UAS-GEOBIA Approach to Sapling Identification in Jack Pine Barrens after Fire

Abstract

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 (...

Forestry Remote Sensing from Unmanned Aerial Vehicles: A Review Focusing on the Data, Processing and Potentialities

“…Considering the studies developed by Shin et al [138], White et al [134] and Fernández-Guisuraga et al [136], authors used MSP sensors. Shin et al [138] evaluated the feasibility of using UAV imagery to estimate forest canopy fuels and its structure in a ponderosa pine (Pinus ponderosa) stand with a small Gambel oak (Quercus gambelii) component.…”

“…This way, UAVs can provide additional data to supplement, or potentially substitute, traditional estimates of canopy fuel. Considering the spatial resolution that can be achieved by UAVs, White et al [134] evaluated the potential of jake pine (Pinus banksiana) saplings identification in post-fire environments. The best results were achieved in the latter epoch due to sapling development compared to ground vegetation cover, combination of RGB and NIR-R bands obtained higher accuracies.…”

Effectiveness of Sentinel-2 in Multi-Temporal Post-Fire Monitoring When Compared with UAV Imagery

“…In the past decade, the use of unmanned aerial vehicles (UAVs) has increased for agroforestry applications [8] and are now being used for forest fire prevention [9], canopy fuel estimation [10], fire monitoring [11,12] and to support firefighting operations [13]. Likewise, studies using UAV-based imagery in post-fire monitoring have been concerned with surveying [14], calibrating satellite-based burn severity indices [15], assessing post-fire vegetation recovery [16], mapping fire severity [17,18], studying forest recovery dynamics [19] and sapling identification [20]. Despite being a cost-effective and a very versatile platform for remote sensed data acquisition that is capable of carrying a wide set of sensors, its usage in surveying big areas can be constrained due to legal [21] and technological limitations such as its autonomy and payload capacity [8].…”

Computer Vision for Fire Detection on UAVs—From Software to Hardware

“…Multispectral/Hyperspectral: These types of cameras capture the information they receive using multiple wavelengths of light. They are devices for obtaining millions of spectral information for each pixel of the image [36,37]. More specifically, multispectral cameras use 3-10 extended bands, while hyperspectral cameras use hundreds of narrow bands in wavelengths of light.…”