使用机器学习算法在尼泊尔西部Terai Sal森林的地面树木生物质建模
Bikram Singh1, Amit Kumar Verma1, Kasip Tiwari2
1Forest Research Institute (Deemed to be) University, Dehradun-248195, Uttarakhand, India.
Heliyon
|November 29, 2023
概括
准确的森林生物质估计对于可持续管理和碳跟踪至关重要. 这项研究发现,随机森林算法,使用卫星数据纹理和原始频段,最好模拟尼泊尔的地表树木生物质.
科学领域:
- 林业科学 林业科学
- 遥感 遥感 遥感 遥感
- 生态生态学 生态生态学
背景情况:
- 森林生物质监测对于可持续森林管理和碳循环跟踪至关重要.
- 准确估计地表树木生物质 (AGTB) 对于生态和经济评估至关重要.
- 尼泊尔西部的特拉伊萨尔森林需要有效的生物质监测来保护和资源管理.
研究的目的:
- 使用机器学习算法 (MLA) 在尼泊尔西部泰拉伊萨尔森林中建模地面以上树木生物质 (AGTB).
- 为了比较支持向量机 (SVM),随机森林 (RF) 和随机梯度增强 (SGB) 的性能,用于AGTB建模.
- 用遥感数据确定最佳数据集和变量,以便使用遥感数据准确估计AGTB.
主要方法:
- 通过系统的库存样本图表量化了AGTB.
- 用 Sentinel-2A 卫星图像来得出光谱和纹理变量.
- 三个MLA (SVM,RF,SGB) 应用于八个分类变量数据集进行建模.
主要成果:
- 随机森林 (RF) 算法,利用灰级共发生矩阵 (GLCM) 纹理和原始带 (RB) 数据集的组合,实现了最佳性能.
- 最优的射频模型为AGTB估计产生了78.81t ha-1的根平均平方误差 (RMSE).
- 确定的重要变量包括传统指数,原始波段和近红外GLCM纹理.
结论:
- 射频算法与GLCM和原始频段相结合,为研究区域的AGTB估计提供了强大而准确的方法.
- 这项研究强调了MLA和多数据集变量的潜力,以推进尼泊尔的生物质和碳估计技术.
- 这些发现为研究人员开发新的森林生物质评估方法提供了有价值的见解.
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