滇中城市群森林植被净初级生产力时空变化特征及其影响因素研究

doi:10.16258/j.cnki.1674-5906.2026.02.005

生态环境学报 ›› 2026, Vol. 35 ›› Issue (2): 209-222.DOI: 10.16258/j.cnki.1674-5906.2026.02.005

滇中城市群森林植被净初级生产力时空变化特征及其影响因素研究

余燕鞠¹^,²^,³(), 丁雪¹^,²^,³^,^*()

1.云南师范大学地理学部，云南昆明 650500
2.云南省高校资源与环境遥感重点实验室，云南昆明 650500
3.云南省地理空间信息技术工程技术研究中心，云南昆明 650500

收稿日期:2025-02-19 修回日期:2025-11-12 接受日期:2025-11-19 出版日期:2026-02-18 发布日期:2026-02-09
通讯作者: 丁雪
作者简介:余燕鞠（2002年生），女，硕士研究生，主要研究方向为土地利用模拟与碳储量估算。E-mail: 1690728905@qq.com
基金资助:
国家自然科学基金项目(42261073);国家自然科学基金项目(41971369);云南省基础研究专项计划面上项目(202401AT0701103)

Study on the Spatiotemporal Patterns and Driving Factors of Forest Vegetation Net Primary Productivity in the Central Yunnan Urban Agglomeration

YU Yanju¹^,²^,³(), DING Xue¹^,²^,³^,^*()

1. Faculty of Geography, Yunnan Normal University, Kunming 650500, P. R. China
2. Yunnan Provincial Key Laboratory of Remote Sensing for Resources and Environment in Universities, Kunming 650500, P. R. China
3. Yunnan Provincial Engineering and Technology Research Center for Geospatial Information Technology, Kunming 650500, P. R. China

Received:2025-02-19 Revised:2025-11-12 Accepted:2025-11-19 Online:2026-02-18 Published:2026-02-09

摘要/Abstract

摘要：

揭示滇中城市群森林生态系统的碳汇动态及其环境响应机制，可为区域森林资源的可持续管理提供重要的理论依据。基于MOD17A3H V6 NPP数据、MCD12Q1 V6土地覆盖类型数据、ERA5气象数据和SRTMGL1_003高程数据等多源遥感数据，结合Hurst指数和偏导数残差法等多元统计方法，系统地研究了2000－2020年滇中城市群森林净初级生产力（NPP）的时空变化特征及其影响因素。研究结果表明，1）2000－2020年，滇中城市群森林总面积为1.77×10⁴ km²，其中阔叶林、针叶林和混交林分别为0.34×10⁴ km²、0.70×10⁴ km²和0.73×10⁴ km²。2）森林NPP总量年均增长C 2.01 Tg∙a⁻¹（增幅60.5%），混交林的年均增速最高（C 1.71 Tg∙a⁻¹），其次为针叶林（C 1.66 Tg∙a⁻¹）和阔叶林（C 0.80 Tg∙a⁻¹）。3）55.8%的区域森林NPP呈增加趋势，6.86%的区域为极显著和显著增加，44.2%的区域呈减少趋势，2.10%的区域显著减少。4）78.0%的区域Hurst指数<0.5，预示未来NPP将呈现逐步减少。5）地形影响显著，NPP在2100－2400 m达峰值，随坡度增加而降低，南坡向NPP最高。6）人类活动贡献率达99.3%，是NPP改善的主导因素（占33.7%），而气候变化和人类活动的共同作用则是导致NPP退化的主要原因（占30.6%）。研究结果可为该区域森林碳循环研究和森林资源可持续管理提供科学参考。

关键词: 森林, 净初级生产力（NPP）, 时空变化, 气候变化, 人类活动, 滇中城市群

Abstract:

