Mir Mustafizur Rahman

Terrestrial photographic imagery combined with structure-from-motion (SfM) provides a relatively easy-to-implement method for monitoring environmental systems, even in remote and rough terrain. However, the collection of in-situ positioning data and the identification of control points required for georeferencing in SfM processing is the primary roadblock to using SfM in difficult-to-access locations; it is also the primary bottleneck for using SfM in a time series. We describe a novel, computationally efficient, and semi-automated approach for georeferencing unreferenced point clouds (UPC) derived from terrestrial overlapping photos to a reference dataset (e.g., DEM or aerial point cloud; hereafter RPC) in order to address this problem. The approach utilizes a Discrete Global Grid System (DGGS), which allows us to capitalize on easily collected rough information about camera deployment to coarsely register the UPC using the RPC. The DGGS also provides a hierarchical set of grids which supports a hierarchical modified iterative closest point algorithm with natural correspondence between the UPC and RPC. The approach requires minimal interaction in a user-friendly interface, while allowing for user adjustment of parameters and inspection of results. We illustrate the approach with two case studies: a close-range (3 km) scene of relatively flat glacier ice reconstructed from four cameras overlooking Nàłùdäy (Lowell Glacier), Yukon, Canada. We assessed the accuracy of the georeferencing by comparing the UPC to the RPC, as well as surveyed control points; the consistency of the registration was assessed using the difference between successive registered surfaces in the time series. The accuracy of the registration is roughly equal to the ground sampling distance and is consistent across time steps. These results demonstrate the promise of the approach for easy-to-implement georeferencing of point clouds from terrestrial imagery with acceptable accuracy, opening the door for new possibilities in remote monitoring for change-detection, such as monitoring calving rates, glacier surges, or other seasonal changes at remote field locations.

Study of bird microhabitat use is time consuming and labour intensive. Our objective was to present a proof of concept of how emerging, high-resolution bird survey methods can be combined with vegetation data collected via unmanned aerial vehicles to accurately and efficiently quantify bird microhabitat. We used sound localization to determine mourning warbler (Geothlypis philadelphia) song posts, and a hybrid light detection and ranging/digital aerial photogrammetry canopy height model to demonstrate how mourning warblers use regenerating vegetation on reclaimed well sites. We identified differences in vegetation heights at locations used by mourning warblers versus random background locations on a reclaimed well site, with sound localization and the canopy height model both providing measurements with 1-m resolution (t = −3.45, p = 0.002). These technologies have the potential to provide large numbers of accurate bird locations that can be associated with high-resolution, spatially explicit vegetation metrics and used in different ecological niche modeling frameworks.

Forest land-use planning and restoration requires effective tools for mapping and attributing linear disturbances such as roads, trails, and asset corridors over large areas. Most existing linear-feature databases are generated by heads-up digitizing. While suitable for cartographic purposes, these datasets often lack the fine spatial details and multiple attributes required for more demanding analytical applications. To address this need, we developed the Forest Line Mapper (FLM), a semi-automated software tool for mapping and attributing linear features using LiDAR-derived canopy height models. Accuracy assessments conducted in the boreal forest of Alberta, Canada showed that the FLM reliably predicts both the center line (polyline) and footprint (extent polygons) of a variety of linear-feature types including roads, pipelines, seismic lines, and power lines. Our analysis showed that FLM outputs were consistently more accurate than publicly available datasets produced by human photo-interpreters, and that the tool can be reliably deployed across large application areas. In addition to accurately delineating linear features, the FLM generates a variety of spatial attributes associated with line geometry and vegetation characteristics from input canopy height data. Our statistical evaluation indicates that spatial attributes generated by the FLM may be useful for studying and classifying linear features based on disturbance type and ground conditions. The FLM is open-source and freely available and is aimed to assist researchers and land managers working in forested environments everywhere.

Forest canopy openings are a key element of forest structure, influencing a host of ecological dynamics. Light detection and ranging (LiDAR) is the de-facto standard for measuring three-dimensional forest structure, but digital aerial photogrammetry (DAP) has emerged as a viable and economical alternative. We compared the performance of LiDAR and DAP data for characterizing canopy openings and no-openings across a 1-km² expanse of boreal forest in northern Alberta, Canada. Structural openings in canopy cover were delineated using three canopy height model (CHM) alternatives, from (i) LiDAR, (ii) DAP, and (iii) a LiDAR/DAP hybrid. From a point-based detectability perspective, the LiDAR CHM produced the best results (87% overall accuracy), followed by the hybrid and DAP models (47% and 46%, respectively). The hybrid and DAP CHMs experienced large errors of omission (9–53%), particularly with small openings up to 20m², which are an important element of boreal forest structure. By missing these, DAP and hybrid datasets substantially under-reported the total area of openings across our site (152,470 m² and 159,848 m², respectively) compared to LiDAR (245,920 m²). Our results illustrate DAP’s sensitivity to occlusions, mismatched tie points, and other optical challenges inherent to using structure-from-motion workflows in complex forest scenes. These under-documented constraints currently limit the technology’s capacity to fully characterize canopy structure. For now, we recommend that operational use of DAP in forests be limited to mapping large canopy openings, and area-based attributes that are well-documented in the literature.

Peatlands are globally significant sources of atmospheric methane (CH₄). In the northern hemisphere, extensive geologic exploration activities have occurred to map petroleum deposits. In peatlands, these activities result in soil compaction and wetter conditions, changes that are likely to enhance CH₄emissions. To date, this effect has not been quantified. Here we map petroleum exploration disturbances on peatlands in Alberta, Canada, where peatlands and oil deposits are widespread. We then estimate induced CH₄emissions. By our calculations, at least 1900 km²of peatland have been affected, increasing CH₄emissions by 4.4–5.1 kt CH₄ yr⁻¹above undisturbed conditions. Not currently estimated in Canada’s national reporting of greenhouse gas (GHG) emissions, inclusion would increase current emissions from land use, land use change and forestry by 7–8%. However, uncertainty remains large. Research further investigating effects of petroleum exploration on peatland GHG fluxes will allow appropriate consideration of these emissions in future peatland management.

