Extrapolation of canopy height and cover metrics of GEDI LiDAR in tropical montane forest ecosystem

Filtering approaches on Global Ecosystem Dynamics Investigation (GEDI) data differ considerably across existing studies and it is yet unclear which method is the most effective. We conducted an in‐depth analysis of GEDI's vertical accuracy in mapping terrain and canopy heights across three study sites in temperate forests and grasslands in Spain, California, and New Zealand. We started with unfiltered data (2,081,108 footprints) and describe a workflow for data filtering using Level 2A parameters and for geolocation error mitigation. We found that retaining observations with at least one detected mode eliminates noise more effectively than sensitivity. The accuracy of terrain and canopy height observations depended considerably on the number of modes, beam sensitivity, landcover, and terrain slope. In dense forests, a minimum sensitivity of 0.9 was required, while in areas with sparse vegetation, sensitivity of 0.5 sufficed. Sensitivity greater than 0.9 resulted in an overestimation of canopy height in grasslands, especially on steep slopes, where high sensitivity led to the detection of multiple modes. We suggest excluding observations with more than five modes in grasslands. We found that the most effective strategy for filtering low‐quality observations was to combine the quality flag and difference from TanDEM‐X, striking an optimal balance between eliminating poor‐quality data and preserving a maximum number of high‐quality observations. Positional shifts improved the accuracy of GEDI terrain estimates but not of vegetation height estimates. Our findings guide users to an easy way of processing of GEDI footprints, enabling the use of the most accurate data and leading to more reliable applications.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Extrapolation of canopy height and cover metrics of GEDI LiDAR in tropical montane forest ecosystem

Cited by 2 publications

References 54 publications

Machine learning for modeling forest canopy height and cover from multi-sensor data in Northwestern Ethiopia

Machine learning for modeling forest canopy height and cover from multi-sensor data in Northwestern Ethiopia

How to Find Accurate Terrain and Canopy Height GEDI Footprints in Temperate Forests and Grasslands?

Contact Info

Product

Resources

About