RCCZO -- GIS / Map Data, LiDAR, Land Cover, Vegetation -- Data for Vegetation Maps for RCEW -- Reynolds Creek Experimental Watershed -- (2015-2015)

The sparse canopy cover and large contribution of bright background soil, along with the heterogeneous vegetation types in close proximity are common challenges for mapping dryland vegetation with remote sensing. Consequently, the results of a single classification algorithm or one type of sensor to characterize dryland vegetation typically show low accuracy and lack robustness. In our study, we improve classification accuracy in a semi-arid ecosystem based on the use of vegetation optical (hyperspectral) and structural (lidar) information combined with the environmental characteristics of the landscape. To accomplish this goal we used both spectral angle mapper (SAM) and multiple endmember spectral mixture analysis (MESMA) for optical vegetation classification. Lidar-derived maximum vegetation height and delineated riparian zones were then used to modify the optical classification. Incorporating the lidar information into the classification scheme increased the overall accuracy from 60% to 89%. Canopy structure can have a strong influence on spectral variability and the lidar provided complementary information for SAM's sensitivity to shape but not magnitude of the spectra. Similar approaches to map large regions of drylands with low uncertainty may be readily implemented with unmixing algorithms applied to upcoming space-based imaging spectroscopy and lidar. As such, widespread studies to develop and understand the nuances associated with these approaches will enable efficient adoption and application.

稀疏冠层覆盖度、高占比的明亮背景土壤，加之近距离分布的异质植被类型，是利用遥感技术绘制干旱区植被分布图时面临的共性难题。因此，仅采用单一分类算法或单一类传感器表征干旱区植被的研究结果，往往精度偏低且鲁棒性不足。本研究基于植被光学（高光谱hyperspectral）与结构（激光雷达lidar）信息，并结合景观环境特征，提升了半干旱生态系统的植被分类精度。为实现该研究目标，本研究同时采用光谱角填图（Spectral Angle Mapper, SAM）与多端元光谱混合分析（Multiple Endmember Spectral Mixture Analysis, MESMA）开展光学植被分类。随后，我们利用激光雷达反演得到的最大植被高度与划定的河岸带，对光学分类结果进行修正。将激光雷达信息融入分类框架后，总体分类精度从60%提升至89%。冠层结构对光谱变异性具有显著影响，而激光雷达可针对光谱角填图对光谱形状（而非光谱幅值）的敏感性提供互补信息。针对大面积干旱区开展低不确定性植被制图的类似方法，可通过将光谱解混算法应用于即将发射的星载成像光谱与激光雷达系统得以快速实施。因此，开展面向这类方法的开发与细节机理研究，将有助于相关技术的高效推广与应用。