The use of remote sensing for characterizing forests in wildlife habitat modeling

Spatially explicit maps of habitat relationships have proven to be valuable tools for conservation and management applications including evaluating how and which species may be impacted by large scale climate change, ongoing fragmentation of habitat, and local land-use practices. Studies have turned to remote sensing datasets as a way to characterize vegetation for the examination of habitat selection and for mapping realized relationships across the landscape. Although the use of remote sensing in wildlife studies has increased in recent years, the use of these datasets is still limited and some data sources and methods are yet to be explored. The overall goal of this dissertation was to look at the state of the wildlife ecology discipline in the use of geospatial data for habitat mapping, and to advance this area through the fusion of remote sensing tools for the mapping of previously difficult to characterize forest metrics for inclusion in avian cavity-nester habitat models. Chapter 2 reviewed over 60 years of selected wildlife literature to examine the wildlife ecology disciple through historic trends and recent advances in the use of remote sensing for habitat characterization focusing on aspects of scale and the use of available technology. We discuss commonly used remote sensing data sources, point out recent advances in the use of geospatial data for characterizing forest wildlife habitat (the use of lidar data and the creation of spatially explicit habitat prediction maps), and provide future suggestions for increased utilization of available datasets (secondary lidar metrics and time series Landsat data). In chapters 3 and 4 we explored the use of remote sensing for characterizing forest components previously difficult to map across landscapes at scales relevant to local wildlife habitat selection. Chapter 3 found promise in the fusion of lidar structure and Landsat time series disturbance products in the modeling and mapping of post-fire snag and shrub distributions at fine scales and at size/cover thresholds relevant for habitat mapping applications for many wildlife species. The study was conducted within the 2003 B&B Fire Complex in central Oregon. Using 164 field calibration plots and remote sensing predictors, we modeled the presence/absence of snag classes (dbh ≥40cm, ≥50cm, and ≥75cm) and woody shrub cover resulting in 10m output predictive grid maps. Remote sensing predictors included various lidar structure and topography variables and Landsat time series products representing the pre-fire forest, disturbance magnitude, and current forest conditions. We were able to model and map all habitat metrics with acceptable predictive performance and low-moderate errors. The utility of these snag and shrub metrics for representing important nesting habitat features for a cavity-nesting species of conservation concern, the Lewis's Woodpecker (Melanerpes lewis), was demonstrated in Chapter 4. We were able to model nesting habitat with good accuracies according to multiple performance measures and then map realized relationships for this species of conservation concern in an identified source habitat type, providing a potential resource for local scale conservation and management efforts and adding to the regional knowledge of habitat selection for the Lewis's Woodpecker. To our knowledge, these chapters represent first attempts to fuse lidar and time series Landsat disturbance metrics in a post-fire landscape and for the mapping of snag and shrub distributions at scales relevant to avian cavity nesting habitat

Mapping post-fire habitat characteristics through the fusion of remote sensing tools

Post-fire snags provide important resources for cavity nesting communities as well as being subject to timber removal through salvage logging practices. Tools that can characterize their distributions along with other features important as wildlife habitat, such as woody shrub cover, would be useful for research and management purposes. Three dimensional lidar data and Landsat time series disturbance products have both shown varying promise in their ability to characterize aspects of dead biomass and understory cover, but studies exploring the combination of the remote sensing datasets calibrated with field data to model difficult to map habitat components are limited. The purpose of this study was to 1) relate lidar and Landsat time series products to field-collected calibration data to produce maps of important post-fire wildlife habitat components including snags of varying sizes and the availability of a woody shrub layer; and 2) compare the individual performance of the Landsat and lidar datasets for predicting the distributions of these difficult to map forest habitat features. Using 164 field calibration plots and remote sensing predictors, we modeled and mapped the distributions of our response variables including snag classes (dbh ≥ 40 cm, ≥ 50 cm, and ≥ 75 cm) and woody shrub cover thresholds (≥ 30 and ≥ 50% cover) at 10 m resolutions. Remote sensing predictors included various lidar structure and topography variables and Landsat time series products representing the pre-fire forest, disturbance magnitude, and current forest condition. A model was chosen for mapping purposes using AIC model selection and then by comparing leave-one-out-cross-validation error matrices to choose among competing models. We were able to predict and map all response variables with moderate accuracies and variable sensitivity (true positive) and specificity (true negative) rates. All snag and shrub models were considered to have “good” predictive performance as indicated by area under the curve values (0.74–0.91), with percent correctly classified values ranging from 69–85% when a probability threshold is chosen that balances false positive and false negative errors. Landsat models performed marginally better than lidar structure models according to classification statistics. Landsat-only models had slightly less accuracy than models that included lidar and Landsat data, but often with greater errors than the combined model. The ability to map the response variables with moderate errors and acceptable accuracies for many applications was through the fusion of these remote sensing datasets.Keywords: Lidar, Post-fire, Habitat, Maps, Snags, Landsat time series, Shrub coverKeywords: Lidar, Post-fire, Habitat, Maps, Snags, Landsat time series, Shrub cove

