Precision and cost considerations for two-stage sampling in a panelized forest inventory design.

Due to the relatively high cost of measuring sample plots in forest inventories, considerable attention is given to sampling and plot designs during the forest inventory planning phase. A two-stage design can be efficient from a field work perspective as spatially proximate plots are grouped into work zones. A comparison between subsampling with units of unequal size (SUUS) and a simple random sample (SRS) design in a panelized framework assessed the statistical and economic implications of using the SUUS design for a case study in the Northeastern USA. The sampling errors for estimates of forest land area and biomass were approximately 1.5-2.2 times larger with SUUS prior to completion of the inventory cycle. Considerable sampling error reductions were realized by using the zones within a post-stratified sampling paradigm; however, post-stratification of plots in the SRS design always provided smaller sampling errors in comparison. Cost differences between the two designs indicated the SUUS design could reduce the field work expense by 2-7 %. The results also suggest the SUUS design may provide substantial economic advantage for tropical forest inventories, where remote areas, poor access, and lower wages are typically encountered.

Avoiding treatment bias of REDD+ monitoring by sampling with partial replacement.

BACKGROUND: Implementing REDD+ renders the development of a measurement, reporting and verification (MRV) system necessary to monitor carbon stock changes. MRV systems generally apply a combination of remote sensing techniques and in-situ field assessments. In-situ assessments can be based on 1) permanent plots, which are assessed on all successive occasions, 2) temporary plots, which are assessed only once, and 3) a combination of both. The current study focuses on in-situ assessments and addresses the effect of treatment bias, which is introduced by managing permanent sampling plots differently than the surrounding forests. Temporary plots are not subject to treatment bias, but are associated with large sampling errors and low cost-efficiency. Sampling with partial replacement (SPR) utilizes both permanent and temporary plots. RESULTS: We apply a scenario analysis with different intensities of deforestation and forest degradation to show that SPR combines cost-efficiency with the handling of treatment bias. Without treatment bias permanent plots generally provide lower sampling errors for change estimates than SPR and temporary plots, but do not provide reliable estimates, if treatment bias occurs, SPR allows for change estimates that are comparable to those provided by permanent plots, offers the flexibility to adjust sample sizes in the course of time, and allows to compare data on permanent versus temporary plots for detecting treatment bias. Equivalence of biomass or carbon stock estimates between permanent and temporary plots serves as an indication for the absence of treatment bias while differences suggest that there is evidence for treatment bias. CONCLUSIONS: SPR is a flexible tool for estimating emission factors from successive measurements. It does not entirely depend on sample plots that are installed at the first occasion but allows for the adjustment of sample sizes and placement of new plots at any occasion. This ensures that in-situ samples provide representative estimates over time. SPR offers the possibility to increase sampling intensity in areas with high degradation intensities or to establish new plots in areas where permanent plots are lost due to deforestation. SPR is also an ideal approach to mitigate concerns about treatment bias.

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

BACKGROUND: Lidar 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. RESULTS: We 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$ 10.5 million for the NFI estimate. CONCLUSIONS: Global 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.

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.

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.