Abstract
We present a method for online detection of land cover change based on remotely sensed time series. Change is detected by monitoring deviations between observations and forecasts made using the time series historical data and similar time series in the geographical region. This method and several others were applied to MODIS 8-day surface reflectance data for problems of detecting settlement expansion in Limpopo Province, South Africa, and detecting deforestation in New South Wales, Australia. The proposed method had significantly shorter median detection delay (DD) for equivalent rates of false alarms compared with the other evaluated methods. We obtained a median DD of seven samples for settlement detection and 14 samples for deforestation detection corresponding to 56 days and 112 days, respectively. This is compared with a median DD of 224 and 544 days for the best other methods evaluated. We suggest that the proposed method is an excellent candidate for land cover change detection where rapid detection is essential.
Highlights
A Forecasting Approach to Online Change Detection in Land Cover Time SeriesAbstract—We present a method for online detection of land cover change based on remotely sensed time series
O NE of the major goals of remote sensing satellite systems is to enable large scale monitoring of the earth’s land cover
We propose modeling the joint distribution over the samples in a temporal window as a multivariate Gaussian
Summary
Abstract—We present a method for online detection of land cover change based on remotely sensed time series. Change is detected by monitoring deviations between observations and forecasts made using the time series historical data and similar time series in the geographical region. This method and several others were applied to MODIS 8-day surface reflectance data for problems of detecting settlement expansion in Limpopo Province, South Africa, and detecting deforestation in New South Wales, Australia. We obtained a median DD of seven samples for settlement detection and 14 samples for deforestation detection corresponding to 56 days and 112 days, respectively This is compared with a median DD of 224 and 544 days for the best other methods evaluated.
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