Lidar-derived canopy architecture predicts brown creeper occupancy of two western coniferous forests

In western conifer-dominated forests where the abundance of old-growth stands is decreasing, species such as the Brown Creeper (Certhia americana) may be useful as indicator species for monitoring the health of old-growth systems because they are strongly associated with habitat characteristics associated with old growth and are especially sensitive to forest management. Light detection and ranging (lidar) is useful for acquiring fine-resolution, three-dimensional data on vegetation structure across broad areas. We evaluated Brown Creeper occupancy of forested landscapes by using lidar-derived canopy metrics in two coniferous forests in Idaho. Density of the upper canopy was the most important variable for predicting Brown Creeper occupancy, although mean height and height variability were also included in the top models. The upper canopy was twice as dense and the mean height was almost 50% higher at occupied than at unoccupied sites. Previous studies have found indicators of canopy density to be important factors for Brown Creeper habitat; however, this represents the first time that lidar data have been used to examine this relationship empirically through the mapping of the upper canopy density that cannot be continuously quantified by field-based methods or passive remote sensing. Our model’s performance was classified as “good” by multiple criteria. We were able to map probabilities of Brown Creeper occupancy in ~50 000 ha of forest, probabilities that can be used at the local, forest-stand, and landscape scales, and illustrate the potential utility of lidar-derived data for studies of avian distributions in forested landscapes.En los bosques dominados por coníferas del oeste, donde está disminuyendo la abundancia de rodales maduros, las especies como Certhia americana pueden ser útiles como especies indicadoras para monitorear la salud de los sistemas maduros debido a que están fuertemente asociadas con las características del hábitat vinculadas con el bosque maduro y son especialmente sensibles al manejo del bosque. El sistema de detección y alcance de luz (denominado lidar, un acrónimo del inglés “light detection and ranging”) es útil para adquirir datos tridimensionales de alta resolución de la estructura de la vegetación a través de grandes áreas. Evaluamos la ocupación de C. americana de paisajes boscosos usando métricas del dosel derivadas de lidar en dos bosques de coníferas en Idaho. La densidad del dosel alto fue la variable más importante para predecir la ocupación de C. americana, aunque la altura media y la variabilidad de la altura también fueron incluidas en los mejores modelos. El dosel alto fue dos veces más denso y la altura media fue casi 50% más alta en los sitios ocupados que en los sitios desocupados. Estudios previos han encontrado que los indicadores de densidad del dosel son factores importantes del hábitat de C. americana; sin embargo, esto representa la primera vez que datos de lidar han sido usados para examinar esta relación de modo empírico a través del mapeo de la densidad del dosel alto, de un modo continuo que no puede ser cuantificado por métodos basados en trabajo de campo o muestreo remoto pasivo. El desempeño de nuestro modelo fue clasificado como “bueno” por múltiples criterios. Fuimos capaces de mapear las probabilidades de ocupación de C. americana en ~50 000 ha de bosque, probabilidades que pueden ser usadas a las escalas local, de rodal de bosque y de paisaje, y que ilustran la utilidad potencial de los datos derivados de lidar para estudios de distribución de aves en paisajes boscosos.Keywords: Brown Creeper, Certhia americana, mapping, habitat, forest, occupancy, lida

