Abstract
Digital aerial photogrammetric (DAP) techniques applied to unmanned aerial system (UAS) acquired imagery have the potential to offer timely and affordable data for monitoring and updating forest inventories. Development of methods for individual tree crown detection (ITCD) and delineation enables the development of individual tree-based, rather than stand based inventories, which are important for harvesting operations, biomass and carbon stock estimations, forest damage assessment, and forest monitoring in mixed species stands. To achieve these inventory goals, consistent and robust DAP estimates are required over time. Currently, the influence of seasonal changes in deciduous tree structure on the consistency of DAP point clouds, from which tree-based inventories can be derived, is unknown. In this study, we investigate the influence of the timing of DAP acquisition on ITCD accuracies and estimation of tree attributes for a deciduous-dominated forest stand in New Brunswick, Canada. UAS imagery was acquired five times between June and September 2017 over the same stand and consistently processed into DAP point clouds. Airborne laser scanning (ALS) data, acquired the same year, was used to reconstruct a digital terrain model (DTM) and served as a reference for UAS-DAP-based ITCD. Marker-controlled watershed segmentation (MCWS) was used to delineate individual tree crowns. Accuracy index percentages between 55% (July 25) and 77.1% (September 22) were achieved. Omission errors were found to be relatively high for the first three DAP acquisitions (June 7, July 5, and July 25) and decreased gradually thereafter. The commission error was relatively high on July 25. Point cloud metrics were found to be predominantly consistent over the 4-month period, however, estimated tree heights gradually decreased over time, suggesting a trade-off between ITCD accuracies and measured tree heights. Our findings provide insight into the potential influence of seasonality on DAP-ITCD approaches to derive individual tree inventories.
Highlights
Forest inventories are a critical component of sustainable forest management and offer key information on a range of spatial and temporal scales [1]
Tree-level inventory information derived from remote sensing data can be extracted from individual tree crown detection (ITCD) methods which refer to the detection of trees, delineation of crowns, and derivation of tree attributes [9]
The highest ITCD accuracy was observed on September 22 in fall (77.1%) when canopy cover is relatively low (Table 5)
Summary
Forest inventories are a critical component of sustainable forest management and offer key information on a range of spatial and temporal scales [1]. Forest inventories have employed a combination of field plots, aerial photographic interpretation, and remote sensing techniques that facilitate forest management decision making such as operational planning, wood supply, and regeneration strategies [2]. Research advancements in remote sensing have demonstrated accurate derivation of key forest attributes, and offer detailed tree-level information when compared to more common stand inventory approaches [5]. This offers the potential for the accurate estimation of parameters such as tree height, canopy cover, age class, tree density, and tree spatial patterns [6], which in turn inform harvesting operations, carbon stock and biomass estimations, forest monitoring, and forest damage assessment [7,8]. Past research on ITCD was focused on passive aerial and satellite imagery, more recently this has shifted to airborne laser scanning (ALS) [9], which is often considered a best available technology for digital terrain model (DTM) generation [10] and forest inventory modeling [11,12]
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