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

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