Interest in the use of unmanned aerial systems (UAS) to estimate the aboveground biomass (AGB) of vegetation in agricultural and non-agricultural settings is growing rapidly but there is no standardized methodology for planning, collecting and analyzing UAS data for this purpose. We synthesized 46 studies from the peer-reviewed literature to provide the first-ever review on the subject. Our analysis showed that spectral and structural data from UAS imagery can accurately estimate vegetation biomass in a variety of settings, especially when both data types are combined. Vegetation-height metrics are useful for trees, while metrics of variation in structure or volume are better for non-woody vegetation. Multispectral indices using NIR and red-edge wavelengths normally have strong relationships with AGB but RGB-based indices often outperform them in models. Including measures of image texture can improve model accuracy for vegetation with heterogeneous canopies. Vegetation growth structure and phenological stage strongly influence model accuracy and the selection of useful metrics and should be considered carefully. Additional factors related to the study environment, data collection and analytical approach also impact biomass estimation and need to be considered throughout the workflow. Our review shows that UASs provide a capable tool for fine-scale, spatially explicit estimations of vegetation AGB and are an ideal complement to existing ground- and satellite-based approaches. We recommend future studies aimed at emerging UAS technologies and at evaluating the effect of vegetation type and growth stages on AGB estimation.

Aim An understanding of the factors that influence species distributions in heterogeneous landscapes is important when making decisions regarding conservation. Moreover, occupancy probabilities based on detection data can reveal important species-habitat relationships. Accounting for the spatial autocorrelation of detection data increases the statistical validity of occupancy models, but is not often considered. Using novel occupancy modelling that explicitly incorporates detectability and spatial autocorrelation, we assessed the influence of habitat on occupancy patterns of woodland caribou (Rangifer tarandus caribou), moose (Alces alces) and wolves (Canis lupus) across a broad biogeographical extent where fire is the dominant agent of disturbance.Location Northern Ontario, Canada.Methods We aerially surveyed 3851 sampling units, each covering 100 km 2 , for woodland caribou, moose and wolves in February-March in 2009, 2010 and 2011, and visited 1663 units more than once to estimate detectability. We used restricted spatial regression to model occupancy probabilities of each species with respect to habitat factors in two ecozones, accounting for both imperfect detection and lack of independence of sampling units. ResultsCovariates influencing species detection varied among ecozones and species. Caribou occupancy was positively related to bogs and negatively related to disturbed areas, while moose occupancy showed opposite responses to these covariates. Wolf occupancy was related to high prey occupancy. Explicitly accounting for spatial autocorrelation in detection data reduced the chance of type I error in occupancy estimates compared with non-spatial models.Main conclusions Habitat relationships and occupancy patterns support the hypothesis that caribou remain spatially segregated from moose to reduce predation risk. The broad scale of analysis indicated changes in species-habitat relationships, suggesting that limiting factors vary across biogeographical gradients. The spatial pattern in caribou occupancy allowed us to identify important areas used by caribou across the region, including the ecotone between fire-driven boreal forests and peatland complexes. The evidence for significant relationships between caribou and land cover, predators and alternate prey underscores the need for careful planning of development and infrastructure in the area.

Understanding wildlife distribution and habitat use is needed for effectively balancing resource development, wildlife conservation, and Alaska Native subsistence on the North Slope of Alaska, USA. This region includes the National Petroleum Reserve‐Alaska (NPR‐A), a 96,000‐km2 remote area of largely undeveloped lands that is important for wildlife, including caribou (Rangifer tarandus), wolves (Canis lupus), and wolverines (Gulo gulo). We focused our study on spring distribution and occupancy of wolverines in the NPR‐A because a baseline distribution estimate is required to understand current distribution and track changes over time. We conducted aerial surveys of wolverine tracks in snow during March and April of 2014 and 2015, surveying over 84,400 km2 using 100‐km2 hexagonal sampling units. We used hierarchical Bayesian occupancy modeling to determine wolverine distribution and estimate probability of occupancy within each hexagon, relative to measured covariates with potential to affect either detection or occupancy. Probability of wolverine occupancy increased as well‐drained soils increased, suggesting that wolverines prefer drier areas or habitat features associated with well‐drained soils. In addition, as standard deviation of elevation increased, wolverine occupancy also increased, indicating that wolverines may prefer areas with more rugged and variable terrain. Mean elevation was not retained as a covariate in the best‐fitting model, supporting the importance of terrain ruggedness rather than elevation on wolverine distribution within the NPR‐A. Spatially, areas of highest wolverine occupancy occurred within the southern and northeastern portions of the study area, with lowest occupancy in the northern portion of the study area west of Teshekpuk Lake. Based on the spatial pattern of wolverine probability of occupancy, we proposed 4 potential wolverine management zones with varying priorities for monitoring and managing wolverine populations. © 2018 The Wildlife Society.