How much does the time lag between wildlife field-data collection and LiDAR-data acquisition matter for studies of animal distributions? A case study using bird communities

Vegetation structure quantified by light detection and ranging (LiDAR) can improve understanding of wildlife occupancy and species-richness patterns. However, there is often a time lag between the collection of LiDAR data and wildlife data. We investigated whether a time lag between the LiDAR acquisition and field-data acquisition affected mapped wildlife distributions ranging from an individual species distribution to total avian species richness in a conifer forest. We collected bird and LiDAR data in 2009 across a 20,000 ha forest in northern Idaho. Using the 2009 LiDAR data, we modelled the probability of occurrence for the brown creeper (Certhia americana). Using the same 2009 LiDAR data, we additionally modelled total avian species richness and richness of three different bird nesting guilds (ground/understory, mid/upper canopy and cavity). We mapped brown creeper occupancy probability and species richness using the 2009 models, and then compared these maps with maps based on the same models applied to a 2003-LiDAR dataset. A prior study identified areas harvested between 2003 and 2009. There was on average a 5% absolute decrease in mapped probabilities of brown creeper occurrence in non-harvest areas between 2003 and 2009. Species richness changed by less than one species in all cases within non-harvest areas between the 2003 and 2009 maps. Although these comparisons were statistically significant at the p < 0.0001 level, it is likely that the high number of map cells (~480,000) influenced this result. Similar patterns between our 2003 and 2009 maps in non-harvest areas for this suite of avian responses suggests that a 6-year difference between field-data collection and LiDAR-data collection has a minimal effect on mapped avian patterns in an undisturbed coniferous forest. However, because this is one case study in one ecosystem, additional work examining the effect of temporal lags between LiDAR and field-data collection on mapping wildlife distributions is warranted in additional ecosystems

Terrain and vegetation structural influences on local avian species richness in two mixed-conifer forests

Using remotely-sensed metrics to identify regions containing high animal diversity and/or specific animal species or guilds can help prioritize forest management and conservation objectives across actively managed landscapes. We predicted avian species richness in two mixed conifer forests, Moscow Mountain and Slate Creek, containing different management contexts and located in north-central Idaho. We utilized general linear models and an AIC model selection approach to examine the relative importance of a wide range of remotely-sensed ecological variables, including LiDAR-derived metrics of vertical and horizontal structural heterogeneities of both vegetation and terrain, and Landsat-derived vegetation reflectance indices. We also examined the relative importance of these remotely sensed variables in predicting nesting guild distributions of ground/understory nesters, mid-upper canopy nesters, and cavity nesters. All top models were statistically significant, with adjusted R²s ranging from 0.05 to 0.42. Regardless of study area, the density of the understory was positively associated with total species richness and the ground/understory nesting guild. However, the relative importance of ecological predictors generally differed between the study areas and among the nesting guilds. For example, for mid-upper canopy nester richness, the best predictors at Moscow Mountain included height variability and canopy density whereas at Slate Creek they included slope, elevation, patch diversity and height variability. Topographic variables were not found to influence species richness at Moscow Mountain but were strong predictors of avian species richness at the higher elevation Slate Creek, where species richness decreased with increasing slope and elevation. A variance in responses between focal areas suggests that we expand such studies to determine the relative importance of different factors in determining species richness. It is also important to note that managers using predictive maps should realize that models from one region may not adequately represent communities in other areas.This is the publisher’s final pdf. The published article is copyrighted by Elsevier and can be found at: http://www.sciencedirect.com/science/journal/00344257Keywords: Avian nesting guilds, Predictive maps, Species richness modeling, Landsat, Forest birds, LiDARKeywords: Avian nesting guilds, Predictive maps, Species richness modeling, Landsat, Forest birds, LiDA

Evaluating GEDI data fusions for continuous characterizations of forest wildlife habitat

