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Assessment of Antarctic moss health from multi-sensor UAS imagery with Random Forest Modelling
International Journal of Applied Earth Observation and Geoinformation ( IF 7.6 ) Pub Date : 2018-03-20 , DOI: 10.1016/j.jag.2018.01.004
Darren Turner , Arko Lucieer , Zbyněk Malenovský , Diana King , Sharon A. Robinson

Moss beds are one of very few terrestrial vegetation types that can be found on the Antarctic continent and as such mapping their extent and monitoring their health is important to environmental managers. Across Antarctica, moss beds are experiencing changes in health as their environment changes. As Antarctic moss beds are spatially fragmented with relatively small extent they require very high resolution remotely sensed imagery to monitor their distribution and dynamics. This study demonstrates that multi-sensor imagery collected by an Unmanned Aircraft System (UAS) provides a novel data source for assessment of moss health. In this study, we train a Random Forest Regression Model (RFM) with long-term field quadrats at a study site in the Windmill Islands, East Antarctica and apply it to UAS RGB and 6-band multispectral imagery, derived vegetation indices, 3D topographic data, and thermal imagery to predict moss health. Our results suggest that moss health, expressed as a percentage between 0 and 100% healthy, can be estimated with a root mean squared error (RMSE) between 7 and 12%. The RFM also quantifies the importance of input variables for moss health estimation showing the multispectral sensor data was important for accurate health prediction, such information being essential for planning future field investigations. The RFM was applied to the entire moss bed, providing an extrapolation of the health assessment across a larger spatial area. With further validation the resulting maps could be used for change detection of moss health across multiple sites and seasons.



中文翻译:

利用随机森林模型从多传感器UAS影像评估南极苔藓健康

苔藓床是南极大陆上很少见的陆生植被类型之一,因此,绘制其分布范围并监测其健康状况对环境管理者很重要。在整个南极洲,随着环境的变化,苔藓床的健康状况也在发生变化。由于南极苔藓床在空间上的分散程度相对较小,因此它们需要非常高分辨率的遥感影像来监测其分布和动态。这项研究表明,无人机系统(UAS)收集的多传感器图像为评估苔藓的健康状况提供了新颖的数据源。在这项研究中,我们在南极东部风车群岛的一个研究地点训练了具有长期场四边形的随机森林回归模型(RFM),并将其应用于UAS RGB和6波段多光谱图像,得出植被指数,3D地形数据和热图像,以预测苔藓的健康状况。我们的结果表明,以0到100%健康之间的百分比表示的苔藓健康状况,可以用7%至12%的均方根误差(RMSE)进行估算。RFM还量化了输入变量对于苔藓健康估计的重要性,表明多光谱传感器数据对于准确的健康预测非常重要,此类信息对于计划未来的野外调查至关重要。RFM应用于整个苔藓床,从而在更大的空间区域内进行了健康评估的推断。经过进一步验证,生成的地图可用于跨多个地点和多个季节进行苔藓健康状况的变化检测。我们的结果表明,以0到100%健康之间的百分比表示的苔藓健康状况可以用7%至12%的均方根误差(RMSE)进行估算。RFM还量化了输入变量对于苔藓健康估计的重要性,表明多光谱传感器数据对于准确的健康预测非常重要,此类信息对于计划未来的野外调查至关重要。RFM应用于整个苔藓床,从而在更大的空间区域内进行了健康评估的推断。经过进一步验证,生成的地图可用于跨多个地点和多个季节进行苔藓健康状况的变化检测。我们的结果表明,以0到100%健康之间的百分比表示的苔藓健康状况可以用7%至12%的均方根误差(RMSE)进行估算。RFM还量化了输入变量对于苔藓健康估计的重要性,表明多光谱传感器数据对于准确的健康预测非常重要,此类信息对于计划未来的野外调查至关重要。RFM应用于整个苔藓床,从而在更大的空间区域内进行了健康评估的推断。经过进一步验证,生成的地图可用于跨多个地点和多个季节进行苔藓健康状况的变化检测。RFM还量化了输入变量对于苔藓健康估计的重要性,表明多光谱传感器数据对于准确的健康预测非常重要,此类信息对于计划未来的野外调查至关重要。RFM应用于整个苔藓床,从而在更大的空间区域内进行了健康评估的推断。经过进一步验证,生成的地图可用于跨多个地点和多个季节进行苔藓健康状况的变化检测。RFM还量化了输入变量对于苔藓健康估计的重要性,表明多光谱传感器数据对于准确的健康预测非常重要,此类信息对于计划未来的野外调查至关重要。RFM应用于整个苔藓床,从而在更大的空间区域内进行了健康评估的推断。经过进一步验证,生成的地图可用于跨多个地点和多个季节进行苔藓健康状况的变化检测。

更新日期:2018-03-20
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