1990—2020年黑龙江省植被覆盖度的时空变化趋势及驱动力

doi:10.13287/j.1001-9332.202305.021

摘要/Abstract

摘要： 植被覆盖度(FVC)作为表征植被生长状况与生态系统变化的定量指标,其时空变化趋势及驱动力是全球及区域生态环境的重要研究内容。本研究基于GEE云计算平台,应用像元二分模型对黑龙江省1990—2020年FVC进行估测,并通过Mann-Kendall突变检验、Sen趋势分析、Mann-Kendall显著性检验、相关性分析及结构方程模型,分析FVC的时空变化趋势及驱动力。结果表明: 基于像元二分模型法计算研究区FVC具有较高精度(R²>0.7、均方根误差<0.1、相对均方根误差<14%)。1990—2020年,黑龙江省年均FVC为0.79,呈波动上升趋势(0.72～0.85),年均增长0.4%,市级行政区年均FVC也呈不同程度的增长。黑龙江省以极高FVC类型为主,其面积占比逐渐增加。FVC呈增加趋势的面积占比为67.4%,呈减小趋势的面积占比为26.2%,其余保持不变。人为活动因子对年均FVC的相关性高于生长季月平均气象因子对年均FVC的相关性。人为活动因子是黑龙江省FVC变化的主要驱动因子,其次为土地利用类型,而生长季月平均气象因子对FVC变化的总影响为负。本研究结果可为黑龙江省长时间植被覆盖监测与驱动力分析提供技术支撑,也为生态环境的恢复与保护、相关土地利用政策的制定提供了依据。

关键词: 植被覆盖度, 像元二分模型, Mann-Kendall突变检验, Sen趋势分析, Mann-Kendall显著性检验, 结构方程模型

Abstract: Fractional vegetation cover (FVC) is a quantitative indicator for vegetation growth conditions and ecosystem change. Clarifying the spatial and temporal trends and driving factors of FVC is an important research content of global and regional ecological environment. Based on Google Earth Engine (GEE) cloud computing platform, we estimated FVC in Heilongjiang Province from 1990 to 2020 using the pixel dichotomous model. We analyzed the temporal and spatial trends and drivers of FVC using Mann-Kendall mutation test, Sen's slope analysis with Mann-Kendall significance test, correlation analysis, and structural equation model. The results showed that the estimated FVC based on the pixel dichotomous model had high accuracy (R²>0.7, root mean square error <0.1, relative root mean square error <14%). From 1990 to 2020, the annual average FVC in Heilongjiang was 0.79, with a fluctuating upward trend (0.72-0.85) and an average annual growth rate of 0.4%. The annual average FVC at the municipal administrative districts level also showed different levels of increase of FVC. The area with extremely high FVC dominated the Heilongjiang Province with a gradual increase proportion. The area with increasing trend of FVC accounted for 67.4% of the total area, whereas the area with decreasing trend only accounted for 26.2%, and the rest remained unchanged. The correlation of human activity factor on annual average FVC was higher than that of growing season monthly average meteorological factor. The human activity factor was the main driver for FVC change in Heilongjiang Province, followed by land use type. The total effect of monthly average meteorological factor during the growing season on FVC change was negative. The results would serve as technical support for long-term FVC monitoring and driving force analysis in Heilongjiang Province, and provide a reference for ecological environment restoration and protection, as well as the formulation of related land use policy.

Key words: fractional vegetation cover, pixel dichotomous model, Mann-Kendall mutation test, Sen's slope analysis, Mann-Kendall significance test, structural equation model

赵楠, 赵颖慧, 邹海凤, 白晓红, 甄贞. 1990—2020年黑龙江省植被覆盖度的时空变化趋势及驱动力[J]. 应用生态学报, 2023, 34(5): 1320-1330.

ZHAO Nan, ZHAO Yinghui, ZOU Haifeng, Bai Xiaohong, ZHEN Zhen. Spatial and temporal trends and drivers of fractional vegetation cover in Heilongjiang Province, China during 1990-2020[J]. Chinese Journal of Applied Ecology, 2023, 34(5): 1320-1330.

