Low-cost methods to measure forest structure are needed to consistently and repeatedly inventory forest conditions over large areas. In this study we investigate low-cost pushbroom Digital Aerial Photography (DAP) to aid in the estimation of forest volume over large areas in Washington State (USA). We also examine the effects of plot location precision (low versus high) and Digital Terrain Model (DTM) resolution (1 m versus 10 m) on estimation performance. Estimation with DAP and post-stratification with high-precision plot locations and a 1 m DTM was 4 times as efficient (precision per number of plots) as estimation without remote sensing and 3 times as efficient when using low-precision plot locations and a 10 m DTM. These findings can contribute significantly to efforts to consistently estimate and map forest yield across entire states (or equivalent) or even nations. The broad-scale, high-resolution, and high-precision information provided by pushbroom DAP facilitates used by a wide variety of user types such a towns and cities, small private timber owners, fire prevention groups, Non-Governmental Organizations (NGOs), counties, and state and federal organizations.
Changes to the fire regime in boreal Alaska are shifting the ratio of coniferous to deciduous dominance on the landscape. The increase in aspen and birch may have important effects on predatory hymenopteran assemblages by providing a source of extrafloral nectar and increasing prey availability. Furthermore, fire-induced changes in successional age alter habitat structure and microclimate in ways that may favor ants. This study is the first to characterize the influence of fire-related vegetation changes on boreal predatory hymenopteran assemblages. We compare the abundance, species richness, and composition of predatory hymenopteran assemblages among forests at different stages of succession and of varying post-fire tree species compositions. Ant assemblages were weakly related to forest composition, but ants were significantly more abundant and speciose in early-successional forests than in mid-late successional forests. In contrast, macropterous wasp morphospecies richness and abundance, and micropterous wasp abundance, were positively related to the basal area of aspen, but were not related to successional stage. The results suggest that shifts in boreal vegetation related to climate warming will result in changes to the predaceous insect community, with ants responding positively to disturbance and wasps responding positively to an increase in the representation of aspen on the landscape.
Using a large dataset compiled from studies over the years covering 23 tree species, we developed methods to estimate total and components (stem, bark, branch, and foliage) of aboveground live tree biomass. Missing components in the dataset were imputed using species-specific or generalized (species combined into softwood and hardwood groups) Dirichlet imputation. Geometric means of the imputed stem wood proportions were 8% and 9% higher than the observed geometric mean of stem wood proportions in softwood and hardwood species, respectively. For other components, the differences were within 1%. On average, the component ratio method (CRM), used for the official United States forest carbon inventories, underestimated the aboveground biomass (AGB, kg) predictions by 3.7% with a very wide range (–70.3% to 31.6%). Compared with the CRM approach, equations developed in this study reduced RMSE of AGB by as much as 145.0%. On average, new equations reduced RMSE in predicting individual-tree AGB by 15.5% compared with the CRM approach and by 3.9% compared with a calibration of CRM AGB. Predicting AGB as a function of stem volume was not as accurate as using direct AGB equations. Generalized component ratio equations may be suitable for the stem wood component but were highly biased for other components.
The U.S. Geological Survey Land Change Monitoring, Assessment and Projection (USGS LCMAP) initiative is working toward a comprehensive capability to characterize land cover and land cover change using dense Landsat time series data. A suite of products including annual land cover maps and annual land cover change maps will be produced using the Landsat 4-8 data record. LCMAP products will initially be created for the conterminous United States (CONUS) and then extended to include Alaska and Hawaii. A critical component of LCMAP is the collection of reference data using the TimeSync tool, a web-based interface for manually interpreting and recording land cover from Landsat data supplemented with fine resolution imagery and other ancillary data. These reference data will be used for area estimation and validation of the LCMAP annual land cover products. Nearly 12,000 LCMAP reference sample pixels have been interpreted and a simple random subsample of these pixels has been interpreted independently by a second analyst (hereafter referred to as “duplicate interpretations”). The annual land cover reference class labels for the 1984-2016 monitoring period obtained from these duplicate interpretations are used to address the following questions: 1) How consistent are the reference class labels among interpreters overall and per class? 2) Does consistency vary by geographic region? 3) Does consistency vary as interpreters gain experience over time? 4) Does interpreter consistency change with improving availability and quality of imagery from 1984 to 2016? Overall agreement between interpreters was 88%. Class-specific agreement ranged from 46% for Disturbed to 94% for Water, with more prevalent classes (Tree Cover, Grass/Shrub and Cropland) generally having greater agreement than rare classes (Developed, Barren and Wetland). Agreement between interpreters remained approximately the same over the 12-month period during which these interpretations were completed. Increasing availability of Landsat and Google Earth fine resolution data over the 1984 to 2016 monitoring period coincided with increased interpreter consistency for the post-2000 data record. The reference data interpretation and quality assurance protocols implemented for LCMAP demonstrate the technical and practical feasibility of using the Landsat archive and intensive human interpretation to produce national, annual reference land cover data over a 30-year period. Protocols to estimate and enhance interpreter consistency are critical elements to document and ensure the quality of these reference data.
The Ecosystem Management Decision Support (EMDS) system has been further enhanced with an analytical engine—BayesFusion’s SMILE (Structural Modeling, Inference, and Learning Engine) that comes with the GeNIe (Graphical Network Interface) software—for creating Bayesian network (BN) models. BNs are graphical networks of variables linked by probabilities, that have proven useful in decision-aiding for risk analysis and risk management.
This report highlights key findings from 2012 data collected by the Forest Inventory and Analysis program across all forested land on four islands in American Samoa, updating previously published findings from data collected in 2001 (Donnegan et al. 2004). We summarize and interpret basic resource information such as estimates of forest area, stem volume, biomass, numbers of trees, damages to trees, and tree size distribution as well as overstory and understory vegetation cover and information on invasive plant species presence and cover. Detailed tables and graphical highlights are included to help inform resource managers and policymakers, as well as educate the public regarding the status and trends of their local natural resources. The appendices provide details on inventory methods and design and include summary tables of data, with statistical error, for the wide variety of forest characteristics inventoried.
This document describes the standards, codes, methods, and definitions for Forest Inventory and Analysis (FIA) field data items used for URBAN FIA inventories of San Diego, CA and Portland, OR by the PNW-FIA unit in 2018.
This manual documents data collection procedures, codes, standards, and definitions used by the Pacific Northwest Research Station, Forest Inventory and Analysis (PNW-FIA) program in the 2018 annual forest inventory of Alaska.