Mapping forest attributes using data from stereophotogrammetry of aerial images and field data from the national forest inventory

Bohlin, Jonas; Bohlin, Inka; Jonzén, Jonas; Nilsson, Mats

doi:10.14214/sf.2021

Cited by 31 publications

(38 citation statements)

References 22 publications

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“…In this study we present the development of forest tree type and timber volume maps for the federal state of Baden-Württemberg in south-west Germany. Recently, several studies have focused on the use of large-scale remote sensing data for mapping forest properties [5,[43][44][45][46]. Our study was motivated by the effort of the forest administration in Baden-Württemberg to support the field-work of the forest management experts by remote sensing-based information on forest resources for establishing forest management plans.…”

Section: Discussionmentioning

confidence: 99%

“…Since many years, remote sensing techniques such as airborne light detection and ranging (LiDAR) or aerial stereo photographs have been studied to obtain vegetation height [1][2][3][4][5][6], and to estimate timber volume or other forest properties in regression models [1,[5][6][7][8]. These models can be applied to the entire area for which a model is representative and for which the remote sensing data are available, resulting in area wide predictions of, for example, timber volume.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

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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“…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. For this reason, the same modeling technique used in double-sampling procedures has also been intensively used to produce exhaustive prediction maps that provide pixelwise estimations of a target variable in high spatial resolution (Bohlin et al 2017;Hill et al 2014;Latifi et al 2010;Nink et al 2015;Tonolli et al 2011).…”

Section: Electronic Supplementary Materialsmentioning

confidence: 99%

“…The number of plots per cluster can, however, vary between 1 and 4 depending on forest/non-forest decisions on the plot level (Bundesministerium and Ernährung 2011). In the field survey of the BWI3, sample trees for timber volume estimations are selected according to the angle count sampling technique (Bitterlich 1984), using a basal area factor of 4 that is, respectively, adjusted for boundary effects at the forest border (Bundesministerium and Ernährung 2011). A further selection criterion for a tree to be recorded is a diameter at breast height (dbh) of at least 7 cm.…”

Section: Terrestrial Inventory Datamentioning

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

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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%).

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“…However, while the primary purpose of NFI plots is to produce statistical estimates, they are today often also used as training data for producing nationwide wall-to-wall raster-type forest resources maps based on either satellite images, laser scanning, or photogrammetric point cloud data (Tomppo 1990;Nord-Larsen and Schumacher 2012;Nilsson et al 2017;Bohlin et al 2017;Tuominen et al 2017). They are typically less accurate and/or less detailed than FMI data, but freely available also for other users than the forest owner.…”

Section: Introductionmentioning

confidence: 99%

Remote sensing and forest inventories in Nordic countries – roadmap for the future

Kangas

Astrup

Breidenbach

et al. 2018

Scandinavian Journal of Forest Research

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140

103

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The Nordic countries have long traditions in forest inventory and remote sensing (RS). In sample-based national forest inventories (NFIs), utilization of aerial photographs started during the 1960s, satellite images during the 1980s, laser scanning during the 2000s, and photogrammetric point clouds during the 2010s. In forest management inventories (FMI), utilization of aerial photos started during the 1940s and laser scanning during the 2000s. However, so far, RS has mostly been used for map production and research rather than for estimation of regional parameters or inference on their accuracy. In recent years, the RS technology has been developing very fast. At the same time, the needs for information are constantly increasing. New technologies have created possibilities for cost-efficient production of accurate, large area forest data sets, which also will change the way forest inventories are done in the future. In this study, we analyse the state-of-the-art both in the NFIs and FMIs in the Nordic countries. We identify the benefits and drawbacks of different RS materials and data acquisition approaches with different user perspectives. Based on the analysis, we identify the needs for further development and emerging research questions. We also discuss alternatives for ownership of the data and cost-sharing between different actors in the field. ARTICLE HISTORY

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Mapping forest attributes using data from stereophotogrammetry of aerial images and field data from the national forest inventory

Cited by 31 publications

References 22 publications

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

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

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

Remote sensing and forest inventories in Nordic countries – roadmap for the future

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