A Review on Mapping Land Cover Change and the Various Techniques Used

Global land cover types in 2001 and 2010 were mapped at 250 m resolution with the data from multiyear time series Moderate Resolution Imaging Spectrometer (MODIS) data. This was done with the help of data acquired in the preceding and subsequent years. Slope data and geographical coordinates of pixels were also used. The classification was done with the finer resolution observation and monitoring of global land cover (FROM-GLC) project and the results were further improved through post processing. A spatial-temporal consistency model, Maximum a Posteriori Markov Random Fields (MAP-MRF), was first applied to improve land cover classification for 3 consecutive years. The MRF outputs for 2001 and 2010 were then processed with a rule-based label adjustment method with MOD44B, slope and composited EVI series as auxiliary data. With maximum probabilities, the label adjustment process re-labelled the over-classified forests, water bodies and barren lands to alter-native classes. The longest record of global-scale medium spatial resolution earth observation data goes to Landsat data. Therefore, the current methods for large area monitoring of land cover change using medium spatial resolution imagery (1050 m) use Landsat data. Forest cover change is quantified by large area products. Forests are an easy cover type to map and are the current focus of great environmental concern. Among existing change products, supervised or knowledge-based characterization methods predominate. Radiometric correction methods vary largely as a function of geographic/algorithmic scale. Temporal updating of cover change varies as a function of regional acquisition frequency, cloud cover and seasonality. With the Landsat archive opened for free access to terrain-corrected data, in the future it is very likely that product generation will be more data intensive. Per scene, interactive analyses will no longer be viable. With both free and open access to large data volumes with improved processing power there will be automated image pre-processing and land cover characterization methods. Such methods will need to grasp high-performance computing capabilities in advancing the land cover monitoring discipline. Robust validation efforts will be an essentiality to quantify product accuracies in determining the optimal change characterization methodologies.