Advancing Land-Use and Land-Cover Analysis by Integrating Optical and Polarimetric Radar Platforms

该研究项目计划将来自新雷达平台（ALOS/PALSAR和RADARSAT2）的数据与现有光学传感器（TM/ETM+， ASTER）的数据整合在一起，以实现持续云层覆盖地区的土地覆盖分类和土地覆盖动态评估。结果将是及时和准确地评估通常经历持续云层覆盖的地区的土地覆盖变化，从而使传统的遥感方法成为不可能。来自印第安纳大学的Moran教授和Lu教授，以及法国国家空间研究机构（INPE）的国际合作伙伴认为，ALOS/PALSAR和RADARSAT2的多极化与光学数据分析相结合，将提供更好、更及时的植被分类精度，并将改善持续云层覆盖地区的土地利用和土地覆盖的时空分析。两个研究区域将是本研究的重点，阿尔塔米拉和圣塔伦，都在巴西亚马逊州帕拉。这将使该研究小组能够使用巢式地理参考方法分析多年来收集的数据，包括土壤分析、植被结构和组成、土地利用历史、制度分析、数百户家庭的人口统计和决策，以及使用20世纪70年代以来的多时间遥感数据（MSS、TM/ETM+和IKONOS/QuickBird）进行土地覆盖分类，从而降低成本并更快地取得结果。pi获得了两个研究区域的RADARSAT2和ALOS/PALSAR的最新图像，以及IKONOS和Quickbird图像。这些数据将与来自Landsat MSS、TM和ETM+（以及ASTER）的研究区域的存档数据融合在一起，以提高土地覆盖评估的知识水平和方法，这些知识和方法适用于世界上任何地区，这些地区的云覆盖限制了土地覆盖变化的季节性观测。分离性分析将用于检查数据融合图像的能力，这些图像是用不同的数据融合技术开发的，在广泛的实地工作和大量使用IKONOS和Quickbird高分辨率数据的基础上，根据训练样本地块区分感兴趣的土地覆盖类别。这项研究的结果将解决科学家目前面临的一个主要障碍，即在持续云层覆盖的地区量化土地覆盖变化。潮湿的热带世界的大部分地区在一年中的大部分时间里都被云覆盖，有时可能需要几年的时间才能获得无云图像，以监测不断变化的森林状况。这妨碍了对森林砍伐事件的及时反应和对土地覆盖随时间变化的了解。该项目解决了及时分析巴西亚马逊地区以及更广泛的潮湿热带地区土地覆盖变化的迫切需要。

This research project plans to integrate data from new radar platforms (ALOS/PALSAR and RADARSAT2) with those from existing optical sensors (TM/ETM+, ASTER) to enable land cover classification and assessment of land cover dynamics in areas of constant cloud cover. The result will be timely and accurate assessment of land cover change in areas that typically experience constant cloud cover and therefore make traditional methods of remote sensing impossible. Professors Moran and Lu from Indiana University, along with international collaborators at the Instituto Nacional de Pesquisas Espaciais (INPE) contend that the multiple polarizations of ALOS/PALSAR and RADARSAT2, fused with analysis of optical data, will provide better and timelier vegetation classification accuracy, and will improve the temporal and spatial analysis of land use and land cover in areas characterized by persistent cloud cover. Two study areas will be the focus of this study, Altamira and Santarem, both in the Brazilian Amazon state of Para. This will enable the analysis of data collected for several years by this research team using a nested-georeferenced approach including soil analyses, vegetation stand structure and composition, land use histories, institutional analyses, demography and decision-making of hundreds of households, and land cover classification using multi-temporal remote sensing data since the 1970s (MSS, TM/ETM+, and IKONOS/QuickBird,) thereby reducing costs and to achieve results much more quickly. The PIs have obtained recent images of RADARSAT2 and ALOS/PALSAR for both study areas, as well as IKONOS and Quickbird images. These data will be fused with archived data for the study areas from Landsat MSS, TM and ETM+ (and ASTER) to advance the state of knowledge and methods in land cover assessment applicable across any part of the world where cloud-cover constrains seasonal observations of changes in land cover. Separatibility analysis will be used to examine the capability of the data fused images, which are developed with different data fusion techniques, in distinguishing the land cover classes of interest based on the training sample plots from extensive field work and the intensive use of the IKONOS and Quickbird very high resolution data. The results of this study will solve a major obstacle currently faced by scientists in quantifying land cover change in areas with persistent cloud cover. Much of the humid tropical world is covered by clouds for most of the year, and it can be sometimes years between dates when it is possible to obtain cloud-free images for monitoring changing forest conditions. This prevents timely responses to deforestation events and understanding of land cover changes over time. This project addresses an urgent need for timely analysis of land cover changes in the Brazilian Amazon, and in humid tropical regions more broadly.