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Estimating stand density, biomass and tree species from very high resolution stereo-imagery- towards an all-in-one sensor for forestry applications?

Fassnacht, Fabian and Mangold, Daniel and Schäfer, Jannika and Immitzer, Markus and Kattenborg, Teja and Koch, Barbara and Latifi, Hooman (2017) Estimating stand density, biomass and tree species from very high resolution stereo-imagery- towards an all-in-one sensor for forestry applications? Forestry, 90 (5), pp. 613-631. Oxford University Press. doi: 10.1093/forestry/cpx014. ISSN 0015-752X.

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Official URL: https://academic.oup.com/forestry/article/doi/10.1093/forestry/cpx014/3083963/Estimating-stand-density-biomass-and-tree-species


The estimation of various forest inventory attributes from high spatial resolution airborne remote sensing data has been widely examined and proved to be successful at the experimental level. Nevertheless, the operational use of these data in automated procedures to support forest inventories and forest management is still limited to a small number of cases. The reasons for this are high data costs, limited availability of remote sensing data over large areas and resistance from practitioners. In this review the main aim is to stimulate debate about spaceborne very high resolution stereo-imagery (VHRSI) as an alternative to airborne remote sensing data by presenting: (1) a case study on the retrieval of stand density, aboveground biomass and tree species using a set of easy-to-calculate variables obtained from VHRSI data combined with image processing and nonparametric classification and modelling approaches; and (2) the results of an expert opinion survey on the potential of VHRSI as compared with Light Detection and Ranging (LiDAR), hyperspectral and airborne digital imagery to derive a range of forest inventory attributes. In the case study, stand density was estimated with r² = 0.71 and RMSE = 156 trees (rel./norm. RMSE = 24.9 per cent/12.4 per cent), biomass with r² = 0.64 and RMSE of 36.7 t/ha (rel./norm. RMSE = 20.0 per cent/12.8 per cent) while tree species classifications with five species reached overall accuracies of 84.2 per cent (kappa = 0.81). These results were comparable to earlier studies in the same test site, obtained with more expensive airborne acquisitions. Expert opinions were more diverse for VHRSI and aerial photographs (Shannon index values of 0.94 and 0.97) than for LiDAR and hyperspectral data (Shannon index values 0.69 and 0.88). In our opinion, this reflects the current state-of-the-art in the application of VHRSI for automatically retrieving forest inventory attributes. The number of studies using these data is still limited, and the full potential of these datasets is not yet completely explored. Compared with LiDAR and hyperspectral data, which both mostly received high scores for forest inventory products matching the sensor systems’ strengths, VHRSI and aerial photographs received more homogeneous scores indicating their potential as multi-purpose instruments to collect forest inventory information. In summary, considering the simpler acquisition, reasonable price and the comparably easy data format and handling of VHRSI compared with other sensor types, we recommend further research on the application of these data for supporting operational forest inventories.

Item URL in elib:https://elib.dlr.de/111943/
Document Type:Article
Title:Estimating stand density, biomass and tree species from very high resolution stereo-imagery- towards an all-in-one sensor for forestry applications?
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Immitzer, Markusboku wienUNSPECIFIED
Journal or Publication Title:Forestry
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:Yes
Page Range:pp. 613-631
Publisher:Oxford University Press
Keywords:very high resolution stereo satellite data, stand density, tree species, biomass, operationality, WorldView 2
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Geoscientific remote sensing and GIS methods
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center
Deposited By: Wöhrl, Monika
Deposited On:23 Oct 2017 12:13
Last Modified:08 Mar 2018 18:30

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