Land Cover Change Monitoring and Mapping Using Remote Sensing Data in Arid and Semiarid Land of United States
报告人:史华 美国地质勘探局地球资源观测与科学中心 高级研究员
Earth Resources Observation and Science (EROS) Center, United States Geology Survey (USGS), 47914 252nd Street, Sioux Falls, SD 57198
Increasing direct and diffuse anthropogenic usage and threats on the arid and semiarid ecosystems create an urgent need to better understand their biodiversity, function, mechanisms of change, biogeochemical cycles, and current distribution. Quantifying western U.S. rangelands as a series of fractional components with remote sensing provides a new way to understand these changing ecosystems. Nine shrubland ecosystem components, including percent shrub, sagebrush (Artemisia spp), big sagebrush, herbaceous, annual herbaceous, litter, and bare ground cover, along with sagebrush and shrub heights, were quantified at 30-m resolution by mapping region. This dataset has been adopted to upgrade (cross walk) the NLCD 2016 product (releasing on May 2019). Moreover, the need to monitor change in vegetation cover in shrubland ecosystem is urgent due to the increasing impacts of climate change, shifting fire regimes, and management practices on ecosystem health. Remote sensing provides a cost effective and reliable method for monitoring change through time and attributing changes to drivers. We developed an automated method of mapping shrubland fractional component cover over of Western United States from 1984 to current using a dense Landsat imagery time-series. These data can be used to answer critical questions regarding the influence of climate change and the suitability of management practices.
With a research specialization in integrated physical geography, landscape change modeling and mapping, and remote sensing and GIS applications, most of Dr. Shi experience and research concentrates on the assessment of human-ecosystem interactions, characterization of landscape changes, and ecological processes in arid and semiarid land. His research focuses on developing and improving algorithms, data collections and inputs, and thematic outputs to enable the mapping of landscape change at regional, continental and global scales. Such datasets can be used to better inform approaches to natural resource management, including ecosystem disturbances (both human and nature) and biodiversity monitoring. The datasets can also be used by scientists as inputs into carbon, climate, ecological, and hydrological modeling studies. Specific research interests including: forecasting and mapping disturbances of ecosystems; assessing the influences of climate change and rangeland management on natural disturbance regimes; evaluating of output from the continuous-monitoring algorithm by assessing the accuracy of the output; and developing models to simulate future landscape changes and their ecological impacts. His current work focuses on integrating ecological concepts to assess natural and human disturbances at broad spatial scales and temporal time-series through the application of field observation, multiple sensor satellite imagery, GIS datasets, and spatial statistics models in arid semiarid ecosystems.