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

摘要

Increases in extreme weather events associated with climate change have the potential to put currently healthyudforests at risk. One option to minimize this risk is the application of forest management measures aimed atudgenerating species mixtures predicted to be more resilient to these threats. In order to apply such measuresudappropriately, forest managers need up-to-date, accurate and consistent forest maps at relatively fine spatialudresolutions. Cost efficiency is a major factor when creating such maps. Taking European spruce (Picea abies) andudScots pine (Pinus sylvestris) as an example, this paper describes an innovative approach for mapping two treeudspecies using a combination of commercial very high resolution WorldView-2 (WV2) images and Landsat timeudseries data. As a first step, this study used a supervised object-based classification of WV2 images coveringudrelatively small test sites distributed across the region of interest. Using these classification maps as traininguddata, wall-to-wall mapping of fractional coverages of spruce and pine was achieved using multi-temporal Landsatuddata and Random Forests (RF) regression. The method was applied for the entire state of Bavaria (Germany),udwhich comprises a total forested area of approximately 26,000 km2. As applied here, this two-step approachudyields consistent and accurate maps of fractional tree cover estimates with a spatial resolution of 1 ha.udIndependent validation of the fractional cover estimates using 3780 reference samples collected through visualudinterpretation of orthophotos produced root-mean-square errors (RMSE) of 11% (for spruce) and 14% (for pine)udwith almost no bias, and R2 values of 0.74 and 0.79 for spruce and pine, respectively. The majority of theudvalidation samples (75% (spruce) and 84% (pine)) were modeled within the assumed uncertainty of± 15% of theudreference 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 capturesudregional 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 greatudpotential for cost-effective operational mapping of dominant forest types over large areas.