Spatio-temporal Changes And Driving Forces Of Net Primary Productivity Of Vegetation In Panxi Area

The Panxi region is located in the transition zone from the Qinghai-Tibet Plateau to the Sichuan Basin and is an important ecological barrier in the upper reaches of the Yangtze River in China.The complex and diverse landforms,crisscrossed by water systems,distinct dry and wet seasons,and extremely concentrated rainfall in the region result in frequent geological disasters,serious soil erosion,and inherent fragile ecological environment.As an important part of terrestrial ecological environment,vegetation plays an important role in exerting ecological efficiency and maintaining ecological environment.The net primary productivity is the source and basis of vegetation growth energy,and the main factor indicating the quality of terrestrial ecosystems and regulating the functions of ecosystems.Therefore,studying the changes of vegetation productivity in Panxi is of great significance for evaluating the sustainable development of ecosystems.Based on the analysis of previous research results,this study uses the“3S”technology to extract LUCC information of Panzhihua City,Huili County and Ningnan County of Liangshan from the multi-type and multi-temporal remote sensing images,discusses the temporal and spatial evolution of regional land use by“dynamic degree”,“spatial analysis”and“landscape pattern indicators”,corrects the ecosystem service value per unit area adapted to the study area,and analysis of the impact of land use type change on ecosystem service value.On this basis,LUCC,Modis NDVI and surface meteorological data of the same period as data sources are applied to the improved CASA model to simulate the monthly vegetation NPP in the study area from 2002 to 2016.With the NPP data set,this study analyzes the spatial and temporal changes of NPP by“simple difference”and“degree analysis”,and discusses the response of NPP to meteorological factors and LUCC based on“correlation analysis”and“Residual analysis”.The results of this study are of great significance to the sustainable development of ecological environment in the study area,and provide a reference for the relevant departments to plan land use rationally and formulate ecological environment protection measures.The main conclusions are as follows:（1）From 1995 to 2016,the study area always maintained the status of land use,with forest land as the main,cultivated land and grassland accounting for a certain proportion,and construction land occupying an important position.With the implementation of urban expansion and the policy of returning farmland to forests,the area conversion between different land use types is significant in the past 22 years,the construction land and forest land increased by 54.94 km² and 339.71 km²,separately,and the cultivated land decreased by 211.37 km².With the change of elevation and gradient,the land use types have obvious characteristics of differentiation.In the overall distribution of landscape pattern,heterogeneity gradually strengthens,uniformity gradually weakens,spread gradually decreases,and landscape fragmentation degree increases.（2）Applying NPP to revise the ecosystem service value per unit area of the study area,the total ESV of the study area is estimated.It is found that the value of ecosystem services showed an increasing trend except for the function of food production during 1995 to 2016.The order of contribution rate of different land use types to ecosystem service value is forest land,cultivated land,grassland,water body and unused land.The total ESV increased by 361 million,indicating that the regional ecological environment continued to improve and the ecosystem service function gradually increased.（3）Based on the improved CASA model,the monthly net primary productivity（NPP）of vegetation in the study area is simulated from 2002 to 2016.The results indicate that the NPP of vegetation has obvious spatial differentiation,showing a decreasing trend from north to south.From the perspective of different administrative regions,the order of NPP mean from high to low is Ningnan County,Miyi County,Yanbian County,Huili County,Renhe District,Western District and Eastern District,and the average NPP is between 386.10 gC·m^-2·a^-1714.68 gC·m^-2·a^-1.From the annual total NPP distribution of different counties,the maximum NPP in Huili County is 2.82 TgC·a^-1,and the minimum NPP in Panzhihua is 0.05 TgC·a^-1.The spatial distribution of annual NPP is not only related to the annual mean value of NPP,but also depends on the size of counties.（4）There are significant differences in NPP of vegetation in different years and seasons in the study area.From 2002 to 2016,the average of vegetation NPP is 658.75gC·m^-2·a^-1.And the interannual variation shows an obvious fluctuation trend which is in the range of 8.68-9.43 TgC·a^-1,the highest value is gC·m^-2·a^-1 in 2009,and the lowest value is 637.15 gC·m^-2·a^-1 in 2004.Vegetation NPP increased significantly from 2002 to 2009,and then decreased gradually,while total and annual averages value in 2016 are higher than those in 2002.From the analysis of seasonal change,the total seasonal NPP of vegetation from largest to smallest is as follows summer,spring,autumn and winter,and the NPP increases continuously when the seasonal change is winter to spring to summer,while the NPP decreases continuously when the seasonal change is summer to autumn to winter.（5）Climate change and human activities have a significant impact on vegetation NPP in the study area.From 2002 to 2016,vegetation NPP is moderately positively correlated with temperature and rainfall,and more significant with rainfall.During the study period,the average NPP_lulculc is 506.97 gC·m^-2·a^-1 due to land cover change and human activity disturbance,and the average NPP_lulc is 927.86 gC·m^-2·a^-1 in the human activities disturbance around towns,industrial and mining land and water bodies,followed by is 605.03 gC·m^-2·a^-11 in agricultural land,shrub forest and grassland distribution areas.The NPP_lulc in the forest areas with excellent vegetation coverage is the lowest,and even negative in some areas,with an average value of 275.58gC·m^-2·a^-1.