Development of area-wide functional indicators using remotely sensed data

该提案旨在制定和实施区域范围的功能指标(蒸散量、初级生产力、水分利用效率)，这些指标可用于监测由于环境变化(土地利用和气候变化)引起的树木水分关系的变化，并确定对水分敏感的指标树。除其他外，将使用闪烁测量仪和冠层上方的涡流协方差测量，以及基于观测的C5项目和其他项目的模型结果，对开发的算法进行校准和质量评估。对于C5模型，将通过LiDAR数据计算所需的树冠照度(日照和遮荫比例)。此外，该提案旨在与C2项目合作，得出适用于制定和实施关于生物多样性和生态系统进程的全区域功能指标的全区域结构性和多/高光谱预测变量。在其他变量中，有与水分胁迫和生产力相关的指数，也有结构参数，如植被高度和郁闭度。第三个目标是为其他平台项目提供基于变化检测和多目标土地分配(MOLA)技术的未来土地利用变化预测。指标和预测变量是利用高分辨率数据(每像素1-6.5米分辨率)在冠状尺度上制定的，然后被提升到景观尺度(约30米像素分辨率)，用于厄瓜多尔南部的区域监测。与非大学合作伙伴一起在业务监测系统中实施需要可从业务上可用的数据来源得出的指标。

The proposal aims at the development and implementation of area-wide functional indicators (Evapotranspiration ET, Primary Production, Water Use Efficiency) which can be used to monitor changes in tree water relations due to environmental change (land use and climate change) and to identify water sensitive indicator trees. Calibration and quality assessment of the developed algorithms will be conducted among others with scintillometer and eddy covariance measurements above the canopy, and observation-based model results of project C5 and others. For the model of C5, the required tree crown illumination (sunlit and shaded fraction) will be calculated by means of LiDAR data. Furthermore, the proposal aims at deriving area-wide structural and multi-/hyperspectral predictor variables applicable to develop and implement area-wide functional indicators on biodiversity and ecosystem processes in collaboration with project C2. Among others, the variables are water stress and productivity related indices but also structural parameters as e.g. vegetation height and canopy density. The third aim is to provide other platform projects with future land use change projections based on change detection and Multi Objective Land Allocation (MOLA) techniques. Indicators and predictor variables are developed on the crown scale with high resolution data (1 - 6.5 m resolution per Pixel) and are then upscaled to the landscape scale (~30 m pixel resolution) for area-wide monitoring in southern Ecuador. The implementation in an operational monitoring system together with the non-university cooperation partners requires indicators which can be derived from operationally available data sources.