Search

The distribution and local abundance of tree species constitute basic information about our forest ecosystems that is relevant to understanding their ecology, diversity, and relationship to people. The US Forest Service conducts a forest inventory across all forest lands in the United States. We developed geospatial models of forest attributes using this sample-based inventory which make this information available for an even wider variety of applications. From these modeled datasets, we created a series of maps for 24 US states in an effort to connect more people to trees, the datasets, and the scientific research behind them. Presenting these maps in an attractive way invites engagement. The sidebar text is presented in accessible scientific language that clearly defines terms, guides readers in interpreting the maps and histograms, and provides source details and links. The resulting maps are inviting, informative, and accessible to a broad range of people of different ages and backgrounds. [ABSTRACT FROM AUTHOR]

Abstract: The paper describes an efficient approach for mapping multiple individual tree species over large spatial domains. The method integrates vegetation phenology derived from MODIS imagery and raster data describing relevant environmental parameters with extensive field plot data of tree species basal area to create maps of tree species abundance and distribution at a 250-m pixel size for the entire eastern contiguous United States. The approach uses the modeling techniques of k-nearest neighbors and canonical correspondence analysis, where model predictions are calculated using a weighting of nearest neighbors based on proximity in a feature space derived from the model. The approach also utilizes a stratification derived from the 2001 National Land-Cover Database tree canopy cover layer. Data pre-processing is also described, which includes the use of Fourier series transformation for data reduction and characterizing seasonal vegetation phenology patterns that are apparent in the MODIS imagery. A suite of assessment procedures is applied to each of the modeled dataset presented. These indicate high accuracies, at the scales of assessments used, for total live-tree basal area per hectare and for many of the most common tree species found in the study area. The end result is an approach that enables the mapping of individual tree species distributions, while retaining much of the species covariance found on the forest inventory plots, at a level of spatial detail approaching that required for many regional management and planning applications. The proposed approach has the potential for operational application for simultaneously mapping the distribution and abundance of numerous common tree species across large spatial domains. [Copyright &y& Elsevier]

Abstract: Geospatial datasets of forest characteristics are modeled representations of real populations on the ground. The continuous spatial character of such datasets provides an incredible source of information at the landscape level for ecosystem research, policy analysis, and planning applications, all of which are critical for addressing current challenges related to climate change, urbanization pressures, and data requirements for monitoring carbon sequestration. However, the effectiveness of these applications is dependent upon the accuracy of the geospatial input datasets. A comprehensive set of robust measures is necessary to provide sufficient information to effectively assess the accuracy of these modeled geospatial datasets being produced. Yet challenges in the availability of reference data, in the appropriateness of assessment methods to dataset use, and in the completeness of assessment methods available have continued to hamper the timely and consistent application of map assessments. In this study we present a suite of assessments that can be used to characterize the accuracy of geospatial datasets of modeled continuous variables—an increasingly common format for modeling such attributes as proportion or probability of forestland as well as more traditionally continuous attributes such as leaf area index and forest biomass. It is a comparative accuracy assessment, in which each modeled dataset is compared to a set of reference data, recognizing both the potential for error in reference data, and probable differences in spatial support between the datasets. When used together, this proposed suite of assessments provides essential information on the type, magnitude, frequency and location of errors in each dataset. The assessments presented depend upon reference data with large sample sizes. The U.S. Forest Service (USFS) Forest Inventory and Analysis (FIA) database is introduced as an available reference dataset of sufficient sampling intensity to take full advantage of these assessments and facilitate their prompt application after modeled datasets are developed. We illustrate the application of this suite of assessments with two modeled datasets of forest biomass, in Minnesota and New York. The information provided by this suite of assessments substantially improves a user''s ability to apply modeled geospatial datasets effectively and to assess the relative strengths and weaknesses of multiple datasets depicting the same forest characteristic. [Copyright &y& Elsevier]

Globally, forests are a crucial natural resource, and their sound management is critical for human and ecosystem health and well-being. Efforts to manage forests depend upon reliable data on the status of and trends in forest resources. When these data come from well-designed natural resource monitoring (NRM) systems, decision makers can make science-informed decisions. National forest inventories (NFIs) are a cornerstone of NRM systems, but requires capacity and skills to implement. Efficiencies can be gained by incorporating auxiliary information derived from remote sensing (RS) into ground-based forest inventories. However, it can be difficult for countries embarking on NFI development to choose among the various RS integration options, and to develop a harmonized vision of how NFI and RS data can work together to meet monitoring needs. The NFI of the United States, which has been conducted by the USDA Forest Service's (USFS) Forest Inventory and Analysis (FIA) program for nearly a century, uses RS technology extensively. Here we review the history of the use of RS in FIA, beginning with general background on NFI, FIA, and sampling statistics, followed by a description of the evolution of RS technology usage, beginning with paper aerial photography and ending with present day applications and future directions. The goal of this review is to offer FIA's experience with NFI-RS integration as a case study for other countries wishing to improve the efficiency of their NFI programs. [ABSTRACT FROM AUTHOR]