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使用堆叠的概括和Landsat时间序列,绘制中国二级森林年龄的地图

Shaoyu Zhang¹, Hanzeyu Xu², Aixia Liu³

¹Key Laboratory of Poyang Lake Wetland and Watershed Research (Ministry of Education), School of Geography and Environment, Jiangxi Normal University, Nanchang, 330022, China.

Scientific data

|March 17, 2024

概括

这项研究引入了一种新的集合方法,利用改进的变化检测算法和Landsat数据,绘制中国的二级森林年龄 (SFA). 由此产生的数据集增强了对森林生态系统和碳库存估计的理解.

科学领域:

林业科学林业科学
遥感遥感遥感遥感
生态系统动力学生态系统动力学

背景情况:

准确地绘制二级森林年龄 (SFA) 对于了解中国的森林生态系统和碳库存至关重要.
以前使用变化检测算法的方法往往无法充分描述整个森林景观.
需要改进的算法来检测森林树木的建立,以进行全面的SFA评估.

研究的目的:

开发和验证数据驱动的整体方法,以提高植被变化追踪器 (VCT) 和持续变化检测和分类 (CCDC) 算法的性能.
为中国生成一个具有高空间分辨率的二级森林年龄 (SFA) 的全国规模数据集.
提高对二级森林的理解,改善森林碳库存估计.

主要方法:

通过整合改进的VCT和CCDC算法,开发了一种集合方法.
使用Dense Landsat时间序列数据来确定二级森林的建立时间.
为全国范围的SFA映射选择了一个最佳的集合模型.

主要成果:

制作了中国国家二级森林年龄 (SFAC) 数据集,涵盖年龄从1岁到34岁.
该数据集提供30m的空间分辨率,提供连续的国家SFA信息.
开发的方法在检测森林树的建立方面表现得更好.

结论:

所生成的SFAC数据集提供了有价值的,高分辨率的空间信息,介绍了中国的二级森林年龄.
这一数据集可以显著提高对二级森林动态的理解,以及对森林碳储存的估计.
数据驱动组合方法为全国范围的森林年龄绘制提供了一个强大的方法.

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