林业科学 ›› 2023, Vol. 59 ›› Issue (4): 88-99.doi: 10.11707/j.1001-7488.LYKX20210712
周小成1(),黄婷婷1,李媛1,肖祥希2,朱洪如3,陈芸芝1,冯芝淸4
收稿日期:
2021-09-17
出版日期:
2023-04-25
发布日期:
2023-07-05
基金资助:
Xiaocheng Zhou1(),Tingting Huang1,Yuan Li1,Xiangxi Xiao2,Hongru Zhu3,Yunzhi Chen1,Zhiqing Feng4
Received:
2021-09-17
Online:
2023-04-25
Published:
2023-07-05
摘要:
目的: 应用XGBoost算法建立包含林龄的遥感因子-蓄积量模型,评价遥感估算的林龄因子与遥感因子相结合提高森林蓄积量估算精度的有效性,为实现高效、快速、精准的大范围森林蓄积量估算提供一种新的思路和方法。方法: 以福建省将乐县为研究示范区,首先,基于1987—2016年时序Landsat影像,采用LandTrendr森林干扰与恢复监测算法监测年度林分更替干扰并估算干扰区林龄;然后,基于GF-1号影像光谱、纹理、地形等特征,采用递归特征消除的随机森林算法(RFE-RF)估算非干扰区林龄;在此基础上,结合GF-1影像光谱、纹理因子和森林资源二类调查小班实测蓄积量数据,采用极端梯度提升算法估算研究区森林蓄积量。对比有无林龄因子的森林蓄积量估算精度,进一步验证遥感林龄因子对提高森林蓄积量估算精度的重要性。结果: 采用LandTrendr森林干扰与恢复监测算法获得的干扰区林分林龄误差仅1~2年,林龄估算精度明显优于传统利用遥感因子估算的林龄精度(误差4~12年)。仅采用常规遥感因子估算森林蓄积量时,XGBoost模型决定系数(R2)为0.59,平均均方根误差(RMSE)为30.72 m3·hm?2,相对均方根误差(rRMSE)为16.46%;加入林龄因子后,模型R2提高至0.73,平均RMSE减少至23.73 m3·hm?2,rRMSE为13.26%,森林蓄积量估算平均总体精度约提高10.4%,达84.4%。结论: 相比仅采用常规遥感因子估算森林蓄积量,应用XGBoost算法建立包含林龄的遥感因子-蓄积量模型,其估算精度接近森林资源调查相关规定要求,可为大范围亚热带森林资源快速调查评估提供重要技术支持。
中图分类号:
周小成, 黄婷婷, 李媛, 肖祥希, 朱洪如, 陈芸芝, 冯芝淸. 结合遥感林龄因子的亚热带森林蓄积量估算方法[J]. 林业科学, 2023, 59(4): 88-99.
Xiaocheng Zhou, Tingting Huang, Yuan Li, Xiangxi Xiao, Hongru Zhu, Yunzhi Chen, Zhiqing Feng. A Method for Estimating Subtropical Forest Stock by Combining Remotely Sensed Forest Age Factors[J]. Scientia Silvae Sinicae, 2023, 59(4): 88-99.
表1
GF-1影像植被指数"
植被指数 Vegetation indices (VIS) | 公式 Formulation | 植被指数 Vegetation indices (VIS) | 公式 Formulation | |
差值植被指数 Differential vegetation index (DVI) | | 归一化差异绿度指数 Normalized difference green vegetation index (NDGI) | | |
环境植被指数 Environmental vegetation index (EVI) | | 重归一化植被指数 Renormalized difference vegetation index (RDVI) | | |
归一化植被指数 Normalized difference vegetation index (NDVI) | | 红边比值植被指数 Red edge ratio vegetation index (RGRI) | | |
红绿植被指数 Green-red normalized difference vegetation index (GRNDVI) | | 比值植被指数 Ratio vegetation index (RVI) | | |
绿化率植被指数 Green red vegetation index (GRVI) | | 转换型植被指数 Transformed normalized difference vegetation index (TNDVI) | | |
改进植被指数 Modified simple ratio index (MSR) | | GF-1波段GF-1 band | / |
表4
XGBoost估算蓄积量精度验证"
基于常规遥感因子蓄积量估算 Estimation of volume based on ordinary remote sensing factors | 结合林龄因子蓄积量估算 Estimation of volume by combining forest age parameters | |||
小班平均每公顷蓄积量 Average accumulation per ha in subcompartment/ (m3·hm?2) | XGBoost估算蓄积量平均精度 XGBoost estimated volume average accuracy(%) | 小班平均每公顷蓄积量 Average accumulation per ha in subcompartment/ (m3·hm?2) | XGBoost估算蓄积量平均精度 XGBoost estimated volume average accuracy | |
75~150 | 71.0 | 75~150 | 83.0 | |
150~195 | 76.5 | 150~195 | 87.2 | |
>195 | 74.1 | >195 | 81.3 |
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