Key Laboratory of 3D Information Acquisition
Key Laboratory of 3D Information Acquisition
Gong Z.,Capital Normal University |
Gong Z.,Key Laboratory of 3D Information Acquisition |
Gong Z.,Key Laboratory of Resources Environment |
Gong Z.,Base of the State Laboratory of Urban Environmental Processes and Digital Modeling |
And 14 more authors.
International Journal of Applied Earth Observation and Geoinformation | Year: 2015
Vegetation abundance is a significant indicator for measuring the coverage of plant community. It is alsoa fundamental data for the evaluation of a reservoir riparian zone eco-environment. In this study, a sub-pixel Markov model was introduced and applied to simulate dynamics of vegetation abundance in theGuanting Reservoir Riparian zone based on seven Landsat Thematic Mapper/Enhanced Thematic MapperPlus/Operational Land Imager data acquired between 2001 and 2013. Our study extended Markov model'sapplication from a traditional regional scale to a sub-pixel scale. Firstly, Linear Spectral Mixture Analysis(LSMA) was used to obtain fractional images with a five-endmember model consisting of terrestrialplants, aquatic plants, high albedo, low albedo, and bare soil. Then, a sub-pixel transitive probabilitymatrix was calculated. Based on the matrix, we simulated statuses of vegetation abundance in 2010and 2013, which were compared with the results created by LSMA. Validations showed that there wereonly slight differences between the LSMA derived results and the simulated terrestrial plants fractionalimages for both 2010 and 2013, while obvious differences existed for aquatic plants fractional images, which might be attributed to a dramatically diversity of water level and water discharge between 2001and 2013. Moreover, the sub-pixel Markov model could lead to an RMSE (Root Mean Square Error) of0.105 and an R2 of 0.808 for terrestrial plants, and an RMSE of 0.044 and an R2 of 0.784 for aquatic plantsin 2010. For the simulated results with the 2013 image, an RMSE of 0.126 and an R2 of 0.768 could beachieved for terrestrial plants, and an RMSE of 0.086 and an R2 of 0.779 could be yielded for aquatic plants. These results suggested that the sub-pixel Markov model could yield a reasonable result in a short period. Additionally, an analysis of dynamics of vegetation abundance from 2001 to 2020 indicated that thereexisted an increasing trend for the average fractional value of terrestrial plants and a decreasing trendfor aquatic plants. © 2014 Elsevier B.V.
Gong Z.N.,Capital Normal University |
Gong Z.N.,Key Laboratory of 3D Information Acquisition |
Gong Z.N.,Key Laboratory of Resources Environment and GIS of Beijing Municipal |
Gong Z.N.,Base of the State Laboratory of Urban Environmental Processes and Digital Modeling |
And 16 more authors.
Shengtai Xuebao/ Acta Ecologica Sinica | Year: 2014
Chlorophyll can be an indicator in photosynthesis capacity and vegetation developmental stages,which is also one of important indicators to monitor health status of wetland vegetation growth. Hyperspectral remote sensing technology can provide a simple, effective and non-destructive data acquisition, which can offer processing method for quantifying diagnosis plant chlorophyll content as well. This study used the Fieldspec 3 spectrometer and a plant probe leaf clip spectral detector to guarantee the spectrum are detected in the same area of the leaf, it is also eliminating the background reflectance, spectral fluctuations caused by bending of the blade surface and the impact caused by leaf internal variability. This study determined the typical wetland plants leaf hyperspectral reflectance data at Wild Duck Lake, and at the same time the corresponding leaf chlorophyll content was measured using a spectrophotometer indoor. The relationship between chlorophyll content and the Trilateral parameters, as well as the ratio of spectral index model (SR) and normalized difference spectral index (ND) were established respectively using linear regression model., then 3-Fold Cross Validation(3K-CV) was used to test the accuracy of the estimation model. The results showed that most of the "trilateral" parameters were significantly correlated with plant leaf chlorophyll content; the maximum correlation coefficient reached 0.867. The correlation coefficient between ratio (SR) and normalized (ND) and chlorophyll content were high in general. Suitable band combinations were 550—700 nm,700—1400nm, 550—700 nm and 1600—1900 nm. The best indices with highest correlation with chlorophyll content were SR (calculated from bands 565 nm and 740 nm) and ND (calculated from bands 565 nm and 735 nm). And then by choosing the best correlation spectrum characteristic parameters based on the Trilateral parameters and ND model index, a plant chlorophyll estimation model was constructed. Among them, a chlorophyll content estimation model established by Red edge position (WP_r) of spectral characteristic parameters and ND (565nm, 735nm) spectral index achieved better test results, and R2 both reached above 0.8, the estimation model were y = 0.113x−78.74, y = 5.5762x + 4.4828. Using 3K-CV method for testing and validation, the prediction accuracies of both plant leaf chlorophyll content estimation models were 93. 9% and 90. 7%, respectively. The quantitative analysis of hyperspectral remote sensing technology shows a strong advantage in detecting vegetation weak spectral differences and provides an important theoretical basis and technical support for the practical application in the diagnosis of plant chlorophyll content. © 2014, Science Press. All rights reserved.