Comparison of Single and Ensemble Regression Model Workflows for Estimating Basal Area by Tree Size Class in Pine Forests of Southeastern U.S
Quantifying basal area in terms of diameter classes is important for informing forest management decisions. It is commonly derived from stand diameter distributions using field measurements, LiDAR, and a distribution function. This study compares alternative methods for directly estimating basal are...
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MDPI AG
2025-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/2/253 |
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| author | Joseph St. Peter Jason Drake Paul Medley Eben Broadbent Gang Chen Victor Ibeanusi |
| author_facet | Joseph St. Peter Jason Drake Paul Medley Eben Broadbent Gang Chen Victor Ibeanusi |
| author_sort | Joseph St. Peter |
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| description | Quantifying basal area in terms of diameter classes is important for informing forest management decisions. It is commonly derived from stand diameter distributions using field measurements, LiDAR, and a distribution function. This study compares alternative methods for directly estimating basal area in three tree diameter classes that are relevant to timber operations and wildlife habitat planning in southern United States pine forests. Specifically, linear modeling, ensemble linear modeling (ELM) and ensemble general additive modeling (EGAM) were compared. The results showed that the EGAM method provided the highest r-squared values and the lowest RMSE, and the ELM method provided good interpretability and 30 times faster processing than the EGAM method. Both ensemble methods produced a spatially explicit standard error estimate output without additional steps, unlike the single linear model. In general, the estimation results of this study were comparable or improved over prior studies’ estimates of basal area by tree diameter class. |
| format | Article |
| id | doaj-art-ffeeace0674442c2aba122dd68340868 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj-art-ffeeace0674442c2aba122dd683408682025-01-24T13:47:53ZengMDPI AGRemote Sensing2072-42922025-01-0117225310.3390/rs17020253Comparison of Single and Ensemble Regression Model Workflows for Estimating Basal Area by Tree Size Class in Pine Forests of Southeastern U.SJoseph St. Peter0Jason Drake1Paul Medley2Eben Broadbent3Gang Chen4Victor Ibeanusi5W.A. Franke College of Forestry, University of Montana, Missoula, MT 59812, USAU.S. Forest Service, National Forests in Florida, Tallahassee, FL 32303, USAU.S. Forest Service, National Forests in Florida, Tallahassee, FL 32303, USASchool of Forest, Fisheries & Geomatics Sciences, University of Florida, Gainesville, FL 32611, USADepartment of Civil & Environmental Engineering, FAMU-FSU College of Engineering, Tallahassee, FL 32310, USASchool of the Environment, Florida Agricultural & Mechanical University, Tallahassee, FL 32307, USAQuantifying basal area in terms of diameter classes is important for informing forest management decisions. It is commonly derived from stand diameter distributions using field measurements, LiDAR, and a distribution function. This study compares alternative methods for directly estimating basal area in three tree diameter classes that are relevant to timber operations and wildlife habitat planning in southern United States pine forests. Specifically, linear modeling, ensemble linear modeling (ELM) and ensemble general additive modeling (EGAM) were compared. The results showed that the EGAM method provided the highest r-squared values and the lowest RMSE, and the ELM method provided good interpretability and 30 times faster processing than the EGAM method. Both ensemble methods produced a spatially explicit standard error estimate output without additional steps, unlike the single linear model. In general, the estimation results of this study were comparable or improved over prior studies’ estimates of basal area by tree diameter class.https://www.mdpi.com/2072-4292/17/2/253LiDARbasal areaensemble modelingsouthern pinediameter at breast heightdiameter class |
| spellingShingle | Joseph St. Peter Jason Drake Paul Medley Eben Broadbent Gang Chen Victor Ibeanusi Comparison of Single and Ensemble Regression Model Workflows for Estimating Basal Area by Tree Size Class in Pine Forests of Southeastern U.S Remote Sensing LiDAR basal area ensemble modeling southern pine diameter at breast height diameter class |
| title | Comparison of Single and Ensemble Regression Model Workflows for Estimating Basal Area by Tree Size Class in Pine Forests of Southeastern U.S |
| title_full | Comparison of Single and Ensemble Regression Model Workflows for Estimating Basal Area by Tree Size Class in Pine Forests of Southeastern U.S |
| title_fullStr | Comparison of Single and Ensemble Regression Model Workflows for Estimating Basal Area by Tree Size Class in Pine Forests of Southeastern U.S |
| title_full_unstemmed | Comparison of Single and Ensemble Regression Model Workflows for Estimating Basal Area by Tree Size Class in Pine Forests of Southeastern U.S |
| title_short | Comparison of Single and Ensemble Regression Model Workflows for Estimating Basal Area by Tree Size Class in Pine Forests of Southeastern U.S |
| title_sort | comparison of single and ensemble regression model workflows for estimating basal area by tree size class in pine forests of southeastern u s |
| topic | LiDAR basal area ensemble modeling southern pine diameter at breast height diameter class |
| url | https://www.mdpi.com/2072-4292/17/2/253 |
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