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Forest Biodiversity Conservation with Remote Sensing Techniques

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A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Inventory, Modeling and Remote Sensing".

Deadline for manuscript submissions: closed (15 April 2020) | Viewed by 37303

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Special Issue Editors

Prof. Dr. Nicholas Coops

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Guest Editor

Integrated Remote Sensing Studio, Department of Forest Resources Management, University of British Columbia, 2424 Main Mall, Vancouver, BC V6T 1Z4, Canada
Interests: active and passive remote sensing technologies for biodiversity assessment; forest structure; species diversity and richness; long time series satellite data; dynamic habitat index
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Prof. Dr. Scott Nielsen

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Guest Editor

Department of Renewable Resources, General Services Building, University of Alberta, Edmonton, AB T6G 2H1, Canada
Interests: conservation biology; biogeography; biodiversity; terrestrial ecology; habitat fragmentation; boreal forest; remote sensing applications in biodiversity and conservation

Special Issue Information

Dear Colleagues,

Mapping the distribution and abundance of species and the traits of their ecosystem is critical to understanding the patterns and trends we observe and for managing biodiversity values. Species respond across a range of scales and to a number of different factors, including among others climate, ecosystem productivity, habitat structure, and disturbance history. Since the launch of the first Earth Observation satellites over 40 years ago, remote sensing techniques have been applied to locating and mapping forest species’ habitat and for scaling up observations to assess threats and monitor change. Remote sensing offers obvious benefits to monitoring and managing biodiversity, including broad coverage, repeat monitoring, and a range of spectral and spatial resolutions that facilitate observations not possible from field-based surveys alone. As remote sensing technologies and techniques continue to evolve, their application to biodiversity conservation become even more relevant and integrated.

This Special Issue of Forests is focused on the assessment of forest biodiversity and conservation utilising remote sensing and Earth Observation technologies. Research articles may focus on any aspect of forest biodiversity and conservation using remote sensing approaches, including the prediction and monitoring of forest species, their environment and habitat, as well as conservation initiatives that utilise remote sensing technologies from local to global scales. We welcome contributions using a range of remote sensing platforms from hand-held mobile devices, unmanned aerial vehicles (UAVs) and aircraft, as well as satellite-based approaches

Prof. Dr. Nicholas Coops
Prof. Dr. Scott Nielsen
Guest Editors

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Keywords

forests
biodiversity
remote sensing
satellites
unmanned aerial vehicles (UAVs)
environment
monitoring
species
scale

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Published Papers (9 papers)

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Research

19 pages, 3837 KiB

Open AccessFeature PaperArticle

Predicting Occurrence, Abundance, and Fruiting of a Cultural Keystone Species to Inform Landscape Values and Priority Sites for Habitat Enhancements

by Scott E. Nielsen, Jacqueline M. Dennett and Christopher W. Bater

Forests 2020, 11(7), 783; https://doi.org/10.3390/f11070783 - 21 Jul 2020

Cited by 11 | Viewed by 3233

Abstract

Environmental niche modeling is an increasingly common tool in conservation and management of non-timber species. In particular, models of species’ habitats have been aided by new advances in remote sensing and it is now possible to relate forest structure variables to understory species at a relatively high resolution over large spatial scales. Here, we model landscape responses for a culturally-valued keystone shrub, velvet-leaf blueberry (Vaccinium myrtilloides Michaux), in northeast Alberta, Canada, to better understand the environmental factors promoting or limiting its occurrence, abundance, and fruit production, and to guide regional planning. Occurrence and abundance were measured at 845 and 335 sites, respectively, with both strongly related to land cover type and topo-edaphic factors. However, their influence varied widely, reflecting differences in the processes affecting occurrence and abundance. We then used airborne laser scanning (ALS) to characterize horizontal forest canopy cover for the study area, and related this and other geospatial variables to patterns in fruit production where we demonstrated a five-fold increase in fruit production from closed to open forest stands. We then simulated forest canopy thinning across the study area to identify places where gains in fruit production would be greatest following natural disturbance or directed management (e.g., thinning, prescribed burning). Finally, we suggest this approach could be used to identify sites for habitat enhancements to offset direct (land use change) or indirect (access) losses of resources in areas impacted with resource extraction activities, or simply to increase a culturally-valued resource through management. Full article

(This article belongs to the Special Issue Forest Biodiversity Conservation with Remote Sensing Techniques)

