Abstract
Broad scale remote sensing promises to build forest inventories at unprecedented scales. A crucial step in this process is to associate sensor data into individual crowns. While dozens of crown detection algorithms have been proposed, their performance is typically not compared based on standard data or evaluation metrics. There is a need for a benchmark dataset to minimize differences in reported results as well as support evaluation of algorithms across a broad range of forest types. Combining RGB, LiDAR and hyperspectral sensor data from the USA National Ecological Observatory Network's Airborne Observation Platform with multiple types of evaluation data, we created a benchmark dataset to assess crown detection and delineation methods for canopy trees covering dominant forest types in the United States. This benchmark dataset includes an R package to standardize evaluation metrics and simplify comparisons between methods. The benchmark dataset contains over 6,000 image-annotated crowns, 400 field-annotated crowns, and 3,000 canopy stem points from a wide range of forest types. In addition, we include over 10,000 training crowns for optional use. We discuss the different evaluation data sources and assess the accuracy of the image-annotated crowns by comparing annotations among multiple annotators as well as overlapping field-annotated crowns. We provide an example submission and score for an open-source algorithm that can serve as a baseline for future methods.
Highlights
Quantifying individual trees is a central task for ecology and management of forested landscapes
The benchmark dataset contains over 6,000 image-annotated crowns, 400 fieldannotated crowns, and 3,000 canopy stem points from a wide range of forest types
We developed a benchmark dataset of individual canopy crowns derived from multi-sensor imagery in the National Ecological Observatory Network (Table 1) that provides: 1) co-registered remote sensing data from multiple sensors (LiDAR, RGB imagery, and hyperspectral imagery) to allow comparisons of methods based on any single sensor, or any combination of sensors, and 2) three types of evaluation data to allow assessing both ‘tree detection’, defined as the identifying the location of individual trees using evaluation data with a point at the crown center [5, 17], and ‘crown delineation’ defined as identifying the boundary edge of crowns [9, 11,12,13] across a broad range of forest types
Summary
Quantifying individual trees is a central task for ecology and management of forested landscapes. [10] proposed a pixel-based algorithm for 50 cm pan-sharpened satellite RGB data from a tropical forest in Brazil evaluated against field-collected tree stem locations, and [11] proposed a vector-based algorithm for 10 cm fixed-winged aircraft RGB data from oak forests in California evaluated against image-annotated crowns. Given these differences, a comparison among algorithms is difficult to make based on reported statistics to interpret the relative accuracy, generality and cost effectiveness
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
