Li Ying, Wang Hong, Li Xiao Bing
State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Resources Science and Technology, Beijing Normal University, Beijing, China; CERI eco Technology Company Limited, Beijing, China.
State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Resources Science and Technology, Beijing Normal University, Beijing, China.
PLoS One. 2015 Apr 23;10(4):e0124608. doi: 10.1371/journal.pone.0124608. eCollection 2015.
Vegetation is an important part of ecosystem and estimation of fractional vegetation cover is of significant meaning to monitoring of vegetation growth in a certain region. With Landsat TM images and HJ-1B images as data source, an improved selective endmember linear spectral mixture model (SELSMM) was put forward in this research to estimate the fractional vegetation cover in Huangfuchuan watershed in China. We compared the result with the vegetation coverage estimated with linear spectral mixture model (LSMM) and conducted accuracy test on the two results with field survey data to study the effectiveness of different models in estimation of vegetation coverage. Results indicated that: (1) the RMSE of the estimation result of SELSMM based on TM images is the lowest, which is 0.044. The RMSEs of the estimation results of LSMM based on TM images, SELSMM based on HJ-1B images and LSMM based on HJ-1B images are respectively 0.052, 0.077 and 0.082, which are all higher than that of SELSMM based on TM images; (2) the R2 of SELSMM based on TM images, LSMM based on TM images, SELSMM based on HJ-1B images and LSMM based on HJ-1B images are respectively 0.668, 0.531, 0.342 and 0.336. Among these models, SELSMM based on TM images has the highest estimation accuracy and also the highest correlation with measured vegetation coverage. Of the two methods tested, SELSMM is superior to LSMM in estimation of vegetation coverage and it is also better at unmixing mixed pixels of TM images than pixels of HJ-1B images. So, the SELSMM based on TM images is comparatively accurate and reliable in the research of regional fractional vegetation cover estimation.
植被是生态系统的重要组成部分，估算植被覆盖度对于监测某一地区的植被生长具有重要意义。本研究以Landsat TM影像和HJ - 1B影像为数据源，提出了一种改进的选择性端元线性光谱混合模型（SELSMM），用于估算中国皇甫川流域的植被覆盖度。我们将该结果与用线性光谱混合模型（LSMM）估算的植被覆盖度进行了比较，并用实地调查数据对这两种结果进行了精度检验，以研究不同模型在植被覆盖度估算中的有效性。结果表明：（1）基于TM影像的SELSMM估算结果的均方根误差（RMSE）最低，为0.044。基于TM影像的LSMM、基于HJ - 1B影像的SELSMM和基于HJ - 1B影像的LSMM估算结果的RMSE分别为0.052、0.077和0.08²，均高于基于TM影像的SELSMM；（2）基于TM影像的SELSMM、基于TM影像的LSMM、基于HJ - 1B影像的SELSMM和基于HJ - 1B影像的LSMM的决定系数（R²）分别为0.668、0.531、0.342和0.336。在这些模型中，基于TM影像的SELSMM估算精度最高，与实测植被覆盖度的相关性也最高。在测试的两种方法中，SELSMM在植被覆盖度估算方面优于LSMM，并且在分解TM影像的混合像元方面比HJ - 1B影像的像元表现更好。因此，基于TM影像的SELSMM在区域植被覆盖度估算研究中相对准确可靠。
