Gap geometry, seasonality and associated losses of biomass – combining UAV imagery and field data from a Central Amazon forest

Abstract

Abstract. Understanding mechanisms of tree mortality and geometric patterns of canopy gaps is relevant for robust estimates of carbon stocks and balance in tropical forests, and for assessing how they are responding to climate change. We combined monthly RGB images acquired from an unmanned aerial vehicle with field surveys to identify gaps in an 18-ha permanent plot in an old-growth Central Amazon forest over a period of 28 months. In addition to detecting, we measured the size and shape of gaps, and analyzed their temporal variation and correlation with rainfall. We further described associated modes of tree mortality or branch fall and quantified associated losses of biomass. Overall, the sensitivity of gap detection differed between field surveys and imagery data. In total, we detected 32 gaps either in the images and field, ranging in area from 9 m2 to 835 m2. Relatively small gaps (

Plant phenotyping relevance

UAV画像とフィールド調査を用いて森林キャノピーギャップを検出・定量化し、検出感度を比較しており、植物群落の構造状態を測定する方法の適用・検証が中心的です。

Code and data availability

The paper's R analysis code is publicly archived on Zenodo (10.5281/zenodo.8298693), and the supporting lidar data are openly available on Zenodo (10.5281/zenodo.7636454). Other data (UAV imagery, field gap measurements) are only available upon request from the co-authors.