使用Sentinel时间序列数据和谷歌地球引擎自动化季节性大米作物映射:在易受气候风险影响的孟加拉国的案例研究
Varun Tiwari1, Mirela G Tulbure1, Júlio Caineta1
1Center for Geospatial Analytics, North Carolina State University (NCSU), USA.
Journal of environmental management
|December 13, 2023
概括
在孟加拉国准确地绘制大米田地图,使用卫星数据. 一个新的框架有效地绘制了大米,帮助气候变化适应和粮食安全.
科学领域:
- 农业遥感 农业遥感
- 适应气候变化 适应气候变化
- 粮食安全 粮食安全
背景情况:
- 高分辨率的田地图对孟加拉国农业,经济和粮食安全至关重要,特别是在气候变化的影响下.
- 准确的绘制地图支持改善大米生产,可持续农业政策和气候风险适应战略.
- 由于不同的环境条件和数据稀缺,现有的方法在及时和准确地绘制大米方面面临挑战.
研究的目的:
- 通过使用卫星时间序列数据,开发和评估一个框架,以便在孟加拉国及时准确地绘制大米地图.
- 为了比较多Otsu自动值方法的性能与随机森林 (RF) 分类器用于米绘图.
- 评估框架是否适合季内决策和解决现场数据稀缺问题.
主要方法:
- 利用Sentinel-1和Sentinel-2时间序列数据来确定关键的水种植季节 (移植和高峰).
- 在高峰季节 (四月至五月) 采用多Otsu自动值方法绘制大米图.
- 与两个射频分类器配置 (D1 RF:移植-高峰季节;D2 RF:移植-收获季节) 的多Otsu进行了比较.
主要成果:
- 多Otsu方法在作物区实现了61.18%至94.43%的整体分类准确度 (OCA).
- D2射频分类器产生了最高的平均OCA (92.15%),其次是D1射频 (89.47%) 和多Otsu (85.27%).
- 多种Otsu有效地绘制了大米收获前的地图,而不需要难以获得的季节性培训样本.
结论:
- 开发的框架提供了重要的季节性大米面积地图,用于及时的适应性管理和生产率预测.
- 该框架的可扩展性解决了现场数据的稀缺性,为在孟加拉国等数据有限的地区的运营性使用提供了潜力.
- 这种方法支持在水种植中为气候变化适应做出明智决策.
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