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    刘时城, 温仲明, 陶宇, 朱朵菊, 张静, 曾鸿文. 不同地形校正方法对刺槐林遥感提取的影响[J]. 北京林业大学学报, 2017, 39(5): 25-33. DOI: 10.13332/j.1000-1522.20160309
    引用本文: 刘时城, 温仲明, 陶宇, 朱朵菊, 张静, 曾鸿文. 不同地形校正方法对刺槐林遥感提取的影响[J]. 北京林业大学学报, 2017, 39(5): 25-33. DOI: 10.13332/j.1000-1522.20160309
    LIU Shi-cheng, WEN Zhong-ming, TAO Yu, ZHU Duo-ju, ZHANG Jing, ZENG Hong-wen. Influence of different topographic correction methods on the remote sensing extraction of Robinia pseudoacacia distribution[J]. Journal of Beijing Forestry University, 2017, 39(5): 25-33. DOI: 10.13332/j.1000-1522.20160309
    Citation: LIU Shi-cheng, WEN Zhong-ming, TAO Yu, ZHU Duo-ju, ZHANG Jing, ZENG Hong-wen. Influence of different topographic correction methods on the remote sensing extraction of Robinia pseudoacacia distribution[J]. Journal of Beijing Forestry University, 2017, 39(5): 25-33. DOI: 10.13332/j.1000-1522.20160309

    不同地形校正方法对刺槐林遥感提取的影响

    Influence of different topographic correction methods on the remote sensing extraction of Robinia pseudoacacia distribution

    • 摘要: 刺槐是黄土丘陵区生态环境建设的主要乔木树种之一,而地形阴影是影响刺槐林遥感提取精度的重要因素。为研究不同地形校正方法对刺槐林分布信息提取的影响,以黄土丘陵区安塞县的刺槐人工林为例, 使用Cosine、SCS、Minnaert、C、SCS+C 5种校正方法对该地区2015年7月份的Landsat8 OLI影像进行地形校正,并采用基于样本、面向对象提取的方法对人工刺槐林的分布信息进行提取。最后对地形校正前后的影像进行视觉比较和回归分析,并对提取结果进行精度评估,从而比较不同地形校正方法对刺槐人工林分布信息提取的影响。结果表明:1) 5种地形校正方法削弱了遥感影像上地形阴影的视觉效果, 其中Cosine、SCS校正存在过度校正的现象。2) 5种地形校正方法使得各波段辐射亮度值的均值和方差较之前发生变化,且SCS+C校正符合预期效果。3) Minnaert、SCS+C及C校正降低了太阳入射角的余弦值cosi与影像各波段的辐射亮度值间的回归直线斜率m的绝对值及相关系数r的绝对值, Cosine、SCS校正使两参数在部分波段上变大。4) 5种地形校正方法都不同程度地降低刺槐提取的漏分误差,但Cosine校正后用户精度降低了2.47%;Minnaert、SCS+C及C校正均提高了用户者精度和生产者精度,以C校正的精度最高, 生产者精度提高了16.66%,用户精度提高了2.82%。5) 5种地形校正方法均提高了Kappa系数值,以C校正最高,Kappa系数值为0.76。本研究说明刺槐林遥感提取有必要进行地形校正,且应结合研究区的情况选择地形校正方法,这为黄土丘陵区刺槐信息提取时合适地形校正方法的选取提供了重要的依据。

       

      Abstract: Robinia pseudoacacia is one of main tree species of environmental construction in loess hilly gully region, and topographic shadow is a main factor affecting the accuracy of its information extraction by remote sensing. A case study was carried out in Ansai County, Shaanxi Province of northwestern China, which belongs to the loess hilly gully region to compare different topographic correction methods in extracting information on the distribution of Robinia pseudoacacia. The topographic effects of the Landsat8 OLI(July, 2015) were removed by 5 common topographic models (Cosine, SCS, Minnaert, C, SCS+C), and the example-based feature extraction was used to extract the distribution of R. pseudoacacia. The results were evaluated by visual comparison, regression analysis and ground-based validation. Results show that: 1)all topographic correction methods could reduce the topographic shadow on the remote sensing image and both the Cosine model and SCS model over-corrected the shade area's image. 2) All topographic correction methods changed the mean and variance of each bands' radiance, and SCS+C model reached the expected goal. 3) Minnaert model, SCS+C model and C model decreased the absolute value of two regression model parameters(slope m and coefficient r) between all bands' radiance and cosi, however Cosine model and SCS model increased the absolute value of two regression model parameters in some bands. 4)All topographic correction methods could correct the image and decrease the omission of the extraction of R. pseudoacacia's distribution to a certain degree, but Cosine model even decreased user's accuracy by 2.47%. Minnaert model, SCS+C model and C model could improve both the producer's accuracy and user's accuracy, and the C model, considered as the best method in the research, increased the producer's accuracy and the user's accuracy by 16.66% and 2.82%, respectively. 5) All topographic correction methods increased the Kappa coefficient, and the Kappa coefficient was highest (0.76) after C model's correction. The result suggested that topographic correction was necessary to the extraction and the model chosen should take the condition of the research area into account, which provided an important basis for choosing a proper topographic method in the process of extracting R. pseudoacacia's distribution in the loess hilly gully region.

       

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