海南省西北部植被净初级生产力的长时序精尺度遥感监测
Long-term and Fine-scale Monitoring of Net Primary Productivity in Northwestern Hainan based on Remote Sensing Data
张顺雪 1沈瑶 1胡中民1
作者信息
- 1. 海南大学 生态与环境学院,海南 海口 570228
- 折叠
摘要
植被净初级生产力(Net Primary Productivity,NPP)是反映生态系统碳循环状态和变化的有效指标,在"双碳"目标背景下,长时序精尺度数据对植被净初级生产力动态监测具有重要意义.海南省作为国家生态文明试验区,其NPP动态监测对于强化陆地生态系统碳汇建设具有典型作用.以海南省西北部为研究区,基于Landsat系列遥感影像数据,采用VPM光能利用率模型,估算得到海南省西北部长时序(2000～2020)精尺度(30 m空间分辨率)的NPP数据,并对其进行时空变化分析.研究结果表明:海南省西北部日平均NPP在年际尺度上呈现明显的波动上升趋势,与其他植被类型相比落叶阔叶林的NPP最高且增长趋势最快,灌丛的NPP最低且增长趋势最慢.海南省西北部NPP在空间上呈现南高北低分布,研究区南部阔叶林及稀树草原地区NPP较高,北部灌丛地区NPP较低.研究区域NPP的整体增长趋势主要是由南部植被所控制.
Abstract
Net Primary Productivity(NPP)is an effective indicator to reflect the state and change of ecosystem carbon cycle.Under the background of"dual carbon"goal,long-term and fine-scale data is of great significance for dynamic monitoring of NPP.As a national ecological civilization pilot zone,the dynamic monitoring of Hain-an is of great significance to strengthen the construction of terrestrial ecosystem carbon sink in order to achieve the goal of"dual carbon".Based on Landsat TM/OLI remote sensing data and VPM light energy utilization model,this paper estimated the long-term(2000～2020)and fine-scale(30 m spatial resolution)data of north-western Hainan Province,and analyzed its spatio-temporal variation.The results showed that:The daily mean NPP in the northwest of Hainan province showed an obvious increasing trend on the interannual scale.Com-pared with other vegetation types,the NPP of deciduous broad-leaved forest was the highest and the trend of growth was the fastest,while that of shrub was the lowest and the trend of growth was the slowest.The spatial distribution of NPP in the northwest of Hainan Province was more in the south than in the north.The NPP was higher in the southern broad-leaved forest and savanna area,and lower in the northern shrub area.The southern part of the study area had a positive influence on the overall growth trend of NPP,and the influence increased year by year,which was mainly regulated by vegetation types.
关键词
遥感/NPP/Landsat/VPM模型/时空变化Key words
Remote sensing/NPP/Landsat/VPM model/Spatio-temporal variation引用本文复制引用
基金项目
海南省重点研发项目(ZDYF2022SHFZ042)
中国工程科技发展战略海南研究院咨询研究项目(21-HN-ZT-09)
海南省自然科学基金(422RC598)
海南大学科研启动基金(KYQDZR-22081)
出版年
2023
NETL
NSTL
维普
万方数据