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15 pages, 2464 KiB

Open AccessArticle

Landscape Patterns of Rare Vascular Plants in the Lower Athabasca Region of Alberta, Canada

by Scott E. Nielsen, Jacqueline M. Dennett and Christopher W. Bater

Forests 2020, 11(6), 699; https://doi.org/10.3390/f11060699 - 24 Jun 2020

Cited by 4 | Viewed by 2723

Abstract

Predicting habitat for rare species at landscape scales is a common goal of environmental monitoring, management, and conservation; however, the ability to meet that objective is often limited by the paucity of location records and availability of spatial predictors that effectively describe their habitat. To address this challenge, we used an adaptive, model-based iterative sampling design to direct four years of rare plant surveys within 0.25 ha plots across 602 sites in northeast Alberta, Canada. We used these location records to model and map rare plant habitats for the region using a suite of geospatial predictors including airborne light detection and ranging (LiDAR) vegetation structure metrics, land cover types, soil pH, and a terrain wetness model. Our results indicated that LiDAR-derived vegetation structural metrics and land cover were the most important individual factors, but all variables contributed to predicting the occurrence of rare plants. For LiDAR variables, rarity was negatively related to maximum canopy height, but positively related to canopy relief ratio. Rarity was therefore more likely in places with shorter canopy heights and greater structural complexity. This included fens, which overall had the highest rates of rare plant occurrence. Model-based allocation of sampling led to detections of uncommon species at nearly all sites, while the rarest species in the region were detected at an average encounter rate of 8%. Landscape predictions of rare plant habitat can improve our understanding of environmental limits in rarity, guide local management decisions and monitoring plans, and provide regional tools for assessing impacts from resource development. Full article

(This article belongs to the Special Issue Forest Biodiversity Conservation with Remote Sensing Techniques)

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14 pages, 12800 KiB

Open AccessArticle

Quantification of Lichen Cover and Biomass Using Field Data, Airborne Laser Scanning and High Spatial Resolution Optical Data—A Case Study from a Canadian Boreal Pine Forest

by Ashley C. Hillman and Scott E. Nielsen

Forests 2020, 11(6), 682; https://doi.org/10.3390/f11060682 - 16 Jun 2020

Cited by 3 | Viewed by 3557

Abstract

Ground-dwelling macrolichens dominate the forest floor of mature upland pine stands in the boreal forest. Understanding patterns of lichen abundance, as well as environmental characteristics associated with lichen growth, is key to managing lichens as a forage resource for threatened woodland caribou (Rangifer tarandus caribou). The spectral signature of light-coloured lichen distinguishes it from green vegetation, potentially allowing for mapping of lichen abundance using multi-spectral imagery, while canopy structure measured from airborne laser scanning (ALS) of forest openings can indirectly map lichen habitat. Here, we test the use of high-resolution KOMPSAT (Korea Multi-Purpose Satellite-3) imagery (280 cm resolution) and forest structural characteristics derived from ALS to predict lichen biomass in an upland jack pine forest in Northeastern Alberta, Canada. We quantified in the field lichen abundance (cover and biomass) in mature jack pine stands across low, moderate, and high canopy cover. We then used generalized linear models to relate lichen abundance to spectral data from KOMPSAT and structural metrics from ALS. Model selection suggested that lichen abundance was best predicted by canopy cover (ALS points > 1.37 m) and to a lesser extent blue spectral data from KOMPSAT. Lichen biomass was low at plots with high canopy cover (98.96 g/m²), while almost doubling for plots with low canopy cover (186.30 g/m²). Overall the model fit predicting lichen biomass was good (R² c = 0.35), with maps predicting lichen biomass from spectral and structural data illustrating strong spatial variations. High-resolution mapping of ground lichen can provide information on lichen abundance that can be of value for management of forage resources for woodland caribou. We suggest that this approach could be used to map lichen biomass for other regions. Full article

(This article belongs to the Special Issue Forest Biodiversity Conservation with Remote Sensing Techniques)

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17 pages, 14851 KiB

Open AccessArticle

Monitoring Broadscale Vegetational Diversity and Change across North American Landscapes Using Land Surface Phenology

