SGER: Applications of Remote Sensing and Image Processing in Studies of Forest Tree Population Dynamics and Ecology

SGER：遥感和图像处理在林木种群动态和生态研究中的应用

• 批准号: 9617555
• 负责人: James McGraw
• 金额: $ 2.5万
• 依托单位: West Virginia University Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1996
• 资助国家: 美国
• 起止时间: 1996-12-15 至 1998-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9617555&HistoricalAwards=false
• 关键词: SGER; Applications; Remote; Sensing; Image

9617555 McGraw This research tests the feasibility of censusing forest tree populations using remote sensing for the broad purposes of inventory, projection of future population levels, and screening the forest for rare, endangered or otherwise unique individuals ('bioprospecting'). High resolution color and color infrared photographic images of mapped, forested plots will be collected with 35mm cameras in fifteen overflights for a period of one year. Methods will be developed to automate the process of circumscribing individual tree canopies from scanned images. New image processing approaches to identify individuals to species will be developed which rely on differential phenological development of species and differing spectral reflectance properties. The ultimate goal of these techniques will be the censusing of populations of trees from remote imagery in a manner suitable for input to size-dependent matrix population models of forest tree population dynamics. In the future, satellite-borne sensors will likely be capable of both high spatial and spectral resolution image capturing. Population-level applications of remote-sensing are in their infancy. If data from such sources can be used for forest inventory, population projection and 'bioprospecting', the studies made possible will have regional and global applicability to numerous serious environmental problems.

9617555麦格劳这项研究测试了使用遥感对森林树木种群进行普查的可行性，目的是进行广泛的清查，预测未来的种群水平，并筛选森林中稀有、濒危或其他独特的个体(“生物勘探”)。将使用35 mm相机在15次飞越中收集绘制的森林地块的高分辨率彩色和彩色红外摄影图像，为期一年。将开发从扫描图像中自动划分单个树冠的过程的方法。将开发新的图像处理方法来识别个体和物种，这依赖于物种不同的物候发育和不同的光谱反射特性。这些技术的最终目标将是以一种适合于输入森林树木种群动态的大小依赖矩阵种群模型的方式从遥感图像中普查树木种群。在未来，星载传感器很可能同时具有高空间和高光谱分辨率的图像捕获能力。人口层面的遥感应用还处于初级阶段。如果这些来源的数据可以用于森林调查、人口预测和生物勘探，那么这些研究将在区域和全球范围内适用于许多严重的环境问题。