From deforestation to urbanization, the human footprint on the land surface is ever expanding, converting natural land covers into land uses or intensifying current land uses. Land use change results in loss of biodiversity, increased greenhouse gas emissions, and alteration of hydrological systems, among other impacts. Rates of land use change can be quantified using time-series earth observation data from satellites. The integrated use of multi-source data dramatically improves monitoring capabilities, reducing the uncertainties around many important land dynamics, such as deforestation rates and crop area estimation. In this forum presentation, a number of themes will be presented with a focus on our improving capabilities to accurately quantify global land change.

Bio

Matthew Hansen is a remote sensing scientist and professor in the University of Maryland’s Department of Geographical Sciences. He specializes in large-area land cover and land use change mapping as well as developing improved algorithms, data inputs and thematic outputs for land change mapping at regional, continental and global scales. Such maps support more informed approaches to natural resource management, including deforestation and biodiversity monitoring and can also be used by other scientists as inputs to carbon, climate and hydrological modeling studies. Prof. Hansen’s work as an Associate Team Member of NASA’s MODIS Land Science Team included the algorithmic development and product delivery of the MODIS Vegetation Continuous Fields land cover layers. In his current research, he is applying the MODIS global processing model to the Landsat archive. Exhaustive mining of the Landsat archive is being used to map forest disturbance in the Congo Basin, Indonesia, European Russia, Mexico, Quebec and the United States, and the methods developed will be used to test global-scale disturbance mapping with Landsat data. Other current research efforts include improving global cropland monitoring capabilities, for example, global soybean cultivated area estimation using MODIS, Landsat and RapidEye data sets.