Continuous characterizations of forest structure are critical for modeling wildlife habitat as well as for assessing trade-offs with additional ecosystem services. To overcome the spatial and temporal limitations of airborne lidar data for studying wide-ranging animals and for monitoring wildlife habitat through time, novel sampling data sources, including the space-borne Global Ecosystem Dynamics Investigation (GEDI) lidar instrument, may be incorporated within data fusion frameworks to scale up satellite-based estimates of forest structure across continuous spatial extents. The objectives of this study were to: 1) investigate the value and limitations of satellite data sources for generating GEDI-fusion models and 30 m resolution predictive maps of eight forest structure measures across six western U.S. states (Colorado, Wyoming, Idaho, Oregon, Washington, and Montana); 2) evaluate the suitability of GEDI as a reference data source and assess any spatiotemporal biases of GEDI-fusion maps using samples of airborne lidar data; and 3) examine differences in GEDI-fusion products for inclusion within wildlife habitat models for three keystone woodpecker species with varying forest structure needs. We focused on two fusion models, one that combined Landsat, Sentinel-1 Synthetic Aperture Radar, disturbance, topographic, and bioclimatic predictor information (combined model), and one that was restricted to Landsat, topographic, and bioclimatic predictors (Landsat/topo/bio model). Model performance varied across the eight GEDI structure measures although all representing moderate to high predictive performance (model testing R2 values ranging from 0.36 to 0.76). Results were similar between fusion models, as well as for map validations for years of model creation (2019–2020) and hindcasted years (2016–2018). Within our wildlife case studies, modeling encounter rates of the three woodpecker species using GEDI-fusion inputs yielded AUC values ranging from 0.76–0.87 with observed relationships that followed our ecological understanding of the species. While our results show promise for the use of remote sensing data fusions for scaling up GEDI structure metrics of value for habitat modeling and other applications across broad continuous extents, further assessments are needed to test their performance within habitat modeling for additional species of conservation interest as well as biodiversity assessments

Evaluating carbon storage, timber harvest, and habitat possibilities for a Western Cascades (USA) forest landscape

Forest policymakers and managers have long sought ways to evaluate the capability of forest landscapes to jointly produce timber, habitat, and other ecosystem services in response to forest management. Currently, carbon is of particular interest as policies for increasing carbon storage on federal lands are being proposed. However, a challenge in joint production analysis of forest management is adequately representing ecological conditions and processes that influence joint production relationships. We used simulation models of vegetation structure, forest sector carbon, and potential wildlife habitat to characterize landscape-level joint production possibilities for carbon storage, timber harvest, and habitat for seven wildlife species across a range of forest management regimes. We sought to (1) characterize the general relationships of production possibilities for combinations of carbon storage, timber, and habitat, and (2) identify management variables that most influence joint production relationships. Our 160 000-ha study landscape featured environmental conditions typical of forests in the Western Cascade Mountains of Oregon (USA). Our results indicate that managing forests for carbon storage involves trade-offs among timber harvest and habitat for focal wildlife species, depending on the disturbance interval and utilization intensity followed. Joint production possibilities for wildlife species varied in shape, ranging from competitive to complementary to compound, reflecting niche breadth and habitat component needs of species examined. Managing Pacific Northwest forests to store forest sector carbon can be roughly complementary with habitat for Northern Spotted Owl, Olive-sided Flycatcher, and red tree vole. However, managing forests to increase carbon storage potentially can be competitive with timber production and habitat for Pacific marten, Pileated Woodpecker, and Western Bluebird, depending on the disturbance interval and harvest intensity chosen. Our analysis suggests that joint production possibilities under forest management regimes currently typical on industrial forest lands (e.g., 40- to 80-yr rotations with some tree retention for wildlife) represent but a small fraction of joint production outcomes possible in the region. Although the theoretical boundaries of the production possibilities sets we developed are probably unachievable in the current management environment, they arguably define the long-term potential of managing forests to produce multiple ecosystem services within and across multiple forest ownerships

Potential for Individual Tree Monitoring in Ponderosa Pine-Dominated Forests Using Unmanned Aerial System Structure from Motion Point Clouds

