Multi-sensor modelling of woody vegetation and canopy cover across natural and modified ecosystems
Remote sensing is an essential tool for monitoring the extent and biophysical attributes of vegetation. Multi-sensor approaches, that can reduce the costs of developing high-quality datasets and improve predictive performance, are increasingly common. Despite this trend, the advantages of these data...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-07-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843225002821 |
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| author | Stephen B. Stewart Melissa Fedrigo Shaun R. Levick Anthony P. O’Grady Daniel S. Mendham |
| author_facet | Stephen B. Stewart Melissa Fedrigo Shaun R. Levick Anthony P. O’Grady Daniel S. Mendham |
| author_sort | Stephen B. Stewart |
| collection | DOAJ |
| description | Remote sensing is an essential tool for monitoring the extent and biophysical attributes of vegetation. Multi-sensor approaches, that can reduce the costs of developing high-quality datasets and improve predictive performance, are increasingly common. Despite this trend, the advantages of these data-fusion techniques are rarely reported beyond statistical performance. We use airborne lidar-derived metrics to develop models of canopy cover (CC, %) and woody vegetation (WV, presence/absence) using dry-season imagery from the Sentinel-1 (S1 C-band, 5.5 cm wavelength, Synthetic Aperture Radar) and Sentinel-2 (S2, multispectral optical) satellite constellations across natural and modified agricultural ecosystems in Tasmania, southeast Australia. Validation statistics at 18,876 sample locations demonstrated strong performance for both CC (R2 = 0.83, RMSE = 0.13) and WV (OA = 0.94, Kappa = 0.87) when using both S1 and S2 variables for prediction. The small improvement in statistical performance provided by SAR variables (typically 1–2 % for CC and WV) understated the benefits of S1 for discriminating woody vegetation and quantifying canopy cover in non-woody ecosystems (e.g., alpine vegetation, heathlands, wetlands, coastal scrub), demonstrating the complementary benefits of multi-sensor prediction. The emergence and growth of natural capital accounting and frameworks such as the Nature Positive Initiative, mean that high-quality, cost-effective spatial datasets will continue to be in demand. Our study demonstrates the potential of non-commercial, publicly accessible remote sensing imagery to improve fine-scale analyses that may otherwise be cost-prohibitive to apply at scale. |
| format | Article |
| id | doaj-art-c56db114065c4fd69862dd9d1b06bce8 |
| institution | Kabale University |
| issn | 1569-8432 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj-art-c56db114065c4fd69862dd9d1b06bce82025-08-20T03:30:43ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322025-07-0114110463510.1016/j.jag.2025.104635Multi-sensor modelling of woody vegetation and canopy cover across natural and modified ecosystemsStephen B. Stewart0Melissa Fedrigo1Shaun R. Levick2Anthony P. O’Grady3Daniel S. Mendham4CSIRO Environment, Sandy Bay, TAS 7005, Australia; Corresponding author.GeoRubix Solutions, Hobart, TAS 7050, AustraliaCSIRO Environment, Urrbrae, SA 5064, AustraliaCSIRO Environment, Sandy Bay, TAS 7005, AustraliaCSIRO Environment, Black Mountain, ACT 2601, AustraliaRemote sensing is an essential tool for monitoring the extent and biophysical attributes of vegetation. Multi-sensor approaches, that can reduce the costs of developing high-quality datasets and improve predictive performance, are increasingly common. Despite this trend, the advantages of these data-fusion techniques are rarely reported beyond statistical performance. We use airborne lidar-derived metrics to develop models of canopy cover (CC, %) and woody vegetation (WV, presence/absence) using dry-season imagery from the Sentinel-1 (S1 C-band, 5.5 cm wavelength, Synthetic Aperture Radar) and Sentinel-2 (S2, multispectral optical) satellite constellations across natural and modified agricultural ecosystems in Tasmania, southeast Australia. Validation statistics at 18,876 sample locations demonstrated strong performance for both CC (R2 = 0.83, RMSE = 0.13) and WV (OA = 0.94, Kappa = 0.87) when using both S1 and S2 variables for prediction. The small improvement in statistical performance provided by SAR variables (typically 1–2 % for CC and WV) understated the benefits of S1 for discriminating woody vegetation and quantifying canopy cover in non-woody ecosystems (e.g., alpine vegetation, heathlands, wetlands, coastal scrub), demonstrating the complementary benefits of multi-sensor prediction. The emergence and growth of natural capital accounting and frameworks such as the Nature Positive Initiative, mean that high-quality, cost-effective spatial datasets will continue to be in demand. Our study demonstrates the potential of non-commercial, publicly accessible remote sensing imagery to improve fine-scale analyses that may otherwise be cost-prohibitive to apply at scale.http://www.sciencedirect.com/science/article/pii/S1569843225002821Woody vegetationCanopy coverMulti-sensorSARLidarSentinel |
| spellingShingle | Stephen B. Stewart Melissa Fedrigo Shaun R. Levick Anthony P. O’Grady Daniel S. Mendham Multi-sensor modelling of woody vegetation and canopy cover across natural and modified ecosystems International Journal of Applied Earth Observations and Geoinformation Woody vegetation Canopy cover Multi-sensor SAR Lidar Sentinel |
| title | Multi-sensor modelling of woody vegetation and canopy cover across natural and modified ecosystems |
| title_full | Multi-sensor modelling of woody vegetation and canopy cover across natural and modified ecosystems |
| title_fullStr | Multi-sensor modelling of woody vegetation and canopy cover across natural and modified ecosystems |
| title_full_unstemmed | Multi-sensor modelling of woody vegetation and canopy cover across natural and modified ecosystems |
| title_short | Multi-sensor modelling of woody vegetation and canopy cover across natural and modified ecosystems |
| title_sort | multi sensor modelling of woody vegetation and canopy cover across natural and modified ecosystems |
| topic | Woody vegetation Canopy cover Multi-sensor SAR Lidar Sentinel |
| url | http://www.sciencedirect.com/science/article/pii/S1569843225002821 |
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