基于MODIS遥感数据的福建植被生产力时空分布与干旱响应分析

doi:10.16258/j.cnki.1674-5906.2024.06.002

生态环境学报 ›› 2024, Vol. 33 ›› Issue (6): 841-852.DOI: 10.16258/j.cnki.1674-5906.2024.06.002

基于MODIS遥感数据的福建植被生产力时空分布与干旱响应分析

李新妹¹^,²(), 吴作航¹^,³, 王震山⁴, 翁升恒¹^,³, 孙朝锋⁵, 关辉⁶, 王宏¹^,³^,⁷^,^*()

1.福建省灾害天气重点实验室/中国气象局海峡灾害天气重点开放实验室，福建福州 350007
2.福建省福州市气象局，福建福州 350008
3.福建省气象科学研究所，福建福州 350007
4.民航西南空管局气象中心，四川成都 610202
5.福建省气象服务中心，福建福州 350007
6.民航福建空中交通管理分局，福建福州 350209
7.厦门市海峡气象开放重点实验室，福建厦门 361012

收稿日期:2023-11-10 出版日期:2024-06-18 发布日期:2024-07-30
通讯作者: * 王宏。E-mail: wh1575@163.com
作者简介:李新妹（1988年生），女，中级工程师，主要从事陆地生态系统碳循环方面研究。E-mail: lixinmei88@126.com
基金资助:
中国气象局创新发展专项(CXFZ2023J046);厦门市科技局社会发展领域指导性项目(3502Z20214ZD4006);福州市科技局社会发展项目(2023-S-31)

Spatio-temporal Dynamics of Vegetation Productivity and Drought Impacts in Fujian Province Using MODIS Data

LI Xinmei¹^,²(), WU Zuohang¹^,³, WANG Zhenshan⁴, WENG Shengheng¹^,³, SUN Chaofeng⁵, GUAN Hui⁶, WANG Hong¹^,³^,⁷^,^*()

1. Fujian Key Laboratory of Severe Weather/Key Laboratory of Straits Severe Weather, China Meteorological Administration, Fuzhou 350007, P. R. China
2. Fuzhou Meteorological Bureau, Fuzhou 350008, P. R. China
3. Fujian Institute of Meteorological Science, Fuzhou 350007, P. R. China
4. The Southwest of Air Traffic Management Meteorological Center, Chengdu 610202, P. R. China
5. Fujian Meteorological Service Center, Fuzhou 350007, P. R. China
6. Fujian Air Traffic Management Branch Bureau of Civil Aviation Administration, Fuzhou 350209, P. R. China
7. Xiamen Key Laboratory of Straits Meteorology, Xiamen 361012, P. R. China

Received:2023-11-10 Online:2024-06-18 Published:2024-07-30

摘要/Abstract

摘要：

全球升温导致干旱发生的范围和强度增加，但干旱对生态系统碳循环的影响还不明确。基于MODIS遥感数据，采用Sen趋势分析及Mann-Kendall检验、Pearson相关性分析等方法，通过干旱对植被生产力标准化异常的影响等信号，分析了2001—2021年福建省植被总初级生产力（Gross Primary Productivity，GPP）、净初级生产力（Net Primary Productivity，NPP）的时空变化特征与干旱对其的影响及累积/滞后效应。结果表明，1）空间上，福建省植被GPP、NPP多年平均空间分布均呈现“东南高，西北低”的特征，区域平均差异分别为37.4%、65.1%；近21年以来，有近80.8%的区域GPP呈现增长趋势，主要分布在福建省南部、东南部少部分地区，主要指被类型为农田，多为热带经济水果种植的集中区域；另外，超过59.7%的区域NPP呈现下降趋势，主要出现在福建省中部及内陆山区的常绿阔叶林地和稀树草原。2）时间上，植被GPP年际波动明显，总体呈现弱增长趋势[4.86 g∙m⁻²∙a⁻²（以C计，下同），p=0.130]，空间上，6.7%的区域年增速显著超过15 g∙m⁻²∙a⁻²；而NPP年际变化总体呈现弱下降趋势（−1.05 g∙m⁻²∙a⁻²，p=0.396），4.9%的区域年降速显著超过6 g∙m⁻²∙a⁻²。3）研究时段内福建省出现的干旱过程主要以中等干旱为主（52.5%），其次是轻旱（34.0%）。植被GPP、NPP年标准化异常指数与干旱指数（SPEI）存在显著的负相关关系，相关系数分别为−0.624（p=0.002）、−0.531（p=0.013），说明旱情越严重的年份GPP、NPP越高，而旱情较轻或无旱的年份GPP、NPP较低。干旱对GPP最明显的影响主要发生在干旱当月，累积/滞后效应时间较短；干旱事件对GPP的影响是“先增-后缓-再减”，干旱发生初期GPP呈增加趋势，而随着干旱的持续发展，GPP的增加幅度明显减弱，甚至出现负效果。

