SGER: A New Tool for Economic and Environmental Planning - Expanding the Boundaries of LiDAR

激光雷达（光探测和测距）是一种主动传感器，现已被联邦应急管理局批准用于构建数字地形模型（dtm）和数字高程模型（dem）。dtm和dem以及适当的GIS层是构建数字洪水保险费率图的关键来源。激光雷达的使用尚未取代使用其他方法生成的dem和dtm，这些方法已经使用了几十年。然而，势头是朝这个方向发展的；这项研究的目标是证明激光雷达与多光谱数据相结合，可以(1)探测城市地区的流域，以社区为尺度，从而可以用于暴雨排水管理；(2)收集城市结构景观的足够细节，以便在特定的灾难性事件（如洪水或地震）中预测财产损失。路易斯安那州新奥尔良市将同时使用LiDAR和IKONUS多光谱图像。通过结合新的分析技术、实地观察以及与标准数据集的比较，许多州和地方司法管辖区现在拥有的激光雷达数据的价值将会增加。该研究的一个关键要素是开发一套信息融合算法，该算法可以回答以下每个问题：(1)通过激光雷达高程数据与多光谱图像融合收集的折线和邻域尺度流域的自动检测，是否可以改善当前的USGS dem和dtm ？(2)建筑物的高度、几何形状和占地面积是否能够精确到足以进行灾害评估？(3)融合产物能否提供一种建模工具，在给定的水位上升等因素下预测潜在的破坏，并为灾前和灾后规划提供有价值的信息？为了解决这些问题，需要一个由一名环境工程师和两名计算机科学家组成的跨学科研究小组。智力优势：使用信号级方法融合来自如此不同模式的感官数据将是一项重大成就。激光雷达数据本身将成为拥有它的社区中更有价值的商品。更广泛的影响：这项研究在两个方面对学术和公共政策做出了贡献。首先，结果将向负责环境评估和补救的州和联邦机构展示新的方法，用已经收集的用于洪泛平原分析的数据来量化现在和过去的州。其次，它将为城市规划者提供一种廉价的方式来获取数据，用于经济分析、项目评估和灾害损害评估。具体的、切实的成果（当与相关的商业工作结合使用时）将是住宅和商业土地所有者的洪水保险费率降低，以及所有司法管辖区对纳税义务的更准确评估。所产生的数据和算法产品将通过陆军工程兵团传播，FEMA或其他机构无需支付任何费用。

LiDAR (Light Detection And Ranging) is an active sensor now approved by FEMA for construction of digital terrain models (DTMs) and digital elevation models (DEMs). DTMs and DEMs, together with appropriate GIS layers, are key sources for the construction of digital flood insurance rate maps. LiDAR use has not yet supplanted the DEMs and DTMs that have been generated using other methods that have been available for decades. However, the momentum is in that direction; the goal of this research is to show that LiDAR - combined with multispectral data - can (1) detect watersheds in urban areas that are at the scale of a neighborhood and thus can be used for storm drainage management, and (2) collect sufficient detail of the urban structural landscape to be of real use in predicting property damage for given catastropic events such as floods or earthquakes. Both LiDAR and IKONUS multispectral imagery for New Orleans, Louisiana will be used. By a combination of new analytical techniques, field observation, and comparison to standard datasets, the value of LiDAR data now owned by many state and local jurisdictions will be increased. A key element of the research is the development of a set of information fusion algorithms-that answer each of the questions: (1) Can present USGS DEMs and DTMs be improved by automatic detection of break lines and neighborhood-scale watersheds gleaned from LiDAR elevation data fused with multi-spectral imagery? (2) Can the heights, geometries, and footprints of buildings be determined with an accuracy sufficient for disaster assessment? (3) Can the fusion product provide a modeling tool to predict, given factors such as water rising level, the potential damage and provide valuable information for pre- and post-disaster planning? To address these issues will require an interdisciplinary research team consisting of an environmental engineer and two computer scientists.Intellectual Merit: Fusing sensory data from such very different modalities using signal-level methods would be a significant achievement. LiDAR data itself will become a much more valuable commodity in the communities that possess it.Broader Impacts: The research contributes to scholarship and public policy in two ways. First, the results will demonstrate to State and Federal agencies responsible for environmental assessment and remediation new methods for quantifying the present and past states with data already collected for floodplain analysis. Second, it will provide urban planners an inexpensive way to acquire data for economic analysis, project evaluation, and disaster damage assessment. A specific, tangible outcome (when taken in conjuction with relevant commercial work) will be lower flood insurance rates for residential and business land owners as well as more accurate assessment of tax liabilities by all jurisdictions. Data and algorithmic products produced will be disseminated through the Army Corps of Engineers at no cost to FEMA or others.