Land of 10,000 pixels: applications of remote sensing & geospatial data to improve forest management in northern Minnesota, USA

2018 Summer.Includes bibliographical references.The use of remote sensing and geospatial data has become commonplace in a wide variety of ecological applications. However, the utility of these applications is often limited by field sampling design or the constraints on spatial resolution inherent in remote sensing technology. Because land managers require map products that more accurately reflect habitat composition at local, operational levels there is a need to overcome these limitations and improve upon currently available data products. This study addresses this need through two unique applications demonstrating the ability of remote sensing to enhance operational forest management at local scales. In the first chapter, remote sensing products were evaluated to improve upon regional estimates of the spatial configuration, extent, and distribution of black ash from forest inventory and analysis (FIA) survey data. To do this, spectral and topographic indices, as well as ancillary geospatial data were combined with FIA survey information in a non-parametric modeling framework to predict the presence and absence of black ash dominated stands in northern Minnesota, USA. The final model produced low error rates (Overall: 14.5%, Presence: 14.3%, Absence: 14.6%; AUC: 0.92) and was strongly informed by an optimized set of predictors related to soil saturation and seasonal growth patterns. The model allowed the production of accurate, fine-scale presence/absence maps of black ash stand dominance that can ultimately be used in support of invasive species risk management. In the second chapter, metrics from low-density LiDAR were evaluated for improving upon estimates of forest canopy attributes traditionally accessed through the LANDFIRE program. To do this, LiDAR metrics were combined with a Landsat time-series derived canopy cover layer in random forest k-nearest neighbor imputation approach to estimate canopy bulk density, two measures of canopy base height, and stand age across the Boundary Waters Canoe Area in northern Minnesota, USA. These models produced strong relationships between the estimates of canopy fuel attributes and field-based data for stand age (R2 = 0.82, RMSE = 10.12 years), crown fuel base height (R2 = 0.79, RMSE = 1.10 m.), live crown base height (R2 = 0.71, RMSE 1.60 m.), and canopy bulk density (R2 = 0.58, RMSE 0.09 kg/m3). An additional standard randomForest model of canopy height was less successful (R2 = 0.33, RMSE 2.08 m). The map products generated from these models improve upon the accuracy of national available canopy fuel products and provide local forest managers with cost-efficient and operationally ready data required to simulate fire behavior and support management efforts

Regional models do not outperform continental models for invasive species

Aim: Species distribution models can guide invasive species prevention and management by characterizing invasion risk across space. However, extrapolation and transferability issues pose challenges for developing useful models for invasive species. Previous work has emphasized the importance of including all available occurrences in model estimation, but managers attuned to local processes may be skeptical of models based on a broad spatial extent if they suspect the captured responses reflect those of other regions where data are more numerous. We asked whether species distribution models for invasive plants performed better when developed at national versus regional extents. Location: Continental United States. Methods: We developed ensembles of species distribution models trained nationally, on sagebrush habitat, or on sagebrush habitat within three ecoregions (Great Basin, eastern sagebrush, and Great Plains) for nine invasive plants of interest for early detection and rapid response at local or regional scales. We compared the performance of national versus regional models using spatially independent withheld test data from each of the three ecoregions. Results: We found that models trained using a national spatial extent tended to perform better than regionally trained models. Regional models did not outperform national ones even when considerable occurrence data were available for model estimation within the focal region. Information was often unavailable to fit informative regional models precisely in those areas of greatest interest for early detection and rapid response. Main conclusions: Habitat suitability models for invasive plant species trained at a continental extent can reduce extrapolation while maximizing information on species’ responses to environmental variation. Standard modeling methods can capture spatially varying limiting factors, while regional or hierarchical models may only be advantageous when populations differ in their responses to environmental conditions, a condition expected to be relatively rare at the expanding boundaries of invasive species’ distributions

Estimating Canopy Fuel Attributes from Low-Density LiDAR

Simulations of wildland fire risk are dependent on the accuracy and relevance of spatial data inputs describing drivers of wildland fire, including canopy fuels. Spatial data are freely available at national and regional levels. However, the spatial resolution and accuracy of these types of products often are insufficient for modeling local conditions. Fortunately, active remote sensing techniques can produce accurate, high-resolution estimates of forest structure. Here, low-density LiDAR and field-based data were combined using randomForest k-nearest neighbor imputation (RF-kNN) to estimate canopy bulk density, canopy base height, and stand age across the Boundary Waters Canoe Area in Minnesota, USA. RF-kNN models produced strong relationships between estimated canopy fuel attributes and field-based data for stand age (Adj. R2 = 0.81, RMSE = 10.12 years), crown fuel base height (Adj. R2 = 0.78, RMSE = 1.10 m), live crown base height (Adj. R2 = 0.7, RMSE = 1.60 m), and canopy bulk density (Adj. R2 = 0.48, RMSE = 0.09kg/m3). These results suggest that low-density LiDAR can help estimate canopy fuel attributes in mixed forests, with robust model accuracies and high spatial resolutions compared to currently utilized fire behavior model inputs. Model map outputs provide a cost-efficient alternative for data required to simulate fire behavior and support local management

