Identifying Old-Growth Forests in Complex Landscapes: A New LiDAR-Based Estimation Framework and Conservation Implications
Old-growth forests provide many ecosystem services and benefits. However, they are becoming increasingly rare and thus are an urgent priority for conservation. Accurately mapping old-growth forests is a critical step in this process. Here, we used LiDAR, an improved individual tree crown delineation...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/1/147 |
| _version_ | 1797358197723365376 |
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| author | Raphaël Trouvé Ruizhu Jiang Patrick J. Baker Sabine Kasel Craig R. Nitschke |
| author_facet | Raphaël Trouvé Ruizhu Jiang Patrick J. Baker Sabine Kasel Craig R. Nitschke |
| author_sort | Raphaël Trouvé |
| collection | DOAJ |
| description | Old-growth forests provide many ecosystem services and benefits. However, they are becoming increasingly rare and thus are an urgent priority for conservation. Accurately mapping old-growth forests is a critical step in this process. Here, we used LiDAR, an improved individual tree crown delineation algorithm for broadleaved forests, Gaussian mixture modelling, and a rule-based classification key to map the extent and location of old-growth forests across a topographically and ecologically complex landscape of 337,548 ha in southeastern Australia. We found that variation in old growth extent was largely driven by the old growth definition, which is a human construct, rather than by uncertainty in the technical aspect of the work. Current regulations define a stand as old growth if it was recruited prior to 1900 (i.e., >120 years old) and is undisturbed (i.e., <10% regrowth canopy cover and no visible disturbance traces). Only 2.7% (95% confidence intervals ranging from 1.4 to 4.9%) of the forests in the study landscape met these criteria. However, this definition is overly restrictive as it leaves many multi-aged stands with ecologically mature elements (e.g., one or more legacy trees amid regrowth) unprotected. Removing the regrowth filter, an indicator of past disturbances, increased the proportion of old-growth forests from 2.7% to 15% of the landscape. Our analyses also revealed that 60% of giant trees (>250 cm in diameter at breast height) were located within 50 m of cool temperate rainforests and cool temperate mixed forests (i.e., streamlines). We discuss the implication of our findings for the conservation and management of high-conservation-value forests in the region. |
| first_indexed | 2024-03-08T14:58:25Z |
| format | Article |
| id | doaj.art-6a3de61f32464ec7a2b3d90f1f44acc8 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-08T14:58:25Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-6a3de61f32464ec7a2b3d90f1f44acc82024-01-10T15:07:39ZengMDPI AGRemote Sensing2072-42922023-12-0116114710.3390/rs16010147Identifying Old-Growth Forests in Complex Landscapes: A New LiDAR-Based Estimation Framework and Conservation ImplicationsRaphaël Trouvé0Ruizhu Jiang1Patrick J. Baker2Sabine Kasel3Craig R. Nitschke4School of Agriculture, Food and Ecosystem Sciences, The University of Melbourne, Melbourne, VIC 3010, AustraliaSchool of Agriculture, Food and Ecosystem Sciences, The University of Melbourne, Melbourne, VIC 3010, AustraliaSchool of Agriculture, Food and Ecosystem Sciences, The University of Melbourne, Melbourne, VIC 3010, AustraliaSchool of Agriculture, Food and Ecosystem Sciences, The University of Melbourne, Melbourne, VIC 3010, AustraliaSchool of Agriculture, Food and Ecosystem Sciences, The University of Melbourne, Melbourne, VIC 3010, AustraliaOld-growth forests provide many ecosystem services and benefits. However, they are becoming increasingly rare and thus are an urgent priority for conservation. Accurately mapping old-growth forests is a critical step in this process. Here, we used LiDAR, an improved individual tree crown delineation algorithm for broadleaved forests, Gaussian mixture modelling, and a rule-based classification key to map the extent and location of old-growth forests across a topographically and ecologically complex landscape of 337,548 ha in southeastern Australia. We found that variation in old growth extent was largely driven by the old growth definition, which is a human construct, rather than by uncertainty in the technical aspect of the work. Current regulations define a stand as old growth if it was recruited prior to 1900 (i.e., >120 years old) and is undisturbed (i.e., <10% regrowth canopy cover and no visible disturbance traces). Only 2.7% (95% confidence intervals ranging from 1.4 to 4.9%) of the forests in the study landscape met these criteria. However, this definition is overly restrictive as it leaves many multi-aged stands with ecologically mature elements (e.g., one or more legacy trees amid regrowth) unprotected. Removing the regrowth filter, an indicator of past disturbances, increased the proportion of old-growth forests from 2.7% to 15% of the landscape. Our analyses also revealed that 60% of giant trees (>250 cm in diameter at breast height) were located within 50 m of cool temperate rainforests and cool temperate mixed forests (i.e., streamlines). We discuss the implication of our findings for the conservation and management of high-conservation-value forests in the region.https://www.mdpi.com/2072-4292/16/1/147airborne laser scanningindividual tree detectionfinite mixture modelsmulti-cohort forestecologically mature forestold growth |
| spellingShingle | Raphaël Trouvé Ruizhu Jiang Patrick J. Baker Sabine Kasel Craig R. Nitschke Identifying Old-Growth Forests in Complex Landscapes: A New LiDAR-Based Estimation Framework and Conservation Implications Remote Sensing airborne laser scanning individual tree detection finite mixture models multi-cohort forest ecologically mature forest old growth |
| title | Identifying Old-Growth Forests in Complex Landscapes: A New LiDAR-Based Estimation Framework and Conservation Implications |
| title_full | Identifying Old-Growth Forests in Complex Landscapes: A New LiDAR-Based Estimation Framework and Conservation Implications |
| title_fullStr | Identifying Old-Growth Forests in Complex Landscapes: A New LiDAR-Based Estimation Framework and Conservation Implications |
| title_full_unstemmed | Identifying Old-Growth Forests in Complex Landscapes: A New LiDAR-Based Estimation Framework and Conservation Implications |
| title_short | Identifying Old-Growth Forests in Complex Landscapes: A New LiDAR-Based Estimation Framework and Conservation Implications |
| title_sort | identifying old growth forests in complex landscapes a new lidar based estimation framework and conservation implications |
| topic | airborne laser scanning individual tree detection finite mixture models multi-cohort forest ecologically mature forest old growth |
| url | https://www.mdpi.com/2072-4292/16/1/147 |
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