A comparative study of algorithms for land cover change (2010)

Ecosystem-related observations from remote sensors on satellites offer huge potential for understanding the location and extent of global land cover change. This paper presents a comparative study of three time series based algorithms for detecting changes in land cover. The techniques are evaluated quantitatively using forest fire ground truth from the state of California for 2000-2009. On relatively high quality data sets, all three schemes perform reasonably well, but their ability to handle noise and natural variability in the vegetation data differs dramatically. In particular, one of the algorithms significantly outperforms the other two since it accounts for variability in the time series. The climate and earth sciences have recently undergone a rapid transformation from a data- poor to a data-rich environment. In particular, climate and ecosystem related observations from remote sensors on satellites, as well as outputs of climate or earth system models from large-scale computational platforms, provide terabytes of temporal, spatial and spatio-temporal data. These massive and information-rich datasets offer huge potential for advancing the science of land cover change, climate change and anthropogenic impacts. One important area where remote sensing data can play a key role is in the study of land cover change. Specifically, the conversion of natural land cover into human-dominated cover types con- tinues to be a change of global proportions with many unknown environmental consequences. In addition, being able to assess the carbon risk of changes in forest cover is of critical importance for both economic and scientific reasons. In fact, changes in forests account for as much as 20% of the greenhouse gas emissions in the atmosphere, an amount second only to fossil fuel emissions. Thus, there is a need in the earth science domain to systematically study land cover change in order to understand its impact on local climate, radiation balance, biogeochemistry, hydrology, and the diversity and abundance of terrestrial species. Land cover conversions include tree harvests in forested regions, urbanization, and agricultural intensification in former woodland and natural grassland areas. These types of conversions also have significant public policy implications due to issues such as water supply management and atmospheric CO2 output. In spite of the importance of this problem and the considerable advances made over the last few years in high-resolution satellite data, data mining, and online mapping tools and services, end users still lack practical tools to help them manage and transform this data into actionable knowledge of changes in forest ecosystems that can be used for decision making and policy planning purposes. For ecosystem data, change detection is the process of identifying changes in the cover type and/or human use of the Earth. Examples include the conversion of forested land to barren land (possibly due to deforestation or a fire), grasslands to golf courses and farmland to housing developments. There is a large body of research in change detection using remotely sensed image data. Most pre- vious change detection studies primarily rely on examining differences between two or more satellite images acquired on different dates (9). However, these techniques have well-known limitations (as