High-Resolution Canopy Height Model Generation and Validation Using USGS 3DEP LiDAR Data in Indiana, USA
Abstract
:1. Introduction
2. Materials and Methods
2.1. Forest Inventory Data
2.2. LiDAR and Digital Terrain Model (DTM) Data
2.3. Canopy Height Model (CHM) Generation
2.4. Accuracy Assessment of LiDAR-Based Height Measurement
3. Results
3.1. Canopy Height Model (CHM)
3.2. Correlation between Inventory and LiDAR Height Metrics
3.3. An Accuracy Assessment of CHM-Based Height
3.3.1. Effect of LiDAR Point Density on Height Accuracy
3.3.2. Effect of Tree Height on Measurement Accuracy
3.3.3. Effect of Data Acquisition Timing on Measurement Accuracy
3.3.4. Height Accuracy When Accurate Location Is Provided
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Acknowledgments
Conflicts of Interest
Appendix A
Requirements | Range |
---|---|
Aggregate nominal pulse spacing (m) | ≦0.71 |
Aggregate nominal pulse density (pulses/m2) | ≧2.0 |
Smooth surface repeatability, RMSD * (m) | ≦0.06 |
Swath overlap difference, RMSD (m) | ≦0.08 |
RMSE (non-vegetated, m) | ≦0.1 |
Non-vegetated vertical accuracy at 95% confidence level (m) | ≦0.196 |
Vegetated vertical accuracy at the 95% confidence level (m) | ≦0.30 |
References
- Leites, L.P.; Robinson, A.P.; Crookston, N.L. Accuracy and Equivalence Testing of Crown Ratio Models and Assessment of Their Impact on Diameter Growth and Basal Area Increment Predictions of Two Variants of the Forest Vegetation Simulator. Can. J. For. Res. 2009, 39, 655–665. [Google Scholar] [CrossRef]
- Brando, P. Tree Height Matters. Nat. Geosci. 2018, 11, 390–391. [Google Scholar] [CrossRef]
- Stereńczak, K.; Mielcarek, M.; Wertz, B.; Bronisz, K.; Zajączkowski, G.; Jagodziński, A.M.; Ochał, W.; Skorupski, M. Factors Influencing the Accuracy of Ground-Based Tree-Height Measurements for Major European Tree Species. J. Environ. Manag. 2019, 231, 1284–1292. [Google Scholar] [CrossRef] [PubMed]
- Bouvier, M.; Durrieu, S.; Fournier, R.A.; Renaud, J.P. Generalizing Predictive Models of Forest Inventory Attributes Using an Area-Based Approach with Airborne LiDAR Data. Remote Sens. Environ. 2015, 156, 322–334. [Google Scholar] [CrossRef]
- Gray, A.; Brandeis, T.; Shaw, J.; McWilliams, W.; Miles, P. Forest Inventory and Analysis Database of the United States of America (FIA). Biodivers. Ecol. 2012, 4, 225–231. [Google Scholar] [CrossRef] [Green Version]
- Wallace, L.; Lucieer, A.; Watson, C.; Turner, D. Development of a UAV-LiDAR System with Application to Forest Inventory. Remote Sens. 2012, 4, 1519–1543. [Google Scholar] [CrossRef] [Green Version]
- Larue, E.A.; Hardiman, B.S.; Elliott, J.M.; Fei, S. Structural Diversity as a Predictor of Ecosystem Function. Environ. Res. Lett. 2019, 14, 114011. [Google Scholar] [CrossRef]
- Shao, G.; Shao, G.; Gallion, J.; Saunders, M.R.; Frankenberger, J.R.; Fei, S. Improving Lidar-Based Aboveground Biomass Estimation of Temperate Hardwood Forests with Varying Site Productivity. Remote Sens. Environ. 2018, 204, 872–882. [Google Scholar] [CrossRef]
- Alexander, C.; Korstjens, A.H.; Hill, R.A. Influence of Micro-Topography and Crown Characteristics on Tree Height Estimations in Tropical Forests Based on LiDAR Canopy Height Models. Int. J. Appl. Earth Obs. Geoinf. 2018, 65, 105–113. [Google Scholar] [CrossRef]
- Andersen, H.E.; Reutebuch, S.E.; McGaughey, R.J. A Rigorous Assessment of Tree Height Measurements Obtained Using Airborne Lidar and Conventional Field Methods. Can. J. Remote Sens. 2006, 32, 355–366. [Google Scholar] [CrossRef]
- Barnes, C.; Balzter, H.; Barrett, K.; Eddy, J.; Milner, S.; Suárez, J.C. Remote Sensing Individual Tree Crown Delineation from Airborne Laser Scanning for Diseased Larch Forest Stands. Remote Sens. 2017, 9, 231. [Google Scholar] [CrossRef] [Green Version]
- Bottalico, F.; Chirici, G.; Giannini, R.; Mele, S.; Mura, M.; Puxeddu, M.; Mcroberts, R.E.; Valbuena, R.; Travaglini, D. Modeling Mediterranean Forest Structure Using Airborne Laser Scanning Data. Int. J. Appl. Earth Obs. Geoinf. 2017, 57, 145–153. [Google Scholar] [CrossRef]