by Bjorn-Gustaf J. Brooks, Danny C. Lee, Lars Y. Pomara and William W. Hargrove

Forests 2020, 11(6), 606; https://doi.org/10.3390/f11060606 - 27 May 2020

Cited by 18 | Viewed by 5174

Abstract

We describe a polar coordinate transformation of vegetation index profiles which permits a broad-scale comparison of location-specific phenological variability influenced by climate, topography, land use, and other factors. We apply statistical data reduction techniques to identify fundamental dimensions of phenological variability and to classify phenological types with intuitive ecological interpretation. Remote sensing-based land surface phenology can reveal ecologically meaningful vegetational diversity and dynamics across broad landscapes. Land surface phenology is inherently complex at regional to continental scales, varying with latitude, elevation, and multiple biophysical factors. Quantifying phenological change across ecological gradients at these scales is a potentially powerful way to monitor ecological development, disturbance, and diversity. Polar coordinate transformation was applied to Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) time series spanning 2000-2018 across North America. In a first step, 46 NDVI values per year were reduced to 11 intuitive annual metrics, such as the midpoint of the growing season and degree of seasonality, measured relative to location-specific annual phenological cycles. Second, factor analysis further reduced these metrics to fundamental phenology dimensions corresponding to annual timing, productivity, and seasonality. The factor analysis explained over 95% of the variability in the metrics and represented a more than ten-fold reduction in data volume from the original time series. In a final step, phenological classes (‘phenoclasses’) based on the statistical clustering of the factor data, were computed to describe the phenological state of each pixel during each year, which facilitated the tracking of year-to-year dynamics. Collectively the phenology metrics, factors, and phenoclasses provide a system for characterizing land surface phenology and for monitoring phenological change that is indicative of ecological gradients, development, disturbance, and other aspects of landscape-scale diversity and dynamics. Full article

(This article belongs to the Special Issue Forest Biodiversity Conservation with Remote Sensing Techniques)

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23 pages, 5745 KiB

Open AccessArticle

Modelling Lichen Abundance for Woodland Caribou in a Fire-Driven Boreal Landscape

by Joseph A. Silva, Scott E. Nielsen, Clayton T. Lamb, Christine Hague and Stan Boutin

Forests 2019, 10(11), 962; https://doi.org/10.3390/f10110962 - 1 Nov 2019

Cited by 18 | Viewed by 5246

Abstract

Woodland caribou (Rangifer tarandus caribou) are reliant on Cladonia spp. ground lichens as a major component of their diet and lichen abundance could be an important indicator of habitat quality, particularly in winter. The boreal forest is typified by large, stand-replacing forest fires that consume ground lichens, which take decades to recover. The large spatial extent of caribou ranges and the mosaic of lichen availability created by fires make it challenging to track the abundance of ground lichens. Researchers have developed various techniques to map lichens across northern boreal and tundra landscapes, but it remains unclear which techniques are best suited for use in the continuous boreal forest, where many of the conflicts amongst caribou and human activities are most acute. In this study, we propose a two-stage regression modelling approach to map the abundance (biomass, kg/ha) of Cladonia spp. ground lichens in the boreal forest. Our study was conducted in Woodland Caribou Provincial Park, a wilderness-class protected area in northwestern Ontario, Canada. We used field sampling to characterize lichen abundance in 109 upland forest stands across the local time-since-fire continuum (2–119 years-since-fire). We then used generalized linear models to relate lichen presence and lichen abundance to forest structure, topographic and remote sensing attributes. Model selection indicated ground lichens were best predicted by ecosite, time-since-fire, and canopy closure. Lichen abundance was very low (<1000 kg/ha) across the time-since-fire continuum in upland forest stands with dense tree cover. Conversely, lichen abundance increased steadily across the time-since-fire continuum in upland forest stands with sparse tree cover, exceeding 3000 kg/ha in mature stands. We interpolated the best lichen presence and lichen abundance models to create spatial layers and combined them to generate a map that provides a reasonable estimation of lichen biomass (R² = 0.39) for our study area. We encourage researchers and managers to use our method as a basic framework to map the abundance of ground lichens across fire-prone, boreal caribou ranges. Mapping lichens will aid in the identification of suitable habitat and can be used in planning to ensure habitat is maintained in adequate supply in areas with multiple land-use objectives. We also encourage the use of lichen abundance maps to investigate questions that improve our understanding of caribou ecology. Full article

(This article belongs to the Special Issue Forest Biodiversity Conservation with Remote Sensing Techniques)

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13 pages, 2327 KiB

Open AccessArticle

Identifying Patterns of Human and Bird Activities Using Bioacoustic Data

by Renjie Li, Saurabh Garg and Alexander Brown

Forests 2019, 10(10), 917; https://doi.org/10.3390/f10100917 - 18 Oct 2019

Viewed by 3521

Abstract

In general, humans and animals often interact within the same environment at the same time. Human activities may disturb or affect some bird activities. Therefore, it is important to monitor and study the relationships between human and animal activities. This paper proposed a system able not only to automatically classify human and bird activities using bioacoustic data, but also to automatically summarize patterns of events over time. To perform automatic summarization of acoustic events, a frequency–duration graph (FDG) framework was proposed to summarize the patterns of human and bird activities. This system first performs data pre-processing work on raw bioacoustic data and then applies a support vector machine (SVM) model and a multi-layer perceptron (MLP) model to classify human and bird chirping activities before using the FDG framework to summarize results. The SVM model achieved 98% accuracy on average and the MLP model achieved 98% accuracy on average across several day-long recordings. Three case studies with real data show that the FDG framework correctly determined the patterns of human and bird activities over time and provided both statistical and graphical insight into the relationships between these two events. Full article

