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林业科学 ›› 2023, Vol. 59 ›› Issue (9): 34-44.doi: 10.11707/j.1001-7488.LYKX20210756

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基于多角度卫星遥感的胡杨林结构参数获取

杨雪峰, 木尼热·买买提   

  1. 新疆师范大学地理科学与旅游学院 新疆干旱区湖泊环境与资源实验室 乌鲁木齐 830054
  • 收稿日期:2021-10-10 修回日期:2022-03-08 发布日期:2023-10-28
  • 基金资助:
    国家自然科学基金项目(41761075,42261062);自治区自然科学基金项目(2022D01A97)

Structural Parameters Acquisition of Populus euphratica by Multi-Angle Remote Sensing Image

Yang Xuefeng, Munire·Maimaiti null   

  1. College of Geography Science and Tourism,Xinjiang Normal University Xinjiang Laboratory of Lake Environment and Resources in Arid Zone Urumqi 830054
  • Received:2021-10-10 Revised:2022-03-08 Published:2023-10-28

摘要: 目的 基于多角度卫星遥感(MISR)和无人机(UAV)摄影测量技术, 采用简单几何光学模型(SGM)和广义简约梯度(GRG)优化方法,反演获取塔里木河下游胡杨林主要结构参数(树高、冠幅、林分密度和覆盖度)。方法 以塔里木河下游典型河岸林为研究对象,基于野外调查、UAV倾斜摄影测量和MISR数据,构建训练集,采用SGM对场景反射进行分解模拟,获取Walthall背景反射模型参数,评价参数敏感性,建立参数回归方程,运用GRG优化算法反演得到测试集结构参数,利用无人机测量数据进行精度验证和评价。结果 从UAV倾斜摄影测量数据中提取的树高、冠幅、林分密度数据与实测数据相比,UAV获取的树高、冠幅、林分密度与实测数据的R2分别为0.90、0.84、0.94,均方根误差(RMSE)分别为0.45 m、0.68 m、4.25株·hm−2。SGM对训练集样地红光模拟反射值与卫星观测反射值的R2最大值为0.99、最小值为0.72、平均值为0.92;反演获取的训练样地树高和覆盖度与参考值相比,覆盖度R2达0.99,RMSE为0.73%,树高R2为0.66,RMSE为0.61 m。对反演获取的Walthall模型参数进行敏感性分析发现,c1对覆盖度和树高影响最大,其次是c4c2c3影响最小,c1~c4对林分密度变化影响很小。建立AN多光谱反射率和AMBRALS模型$ {f}_{\mathrm{i}\mathrm{s}\mathrm{o}} $、$ {f}_{\mathrm{v}\mathrm{o}\mathrm{l}} $、$ {f}_{\mathrm{g}\mathrm{e}\mathrm{o}} $系数与c1~c4的多元线性回归方程,预测训练集样地c1~c4参数值与反演值相比,调整R2分别为0.78、0.98、0.59和0.75。MISR反演获取的胡杨覆盖度、树高、密度和冠幅与无人机数据在275 m2尺度上相比,线性模型R2分别为0.54、0.47、0.41和0.24,RMSE分别为3%、0.76 m、112株和0.64 m;平均相对误差(MRE)分别为24.7%、8.9%、22.5%和10%;使用幂函数模型时,覆盖度、树高、林分密度、冠幅的R2分别为0.80、0.53、0.55和0.30,RMSE分别为1%、0.4 m、33 株和 0.32 m,MRE分别为10%、5%、6%和6%。结论 无人机获取的胡杨林结构数据可代替地面调查数据,作为SGM的定标参数以及最终精度验证;SGM能较好模拟MISR观测样地的各向异性反射特征;SGM反演获取的Walthall背景反射模型参数精度以及由此建立的参数回归方程精度对最终结构参数反演结果存在较大影响。多角度卫星遥感和无人机摄影测量技术结合可在区域尺度上获取较为准确的森林结构信息。

