| 利用ESAR极化数据的复杂地形区森林地上生物量估算 |
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| 引用本文： | 张海波,汪长城,朱建军,付海强.利用ESAR极化数据的复杂地形区森林地上生物量估算[J].测绘学报,2018,47(10):1353-1362. |
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| 作者姓名： | 张海波 汪长城 朱建军 付海强 |
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| 作者单位： | 1. 中南大学地球科学与信息物理学院, 湖南 长沙 410083;2. 中南大学有色金属成矿预测教育部重点实验室, 湖南 长沙 410083 |
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| 基金项目： | 国家自然科学基金（41531068；41371335；41671356）；湖南省自然科学基金（2016JJ2141）；欧空局数据合作计划（14655）；中南大学研究生自主探索创新项目（2017zzts179）第10期张海波，等：利用ESAR极化数据的复杂地形区森林地上生物量估算 |
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| 摘 要： | 利用机载E-SAR传感器获取的P-波段全极化SAR数据与实测林分样地数据，分析不同极化方式后向散射系数在地形起伏区与森林地上生物量（AGB）的响应关系，以改进的水云模型为基础，建立了融入地形因子的分析性模型。采用遗传算法确定模型的最优参数，并对模型在不同坡度情况下的可靠性、稳定性进行分析，同时通过与常用模型相对比，确定水云分析模型在复杂地形区估算AGB的优势。结果表明：在森林AGB处于较低值的情况下，后向散射系数（HH、HV、VV）变化趋势与AGB变化趋势保持一致，但随着AGB值的增大，这种一致性仅在HV极化方式下继续保持，因此相比之下，HV极化方式更适用于复杂地形区生物量的估算。地形对森林AGB的估算具有极大的影响，后向散射系数与AGB的相关性随着地形坡度的增加而减小。5种模型估算森林AGB的能力大小排序为：水云分析模型 > 二次模型 > 对数模型 > 指数模型 > 线性模型。地形起伏较小的地区估算稳定性排序为：水云分析模型 > 二次模型 > 对数模型 > 指数模型>线性模型。地形起伏较大的地区估算稳定性排序为。水云分析模型 > 二次模型 > 线性模型 > 指数模型 > 对数模型。利用水云分析模型对研究区AGB估算，其实测AGB与模型估算的生物量值决定系数为0.597，RMSE为30.876 t/hm2，拟合精度为77.40%。
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| 关 键 词： | 极化SAR 森林地上生物量 地形 局部入射角 遗传算法 |
| 收稿时间： | 2017-03-14 |
| 修稿时间： | 2017-10-19 |
Forest Above-ground Biomass Estimation for Rugged Terrain by Using ESAR Polarization Data |
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| ZHANG Haibo,WANG Changcheng,ZHU Jianjun,FU Haiqiang.Forest Above-ground Biomass Estimation for Rugged Terrain by Using ESAR Polarization Data[J].Acta Geodaetica et Cartographica Sinica,2018,47(10):1353-1362. |
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| Authors: | ZHANG Haibo WANG Changcheng ZHU Jianjun FU Haiqiang |
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| Institution: | 1. School of Geosciences and Info-Physics, Central South University, Changsha 410083, China;2. Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring(Central South University), Ministry of Education, Changsha 410083, China |
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| Abstract: | The influence of the ground slope on radar backscatter has been proven to be greater for lower radar frequencies due to deeper canopy penetration. In order to solve this problem and obtain accurate estimation of forest above-ground biomass (AGB) in the region of rugged terrain, the analytic model integrating the topographic factors was presented based on the modified water-cloud model (WCM) and the relationship between different backscattering coefficients and the forest AGB using the airborne P-band full polarimetric SAR (PolSAR) data acquired by E-SAR. In this study, genetic algorithm (GA) was used to determine the optimal parameter values for the model, the terrain slope was divided into three grades (0~5°、5°~10°、≥ 10°). Then we analyzed the reliability and stability of the model under the condition of different slope. Meanwhile, in order to determine advantage of the water-cloud analysis model in evaluating AGB, we used common models include linear model、logarithm model、exponential model、quadratic model to comparison and analysis. Through the comparative analysis, we found that when the forest AGB at lower level, the variational trend of backscatter coefficients (HH、HV、VV) kept the same with the vatiational trend of AGB. With the increase of AGB values, this consistency in HV backscatter coefficient values to keep alone, therefore, HV polarization was the best to estimate biomass in the complex terrain region. The terrain has a great impact on estimating forest AGB, a phenomenon was that the correlation of backscatter coefficients and forest AGB decreased with the increase of ground slope. The capabilities of estimate biomass in the five models were different, from strong to weak was that water-cloud analysis model > quadratic model > logarithm model > exponential model > linear model. Meanwhile, through comparing the change of the determination coefficients (R2), these models were found that have different stabilities to estimate forest AGB in different slope levels. When the slope changed from 0~5° to 5°~10°, the stability from strong to weak was water-cloud analysis model > quadratic model > logarithm model > exponential model > linear model. With the slope from 5°~10° to ≥ 10°, this sequence became that water-cloud analysis model > exponential model > linear model > quadratic model > logarithm model. In addition, between 0~5° to ≥ 10°, this sequence was water-cloud analysis model > quadratic model > linear model > exponential model > logarithm model respectively. Although, there was different sequence in five models, the stability of the water-cloud analysis model was higher than other models. So, we tried to use water-cloud analysis model to estimate forest AGB for the study area. The result showed that the R2 between the field AGB and estimated AGB was 0.597, the root mean squared error (RMSE) was 30.876 t/hm2, the overall accuracy was 77.40%. |
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