Collaborative Research: SEI(BIO)--Automated Methods for Generating High-Resolution GIS Databases from Remotely Sensed Data for Biodiversity Predictions

全球变化对土地覆盖、碳循环和生物多样性丧失的影响涉及精细尺度上的复杂相互作用，例如森林下层资源的可用性与区域土地覆盖、气候和二氧化碳的相互作用。全球变化研究需要通过对局部现象的仔细研究来开发模型，这些模型可以扩展到景观、区域和全球尺度。不幸的是，环境科学家在确定不同规模的因素如何影响景观的能力方面一直受到限制。这项研究的主要长期目标是提高生物学和地学研究项目获取、分析和分发重要环境属性的高分辨率地理信息系统数据库的能力。为支持这一目标，计算机科学小组将开发新技术，从遥感数据中提取地理信息系统数据库形式的森林属性。计算机科学小组将建立一个航空遥感平台和一套分析工具，用于创建具有亚米级地理登记和高程精度的环境属性地理信息系统数据库。马萨诸塞州大学和MHC开发的图像采集、分析和地理信息系统工具提供了对用于研究全球变化的模型进行参数化和验证所需的关键的广泛但高分辨率的数据。这些分析的产品将被整合到杜克大学的建模框架中，该框架包括广泛的现场数据、新的统计计算方法的应用以及林分模拟器的开发。这项综合努力将用于根据对环境影响和不确定性的全面核算，确定如何维持林地的多样性。

AbstractNSF-0430742Consequences of global change for land cover, carbon cycles, and biodiversity loss involve complex interactions at fine scales, such as resource availability in forest understories, to regional land-cover, climate, and CO2. Global change research requires models developed through careful study of local phenomena that can be extended to landscape, regional, and global scales. Unfortunately, environmental scientists have been limited in their ability to determine how factors that operate at different scales impact landscapes. The primary long-term goal of the research is to enhance the ability of biology and geoscience research programs to acquire, analyze, and distribute high-resolution GIS databases of important environmental attributes. In support of this goal the computer science team will develop new techniques to extract forest attributes in the form of GIS databases from remotely sensed data. The computer science team will build an aerial remote sensing platform and a suite of analysis tools for creating GIS databases of environmental attributes with sub-meter geo-registration and elevation accuracies. The image acquisition, analysis and GIS tools developed by UMass and MHC provide the critical broad-scale, yet high-resolution, data needed to parameterize and validate models used to study global change. The products of these analyses will be integrated within a modeling framework at Duke University that includes extensive field data, application of new statistical computation methods, and development of a stand simulator. The combined effort will be used to determine how diversity is maintained in forest stands based on a comprehensive accounting of environmental impacts and uncertainties.