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

通过集成光学和偏振雷达平台推进土地利用和土地覆盖分析

• 批准号: 0850615
• 负责人: Emilio Moran
• 金额: $ 19.96万
• 依托单位: Indiana University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-06-01 至 2012-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0850615&HistoricalAwards=false
• 关键词: Advancing; Land; Use; Cover; Analysis

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.

该研究项目计划将新的雷达平台（ALOS/PALSAR和RADARSAT2）与现有光学传感器（TM/ETM+，Aster）的数据集成，以使土地覆盖分类和评估恒定云覆盖区域的土地覆盖动态。结果将是对通常经历恒定云覆盖的区域的土地覆盖变化的及时，准确的评估，因此使传统的遥感方法不可能。 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.这项研究的重点将是两个研究领域，即阿尔塔米拉（Altamira）和桑塔雷姆（Santarem），均在巴西亚马逊州。这将使该研究团队使用嵌套地向往的方法对收集的数据进行分析，包括土壤分析，植被支架结构和组成，土地使用历史，制度分析，人口统计学和数百个家庭的决策，以及自1970年代以来使用多型临时远程感应数据（MSS，TM/TM/TM/TM/TM/tm/eTM/etm+），并使用多个远程远程感应数据来进行土地覆盖分类。结果更快。 PI在两个研究领域以及Ikonos和Quickbird图像中获得了最近的RadarSat2和Alos/Palsar的图像。这些数据将与Landsat MSS，TM和ETM+（和Aster）的研究区域的存档数据融合，以推进适用于世界任何地方的土地覆盖评估中的知识和方法状态，云覆盖范围限制了土地覆盖变化的季节性观察。分离性分析将用于检查数据融合图像的能力，这些图像的能力是通过不同的数据融合技术开发的，可以根据培训样本图与广泛的现场工作以及Ikonos和QuickBird非常高的分辨率数据的大量使用来区分土地覆盖类别的覆盖范围。这项研究的结果将解决科学家目前面临的主要障碍，以量化持续云覆盖的地区的土地覆盖变化。一年中的大部分时间，潮湿的热带世界都被云所覆盖，并且在可能获得无云的图像以监视不断变化的森林状况的情况下，有时可能会在日期之间几年。这防止了对森林砍伐事件的及时回应，并了解土地覆盖的时间随时间变化。该项目迫切需要及时分析巴西亚马逊的土地覆盖变化以及潮湿的热带地区。