- González-Ferreiro, E.; Diéguez-Aranda, U.; Barreiro-Fernández, L.; Buján, S.; Barbosa, M.; Suárez, J.C.; Bye, I.J.; Miranda, D. A Mixed Pixel-and Region-Based Approach for Using Airborne Laser Scanning Data for Individual Tree Crown Delineation in Pinus Radiata D. Don Plantations. Int. J. Remote Sens. 2013, 34, 7671–7690. [Google Scholar] [CrossRef]
- Mohan, M.; Araujo, B.; de Mendonça, F.; Silva, C.A.; Klauberg, C.; Santos De Saboya Ribeiro, A.; Gomes De Araújo, E.J.; Monte, M.A.; Cardil, A. Optimizing Individual Tree Detection Accuracy and Measuring Forest Uniformity in Coconut (Cocos Nucifera L.) Plantations Using Airborne Laser Scanning. Ecol. Model. 2019, 409, 108736. [Google Scholar] [CrossRef]
- Mielcarek, M.; Stereńczak, K.; Khosravipour, A. Testing and Evaluating Different LiDAR-Derived Canopy Height Model Generation Methods for Tree Height Estimation. Int. J. Appl. Earth Obs. Geoinf. 2018, 71, 132–143. [Google Scholar] [CrossRef]
- Sibona, E.; Vitali, A.; Meloni, F.; Caffo, L.; Dotta, A.; Lingua, E.; Motta, R.; Garbarino, M. Direct Measurement of Tree Height Provides Different Results on the Assessment of LiDAR Accuracy. Forests 2017, 8, 7. [Google Scholar] [CrossRef]
- Gatziolis, D.; Fried, J.S.; Monleon, V.S. Challenges to Estimating Tree Height via LiDAR in Closed-Canopy Forests: A Parable from Western Oregon. For. Sci. 2010, 56, 139–155. [Google Scholar] [CrossRef]
- Kwak, D.A.; Lee, W.K.; Lee, J.H.; Biging, G.S.; Gong, P. Detection of Individual Trees and Estimation of Tree Height Using LiDAR Data. J. For. Res. 2007, 12, 425–434. [Google Scholar] [CrossRef]
- Kotivuori, E.; Korhonen, L.; Packalen, P. Nationwide Airborne Laser Scanning Based Models for Volume, Biomass and Dominant Height in Finland. Silva Fenn. 2016, 50, 1567. [Google Scholar] [CrossRef] [Green Version]
- Moudrý, V.; Urban, R.; Štroner, M.; Komárek, J.; Brouček, J.; Prošek, J. Comparison of a Commercial and Home-Assembled Fixed-Wing UAV for Terrain Mapping of a Post-Mining Site under Leaf-off Conditions. Int. J. Remote Sens. 2019, 40, 555–572. [Google Scholar] [CrossRef]
- Nilsson, M.; Nordkvist, K.; Jonzén, J.; Lindgren, N.; Axensten, P.; Wallerman, J.; Egberth, M.; Larsson, S.; Nilsson, L.; Eriksson, J.; et al. A Nationwide Forest Attribute Map of Sweden Predicted Using Airborne Laser Scanning Data and Field Data from the National Forest Inventory. Remote Sens. Environ. 2017, 194, 447–454. [Google Scholar] [CrossRef]
- Karl Heidemann, H. National Geospatial Program Lidar Base Specification Lidar Base Specification Techniques and Methods 11-B4; U.S. Geological Survey: Fairfax County, VA, USA, 2012.
- Hudak, A.T.; Fekety, P.A.; Kane, V.R.; Kennedy, R.E.; Filippelli, S.K.; Falkowski, M.J.; Tinkham, W.T.; Smith, A.M.S.; Crookston, N.L.; Domke, G.M.; et al. A Carbon Monitoring System for Mapping Regional, Annual Aboveground Biomass across the Northwestern USA. Environ. Res. Lett. 2020, 15, 095003. [Google Scholar] [CrossRef]
- Obata, S.; Cieszewski, C.J.; Lowe, R.C.; Bettinger, P. Random Forest Regression Model for Estimation of the Growing Stock Volumes in Georgia, Usa, Using Dense Landsat Time Series and Fia Dataset. Remote Sens. 2021, 13, 218. [Google Scholar] [CrossRef]
- Vogel, J.; Wu, Z.; Dye, D.; Stoker, J.; Velasco, M.; Middleton, B. Evaluating Lidar Point Densities for Effective Estimation of Aboveground Biomass. Int. J. Adv. Remote Sens. GIS 2016, 5, 1483–1499. [Google Scholar] [CrossRef] [Green Version]
- Parker, R.C.; Glass, P.A.; Londo, H.A.; Evans, D.L.; Belli, K.L.; Matney, T.G.; Schultz, E.B. Use of Computer and Spatial Technologies in Large Area Inventories; Forest and Wildlife Research Center, Mississippi State University: Starkville, MS, USA, 2007; pp. 3–9. [Google Scholar]
- Guidelines for Digital Elevation Data. Available online: https://giscenter.isu.edu/pdf/NDEPElevationGuidelinesVer1.pdf (accessed on 27 December 2021).
