Mapping Black Ash Dominated Stands Using Geospatial and Forest Inventory Data in Northern Minnesota, USA Peder S

892 ARTICLE

Mapping black ash dominated stands using geospatial and forest inventory data in northern Minnesota, USA Peder S. Engelstad, Michael J. Falkowski, Anthony W. D’Amato, Robert A. Slesak, Brian J. Palik, Grant M. Domke, and Matthew B. Russell

Abstract: Emerald ash borer (EAB; Agrilus planipennis Fairmaire, 1888) has been a persistent disturbance for ash forests in the United States since 2002. Of particular concern is the impact that EAB will have on the ecosystem functioning of wetlands dominated by black ash (Fraxinus nigra Marsh.). In preparation, forest managers need reliable and complete maps of black ash dominated stands. Traditionally, forest survey data from the United States Forest Inventory and Analysis (FIA) Program have provided rigorous measures of tree species at large spatial extents but are limited when providing estimates for smaller management units (e.g., stands). Fortunately, geospatial data can extend forest survey information by generating predictions of forest attributes at scales ﬁner than those of the FIA sampling grid. In this study, geospatial data were integrated with FIA data in a randomForest model to estimate and map black ash dominated stands in northern Minnesota in the United States. The model produced low error rates (overall error = 14.5%; area under the curve (AUC) = 0.92) and was strongly informed by predictors from soil saturation and phenology. These results improve upon FIA-based spatial estimates at national extents by providing forest managers with accurate, ﬁne-scale maps (30 m spatial resolution) of black ash stand dominance that could ultimately support landscape-level EAB risk and vulnerability assessments.

Key words: compound topographic index (CTI), remote sensing, black ash, emerald ash borer, forest inventory. Résumé : L’agrile du frêne (AF; Agrilus planipennis Fairmaire, 1888) est une source de perturbation persistante dans les forêts de frêne depuis 2002 aux États-Unis. L’impact que l’AF aura sur le fonctionnement de l’écosystème des milieux humides dominés par le frêne noir (Fraxinus nigra Marsh.) est particulièrement préoccupant. Pour se préparer, les gestionnaires forestiers ont besoin de cartes exhaustives et fiables des peuplements dominés par le frêne noir. Traditionnellement, les données d’inventaire forestier du programme d’analyse et d’inventaire forestier (AIF) des États-Unis ont fourni des mesures rigoureuses concernant les espèces arborescentes pour de vastes étendues mais elles sont limitées lorsqu’il s’agit de fournir des estimations pour de plus petites unités d’aménagement (p. ex des peuplements). Heureusement, les données géospatiales peuvent étendre la portée des informations de l’inventaire forestier en générant des prédictions des attributs forestiers à des échelles plus fines que la grille d’échantillonnage du programme d’AIF. Dans cette étude, des données géospatiales ont été intégrées avec des données du programme d’AIF dans une forêt d’arbres décisionnels pour estimer et cartographier les peuplements dominés par le frêne noir dans le nord du Minnesota, aux États-Unis. Le modèle a produit de faibles taux d’erreur (erreur globale = 14,5 %; surface sous la courbe (AUC) = 0,92) et était fortement informé par des prédicteurs ayant trait à la saturation du sol et à la phénologie. Ces résultats améliorent les estimations spatiales fondées sur le programme d’AIF à l’échelle nationale en fournissant aux gestionnaires forestiers des cartes précises et à petite échelle (résolution spatiale de 30 m) des peuplements dominés par le frêne noir qui pourraient en fin de compte servir de base à l’évaluation des risques et de la vulnérabilité à l’AL. [Traduit par la Rédaction]

Mots-clés : indice topographique composé (CTI), télédétection, frêne noir, agrile du frêne, inventaire forestier.

1. Introduction planipennis Fairmaire, 1888), a wood-boring beetle that was first A variety of exotic flora, fauna, and pathogens induce ecological discovered in southeast Michigan in 2002. EAB has been found to changes to forest ecosystems worldwide, ultimately disrupting attack all ash species with >2.5 cm diameter, regardless of health ecosystem processes and functions, including nutrient cycling, (Herms and McCullough 2014), and kills hosts in as little as 2 years productivity, and wildlife habitat (Lovett et al. 2016). In the short (Knight et al. 2013). Because of widespread municipal planting of term, some invasive species can alter forest floor environments ash trees, much of the research on EAB impact and mitigation and create canopy gaps, whereas long-term changes may affect strategies has been restricted to urban environments (i.e., Crook entire forest successional trajectories (Gandhi and Herms 2010). et al. 2008; Poland and McCullough 2006; Kovacs et al. 2011). How- One such invasive species is the emerald ash borer (EAB; Agrilus ever, EAB range expansion and the associated threat to nonurban

Received 8 November 2018. Accepted 3 March 2019. P.S. Engelstad and M.J. Falkowski. Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO 80523, USA. A.W. D’Amato. Rubenstein School of Environment and Natural Resources, University of Vermont, Burlington, VT 05405, USA. R.A. Slesak. Minnesota Forest Resources Council, St. Paul, MN 55108, USA; Department of Forest Resources, University of Minnesota, St. Paul, MN 55108, USA. B.J. Palik. Northern Research Station, USDA Forest Service, Grand Rapids, MN 55744, USA. G.M. Domke. Northern Research Station, Forest Inventory and Analysis, USDA Forest Service, St. Paul, MN 55108, USA. M.B. Russell. Department of Forest Resources, University of Minnesota, St. Paul, MN 55108, USA. Corresponding author: Peder S. Engelstad (email: [email protected]). Copyright remains with the author(s) or their institution(s). Permission for reuse (free in most cases) can be obtained from RightsLink.

