Using Aerial LiDAR Data to Map Vegetation Structural Types in Arid and Semi-Arid Rangelands.
Saved in:
| Title: | Using Aerial LiDAR Data to Map Vegetation Structural Types in Arid and Semi-Arid Rangelands. |
|---|---|
| Authors: | Ruscalleda-Alvarez, Jaume1,2 (AUTHOR) jaume.ruscalledaalvarez@dbca.wa.gov.au, Page, Gerald F. M.2,3 (AUTHOR), Zdunic, Katherine2,3 (AUTHOR), Prober, Suzanne M.4 (AUTHOR) |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 10, p1641. 23p. |
| Subjects: | LIDAR, Vegetation classification, Arid regions, Range management, Fuzzy clustering technique, Remote sensing, Ecological assessment |
| Geographic Terms: | Western Australia |
| Abstract: | Highlights: What are the main findings? Eight distinct vegetation structural types across 370,000 ha of arid and semi-arid rangelands were identified and mapped using high-density aerial LiDAR. Unsupervised fuzzy c-means classification allowed for quantifying mapping confidence for each pixel. What are the implications of the main findings? We revealed previously unknown vegetation structural heterogeneity in the study area. The vegetation structural maps presented here are a baseline for rangeland ecological condition assessments and a key input for rangeland management and restoration planning. Rangelands occupy over half of the Earth's terrestrial surface and play an important role in supporting biodiversity and livelihoods. However, widespread degradation—particularly in arid and semi-arid regions—has compromised their ecological function. Traditional monitoring approaches that rely on vegetation cover metrics from optical satellite imagery fail to capture the three-dimensional structure of vegetation, which is critical for assessing ecosystem condition and guiding restoration and management efforts. This study demonstrates the application of high-density airborne LiDAR (ALS) data (~15–20 points/m2) to identify and map vegetation structural types across 370,000 hectares of semi-arid rangelands in Western Australia. Using an unsupervised fuzzy c-means clustering algorithm on seven minimally correlated ALS-derived structural metrics, we identified eight statistically distinct vegetation structural classes. The resulting structural map revealed spatial heterogeneity in vegetation structure, including in areas with similar vegetation cover, with high confidence in structural attribution in 74.5% of the study area. The rangeland-specific structural classes developed in this study, which incorporate measures of classification certainty, offer a robust framework for vegetation structural mapping in field data-scarce environments. This framework can support ecological condition assessments and provide a basis for rangeland management and restoration planning. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
Be the first to leave a comment!
