Monitoring changes of forest height in California

Forests of California are undergoing large-scale disturbances from wildfire and tree mortality, caused by frequent droughts, insect infestations, and human activities. Mapping and monitoring the structure of these forests at high spatial resolution provides the necessary data to better manage forest health, mitigate wildfire risks, and improve carbon sequestration. Here, we use LiDAR measurements of top of canopy height metric (RH98) from NASA’s Global Ecosystem Dynamics Investigation (GEDI) mission to map vegetation height across the entire California for two different time periods (2019–2020 and 2021–2022) and explore the impact of disturbance. Exploring the reliability of machine learning methods for temporal monitoring of forest is still a developing field. We train a deep neural network to predict forest height metrics at 10-m resolution from radar and optical satellite imagery. Model validation against independent airborne LiDAR data showed a <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m1"><mml:mrow><mml:msup><mml:mrow><mml:mi>R</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup><mml:mo>≥</mml:mo><mml:mn>0.65</mml:mn></mml:mrow></mml:math> for the top of canopy height outperforming existing GEDI-based height maps and with improved sensitivity for mapping tall trees (RH98 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m2"><mml:mrow><mml:mo>≥</mml:mo></mml:mrow></mml:math> 50 m) across California. Height showed distinct spatial variations across forest types offering quantitative and spatial information to evaluate forest conditions. The model, trained on data from 2019 to 2020, showed a similar accuracy when applied to satellite imagery acquired in 2021–2022 allowing a robust detection of changes caused by natural and man-made disturbances of forest. Changes of height captured impacts of tree mortality and fire intensity, pointing to the influence of wildfire across landscapes. Fires caused more than 60% of the large forest disturbances between the two time periods. This study demonstrates the benefits of using locally trained ML models to rapidly modernize forest management techniques in the age of increasing climate risks.