Considerations towards a Novel Approach for Integrating Angle-Count Sampling Data in Remote Sensing Based Forest Inventories

Kirchhoefer, Melanie; Schumacher, Johannes; Adler, Petra; Kändler, Gerald

doi:10.3390/f8070239

Cited by 12 publications

(7 citation statements)

References 23 publications

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“…For example, it may be dicult to link a line transect, as used in distance sampling, to remotely sensed data and to tesselate the study area according to the transect as required for unit-level estimators (Bäuerle et al, 2009). Similar issues arise with variable radius plots although workarounds may be feasible (Kirchhoefer et al, 2017). In that way, area-level models may simplify the use of with model assumptions.…”

Section: Introductionmentioning

confidence: 97%

Unit-level and area-level small area estimation under heteroscedasticity using digital aerial photogrammetry data

Breidenbach

Magnussen

Rahlf

et al. 2018

Remote Sensing of Environment

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In many applications, estimates are required for small sub-populations with so few (or no) sample plots that direct estimators that do not utilize auxiliary variables (e.g. remotely sensed data) are not applicable or result in low precision. This problem is overcome in small area estimation (SAE) by linking the variable of interest to auxiliary variables using a model. Two types of models can be distinguished based on the scale on which they operate: i) Unit-level models are applied in the well-known area-based approach (ABA) and are commonly used in forest inventories supported by ne-resolution 3D remote sensing data such as airborne laser scanning (ALS) or digital aerial photogrammetry (AP); ii) Area-level models, where the response is a direct estimate based on a sample within the domain and the explanatory variables are aggregated auxiliary variables, are less frequently applied. Estimators associated with these two model types can make use of sample plots within domains if available and reduce to so-called synthetic estimators in domains where no sample plots are available. We used both model types and their associated model-based estimators in the same study area with AP data as auxiliary variables. Heteroscedasticity, i.e. for continuous dependent variables typically an increasing dispersion of residuals with increasing predictions, is often observed in models linking eld-and remotely sensed data. This violates the model assumption that the distribution of the residual errors is constant. Complying with model assumptions is required for model-based methods to result in reliable estimates. Addressing heteroscedasticity in models had considerable impacts on standard errors. When complying

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Section: Introductionmentioning

confidence: 97%

Unit-level and area-level small area estimation under heteroscedasticity using digital aerial photogrammetry data

Breidenbach

Magnussen

Rahlf

et al. 2018

Remote Sensing of Environment

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“…At each tract corner point located in the forest, nested circular sub-plots of 5 different radii and a Bitterlich sub-plot [53], so-called angle count sampling (ACS), were established to record the set of inventory variables regarding trees, stand structure and site characteristics. With ACS, sample trees are selected with a probability proportional to their basal area, meaning that the range of each plot differs [54].…”

Section: Nfi Datamentioning

confidence: 99%

Forest Cover Mapping Based on a Combination of Aerial Images and Sentinel-2 Satellite Data Compared to National Forest Inventory Data

Ganz

Adler

Kändler

2020

Forests

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Research Highlights: This study developed the first remote sensing-based forest cover map of Baden-Württemberg, Germany, in a very high level of detail. Background and Objectives: As available global or pan-European forest maps have a low level of detail and the forest definition is not considered, administrative data are often oversimplified or out of date. Consequently, there is an important need for spatio-temporally explicit forest maps. The main objective of the present study was to generate a forest cover map of Baden-Württemberg, taking the German forest definition into account. Furthermore, we compared the results to NFI data; incongruences were categorized and quantified. Materials and Methods: We used a multisensory approach involving both aerial images and Sentinel-2 data. The applied methods are almost completely automated and therefore suitable for area-wide forest mapping. Results: According to our results, approximately 37.12% of the state is covered by forest, which agrees very well with the results of the NFI report (37.26% ± 0.44%). We showed that the forest cover map could be derived by aerial images and Sentinel-2 data including various data acquisition conditions and settings. Comparisons between the forest cover map and 34,429 NFI plots resulted in a spatial agreement of 95.21% overall. We identified four reasons for incongruences: (a) edge effects at forest borders (2.08%), (b) different forest definitions since NFI does not specify minimum tree height (2.04%), (c) land cover does not match land use (0.66%) and (d) errors in the forest cover layer (0.01%). Conclusions: The introduced approach is a valuable technique for mapping forest cover in a high level of detail. The developed forest cover map is frequently updated and thus can be used for monitoring purposes and for assisting a wide range of forest science, biodiversity or climate change-related studies.

