Double sampling with ratio or regression estimator examples
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==Example 1== | ==Example 1== | ||
| + | Aerial photographs or satellite images are used to measure the [[Ancillary variable|ancillary variable]], for example percentage crown cover. In the second phase, field plots are selected to measure the target variable such as [[volume functions|volume]] or [[Biomass functions and carbon estimation|biomass]] per ha and the ancillary variables. Thus, a regression can be established which allows to predict the target variable once the ancillary variable is known. In many cases, this regression, however, is not very strong so that the overall [[Accuracy and precision|precision]] that can be achieved is moderate. One of the main issues and source of errors in this example is the accuracy of co-registration between [[Remote sensing|remote sensing]] imagery and [sample plot|field plots]] <ref name="kleinn2007">Kleinn, C. 2007. Lecture Notes for the Teaching Module Forest Inventory. Department of Forest Inventory and Remote Sensing. Faculty of Forest Science and Forest Ecology, Georg-August-Universität Göttingen. 164 S.</ref>. | ||
| − | + | ==Example 2== | |
| + | This example is on the estimation of [[leaf area]] of a tree, as, for example, needed to determine the [http://en.wikipedia.org/wiki/Leaf_Area_Index leaf area index]. Here, leaf area is difficult to measure; it is much easier to observe leaf weight. Therefore, a regression is established in the second phase that allows predicting leaf area from leaf weight; a sample of leaves is taken in the second phase sample of which both leaf area and leaf weight are determined. In order to apply this regression, the mean (or total) leaf weight needs to be determined: for this purpose, a large sample is taken in the first phase. In this example, a major issue is the [[population|sampling frame]] for the first phase sample, that needs to be carefully defined (or a [[:category:sampling design|sampling technique]] is applied that does not require the a-priori definition of the sampling frame such as [[randomized branch sampling]])<ref name="kleinn2007" />. | ||
| + | ==References== | ||
| − | + | <references/> | |
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| − | + | [[Category:Forest Inventory Examples]] | |
Latest revision as of 17:17, 26 October 2013
[edit] Example 1
Aerial photographs or satellite images are used to measure the ancillary variable, for example percentage crown cover. In the second phase, field plots are selected to measure the target variable such as volume or biomass per ha and the ancillary variables. Thus, a regression can be established which allows to predict the target variable once the ancillary variable is known. In many cases, this regression, however, is not very strong so that the overall precision that can be achieved is moderate. One of the main issues and source of errors in this example is the accuracy of co-registration between remote sensing imagery and [sample plot|field plots]] [1].
[edit] Example 2
This example is on the estimation of leaf area of a tree, as, for example, needed to determine the leaf area index. Here, leaf area is difficult to measure; it is much easier to observe leaf weight. Therefore, a regression is established in the second phase that allows predicting leaf area from leaf weight; a sample of leaves is taken in the second phase sample of which both leaf area and leaf weight are determined. In order to apply this regression, the mean (or total) leaf weight needs to be determined: for this purpose, a large sample is taken in the first phase. In this example, a major issue is the sampling frame for the first phase sample, that needs to be carefully defined (or a sampling technique is applied that does not require the a-priori definition of the sampling frame such as randomized branch sampling)[1].
