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Open AccessArticle

A Double-Sampling Extension of the German National Forest Inventory for Design-Based Small Area Estimation on Forest District Levels

1
Department of Environmental Systems Science, ETH Zurich, 8092 Zurich, Switzerland
2
State Forest Service Rhineland-Palatinate, 56281 Emmelshausen, Germany
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(7), 1052; https://doi.org/10.3390/rs10071052
Received: 17 May 2018 / Revised: 25 June 2018 / Accepted: 26 June 2018 / Published: 3 July 2018
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Abstract

The German National Forest Inventory consists of a systematic grid of permanent sample plots and provides a reliable evidence-based assessment of the state and the development of Germany’s forests on national and federal state level in a 10 year interval. However, the data have yet been scarcely used for estimation on smaller management levels such as forest districts due to insufficient sample sizes within the area of interests and the implied large estimation errors. In this study, we present a double-sampling extension to the existing German National Forest Inventory (NFI) that allows for the application of recently developed design-based small area regression estimators. We illustrate the implementation of the estimation procedure and evaluate its potential for future large-scale operational application by the example of timber volume estimation on two small-scale management levels (45 and 405 forest district units respectively) over the entire area of the federal German state of Rhineland-Palatinate. An airborne laserscanning (ALS) derived canopy height model and a tree species classification map based on satellite data were used as auxiliary data in an ordinary least square regression model to produce the timber volume predictions. The results support that the suggested double-sampling procedure can substantially increase estimation precision on both management levels: the two-phase estimators were able to reduce the variance of the one-phase simple random sampling estimator by 43% and 25% on average for the two management levels respectively. View Full-Text
Keywords: National Forest Inventory; small area estimation; forest districts; double sampling for regression within strata; cluster sampling; canopy height model; tree species classification National Forest Inventory; small area estimation; forest districts; double sampling for regression within strata; cluster sampling; canopy height model; tree species classification
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Hill, A.; Mandallaz, D.; Langshausen, J. A Double-Sampling Extension of the German National Forest Inventory for Design-Based Small Area Estimation on Forest District Levels. Remote Sens. 2018, 10, 1052.

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