Can. J. For. Res. 49: 892–902 (2019) dx.doi.org/10.1139/cjfr-2018-0481 Published at www.nrcresearchpress.com/cjfr on 6 March 2019. Engelstad et al. 893 ash forests is expected to grow because of migrating populations niches of target species. For example, Evans and Cushman (2009) (Taylor et al. 2010), firewood transportation (BenDor et al. 2006), used topographic indices and Landsat data to estimate the occu- and warming climate conditions (Liang and Fei 2014). pancy of four conifer species in northern Idaho. In Maine, One ash species of particular concern is black ash (Fraxinus nigra Dunckel et al. (2015) mapped eastern hemlock (Tsuga canadensis (L.) Marsh.), a slow-growing hardwood tree often found in relatively Carrière) occurrence by combining climate, soil, and topographic pure stands on poorly drained soils and in wetlands of the Upper indices with Landsat data. In Utah, Zimmermann et al. (2007) Midwest region of the United States (USA). The native range of compared the effectiveness of topographic and bioclimatic pre- black ash spans from western Newfoundland to southeastern dictors with vegetation indices from Landsat for species-level clas- Manitoba (Canada), south to Iowa and Ohio, and east to northern sification of over a dozen tree species. Similar approaches may be Virginia (Wright and Rauscher 1990). Black ash is considered to particularly effective in mapping black ash given its common function as a foundational tree species and thereby strongly reg- occurrence in wetlands and unique phenology compared with ulates ecosystem processes (Ellison et al. 2005; Youngquist et al. other tree species. 2017). Rapid stand mortality of black ash from EAB attack is ex- In this study, spectral and topographic indices and auxiliary pected to have substantial impacts on ecosystem structure and geospatial layers were combined with data from FIA field surveys function (Slesak et al. 2014). Indeed, dramatic disruptions are an- to classify black ash dominated stands across a diverse forest land- ticipated in native plant communities and wildlife food webs scape in northern Minnesota, USA. It was hypothesized that pre- (DeSantis et al. 2013; Looney et al. 2017; Youngquist et al. 2017), dictors related to soil moisture and phenology could serve as biogeochemical processes (Flower et al. 2013), and resistance to novel predictors to capture the unique ecology of black ash and invasion by exotic flora (Kenis et al. 2009; Klooster et al. 2014). that this could help to develop accurate estimates and maps of the Additionally, the reduction in evapotranspiration accompanying location, extent, and distribution of black ash dominated stands. the loss of black ash may shift vegetation types in these communities from wetland forests to marshlike ecosystems (Diamond In the development of this approach, a model selection procedure et al. 2018) and may ultimately increase flood risk (Telander et al. was used to evaluate the most important predictors for classifying 2015). The loss of black ash also poses a distinct threat to the black ash stands and generate a highly accurate, parsimonious cultural practices of multiple Indigenous groups in the northern model that accounted for class imbalance. Model performance Great Lakes states by reducing wood sources traditionally used for was evaluated via traditional accuracy assessment statistics and basketmaking (Costanza et al. 2017; Willow 2011). by conditional density plots to determine if the model produced To help proactively address these risks and implement mitiga- ecologically meaningful results. Furthermore, a spatial assess- tion strategies (i.e., planting replacement tree species, employing ment of classification stability was performed to explore the po- adaptive silvicultural techniques, and releasing biological con- tential sources of error when classifying stand dominance across a trols), forest managers need reliable and complete maps depicting diverse array of forest conditions. the spatial configuration, extent, and distribution of black ash dominated forests. Large-area estimates of tree species distribu- 2. Methods tions can be generated from systematic forest inventory data col- 2.1. Study area lected across large geographic extents (i.e., Blackard et al. 2008); The study area is defined by the extent of a Landsat scene however, such data do not provide specific predictions of species (WRS-2 Path 28/Row 27) in northern Minnesota (Fig. 1). The area occurrence at spatial extents smaller than that of the systematic (ϳ36 000 km2) sits on the transition zone between boreal and sampling grid (McRoberts and Tomppo 2007). The United States mixed forest types resulting in a diverse, heterogeneous mix of Department of Agriculture’s Forest Inventory and Analysis (FIA) hardwood and conifer species that occur in well-drained uplands Program provides forest inventory information through annual and poorly drained swamps and bogs. Historical climate records surveys of permanent sample plots (Bechtold and Patterson 2005). (1981–2010) indicate a mean annual precipitation of 540 to 810 mm The strategic-level sampling methodology of FIA ensures that pop- (Minnesota Department of Natural Resources 2017), and elevation ulation estimates at county, state, regional, and national levels ϳ meet specific precision standards. However, smaller spatial units ranges from 350 to 600 m above sea level. The study area con- (e.g., forest district management units and stands) often contain tains a mix of public and private lands, including the entirety of an insufficient number of plots to generate precise local estimates Chippewa National Forest. The study area was selected because of of forest attributes and descriptions of their spatial characteristics the availability of statewide LiDAR products in Minnesota and (Goerndt et al. 2013). These local estimates of forest conditions are cloud-free Landsat imagery coterminous with the most recent needed to quantify the effects of forest health agents (e.g., EAB) year of publicly accessible FIA data. Placing the study in the forests because stands of varying host-tree (e.g., black ash) composition of northern Minnesota is also relevant because of its proximity to are expected to be differentially affected. ongoing EAB outbreaks and associated urgency for adaptive man- Fortunately, this problem of small-area estimation has been agement strategies for black ash forests (D’Amato et al. 2018), as successfully addressed in previous studies by combining FIA data well as the predicted northward shifts in EAB range (Liang and Fei with predictors obtained from a variety of remote sensing sources 2014). to generate spatial predictions of forest attributes in the geographic space between FIA survey locations. For example, multi- 2.2. Data collection spectral data from passive optical sensors (ranging from 5 to 30 m Field data used in this study consisted of 1765 FIA plots mea- spatial resolution) have been leveraged to estimate basal area sured during a single inventory cycle between 2011 and 2015. The (McRoberts and Tomppo 2007), forest disturbance (Schroeder sampling design of FIA consists of long-term survey sites system- et al. 2014), stand density (McRoberts 2009), successional stage (Liu atically stratified across public and private lands, with each site et al. 2008), and canopy cover (Coulston et al. 2012), among others. consisting of four 0.02 ha fixed-radius subplots (i.e., phase 2 plots; Active remote sensing techniques such as light detection and Bechtold and Patterson 2005). The large number of forest attri- ranging (LiDAR) have also been used in conjunction with data butes collected at each site generally fall into two categories: site from FIA to predict biomass (Andersen et al. 2009), stand volume description and direct measurement. Site descriptions include (Sheridan et al. 2014), and fire effects (Alonzo et al. 2017). observations of forest type, condition (e.g., forest or nonforest), Species occurrence data from FIA have also been used to classify and disturbance. Measurements recorded at the individual-tree forest types and are often combined with multiple remote sensing level include standard forestry metrics such as diameter at breast or geospatial resources that help define the biotic and abiotic height (DBH; breast height = 1.35 m), tree height, age, and species.

Published by NRC Research Press 894 Can. J. For. Res. Vol. 49, 2019

Fig. 1. Spatial extent of the study area in northern Minnesota. Forest Inventory and Analysis (FIA) plots (n = 1765) evaluated during model development are shown for the 2011–2015 data collection cycle. As displayed, plot locations are “fuzzed” (within 0.8 km of true locations) for privacy purposes, but true plot coordinates were used during model development via an agreement with FIA. Surface water is shown in darker gray.

To quantify stand dominance, basal area was calculated for each study, a LiDAR-derived digital terrain model (MNGeo 2017) was species using DBH. Basal area is understood as a correlate of stand used to derive 20 m spatial resolution topographic indices to de- density and has been successfully used in previous remote sensing scribe a variety of soil attributes (Table 1). To characterize the wet studies of forest species occurrence (Duveneck et al. 2015; Goerndt soils typical of black ash stands, indices were chosen that reflect et al. 2013; Martin et al. 1998; Moisen et al. 2006; Ohmann and soil moisture potential (compound topographic index, CTI), solar Gregory 2002). Total basal area for the plot was determined by temperature effects (heat load index; McCune and Keon 2002), scaling to the hectare level using an expansion factor listed in the and water-holding capacity (integrated moisture index; Iverson FIA database for living trees ≥ 12.7 cm DBH. The relative dominance for each species in the plot was calculated as the total basal et al. 1997), in addition to standard topography metrics such as area by species divided by the total plot basal area. elevation, slope, and aspect. To explore a measure of stand dominance, plots with a simple Although LiDAR point-cloud data can be extremely useful for majority of black ash (≥50% of total plot basal area) were assigned characterizing forest structure, the low-density data set used in a binary value of presence (1), whereas plots where black ash this study was not explicitly designed for assessing vegetation accounted for less than 50% of total basal area were assigned a structure; rather, acquisition parameters were optimized for top- value of absence (0). From the 1765 field plots available during the ographic mapping. Thus, acquisition parameters such as scan inventory cycle, 923 remained after retaining only undisturbed, angle, flight-line overlap, and point density are far below rec- forested plots with single land cover condition. The application of ommended thresholds for vegetation mapping and structural the stand-dominance threshold resulted in training data that in- assessment (e.g., Evans et al. 2009). Fortunately, multispectral sat- cluded 874 absence and 49 presence points. ellite sensors collect data with higher spectral and temporal res- 2.3. Remote sensing data olution, facilitating species differentiation and the computation The use of active and passive remote sensing data is common in of seasonal means from vegetation indices. In this study, Landsat ecological research. Specific to species-level classification, these 8 single-date (13 September 2015), cloud-free, surface-reflectance data can potentially describe the biophysical characteristics of the vegetation indices were acquired from the U.S. Geological Survey target species’ environmental niche and growth patterns. For this (USGS 2017; Table 1) at a 30 m spatial resolution.

