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The boreal biome, the largest terrestrial biome on Earth, is increasingly vulnerable to climate change due to warming twice as rapidly as the global average. Climate change has increased the temperature, frequency, severity, and amount of area burned, which is leading to changes in the spatial extent of forest type and species range. These rapid ecological shifts necessitate fine-scale monitoring of forest type to detect potential type conversions and guide management interventions. In this study, we present a framework for forest type classification combining field plots and high-resolution remote sensing data using machine learning models in the boreal forest of Interior Alaska. For this purpose, we conducted forest type classification at three different levels, including 1. forest and nonforest, 2. hardwood, softwood, and nonforest, and 3. three dominant forest types, including paper birch, black spruce, white spruce, and nonforest. To achieve this goal, we compared the performance of two advanced modeling approaches, the convolutional neural network (CNN) and the XGBoost model. Our datasets included field and high-resolution topographic metrics including elevation, slope, aspect, and solar radiation and canopy height derived from lidar (1 m) and 44 vegetation indices derived from high-resolution (1 m) visible to near infrared (VNIR) hyperspectral data collected by NASA Goddard's Lidar, Hyperspectral and Thermal Imager (G-LiHT) sensor. The remote sensing data were collected under variable sky conditions (clear to overcast) throughout a 1-month growing-season period, and field data collected by United States Department of Agriculture (USDA) Forest Service, Forest Inventory and Analysis program (FIA). In this framework, we also studied the importance of topographic and remote sensing variables for the classification of forest types. We found the CNN model outperformed the XGBoost model in terms of overall accuracy and a macro average F1 score for all three different forest type classifications. The CNN model achieved an overall accuracy of 93.1% for forest or nonforest, 82.6% for hardwood, softwood, and nonforest, and 74.7% for three dominant forest types including paper birch, black spruce, and white spruce along with nonforest. Among the various topographic factors, we found that elevation was the most important factor for discriminating all forest types. In addition, we found that canopy height and vegetation indices including Photochemical Reflectance Index (PRI) (R531 & R570), Pigment Specific Normalized Difference (PSND) (R635 & R800), and Gitelson and Merzlyak (GM1) (R550 & R750) were important for differentiating between hardwood and softwood while Anthocyanin Reflectance Index (ARI1) (R550 & R700) was important for differentiating between forest and nonforest. The high-resolution forest type information can improve our ecological understanding of boreal forest dynamics, estimate above ground biomass, and carbon, and support the national forest inventory and forest managers.

Contemporary reference sites in California's Sierra Nevada represent areas where a frequent, low-intensity fire regime – an integral ecological process in temperate dry forests – has been reintroduced after several decades of fire suppression. Produced by an intact fire regime, forest structural patterns in these sites are likely more resilient to future disturbances and climate, and thus can provide reference conditions to guide management and ecological research. In this paper, we present a set of 119 delineated contemporary reference sites in the Sierra Nevada yellow pine and mixed-conifer zone along with a suite of key remote sensing-derived forest structure metrics representing conditions within these sites. We also provide a set of summary figures for individual reference sites and sites grouped by dominant climate class. We identified restored frequent-fire landscapes using a combination of fire history, burn severity, management history, and forest type datasets and we delineated individual polygons using catchment basins, fire perimeters, and imagery. Reference sites ranged in size from 101-966 ha with a mean size of 240 ha. Where available (for 59 sites), we used airborne lidar datasets to characterize a suite of key forest structure metrics within reference sites. Across all 119 sites, we provide a set of forest structure metrics produced by the California Forest Observatory. Reference sites were categorized based on their dominant climate class to assist users in identifying the most climatically relevant reference conditions for their project or study area. We encourage the use of the reference sites and associated forest structure datasets for guiding ecologically focused forest management and research in the Sierra Nevada.