Abstract
Tropical forests exhibit complex but poorly understood patterns of leaf phenology. Understanding species- and individual-level phenological patterns in tropical forests requires datasets covering large numbers of trees, which can be provided by Unmanned Aerial Vehicles (UAVs). In this paper, we test a workflow combining high-resolution RGB images (7 cm/pixel) acquired from UAVs with a machine learning algorithm to monitor tree and species leaf phenology in a tropical forest in Panama. We acquired images for 34 flight dates over a 12-month period. Crown boundaries were digitized in images and linked with forest inventory data to identify species. We evaluated predictions of leaf cover from different models that included up to 14 image features extracted for each crown on each date. The models were trained and tested with visual estimates of leaf cover from 2422 images from 85 crowns belonging to eight species spanning a range of phenological patterns. The best-performing model included both standard color metrics, as well as texture metrics that quantify within-crown variation, with r2 of 0.84 and mean absolute error (MAE) of 7.8% in 10-fold cross-validation. In contrast, the model based only on the widely-used Green Chromatic Coordinate (GCC) index performed relatively poorly (r2 = 0.52, MAE = 13.6%). These results highlight the utility of texture features for image analysis of tropical forest canopies, where illumination changes may diminish the utility of color indices, such as GCC. The algorithm successfully predicted both individual-tree and species patterns, with mean r2 of 0.82 and 0.89 and mean MAE of 8.1% and 6.0% for individual- and species-level analyses, respectively. Our study is the first to develop and test methods for landscape-scale UAV monitoring of individual trees and species in diverse tropical forests. Our analyses revealed undescribed patterns of high intraspecific variation and complex leaf cover changes for some species.
Highlights
Plant phenology has long been recognized as a critical driver of ecosystem processes, and one heavily influenced by climate
Our analyses demonstrate that Unmanned Aerial Vehicles (UAVs) images, including images collected under cloudy conditions and with variable illumination, can provide high-resolution and high-quality quantitative data on leaf cover, which should allow for long-term observations of large numbers of individuals and species
Our study demonstrates three key components of a research program that combines UAV images and machine learning to quantify tropical forest phenology
Summary
Plant phenology has long been recognized as a critical driver of ecosystem processes, and one heavily influenced by climate. Satellite observations and field data have provided high confidence that phenological shifts have been caused by climate change in recent decades in temperate and boreal ecosystems [1]. Compared to temperate and boreal ecosystems, relatively little is known about the leaf phenology of tropical forests, how it responds to climate variability, and how phenological shifts might impact tropical forest ecosystems [2,3]. To better understand the potential drivers of variation in tropical tree leaf phenology, a critical first step is to quantify patterns in tropical forest phenology at individual and species levels. Few datasets have the combination of high spatial and temporal resolution and comprehensive coverage needed to quantify intra- and interspecific variation in tropical leaf phenology, which requires individual tree-level monitoring for many species
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