- IGIC Indiana’s New 3DEP LiDAR Data and Informational Resources. Available online: https://igic.memberclicks.net/indiana-s-new-3dep-lidar-data-and-informational-resources (accessed on 4 August 2021).
- Homer, C.; Dewitz, J.; Jin, S.; Xian, G.; Costello, C.; Danielson, P.; Gass, L.; Funk, M.; Wickham, J.; Stehman, S.; et al. Conterminous United States Land Cover Change Patterns 2001–2016 from the 2016 National Land Cover Database. ISPRS J. Photogramm. Remote Sens. 2020, 162, 184–199. [Google Scholar] [CrossRef]
- Jung, J.; Oh, S. Indiana Statewide Normalized Digital Height Model (2016–2019). Available online: Lidar.jinha.org (accessed on 27 December 2021).
- Jung, J.; Oh, S. LiDAR Data Hosted by IDiF. Available online: Lidar.digitalforestry.org (accessed on 27 December 2021).
- Grubinger, S.; Coops, N.C.; Stoehr, M.; El-Kassaby, Y.A.; Lucieer, A.; Turner, D. Modeling Realized Gains in Douglas-Fir (Pseudotsuga Menziesii) Using Laser Scanning Data from Unmanned Aircraft Systems (UAS). For. Ecol. Manag. 2020, 473, 118284. [Google Scholar] [CrossRef]
- Hyyppä, J.; Hyyppä, H.; Leckie, D.; Gougeon, F.; Yu, X.; Maltamo, M. Methods of Small-Footprint Airborne Laser Scanning for Extracting Forest Inventory Data in Boreal Forests. Int. J. Remote Sens. 2008, 29, 1339–1366. [Google Scholar] [CrossRef]
- Piedallu, C.; Gégout, J.C. Effects of Forest Environment and Survey Protocol on GPS Accuracy. Photogramm. Eng. Remote Sens. 2005, 71, 1071–1078. [Google Scholar] [CrossRef]
- Sigrist, P.; Coppin, P.; Hermy, M. Impact of Forest Canopy on Quality and Accuracy of GPS Measurements. Int. J. Remote Sens. 1999, 20, 3595–3610. [Google Scholar] [CrossRef]
- Swathi, N.; Dutt, V.B.S.S.I.; Rao, G.S. An Adaptive Filter Approach for GPS Multipath Error Estimation and Mitigation. In Lecture Notes in Electrical Engineering; Springer: New Delhi, India, 2016; Volume 372, pp. 539–546. [Google Scholar]
- Khosravipour, A.; Skidmore, A.K.; Isenburg, M.; Wang, T.; Hussin, Y.A. Generating Pit-Free Canopy Height Models from Airborne Lidar. Photogramm. Eng. Remote Sens. 2014, 80, 863–872. [Google Scholar] [CrossRef]
Clipping Radius (m) | Correlation of Tree Heights Obtained by 2011–2013 LiDAR and 2008–2012 Inventory Data | Correlation of Tree Heights Obtained by 2017–2020 LiDAR and 2013–2017 Inventory Data | ||
---|---|---|---|---|
Positional Difference of Plot Center (GPS) | Positional Difference of Plot Center (GPS) | |||
Less Than 0.1 m (n = 25) | Unspecified (n = 4845) | Less Than 0.1 m (n = 25) | Unspecified (n = 4845) | |
3.7 | 0.84 | 0.55 | 0.78 | 0.42 |
7.3 | 0.91 | 0.60 | 0.84 | 0.48 |
11.0 | 0.89 | 0.56 | 0.86 | 0.49 |
14.6 | 0.88 | 0.53 | 0.84 | 0.47 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Oh, S.; Jung, J.; Shao, G.; Shao, G.; Gallion, J.; Fei, S. High-Resolution Canopy Height Model Generation and Validation Using USGS 3DEP LiDAR Data in Indiana, USA. Remote Sens. 2022, 14, 935. https://doi.org/10.3390/rs14040935
Oh S, Jung J, Shao G, Shao G, Gallion J, Fei S. High-Resolution Canopy Height Model Generation and Validation Using USGS 3DEP LiDAR Data in Indiana, USA. Remote Sensing. 2022; 14(4):935. https://doi.org/10.3390/rs14040935
Chicago/Turabian StyleOh, Sungchan, Jinha Jung, Guofan Shao, Gang Shao, Joey Gallion, and Songlin Fei. 2022. "High-Resolution Canopy Height Model Generation and Validation Using USGS 3DEP LiDAR Data in Indiana, USA" Remote Sensing 14, no. 4: 935. https://doi.org/10.3390/rs14040935