Stereo-imagery-based post-stratification by regression-tree modelling in Swiss National Forest Inventory

Pulkkinen, Minna; Ginzler, Christian; Traub, Berthold; Lanz, Adrian

doi:10.1016/j.rse.2018.04.052

Cited by 16 publications

(9 citation statements)

References 22 publications

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“…264-269). PS using remotely sensed auxiliary information has been shown to improve the precision at the national and regional levels compared with estimation based on field sample plots only (McRoberts et al 2002(McRoberts et al , 2006Nilsson et al 2003Nilsson et al , 2005Pulkkinen et al 2018;Haakana et al 2019). Accordingly, it is applied in operational NFIs in several countries, including France, Netherlands, Slovakia, Sweden, Switzerland, and the United States (Barrett et al 2016;Pulkkinen et al 2018).…”

Section: Introductionmentioning

confidence: 99%

“…In many NFIs, continuous field inventories and remotely sensed data acquired on a regular basis provide external information; data from a previous inventory can be used in fitting the model (Pulkkinen et al 2018;Haakana et al 2019). For example, in Finland, forest resources maps based on NFI sample plot data, satellite images, and numerical map data, extending over the whole country, are produced by the multisource NFI (MS-NFI) method every 2 years (Tomppo et al 2008;Mäkisara et al 2016Mäkisara et al , 2019.…”

Section: Introductionmentioning

confidence: 99%

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Precision of exogenous post-stratification in small-area estimation based on a continuous national forest inventory

et al. 2020

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National forest inventories (NFIs) are designed to provide accurate information on forest resources at the national and regional levels, but there is also a demand for such information at smaller spatial scales. Auxiliary data such as satellite imagery have been used to facilitate small-area estimation. The commonly used method, k-nearest neighbour (k-NN), provides a model-based estimator for small areas, but a design-unbiased estimator for the mean square error is not available. Post-stratification (PS) is an alternative approach to using auxiliary information that allows for design-based variance estimation. In a case study using real inventory data of the Finnish NFI, we applied this method to the municipality level to explore the lower limit to the area for which the key forest parameters, forest area and growing stock volumes, can be estimated with sufficient precision. For PS, we employed exogenous forest resources maps based on the previous NFI round. In the municipalities of the two study provinces, the relative standard errors of total volume estimates ranged from 2.3% to 26.9%. They were smaller than 10% for municipalities with an area of 390 km2 or larger, corresponding to approximately 100 or more sample plots on forestland. We also demonstrated the usefulness of design-unbiased variance estimation in showing discrepancies between design-based PS and model-based k-NN estimates.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Precision of exogenous post-stratification in small-area estimation based on a continuous national forest inventory

et al. 2020

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“…Development of stand-volume models based on remote sensing data usually involves measurements of independent variables through visual interpretation method on the image [1,2]. The visual interpretation mainly relies more on the capability of interpreters that are quite varied from person to person, depending on the their skill and experiences that may provide inconsistence results.…”

Section: Introductionmentioning

confidence: 99%

Examining the object-based and pixel-based image analyses for developing stand volume estimator model

Apriyanto

Jaya

Puspaningsih

2019

IJEECS

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In the last two decades there has been significant leap on the spatial resolution of the satellite digital images which may be very useful for estimating stand parameter required for forest as well as environment management. This paper describes development of stand volume estimator models using SPOT 6 panchromatic and multispectral images with an object-based digital image analysis (OBIA) and conventional pixel-based approaches. The data used include panchromatic band with1.5m spatial resolution, and multispectral band with6m spatial resolution. The proposed OBIA technique with mean-shift algorithm was functioned to derive a canopy cover variable from the fusion of the panchromatic and multispectral, while the pixel-based vegetation index was used to develop model with an original pixel-size of 6 m. The estimator models were established based on 65 sample plots both measured in the field and images. The study found that the OBIA provides more accurate identification with Kappa Accuracy (KA) of 71% and Overall Accuracy (OA) of 86%. The study concluded that the best stand volume estimation model is the model that developed from the canopy cover (C) derived from OBIA i.e., v = 13.47e<sup>0.032C</sup> with mean deviation of only 0.92%, better than the model derived from conventional pixel-based approach, i.e., v = 0.0000067e<sup>16.48TNDVI</sup> with a mean deviation of 5.37%.

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“…McConville and Toth [49] explore the theoretical properties of a post-stratified estimator where the bins are created by a regression tree. In the context of forest inventory, Pulkkinen et al [50] and Myllymäki et al [51] explore the utility of post-strata generated by regression trees.…”

Section: Post-stratified Estimatormentioning

confidence: 99%

A Tutorial on Model-Assisted Estimation with Application to Forest Inventory

McConville

Moisen

Frescino

2020

Forests

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National forest inventories in many countries combine expensive ground plot data with remotely-sensed information to improve precision in estimators of forest parameters. A simple post-stratified estimator is often the tool of choice because it has known statistical properties, is easy to implement, and is intuitive to the many users of inventory data. Because of the increased availability of remotely-sensed data with improved spatial, temporal, and thematic resolutions, there is a need to equip the inventory community with a more diverse array of statistical estimators. Focusing on generalized regression estimators, we step the reader through seven estimators including: Horvitz Thompson, ratio, post-stratification, regression, lasso, ridge, and elastic net. Using forest inventory data from Daggett county in Utah, USA as an example, we illustrate how to construct, as well as compare the relative performance of, these estimators. Augmented by simulations, we also show how the standard variance estimator suffers from greater negative bias than the bootstrap variance estimator, especially as the size of the assisting model grows. Each estimator is made readily accessible through the new R package, mase. We conclude with guidelines in the form of a decision tree on when to use which an estimator in forest inventory applications.

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Stereo-imagery-based post-stratification by regression-tree modelling in Swiss National Forest Inventory

Cited by 16 publications

References 22 publications

Precision of exogenous post-stratification in small-area estimation based on a continuous national forest inventory

Precision of exogenous post-stratification in small-area estimation based on a continuous national forest inventory

Examining the object-based and pixel-based image analyses for developing stand volume estimator model

A Tutorial on Model-Assisted Estimation with Application to Forest Inventory

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