Author:
Radke David,Radke Daniel,Radke John
Abstract
Measuring and monitoring the height of vegetation provides important insights into forest age and habitat quality. These are essential for the accuracy of applications that are highly reliant on up-to-date and accurate vegetation data. Current vegetation sensing practices involve ground survey, photogrammetry, synthetic aperture radar (SAR), and airborne light detection and ranging sensors (LiDAR). While these methods provide high resolution and accuracy, their hardware and collection effort prohibits highly recurrent and widespread collection. In response to the limitations of current methods, we designed Y-NET, a novel deep learning model to generate high resolution models of vegetation from highly recurrent multispectral aerial imagery and elevation data. Y-NET’s architecture uses convolutional layers to learn correlations between different input features and vegetation height, generating an accurate vegetation surface model (VSM) at 1×1 m resolution. We evaluated Y-NET on 235 km2 of the East San Francisco Bay Area and find that Y-NET achieves low error from LiDAR when tested on new locations. Y-NET also achieves an R2 of 0.83 and can effectively model complex vegetation through side-by-side visual comparisons. Furthermore, we show that Y-NET is able to identify instances of vegetation growth and mitigation by comparing aerial imagery and LiDAR collected at different times.
Subject
General Earth and Planetary Sciences
Cited by
5 articles.
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1. Forest Height Estimation Using Sentinel-1/2 and ALOS-2;2023 8th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR);2023-10-23
2. Multispectral vs Synthetic Aperture Radar Data for Canopy Height Estimation;IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium;2023-07-16
3. Using Sentinel-2 and Stacking Regressors for Forest Height Estimation;IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium;2023-07-16
4. Forest Height Estimation Using Multi-Frequency Sar and a Stacking Regression;IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium;2023-07-16
5. MASANet: Multi-Angle Self-Attention Network for Semantic Segmentation of Remote Sensing Images;Tehnicki vjesnik - Technical Gazette;2022-10-15