National forest inventories in the service of small area estimation of stem volume

Magnussen, Steen; Mandallaz, Daniel; Breidenbach, Johannes; Lanz, Adrian; Ginzler, Christian

doi:10.1139/cjfr-2013-0448

Cited by 34 publications

(33 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the framework of the Swiss National Forest Inventory, the data is used as auxiliary information to post-stratify forest areas according to canopy height for the estimation of different forest characteristics, see also [8,44]. This allows for a further reduction of estimation errors, especially when it comes to small area estimations [1,45].…”

Section: Applications Within the National Forest Inventorymentioning

confidence: 99%

Countrywide Stereo-Image Matching for Updating Digital Surface Models in the Framework of the Swiss National Forest Inventory

2015

Self Cite

View full text Add to dashboard Cite

Surface models provide key knowledge of the 3-d structure of forests. Aerial stereo imagery acquired during routine mapping campaigns covering the whole of Switzerland (41,285 km 2 ), offers a potential data source to calculate digital surface models (DSMs). We present an automated workflow to generate a nationwide DSM with a resolution of 1 × 1 m based on photogrammetric image matching. A canopy height model (CHM) is derived in combination with an existing digital terrain model (DTM). ADS40/ADS80 summer images from 2007 to 2012 were used for stereo matching, with ground sample distances (GSD) of 0.25 m in lowlands and 0.5 m in high mountain areas. Two different image matching strategies for DSM calculation were applied: one optimized for single features such as trees and for abrupt changes in elevation such as steep rocks, and another optimized for homogeneous areas such as meadows or glaciers. The country was divided into 165,500 blocks, which were matched independently using an automated workflow. The completeness of successfully matched points was high, 97.9%. To test the accuracy of the derived DSM, two reference data sets were used: (1) topographic survey points (n = 198) and (2) stereo measurements (n = 195,784) within the framework of the Swiss National Forest Inventory (NFI), in order to distinguish various land cover types. An overall median accuracy of 0.04 m with a normalized median absolute deviation (NMAD) of 0.32 m was found using the topographic survey points. The agreement between the stereo measurements and the values of the DSM revealed acceptable NMAD values between 1.76 and 3.94 m for forested areas. A good correlation (Pearson's r = 0.83) was found between terrestrially OPEN ACCESSRemote Sens. 2015, 7 4344 measured tree height (n = 3109) and the height derived from the CHM. Optimized image matching strategies, an automatic workflow and acceptable computation time mean that the presented approach is suitable for operational usage at the nationwide extent. The CHM will be used to reduce estimation errors of different forest characteristics in the Swiss NFI and has high potential for change detection assessments, since an aerial stereo imagery update is available every six years.

show abstract

Section: Applications Within the National Forest Inventorymentioning

confidence: 99%

Countrywide Stereo-Image Matching for Updating Digital Surface Models in the Framework of the Swiss National Forest Inventory

2015

Self Cite

View full text Add to dashboard Cite

show abstract

“…The specific scope of double-sampling is to enlarge the terrestrial sample size by a much larger sample of predictions of the target variable in order to gain higher estimation precision without performing additional expensive terrestrial measurements. Model-dependent and design-based regression estimators are used in a broad range of doublesampling concepts and methods (Gregoire and Valentine 2007;Köhl et al 2006;Mandallaz 2013a, b;Saborowski et al 2010;Schreuder et al 1993) and have been applied to existing inventory systems (Breidenbach and Astrup 2012;von Lüpke and Saborowski 2014;Mandallaz et al 2013;Magnussen et al 2014;Massey et al 2014). While double-sampling methods provide reliable estimates for a given spatial unit, e.g., a forest district, they do not provide information about the spatial distribution of the estimated quantity within this area.…”

Section: Electronic Supplementary Materialsmentioning

confidence: 99%

Combining canopy height and tree species map information for large-scale timber volume estimations under strong heterogeneity of auxiliary data and variable sample plot sizes

2018

Self Cite

View full text Add to dashboard Cite

A timber volume regression model applicable to the state and communal forest area of the federal German state of RhinelandPalatinate is identified using a combination of airborne laser scanning (ALS)-derived metrics and information from a satellitebased tree species classification map available on the federal state level. As is common in many forest inventory datasets, strong heterogeneity in the ALS data due to different acquisition dates and misclassifications in the tree species classification map had noticeable effects on the regression model's performance. This article specifically addresses techniques that improve the performance of ordinary least square regression models under such restricting conditions. We introduce a calibration technique to neutralize the effect of misclassifications in the tree species variable that originally caused a residual inflation of 0.05 in adjusted R 2 . Incorporating the calibrated tree species information improved the model accuracy by up to 0.07 in adjusted R 2 and suggests the use of such information in forthcoming inventories. We also found that including ALS quality information as categorical variables within the regression model considerably mitigates issues with time lags between the ALS and terrestrial data acquisition and ALS quality variations (increase of 0.09 in adjusted R 2 ). The model achieved an adjusted R 2 of 0.48 and a cross-validated root-mean-square error (RMSE cv ) of 46.7% under incorporation of the tree species and ALS quality information and was thus improved by 0.12 in adjusted R 2 (5% in RMSE cv ) compared to the simple model only containing ALS height metrics (adjusted R 2 = 0.36 , RMSE cv = 51.7%).

show abstract

“…The estimation errors under the applied model-dependent and design-based small area estimators turned to be markedly smaller than under the standard one-phase estimator. Another example is Magnussen et al [29] who recently used the Swiss NFI data to estimate timber volume within 108 Swiss forest districts with sample sizes between 9 and 206. Similar studies using German NFI data for small area estimation have been lacking.…”

Section: Introductionmentioning

confidence: 99%

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

Hill

Mandallaz

Langshausen³

2018

Remote Sensing

Self Cite

View full text Add to dashboard Cite

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.

show abstract

National forest inventories in the service of small area estimation of stem volume

Cited by 34 publications

References 58 publications

Countrywide Stereo-Image Matching for Updating Digital Surface Models in the Framework of the Swiss National Forest Inventory

Countrywide Stereo-Image Matching for Updating Digital Surface Models in the Framework of the Swiss National Forest Inventory

Combining canopy height and tree species map information for large-scale timber volume estimations under strong heterogeneity of auxiliary data and variable sample plot sizes

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

Contact Info

Product

Resources

About