NPP estimation and seasonal change research of Gansu province in northwest China

Tao Han; Dawei Wang; Xiaocui Hao; Youyan Jiang

doi:10.1117/12.2286294

8 March 2018 NPP estimation and seasonal change research of Gansu province in northwest China

Tao Han, Dawei Wang, Xiaocui Hao, Youyan Jiang

Proceedings Volume 10611, MIPPR 2017: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications; 106110K (2018) https://doi.org/10.1117/12.2286294
Event: Tenth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2017), 2017, Xiangyang, China

Abstract

Based on GIS and remote sensing technology, this paper estimates the NPP of the 2015 year-round and every season of Gansu province in northwest China by using the CASA(Carnegie Ames Stanford Approach) light energy utilization model. The result shows that the total annual NPP of Gansu province gradually decline from southeast to northwest in the space, which is in accordance with the water and heat condition in Gansu province. The results show that the summer NPP in Gansu Province is the maximum in each season. The maximum value of summer NPP in Gansu Province reached 695 (gCm-2•season-1), and the maximum value was 473 in spring, and 288 in the autumn, and the NPP in the winter in Gansu province were under 60. The fluctuation range of NPP value is large, this is due to the diversity of ecosystem types in Gansu province, including desert, grassland, farmland and forest, among them, the grassland area is the largest, and the grassland type is very diverse, the grassland coverage is obviously different, especially the low coverage grassland growth is affected by precipitation and temperature and other meteorological factors obviously.

Citation Download Citation

Tao Han, Dawei Wang, Xiaocui Hao, and Youyan Jiang "NPP estimation and seasonal change research of Gansu province in northwest China", Proc. SPIE 10611, MIPPR 2017: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 106110K (8 March 2018); https://doi.org/10.1117/12.2286294

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KEYWORDS

Vegetation

Radiation effects

Meteorology

Atmospheric modeling

Data modeling

Wind energy

Climatology

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