Published by NRC Research Press Engelstad et al. 895

Table 1. The 44 candidate predictor variables evaluated during development of the randomForest classification of black ash stand dominance in northern Minnesota. Source Variable Description LiDAR DTM (20 m) CTI Compound topographic index LiDAR DTM (20 m) CURVE Slope curvature Landsat (30 m) doy_EVI Single-date enhanced vegetation index Landsat (30 m) doy_IFZ Single-date integrated forest Z score Landsat (30 m) doy_MSAVI Single-date modified soil-adjusted vegetation index Landsat (30 m) doy_NBR Single-date normalized burn ratio Landsat (30 m) doy_NDMI Single-date normalized difference moisture index Landsat (30 m) doy_NDVI Single-date normalized difference vegetation index Landsat (30 m) doy_SAVI Single-date soil-adjusted vegetation index Landsat (30 m) dNBR Summer minus fall median differenced normalized burn ratio Landsat (30 m) dNDMI Summer minus fall median normalized difference moisture index Landsat (30 m) dNDVI Summer minus fall median normalized difference vegetation index LiDAR DTM (20 m) ELEV Elevation (m) Landsat (30 m) fall_NBR Median normalized burn ratio (September–October) Landsat (30 m) fall_NDMI Median normalized difference moisture index (September–October) Landsat (30 m) fall_NDVI Median normalized difference vegetation index (September–October) Landsat (30 m) grow_NBR Median normalized burn ratio (April–October) Landsat (30 m) grow_NDMI Median normalized difference moisture index (April–October) Landsat (30 m) grow_NDVI Median normalized difference vegetation index (April–October) LiDAR DTM (20 m) HLI Heat load index LiDAR DTM (20 m) IMI Integrated moisture index Landsat (30 m) JSmax_NBR Maximum normalized burn ratio (June–September) Landsat (30 m) JSmax_NDMI Maximum normalized difference moisture index (June–September) Landsat (30 m) JSmax_NDVI Maximum normalized difference vegetation index (June–September) Landsat (30 m) JSmin_NBR Minimum normalized burn ratio (June–September) Landsat (30 m) JSmin_NDMI Minimum normalized difference moisture index (June–September) Landsat (30 m) JSmin_NDVI Minimum normalized difference vegetation index (June–September) Landsat (30 m) JSmed_NBR Median normalized burn ratio (June–September) Landsat (30 m) JSmed_NDMI Median normalized difference moisture index (June–September) Landsat (30 m) JSmed_NDVI Median normalized difference vegetation index (June–September) LiDAR DTM (20 m) ROUGH Terrain roughness SSURGO (30 m) SOILS_DI SSURGO soil drainage index SSURGO (30 m) SOILS_PI SSURGO soil productivity index Landsat (30 m) summ_NBR Summer normalized burn ratio (July–August) Landsat (30 m) summ_NDMI Summer normalized difference moisture index (July–August) Landsat (30 m) summ_NDVI Summer normalized difference vegetation index (July–August) Landsat (30 m) TCAP_A Harmonized tasseled cap angle Landsat (30 m) TCAP_B Harmonized tasseled cap brightness (Vogeler et al. 2018) Landsat (30 m) TCAP_D Harmonized tasseled cap distance Landsat (30 m) TCAP_DI Harmonized tasseled cap disturbance index Landsat (30 m) TCAP_G Harmonized tasseled cap greenness (Vogeler et al. 2018) Landsat (30 m) TCAP_W Harmonized tasseled cap wetness (Vogeler et al. 2018) LiDAR DTM (20 m) TRASP Transformed aspect NWI (30 m) WETLAND National Wetlands Inventory category Note: Variables included in the final model are indicated in boldface type. LiDAR, light detection and ranging; DTM, digital terrain model; SSURGO, Soil Survey Geographic Database; NWI, National Wetlands Inventory.

Additionally, Google Earth Engine (https://earthengine.google. (Wright and Rauscher 1990), NWI vector data were used to develop com) was used to generate minimum, maximum, median, and a 30 m spatial resolution categorical raster (Table 1) that would differenced values for normalized burn ratio (NBR), normalized discriminate forested from nonforested wetlands. Soil indices difference vegetation index (NDVI), and normalized difference (Table 1) from the Soil Survey Geographic Database (SSURGO; Soil moisture index (NDMI) at a 30 m spatial resolution (Table 1) within Survey Staff 2017) also provided 30 m spatial resolution rasters of a date range long enough to capture the growth cycles of multiple soil inundation and nutrient quality. First, a drainage index, de- forest species (1 April to 31 October 2015). The goal of including signed to measure long-term soil wetness, was used to relate black seasonally descriptive predictors was to differentiate the phe- ash with heavily inundated soils. Second, the incorporation of a nological signature of black ash, deﬁned by smaller annual leaf- productivity index, interpreted from soil taxonomy, provided a area growth and a restricted number of growing days (Telander measure related to the nutrient content of soils where black ash is et al. 2015) from co-occurring deciduous tree species. found.

2.4. Auxiliary geospatial data 2.5. Predictor variable development To further capture the abiotic niche of black ash, geospatial FIA plots are located on their own grid system irrespective of data were acquired from the National Wetlands Inventory (NWI), any predictor variable georegistration present in this study. To which uses manual interpretation of aerial imagery (1 m spatial address this,a3×3focal mean moving window was applied to resolution) to produce a detailed hierarchical classiﬁcation of wet- generate mean values for all continuous predictor variables over land habitat types (Dahl et al. 2009). Following previous research the area encompassing all four FIA subplots (ϳ90m×90m)while establishing forest wetlands as the primary habitat of black ash resampling the predictor rasters to a common spatial resolution

Published by NRC Research Press 896 Can. J. For. Res. Vol. 49, 2019

(30m×30m).Despite the potential to describe only general forest Table 2. Classiﬁcation accuracy statistics for the randomForest model conditions, this method has been used to partially alleviate coreg- of presence and absence of black ash stand dominance. istration errors between remotely sensed predictors and FIA data Omission Commission Class incorporating all four subplots (Dunckel et al. 2015; Moisen et al. Class error (producer) error (user) accuracy (%) 2006; Nelson et al. 2009; Powell et al. 2010). An additional conse- quence of using all four subplots is a potential reduction in train- Presence 4.7 14.3 85.7 Absence 24.7 14.6 85.4 ing data size if ﬁltering out nonhomogeneous forest-cover classes (McRoberts 2009). Overall accuracy (%) 85.5

2.6. Model development A presence and absence classification model of black ash stand Fig. 2. Plot of variable importance values for the predictors used dominance was developed using the randomForest algorithm (RF; in the final randomForest model of black ash dominated stand Breiman 2001) as implemented in the randomForest package presence or absence. Percent Mean Decrease Accuracy measures the (Liaw and Wiener 2002) of the R statistical software (R Core Team percent accuracy lost with the removal of individual predictor 2017). The RF model was run with 4000 bootstrapped replicates variables (listed on the y axis). See Table 1 for definitions of the (ntrees), with replacement, to reach convergence on error rates abbreviations. and stabilize variable importance and variable interaction (Evans and Cushman 2009). Although RF is considered robust to col- linearity (Cutler et al. 2007), a model-selection procedure was used to reduce the number of redundant predictors while retaining ecological interpretability (Falkowski et al. 2009). Prior to model selection, a Gram-Schmidt QR decomposition procedure, which is in the R rfUtilities package (Evans et al. 2011), was used to identify and remove multicollinear predictor variables. To further opti- mize model parsimony, a model-selection function, also available in rfUtilities, was implemented based on its successful use in previous studies (i.e., Falkowski et al. 2009; Olaya-Marin et al. 2013; Tinkham et al. 2014). This function iterates through and evaluates potential models (i.e., suites of potential predictor variables) by assessing possible variable combinations and comparing them based on a model improvement ratio statistic (Murphy et al. 2010) that measures the difference in percent change in overall out-of- bag error.

