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Fractional cover mapping of spruce and pine at 1 ha resolution combining very high and medium spatial resolution satellite imagery

Immitzer, Markus and Böck, Sebastian and Einzmann, Kathrin and Vuolo, Francesco and Pinnel, Nicole and Wallner, Adelheid and Atzberger, Clement (2018) Fractional cover mapping of spruce and pine at 1 ha resolution combining very high and medium spatial resolution satellite imagery. Remote Sensing of Environment, 204, pp. 690-703. Elsevier. DOI: 10.1016/j.rse.2017.09.031 ISSN 0034-4257

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Official URL: http://dx.doi.org/10.1016/j.rse.2017.09.031

Abstract

Increases in extreme weather events associated with climate change have the potential to put currently healthy forests at risk. One option to minimize this risk is the application of forest management measures aimed at generating species mixtures predicted to be more resilient to these threats. In order to apply such measures appropriately, forest managers need up-to-date, accurate and consistent forest maps at relatively fine spatial resolutions. Cost efficiency is a major factor when creating such maps. Taking European spruce (Picea abies) and Scots pine (Pinus sylvestris) as an example, this paper describes an innovative approach for mapping two tree species using a combination of commercial very high resolution WorldView-2 (WV2) images and Landsat time series data. As a first step, this study used a supervised object-based classification of WV2 images covering relatively small test sites distributed across the region of interest. Using these classification maps as training data, wall-to-wall mapping of fractional coverages of spruce and pine was achieved using multi-temporal Landsat data and Random Forests (RF) regression. The method was applied for the entire state of Bavaria (Germany), which comprises a total forested area of approximately 26,000 km2. As applied here, this two-step approach yields consistent and accurate maps of fractional tree cover estimates with a spatial resolution of 1 ha. Independent validation of the fractional cover estimates using 3780 reference samples collected through visual interpretation of orthophotos produced root-mean-square errors (RMSE) of 11% (for spruce) and 14% (for pine) with almost no bias, and R2 values of 0.74 and 0.79 for spruce and pine, respectively. The majority of the validation samples (75% (spruce) and 84% (pine)) were modeled within the assumed uncertainty of± 15% of the reference sample. Accuracies were significantly better compared to those achieved using a single-step classification of Landsat time series data at the pixel level (30 m), because the two-step approach better captures regional variation in the spectral signatures of target classes. Moreover, the increased number of available reference cells mitigates the impact of occasional errors in the reference data set. This two-step approach has great potential for cost-effective operational mapping of dominant forest types over large areas.

Item URL in elib:https://elib.dlr.de/114500/
Document Type:Article
Additional Information:LWF Projekt VitTree
Title:Fractional cover mapping of spruce and pine at 1 ha resolution combining very high and medium spatial resolution satellite imagery
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Immitzer, Markusboku wienUNSPECIFIED
Böck, Sebastianboku wienUNSPECIFIED
Einzmann, Kathrinboku wienUNSPECIFIED
Vuolo, Francescoboku wienUNSPECIFIED
Pinnel, NicoleNicole.Pinnel (at) dlr.dehttps://orcid.org/0000-0003-1978-3204
Wallner, Adelheidadelheid.wallner (at) lwf.bayern.deUNSPECIFIED
Atzberger, Clementboku wienUNSPECIFIED
Date:1 January 2018
Journal or Publication Title:Remote Sensing of Environment
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:204
DOI :10.1016/j.rse.2017.09.031
Page Range:pp. 690-703
Editors:
EditorsEmail
Hu, ChuanminUNSPECIFIED
Chen, ChenUNSPECIFIED
Chuvieco, EmilioUNSPECIFIED
Schaaf, CrystalUNSPECIFIED
Publisher:Elsevier
ISSN:0034-4257
Status:Published
Keywords:Upscaling, Random Forest regression, WorldView-2, Landsat, Fractional cover, Tree species mapping
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren, R - Remote sensing and geoscience, R - Vorhaben Optische Fernerkundung für sicherheitsrelevante Anwendungen
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Land Surface
Deposited By: Pinnel, Dr.rer.nat Nicole
Deposited On:21 Nov 2017 13:29
Last Modified:23 Feb 2019 00:21

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