
Proceedings Paper
Monitoring of vegetation variation in Jiangsu province based on MODIS-LAI dataFormat | Member Price | Non-Member Price |
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Paper Abstract
Vegetation is the essential cornerstone of ecosystem cycling, Leaf area index (LAI) is a key parameter to characterize vegetation growth statue. In this study, Jiangsu province as an important coastal province was chosen as the study area, the finished product data of LAI with 500-meter resolution acquired from MODIS sensor were used to reflect the vegetation statue variation and assess the ecological environment. The variation of the mean LAIs in the whole year, in the withering period and in the flourishing period of 2005, 2008, 2011, 2014, 2017 were explored, their spatial distributions were mapped, the stability and trend of vegetation variation were assessed respectively based on the coefficient of variation (CV) and variation rate (VR), the future vegetation statue was simulated by integrating Cellular Automata model and Markov model. Results showed that the mean LAI values in above three periods of 2017 were respectively 0.82, 0.34 and 1.6. From 2005 to 2017, the variation of the mean LAI values was flat except that in flourishing period, their spatial distributions were similar at the same period, northern vegetation statue was better than that in the south especially in the flourishing period. The stability in the whole year was the best of three periods, that in suburban areas was generally better than that in urban areas. Stable trend dominated Jiangsu province all the time, the vegetation in the flourishing period was significantly fluctuant. The vegetation would generally show an improving trend in future six years after 2017.
Paper Details
Date Published: 3 October 2019
PDF: 12 pages
Proc. SPIE 11156, Earth Resources and Environmental Remote Sensing/GIS Applications X, 111561C (3 October 2019); doi: 10.1117/12.2525146
Published in SPIE Proceedings Vol. 11156:
Earth Resources and Environmental Remote Sensing/GIS Applications X
Karsten Schulz; Ulrich Michel; Konstantinos G. Nikolakopoulos, Editor(s)
PDF: 12 pages
Proc. SPIE 11156, Earth Resources and Environmental Remote Sensing/GIS Applications X, 111561C (3 October 2019); doi: 10.1117/12.2525146
Show Author Affiliations
Published in SPIE Proceedings Vol. 11156:
Earth Resources and Environmental Remote Sensing/GIS Applications X
Karsten Schulz; Ulrich Michel; Konstantinos G. Nikolakopoulos, Editor(s)
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