关键词: 植被生产力, 时空分布, 标准化异常指数, 累积/滞后效应, 福建省

Abstract:

Global warming has intensified drought occurrences, but their impacts on ecosystem carbon cycle remain unclear. This study analyzes the spatio-temporal variations of Gross Primary Productivity (GPP) and Net Primary Productivity (NPP) in Fujian Province from 2001 to 2021 using MODIS data. We employed Sen’s trend analysis, the Mann-Kendall test, Pearson’s correlation analysis and other methods to assess drought’s influence on vegetation productivity anomalies. Additionally, we explored cumulative and lagged effects of drought events. The results showed that: 1) Annual average GPP and NPP exhibited a “high southeast, low northwest” pattern, with regional differences of 37.4% and 65.1%, respectively. Nearly 80.8% of the region showed an increasing GPP trend, concentrated in southern and southeastern agricultural lands and fruit plantations. Conversely, NPP showed a downward trend in over 59.7% of areas, mainly in central and inland mountainous evergreen broad-leaved forests and savannas. 2) Vegetation GPP exhibited significant year-to-year fluctuations with a weak overall upward trend (4.86 g∙m⁻²∙a⁻² [in terms of carbon, the same below], p=0.130). However, pronounced annual increases exceeding 15 g∙m⁻²∙a⁻² occurred in 6.7% of the regions. NPP showed a weak declining trend (−1.05 g∙m⁻²∙a⁻², p=0.396), with annual decreases exceeding 6 g∙m⁻²∙a⁻² in 4.9% of the regions. 3) Moderate drought (52.5%) was the dominant type during the study period, followed by light drought (34.0%). A significant negative correlation existed between the standardized anomaly indices of GPP and NPP and the drought index (SPEI), with the correlation coefficients (r) −0.624 (p=0.002) and −0.531 (p=0.013), respectively. This implies higher GPP and NPP during severe droughts and lower values during light drought years or no drought years. The most pronounced GPP response to drought occurred in the drought month with a short cumulative/lagging effect. The initial drought stage exhibited an increasing GPP trend, but this growth weakened and even reversed with prolonged drought.

Key words: vegetation productivity, spatio-temporal distribution, standardized anomaly index, cumulative/lagging effect, Fujian province

中图分类号:

Q948
X173

李新妹, 吴作航, 王震山, 翁升恒, 孙朝锋, 关辉, 王宏. 基于MODIS遥感数据的福建植被生产力时空分布与干旱响应分析[J]. 生态环境学报, 2024, 33(6): 841-852.

LI Xinmei, WU Zuohang, WANG Zhenshan, WENG Shengheng, SUN Chaofeng, GUAN Hui, WANG Hong. Spatio-temporal Dynamics of Vegetation Productivity and Drought Impacts in Fujian Province Using MODIS Data[J]. Ecology and Environment, 2024, 33(6): 841-852.