Revealing the carbon sink dynamics of forest ecosystems and their environmental response mechanisms in the Central Yunnan Urban Agglomeration provides crucial theoretical foundations for sustainable management of regional forest resources. Utilizing multi-source remote sensing data including MOD17A3H V6 NPP data, MCD12Q1 V6 land cover classification, ERA5 meteorological data, and SRTMGL1_003 elevation data, combined with multivariate statistical methods such as the Hurst index and partial derivative residual analysis, this study systematically investigated the spatiotemporal variation characteristics of forest net primary productivity (NPP) and its influencing factors in the Central Yunnan Urban Agglomeration from 2000 to 2020. The main findings include: 1) From 2000 to 2020, the total forest area in the Central Yunnan Urban Agglomeration was 1.77×104 km², of which broadleaf forests, coniferous forests, and mixed forests accounted for 0.34×104 km², 0.70×104 km² and 0.73×104 km², respectively. 2) The total NPP of forests increased by an average of C 2.01 Tg∙a⁻¹per year (an increase of 60.5%), with the highest average annual growth rate in mixed forests (C 1.71 Tg∙a⁻¹), followed by coniferous forests (C 1.66 Tg∙a⁻¹) and broadleaf forests (C 0.80 Tg∙a⁻¹). 3) In 55.8% of the forest areas, NPP showed an increasing trend, with 6.86% of the areas experiencing highly significant and significant increases. In contrast, 44.2% of the forest areas showed a decreasing trend, with 2.10% of the areas experiencing significant decreases. 4) The Hurst index was <0.5 in 78.0% of the areas, indicating that future NPP is likely to decrease gradually. 5) Topography had a significant impact, with NPP peaking at elevations of 2100‒2400 m and decreasing with increasing slope. The southern aspect had the highest NPP. 6) Human activities contributed to 99.3% of the changes, making them the dominant factor for NPP improvement (accounting for 33.7%). However, the combined effects of climate change and human activities were the main cause of NPP degradation (accounting for 30.6%). The findings can provide a scientific reference for forest carbon cycle research and sustainable forest resource management in the region.

Key words: forest, net primary productivity, spatiotemporal patterns, climate change, human activities, the Central Yunnan Urban Agglomeration

中图分类号:

Q948
X173

余燕鞠, 丁雪. 滇中城市群森林植被净初级生产力时空变化特征及其影响因素研究[J]. 生态环境学报, 2026, 35(2): 209-222.

YU Yanju, DING Xue. Study on the Spatiotemporal Patterns and Driving Factors of Forest Vegetation Net Primary Productivity in the Central Yunnan Urban Agglomeration[J]. Ecology and Environmental Sciences, 2026, 35(2): 209-222.

图/表 18

图1 滇中城市群位置本图根据审图号GS（2024）0650号地图制作；地图边界无修改

Figure 1 Location of the Central Yunnan Urban Agglomeration

表1 数据来源

Table1 Data resources

数据类型	数据名称	空间分辨率	数据来源
森林净初级生产力	MOD17A3H V6	500 m	NASA（https://search.earthdata.nasa.gov/）
森林类型	MCD12Q1 V6	500 m	NASA（https://search.earthdata.nasa.gov/）
降水	ERA5	0.1°	ECMWF（https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5）
气温	ERA5	0.1°
高程	USGS/SRTMGL1_003	30 m	中科院地理空间数据云平台（http://www.gscloud.cn/）
坡度		30 m
坡向		30 m
气象站点数据			NOAA（https://www.ncei.noaa.gov/data/global-summary-of-the-day/archive/）

图2 ERA5数据集与站点地表温度散点图

Figure 2 Scatter plots of the surface temperature from ERA5 dataset and station

表2 Theil-Sen Median趋势分析变化趋势划分表

Table 2 Classification of theil-sen median trend analysis change trend

N	Z	变化趋势
N>0	2.58<Z	极显著增加
	1.96<Z≤2.58	显著增加
	1.65<Z≤1.96	微显著增加
	0<Z≤1.65	不显著增加
N=0	Z=0	无变化
N<0	−1.65≤Z<0	不显著减少
	−1.96≤Z<−1.65	微显著减少
	−2.58≤Z<−1.96	显著减少
	Z<−2.58	极显著减少