2.7. Addressing class imbalance In machine learning, class-size imbalance remains a regular source of bias, often resulting in the majority class being favored over the minority class (He and Garcia 2009; Japkowicz and Stephen 2002). Specific to RF, the iterative bootstrap sample of quality of model predictions. Finally, conditional density plots, training data often lacks a representative sample from the minor- which display the probability of class occurrence across the range ity class. To address this, downsampling the majority class has of values for a single predictor, were examined for the most im- previously been shown to be more effective in overcoming majority-class bias when compared with oversampling and portant predictors (identified by the final RF model) to verify the weighted class methods (Chen et al. 2004; Drummond and Holte production of an ecologically reasonable model. 2003). In this study, the majority class (i.e., absence) was down- 3. Results sampled to match the size of the minority class (i.e., presence; n = 49) for each bootstrap sample to minimize bias from class 3.1. Model selection imbalance. The Gram-Schmidt QR decomposition procedure identified and removed 33 multicollinear predictors. The secondary model selec- 2.8. Accuracy assessment and model evaluation tion function (model improvement ratio statistic) did not identify Classification accuracy was measured based on internal error further model parsimony optimizations for the binary classification estimates from out-of-bag samples generated by the RF model. Overall accuracy and errors of omission and commission were of black ash dominated stands. This left 12 predictor variables that calculated to explore the accuracy of presence and absence classes were used to develop the RF classification model: CTI, summer produced by the model. The model was also assessed using the minus fall median differenced NBR (dNBR), June–September mini- area under the curve (AUC) statistic, generated from a receiver mum NDMI (JSmin_NDMI), tasseled cap (TC) angle, TC brightness, TC operating characteristic (ROC) curve comparing the true positive greenness, TC wetness, TC disturbance index (TCAP_DI), single-date rate (sensitivity) with the true negative rate (specificity). Following NBR, single-date enhanced vegetation index (EVI), single-date NDMI, this, the model was applied to the geospatial data sets to produce and NWI wetland class (Table 1). a binary classification of presence and absence of black ash dominance across the study area. Additionally, a class probability map 3.2. Classification accuracy and variable importance (following Falkowski et al. 2009) was created by assigning the The overall accuracy of the final model was 85.5%, and the AUC maximum value from the binary model’s RF class vote matrix for value was 0.92. Individual class and omission and commission errors each cell across the study area. For example, if a grid cell received were low (Table 2); however, some confusion did occur in the form 80% of the RF votes for a particular class, it would be assigned a of false positives, resulting in a 24.7% error rate. The investigation of value of 0.80. The class probability map was used in conjunction variable importance (Fig. 2) indicated CTI as the most important, with high spatial resolution imagery (1 m) to visually assess the followed closely by TCAP_DI, dNBR, and JSmin_NDMI.

Published by NRC Research Press Engelstad et al. 897

Fig. 3. Binary classiﬁcation (left), maximum class probability (classiﬁcation stability) map (right; both 30 m resolution), and associated output summary statistics produced by the randomForest model of black ash stand dominance in northern Minnesota. The color of each bar corresponds to the associated stability level in the legend and probability map. The numbers listed above each bar represent the percentages of total land area for each probability level in the study area.

3.3. Model evaluation Conditional density plots (Fig. 5) were examined to verify that the The class probability map (Fig. 3) illustrates the prediction sta- model produced ecologically meaningful results. The plots repre- bility of both presences and absences. The majority of the study sent the relationships among the probability of presence or ab- area (ϳ60%) had a classiﬁcation stability of over 80%. In contrast, sence classes and the four most important predictors. For the approximately 14% of the study area was represented by stability presence class, an increasing likelihood of presence is generally under 60%. Initial visual interpretation of these pixels indicated associated with increasing values of CTI, TCAP_DI, and dNBR. The that higher instability typically occurred in areas of forest distur- inverse is true for JSmin_NDMI, with the likelihood of presence bance (i.e., harvest) during the years of data collection (Fig. 4). quickly decreasing for values above ϳ0.25.

Published by NRC Research Press 898 Can. J. For. Res. Vol. 49, 2019

Fig. 4. Detailed views of the full class probability map (left) overlaying the extent of a known area of forest disturbance (harvest activity) occurring during the FIA data collection cycle (2011–2015). High-resolution (1 m) DigitalGlobe imagery (right) was visually assessed in Google Earth Pro to investigate areas of low prediction stability.

Fig. 5. Conditional density plots for the top four predictors from the randomForest model. The light gray area represents the probability of presence and the dark gray area represents the probability of absence across the range of values for each individual predictor.

Published by NRC Research Press Engelstad et al. 899

4. Discussion of presence is higher may be explained by variations in other The RF model developed in this study effectively classified the correlates, including organic matter content, pH, and phosphorus presence and absence of black ash dominated stands while ad- levels (Moore et al. 1993), conducive to black ash establishment. The discriminatory power of CTI is encouraging, but it is also dressing potential class imbalance bias. The maps produced are notable that the other derived topographic and soil-moisture in- operationally ready estimates of the spatial configuration, distri- dices were less important than hypothesized. This may partially bution, and extent of black ash in northern Minnesota. These be because of a general lack of landform variation in northern results successfully overcome the spatial limitations inherent in Minnesota relative to areas with complex terrain, where multiple using forest inventory data alone to represent local estimates of topographic indices have been used to develop species-occurrence black ash and allow managers to understand the landscape-level models (Evans and Cushman 2009). Additionally, soil-moisture patterns of a species at risk of potential habitat loss due to EAB. indices may lack precision at higher resolutions, leading to lower The accuracies presented here are similar to, or improve upon, model importance (McEachran et al. 2018). the limited research generating species-level classifications of TCAP_DI, a normalized, linear combination of the three stan- black ash from remotely sensed data at the spatial resolution used dard TC indices (brightness, greenness, and wetness), was also in this study (30 m). For example, Wolter et al. (1995) used multi- important in the model and displayed a similar trend of values date Landsat imagery in a partial least squares (PLS) regression to increasing in parallel with the likelihood of presence. Originally produce a mean class accuracy of 91% for black ash stands in developed to highlight the spectral differences between vegetated northwestern Wisconsin after masking all nonhardwood and oak and unvegetated pixels, TCAP_DI scales from low values repre- cover types. Wolter and White (2002) used the same methodology senting “mature” forest to high values representing “bare soil” and data sources to map black ash cover in northeastern Minne- (Healey et al. 2005). In its conditional density plot, higher TCAP_DI sota (overall accuracy = 83.5%). Again, with a PLS-regression ap- values are generally associated with a higher probability of pres- proach and multidate Landsat, Wolter and Townsend (2011) ence (Fig. 5). The most likely explanation is the unique phenology estimated forest composition in northeastern Minnesota and pro- of black ash relative to neighboring deciduous species. Black ash duced a mean accuracy of 89.5% for black ash. Additional studies, is often the last leaf-on and first leaf-off species (Wolter and focused more on black ash as a component of forest cover type, Townsend 2011), leading to less light being absorbed by its canopy. have generally shown lower classification accuracies. Reese et al. Supporting this explanation were the distinct thresholds of the (2002) achieved 81.8% accuracy when combining black ash with conditional density plots for dNBR and JSmin_NDMI (Fig. 5). For red maple (Acer rubrum L.) and silver maple (Acer saccharinum L.) in dNBR, high values represent more rapid senescence from April to a “broad leaf deciduous forest” class while mapping land cover in October and were highly associated with black ash presence. For Wisconsin. A study by Bergen and Dronova (2007) in northern JSmin_NDMI, presence was associated with low values, which are Michigan produced moderate success (accuracy = 75.8%) using a often found in areas with sparse canopy closure, including for- “wet deciduous” category that included trembling aspen (Populus ested wetland habitat. The base indices for these two metrics, NBR tremuloides Michx.), red maple, black ash, and white birch (Betula and NDMI, are both highly influenced by the inclusion of the papyrifera Marsh.). mid-infrared (MIR) bands, which have shown the ability to dis- Although direct comparisons cannot be made with the accura- criminate a gradient of foliar moisture content (Hunt et al. 1987). cies from models using regression instead of classification, previ- Though model stability is high in much of the study area, the ous studies on abundance have also included black ash at the class probability map indicates that low stability values occur over species level but often at spatial resolutions coarser than those in areas of disturbance, commonly because of forest harvest (Fig. 4). this study. For example, Chambers et al. (2013) reported an R2 of These areas likely account for the notable rate of false positives 0.37 when estimating black ash basal area across eastern North produced by the model and may be due to unanticipated similar- America using forest inventory data combined with topographic, ities in photosynthetic activity between areas of forest regrowth climate, and soil predictors using 20 km grid cells. Wilson et al. and black ash stands or model confusion driven by mixed forest (2013) predicted black ash abundance (R2 = 0.71) using data from classes generated by aggregating predictors across the extent of the Moderate Resolution Imaging Spectroradiometer (MODIS; the FIA plot (i.e., averaging among subplots). Alternatively, 250 m spatial resolution) across the eastern United States. Studies classification confusion could be due to the temporal mismatch with similar methodologies using different species have produced between the field data collection period (2011–2015) and the mul- higher accuracies compared with this study. Dunckel et al. (2015) tispectral data (2015 only). To overcome these errors, the use of reported an AUC value of 0.91 when combining relative basal area disturbance metrics from time-series data such as those from from FIA with climatic, soil, and Landsat data in an RF model to LandTrendr (Kennedy et al. 2010) could be used to highlight classify the presence and absence of a rare forest species (eastern changes in spectral trajectories indicative of significant vegeta- hemlock) in Maine. Evans and Cushman (2009) saw high model tion change. performance (AUC ≥ 0.98) for the classification of four conifer The classification produced in this study can be used to enhance species in northern Idaho based on FIA data and a downsampling forest management actions at local levels, acting as a complement technique in RF with predictors from topography, climate, and to additional data sources used to assess the risks to black ash multispectral data. dominated ecosystems posed by EAB. Additionally, the map prod- Overall, the RF classification algorithm produced accurate clas- ucts may help identify locations for enhanced monitoring efforts sifications and generated an interpretable importance metric for investigating the possible factors mitigating EAB impact, includ- each of the predictors used in the final model. The conditional ing woodpecker habitat use (Flower et al. 2014; Lyons 2015) and the density plot for the most important predictor, CTI, indicated in- release of biocontrols such as known EAB parasitoids (Duan et al. creasing values to be generally associated with an increasing like- 2018). The methods presented here could also be applied to mod- lihood of presence (Fig. 5). As CTI values increase, drainage els of presence and absence of other ash species across the antic- depressions tend to form on the landscape, giving the index ipated invasion range of EAB. Dominant stands identified in this strong correlations with soil moisture content and surface water study may also be useful in establishing a baseline of black ash pooling (Moore et al. 1991). The ability of CTI to discriminate these population health (Palik et al. 2012), helping to distinguish between topographic features is important in capturing the location of periodic crown dieback and EAB infestation. Finally, these results saturated soils (i.e., bogs and streambanks), where few tree species could also complement the growing body of research assessing the other than black ash can establish or grow (Wright and Rauscher early detection of EAB infestation through remote sensing. Previous 1990). Furthermore, CTI values for cases in which the probability work has investigated hyperspectral (Pontius et al. 2008; Zhang et al.

