Image to Image Deep Learning for Enhanced Vegetation Height Modeling in Texas

Abstract

Vegetation canopy height mapping is vital for forest monitoring. However, the high cost and inefficiency of manual tree measurements, coupled with the irregular and limited local-scale acquisition of airborne LiDAR data, continue to impede its widespread application. The increasing availability of high spatial resolution imagery is creating opportunities to characterize forest attributes at finer resolutions over large regions. In this study, we investigate the synergy of airborne lidar and high spatial resolution USDA-NAIP imagery for detailed canopy height mapping using an image-to-image deep learning approach. Our main inputs were 1 m NAIP image patches which served as predictor layers and corresponding 1 m canopy height models derived from airborne lidar data, which served as output layers. We adapted a U-Net model architecture for canopy height regression, training and validating the models with 10,000 256-by-256 pixel image patches. We evaluated three settings for the U-Net encoder depth and used both 1 m and 2 m datasets to assess their impact on model performance. Canopy height predictions from the fitted models were highly correlated (R2 = 0.70 to 0.89), precise (MAE = 1.37–2.21 m), and virtually unbiased (Bias = −0.20–0.07 m) with respect to validation data. The trained models also performed adequately well on the independent test data (R2 = 0.62–0.78, MAE = 3.06–4.1 m). Models with higher encoder depths (3,4) and trained with 2 m data provide better predictions than models with encoder depth 2 and trained on 1 m data. Inter-comparisons with existing canopy height products also showed our canopy height map provided better agreement with reference airborne lidar canopy height estimates. This study shows the potential of developing regional canopy height products using airborne lidar and NAIP imagery to support forest productivity and carbon modeling at spatially detailed scales. The 30 m canopy height map generated over Texas holds promise in advancing economic and sustainable forest management goals and enhancing decision-making in natural resource management across the state.