Estimation of fine-scale vegetation distribution information from RPAS-generated imagery and structure to aid restoration monitoring
Detailed maps of vegetation composition are vital for restoration planning, implementation, and monitoring, particularly at early stages of succession. This is usually accomplished through ground surveys, which can be costly and impractical depending on extent and accessibility, or conducted at too...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-06-01
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| Series: | Science of Remote Sensing |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666017223000391 |
| _version_ | 1827228898795978752 |
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| author | Rik J.G. Nuijten Nicholas C. Coops Dustin Theberge Cindy E. Prescott |
| author_facet | Rik J.G. Nuijten Nicholas C. Coops Dustin Theberge Cindy E. Prescott |
| author_sort | Rik J.G. Nuijten |
| collection | DOAJ |
| description | Detailed maps of vegetation composition are vital for restoration planning, implementation, and monitoring, particularly at early stages of succession. This is usually accomplished through ground surveys, which can be costly and impractical depending on extent and accessibility, or conducted at too broad a spatial scale. In this study, we propose a methodology for mapping regenerating vegetation composition at 2 × 2 m2 spatial resolution, using very high spatial resolution (<1 m) remote sensing imagery obtained from remotely piloted aerial systems (RPAS) in conjunction with digital aerial photogrammetry (DAP) techniques for reconstructing vegetation structure. We applied logistic regression on multispectral orthomosaics, clusters of vegetation structure, and local illumination estimates to develop presence-absence models for eight key plant groups at various taxonomic levels as well as six plant functional types (conifer tree seedlings, grasses, tall- and low-growing forbs, shrubs, and mosses). Our results show higher accuracies for plant functional types (mean F-score = 0.67) compared to lower taxonomic levels (0.57). Notably, shrubs (F-score = 0.79), low-growing forbs (0.70), and mosses (0.69) exhibited the highest accuracies, while grasses (0.46), the aster family (Asteraceae spp; 0.48), and spruce seedlings (Picea spp; 0.54) demonstrated lower accuracies. Vegetation structure variables were identified as the most influential in the models, with mean NIRv ranking highest among spectral variables. High average ranks of spectral variation metrics (e.g., standard deviation of NIRv) implied the influence of environmental determinants such as plant co-occurrences and micro-habitat conditions, which drive spectral variation. Discrete composition maps were produced for three restoration sites and analogous wildfire-disturbed sites. Plant compositions found at one site pair exhibited similarity (Bray-Curtis = 0.28), however, certain key plant groups covered larger extents of the restoration site than anticipated. Willows (Salix spp; 25.4% vs. 9.3%), which are typically planted for soil stabilization and obstruction, and clovers (Trifolium spp; 11.1% vs. 3.6%), which represent non-native agronomic vegetation, were prominent. The developed methodology facilitates the generation of detailed plant composition maps, aiding evaluations of vegetation patterns that are difficult to discern visually or through conventional field sampling. This approach can effectively help assess restoration goals and guide adaptive management strategies, especially when incorporating the expertise of restoration ecologists in understanding how different vegetation types affect habitat quality. |
| first_indexed | 2024-03-08T16:50:49Z |
| format | Article |
| id | doaj.art-2a8d87f3977d4eb5b75a6cfceed17771 |
| institution | Directory Open Access Journal |
| issn | 2666-0172 |
| language | English |
| last_indexed | 2025-03-21T18:21:10Z |
| publishDate | 2024-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Science of Remote Sensing |