Roads are an overwhelming component of the global human footprint and their absence helps identify intact areas with high ecological value. Road‐free areas are decreasing globally, making accurate estimation of their location and size of great importance. Identification of such regions requires accurate data, but substantial variability exists in road network datasets created and maintained at different spatial scales. We compared estimates of road length, density, and roadless areas across Canada, which contains a high proportion of the world's remaining undisturbed and road‐free areas. Global‐ and national‐scale datasets included, on average, only 11%–14% of roads represented in regional‐scale data or volunteered geographic information (VGI), with the most pronounced differences in less‐developed areas. Regional‐scale datasets, with the lowest estimates of amount of roadless area and smallest mean roadless patch size, are likely the most complete road datasets but are not available for all jurisdictions, limiting their national‐scale utility. VGI provides a national‐scale alternative but still lacks many low‐use roads. Available global and national datasets have insufficient information for accurate assessments of roadless areas in Canada, which will require detailed, consistent subnational datasets assembled and maintained by each province and territory in a coordinated fashion to achieve national coverage.

Aim:We used data from aerial surveys of wolverine tracks collected in seven winters over a 10-year period (2003-2012) within a 574,287 km 2 study area to evaluate the broad-scale pattern of wolverine occurrence across a remote northern boreal forest region, identifying areas of high and low occupancy.Location: Northern Ontario, Canada.Taxon: Wolverine (Gulo gulo Linnaeus, 1758). Methods:We collected wolverine tracks and observations in 100-km 2 hexagonal survey units, making a total of 6,664 visits to 3,039 units, visiting each 1-9 times.We used hierarchical Bayesian occupancy modelling to model wolverine occurrence, and included covariates with the potential to affect detection and/or occupancy probability of wolverines.Results: we detected wolverines on 946 visits, 14.2% of total visits. Probability of detecting a wolverine varied among years and between the two ecozones in the study area. Wolverine occupancy was negatively related to two important covariates, the geographical coordinate Easting and thawing degree-days. A site occupancy probability map indicated that wolverine occupancy probabilities were highest, and standard error lowest, in the western and northern portions of the study area. Main conclusions:The occupancy framework enabled us to use observation data from tracks of this elusive, wide-ranging carnivore over a vast, remote area while explicitly considering detectability and spatial autocorrelation, yielding a map of probable wolverine distribution in northern Ontario that would not be possible using other methods of detection across a large region. With resource development pressures increasing in this globally significant region in the face of a changing climate, it is important to monitor changes in distribution of species like wolverines that have low population growth rates, large spatial requirements and sensitivity to human disturbance. This study demonstrates a relatively cost-effective and noninvasive alternative to monitoring based on wolverine harvest records, which have not been available since 2009 in Ontario due to changes in the provincial regulatory regime for this threatened species.

Shrub-dominated ecosystems support biodiversity and play an important storage role in the global carbon cycle. However, it is challenging to characterize biophysical properties of low-stature vegetation like shrubs from conventional ground-based or remotely sensed data. We used spectral and structural variables derived from high-resolution unmanned aerial system (UAS) imagery to estimate the aboveground biomass of shrubs in the Betula and Salix genera in a montane meadow in Banff National Park, Canada using an area-based approach. In single-variable linear regression models, visible light (RGB) indices outperformed multispectral or structural data. A linear model based on the red ratio vegetation index (VI) accumulated over shrub area could model biomass (calibration R2 = 0.888; validation R2 = 0.774) nearly as well as the top multivariate linear regression models (calibration R2 = 0.896; validation R2 > 0.750), which combined an accumulated RGB VI with a multispectral metric. The excellent performance of accumulated RGB VIs represents a novel approach to fine-scale vegetation biomass estimation, fusing spectral and spatial information into a single parsimonious metric that rivals the performance of more complex multivariate models. Methods developed in this study will be relevant to researchers interested in estimating fine-scale shrub aboveground biomass within a range of ecosystems.

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