中国农业气象 ›› 2022, Vol. 43 ›› Issue (05): 408-420.doi: 10.3969/j.issn.1000-6362.2022.05.007

• 农业气象信息技术 栏目 • 上一篇    

基于高分六号卫星红边波段的森林蓄积量遥感反演——以西宁市针叶林为例

任枫,王琦,杨佳,任庆福   

  1. 1. 西安绿环林业技术服务有限责任公司,西安 710048;2.广东省科学院生态环境与土壤研究所,广州 510650;3.航天宏图信息技术股份有限公司,北京 100089
  • 收稿日期:2021-08-25 出版日期:2022-05-20 发布日期:2022-05-19
  • 通讯作者: 任庆福,工程师,主要从事“3S”技术在生态与水文方面应用工作。 E-mail:wxws.2008@163.com
  • 作者简介:任枫,E-mail: 65511157@qq.com
  • 基金资助:
    国家林业与草原局西北调查规划设计院2021年科技创新项目“青海省天然林生态系统服务功能量化研究”(XBJ-KJCX-2021-16);广东省自然科学基金杰出青年项目“基于人工智能深度学习的土壤重金属污染演变格局过程和机制解析”(2020B1515020020)

Retrieving of the Forest Stock Volume Based on the Red Edge Bands of GF-6 Remote Sensing Satellite: A Case Study of Coniferous Forest in Xining City

REN Feng, WANG Qi, YANG Jia, REN Qing-fu   

  1. 1. Xi'an Lvhuan Forestry Technical Service Co.Ltd., Xi'an 710048,China; 2. Institute of Eco-environmental and Soil Sciences, Guangdong Academy of Sciences, Guangzhou 510650; 3. PIESAT Information Technology Co.Ltd., Beijing 100089
  • Received:2021-08-25 Online:2022-05-20 Published:2022-05-19

摘要: 红光波段和近红外波段常被作为森林蓄积量反演的敏感波段,介于两者之间的红边波段往往被忽略,为了明确红边波段对森林蓄积量的遥感反演精度的影响,本研究基于高分六号卫星宽幅影像(GF6-WFV),结合西宁市2014年森林资源二类调查数据,从光谱特征、植被指数、地形因子、影像纹理等4个方面选取蓄积量反演的自变量,采用多元线性回归(MLR)、随机森林(RF)模型,分析比较了有无红边波段对西宁市针叶林蓄积量遥感反演精度的影响。结果表明:(1)无红边波段(No Red-Edge)和加入红边波段(Red-Edge Added)两组处理的纹理特征变量降维后,其主成分信息主要解释了红外、近红外以及红边1波段的纹理特征。(2)与蓄积量相关的光谱特征变量:No Red-Edge处理主要包括红边波段和近红外波段的地表反射率,Red-Edge Added处理主要为红边1波段的地表反射率;植被指数变量:No Red-Edge处理主要包括NDVI和SAVI,Red-Edge Added处理为MTCI。(3)加入红边波段后,RF模型的R2优于MLR模型,分别为0.6719和0.5487,RF模型的均方根误差(RMSE)小于MLR模型,分别为26.3m3hm−2和20.8m3hm−2。(4)去除模型对反演精度的影响后,相对于No Red-Edge,Red-Edge Added处理的反演结果与观测值拟合的R2提高了11.6%,RMSE降低了9.1%。说明加入红边波段可显著提高西宁市针叶林蓄积量的反演精度,研究结果可为森林蓄积量的遥感反演提供科学依据。

关键词: 高分六号卫星, 红边波段, 蓄积量遥感反演, 随机森林模型, 西宁市

Abstract: The red band and the near-infrared band are often used as sensitive bands for remote sensing retrieval of forest stock volume, however the red edge band between the two is often ignored. To investigate whether the red edge band is sensitive to the accuracy of forest stock volume retrieval, the wide imagery data of GF-6 remote sensing satellite (GF-6 WFV), the DEM, the second survey data of forest resources in 2014 of Xining city were used, and the multiple linear regression model (MLR), the random forest regression model (RF) were employed. The predicting variables were collected from spectral characteristic, vegetation index, topographic factor and image texture. These variables were divided into two groups, the one is no red edge band (No Red-Edge), and the other is red edge band added (Red-Edge Added). The results show that: (1) the selected principal components which were derived from the texture variables of two groups by PCA method mainly explained the texture features of red, near-infrared and red-edge 1 band of the image. (2) Regarding the spectral variables, the surface reflectance of red and near-infrared band were selected because of the high correlation with the forest stock volume in No Red-Edge group, and in Red-Edge Added group, the surface reflectance of the red-edge 1 band was selected. Regarding the vegetation index variables, the Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) were selected in No Red-Edge group, and in Red-Edge Added group, the MERIS Terrestrial Chlorophyll Index (MTCI) was selected. (3) In Red-Edge Added group, the R2 of the RF model is better than that of the MLR model, 0.6719 and 0.5487, respectively, and the root mean square error (RMSE) of the RF model is smaller than that of the MLR model, 26.3m3·ha−1 and 20.8m3·ha−1, respectively. (4) After eliminating the effect of the model on the accuracy of forest stock volume retrieval, the R2 between the retrieval and the observed values in Red-Edge Added group increased by 11.6%, and the RMSE in Red-Edge Added group decreased by 9.1% compared to No Red-Edge group. Our results suggested that the red-edge band significantly improved the accuracy of the coniferous forest stock volume retrieval in Xining city. This study has high potential value in the remote sensing retrieval of forest stock volume.

Key words: GF-6 remote sensing satellite, Red edge band, Forest volume retrieval, Random forest model, Xining city