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

REMOTE SENSING OF ENVIRONMENT

卷:204

Article

Immitzer, Markus¹  Boeck, Sebastian¹  Einzmann, Kathrin¹  Vuolo, Francesco¹  Pinnel, Nicole²  Wallner, Adelheid³  Atzberger, Clement¹ 

[1] Univ Nat Resources & Life Sci Vienna BOKU, Inst Surveying Remote Sensing & Land Informat, Peter Jordan Str 82, A-1190 Vienna, Austria

[2] German Aerosp Ctr DLR, German Remote Sensing Data Ctr DFD, Dept Land Surface, Munchner Str 20, D-82234 Wessling, Germany

[3] Bavarian State Inst Forestry LWF, Dept Informat Technol, Res Grp Remote Sensing, Hans Carl von Carlowitz Pl 1, D-85354 Freising Weihenstephan, Germany

关键词: Upscaling;    Random Forest regression;    WorldView-2;    Landsat;    Fractional cover;    Tree species mapping;   

DOI  :  10.1016/j.rse.2017.09.031

来源: Elsevier

【 摘 要 】

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 km(2). 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 R-2 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.

【 授权许可】

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