A forecasting approach to online change detection in land cover time series

We present a method for online detection of land coverchange based on remotely sensed time series. Change is detectedby monitoring deviations between observations and forecasts madeusing the time series historical data and similar time series in thegeographical region. This method and several others were appliedto MODIS 8-day surface reflectance data for problems of detectingsettlement expansion in Limpopo Province, South Africa, and detecting deforestation in New South Wales, Australia. The proposedmethod had significantly shorter median detection delay (DD) forequivalent rates of false alarms compared with the other evaluatedmethods. We obtained a median DD of seven samples for settlementdetection and 14 samples for deforestation detection correspondingto 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 forland cover change detection where rapid detection is essential

Probabilistic methods for land cover change detection

One of the most powerful tools we possess for global scale monitoring of the surface of the earth are spacebourne remote sensing satellites. Every day they capture vast amounts of data at different spatial, spectral and temporal resolutions which must be processed in order to generate meaningful insights. This thesis focuses on the particular problem of detecting if and when a particular region of land cover experiences a change from one type to another. This problem is made difficult by the fact that the majority of land cover on earth, especially vegetated, undergoes natural variations on both annual and inter-annual time scales driven by changes in season and climate. In this thesis we argue that the key to detecting unnatural changes as accurately and rapidly as possible is to do so with respect to a probabilistic model of the natural variations estimated for the particular region of interest. A method each for change detection, change point estimation and online change monitoring are proposed that follow this strategy. These methods are evaluated on reflectance time series from the Moderate Resolution Imaging Spectroradiometer (MODIS) for two change detection problems, detecting unplanned settlement expansion in South Africa and detecting deforestation of protected areas in Australia. In each case the proposed methods are shown to be effective and require little human supervision suggesting that this approach has potential for use in production systems