表3 气候变化和人类活动对滇中城市群森林NPP变化影响的情景

Table 3 Different scenarios of contributions of climate change and human activities on forest NPP changes

情景	N	C	H	气候变化相对作用	人类活动相对作用	说明
植被改善	>0	>0	>0	K_c	K_h	气候变化和人类活动共同促进植被恢复
		>0	<0	100	0	气候变化促进植被恢复
		<0	>0	0	100	人类活动促进植被恢复
植被退化	<0	<0	<0	K_c	K_h	气候变化和人类活动共同促使植被退化
		<0	>0	100	0	气候变化促使植被退化
		>0	<0	0	100	人类活动促使植被退化

图3 滇中城市群森林植被净初级生产力（NPP）年总量变化

Figure 3 Variation in the annual total net primary productivity (NPP) of forest in the Central Yunnan Urban Agglomeration

图4 滇中城市群不同森林类型NPP年总量变化

Figure 4 Variation in the annual total NPP of different forest types in the Central Yunnan Urban Agglomeration

图5 滇中城市群森林年均NPP 变化

Figure 5 Variation in annual average NPP of forest in the Central Yunnan Urban Agglomeration

图6 滇中城市群不同森林类型年均NPP变化

Figure 6 Variation in annual average NPP of different forest types in the Central Yunnan Urban Agglomeration

图7 滇中城市群森林NPP变化趋势

Figure 7 Forest NPP change trend of the the Central Yunnan Urban Agglomeration

图8 滇中城市群森林NPP变化趋势显著性

Figure 8 Significance of forest NPP change trend of the Central Yunnan Urban Agglomeration

图9 滇中城市群森林NPP的Hurst指数

Figure 9 Hurst index of forest NPP in the Central Yunnan Urban Agglomeration

图10 地形因子对NPP的影响

Figure 10 The impact of topographic factors on NPP

图11 滇中城市群2000－2020年平均气温和累计降水量年际变化

Figure 11 Variation of annual average temperature and cumulative precipitation in the Central Yunnan Urban Agglomeration

图12 滇中城市群气温和降水对NPP变化的贡献

Figure 12 The contributions of temperature and precipitation to the changes in NPP in the Central Yunnan Urban Agglomeration

图13 滇中城市群气候因子和人类活动对NPP变化的贡献

Figure 13 The contributions of climatic factors and human activities to the changes in NPP in the Central Yunnan Urban Agglomeration

图14 滇中城市群气候变化和人类活动对NPP变化的相对贡献率

Figure 14 The contribution proportions of climate changes and human activities to forest NPP in the Central Yunnan Urban Agglomeration

图15 影响滇中城市群森林NPP变化的驱动因子

Figure 15 The dominant influence on the changes of forest NPP in the Central Yunnan Urban Agglomeration