Published by NRC Research Press 900 Can. J. For. Res. Vol. 49, 2019

2014), LiDAR (Hu et al. 2014), and high spatial resolution data (Murfitt Costanza, K.K.L., Livingston, W.H., Kashian, D.M., Slesak, R.A., Tardif, J.C., et al. 2016), but approaches utilizing time-series metrics and phe- Dech, J.P., Diamond, A.K., Daigle, J.J., Ranco, D.J., Neptune, J.S., Benedict, L., Fraver, S.R., Reinikainen, M., and Siegert, N.W. 2017. The precarious state of nological signatures remain unexplored. a cultural keystone species: tribal and biological assessments of the role and future of black ash. J. For. 115(5): 435–446. doi:10.5849/jof.2016-034R1. 5. Conclusion Coulston, J.W., Moisen, G.G., Wilson, B.T., Finco, M.V., Cohen, W.B., and Brewer, C.K. 2012. Modeling percent tree canopy cover: a pilot study. Photo- Known limitations of systematically collected forest inventory gramm. Eng. Remote Sens. 78(7): 715–727. doi:10.14358/PERS.78.7.715. data can be overcome using remotely sensed data in support of Crook, D.J., Khrimian, A., Francese, J.A., Fraser, I., Poland, T.M., Sawyer, A.J., and forest-species classification and small-area estimation. In this Mastro, V.C. 2008. Development of a host-based semiochemical lure for trap- study, we hypothesized that spatially referenced estimates of ping emerald ash borer Agrilus planipennis (Coleoptera: Buprestidae). Environ. black ash dominated (>50% basal area) stands in northern Minne- Entomol. 37(2): 356–365. doi:10.1603/0046-225X(2008)37[356:DOAHSL]2.0.CO;2. Cutler, D.R., Edwards, T.C., Beard, K.H., Cutler, A., Hess, K.T., Gibson, J., and sota could be generated by combining FIA survey data with spatial Lawler, J.J. 2007. Random forests for classification in ecology. Ecology, 88(11): predictors related to soil characteristics and phenology. The class- 2783–2792. doi:10.1890/07-0539.1. balanced RF modelling framework presented herein achieved an Dahl, T.E., Dick, J., Swords, J., and Wilen, B.O. 2009. Data collection require- overall accuracy of 85% and AUC of 0.92 (with class accuracies of ments and procedures for mapping wetland, deepwater and related habitats 85.4% for absence and 85.7% for presence), indicating robust of the United States. National Standards and Support Team, Division of Hab- itat and Resource Conservation, U.S. Fish and Wildlife Service, Madison, Wis. model performance relative to previous studies conducted with D’Amato, A., Palik, B., Slesak, R., Edge, G., Matula, C., and Bronson, D. 2018. data at similar spatial resolutions. The results support the hypoth- Evaluating adaptive management options for black ash forests in the face of esis that remote sensing of a finer scale and geospatial products emerald ash borer invasion. Forests, 9(6): 348. doi:10.3390/f9060348. characterizing soils and phenology can extend forest survey data DeSantis, R.D., Moser, W.K., Gormanson, D.D., Bartlett, M.G., and Vermunt, B. 2013. Effects of climate on emerald ash borer mortality and the potential for in support of land management actions in smaller management ash survival in North America. Agric. For. Meteorol. 178: 120–128. doi:10.1016/ units. The map outputs produced in this study improve upon j.agrformet.2013.04.015. similar map products at coarse spatial resolutions and can aid Diamond, J.S., McLaughlin, D., Slesak, R., D’Amato, A., and Palik, B. 2018. For- local forest managers as they assess EAB risk and vulnerability in ested versus herbaceous wetlands: can management mitigate ecohydrologic black ash dominated stands. Specifically, these map outputs can regime shifts from invasive emerald ash borer? J. Environ. Manage. 222: 436–446. doi:10.1016/j.jenvman.2018.05.082. be paired with hydrology, understory community, and wildlife Drummond, C., and Holte, R.C. 2003. C4.5, class imbalance, and cost sensitivity: data to anticipate local ecosystem impacts from the effects of why under-sampling beats over-sampling [online]. In Workshop on Learning rapid stand mortality due to EAB. from Imbalanced Datasets II. Available from http://www.site.uottawa.ca/ϳnat/ Workshop2003/drummondc.pdf [accessed 5 November 2018]. Acknowledgements Duan, J., Bauer, L., van Driesche, R., and Gould, J. 2018. Progress and challenges of protecting North American ash trees from the emerald ash borer using This work was supported through funding from the Minnesota biological control. Forests, 9(3): 142. doi:10.3390/f9030142. Environmental and Natural Resources Trust Fund, Upper Great Dunckel, K., Weiskittel, A., Fiske, G., Sader, S.A., Latty, E., and Arnett, A. 2015. Lakes and Midwest Landscape Conservation Cooperative, Depart- Linking remote sensing and various site factors for predicting the spatial distribution of eastern hemlock occurrence and relative basal area in Maine, ment of Interior’s Northeast Climate Adaptation Science Center, USA. For. Ecol. Manage. 358: 180–191. doi:10.1016/j.foreco.2015.09.012. and Colorado State University. Actual plot locations were ob- Duveneck, M.J., Thompson, J.R., and Wilson, B.T. 2015. An imputed forest com- tained through a memorandum of understanding (MOU) with the position map for New England screened by species range boundaries. For. Forest Inventory and Analysis (FIA) Program (USDA Forest Service, Ecol. Manage. 347: 107–115. doi:10.1016/j.foreco.2015.03.016. Northern Research Station). The authors would like to thank Ellison, A.M., Bank, M.S., Clinton, B.D., Colburn, E.A., Elliott, K., Ford, C.R., Foster, D.R., Kloeppel, B.D., Knoepp, J.D., Lovett, G.M., Mohan, J., Orwig, D.A., Wilfred Previant of the Colorado State Forest Service for his FIA Rodenhouse, N.L., Sobczak, W.V., Stinson, K.A., Stone, J.K., Swan, C.M., knowledge and expertise. Thompson, J., Von Holle, B., and Webster, J.R. 2005. Loss of foundation species: consequences for the structure and dynamics of forested ecosystems. References Front. Ecol. Environ. 3(9): 479–486. doi:10.1890/1540-9295(2005)003[0479: LOFSCF]2.0.CO;2. Alonzo, M., Morton, D.C., Cook, B.D., Andersen, H.E., Babcock, C., and Pattison, R. 2017. Patterns of canopy and surface layer consumption in a Evans, J.S., and Cushman, S.A. 2009. Gradient modeling of conifer species using boreal forest fire from repeat airborne lidar. Environ. Res. Lett. 12(6): 065004. random forests. Landsc. Ecol. 24(5): 673–683. doi:10.1007/s10980-009-9341-0. doi:10.1088/1748-9326/aa6ade. Evans, J.S., Hudak, A.T., Faux, R., and Smith, A. 2009. Discrete return lidar in Andersen, H.E., Barrett, T., Winterberger, K., Strunk, J., and Temesgen, H. 2009. natural resources: recommendations for project planning, data processing, Estimating forest biomass on the western lowlands of the Kenai Peninsula of and deliverables. Remote Sens. 1(4): 776–794. doi:10.3390/rs1040776. Alaska using airborne lidar and field plot data in a model-assisted sampling Evans, J.S., Murphy, M.A., Holden, Z.A., and Cushman, S.A. 2011. Modeling spe- design. In Proceedings of the IUFRO Division 4 Conference: Extending Forest cies distribution and change using random forest. In Predictive species and Inventory and Monitoring over Space and Time, Quebec City, Que. pp. 19–22. habitat modeling in landscape ecology: concepts and applications. Edited by Bechtold, W.A., and Patterson, P.L. 2005. The enhanced Forest Inventory and C.A. Drew, Y.F. Wiersma, and F. Huettmann. Springer, New York. pp. 153–159. Analysis program — national sampling design and estimation procedures. doi:10.1007/978-1-4419-7390-0_8. USDA For. Serv. Gen. Tech. Rep. SRS-80. USDA Forest Service, Southern Re- Falkowski, M.J., Evans, J.S., Martinuzzi, S., Gessler, P.E., and Hudak, A.T. 2009. search Station, Asheville, N.C. doi:10.2737/SRS-GTR-80. Characterizing forest succession with lidar data: an evaluation for the Inland BenDor, T.K., Metcalf, S.S., Fontenot, L.E., Sangunett, B., and Hannon, B. 2006. Northwest, USA. Remote Sens. Environ. 113(5): 946–956. doi:10.1016/j.rse.2009. Modeling the spread of the Emerald Ash Borer. Ecol. Model. 197(1–2): 221–236. 01.003. doi:10.1016/j.ecolmodel.2006.03.003. Flower, C.E., Knight, K.S., and Gonzalez-Meler, M.A. 2013. Impacts of the emerald Bergen, K.M., and Dronova, I. 2007. Observing succession on aspen-dominated ash borer (Agrilus planipennis Fairmaire) induced ash (Fraxinus spp.) mortality landscapes using a remote sensing-ecosystem approach. Landsc. Ecol. 22(9): on forest carbon cycling and successional dynamics in the eastern United 1395–1410. doi:10.1007/s10980-007-9119-1. States. Biol. Invasions, 15(4): 931–944. doi:10.1007/s10530-012-0341-7. Blackard, J.A., Finco, M.V., Helmer, E.H., Holden, G.R., Hoppus, M.L., Flower, C.E., Long, L.C., Knight, K.S., Rebbeck, J., Brown, J.S., Gonzalez-Meler, M.A., and Jacobs, D.M., and Ruefenacht, B. 2008. Mapping US forest biomass using Whelan, C.J. 2014. Native bark-foraging birds preferentially forage in infected nationwide forest inventory data and moderate resolution information. ash (Fraxinus spp.) and prove effective predators of the invasive emerald ash Remote Sens. Environ. 112(4): 1658–1677. doi:10.1016/j.rse.2007.08.021. borer (Agrilus planipennis Fairmaire). For. Ecol. Manage. 313: 300–306. doi:10. Breiman, L. 2001. Random forests. Mach. Learn. 45(1): 5–32. doi:10.1023/ 1016/j.foreco.2013.11.030. A:1010933404324. Gandhi, K.J.K., and Herms, D.A. 2010. Direct and indirect effects of alien insect Chambers, D., Périé, C., Casajus, N., and de Blois, S. 2013. Challenges in model- herbivores on ecological processes and interactions in forests of eastern ling the abundance of 105 tree species in eastern North America using cli- North America. Biol. Invasions, 12(2): 389–405. doi:10.1007/s10530-009-9627-9. mate, edaphic, and topographic variables. For. Ecol. Manage. 291(1): 20–29. Goerndt, M.E., Monleon, V.J., and Temesgen, H. 2013. Small-area estimation of doi:10.1016/j.foreco.2012.10.046. county-level forest attributes using ground data and remote sensed auxiliary Chen, C., Liaw, A., and Breiman, L. 2004. Using random forest to learn imbal- information. For. Sci. 59(5): 536–548. doi:10.5849/forsci.12-073. anced data [online]. University of California, Berkeley. Available from https:// He, H., and Garcia, E.A. 2009. Learning from imbalanced data. IEEE Trans. statistics.berkeley.edu/sites/default/files/tech-reports/666.pdf [accessed Knowl. Data Eng. 21(9): 1263–1284. doi:10.1109/TKDE.2008.239. 5 November 2018]. Healey, S.P., Cohen, W.B., Zhiqiang, Y., and Krankina, O.N. 2005. Comparison of

