基于MODIS时序数据的中国西南地区主要植被生态系统干扰动态监测及分析

Dynamic monitoring and analysis of ecosystem disturbances in major vegetation types based on MODIS time series data in Southwest China

在自然或人为活动的干扰下,生态系统的正常功能或多或少受到影响。生态系统干扰信息可为跟踪气候变化响应、探寻全球碳循环路径和维系生态系统功能提供重要参考。飞速发展的遥感技术为生态系统干扰信息的获取提供了新的思路,高时空分辨率的遥感影像能及时有效地监测干扰事件发生的时间和位置。本文以中国西南地区为例,选用2005—2016年MODIS影像的地表温度LST(Land Surface Temperature)和增强型植被指数EVI(Enhanced Vegetation Index)产品,首先应用遥感干扰指数模型提取2007—2016年的逐年生态系统干扰数据,然后研究了西南地区森林与草地灌丛生态系统干扰的时空分布,并通过地面着火点观测记录、遥感火烧迹地数据、Google Earth和政府报告等验证干扰指数模型的有效性,最后明晰中国西南地区森林与草地灌丛干扰强度的分布特征。结果表明:(1)本文使用的遥感干扰指数模型对干扰事件十分敏感,明显优于单独使用EVI或LST。(2)本文提取的森林生态系统干扰和草地灌丛生态系统干扰与Google Earth、气象数据等保持高度一致。(3)森林与草地灌丛干扰是研究区内最主要的生态系统干扰,约占每年总干扰面积的90%。(4)森林干扰最强烈区在川西高原地区,可能的干扰类型为森林火灾,草地灌丛干扰最强烈区为青藏高原农牧区,可能的干扰类型为气象干旱干扰。

Under the disturbance of natural conditions or human activities, the normal function of the ecosystem is more or less affected. The acquisition of ecosystem disturbance information can provide an important reference for tracking the responses of climate change, exploring global carbon cycle paths and sustaining normal ecosystem functions. The rapid development of satellite technology provides new ideas for the acquisition of ecosystem disturbance information. The remote sensing images with high spatio-temporal resolutions can effectively monitor the time and location of disturbance events in time. With Southwest China as the study area(involving Yunnan Province, Guangxi Zhuang Autonomous Region, Guizhou Province,Chongqing Municipality, Sichuan Province, part of Qinghai Province, and part of Tibet Autonomous Region), this research first calculated the annual data on ecosystem disturbances from 2007 to 2016 by applying a disturbance index model of remote sensing based on the 2005-2016 MODIS datasets on Land Surface Temperature(LST) and Enhanced Vegetation Index(EVI). The spatio-temporal patterns of ecosystem disturbances within forests and grasslands/shrubs were then investigated, and the effectiveness of the disturbance index model was validated by field observation points of fire, remotely sensed burned area data, Google Earth images, and government work reports. Finally, the study clarified the distribution characteristics of different intensity levels of ecosystem disturbances within forests and grasslands/shrubs. The results show that: 1) the disturbance index model of remote sensing in this paper was sensitive to ecosystem disturbance events, and it was significantly better than using enhanced vegetation index or land surface temperature alone. 2) The forest ecosystem disturbance and grassland-shrubs ecosystem disturbance extracted in this paper were highly consistent with Google Earth imagery and meteorological data. 3) Forest and grassland-shrub disturbances were the predominant disturbances in the study area, accounting for ~90% of the total disturbance area per year. 4) The areas with the most serious forest disturbances were mainly located in the western Sichuan Plateau, and fire was the main type of disturbance. The areas with the strongest disturbance of grassland-shrubs were pastoral zones on the QinghaiTibet Plateau, where drought caused by weather was the main type of disturbance.