2000—2020年赣南植被覆盖时空变化特征及驱动因素

doi:10.13287/j.1001-9332.202311.023

摘要/Abstract

摘要： 植被在水、碳循环和能量流动中起着重要作用,是调节陆地碳平衡和反映气候变化、人类活动的重要指标。本研究采用Mann-Kendall检验、Theil-Sen Median分析、Hurst指数和变异系数等方法分析2000—2020年赣南生长季植被归一化植被指数(NDVI)的时空变化,用地理探测器综合分析气候、地形、土壤和人为因子等对植被NDVI空间分异的影响。结果表明: 2000—2020年,植被NDVI以0.003·a^-1的速率波动上升。高等级和中高等级植被NDVI面积占比分别为55.8%和41.9%,低波动变化和较低波动变化的区域面积占比为92.3%。植被NDVI极显著改善和显著改善面积占比分别为40.4%、19.4%,极显著退化和显著退化面积总占比仅为2.2%,持续改善和未来改善面积占比分别为28.0%和60.2%。高程、降水量、相对湿度、温度、地貌类型、土地利用类型、人口密度和夜间灯光指数为研究区植被NDVI的主要影响因子,其次是坡度、土壤类型和GDP,坡向和植被类型为间接影响因子。研究期间,赣南植被NDVI整体上稳定性较好,未来植被变化趋势以改善为主,人为因子中土地利用类型、人口密度和夜间灯光指数对植被NDVI的影响整体呈上升趋势。

关键词: 归一化植被指数, 地理探测器, 自然因素, 人为因素, 赣南

Abstract: Vegetation plays a critical role in the water and carbon cycling and energy flow, serving as an indicator for regulating land carbon balance and reflecting climate change and human activities. We analyzed the spatiotemporal variations of normalized difference vegetation index (NDVI) during the growing season in southern Jiangxi from 2000 to 2020, using statistical methods, including the Mann-Kendall test, Theil-Sen Median analysis, Hurst index, and coefficient of variation. We employed the geodetector model to comprehensively assess the impacts of climate, topography, soil and human factors on spatial differentiation of vegetation NDVI. The results showed NDVI exhibited an upward fluctuating trend with a rate of 0.003 per year from 2000 to 2020. The proportion of high-grade and medium-high-grade NDVI areas were 55.8% and 41.9%, respectively, while the areas with low and relatively low fluctuations accounted for 92.3%. The proportions of areas showing extremely significant improvement and significant improvement were 40.4% and 19.4%, respectively. In contrast, the combined proportion of areas displaying extremely significant degradation and significant degradation was only 2.2%. The proportions of areas demonstrating continuous improvement and future improvement were 28.0% and 60.2%, respectively. Elevation, precipitation, relative humidity, temperature, landform type, land use type, population density, and nighttime light were identified as the major factors for the vairations of NDVI in the study area, followed by slope, soil type, and GDP, while slope aspect and vegetation type had indirect influence. Throughout the study period, NDVI in southern Jiangxi was overall stable, with future changes primarily indicating improvement. Notably, human factors such as land use type, population density, and nighttime light index exhibited an upward trend in their impacts on NDVI.

Key words: normalized difference vegetation index, geographic detector, natural factor, human factor, southern Jiangxi Province

刘冬冬, 潘萍, 付佳, 欧阳勋志. 2000—2020年赣南植被覆盖时空变化特征及驱动因素[J]. 应用生态学报, 2023, 34(11): 2919-2928.

LIU Dongdong, PAN Ping, FU Jia, OUYANG Xunzhi. Spatiotemporal variation and driving factor of vegetation coverage from 2000 to 2020 in southern Jiangxi Province, China.[J]. Chinese Journal of Applied Ecology, 2023, 34(11): 2919-2928.

