使用高分辨率卫星图像和机器学习绘制温带高地息地的地图
Charmaine Cruz1, Philip M Perrin2, James R Martin2
1Department of Geography, Trinity College Dublin, The University of Dublin, Dublin, Ireland. cruzc@tcd.ie.
Environmental monitoring and assessment
|August 31, 2024
概括
高分辨率卫星图像和随机森林机器学习准确地绘制复杂的高地息地. 这种方法通过捕捉精细的空间细节和息地过渡来改善保护监测.
科学领域:
- 生态生态学 生态生态学
- 遥感 遥感 遥感 遥感
- 地理空间分析的研究.
背景情况:
- 高地息地提供重要的生态服务,但面临着自然和人为压力因素的重大威胁.
- 有效的保护需要对高地息地分布和状况进行准确的监测.
- 传统的遥感方法与高地息地细微的空间尺度和异质的马赛克性质作斗争.
研究的目的:
- 评估不同空间分辨率对爱尔兰高地息地的绘图精度的影响.
- 开发和评估一种互补的遥感方法,用于绘制复杂的高地息地马赛克.
- 为了利用高分辨率的星卫星图像和随机森林 (RF) 机器学习来进行息地分类.
主要方法:
- 在两个爱尔兰高地地点使用高分辨率 (2米) 星卫星图像进行息地绘制.
- 利用随机森林 (RF) 机器学习算法进行息地分类.
- 研究了不同空间分辨率对分类准确性的影响,并用于马赛克表示的模糊分类.
主要成果:
- 分类准确性通常通过更精细的空间分辨率数据得到改善.
- 最高的精度 (80.34%和79.64%) 是使用2米分辨率的星图像实现的.
- 基于射频的模糊分类有效地代表了复杂的息地马赛克和渐进的过渡.
结论:
- 高分辨率卫星图像与射频机器学习相结合,显著提高了高地息地绘图的准确性.
- 拟议的模糊分类方法提供了比传统方法更现实的复杂高地生态系统的表现.
- 这种方法提供了一种有价值的工具,可以改善空间时间的理解和大规模高地息地的保护.
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