Data · dataset · 2024
Comparing the Accuracy of Polarimetric Synthetic Aperture Radar (PolSAR) Canopy Height Models for Forests in the Pacific Maritime and Montane Cordillera Ecozones
Listed in Borealis
Land management practices increasingly depend on detailed forest structure information, including canopy height, necessitating the adoption of advanced remote sensing methodologies.
Description
This research contributes to meeting this demand by leveraging polarimetric synthetic aperture radar (PolSAR) data, integrated with multispectral imagery, to model forest canopy height in the Pacific Maritime and the Montane Cordillera ecozones of British Columbia.
PolSAR technology employs microwave wavelengths of vertical and/or horizontal orientations to map the Earth’s surface. SAR systems stand out in their global coverage, cost-effectiveness, and are uniquely capable of providing reliable observations under any weather condition. A distinctive aspect of the research is the comparative analysis of PolSAR data behavior across different ecosystem-specific characteristics.
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The approach takes an exploratory analysis of polarimetric parameter extraction from PolSAR data— a method not commonly outlined in literature for canopy height predictions using C-band wavelengths. This exploratory technique was adopted due to the challenges presented by the reduced canopy penetration of C-band data. The results of the study indicate notable accuracy in canopy height predictions, achieving a coefficient of determination (R2) of 0.79 and a root mean square error (RMSE) of 3.57 meters for the Pacific Maritime ecozone study site.
For the Montane Cordillera ecozone study site, an R2 of 0.66 and an RMSE of 2.83 meters were achieved. These findings highlight the promise of integrating C-band PolSAR with multispectral data for developing accurate forest structure models. These results challenge the conventional reliance on light detecting and ranging (LiDAR) technology exclusively, enhancing accessibility and facilitating broader-scale applications.
Links
Where it is published
- Dataverse dataset page borealisdata.ca/dataset.xhtml?persistentId=doi%3A10.5683%2FSP3%2FSWAZYB ↗
landing page · from borealisdata ca
- DOI doi.org/10.5683/sp3/swazyb ↗
DOI / persistent id · from borealisdata ca
Catalogue records · 1
- Dataverse API borealisdata.ca/api/datasets/:persistentId/?persistentId=doi%3A10.5683%2FSP3%2… ↗
metadata API · from borealisdata ca
Topics
- Stated by source
- Earth and Environmental Sciences
Provenance · 1 source records, 12 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| Borealis | doi:10.5683/SP3/SWAZYB | 8 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| concepts[field].anzsrc:field:401304 | mapping · borealisdata ca | vocabulary-mapper@1.0.0 | keywords['Remote Sensing'] |
| concepts[field].anzsrc:group:4611 | mapping · borealisdata ca | vocabulary-mapper@1.0.0 | keywords['Machine Learning'] |
| concepts[field].dataverse_subject:earth-and-environmental-sciences | source · borealisdata ca | connector:borealisdata_ca@1.0.0 | /subjects |
| concepts[field].local:field:computer-science-ai | mapping · borealisdata ca | connector:borealisdata_ca@1.0.0 | /subjects |
| concepts[field].local:field:earth-environmental | mapping · borealisdata ca | connector:borealisdata_ca@1.0.0 | /subjects |
| concepts[modality].local:modality:remote-sensing | mapping · borealisdata ca | vocabulary-mapper@1.0.0 | keywords['Remote Sensing'] |
| created_date | source · borealisdata ca | connector:borealisdata_ca@1.0.0 | |
| description | source · borealisdata ca | connector:borealisdata_ca@1.0.0 | /description |
| publication_date | source · borealisdata ca | connector:borealisdata_ca@1.0.0 | |
| title | source · borealisdata ca | connector:borealisdata_ca@1.0.0 | /name |
| updated_date | source · borealisdata ca | connector:borealisdata_ca@1.0.0 | |
| version_label | source · borealisdata ca | connector:borealisdata_ca@1.0.0 |