Methods and uncertainty modeling for land cover change detection from multi-resolution remotely sensed data

时间序列遥感环境数据已被广泛用于监测地球表面和生物物理变量的变化。 随着遥感技术的迅速发展和广泛应用，积累了大量的空间、光谱和时间尺度广泛的航空和卫星数据。这些发展要求对分析和整合这些数据的方法进行重大变革。传统的时间序列遥感数据变化检测方法已有很多发展。理想情况下，通过利用来自同一传感器的按时间分离的图像来进行变化检测。然而，许多条件将不可避免地结束理想的时间变化检测情况。许多传统的方法不能很好地工作在变化检测涉及多光谱和空间分辨率的图像。与快速增长的数据供应相比，在开发有效的变化检测模型和多尺度分析框架以处理这些复杂性和数量不断增加的多时相遥感数据方面明显滞后。本项目的目标是：（a）审查多传感器时间遥感数据对土地覆盖分类和变化检测程序的误差和不确定性的尺度敏感性;（B）评价不同亚像素方法绘制不同景观土地覆盖及其变化的准确性;（c）开发新的方法，改进从多光谱、多空间分辨率遥感数据中探测土地覆盖变化的工作。这项研究的成果将大大有助于在许多应用中最大限度地利用遥感数据，在拟议的预算和研究范围内，每年将至少培训四名研究生和两名本科生。 ******

Time series remotely sensed environmental data has been widely used in monitoring changes in earth surfaces and biophysical variables. With rapid development and widespread use of remote sensing technologies, a great deal of airborne and satellite data with a wide range of spatial, spectral and temporal scales has been accumulated. These developments demand significant changes in the approach of analyzing and integrating these data. Many traditional change detection methods in time series remotely sensed data have been developed in the past. Change detection is ideally conducted by utilizing images, separated by time, from the same sensor. However, many conditions will inevitably bring an end to the ideal temporal change detection situation. Many traditional approaches do not work well in change detection involving images with multiple spectral and spatial resolutions. Compared with rapidly growing data availability, there is a clear lag in the development of efficient change detection models and multi-scale analysis framework for handling these multi-temporal remotely sensed data of increasing complexity and quantity.****The objectives of this project is to (a) to examine the scale sensitivity of error and uncertainty on land cover classification and change detection procedures from multi-sensor temporal remotely sensed data; (b) to evaluate the accuracy of different sub-pixel approaches for mapping land cover and its change for different landscapes; and (c) to develop novel approaches to improving land cover change detection from multi-spectral, multi-spatial resolution remotely sensed data. The outcomes from this research will significantly contribute to the maximum use of remote sensing data in many applications.Within the proposed budget and research scope at least four graduate students and two undergraduate students will be trained each year. ******