Professor, University of Maryland - Department of Geographical Sciences
How I work with Harvest
Dr. Hansen is part of the University of Maryland Harvest Hub, providing support for Harvest activities and research. His primary goal in quantifying land cover extent and change is to bring improved information to natural resource monitoring, management and modeling. He aims to develop research ideas that are tested and validated and then judged to be of practical value. The most rewarding outcome of my research is the adoption of developed methods in operational contexts.
Dr. Hansen believes in an interdisciplinary research approach. The task of improving land cover monitoring requires engineering skills in terms of data calibration and processing, geographic domain knowledge concerning land cover types and the drivers of land change, and understanding of the value of land information to downstream science applications.
Prof. Matthew Hansen is a remote sensing scientist with a research specialization in large area land cover and land use change mapping. His research is focused on developing improved algorithms, data inputs and thematic outputs which enable the mapping of land cover change at regional, continental and global scales. Such maps enable better 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 Field land cover layers. His current research includes taking the global processing model for MODIS and applying it to the Landsat archive. Exhausting mining of the Landsat archive has been used to map forest disturbance in the Congo Basin, Indonesia, European Russia, Mexico, Quebec and the United States. The methods developed in these efforts will be used to test global-scale disturbance mapping with Landsat data. Other current research efforts focus on improving global cropland monitoring capabilities, for example global soybean cultivated area estimation using MODIS, Landsat and RapidEye data sets.