| spelling | doaj.art-2a8d87f3977d4eb5b75a6cfceed177712024-06-09T05:29:18ZengElsevierScience of Remote Sensing2666-01722024-06-019100114Estimation of fine-scale vegetation distribution information from RPAS-generated imagery and structure to aid restoration monitoringRik J.G. Nuijten0Nicholas C. Coops1Dustin Theberge2Cindy E. Prescott3Faculty of Forestry, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada; Corresponding author.Faculty of Forestry, University of British Columbia, Vancouver, BC, V6T 1Z4, CanadaTC Energy, Calgary, AB, T2P 5H1, CanadaFaculty of Forestry, University of British Columbia, Vancouver, BC, V6T 1Z4, CanadaDetailed maps of vegetation composition are vital for restoration planning, implementation, and monitoring, particularly at early stages of succession. This is usually accomplished through ground surveys, which can be costly and impractical depending on extent and accessibility, or conducted at too broad a spatial scale. In this study, we propose a methodology for mapping regenerating vegetation composition at 2 × 2 m2 spatial resolution, using very high spatial resolution (<1 m) remote sensing imagery obtained from remotely piloted aerial systems (RPAS) in conjunction with digital aerial photogrammetry (DAP) techniques for reconstructing vegetation structure. We applied logistic regression on multispectral orthomosaics, clusters of vegetation structure, and local illumination estimates to develop presence-absence models for eight key plant groups at various taxonomic levels as well as six plant functional types (conifer tree seedlings, grasses, tall- and low-growing forbs, shrubs, and mosses). Our results show higher accuracies for plant functional types (mean F-score = 0.67) compared to lower taxonomic levels (0.57). Notably, shrubs (F-score = 0.79), low-growing forbs (0.70), and mosses (0.69) exhibited the highest accuracies, while grasses (0.46), the aster family (Asteraceae spp; 0.48), and spruce seedlings (Picea spp; 0.54) demonstrated lower accuracies. Vegetation structure variables were identified as the most influential in the models, with mean NIRv ranking highest among spectral variables. High average ranks of spectral variation metrics (e.g., standard deviation of NIRv) implied the influence of environmental determinants such as plant co-occurrences and micro-habitat conditions, which drive spectral variation. Discrete composition maps were produced for three restoration sites and analogous wildfire-disturbed sites. Plant compositions found at one site pair exhibited similarity (Bray-Curtis = 0.28), however, certain key plant groups covered larger extents of the restoration site than anticipated. Willows (Salix spp; 25.4% vs. 9.3%), which are typically planted for soil stabilization and obstruction, and clovers (Trifolium spp; 11.1% vs. 3.6%), which represent non-native agronomic vegetation, were prominent. The developed methodology facilitates the generation of detailed plant composition maps, aiding evaluations of vegetation patterns that are difficult to discern visually or through conventional field sampling. This approach can effectively help assess restoration goals and guide adaptive management strategies, especially when incorporating the expertise of restoration ecologists in understanding how different vegetation types affect habitat quality.http://www.sciencedirect.com/science/article/pii/S2666017223000391Forest regenerationEcological restorationPlant composition mapsBoreal forestsLogistic regressionRemotely piloted aircraft systems (RPAS) |
| spellingShingle | Rik J.G. Nuijten Nicholas C. Coops Dustin Theberge Cindy E. Prescott Estimation of fine-scale vegetation distribution information from RPAS-generated imagery and structure to aid restoration monitoring Science of Remote Sensing Forest regeneration Ecological restoration Plant composition maps Boreal forests Logistic regression Remotely piloted aircraft systems (RPAS) |
| title | Estimation of fine-scale vegetation distribution information from RPAS-generated imagery and structure to aid restoration monitoring |
| title_full | Estimation of fine-scale vegetation distribution information from RPAS-generated imagery and structure to aid restoration monitoring |
| title_fullStr | Estimation of fine-scale vegetation distribution information from RPAS-generated imagery and structure to aid restoration monitoring |
| title_full_unstemmed | Estimation of fine-scale vegetation distribution information from RPAS-generated imagery and structure to aid restoration monitoring |
| title_short | Estimation of fine-scale vegetation distribution information from RPAS-generated imagery and structure to aid restoration monitoring |
| title_sort | estimation of fine scale vegetation distribution information from rpas generated imagery and structure to aid restoration monitoring |
| topic | Forest regeneration Ecological restoration Plant composition maps Boreal forests Logistic regression Remotely piloted aircraft systems (RPAS) |
| url | http://www.sciencedirect.com/science/article/pii/S2666017223000391 |
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