Forest information at multiple scales: development, evaluation and application of the Norwegian forest resources map SR16

Astrup, Rasmus; Rahlf, Johannes; Bjørkelo, Knut; Debella-Gilo, Misganu; Gjertsen, Arnt-Kristian; Breidenbach, Johannes

doi:10.1080/02827581.2019.1588989

Cited by 37 publications

(44 citation statements)

References 44 publications

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“…The forest species composition and structure employed in model fitting were based on the forest resource map "SR16", which was developed using photogrammetric and LiDAR point cloud data with ground plots from the Norwegian National Forest Inventory (NFI) [58]. The forest cover in SR16 is based on updating the forest cover in AR5 with object-based image analysis methods [59].…”

Section: Forest Cover and Structure Datamentioning

confidence: 99%

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Combining MODIS and National Land Resource Products to Model Land Cover-Dependent Surface Albedo for Norway

Bright

Astrup

2019

Remote Sensing

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Surface albedo is an important physical attribute of the climate system and satellite retrievals are useful for understanding how it varies in time and space. Surface albedo is sensitive to land cover and structure, which can vary considerably within the area comprising the effective spatial resolution of the satellite-based retrieval. This is particularly true for MODIS products and for topographically complex regions, such as Norway, which makes it difficult to separate the environmental drivers (e.g., temperature and snow) from those related to land cover and vegetation structure. In the present study, we employ high resolution datasets of Norwegian land cover and structure to spectrally unmix MODIS surface albedo retrievals (MCD43A3 v6) to study how surface albedo varies with land cover and structure. Such insights are useful for constraining land cover-dependent albedo parameterizations in models employed for regional climate or hydrological research and for developing new empirical models. At the scale of individual land cover types, we found that the monthly surface albedo can be predicted at a high accuracy when given additional information about forest structure, snow cover, and near surface air temperature. Such predictions can provide useful empirical benchmarks for climate model predictions made at the land cover level, which is critical for instilling greater confidence in the albedo-related climate impacts of anthropogenic land use/land cover change (LULCC).

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Section: Forest Cover and Structure Datamentioning

confidence: 99%

“…Stand attributes, such as tree species, tree height (Lorey's), biomass, and volume are predicted with generalized linear models at a resolution of 16 m × 16 m with an accuracy (normalized RMSE) of 50% [58]. The forests in SR16 are classified as one of four classes: (1) newly clear-cut; (2) spruce;…”

Section: Forest Cover and Structure Datamentioning

confidence: 99%

Combining MODIS and National Land Resource Products to Model Land Cover-Dependent Surface Albedo for Norway

Bright

Astrup

2019

Remote Sensing

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“…For example, Nilsson et al (2017) utilized National Forest Inventory (NFI) field data and ALS data collected systematically over the country by the Swedish National Land Survey to obtain a nationwide forest attribute map for Sweden. Astrup et al (2019) constructed a similar map product for Norway, employing photogrammetric point clouds instead of ALS data. Other studies merged data from several ALS inventory projects to construct large area prediction models for forest attributes such as volume, biomass and dominant height (Gopalakrishnan et al 2015;Kotivuori et al 2016Kotivuori et al , 2018.…”

Section: Introductionmentioning

confidence: 99%

Predicting stand age in managed forests using National Forest Inventory field data and airborne laser scanning

et al. 2020

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Background: The aim of this study was to construct a nationwide stand age model by using National Forest Inventory (NFI) data and nationwide airborne laser scanning (ALS) data. In plantation forestry, age is usually known. While this is not the case in boreal managed forests, age is still seldom predicted in forest management inventories. Measuring age accurately in situ is also very laborious. On the other hand, tree age is one of the accurately measured sample tree attributes in NFI field data. Many countries also have a nationwide coverage of airborne laser scanning (ALS) data. In this study, we merged these data sources and constructed a nationwide, area-based model for stand age. Results: While constructing the model, we omitted old forests from the data, since the correlation between ALS height metrics and stand age diminished at stands with age > 100 years. Additionally, the effect of growth conditions was considerable, so we also utilized different geographical and NFI variables such as site fertility and soil type in the modeling. The resultant nationwide model for the stand age of managed forests yielded a root mean square error (RMSE) of about 14 years. The model could be improved further by additional forest structure variables, but such information may not be available in practice. Conclusions: The results showed that the prediction of stand age by ALS, geographical and NFI information was challenging, but still possible with moderate success. This study is an example of the joint use of NFI and nationwide ALS data and re-use of NFI data in research.

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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%

“…These results fit into the general picture in this context and are in line with other studies. In a large-scale study in Norway, where three tree species spruce, pine, and broadleaf trees were classified using only a single date S2 scene, classification resulted in kappa of 0.59 for plots dominated by only one species, and in kappa of 0.5 when plots were included that were not dominated by a species group [46]. In another study in central Sweden on a small test area, classification of five tree species spruce, pine, larch, birch, and oak using a time series of S2 scenes resulted in a kappa of 0.84 [20].…”

Section: Discussionmentioning

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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Forest information at multiple scales: development, evaluation and application of the Norwegian forest resources map SR16

Cited by 37 publications

References 44 publications

Combining MODIS and National Land Resource Products to Model Land Cover-Dependent Surface Albedo for Norway

Combining MODIS and National Land Resource Products to Model Land Cover-Dependent Surface Albedo for Norway

Predicting stand age in managed forests using National Forest Inventory field data and airborne laser scanning

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

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