安如, 陆彩红, 王慧麟, 姜丹萍, 孙梦秋, JonathanArthur Quaye Ballard. 三江源典型区草地退化Hyperion高光谱遥感识别研究[J]. 武汉大学学报 ( 信息科学版), 2018, 43(3): 399-405. DOI: 10.13203/j.whugis20150168
引用本文: 安如, 陆彩红, 王慧麟, 姜丹萍, 孙梦秋, JonathanArthur Quaye Ballard. 三江源典型区草地退化Hyperion高光谱遥感识别研究[J]. 武汉大学学报 ( 信息科学版), 2018, 43(3): 399-405. DOI: 10.13203/j.whugis20150168
AN Ru, LU Caihong, WANG Huilin, JIANG Danping, SUN Mengqiu, Jonathan Arthur Quaye Ballard. Remote Sensing Identification of Rangeland Degradation Using Hyperion Hyperspectral Image in a Typical Area for Three-River Headwater Region, Qinghai, China[J]. Geomatics and Information Science of Wuhan University, 2018, 43(3): 399-405. DOI: 10.13203/j.whugis20150168
Citation: AN Ru, LU Caihong, WANG Huilin, JIANG Danping, SUN Mengqiu, Jonathan Arthur Quaye Ballard. Remote Sensing Identification of Rangeland Degradation Using Hyperion Hyperspectral Image in a Typical Area for Three-River Headwater Region, Qinghai, China[J]. Geomatics and Information Science of Wuhan University, 2018, 43(3): 399-405. DOI: 10.13203/j.whugis20150168

三江源典型区草地退化Hyperion高光谱遥感识别研究

Remote Sensing Identification of Rangeland Degradation Using Hyperion Hyperspectral Image in a Typical Area for Three-River Headwater Region, Qinghai, China

  • 摘要: 三江源草地退化直接影响当地及长江、黄河和澜沧江中上游地区的生态安全、水资源合理利用、经济可持续发展和社会稳定,一直是人们关注的焦点问题。近年来,该地区草地群落毒杂草组分增大,退化严重。高光谱遥感具有精细识别草地种群的潜在能力,如何利用其进行草地群落组成信息探测并进行退化识别是当前研究的热点和难点问题。利用卫星高光谱Hyperion影像和地面实测高光谱遥感数据,通过多端元混合像元分解技术,提取可食牧草、毒杂草以及裸土组分信息;以此为指标对研究区草地退化程度进行分类识别,依次为:未退化、轻度退化、中度退化、重度退化和极度退化。利用野外样方实测数据验证监测结果,精度达到84.2%,表明高光谱遥感对草地退化探测具有良好的应用潜力。

     

    Abstract: Rangeland degradation at the Three-River Headwater Region(TRHR) has been a major concern to the public in recent years, especially toxic weeds. The application of hyperspectral remote sensing in detecting meadow composition and identifying degradation become a major tool for research. Using MESMA algorithm and Hyperion image combined with the measured hyperspectral data, information about edible grass, toxic weeds and bare land were extracted to classify the degree of rangeland degradation. The study area was divided into five levels of degradation, namely:no degradation, mild degradation, moderate degradation, severe degradation and extremely severe degradation. Compared with the measurement data samples on monitoring, the precision attained was 84.2%. This shows that hyperspectral remote sensing is effective in detecting rangeland degradation at TRHR.

     

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