Three-phase hierarchical model-based and hybrid inference.

Global commitments to mitigating climate change and halting biodiversity loss require reliable information about Earth's ecosystems. Increasingly, such information is obtained from multiple sources of remotely sensed data combined with data acquired in the field. This new wealth of data poses challenges regarding the combination of different data sources to derive the required information and assess uncertainties. In this article, we show how predictors and their variances can be derived when hierarchically nested models are applied. Previous studies have developed methods for cases involving two modeling steps, such as biomass prediction relying on tree-level allometric models and models linking plot-level field data with remotely sensed data. This study extends the analysis to cases involving three modeling steps to cover new important applications. The additional step might involve an intermediate model, linking field and remotely sensed data available from a small sample, for making predictions that are subsequently used for training a final prediction model based on remotely sensed data:•In cases where the data in the final step are available wall-to-wall, we denote the approach three-phase hierarchical model-based inference (3pHMB),•In cases where the data in the final step are available as a probability sample, we denote the approach three-phase hierarchical hybrid inference (3pHHY).

A spatial and temporal assessment of nonresponse in the national forest inventory of the U.S.

In nearly all national forest inventories (NFI), some sample plots are unable to be measured such that nonresponse may be an issue of concern. Thus, it is of particular interest to understand the phenomenon in terms of current status and temporal change in nonresponse rates and the associated spatial distribution on the landscape. In the NFI of the USA, denial of access permission on privately owned forest land and hazardous conditions has led to an overall nonresponse rate of 9.8% with some areas exceeding 20% of plots being inaccessible. Further, it was found that nearly 50% of the areas studied were exhibiting increasing rates of nonresponse over time. Comparisons between response and nonresponse plots via remote sensing characteristics suggested there may be systematic differences in some parts of the country, which may cause bias in the sample and resulting estimates. The findings indicate that improved communication strategies with private landowners are needed to reduce nonresponse rates. Due to the unlikelihood of eliminating nonresponse entirely, methods to mitigate potential nonresponse bias should be considered for incorporation into the estimation of population parameters.

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.

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

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.