(This article belongs to the Special Issue Forest Biodiversity Conservation with Remote Sensing Techniques)

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18 pages, 10514 KiB

Open AccessArticle

Application of Least-Cost Movement Modeling in Planning Wildlife Mitigation Measures along Transport Corridors: Case Study of Forests and Moose in Lithuania

by Jack Wierzchowski, Andrius Kučas and Linas Balčiauskas

Forests 2019, 10(10), 831; https://doi.org/10.3390/f10100831 - 21 Sep 2019

Cited by 17 | Viewed by 3225

Abstract

The present work presents the development of a moose movement model to explore the value of wildlife mitigation structures and examine how hypothetical changes in land use patterns could alter wildlife habitats at landscape scales. Collisions between vehicles and animals pose a threat to humans and wildlife populations, the most dangerous collisions being with moose. Migrations of moose are generally predictable and habitat-dependent. Here, we use GIS-based simulations of moose movements to examine road-related habitat fragmentation around the main highways A1 and A2 in Lithuania. From forest data, we develop a moose habitat suitability map. Then, by running multiple simulation iterations, we generate potential moose pathways and statistically describe the most efficient potential long-range movement routes that are based on the principles of habitat utilization. Reflecting the probabilities of cross-highway moose movement, ranks are assigned to all 1 km highway segments, characterizing them in terms of their likelihood of moose movement, and thus identifying discrete migration corridors and highway crossing zones. Bottlenecks are identified through simulation, such as where sections of wildlife fencing end without highway crossing structures, thereby creating a ‘spillover’ effect, i.e., moose moving parallel to the highway, then crossing. The tested model has proven the prognostic capacity of the tool to foresee locations of moose-vehicle collisions with high accuracy, thus allowing it to be a valuable addition to the toolbox of highway planners. Full article

(This article belongs to the Special Issue Forest Biodiversity Conservation with Remote Sensing Techniques)

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22 pages, 10446 KiB

Open AccessArticle

Mapping Coarse Woody Debris with Random Forest Classification of Centimetric Aerial Imagery

by Gustavo Lopes Queiroz, Gregory J. McDermid, Guillermo Castilla, Julia Linke and Mir Mustafizur Rahman

Forests 2019, 10(6), 471; https://doi.org/10.3390/f10060471 - 30 May 2019

Cited by 18 | Viewed by 6207

Abstract

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 (<2%), but aided significantly (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. Full article

(This article belongs to the Special Issue Forest Biodiversity Conservation with Remote Sensing Techniques)

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12 pages, 2246 KiB

Open AccessArticle

High Precision Altimeter Demonstrates Simplification and Depression of Microtopography on Seismic Lines in Treed Peatlands

by Cassondra J. Stevenson, Angelo T. Filicetti and Scott E. Nielsen

Forests 2019, 10(4), 295; https://doi.org/10.3390/f10040295 - 28 Mar 2019

Cited by 29 | Viewed by 3579

Abstract

Seismic lines are linear forest clearings used for oil and gas exploration. The mechanical opening of forests for these narrow (3–10 meter) lines is believed to simplify microtopographic complexity and depress local topographic elevation. In treed peatlands, simplified microtopography limits tree regeneration by removing favourable microsites (hummocks) for tree recruitment and increasing the occurrence of flooding that reduces survival of tree seedlings. Little, however, has been done to quantify the microtopography of seismic lines and specifically the degree of alteration. Here, we measured microtopography at 102 treed peatland sites in northeast Alberta, Canada using a high precision hydrostatic altimeter (ZIPLEVEL PRO-2000) that measured variation in local topography of seismic lines and adjacent paired undisturbed forests. Sites were separated into four peatland ecosite types and the presence or absence of recent (<22 years) wildfires. Paired t-tests were used to compare microtopographic complexity and depression depth of seismic lines compared with adjacent forests. Microtopographic complexity on seismic lines was simplified by 20% compared to adjacent stands with no significant change between recently burned and unburned sites, nor between ecosites. Not only were seismic lines simplified, but they were also depressed in elevation by an average of 8 cm compared to adjacent forests with some minor variation between ecosites observed, but again not with recent wildfires. Thus, simplification of microtopographic complexity and the creation of depressions can persist decades after initial disturbance with some differences between peatland ecosites, implying the need for ecosite-specific restoration of topographic complexity. The importance of microtopography for tree regeneration on seismic lines remains an important question for reforestation of these disturbances and thus long-term recovery of habitat for species dependent on undisturbed peatlands, including woodland caribou. Full article

(This article belongs to the Special Issue Forest Biodiversity Conservation with Remote Sensing Techniques)

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