参考文献 33

[1]	BROUWERS N C, COOPS N C, 2016. Decreasing net primary production in forest and shrub vegetation across Southwest Australia[J]. Ecological Indicators, 66: 10-19. DOI URL
[2]	FIELD C B, RANDERSON J T, MALMSTRÖM C M, 1995. Global net primary production: Combining ecology and remote sensing[J]. Remote Sensing of Environment, 51(1): 74-88. DOI URL
[3]	LI W W, ZHOU J L, XU Z Y, et al., 2023. Climate impact greater on vegetation NPP but human enhance benefits after the Grain for Green Program in Loess Plateau[J]. Ecological Indicators, 157: 111201. DOI URL
[4]	SARKAR S, KAFATOS M, 2004. Interannual variability of vegetation over the Indian sub-continent and its relation to the different meteorological parameters[J]. Remote Sensing of Environment, 90(2): 268-280. DOI URL
[5]	TENG M J, ZENG L X, HU W J, et al., 2020. The impacts of climate changes and human activities on net primary productivity vary across an ecotone zone in Northwest China[J]. Science of the Total Environment, 714: 136691. DOI URL
[6]	WANG Q J, LIANG L, WANG S G, et al., 2023. Insights into spatiotemporal variations in the NPP of terrestrial vegetation in Africa from 1981 to 2018[J]. Remote Sensing, 15(11): 2748. DOI URL
[7]	YANG H F, MU S J, LI J L, 2014. Effects of ecological restoration projects on land use and land cover change and its influences on territorial NPP in Xinjiang, China[J]. Catena, 115: 85-95. DOI URL
[8]	ZHANG M, YUAN N Q, LIN H, et al., 2022. Quantitative estimation of the factors impacting spatiotemporal variation in NPP in the Dongting Lake wetlands using Landsat time series data for the last two decades[J]. Ecological Indicators, 135: 108544. DOI URL
[9]	常屹冉, 张弛, 魏嘉诚, 等, 2023. 气候变化和人类活动对内蒙古植被净初级生产力的影响[J]. 草地学报, 31(11): 3444-3452. DOI
	CHANG Y R, ZHANG C, WEI J C, et al., 2023. Impacts of climate change and human activities on the net primary productivity of vegetation in Inner Mongolia[J]. Acta Agrestia Sinica, 31(11): 3444-3452.
[10]	陈皓锐, 经思思, 叶苏蒙, 等, 2025. 库布其沙漠植被净初级生产力时空演变规律及其气象驱动因素分析[J]. 农业机械学报, 56(1): 424-433.
	CHEN H R, JING S S, YE S M, et al., 2025. Analysis of spatial-temporal evolution and meteorological driving factors of vegetation net primary productivity in Kubuqi Desert[J]. Transactions of the Chinese Society for Agricultural Machinery, 56(1): 424-433.
[11]	方贺, 樊高峰, 王阔, 等, 2024. 2000-2022年长江三角洲地区植被净初级生产力时空演化特征及驱动因素[J]. 环境科学, 46(9): 5788-5799.
	FANG H, FAN G F, WANG K, et al., 2024. Spatiotemporal variation characteristics and driving factors of vegetation NPP in the Yangtze River Delta from 2000 to 2022[J]. Environmental Science, 46(9): 5788-5799.
[12]	葛丽娟, 党虹, 戎战磊, 等, 2018. 祁连山中段植被群落的时空格局变化[J]. 干旱区研究, 35(2): 346-353. DOI
	GE L J, DANG H, RONG Z L, et al., 2018. Spatiotemporal distribution pattern of vegetation communities in the Middle Section of the Qilian Mountains[J]. Arid Zone Research, 35(2): 346-353.
[13]	郭睿妍, 田佳, 杨志玲, 等, 2022. 基于GEE平台的黄河流域森林植被净初级生产力时空变化特征[J]. 生态学报, 42(13): 5437-5445.
	GUO R Y, TIAN J, YANG Z L, et al., 2022. Spatio-temporal vegetation characteristics of forest net primary productivity in the Yellow River Basin based on the Google Earth Engine cloud platform[J]. Acta Ecologica Sinica, 42(13): 5437-5445.
