Jack pine (pinus banksiana) forests are unique ecosystems controlled by wildfire. Understanding the traits of revegetation after wildfire is important for sustainable forest management, as these forests not only provide economic resources, but also are home to specialized species, like the Kirtland Warbler (Setophaga kirtlandii). Individual tree detection of jack pine saplings after fire events can provide information about an environment's recovery. Traditional satellite and manned aerial sensors lack the flexibility and spatial resolution required for identifying saplings in early post-fire analysis. Here we evaluated the use of unmanned aerial systems and geographic object-based image analysis for jack pine sapling identification in a region burned during the 2012 Duck Lake Fire in the Upper Peninsula of Michigan. Results of this study indicate that sapling identification accuracies can top 90%, and that accuracy improves with the inclusion of red and near infrared spectral bands. Results also indicated that late season imagery performed best when discriminating between young (<5 years) jack pines and herbaceous ground cover in these environments.Drones 2018, 2, 40 2 of 15 as a valid and low-cost method to generate both orthomosaics and digital surface models (DSMs) derived from 2D image sequences [5,6]. In their study of SfM derived IDT, Reference [7] performed ITD using UAS-SfM derived canopy height models based on algorithms designed for LiDAR data processing. The authors achieved the most accurate results for smoothing window size (SWS) at 3 × 3 irrespective of the fixed window size (FWS). In their assessment of models utilizing SWS and FWS of 3 × 3, the authors achieved a statistical F-scores greater than 0.80. Reference [8] reconstructed poplar saplings using digital photographs and terrestrial LiDAR (T-LiDAR), finding that T-LiDAR was more accurate at 3D construction than digital photographs, but at a much higher cost. Reference [9] examined the potential contribution hyperspectral imagery makes to IDT, achieving accuracies between 40% and 95% in tree detection. A comparison of LiDAR and SfM technology by Reference [10] indicated achieved accuracies of 96% and 80%, respectively, and the authors concluded that the technologies were capable of producing equally acceptable results for plot level estimates. These studies indicate that photogrammetric methods can provide accurate results for identifying tree crowns; however, none of these studies addressed sapling identification in natural environments. Additionally, processing photogrammetric datasets like SfM and LiDAR are computationally intensive for large areas. Finally, Reference [11] developed a land cover classification using multi-view data using a conditional random field (CRF) model, leading to accuracy improvements between 6% and 16.4% for a variety of classification methods. While these methods show promise of integrating multiple image view points for constructing classifications, we posit that there is still a need to develop robust low-cost (...
Abstract. Fire serves as a successional initiation in jack pine (Pinus banksiana) forests of North America, as jack pine reproduce using seratonous cones that open only in intense heat. Jack pine seedling resilience after fire is characterized by high numbers of mortality. The estimation of sapling survivability and density is useful for understanding dynamics of carbon sequestration, forest structure and dynamic, and supporting management of the landscape. Most studies concerning the interaction of forest disturbances occurs at moderate spatial resolution. These moderate resolution data analyses do not adequately capture the fine scale spatial variation of the landscape after fire for understanding sapling survival. Thus, high-resolution data, such as aerial photography may provide more detailed information to support decision-making. A key to the types of spatial patterns that emerge in these early years is the pre-fire stand condition. In heavily managed areas, the mosaic of forest patches may include extensive variety in disturbance conditions. In this current research we address the problem of scale in relation to understanding the influence of pre-fire condition on post-fire early recovery patterns. To do this, we combine data output from the LandTrendr algorithm in Google Earth Engine with spectral data from aerial photography collected by airplane and Unmanned Aerial System to perform a random forest classification. The result is a finer scale resolution map of forest conditions of varying sapling density.
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