基于GF-6的翅碱蓬生长密度遥感定量反演  

Quantitative remote sensing inversion of Suaeda salsa growth density based on GF-6

作　　者：苏岫[1] 李悦铭 景昕蒂[1] 宋德瑞[1] 柳圭泽[1] 徐京萍[1] 王祥[1] SU Xiu;LI Yue-ming;JING Xin-di;SONG De-rui;LIU Gui-ze;XU Jing-ping;WANG Xiang(National Marine Environmental Monitoring Center,Dalian 116023,China)

机构地区：[1]国家海洋环境监测中心,辽宁大连116023

出　　处：《海洋环境科学》2023年第1期151-159,共9页Marine Environmental Science

基　　金：国家重点研发计划项目(2017YFA0604902):“海岸带和沿海地区全球变化综合风险评估研究”。

摘　　要：翅碱蓬作为辽东湾湿地典型生境植被,其生长密度可直观地反映盘锦蓝色海湾整治行动修复效果。本研究以高分六号(GF-6)卫星影像为数据源,基于野外实测数据,开展翅碱蓬生长密度遥感定量反演。研究发现:(1)翅碱蓬生物量湿重与各植被指数的相关性比干重更为显著,基于转换型土壤调整指数(transformed soil-adjusted vegetation index, TSAVI)的二次多项式回归分析模型是翅碱蓬生物量遥感反演的最优模型;(2)本研究基于实测数据构建的翅碱蓬生长密度模型,对不同覆盖情况的翅碱蓬群落均具有较好适用性,实测验证R^(2)>0.97,符合生长密度遥感反演精度要求,且算法中的土壤线系数具有一定的鲁棒性。本研究可为滨海湿地保护修复效果评估提供技术支持。Suaeda salsa is a kind of typical habitat vegetation in the coastal wetlands of Liaodong Bay, its growth density can directly reflect the restoration effect of Panjin blue bay Remediation Action. Using GF-6satellite image as data source and based on field measured data, the study analysed the remote sensing quantitative inversion of Suaeda salsa density, The results showed that:(1) The biomass wet weight was more significantly correlated with each vegetation index than the dry weight. The quadratic polynomial regression analysis model based on TSAVI was the optimal model for biomass remote sensing inversion.(2) Based on insitu data,the growth density model constructed in this study has good applicability to the communities with different cover conditions. The correlation coefficient R^(2) verified by field measurements was greater than 0.97,which met the accuracy requirements of quantitative retrieval of growth density. And the soil coefficient in the model has some robustness. This study can provide technical support for the evaluation of the effect of coastal wetland conservation and restoration.

关 键 词：翅碱蓬 GF-6 生物量 生长密度 遥感定量反演 

分 类 号：TP79[自动化与计算机技术—检测技术与自动化装置]