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“…For each inventory plot, 25 plot-based descriptive metrics were calculated from the extracted CHM and DTM pixels. Those were 18 metrics related to canopy height-5th (p5), 10th (p10), 15th (p15), 20th (p20), 25th (p25), 50th (p50), 75th (p75), 80th (p80), 85th (p85), 90th (p90), 95th (p95), and 99th (p99) percentiles, arithmetic mean (CHMmean), minimum (CHMmin), maximum (CHMmax), standard deviation (CHMsd), variance (CHMvar), coefficient of variation (CHMcv), which are commonly used in other studies (e.g., [39,40]). One variable related to canopy cover and calculated as the percentage of CHM pixels ≥ 6 m (CHMcc).…”

Section: Regression Modelling Of Timber Volume Using 3d Metrics From mentioning

confidence: 99%

“…Non-linear logistic regression models ( Table 1 Equation (1)), proposed by [41] were fitted to the data set, and various combinations of the predictor variables selected in the correlation analysis were tested. As noted in [40]: "This logistic regression model should not be confused with the binomial or multinomial logistic regression models that are often used with categorical data". An advantage of this type of model is that the predictions are non-negative and are constrained by the upper horizontal asymptote β K+2 , which is estimated from the sample data.…”

Section: Regression Modelling Of Timber Volume Using 3d Metrics From mentioning

confidence: 99%

Combination of Multi-Temporal Sentinel 2 Images and Aerial Image Based Canopy Height Models for Timber Volume Modelling

Schumacher

Rattay

Kirchhöfer

et al. 2019

Forests

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Multi-temporal Sentinel 2 optical images and 3D photogrammetric point clouds can be combined to enhance the accuracy of timber volume models on large spatial scale. Information on the proportion of broadleaf and conifer trees improves timber volume models obtained from 3D photogrammetric point clouds. However, the broadleaf-conifer information cannot be obtained from photogrammetric point clouds alone. Furthermore, spectral information of aerial images is too inconsistent to be used for automatic broadleaf-conifer classification over larger areas. In this study we combined multi-temporal Sentinel 2 optical satellite images, 3D photogrammetric point clouds from digital aerial stereo photographs, and forest inventory plots representing an area of 35,751 km 2 in south-west Germany for (1) modelling the percentage of broadleaf tree volume (BL%) using Sentinel 2 time series and (2) modelling timber volume per hectare using 3D photogrammetric point clouds. Forest inventory plots were surveyed in the same years and regions as stereo photographs were acquired (2013-2017), resulting in 11,554 plots. Sentinel 2 images from 2016 and 2017 were corrected for topographic and atmospheric influences and combined with the same forest inventory plots. Spectral variables from corrected multi-temporal Sentinel 2 images were calculated, and Support Vector Machine (SVM) regressions were fitted for each Sentinel 2 scene estimating the BL% for corresponding inventory plots. Variables from the photogrammetric point clouds were calculated for each inventory plot and a non-linear regression model predicting timber volume per hectare was fitted. Each SVM regression and the timber volume model were evaluated using ten-fold cross-validation (CV). The SVM regression models estimating the BL% per Sentinel 2 scene achieved overall accuracies of 68%-75% and a Root Mean Squared Error (RMSE) of 21.5-26.1. The timber volume model showed a RMSE% of 31.7%, a mean bias of 0.2%, and a pseudo-R 2 of 0.64. Application of the SVM regressions on Sentinel 2 scenes covering the state of Baden-Württemberg resulted in predictions of broadleaf tree percentages for the entire state. These predicted values were used as additional predictor in the timber volume model, allowing for predictions of timber volume for the same area. Spatially high-resolution information about growing stock is of great practical relevance for forest management planning, especially when the timber volume of a smaller unit is of interest, for example of a forest stand or a forest district where not enough terrestrial inventory plots are available to make reliable estimations. Here, predictions from remote-sensing based models can be used. Furthermore, information about broadleaf and conifer trees improves timber volume models and reduces model errors and, thereby, prediction uncertainties.

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Considerations towards a Novel Approach for Integrating Angle-Count Sampling Data in Remote Sensing Based Forest Inventories

Cited by 12 publications

References 23 publications

Unit-level and area-level small area estimation under heteroscedasticity using digital aerial photogrammetry data

Unit-level and area-level small area estimation under heteroscedasticity using digital aerial photogrammetry data

Forest Cover Mapping Based on a Combination of Aerial Images and Sentinel-2 Satellite Data Compared to National Forest Inventory Data

Combination of Multi-Temporal Sentinel 2 Images and Aerial Image Based Canopy Height Models for Timber Volume Modelling

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