Study On Temporal Pattern And Driving Mechanism Of Vegetation Change

Vegetation plays an important role in terrestrial ecosystems.Climate change,natural disasters and socio-economic factors can cause vegetation changes,therefore,it is of great significance to carry out research on vegetation changes and driving mechanisms.In this paper,we have studied the temporal pattern and the driving mechanism of vegetation changes in the Mainland of China from 2001 to 2016 using the 8 day maximum synthesis of 500 m MODIS data.The main contents and results of the study are as follows:1.A method on detecting vegetation changes was established.The method was efficient and accurate,which provides a good tool for the study of vegetation changes.In addition,an innovative method for establishing multi-dimension index trends to identify vegetation changes regions was proposed based on the disturbance index and vegetation abundance index.The method can effectively eliminate areas where Vegetation Abundance has a significant tendency to change due to differences in phenology,seasons,and planting crops.Furthermore,the relationship between the trend combination of multi-dimension indexes and the temporal pattern of vegetation changes(vegetation cover growth type,vegetation cover decreasing type and vegetation disturbance recovery type)were established.The area of vegetation changes accounts for 14.33%of the total area of the Chinese mainland in the past 16 years.The proportion of vegetation cover growth type,vegetation cover decreasing type and vegetation disturbance restoration pattern was 2.81%9.10%and 2.422%,respectively.The overall accuracy of identifying vegetation changes areas was 88.52 and the Kappa coefficient was 0.7683,meanwhile,the recognition accuracy of vegetation changes temporal pattern was 88.17%and Kappa coefficient was 0.8027 based on 4120 ground reference point data.2.The information of vegetation evolution process(rate of change,change time and duration)were extracted based on the continuous monitoring research framework of vegetation changes.The changes process was divided into abrupt and gradual changes according to the rate of change,and the information characteristics of temporal pattern changes process of vegetation changes were further analyzed.Among them,the largest area was the temporal pattern of abrupt growth of vegetation cover,which accounts for 31.88%of the area of vegetation change,mainly distributed in the Loess Plateau and Songnen Plain.The temporal pattern of abrupt decrease of vegetation cover was mainly distributed in the Yangtze River Delta urban agglomeration,coastal cities and provincial capital cities.In addition,the abrupt recovery pattern of vegetation disturbance was mainly distributed in southern China.The largest area of vegetation change year was 2006.The duration of vegetation change was about 2 years.3.Taking climate,natural disasters and social economic factors into account and taking the urban area as the unit,we have carried a reach about the mechanism of vegetation changes by the geographic detector in different periods in order to reveal the mechanism of vegetation changes.The results show that the precipitation was the most important driving factor for the vegetation cover growth pattern and the vegetation disturbance and recovery pattern,and the temperature also plays a vital role.The implementation of the three North projects contributes to the growth of vegetation cover,and other social economic factors(per capita GDP,population density,etc.)have very little explanatory power for the two temporal pattern.The temporal pattern of vegetation cover decreasing was not only driven by a single socio-economic factor(per capita GDP,population density,total fixed investment per unit area and GDP ratio of the secondary and tertiary industries),but also the result of the comprehensive action of multiple factors.Forest pests and rodent pests,forest fires,and crop catastrophes were the main driving factors of vegetation disturbance recovery temporal pattern.The interactive detection of natural disasters and climatic factors further strengthened the explanatory power.