Published by NRC Research Press Engelstad et al. 901

Tasseled Cap-based Landsat data structures for use in forest disturbance Moisen, G.G., Freeman, E.A., Blackard, J.A., Frescino, T.S., Zimmermann, N.E., detection. Remote Sens. Environ. 97(3): 301–310. doi:10.1016/j.rse.2005.05.009. and Edwards, T.C. 2006. Predicting tree species presence and basal area in Herms, D.A., and McCullough, D.G. 2014. Emerald ash borer invasion of North Utah: a comparison of stochastic gradient boosting, generalized additive America: history, biology, ecology, impacts, and management. Annu. Rev. models, and tree-based methods. Ecol. Model. 199(2): 176–187. doi:10.1016/j. Entomol. 59(1): 13–30. doi:10.1146/annurev-ento-011613-162051. ecolmodel.2006.05.021. Hu, B., Li, J., Wang, J., and Hall, B. 2014. The early detection of the emerald ash Moore, I.D., Grayson, R.B., and Ladson, A.R. 1991. Digital terrain modelling: a borer (EAB) using advanced geospacial technologies. Int. Arch. Photogramm. review of hydrological, geomorphological, and biological applications. Hy- Remote Sens. Spatial Inf. Sci. 40(2): 213–219. doi:10.5194/isprsarchives-XL-2- drol. Proccess. 5(1): 3–30. doi:10.1002/hyp.3360050103. 213-2014. Moore, I.D., Gessler, P.E., Nielsen, G.A., and Peterson, G.A. 1993. Soil attribute Hunt, E.R., Rock, B.N., and Nobel, P.S. 1987. Measurement of leaf relative water prediction using terrain analysis. Soil Sci. Soc. Am. J. 57(2): 443–452. doi:10. content by infrared reflectance. Remote Sens. Environ. 22(3): 429–435. doi: 2136/sssaj1993.03615995005700020026x. 10.1016/0034-4257(87)90094-0. Murfitt, J., He, Y., Yang, J., Mui, A., and De Mille, K. 2016. Ash decline assessment Iverson, L.R., Dale, M.E., Scott, C.T., and Prasad, A. 1997. A GIS-derived integrated in emerald ash borer infested natural forests using high spatial resolution moisture index to predict forest composition and productivity of Ohio for- images. Remote Sens. 8(3): 256. doi:10.3390/rs8030256. ests (U.S.A.). Landsc. Ecol. 12(5): 331–348. doi:10.1023/A:1007989813501. Murphy, M.A., Evans, J.S., and Storfer, A. 2010. Quantifying Bufo boreas connec- Japkowicz, N., and Stephen, S. 2002. The class imbalance problem: a systematic tivity in Yellowstone National Park with landscape genetics. Ecology, 91(1): study. Intell. Data Anal. 6(5): 429–449. doi:10.3233/IDA-2002-6504. 252–261. doi:10.1890/08-0879.1. Kenis, M., Auger-Rozenberg, M.-A., Roques, A., Timms, L., Péré, C., Cock, M.J.W., Nelson, M.D., Healey, S.P., Moser, W.K., and Hansen, M.H. 2009. Combining Settele, J., Augustin, S., and Lopez-Vaamonde, C. 2009. Ecological effects of satellite imagery with forest inventory data to assess damage severity follow- invasive alien insects. In Ecological impacts of non-native invertebrates and ing a major blowdown event in northern Minnesota, USA. Int. J. Remote Sens. fungi on terrestrial ecosystems. Edited by D. Langor and J. Sweeney. Springer, 30(19): 5089–5108. doi:10.1080/01431160903022951. Dordrecht, Netherlands. pp. 21–45. doi:10.1007/978-1-4020-9680-8_3. Ohmann, J.L., and Gregory, M.J. 2002. Predictive mapping of forest composition Kennedy, R.E., Yang, Z., and Cohen, W.B. 2010. Detecting trends in forest disturbance and structure with direct gradient analysis and nearest-neighbor imputation and recovery using yearly Landsat time series: 1. LandTrendr — temporal in coastal Oregon, U.S.A. Can. J. For. Res. 32(4): 725–741. doi:10.1139/x02-011. segmentation algorithms. Remote Sens. Environ. 114(12): 2897–2910. doi:10. Olaya-Marín, E.J., Martínez-Capel, F., and Vezza, P. 2013. A comparison of artifi- 1016/j.rse.2010.07.008. cial neural networks and random forests to predict native fish species rich- Klooster, W.S., Herms, D.A., Knight, K.S., Herms, C.P., McCullough, D.G., ness in Mediterranean rivers. Knowl. Manage. Aquat. Ecosyst. 409(7): 19. Smith, A., Gandhi, K.J.K., and Cardina, J. 2014. Ash (Fraxinus spp.) mortality, doi:10.1051/kmae/2013052. regeneration, and seed bank dynamics in mixed hardwood forests following Palik, B.J., Ostry, M.E., Venette, R.C., and Abdela, E. 2012. Tree regeneration in invasion by emerald ash borer (Agrilus planipennis). Biol. Invasions, 16(4): 859– black ash (Fraxinus nigra) stands exhibiting crown dieback in Minnesota. For. 873. doi:10.1007/s10530-013-0543-7. Ecol. Manage. 269: 26–30. doi:10.1016/j.foreco.2011.12.020. Knight, K.S., Brown, J.P., and Long, R.P. 2013. Factors affecting the survival of ash Poland, T.M., and McCullough, D.G. 2006. Emerald ash borer: invasion of the (Fraxinus spp.) trees infested by emerald ash borer (Agrilus planipennis). Biol. urban forest and the threat to North America’s ash resource. J. For. 104(3): Invasions, 15(2): 371–383. doi:10.1007/s10530-012-0292-z. 118–124. Kovacs, K.F., Mercader, R.J., Haight, R.G., Siegert, N.W., McCullough, D.G., and Pontius, J., Martin, M., Plourde, L., and Hallett, R. 2008. Ash decline assessment Liebhold, A.M. 2011. The influence of satellite populations of emerald ash in emerald ash borer-infested regions: a test of tree-level, hyperspectral tech- borer on projected economic costs in U.S. communities, 2010–2020. J. Environ. nologies. Remote Sens. Environ. 112(5): 2665–2676. doi:10.1016/j.rse.2007. Manage. 92(9): 2170–2181. doi:10.1016/j.jenvman.2011.03.043. 12.011. Liang, L., and Fei, S. 2014. Divergence of the potential invasion range of emerald ash borer and its host distribution in North America under climate change. Powell, S.L., Cohen, W.B., Healey, S.P., Kennedy, R.E., Moisen, G.G., Pierce, K.B., Clim. Change, 122(4): 735–746. doi:10.1007/s10584-013-1024-9. and Ohmann, J.L. 2010. Quantification of live aboveground forest biomass dynamics with Landsat time-series and field inventory data: a comparison of Liaw, A., and Wiener, M. 2002. Classification and regression by randomForest empirical modeling approaches. Remote Sens. Environ. 