Paul L. Patterson

Use of models in large-area forest surveys: comparing model-assisted, model-based and hybrid estimation

This paper focuses on the use of models for increasing the precision of estimators in large-area forest surveys. It is motivated by the increasing availability of remotely sensed data, which facilitates the development of models predicting the variables of interest in forest surveys. We present, review and compare three different estimation frameworks where models play a core role: model-assisted, model-based, and hybrid estimation. The first two are well known, whereas the third has only recently been introduced in forest surveys. Hybrid inference mixes design-based and model-based inference, since it relies on a probability sample of auxiliary data and a model predicting the target variable from the auxiliary data..We review studies on large-area forest surveys based on model-assisted, model-based, and hybrid estimation, and discuss advantages and disadvantages of the approaches. We conclude that no general recommendations can be made about whether model-assisted, model-based, or hybrid estimation should be preferred. The choice depends on the objective of the survey and the possibilities to acquire appropriate field and remotely sensed data. We also conclude that modelling approaches can only be successfully applied for estimating target variables such as growing stock volume or biomass, which are adequately related to commonly available remotely sensed data, and thus purely field based surveys remain important for several important forest parameters.

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

A primer for nonresponse in the US forest inventory and analysis program

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.

The enhanced forest inventory and analysis program - national sampling design and estimation procedures

Nonresponse caused by denied access and hazardous conditions are a concern for the USDA Forest Service, Forest Inventory and Analysis (FIA) program, whose mission is to quantify status and trends in forest resources across the USA. Any appreciable amount of nonresponse can cause bias in FIA’s estimates of population parameters. This paper will quantify the magnitude of nonresponse and describe the mechanisms that result in nonresponse, describe and qualitatively evaluate FIA’s assumptions regarding nonresponse, provide a recommendation concerning plot replacement strategies, and identify appropriate strategies to pursue that minimize bias. The nonresponse rates ranged from 0% to 21% and differed by land owner group; with denied access to private land the leading cause of nonresponse. Current FIA estimators assume that nonresponse occurs at random. Although in most cases this assumption appears tenable, a qualitative assessment indicates a few situations where the assumption is not tenable. In the short-term, we recommend that FIA use stratification schemes that make the missing at random assumption tenable. We recommend the examination of alternative estimation techniques that use appropriate weighting and auxiliary information to mitigate the effects of nonresponse. We recommend the replacement of nonresponse sample locations not be used.