Coarse woody debris (CWD; large parts of dead trees) is a vital element of forest ecosystems, playing an important role in nutrient cycling, carbon storage, fire fuel, microhabitats, and overall forest structure. However, there is a lack of effective tools for identifying and mapping both standing (snags) and downed (logs) CWD in complex natural settings. We applied a random forest machine learning classifier to detect CWD in centimetric aerial imagery acquired over a 270-hectare study area in the boreal forest of Alberta, Canada. We used a geographic object-based image analysis (GEOBIA) approach in the classification with spectral, spatial, and LiDAR (light detection and ranging)-derived height predictor variables. We found CWD to be detected with great accuracy (93.4 ± 4.2% completeness and 94.5 ± 3.2% correctness) when training samples were located within the application area, and with very good accuracy (84.2 ± 5.2% completeness and 92.2 ± 3.2% correctness) when training samples were located outside the application area. The addition of LiDAR-derived variables did not increase the accuracy of CWD detection overall (p < 0.001) in the distinction between logs and snags. Foresters and researchers interested in CWD can take advantage of these novel methods to produce accurate maps of logs and snags, which will contribute to the understanding and management of forest ecosystems.

Allometric equations for estimating aboveground biomass (AGB) from easily measured plant attributes are unavailable for most species common to mid-continental boreal peatlands, where shrubs comprise a large component of the vegetation community. Our study develops allometric equations for three dominant genera found in boreal fens: Alnus spp. (alder), Salix spp. (willow) and Betula pumila (bog birch). Two different types of local equations were developed: (1) individual equations based on genus/phylogeny, and (2) a general equation that pooled all individuals regardless of genera. The general equation had a R² = 0.97 (n = 82), and was not significantly different (p > 0.05) than any of the phylogenetic equations. This indicated that a single generalized equation is sufficient in estimating AGB for all three genera occurring in our study area. A closer look at the performance of the general equation revealed that smaller stems were predicted less accurately than larger stems because of the higher variability of leafy biomass found in small individuals. Previously published equations developed in other ecoregions did not perform as well as our local equations.

Peatlands are globally significant stores of soil carbon, where local methane (CH₄) emissions are strongly linked to water table position and microtopography. Historically, these factors have been difficult to measure in the field, constraining our capacity to observe local patterns of variability. In this paper, we show how remote sensing surveys conducted from unmanned aerial vehicle (UAV) platforms can be used to map microtopography and depth to water over large areas with good accuracy, paving the way for spatially explicit estimates of CH₄ emissions. This approach enabled us to observe—for the first time—the effects of low‐impact seismic lines (LIS; petroleum exploration corridors) on surface morphology and CH₄ emissions in a treed‐bog ecosystem in northern Alberta, Canada. Through compaction, LIS lines were found to flatten the observed range in microtopographic elevation by 46 cm and decrease mean depth to water by 15.4 cm, compared to surrounding undisturbed conditions. These alterations are projected to increase CH₄ emissions by 20–120% relative to undisturbed areas in our study area, which translates to a total rise of 0.011–0.027 kg CH₄ day⁻¹ per linear kilometer of LIS (~2 m wide). The ~16 km of LIS present at our 61 ha study site were predicted to boost CH₄ emissions by 20–70 kg between May and September 2016.

Groundwater level (GWL) and depth to water (DTW) are related metrics aimed at characterizing groundwater-table positions in peatlands, and two of the most common variables collected by researchers working in these ecosystems. While well-established field techniques exist for measuring GWL and DTW, they are generally difficult to scale. In this study, we present a novel workflow for mapping groundwater using orthophotography and photogrammetric point clouds acquired from unmanned aerial vehicles. Our approach takes advantage of the fact that pockets of surface water are normally abundant in peatlands, which we assume to be reflective of GWL in these porous, gently sloping environments. By first classifying surface water and then extracting a sample of water elevations, we can generate continuous models of GWL through interpolation. Estimates of DTW can then be obtained through additional efforts to characterize terrain. We demonstrate our methodology across a complex, 61-ha treed bog in northern Alberta, Canada. An independent accuracy assessment using 31 temporally coincident water-well measurements revealed accuracies (root mean square error) in the 20-cm range, though errors were concentrated in small upland pockets in the study area, and areas of dense tree covers. Model estimates in the open peatland areas were considerably better.

Microtopographic variability in peatlands has a strong influence on greenhouse gas fluxes, but we lack the ability to characterize terrain in these environments efficiently over large areas. To address this, we assessed the capacity of photogrammetric data acquired from an unmanned aerial vehicle (UAV or drone) to reproduce ground elevations measured in the field. In particular, we set out to evaluate the role of (i) vegetation/surface complexity and (ii) supplementary LiDAR data on results. We compared remote-sensing observations to reference measurements acquired with survey grade GPS equipment at 678 sample points, distributed across a 61-hectare treed bog in northwestern Alberta, Canada. UAV photogrammetric data were found to capture elevation with accuracies, by root mean squares error, ranging from 14–42 cm, depending on the state of vegetation/surface complexity. We judge the technology to perform well under all but the most-complex conditions, where ground visibility is hindered by thick vegetation. Supplementary LiDAR data did not improve results significantly, nor did it perform well as a stand-alone technology at the low densities typically available to researchers.