A remote sensing derived data set of 100 million individual tree crowns for the National Ecological Observatory Network

Abstract

Forests provide biodiversity, ecosystem, and economic services. Information on individual trees is important for understanding forest ecosystems but obtaining individual-level data at broad scales is challenging due to the costs and logistics of data collection. While advances in remote sensing techniques allow surveys of individual trees at unprecedented extents, there remain technical challenges in turning sensor data into tangible information. Using deep learning methods, we produced an open-source data set of individual-level crown estimates for 100 million trees at 37 sites across the United States surveyed by the National Ecological Observatory Network’s Airborne Observation Platform. Each canopy tree crown is represented by a rectangular bounding box and includes information on the height, crown area, and spatial location of the tree. These data have the potential to drive significant expansion of individual-level research on trees by facilitating both regional analyses and cross-region comparisons encompassing forest types from most of the United States.

Individual-based multiple-unit dissimilarity: novel indices and null model for assessing temporal variability in community composition

“…Both the ForestGEO and the monitoring 1000 projects feature individual tracking; my methods are applicable. In addition, recent developments in remote sensing techniques have rendered it possible to identify individual trees (e.g., Guillén-Escribà et al 2021;Weinstein et al 2021), although not (yet) to track individuals. In the near future, remote sensing will yield individual tracking datasets on a global scale.…”

Individual-based multiple-unit dissimilarity: novel indices and null model for assessing temporal variability in community composition

“…Both the ForestGEO and the monitoring 1000 projects feature individual tracking; my methods are applicable. In addition, recent developments in remote sensing techniques have rendered it possible to identify individual trees (e.g., Guillén-Escribà et al 2021; Weinstein et al 2021), although not (yet) to track individuals. In the near future, remote sensing will yield individual tracking datasets on a global scale.…”

Optimizing aerial imagery collection and processing parameters for drone-based individual tree mapping in structurally complex conifer forests

“…SfM-derived point cloud data share many characteristics with aerial light detection and ranging (LiDAR) or aerial laser scanning (ALS) data, which can also be used for ITD (Jeronimo et al, 2018;Zaforemska et al, 2019). A major difference is that SfM-derived point clouds are usually substantially denser and higher resolution (e.g., > 100 points m -2 , this study) than LiDAR-derived point clouds (often < 8 points m -2 ; USGS, 2018; Weinstein et al, 2021). Drone-based data is also much less costly to obtain and can be collected from specific focal areas with high frequency and little advance planning (Camarretta et al, 2020;Mlambo et al, 2017).…”

“…Even when using LiDAR, which usually can penetrate the canopy to some extent, understory and mid-story detail, and thus potential to detect trees there, is limited (Richardson & Moskal, 2011) and has led some to re-focus detection and mapping of individual trees (ITD) toward detection and mapping of tree-approximate objects (TAOs), which can include single trees and clusters of trees that are not differentiable (Jeronimo et al, 2018;North et al, 2017). Maps of the size and arrangement of TAOs may be valuable for some management applications (Jeronimo et al, 2018;North et al, 2017), and important ecological questions can be addressed using maps of the specific trees visible from above (Brandt et al, 2020;Weinstein et al, 2021) or detectable using SfM that is not canopy-penetrating (Koontz et al, 2021). Our calculation of ITD accuracy metrics specifically for "overstory" trees helps to provide a sense of TAO mapping accuracy.…”