Author:
Huo Sihui,Sun Qing,Shuai Yanmin,Shao Congying,Huang Jiapeng
Abstract
Abstract
Global spatial distribution of vegetation is determined jointly by climate, monsoon, ocean current and human activities. In recent decades, surface vegetation changes caused by global warming, extreme climate events and intensified human activities need a further understood. In this paper, we adopted available MODIS land cover dataset (MCD12Q1) to investigate the spatial distribution feature of global vegetation sand dynamics during the past 20 years. Results show that: (1) the spatial distribution pattern of global vegetation formed by the long-term ecosystem evolution exhibits stable with less variation in both latitude and vertical dimensions. Generally, the percent of forest, shrubland, grass and cropland respectively are 15.27%, 9.39%, 39.46% and 8.91% of surface covers, with a distinct land cover transition trend from rainforest at equator region to moss and lichen at the north and south pole. (2) In the past 20 years, the global forest land decreased by 3.0×105 km2, with an average annual decrease of 1.255%, open shrubs experienced slight fluctuations in 2001-2017 and increased sharply by 1.6×105 km2 in 2017-2018. And the grassland, croplands and cropland/natural vegetation mosaics had different degrees of growth rate, with an average annual dynamic increase of 0.021%, 0.019% and 0.502%, respectively. (3) There are about 4.2×106 km2 of forest changed into grassland in West Siberia and the Amazon, and 2.5×106 km2 of grassland into shrubs, as well as 1.9×106 km2 of grassland vegetation into farmland, and 9.4×104 km2 of forest into shrubs.
Reference23 articles.
1. Temporal variation of vegetation phenology in northeastern China;Zu;Acta Ecologica Sinica.,2016
2. Vegetation phenology dynamics and its response to climate change on the Tibetan Plateau;Ma;Acta Prataculturae Sinica.,2016
3. Impact of topography on the spatial distribution pattern of net primary productivity in a meadow;Chang;Acta Ecologica Sinica.,2015
4. Modelling and mapping damage to forests from an ice storm using remote sensing and environmental data;King;Nat. Hazards.,2005
5. Implications of Classification of Methodological Decisions in Flooding Analysis from Hurricane Katrina;Khatami;Remote Sens.,2012