图/表 13

图1 2001—2021年福建省植被GPP、NPP多年平均状态空间分布

Figure 1 Spatial distribution of average annual vegetation GPP and NPP in Fujian Province from 2001 to 2021

图2 2001—2021年福建省植被GPP、NPP空间年变化及趋势检验红色虚线框代表主要下降区域，蓝色虚线框代表主要上升区域

Figure 2 Annual trend analyses and significance tests of vegetation GPP and NPP in Fujian Province from 2001 to 2021

图3 2001—2021年福建省植被GPP、NPP区域年均值时间变化

Figure 3 Temporal changes in regional mean annual vegetation GPP and NPP in Fujian Province from 2001 to 2021

图4 基于GOSIF-GPP_v2产品2001—2021年植被GPP区域年均值时间变化

Figure 4 Temporal changes in regional mean annual vegetation GPP (GOSIF-GPP_ v2) from 2001 to 2021

图5 2001—2021年福建省干旱变化概况

Figure 5 Overview of drought changes in Fujian Province from 2001 to 2021

图6 2001—2021年间福建省植被GPP、NPP与SPEI-12标准化异常变化

Figure 6 Changes in standardization anomalies of vegetation GPP, NPP, and SPEI-12 in Fujian Province from 2001 to 2021

图7 福建省植被GPP、NPP区域年均值与SPEI-12标准化异常相关性分析

Figure 7 Correlation analysis between regional annual mean of GPP and NPP and SPEI-12 standardized anomalies in Fujian Province

图8 AGOSIF-GPP_v2与ASPEI-12的相关系数及置信区间

Figure 8 Correlation coefficients and confidence intervals between AGOSIF-GPP_v2 and ASPEI-12

表1 不同时间尺度的SPEI与AGPP的相关系数

Table 1 Correlation coefficients between SPEI of different timescales and AGPP

指标	SPEI-01	SPEI-02	SPEI-03	SPEI-04	SPEI-05	SPEI-06	SPEI-07	SPEI-08	SPEI-09	SPEI-10	SPEI-11	SPEI-12
A_GPP	−0.543^**	−0.348^**	−0.327^**	−0.312^**	−0.292^**	−0.252^**	−0.226^**	−0.180^**	−0.152^*	−0.120	−0.096	−0.069

图9 MODIS-GPP与GOSIF-GPP月均值长时间序列值相关性分析

Figure 9 Correlation analysis of long time series of monthly mean values of MODIS-GPP and GOSIF-GPP

表2 不同尺度的SPEI与AGPP（GOSIF-GPP_v2）的相关系数

Table 2 Correlation coefficient between SPEI of different scales and AGPP (GOSIF-GPP_v2)

指标	SPEI-01	SPEI-02	SPEI-03	SPEI-04	SPEI-05	SPEI-06	SPEI-07	SPEI-08	SPEI-09	SPEI-10	SPEI-11	SPEI-12
A_GPP	−0.0004	−0.0060	−0.0156	−0.0317	−0.0343	−0.0454	−0.0448	−0.0392	−0.0244	−0.0014	0.0233	0.0483

表3 1个月尺度SPEI与滞后0—12个月AGPP的相关系数

Table 3 Correlation coefficients between one month scale SPEI and AGPP with lags of 0?12 months

指标	A_GPP	A_GPP-1	A_GPP-2	A_GPP-3	A_GPP-4	A_GPP-5	A_GPP-6	A_GPP-7	A_GPP-8	A_GPP-9	A_GPP-10	A_GPP-11	A_GPP-12
SPEI-01	−0.543^**	−0.009	−0.117	−0.129	−0.038	−0.032	0.017	0.091	0.059	0.059	0.102	0.083	0.068

表4 1个月尺度SPEI与滞后0—12个月AGPP(GOSIF-GPP_v2)的相关系数

Table 4 Correlation coefficients between one month scale SPEI and AGPP (GOSIF-GPP_ v2) with lags of 0-12 months

指标	A_GPP	A_GPP-1	A_GPP-2	A_GPP−3	A_GPP-4	A_GPP-5	A_GPP-6	A_GPP-7	A_GPP-8	A_GPP-9	A_GPP-10	A_GPP-11	A_GPP-12
SPEI-01	−0.0004	−0.0050	−0.0298	−0.0561	−0.0317	−0.0187	0.0289	0.0389	0.0527	0.0752	0.0714	0.0678	0.0291

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基于MODIS遥感数据的福建植被生产力时空分布与干旱响应分析

Spatio-temporal Dynamics of Vegetation Productivity and Drought Impacts in Fujian Province Using MODIS Data

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