参考文献

[1] Deng C, Zhang B, Cheng L, et al. Vegetation dynamics and their effects on surface water-energy balance over the Three-North Region of China. Agricultural and Forest Meteorology, 2019, 275: 79-90
[2] Fang X, Zhu Q, Ren L, et al. Large-scale detection of vegetation dynamics and their potential drivers using MODIS images and BFAST: A case study in Quebec, Canada. Remote Sensing of Environment, 2018, 206: 391-402
[3] Yuan J, Xu Y, Xiang J, et al. Spatiotemporal variation of vegetation coverage and its associated influence factor analysis in the Yangtze River Delta, eastern China. Environmental Science and Pollution Research, 2019, 26: 32866-32879
[4] Beck PSA, Atzberger C, Høgda KA, et al. Improved monitoring of vegetation dynamics at very high latitudes: A new method using MODIS NDVI. Remote Sensing of Environment, 2006, 100: 321-334
[5] Pettorelli N, Vik JO, Mysterud A, et al. Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends in Ecology & Evolution, 2005, 20: 503-510
[6] 向珈瑶, 彭文甫, 陶帅. 2000—2020年岷江上游植被NDVI时空变化及其地形响应. 长江流域资源与环境, 2022, 31(7): 1534-1547
[7] Huo H, Sun C. Spatiotemporal variation and influencing factors of vegetation dynamics based on Geodetector: A case study of the northwestern Yunnan Plateau, China. Ecological Indicators, 2021, 130: 108005
[8] Zhou L, Tucker CJ, Kaufmann RK, et al. Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999. Journal of Geophysical Research: Atmospheres, 2001, 106: 20069-20083
[9] Suzuki R, Masuda KG, Dye D. Interannual covariability between actual evapotranspiration and PAL and GIMMS NDVIs of northern Asia. Remote Sensing of Environment, 2007, 106: 387-398
[10] 马海云, 张林林, 魏学琼, 等. 2000—2015年西南地区土地利用与植被覆盖的时空变化. 应用生态学报, 2021, 32(2): 618-628
[11] Wang J, Wang K, Zhang M, et al. Impacts of climate change and human activities on vegetation cover in hilly southern China. Ecological Engineering, 2015, 81: 451-461
[12] Maeda EE, Pellikka PKE, Siljander M, et al. Potential impacts of agricultural expansion and climate change on soil erosion in the Eastern Arc Mountains of Kenya. Geomorphology, 2010, 123: 279-289
[13] Nunes AN, de Almeida AC, Coelho COA. Impacts of land use and cover type on runoff and soil erosion in a marginal area of Portugal. Applied Geography, 2011, 31: 687-699
[14] Zhang Y, Jiang X, Lei Y, et al. The contributions of natural and anthropogenic factors to NDVI variations on the Loess Plateau in China during 2000-2020. Ecological Indicators, 2022, 143: 109342
[15] Tao S, Peng W, Xiang J. Spatiotemporal variations and driving mechanisms of vegetation coverage in the Wumeng Mountainous Area, China. Ecological Informatics, 2022, 70: 101737
[16] 王劲峰, 徐成东. 地理探测器:原理与展望. 地理学报, 2017, 72(1): 116-134
[17] Zhang Y, Ai J, Sun Q, et al. Soil organic carbon and total nitrogen stocks as affected by vegetation types and altitude across the mountainous regions in the Yunnan Province, south-western China. Catena, 2021, 196: 104872
[18] 林妍敏, 李文慧, 南雄雄, 等. 基于地理探测器的宁夏贺兰山植被覆盖度时空分异及驱动因子. 应用生态学报, 2022, 33(12): 3321-3327
[19] 沈钰仟, 杜傲, 林子雁, 等. 赣州市山水林田湖草修复工程生态保护效益. 生态学报, 2023, 43(2): 650-659
[20] Ma Y, Guan Q, Sun Y, et al. Three-dimensional dynamic characteristics of vegetation and its response to climatic factors in the Qilian Mountains. Catena, 2022, 208: 105694
[21] Yang J, Huang X. The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019. Earth System Science Data, 2021, 13: 3907-3925
[22] 周成虎, 程维明. 《中华人民共和国地貌图集》的研究与编制. 地理研究, 2010, 29(6): 970-979
[23] 史学正, 于东升, 高鹏, 等. 中国土壤信息系统(SISChina)及其应用基础研究. 土壤, 2007, 39(3): 329-333
[24] 张新时. 中华人民共和国植被图(1∶1 000 000). 北京: 地质出版社, 2007
[25] Wu Y, Shi K, Chen Z, et al. Developing improved time-series DMSP-OLS-Like data (1992-2019) in China by integrating DMSP-OLS and SNPP-VIIRS. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 1-14
[26] Jiang W, Yuan L, Wang W, et al. Spatio-temporal analysis of vegetation variation in the Yellow River Basin. Ecological Indicators, 2015, 51: 117-126
[27] Ali R, Kuriqi A, Abubaker S, et al. Long-term trends and seasonality detection of the observed flow in Yangtze River using Mann-Kendall and Sen’s innovative trend method. Water, 2019, 11: 1855
[28] Sen PK. Estimates of the regression coefficient based on Kendall’s Tau. Journal of the American Statistical Association, 1968, 63: 1379-1389
[29] Hurst HE. Method of using Long-term storage in reserviors. Proceedings of the Institution of Civil Engineers, 1956, 5: 519-543
[30] 张强, 姚玉璧, 王莺, 等. 中国南方干旱灾害风险特征及其防控对策. 生态学报, 2017, 37(21): 7206-7218
[31] 李倩, 闫正涛, 许映军. 1951—2020年中国南方地区低温雨雪冰冻危险性评估. 地理科学, 2023, 43(1): 163-172
[32] 程东亚, 李旭东. 喀斯特地区植被覆盖度变化及地形与人口效应研究. 地球信息科学学报, 2019, 21(8): 1227-1239
[33] 周夏飞, 朱文泉, 马国霞, 等. 江西省赣州市稀土矿开采导致的水土保持价值损失评估. 自然资源学报, 2016, 31(6): 982-993
[34] 王瑞杰, 闫峰. 2000—2018年西北砒砂岩区植被覆盖度与地形效应. 应用生态学报, 2020, 31(4): 1194-1202
[35] Emmett KD, Renwick KM, Poulter B. Disentangling climate and disturbance effects on regional vegetation greening trends. Ecosystems, 2019, 22: 873-891
[36] Qu S, Wang L, Lin AY, et al. Distinguishing the impacts of climate change and anthropogenic factors on vegetation dynamics in the Yangtze River Basin, China. Ecological Indicators, 2020, 108: 105724
[37] 段艺芳, 任志远, 孙艺杰. 陕北黄土高原植被生态系统水分利用效率气候时滞效应. 生态学报, 2020, 40(10): 3408-3419
[38] Wang J, Lin Y, Glendinning A, et al. Land-use changes and land policies evolution in China’s urbanization processes. Land Use Policy, 2018, 75: 375-387
[39] Burrell AL, Evans JP, de Kauwe MG. Anthropogenic climate change has driven over 5 million km² of drylands towards desertification. Nature Communications, 2020, 11: 3853