State | Stand | Soil | Flat | Mound | Valley | Sub Total |
---|---|---|---|---|---|---|
AL | ALNorth1 | B4SyxfSrPD | −9% | −11% | ||
C2WextScPD | 1% | −3% | 3% | 0% | ||
Sub Total | 3% | −3% | 3% | 20.2 m | ||
ALSouth1 | B1WeaoBgPD | 0% | −3% | 3% | 0% | |
B4SyxfBgPD | 4% | −12% | −2% | 3% | ||
C2WextBgPD | −7% | −7% | −5% | |||
C2WextScPD | −1% | −8% | 2% | 0% | ||
Sub Total | 0% | −3% | 3% | 19.6 m |
State | Stand | Soil | Flat | Mound | Valley | Sub Total |
---|---|---|---|---|---|---|
NC | NCNorth1 | B2WekoAuPD | −1% | 0% | 1% | −1% |
E2WykoAuPD | 8% | 16% | 0% | 13% | ||
Sub Total | −1% | −1% | 2% | 19.7 m | ||
NCNorth2 | B2WekoAuPD | 0% | −2% | 3% | 0% | |
Sub Total | 0% | −2% | 3% | 19.8 m | ||
NCSouth1 | B2WexvAuPD | 1% | −5% | −3% | 0% | |
B3WekoAuPD | 0% | −3% | 3% | 0% | ||
E2WykoAmPD | 3% | 2% | 5% | 3% | ||
E2WyxvAuPD | −7% | −7% | −7% | −7% | ||
Sub Total | 0% | −3% | 3% | 23.8 m | ||
NCSouth2 | B2WexvAuPD | 1% | −2% | 18% | 11% | |
B3WekoAuPD | −2% | −5% | 0% | −3% | ||
E2WyxvAuPD | −1% | −4% | 6% | −1% | ||
Sub Total | −1% | −4% | 10% | 19.1 m |
State | Stand | Soil | Flat | Mound | Valley | Sub Total |
---|---|---|---|---|---|---|
TX | TXSouth1 | B3WeioLbGF | −1% | 1% | −3% | −2% |
C3MeioLbGF | −6% | −2% | 0% | −3% | ||
C3PyaoLbGF | −1% | 0% | 1% | −1% | ||
C3WeioLbGF | 0% | 0% | −1% | 0% | ||
Sub Total | 0% | 0% | 0% | 19.3 m | ||
TXSouth2 | C2MeioLbGF | −2% | −2% | 2% | −1% | |
C3MeioLbGF | 4% | 3% | 2% | 3% | ||
C3PyaoLbGF | #NAME? | 0% | 0% | 2% | ||
C4WoioLbGF | −4% | −5% | 0% | −4% | ||
C6MyioLbGF | −1% | 0% | 2% | 0% | ||
C6PyanLbGF | 0% | −1% | 0% | 0% | ||
Sub Total | 0% | 0% | 2% | 24.4 m |
Stand Scale | Soils | Microtopography | Soils and Microtopography | Plot Simulation |
---|---|---|---|---|
Full Stand | 0.0225 | 0.0547 | 0.0078 | 0.2500 |
Soils | 0.6406 | 0.0078 | 0.3828 | |
Microtopography | 0.0225 | 0.3125 | ||
Soils and Microtopography | 0.6406 |
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Raigosa-García, I.; Rathbun, L.C.; Cook, R.L.; Baker, J.S.; Corrao, M.V.; Sumnall, M.J. Rethinking Productivity Evaluation in Precision Forestry through Dominant Height and Site Index Measurements Using Aerial Laser Scanning LiDAR Data. Forests 2024, 15, 1002. https://doi.org/10.3390/f15061002
Raigosa-García I, Rathbun LC, Cook RL, Baker JS, Corrao MV, Sumnall MJ. Rethinking Productivity Evaluation in Precision Forestry through Dominant Height and Site Index Measurements Using Aerial Laser Scanning LiDAR Data. Forests. 2024; 15(6):1002. https://doi.org/10.3390/f15061002
Chicago/Turabian StyleRaigosa-García, Iván, Leah C. Rathbun, Rachel L. Cook, Justin S. Baker, Mark V. Corrao, and Matthew J. Sumnall. 2024. "Rethinking Productivity Evaluation in Precision Forestry through Dominant Height and Site Index Measurements Using Aerial Laser Scanning LiDAR Data" Forests 15, no. 6: 1002. https://doi.org/10.3390/f15061002