[14]	郝成元, 曾秋强, 2025. 量化极端气候指数对秦巴山区植被净初级生产力动态的贡献[J]. 水土保持学报, 39(2): 135-143, 154.
	HAO C Y, ZENG Q Q, 2025. Quantifying the contribution of extreme climate indices to the dynamics of net primary productivity of vegetation in the Qinba Mountain area[J]. Journal of Soil and Water Conservation, 39(2): 135-143, 154.
[15]	何旭洋, 张福平, 李玲, 等, 2022. 气候变化与人类活动对中国西北内陆河流域植被净初级生产力影响的定量分析[J]. 兰州大学学报(自然科学版), 58(5): 650-660.
	HE X Y, ZHANG F P, LI L, et al., 2022. Quantitative analysis of the impact of climate change and human activities on the NPP of vegetation in the inland river basins of northwest China[J]. Journal of Lanzhou University (Natural Sciences), 58(5): 650-660.
[16]	江田汉, 邓莲堂, 2004. Hurst指数估计中存在的若干问题——以在气候变化研究中的应用为例[J]. 地理科学, 24(2): 177-182.
	JIANG T H, DENG L T, 2004. Some problems in estimating a Hurst exponent: A case study of applications to climatic change[J]. Scientia Geographica Sinica, 24(2): 177-182.
[17]	李文斌, 曹生奎, 曹广超, 等, 2024. 2000-2020年青海湖流域植被净初级生产力时空格局及驱动分析[J]. 水土保持研究, 31(5): 327-336, 343.
	LI W B, CAO S K, CAO G C, et al., 2024. Spatiotemporal patterns of vegetation net primary productivity and their drivers in Qinghai Lake Basin from 2000 to 2020[J]. Research of Soil and Water Conservation, 31(5): 327-336, 343.
[18]	李益敏, 冯显杰, 李媛婷, 等, 2024. 云南省植被覆盖时空变化特征及影响因素研究[J]. 自然资源遥感, 36(2): 116-125.
	LI Y M, FENG X J, LI Y T, et al., 2024. Exploring the spatio-temporal variations and influencing factors of vegetation cover in Yunnan Province[J]. Remote Sensing of Natural Resources, 36(2): 116-125.
[19]	彭双云, 陈明潇, 张老伟, 等, 2024. 滇中城市群碳储量时空演变及其对LULC变化的响应[J]. 水土保持学报, 38(4): 246-256, 266.
	PENG S Y, CHEN M X, ZHANG L W, et al., 2024. Spatiotemporal evolution of carbon storage in the Central Yunnan Urban Agglomeration and its response to LULC change[J]. Journal of Soil and Water Conservation, 38(4): 246-256, 266. DOI URL
[20]	吴丽媛, 神祥金, 刘奕雯, 等, 2024. 青藏高原草本沼泽植被净初级生产力时空变化及其对气候变化的响应[J]. 生态学报, 44(5): 2115-2126.
	WU L Y, SHEN X J, LIU Y W, et al., 2024. Spatio-temporal variation in net primary productivity and its response to climate change in herbaceous marsh on the Qinghai-Tibet Plateau[J]. Acta Ecologica Sinica, 44(5): 2115-2126.
[21]	肖晶, 饶良懿, 2024. 2001-2020年乌梁素海流域植被NPP时空变化及驱动因素分析[J]. 环境科学, 45(8): 4744-4755.
	XIAO J, RAO L Y, 2024. Spatiotemporal variations characteristics and driving factors of vegetation NPP in the Ulansuhai Nur Basin from 2001 to 2020[J]. Environmental Science, 45(8): 4744-4755.
[22]	徐虹, 程晋昕, 何雨芩, 等, 2024. 气候变化和人类活动对云南省植被净初级生产力的影响[J]. 高原气象, 43(4): 1064-1075. DOI
	XU H, CHENG J X, HE Y Q, et al., 2024. Effects of climate change and human activities on net primary productivity in Yunnan Province[J]. Plateau Meteorology, 43(4): 1064-1075. DOI
[23]	徐雨晴, 肖风劲, 於琍, 2020. 中国森林生态系统净初级生产力时空分布及其对气候变化的响应研究综述[J]. 生态学报, 40(14): 4710-4723.
	XU Y Q, XIAO F J, YU L, 2020. Review of spatio-temporal distribution of net primary productivity in forest ecosystem and its response to climate change in China[J]. Acta Ecologica Sinica, 40(14): 4710-4723.
[24]	杨安乐, 张小平, 李宗省, 等, 2023. 气候变化和人类活动对祁连山国家公园植被净初级生产力的定量影响[J]. 生态学报, 43(5): 1784-1792.
