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

• Published in: Geospatial health Vol. 11; no. 1; p. 310
• Main Authors: Bayr, Caroline, Gallaun, Heinz, Kleb, Ulrike, Kornberger, Birgit, Steinegger, Martin, Winter, Martin
• Format: Journal Article
• Language: English; Italian
• Published: Italy PAGEPress Publications 18.04.2016
• Subjects: Autoregressive integrated moving average; Data analysis; Distance learning; Environmental Monitoring - methods; Europe; Forest monitoring; Forests; Functional time series analysis; Infrared Rays; Models, Statistical; Online random regression forests; Satellite Imagery - methods; Satellite images; Spacecraft; Spatio-Temporal Analysis; Time series
• ISSN: 1827-1987; 1970-7096
• DOI: 10.4081/gh.2016.310

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.