Mapping Kenyan Grassland Heights Across Large Spatial Scales with Combined Optical and Radar Satellite Imagery
Mapping Kenyan Grassland Heights Across Large Spatial Scales with Combined Optical and Radar Satellite Imagery
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DOI：
10.3390/rs12071086
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发表时间：
2020-03
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通讯作者：
O. Spagnuolo;Julie C. Jarvey;M. Battaglia;Zachary M. Laubach;M. E. Miller;K. Holekamp;L. Bourgeau-Chavez
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作者：
O. Spagnuolo;Julie C. Jarvey;M. Battaglia;Zachary M. Laubach;M. E. Miller;K. Holekamp;L. Bourgeau-Chavez
草地监测可能具有挑战性，因为在大空间尺度上测量草地状况既耗时又昂贵。遥感为大空间范围和精细时间分辨率的草地状况测绘和监测提供了一种既省时又省钱的方法。遥感光学图像和雷达图像的组合特别有希望，因为它们结合在一起可以测量不同土地覆盖类型之间的水分、结构和反射率的差异。我们将多日期雷达(PALSAR-2和Sentinel-1)和光学(Sentinel-2)图像与现场数据和航空图像的视觉解译相结合，使用机器学习(随机森林)对肯尼亚马赛马拉国家保护区的土地覆盖进行分类。该研究区域包括一系列不同的土地覆盖类型和随时间的变化，这是由于降水的季节性变化、大量居留和迁徙的有蹄类动物的季节性迁移、火灾和牲畜放牧。我们分类了12种土地覆盖类型，用户和生产者的准确率在66%-100%之间，总体准确率为86%。这些方法能够区分短、中、高草被，用户的准确率分别为83%、82%和85%。通过制作一张高精度、高分辨率的地图，区分不同高度的草，这项工作不仅为未来的草原测绘工作勾勒出了一种可行的方法，而且将有助于为马赛马拉国家级保护区的当地管理决策和研究提供信息。
Grassland monitoring can be challenging because it is time-consuming and expensive to measure grass condition at large spatial scales. Remote sensing offers a time- and cost-effective method for mapping and monitoring grassland condition at both large spatial extents and fine temporal resolutions. Combinations of remotely sensed optical and radar imagery are particularly promising because together they can measure differences in moisture, structure, and reflectance among land cover types. We combined multi-date radar (PALSAR-2 and Sentinel-1) and optical (Sentinel-2) imagery with field data and visual interpretation of aerial imagery to classify land cover in the Masai Mara National Reserve, Kenya using machine learning (Random Forests). This study area comprises a diverse array of land cover types and changes over time due to seasonal changes in precipitation, seasonal movements of large herds of resident and migratory ungulates, fires, and livestock grazing. We classified twelve land cover types with user’s and producer’s accuracies ranging from 66%–100% and an overall accuracy of 86%. These methods were able to distinguish among short, medium, and tall grass cover at user’s accuracies of 83%, 82%, and 85%, respectively. By yielding a highly accurate, fine-resolution map that distinguishes among grasses of different heights, this work not only outlines a viable method for future grassland mapping efforts but also will help inform local management decisions and research in the Masai Mara National Reserve.