Combining geostatistical models and remotely sensed data to improve vegetation classification in Horqin sandy land

Chujiang Liao

doi:10.1117/12.2188158

10 August 2015 Combining geostatistical models and remotely sensed data to improve vegetation classification in Horqin sandy land

Chujiang Liao

Author Affiliations +

Proceedings Volume 9620, 2015 International Conference on Optical Instruments and Technology: Optical Sensors and Applications; 96200M (2015) https://doi.org/10.1117/12.2188158
Event: International Conference on Optical Instruments and Technology 2015, 2015, Beijing, China

Abstract

On different degrees of desertification land, there exists different vegetation communities, and spatial structure differences are obvious among different vegetation communities. This study implemented variogram calculation using typical sample selected from the image, adopting a common global optimization method to fit them into the spherical model. The results showed that the difference is obvious among different vegetation communities for the sill and range, such as, the sill and range are smaller for sample variogram of Artemisia halodendron and Salix flavida community than that of Artemisia halodendron and Caragana microphylla community, and the range for sample variogram of Agriophyllum arenarium community is bigger than that of Artemisia halodendron and Salix flavida community, but smaller than that of Artemisia halodendron and Caragana microphylla community. Incorporating the difference of the spatial structure characterization into the vegetation classification can improve sample separation, thereby increasing the overall classification accuracy.

Citation Download Citation

Chujiang Liao "Combining geostatistical models and remotely sensed data to improve vegetation classification in Horqin sandy land", Proc. SPIE 9620, 2015 International Conference on Optical Instruments and Technology: Optical Sensors and Applications, 96200M (10 August 2015); https://doi.org/10.1117/12.2188158

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