	YANG A L, ZHANG X P, LI Z S, et al., 2023. Quantitative analysis of the impacts of climate change and human activities on vegetation NPP in the Qilian Mountain National Park[J]. Acta Ecologica Sinica, 43(5): 1784-1792.
[25]	杨紫妍, 袁金国, 李卓琳, 等, 2024. 水热及下垫面因子影响下的滹沱河流域植被NPP时空演变分析[J]. 环境科学, 46(9): 5825-5838.
	YANG Z Y, YUAN J G, LI Z L, et al., 2024. Analysis of spatiotemporal evolution of vegetation NPP in the Hutuo River Basin under the influence of hydro-thermal factors and underlying surface factors[J]. Environmental Science, 46(9): 5825-5838.
[26]	张格语, 陈文红, 温仲明, 等, 2025. 延河流域植被净初级生产力时空演变及其驱动机制[J]. 水土保持研究, 32(3): 206-213, 221.
	ZHANG G Y, CHEN W H, WEN Z M, et al., 2025. Spatiotemporal evolution of vegetation net primary productivity and its driving mechanisms in the Yanhe River Basin[J]. Research of Soil and Water Conservation, 32(3): 206-213, 221.
[27]	张静, 李状, 杨俊泉, 等, 2024. 气候变化和人类活动对黄淮海平原植被净初级生产力的影响[J/OL]. 地质通报, 1-19 [2025-11-12]. https://link.cnki.net/urli/d/11.4648.P.20241029.1454.006.
	ZHANG J, LI Z, YANG J Q, et al., 2024. Impacts of climate change and human activities on net primary productivity of vegetation in the Huang-Huai-Hai Plain[J/OL]. Geological Bulletin of China, 1-19 [2025-11-12]. https://link.cnki.net/urli/d/11.4648.P.20241029.1454.006.
[28]	张良侠, 岳笑, 周德成, 等, 2023. 气候变化和人类活动对我国典型草原区植被恢复的影响[J]. 环境科学, 44(5): 2694-2703.
	ZHANG L X, YUE X, ZHOU D C, et al., 2023. Impacts of climate change and human activities on vegetation restoration in typical grasslands of China[J]. Environmental Science, 44(5): 2694-2703.
[29]	张艺菲, 张俊玲, 2024. 气候变化和人类活动对云贵高原植被净初级生产力的影响[J]. 环境科学, 46(8): 5217-5228.
	ZHANG Y F, ZHANG J L, 2024. Impacts of climate change and human activities on net primary productivity of vegetation in the Yunnan-Guizhou Plateau[J]. Environmental Science, 46(8): 5217-5228.
[30]	张仲芝, 赵俊三, 陈国平, 等, 2025. 基于OPGD模型的昆明市植被NPP时空演变及驱动机制[J]. 水土保持学报, 39(2): 298-308.
	ZHANG Z Z, ZHAO J S, CHEN G P, et al., 2025. Spatiotemporal evolution of NPP and its influencing factors in Kunming City based on the OPGD model[J]. Journal of Soil and Water Conservation, 39(2): 298-308.
[31]	周娜芳, 贡恩军, 白天豪, 等, 2025. 基于CASA模型的秦巴山区NPP时空动态及影响因素分析[J]. 生态学报, 45(4): 1829-1843.
	ZHOU N F, GONG E J, BAI T H, et al., 2025. Analysis of temporal and spatial dynamics and influencing factors of NPP in the Qinba Mountain area based on the CASA model[J]. Acta Ecologica Sinica, 45(4): 1829-1843.
[32]	周治刚, 丁晔, 王敏丽, 等, 2025. 湖北省植被净初级生产力时空分异格局及多元驱动力定量解析[J]. 环境科学, 46(5): 2997-3008.
	ZHOU Z G, DING Y, WANG M L, et al., 2025. Quantitative analysis of the spatial and temporal distribution pattern of net primary productivity of vegetation in Hubei Province and its multiple driving forces[J]. Environmental Science, 46(5): 2997-3008.
[33]	左丽媛, 高江波, 2020. 基于地理探测器的喀斯特植被NPP定量归因[J]. 生态环境学报, 29(4): 686-694. DOI
	ZUO L Y, GAO J B, 2020. Quantitative attribution analysis of NPP in karst peak cluster depression based on Geographical Detector[J]. Ecology and Environmental Sciences, 29(4): 686-694.

滇中城市群森林植被净初级生产力时空变化特征及其影响因素研究

Study on the Spatiotemporal Patterns and Driving Factors of Forest Vegetation Net Primary Productivity in the Central Yunnan Urban Agglomeration

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