Forest harvest dataset for northern Colorado Rocky Mountains (1984â2015) generated from a Landsat time series and existing forest harvest records

This dataset provides a shapefile containing approximately 3500 polygons with the location, extent, size, and year of clearcut harvest events occurring between 1984 and 2015 in forested areas of the northern Colorado, Landsat WRS-2 scene Path 034, Row 032. Harvest events were modeled and mapped using a 32 year time series of Landsat imagery, the LandTrendr algorithm, and ancillary datasets. The dataset also conveys information on the elevation, aspect, ownership, distance to roads, and the watershed where each harvest event occurred. Keywords: Forestry, Harvests, Landsat, LandTrend

Shifting hotspots: Climate change projected to drive contractions and expansions of invasive plant abundance ranges

This file contains maps of current and future abundance suitable habitat for 144 invasive plant species in the United States. Each tiff file represents the current or future range prediction maps of habitat suitable for supporting abundant populations (greater than or equal to 5% cover) of 144 invasive plant taxa, projected across the lower 48 States of the United States. Each tiff file is named with the USDA species code (SpCode) (see \u271Species_information_Nov15.xlsx\u27 file for full species names), with species codes followed by .2c indicating maps related to future climatic conditions under a +2oC warming scenario. Areas predicted to be climatically suitable for supporting abundant populations is based on model agreement and range from 0 (no models identify that area as suitable) to 15 (all model outputs identify the area as suitable). Values of 300 represent areas that are masked due to climate dissimilarity. See main publication for model fitting details.https://scholarworks.umass.edu/data/1167/thumbnail.jp

Data for \u3c\u3c Shifting hotspots: Climate change projected to drive contractions and expansions of invasive plant abundance ranges\u3e\u3e

Invasive plant abundance data. Abundance data for 175 invasive plant species across the lower 48 United States. Each abundance record includes a UniqueID (numbers or characters that appeared to be a unique ID from the original dataset), the dataset from which the datapoint was derived from (see dataset_information.csv file for additional information on each dataset), decimal Longitude (Long), decimal Latitude (Lat), Species Code (SpCode, unique species identifier from USDA PLANTS database), cover (percentage cover), and CoverType (the type of abundance measurement in the cover column; either PercentCover, CoverClass, or AverageCoverClass).https://scholarworks.umass.edu/data/1168/thumbnail.jp

A modeling workflow that balances automation and human intervention to inform invasive plant management decisions at multiple spatial scales.

Predictions of habitat suitability for invasive plant species can guide risk assessments at regional and national scales and inform early detection and rapid-response strategies at local scales. We present a general approach to invasive species modeling and mapping that meets objectives at multiple scales. Our methodology is designed to balance trade-offs between developing highly customized models for few species versus fitting non-specific and generic models for numerous species. We developed a national library of environmental variables known to physiologically limit plant distributions and relied on human input based on natural history knowledge to further narrow the variable set for each species before developing habitat suitability models. To ensure efficiency, we used largely automated modeling approaches and human input only at key junctures. We explore and present uncertainty by using two alternative sources of background samples, including five statistical algorithms, and constructing model ensembles. We demonstrate the use and efficiency of the Software for Assisted Habitat Modeling [SAHM 2.1.2], a package in VisTrails, which performs the majority of the modeling analyses. Our workflow includes solicitation of expert feedback on model outputs such as spatial prediction results and variable response curves, and iterative improvement based on new data availability and directed field validation of initial model results. We highlight the utility of the models for decision-making at regional and local scales with case studies of two plant species that invade natural areas: fountain grass (Pennisetum setaceum) and goutweed (Aegopodium podagraria). By balancing model automation with human intervention, we can efficiently provide land managers with mapped predicted distributions for multiple invasive species to inform decisions across spatial scales

Mapping Black Ash Dominated Stands Using Geospatial Forest Inventory Data in Northern Minnesota, USA.

Emerald ash borer (EAB; Argilus planipennis Fairemaire) has been a persistent disturbance on ash forests in the United States since 2002. Of particular concern is the impact EAB will have on the ecosystem functioning of wetlands dominated by black ash (Fraxinus nigra Marshall). 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 has 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 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, USA. The model produced low error rates (Overall error: 14.5%, 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.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