Andrew J. Lister

Mapping host-species abundance of three major exotic forest pests

Periodically over the last century, forests of the Eastern United States devastated by invasive pests. We used existing data to predict the geographical extent of future damage from beech bark disease (BBD), hemlock woolly adelgid (HWA), and gypsy moth. The distributions of host species of these alien pests were mapped in 1-km2 cells by interpolating host basal area/ha from 93,611 forest-inventory plots in 37 states. The interpolated surfaces were adjusted for forest density (percent land cover) by multiplying values by an estimate of percent forest cover derived from existing land-cover maps (30-m2 cells). According to our estimates, BBD currently occupies only about 27 percent of its potential range in land area, but has invaded more than 54 percent in total host density. HWA occupies nearly 26 percent of its potential range in land area, and about one-quarter in total host density. Gypsy moth occupies only 23 percent of its potential range in the Eastern United States, and only 26 percent in total host density.

The forests of Maine: 2003

In 1999, the Maine Forest Service and USDA Forest Services Forest Inventory and Analysis program implemented a new system for inventorying and monitoring Maines forests. The effects of the spruce budworm epidemic continue to affect the composition, structure, and distribution of Maines forested ecosystems. The area of forest land in Maine has remained stable since the 1970s. Although relatively small acreages of forest are converted to other land uses, these conversions often remove highly valued forests such as white pine. The total inventory volume of live trees increased slightly, indicating the beginning of a response of Maines forest to the tremendous devastation from spruce budworm.

Approaches to monitoring changes in carbon stocks for REDD

Reducing emissions from deforestation and forest degradation plus improving forest-management (REDD+) is a mechanism to facilitate tropical countries’ participation in climate change mitigation. In this review we focus on the current state of monitoring systems to support implementing REDD+. The main elements of current monitoring systems – Landsat satellites and traditional forest inventories – will continue to be the backbone of many forest-monitoring systems around the world, but new remote-sensing and analytical approaches are addressing monitoring problems specific to the tropics and implementing REDD+. There is increasing recognition of the utility of combining remote sensing with field data using models that integrate information from many sources, which will continue to evolve as new sensors are deployed and as the availability of field data increases.

Implications of sampling design and sample size for national carbon accounting systems

BackgroundCountries willing to adopt a REDD regime need to establish a national Measurement, Reporting and Verification (MRV) system that provides information on forest carbon stocks and carbon stock changes. Due to the extensive areas covered by forests the information is generally obtained by sample based surveys. Most operational sampling approaches utilize a combination of earth-observation data and in-situ field assessments as data sources.ResultsWe compared the cost-efficiency of four different sampling design alternatives (simple random sampling, regression estimators, stratified sampling, 2-phase sampling with regression estimators) that have been proposed in the scope of REDD. Three of the design alternatives provide for a combination of in-situ and earth-observation data. Under different settings of remote sensing coverage, cost per field plot, cost of remote sensing imagery, correlation between attributes quantified in remote sensing and field data, as well as population variability and the percent standard error over total survey cost was calculated. The cost-efficiency of forest carbon stock assessments is driven by the sampling design chosen. Our results indicate that the cost of remote sensing imagery is decisive for the cost-efficiency of a sampling design. The variability of the sample population impairs cost-efficiency, but does not reverse the pattern of cost-efficiency of the individual design alternatives.Conclusions, brief summary and potential implicationsOur results clearly indicate that it is important to consider cost-efficiency in the development of forest carbon stock assessments and the selection of remote sensing techniques. The development of MRV-systems for REDD need to be based on a sound optimization process that compares different data sources and sampling designs with respect to their cost-efficiency. This helps to reduce the uncertainties related with the quantification of carbon stocks and to increase the financial benefits from adopting a REDD regime.

Use of space-filling curves to select sample locations in natural resource monitoring studies

The establishment of several large area monitoring networks over the past few decades has led to increased research into ways to spatially balance sample locations across the landscape. Many of these methods are well documented and have been used in the past with great success. In this paper, we present a method using geographic information systems (GIS) and fractals to create a sampling frame, superimpose a tessellation and draw a sample. We present a case study that illustrates the technique and compares results to those from other methods using data from Voyageurs National Park in Minnesota. Our method compares favorably with results from a popular plot selection method, Generalized Random Tessellation Stratified Design, and offers several additional advantages, including ease of implementation, intuitive appeal, and the ability to maintain spatial balance by adding new plots in the event of an inaccessible plot encountered in the field.