关键词: 森林结构参数, 胡杨, 多角度卫星遥感, 简单几何光学模型

Abstract: Objective Forest structure characteristic is an important index to evaluate forest ecological function, which can directly present the growth status of forest. It is not only the research object in the fields of forestry, ecology and geoscience, but also an important input parameter of many terrestrial ecological models. How to obtain the characteristics of large-scale forest structure effectively has important scientific research value. Based on MISR(multi-angle imaging spectro-radiometer) satellite remote sensing data and UAV(unmanned aerial vehicle) photogrammetry data, SGM(simple geometric optical model) and the optimization methods of GRG(generalized reduced gradient) were used to obtain the key structure information of Populus euphratica forest in Tarim River, so as to provide a new method for the quantitative study of forest remote sensing.Method Taking the typical riparian forest near Yingsu in the lower reaches of Tarim River as the study area, based on field investigation, UAV remote sensing aerial survey, multi-angle satellite remote sensing, the inversion of SGM is carried out, the process is divided into three steps: firstly, the multi-angle reflectance data and forest structure parameters of the train dataset are input into the SGM model as known fixed variables, and the coefficients of Walthall model are retrieved by GRG optimization algorithm. Secondly, the regression equation of Walthall model coefficients is established by using the AMBRAL model coefficients of the train dataset and the multi-spectral reflectance data of MISR AN camera. Thirdly, an regression equation of the Walthall model coefficient is used to obtain the soil background reflectance of the test dataset, and the structural parameters of the test dataset are retrieved by GRG nonlinear optimization algorithm, Finally, the accuracy is verified and evaluated by UAV measurement data.Result The tree height, crown diameter and density data extracted by OBIA (object based image analysis) technology were compared with the measured samples. It was found that the R2 of the height, crown diameter, density obtained by UAV oblique photogrammetry between the field-measured value was 0.90, 0.84, 0.94 and RMSE(root mean square error) was 0.45 m, 0.68 m and 4.25 trees·hm-2 respectively. Compares the SGM simulated value with satellite observation reflection value of the train dataset, the maximum value of R2 is 0.99, the minimum value of R2 is 0.72 and the mean value of R2 is 0.92. The tree height and coverage obtained from the inversion of the train dataset are consistent with the reference value. R2 reaches 0.99 and RMSE is 0.73%. The consistency of tree height is slightly poor, R2 is 0.66 and RMSE is 0.61m. c1 parameter of Walthall model has the greatest impact on FVC and tree height, followed by c4, c2 and c3. c1-c4 have relative little effect on the change of density. By establishing the multiple linear regression equation between the reflectance of AN camera multispectral band and AMBRALS model coefficients with c1-c4, the c1-c4 parameters of the train dataset are predicted. Compared with the inversion values, the adjusted R2 of c1-c4 parameters are 0.78, 0.98, 0.59 and 0.75 respectively. Compared with the UAV measured data on the 275 m2 scale, the R2 of linear models of FVC, tree height, density and crown diameter of Populus euphratica obtained by MISR multi-angle satellite remote sensing were 0.54, 0.47, 0.41 and 0.24 respectively, and the RMSE were 3%, 0.76 m, 112 trees and 0.31 m respectively; MRE(mean relative error) was 24.7%, 8.9%, 22.5% and 10% respectively. When the power function model is used, the R2 of FVC, tree height, density and crown diameter are 0.80, 0.53, 0.55 and 0.30; RMSE is 1%, 0.4m, 33 trees, 0.16 m; MRE is 10%, 5%, 6%, 6% respectively.Conclusion The structural data of Populus euphratica forest obtained by UAV can be used as the calibration parameters of the model and the accuracy verification instead of the field-measured data. SGM model can well simulate the anisotropic reflection characteristics of observation samples. The accuracy of background reflection simulated by Walthall model has a great impact on the final inversion results of structural parameters. The combination of MISR multi-angle satellite remote sensing and UAV technology can obtain more accurate forest structure information on a regional scale.

Key words: forest structural parameters, Populus euphratica, multi-angle satellite remote sensing, simple geometric-optical model (SGM)

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