Characterization of forest structure is important for management-related decision making, monitoring, and adaptive management. Increasingly, observations of forest structure are needed at both finer resolutions and across greater extents to support spatially explicit management planning. Unmanned aerial system (UAS)-based photogrammetry provides an airborne method of forest structure data acquisition at a significantly lower cost and time commitment than existing methods such as airborne laser scanning (LiDAR). This study utilizes nearly 5,000 stem-mapped trees in ponderosa pine-dominated forests to evaluate several algorithms for detecting individual tree locations and characterizing crown area across tree sizes. Our results indicate that adaptive variable-window detection methods with UAS-based canopy height models have greater tree detection rates compared to fixed window analysis across a range of tree sizes. Using the UAS approach, probability of detecting individual trees decreases from 97% for dominant overstory to 67% for suppressed understory trees. Additionally, crown radii were correctly determined within 0.5 m for approximately two-thirds of sampled trees. These findings highlight the potential for UAS photogrammetry to characterize forest structure through the detection of trees and tree groups in open-canopy ponderosa pine forests. Further work should investigate how these methods transfer to more diverse species compositions and forest structures.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Assessing GEDI data fusions to map woodpecker distributions and biodiversity hotspots

In forested systems, woodpecker species richness has been linked with songbird diversity, and identifying woodpecker biodiversity hotspots may contribute important information for conservation planning. The availability of global forest structure data via the Global Ecosystem Dynamics Investigation (GEDI) instrument provides a new tool for examining broad extent relationships amongst environmental variables, forest structure, and woodpecker diversity hotspots. Within the Marine West Coast Forest ecoregion, USA, we used eBird data for 7 woodpecker species to model encounter rates based on bioclimatic variables, process data (e.g. duration and timing of survey), MODIS forest land cover data, and GEDI-fusion metrics. The GEDI-fusion metrics included foliage height diversity (fhd), rh98 (a representation of canopy height), and canopy cover, which were created by combining GEDI data with Landsat, Sentinel-1, topographic, and climatic information within a random forest modeling framework. AUCs for the species-specific models ranged from 0.77–0.98, where bioclimatic and process predictors were amongst the most important variables for all species. GEDI-fusion forest structure metrics were highly ranked for all species, with fhd included as a highly ranked predictor for all species. The structural metrics included as top predictors for each species were reflective of known species-specific habitat associations. Hotspots in this ecoregion tended to be inland and occurred most often on privately-owned lands. Identification of hotspots is the first step towards management plans focused on biodiversity, and understanding ownership patterns is important for future conservation efforts. The near-global extent of GEDI data, along with recent studies that recommend woodpeckers as indicators of biodiversity across multiple forest types at local and global scales, suggest that synthesis of GEDI-derived data applied to woodpecker detection information might be a powerful approach to identifying biodiversity hotspots

Influence of UAS Flight Altitude and Speed on Aboveground Biomass Prediction

The management of low-density savannah and woodland forests for carbon storage presents a mechanism to offset the expense of ecologically informed forest management strategies. However, existing carbon monitoring systems draw on vast amounts of either field observations or aerial light detection and ranging (LiDAR) collections, making them financially prohibitive in low productivity systems where forest management focuses on promoting resilience to disturbance and multiple uses. This study evaluates how UAS altitude and flight speed influence area-based aboveground forest biomass model predictions. The imagery was acquired across a range of UAS altitudes and flight speeds that influence the efficiency of data collection. Data were processed using common structures from motion photogrammetry algorithms and then modeled using Random Forest. These results are compared to LiDAR observations collected from fixed-wing manned aircraft and modeled using the same routine. Results show a strong positive relationship between flight altitude and plot-based aboveground biomass modeling accuracy. UAS predictions increasingly outperformed (2–24% increased variance explained) commercial airborne LiDAR strategies as acquisition altitude increased from 80–120 m. The reduced cost of UAS data collection and processing and improved biomass modeling accuracy over airborne LiDAR approaches could make carbon monitoring viable in low productivity forest systems