摘要
基于2015—2020年四川省植被总初级生产力(GPP)数据,利用空间统计与时间序列分析方法,定量分析了2015—2020年四川省GPP时空变化趋势。结果表明,2020年四川省的GPP空间差异较明显,以岷山—横断山脉为中心向北向南延伸的山地地区年GPP值较高,以此为界东西两侧区域其值较低。GPP均值最大的区域为川西南山地区[214.00(g·c)/m^(2)],最小为川西北高原区[104.51(g·c)/m^(2)]。市州尺度上,GPP均值最大和最小分别为巴中市[226.05(g·c)/m^(2)]和甘孜州[103.72(g·c)/m^(2)]。2015—2020年,四川省GPP>150(g·c)/m^(2)的区域面积占比大于50%,且GPP<100(g·c)/m^(2)的区域面积占比减少了3.41%,GPP>200(g·c)/m^(2)的区域面积占比增加了1.14%,GPP年均值由155.19(g·c)/m^(2)上升至157.35(g·c)/m^(2),表明四川省的植被生物量呈增加的趋势,植被固碳能力有所增强,植被状况整体呈现向好的趋势发展。
Based on the Gross Primary Productivity(GPP)data of Sichuan Province from 2015 to 2020,this paper quantitatively analyzed the temporal and spatial variation trend of GPP in Sichuan Province from 2015 to 2020 using spatial statistics and time series analysis methods.The results showed that the spatial difference of GPP in Sichuan Province was obvious in 2020,and the annual GPP value of the mountainous area extending north to south with Minshan-Hengduan Mountains as the center was higher,while the value in the east and west sides of this boundary was lower.The area with the largest GPP average value was the mountainous area in Southwest Sichuan(214.00(g·c)/m^(2)),and the smallest area was the Northwest Sichuan Plateau(104.51(g·c)/m^(2)).On the city state scale,the maximum and minimum GPP average values were Bazhong(226.05(g·c)/m^(2))and Ganzi(103.72(g·c)/m^(2)),respectively.From 2015 to 2020,the area with GPP>150(g·c)/m^(2)in Sichuan Province accounted for more than 50%,the area proportion of GPP<100(g·c)/m^(2)decreased by 3.41%,the area proportion of GPP>200(g·c)/m^(2)increased by 1.14%,and the average annual value of GPP increased from 155.19(g·c)/m^(2)to 157.35(g·c)/m^(2),indicating that the vegetation biomass in Sichuan Province showed an increasing trend,the carbon sequestration capacity of vegetation was enhanced,and the overall vegetation status showed a good trend.
出处
《环境保护与循环经济》
2022年第12期49-52,共4页
environmental protection and circular economy
基金
四川省重点研发项目(2022YFS0470)
四川省环保科技计划项目(2022HB29)。
关键词
总初级生产力
时空变化
遥感
四川省
gross primary productivity
spatial and temporal variability
remote sensing
Sichuan Province