Regeneration Strategies, Disturbance and Plant Interactions as Organizers of Vegetation Spatial Patterns in a Pine Forest

To determine how vegetation pattern in early successional forests may be related to plant traits and types of disturbance, we measured percent cover of individual taxa annually in a South Carolina Pinus elliottii forest, starting one year before, and ending four years after harvest and tree girdling disturbances were applied. The 17 most important taxa surveyed were grouped into four regeneration strategies chosen a priori, and the spatial patterns of these groups and of the soil were investigated using global variability, semivariograms and kriged maps. We also examined spatial correlations across years, across taxa, and between species and soil disturbance. Seed bank taxa represented by Dichanthelium spp. increased rapidly and formed large patches, and then quickly declined. Taxa that regenerate by newly dispersed seeds, represented by Rhus copallina and Rubus spp. occurred at first in a few patches, and became widespread later. Stump sprouters, represented by Quercus spp. and Myrica cerifera, had rapid increases in cover, but their spatial patterns were largely determined by their pre-disturbance patterns. Prunus serotina, which relies on both sprouting and dispersed seed, had moderate cover and a random distribution. Within-species temporal correlation of spatial pattern was lower in girdled than in harvested plots, and was not clearly related to regeneration strategy. Forest floor disturbance was patchy and affected the pattern of Dichanthelium spp. in the harvested plots. Negative correlations between herbs and woody plants in harvested plots reflected the role of biotic (i.e., successional) filters on vegetation pattern. Surprisingly, no spatial correlations were detected between the nitrogen fixer, Myrica cerifera and other taxa in this N-limited system. In comparing the spatial and temporal patterns, we found kriged maps more informative than analysis of semivariograms alone. The maps and correlation statistics demonstrated that regeneration traits, spatial patterns of soil disturbances, and interactions among taxa influence dynamics of the spatial patterns of the plants. We also demonstrated that disturbance types affected the importance and interactions among these three factors, and caused different spatial patterns of the plant taxa.

A sample design for globally consistent biomass estimation using lidar data from the Geoscience Laser Altimeter System (GLAS)

BackgroundLidar height data collected by the Geosciences Laser Altimeter System (GLAS) from 2002 to 2008 has the potential to form the basis of a globally consistent sample-based inventory of forest biomass. GLAS lidar return data were collected globally in spatially discrete full waveform “shots,” which have been shown to be strongly correlated with aboveground forest biomass. Relationships observed at spatially coincident field plots may be used to model biomass at all GLAS shots, and well-established methods of model-based inference may then be used to estimate biomass and variance for specific spatial domains. However, the spatial pattern of GLAS acquisition is neither random across the surface of the earth nor is it identifiable with any particular systematic design. Undefined sample properties therefore hinder the use of GLAS in global forest sampling.ResultsWe propose a method of identifying a subset of the GLAS data which can justifiably be treated as a simple random sample in model-based biomass estimation. The relatively uniform spatial distribution and locally arbitrary positioning of the resulting sample is similar to the design used by the US national forest inventory (NFI). We demonstrated model-based estimation using a sample of GLAS data in the US state of California, where our estimate of biomass (211 Mg/hectare) was within the 1.4% standard error of the design-based estimate supplied by the US NFI. The standard error of the GLAS-based estimate was significantly higher than the NFI estimate, although the cost of the GLAS estimate (excluding costs for the satellite itself) was almost nothing, compared to at least US

Pennsylvania's forest 2004.

10.5 million for the NFI estimate.ConclusionsGlobal application of model-based estimation using GLAS, while demanding significant consolidation of training data, would improve inter-comparability of international biomass estimates by imposing consistent methods and a globally coherent sample frame. The methods presented here constitute a globally extensible approach for generating a simple random sample from the global GLAS dataset, enabling its use in forest inventory activities.

Inventory methods for trees in nonforest areas in the great plains states

Pennsylvanias forest-land base is stable, covering 16.6 million acres or 58 percent of the land area. Sawtimber volume totals 88.9 billion board feet, an average of about 5,000 board feet per acre. Currently, only half of the forest land that should have advance tree seedling and sapling regeneration is adequately stocked with high-canopy species, and only one-third has adequate regeneration for commercially desirable timber species. Several exotic diseases and insects threaten the health of Pennsylvanias forests. Stressors such as drought, acidic deposition, and ground-level ozone pollution are adversely affecting the States forests.

The forests of Connecticut

The US Forest Service’s Forest Inventory and Analysis (FIA) program collects information on trees in areas that meet its definition of forest. However, the inventory excludes trees in areas that do not meet this definition, such as those found in urban areas, in isolated patches, in areas with sparse or predominantly herbaceous vegetation, in narrow strips (e.g., shelterbelts), or in riparian areas. In the Great Plains States, little is known about the tree resource in these noninventoried, nonforest areas, and there is a great deal of concern about the potential impact of invasive pests, such as the emerald ash borer. To address this knowledge gap, FIA’s National Inventory and Monitoring Applications Center has partnered with state cooperators and others in a project called the Great Plains Initiative to design and implement an inventory of trees in nonforest areas. The goal of the inventory is to characterize the nonforest tree resource using methods compatible with those of FIA so a holistic understanding of the resource can be obtained by integrating the two surveys. The goal of this paper is to describe the process of designing and implementing the survey, including plot and sample design, and to present some example results from a reporting tool we developed.