114(5): 1053–1068. [online]. R News, 2/3(December 2002): 18–22. Available from https://cran. r-project.org/doc/Rnews/Rnews_2002-3.pdf [accessed 5 November 2018]. doi:10.1016/j.rse.2009.12.018. Liu, W., Song, C., Schroeder, T.A., and Cohen, W.B. 2008. Predicting forest suc- R Core Team. 2017. R: a language and environment for statistical computing cessional stages using multitemporal Landsat imagery with forest inventory [online]. R Foundation for Statistical Computing, Vienna, Austria. Available and analysis data. Int. J. Remote Sens. 29(13): 3855–3872. doi:10.1080/ from https://www.R-project.org/. 01431160701840166. Reese, H.M., Lillesand, T.M., Nagel, D.E., Stewart, J.S., Goldmann, R.A., Looney, C.E., D’Amato, A.W., Palik, B.J., Slesak, R.A., and Slater, M.A. 2017. The Simmons, T.E., Chipman, J.W., and Tessar, P.A. 2002. Statewide land cover response of Fraxinus nigra forest ground-layer vegetation to emulated emer- derived from multiseasonal Landsat TM data: a retrospective of the WIS- ald ash borer mortality and management strategies in northern Minnesota, CLAND project. Remote Sens. Environ. 82(2–3): 224–237. doi:10.1016/S0034- USA. For. Ecol. Manage. 389(2017): 352–363. doi:10.1016/j.foreco.2016.12.028. 4257(02)00039-1. Lovett, G.M., Weiss, M., Liebhold, A.M., Holmes, T.P., Leung, B., Lambert, K.F., Schroeder, T.A., Healey, S.P., Moisen, G.G., Frescino, T.S., Cohen, W.B., Orwig, D.A., Campbell, F.T., Rosenthal, J., McCullough, D.G., Wildova, R., Huang, C., Kennedy, R.E., and Yang, Z. 2014. Improving estimates of forest Ayres, M.P., Canham, C.D., Foster, D.R., LaDeau, S.L., and Weldy, T. 2016. disturbance by combining observations from Landsat time series with U.S. Nonnative forest insects and pathogens in the United States: impacts and Forest Service Forest Inventory and Analysis data. Remote Sensing of Envi- policy options. Ecol. Appl. 26(5): 1437–1455. doi:10.1890/15-1176. ronment, 154(November): 61–73. doi:10.1016/j.rse.2014.08.005. Lyons, D.B. 2015. What’s killing the green menace: mortality factors affecting Sheridan, R., Popescu, S., Gatziolis, D., Morgan, C., and Ku, N.-W. 2014. Modeling the emerald ash borer (Coleoptera: Buprestidae) in North America? Can. forest aboveground biomass and volume using airborne LiDAR metrics and Entomol. 147(3): 263–276. doi:10.4039/tce.2014.62. forest inventory and analysis data in the Pacific Northwest. Remote Sens. 7(1): Martin, M.E., Newman, S.D., Aber, J.D., and Congalton, R.G. 1998. Determining 229–255. doi:10.3390/rs70100229. forest species composition using high spectral resolution remote sensing Slesak, R.A., Lenhart, C.F., Brooks, K.N., D’Amato, A.W., and Palik, B.J. 2014. data. Remote Sens. Environ. 65(3): 249–254. doi:10.1016/S0034-4257(98)00035-2. Water table response to harvesting and simulated emerald ash borer mortal- McCune, B., and Keon, D. 2002. Equations for potential annual direct incident ity in black ash wetlands in Minnesota, USA. Can. J. For. Res. 44(8): 961–968. radiation and heat load. J. Veg. Sci. 13(4): 603–606. doi:10.1111/j.1654-1103.2002. doi:10.1139/cjfr-2014-0111. tb02087.x. Soil Survey Staff. 2017. Gridded Soil Survey Geographic (gSSURGO) Database for McEachran, Z.P., Slesak, R.A., and Karwan, D.L. 2018. From skid trails to land- Minnesota [online]. United States Department of Agriculture, Natural Re- scapes: vegetation is the dominant factor influencing erosion after forest sources Conservation Service. Available from https://gdg.sc.egov.usda.gov harvest in a low relief glaciated landscape. For. Ecol. Manage. 430: 299–311. [accessed 5 November 2018]. doi:10.1016/j.foreco.2018.08.021. Taylor, R.A.J., Bauer, L.S., Poland, T.M., and Windell, K.N. 2010. Flight perfor- McRoberts, R.E. 2009. A two-step nearest neighbors algorithm using satellite mance of Agrilus planipennis (Coleoptera: Buprestidae) on a flight mill and in imagery for predicting forest structure within species composition classes. free flight. J. Insect Behav. 23(2): 128–148. doi:10.1007/s10905-010-9202-3. Remote Sens. Environ. 113(3): 532–545. doi:10.1016/j.rse.2008.10.001. Telander, A.C., Slesak, R.A., D’Amato, A.W., Palik, B.J., Brooks, K.N., and McRoberts, R.E., and Tomppo, E.O. 2007. Remote sensing support for national Lenhart, C.F. 2015. Sap flow of black ash in wetland forests of northern forest inventories. Remote Sens. Environ. 110(4): 412–419. doi:10.1016/j.rse. Minnesota, USA: hydrologic implications of tree mortality due to emerald 2006.09.034. ash borer. Agric. For. Meteorol. 206: 4–11. doi:10.1016/j.agrformet.2015.02.019. Minnesota Department of Natural Resources. 2017. Ecological Classification Tinkham, W.T., Smith, A., Marshall, H., Link, T.E., Falkowski, M.J., and System: Laurentian Mixed Forest Province [online]. Available from http:// Winstral, A.M. 2014. Quantifying spatial distribution of snow depth errors www.dnr.state.mn.us/ecs/212/index.html [accessed 25 April 2017]. from LiDAR using random forest. Remote Sens. Environ. 141: 105–115. doi:10. MNGeo. 2017. Metadata: LiDAR elevation, Arrowhead Region, NE Minnesota, 1016/j.rse.2013.10.021. 2011 [online]. Minnesota Department of Natural Resources. Available from U.S. Geological Survey. 2017. Earth Resources Observation and Science (EROS) http://www.mngeo.state.mn.us/chouse/metadata/lidar_arrowhead2011.html Center Science Processing Architecture (ESPA) on Demand Interface [online]. [accessed 25 April 2017]. Product Guide Version 4.0. Department of Interior, U.S. Geological Survey.

