Alternative methods for assessing and monitoring mangrove forests

虽然红树林被认为是世界上最具生产力的生态系统之一，但它也被确定为最受威胁的生态系统之一。一个特别令人关切的问题是无法收到关于许多这些森林湿地的范围和状况的最新资料。因此，拟议研究的一个重点是继续评价从卫星获得的图像的使用，作为定期绘制这些湿地地图的一种手段，其规模足够详细，以协助研究人员和资源管理人员。例如，我研究的一个目标是确定是否可以使用多日期高分辨率光学卫星数据在物种水平上绘制红树林生产力的变化，这些变化是通过叶面积指数（LAI）（即叶覆盖率）间接估算的。此外，对于准永久云覆盖限制光学传感器的地区，我建议研究使用雷达卫星RADARSAT-2作为估计类似红树林生物物理参数数据（例如LAI，树高）的替代平台，因为雷达可以有效地通过云层成像。最近关于当地居民使用红树林的研究表明，这些人依赖从这些森林中提取的木材用于各种当地用途（例如房屋建造）。这些研究的结果还表明，物种组成、树高和与定居点的距离等因素可以解释红树林某些部分发生的采伐程度。因此，我建议开发一个独特的空间数据库，以确定适合红树林生存采伐的地区。这一管理工具将以综合各种数据来源为基础，包括从卫星获得的信息和从当地渔民收集的知识。最后，我打算评估树木年代学（即树木年轮测年）在确定红树林退化的具体历史原因方面的潜在用途。特别是，我预计树木年轮特征的变化将反映过去的扰动，从而帮助研究人员确定红树林消失的关键原因。

Although considered one of the most productive ecosystems in the world, mangrove forests are also identified as one of the most threatened.  One particular concern is the inability to receive updated information regarding the extent and condition of many of these forested wetlands.  Consequently, one focus of the proposed research is a continued evaluation of the use of imagery acquired from satellites as a means of regularly mapping these wetlands at a scale detailed enough to assist researchers and resource managers.  For example, one objective of my study is to determine whether changes in mangrove productivity, indirectly estimated using Leaf Area Index (LAI) (i.e. leaf coverage), can be mapped at the species level using multi-date very high resolution optical satellite data.  In addition, for regions where quasi-permanent cloud cover limits optical sensors, I am proposing to examine the use of a radar satellite, RADARSAT-2, as an alternative platform for estimating similar mangrove biophysical parameter data (e.g. LAI, tree height) since radar can effectively image through clouds.      Recent studies on the use of mangrove forests by local inhabitants indicate that these people depend on the timber extracted from these forests for a variety of local uses (e.g. house construction).  The results of these studies also indicate that factors such as species composition, tree height and distance from settlements could explain the extent of harvesting occurring for select parts of the mangrove forest. Consequently, I propose to develop a unique spatial database to identify suitable areas for mangrove subsistence harvesting.  This management tool will be based on the integration of various sources of data including information derived from satellites and knowledge gathered from local fishermen.  Finally, I intend to assess the potential use of dendrochronology (i.e. tree ring dating) for identifying specific historical causes of mangrove forest degradation.  In particular, I anticipate that changes in tree ring characteristics will reflect past perturbations and thus assist researchers in identifying key causes of mangrove loss.