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Monitoring and dynamic analysis of fractional vegetation cover in southwestern China over the past 15 years based on MODIS data |
| ZHENG Zhaoju1, 2, ZENG Yuan1, ZHAO Yujin1, ZHAO Dan1, WU Bingfang1 |
1. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China;
2. University of Chinese Academy of Sciences, Beijing 100049, China |
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Abstract Fractional vegetation cover (FVC) is a critical indicator for vegetation and eco-environment. It is frequently used as a basic input for hydrology, meteorology and water-soil protection studies at regional or global scales. Southwestern China is an important ecological barrier and the major water supplying area in China. It is important to carry out the study of changes of regional fractional vegetation cover for the protection of eco-environment. In this paper, based on the MODIS-NDVI data obtained from 2000 to 2014, the authors estimated fractional vegetation cover of southwestern China by using the method of dimidiate pixel model, and analyzed the spatial-temporal variation characteristics of the FVC. The results show that, in the past 15 years, the FVC of southwestern China has shown an increasing trend in general but decreased in some meadow areas over the northwest of the study area and the urban expanded areas. In different kinds of ecosystem types, the forest shows the largest average increase of the annual maximum FVC (0.096 2 a-1, p<0.05), while the grassland shows the smallest increase (0.031 1 a-1, p=0.582). Fractional vegetation cover has increased in different degrees in most seasons in the past 15 years in southwestern China, with the increase in autumn being most rapid (0.229 8 a-1) and has most significant trend (p<0.01), followed by spring. For better understanding the effects of climate change on FVC, the correlation coefficients of climatic factors and the annual maximum FVC in different temporal durations were calculated. The results suggest that the annual maximum FVC is significantly related to accumulated precipitation of autumn and mean temperature in summer, showing correlation coefficients of 0.320 and 0.281. In addition, human activities are also important causes resulting in FVC change and the effect has increased in both positive and negative aspects.
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| Keywords
image retrieval
context-sensitive Bayesian network(CSBN)
direction relations
average high frequency signal strength(AHFSS)
double-semantic retrieval
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Issue Date: 15 August 2017
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2017,
Vol. 29