Published by NRC Research Press 902 Can. J. For. Res. Vol. 49, 2019

Available from https://landsat.usgs.gov/sites/default/files/documents/ Wolter, P.T., Mladenoff, D.J., Host, G.E., and Crow, T.R. 1995. Improved forest espa_odi_userguide.pdf [accessed 5 April 2017]. classification in the northern Lake States using multi-temporal Landsat im- Vogeler, J.C., Braaten, J.D., Slesak, R.A., and Falkowski, M.J. 2018. Extracting the agery. Photogram. Eng. Remote Sens. 61(9): 1129–1143. full value of the Landsat archive: inter-sensor harmonization for the map- Wright, J.W., and Rauscher, H.M. 1990. Fraxinus nigra Marsh. black ash [online]. In ping of Minnesota forest canopy cover (1973–2015). Remote Sens. Environ. Silvics of North America: Volume 2. Hardwoods. Edited by R.M. Burns and 209: 363–374. doi:10.1016/j.rse.2018.02.046. B.H. Honkala. USDA Forest Service, Washington, D.C. pp. 344–347. Available Willow, A.J. 2011. Indigenizing invasive species management: Native North from https://www.na.fs.fed.us/spfo/pubs/silvics_manual/volume_2/fraxinus/ Americans and the emerald ash borer (EAB) beetle. Cult. Agric. Food Environ. nigra.htm [accessed 5 November 2018]. 33(2): 70–82. doi:10.1111/j.2153-9561.2011.01051.x. Youngquist, M.B., Eggert, S.L., D’Amato, A.W., Palik, B.J., and Slesak, R.A. 2017. Wilson, B.T., Lister, A.J., Riemann, R.I., and Griffith, D.M. 2013. Live tree species Potential effects of foundation species loss on wetland communities: a case basal area of the contiguous United States (2000–2009). USDA Forest Service, study of black ash wetlands threatened by emerald ash borer. Wetlands, Rocky Mountain Research Station, Newtown Square, PA. doi:10.2737/RDS- 37(4): 787–799. doi:10.1007/s13157-017-0908-2. 2013-0013. Zhang, K., Hu, B., and Robinson, J. 2014. Early detection of emerald ash borer Wolter, P.T., and Townsend, P.A. 2011. Multi-sensor data fusion for estimating infestation using multisourced data: a case study in the town of Oakville, forest species composition and abundance in northern Minnesota. Remote Ontario, Canada. J. Appl. Remote Sens. 8(1): 83602. doi:10.1117/1.JRS.8.083602. Sens. Environ. 115(2): 671–691. doi:10.1016/j.rse.2010.10.010. Zimmermann, N.E., Edwards, T.C., Moisen, G.G., Frescino, T.S., and Blackard, J.A. Wolter, P.T., and White, M.A. 2002. Recent forest cover type transitions and 2007. Remote sensing-based predictors improve distribution models of rare, landscape structural changes in northeast Minnesota, USA. Landsc. Ecol. early successional, and broadleaf tree species in Utah. J. Appl Ecol. 44(5): 17(2): 133–155. doi:10.1023/A:1016522509857. 1057–1067. doi:10.1111/j.1365-2664.2007.01348.x.

Published by NRC Research Press