Satellite-based forest monitoring: spatial and temporal forecast of growing index and short-wave infrared band

  • Caroline Bayr Joanneum Research, Statistical Applications Research Group, Graz, Austria.
  • Heinz Gallaun Joanneum Research, Remote Sensing and Geoinformation Research Group, Graz, Austria.
  • Ulrike Kleb Joanneum Research, Statistical Applications Research Group, Graz, Austria.
  • Birgit Kornberger | birgit.kornberger@joanneum.at Joanneum Research, Statistical Applications Research Group, Graz, Austria.
  • Martin Steinegger Joanneum Research, Remote Sensing and Geoinformation Research Group, Graz, Austria.
  • Martin Winter Joanneum Research, Digital, Audiovisual Media Group, Graz, Austria.

Abstract

For detecting anomalies or interventions in the field of forest monitoring we propose an approach based on the spatial and temporal forecast of satellite time series data. For each pixel of the satellite image three different types of forecasts are provided, namely spatial, temporal and combined spatio-temporal forecast. Spatial forecast means that a clustering algorithm is used to group the time series data based on the features normalised difference vegetation index (NDVI) and the short-wave infrared band (SWIR). For estimation of the typical temporal trajectory of the NDVI and SWIR during the vegetation period of each spatial cluster, we apply several methods of functional data analysis including functional principal component analysis, and a novel form of random regression forests with online learning (streaming) capability. The temporal forecast is carried out by means of functional time series analysis and an autoregressive integrated moving average model. The combination of the temporal forecasts, which is based on the past of the considered pixel, and spatial forecasts, which is based on highly correlated pixels within one cluster and their past, is performed by functional data analysis, and a variant of random regression forests adapted to online learning capabilities. For evaluation of the methods, the approaches are applied to a study area in Germany for monitoring forest damages caused by wind-storm, and to a study area in Spain for monitoring forest fires.

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Published
2016-04-18
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Section
Original Articles
Keywords:
Satellite images, Forest monitoring, Functional time series analysis, Autoregressive integrated moving average, Online random regression forests
Statistics
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How to Cite
Bayr, C., Gallaun, H., Kleb, U., Kornberger, B., Steinegger, M., & Winter, M. (2016). Satellite-based forest monitoring: spatial and temporal forecast of growing index and short-wave infrared band. Geospatial Health, 11(1). https://doi.org/10.4081/gh.2016.310