International Research Fellowship: Application of Global Biophysical Fields Derived from Large-Swath Remote Sensing Data to the Detection and Categorization of Land-Cover Change

0301307林德曼国际研究奖学金计划使美国科学家和工程师能够在国外进行三到二十四个月的研究。 该计划的奖项提供了联合研究的机会，以及使用国外独特或互补的设施，专业知识和实验条件。 该奖项将支持马克·林德曼博士与埃里克·兰宾博士在比利时新鲁汶天主教大学进行为期24个月的研究，将大幅遥感数据产生的全球生物物理场应用于土地覆盖的探测和分类，准确监测土地覆盖和土地利用变化的能力对于准确评估影响率、模拟生态系统、并有效地减轻未来自然区域的损失。 作为一项新的欧洲倡议（CYCLOPES）的一部分，这一项目将审查将自然和人类土地覆盖物变化分开和分类的方法。 CYCLOPES将采用大测绘带遥感数据和创新方法，如融合若干数据源和利用人工神经网络反演当前的辐射转移模型，以开发在空间和时间上一致的生物物理数据。 然后，土地覆被变化探测方法将比较遥感土地覆被指标连续几年的季节性变化曲线，特别强调比较生物物理数据的效用，以相对于常用的植被指数区分土地覆被变化的原因。 与CYCLOPES和Lambin博士的合作将为PI提供检查大规模土地覆盖分析的机会，并在数据和算法开发以及全球建模方面获得更多经验。 这里开发的方法将使他能够进一步研究人类对生态系统过程的影响，从地方到全球规模。

0301307LindermanThe International Research Fellowship Program enables U.S. scientists and engineers to conduct three to twenty-four months of research abroad. The program's awards provide opportunities for joint research, and the use of unique or complementary facilities, expertise and experimental conditions abroad. This award will support a twenty-four month research fellowship by Dr. Marc Linderman to work with Dr. Eric Lambin at Universite Catholique de Louvain, in Louvain-la-Neuve, Belgium, on the application of global biophysical fields derived from large-swath remote sensing data to the detection and categorization of land cover-change.The ability to accurately monitor changes to land cover and land use is crucial for accurate assessments of the rates of impact, to model ecological systems, and to effectively mitigate future losses of natural areas. As part of a new European initiative (CYCLOPES), this project will examine methods to decouple and categorize natural and human land-cover change. CYCLOPES will employ large-swath remote sensing data and innovative methods such as the fusion of several data sources and inversion of current radiative transfer models using artificial neural networks to develop spatially and temporally consistent biophysical data. The land-cover change detection approach will then compare seasonal development curves for successive years of remotely-sensed land-cover indicators with particular emphasis on comparing the utility of biophysical data to discriminate between causes of land-cover change relative to commonly used vegetation indices. Collaboration with CYCLOPES and Dr. Lambin will provide opportunities for the PI to examine broad-scale land-cover analyses, and gain additional experience in data and algorithm development and global modeling. The methods developed here will enable him to further examine human impacts on ecosystem processes from local to global scales.