参考文献

[1] 孟琪, 武志涛, 杜自强, 等. 京津风沙源区不同分区植被覆盖度变化及归因分析. 应用生态学报, 2021, 32(8): 2895-2905
[2] Jia K, Liang SL, Liu S, et al. Global land surface fractional vegetation cover estimation using general regression neural networks from MODIS surface reflectance. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53: 4787-4796
[3] Purevdorj T, Tateishi R, Ishiyama T, et al. Relationships between percent vegetation cover and vegetation indices. International Journal of Remote Sensing, 1998, 19: 3519-3535
[4] 吴志杰, 何国金, 黄绍霖, 等. 南方丘陵区植被覆盖度遥感估算的地形效应评估. 遥感学报, 2017, 21(1): 159-167
[5] 王晓蕾, 石守海. 基于GEE的黄河流域植被时空变化及其地形效应研究. 地球信息科学学报, 2022, 24(6): 1087-1098
[6] 赵旦, 张淼, 于名召. 汶川地震灾后农田和植被恢复遥感监测. 遥感学报, 2014, 18(4): 958-970
[7] Gitelson AA, Kaufman YJ, Stark R, et al. Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment, 2002, 80: 76-87
[8] 邢著荣, 冯幼贵, 杨贵军, 等. 基于遥感的植被覆盖度估算方法述评. 遥感技术与应用, 2009, 24(6): 6
[9] 秦伟, 朱清科, 张学霞, 等. 植被覆盖度及其测算方法研究进展. 西北农林科技大学学报:自然科学版, 2006, 34(9): 163-170
[10] Martínez B, Gilabert MA. Vegetation dynamics from NDVI time series analysis using the wavelet transform. Remote Sensing of Environment, 2009, 113: 1823-1842
[11] Jiménez-Muñoz JC, Sobrino JA, Antonio P, et al. Comparison between fractional vegetation cover retrievals from vegetation indices and spectral mixture analysis: Case study of PROBA/CHRIS data over an agricultural area. Sensors, 2009, 9: 768-793
[12] Li S, Wang J, Zhang M, et al. Characterizing and attributing the vegetation coverage changes in North Shanxi coal base of China from 1987 to 2020. Resources Policy, 2021, 74: 102331
[13] 李登科, 范建忠, 王娟. 陕西省植被覆盖度变化特征及其成因. 应用生态学报, 2010, 21(11): 2896-2903
[14] 程红芳, 章文波, 陈锋. 植被覆盖度遥感估算方法研究进展. 国土资源遥感, 2008, 75(1): 13-18
[15] Li J, Wang JL, Zhang J, et al. Dynamic changes of vege-tation coverage in China-Myanmar economic corridor over the past 20 years. International Journal of Applied Earth Observation and Geoinformation, 2021, 102: 102378
[16] 刘垚燚, 曾鹏, 张然, 等. 基于GEE和BRT的1984—2019年长三角生态绿色一体化发展示范区植被覆盖度变化. 应用生态学报, 2021, 32(3): 1033-1044
[17] Zhang XF, Liao CH, Li J, et al. Fractional vegetation cover estimation in arid and semi-arid environments using HJ-1 satellite hyperspectral data. International Journal of Applied Earth Observation and Geoinformation, 2013, 21: 506-512
[18] 姚春阳. 基于Google Earth Engine和Sentinel-2数据的植被覆盖度动态监测研究. 经纬天地, 2021(3): 36-38
[19] 王亭月. 2012—2018年黑龙江省植被覆盖度动态变化分析. 国土与自然资源研究, 2020(4): 16-21
[20] 张晓慧, 张丽娟, 姜春艳, 等. 基于MODIS影像研究黑龙江省植被变化以及与降水和气温的相关关系. 哈尔滨师范大学自然科学学报, 2015, 31(3): 144-150
[21] 王小娜, 田金炎, 李小娟, 等. Google Earth Engine云平台对遥感发展的改变. 遥感学报, 2022, 26(2): 299-309
[22] 王子龙, 朱彤, 姜秋香. 黑龙江省土地利用/覆被变化及生境质量特征变化分析. 东北农业大学学报, 2022, 53(1): 77-86
[23] 程春香, 于敏, 吴门新, 等. 2000—2020年黑龙江省陆地植被NPP驱动因子分析. 东北农业大学学报, 2022, 53(6): 20-28
[24] 谭美宝. 黑河流域植被覆盖度与灌溉耕地遥感监测研究. 硕士论文. 兰州: 兰州大学, 2021
[25] Jia K, Liang SL, Wei XQ, et al. Validation of Global Land Surface Satellite (GLASS) fractional vegetation cover product from MODIS data in an agricultural region. Remote Sensing Letters, 2018, 9: 847-856
[26] Yang LQ, Jia K, Liang SL, et al. Comparison of four machine learning methods for generating the GLASS fractional vegetation cover product from MODIS data. Remote Sensing, 2016, 8: 682
[27] Song DX, Wang ZH, He T, et al. Estimation and validation of 30 m fractional vegetation cover over China through integrated use of Landsat 8 and Gaofen 2 data. Science of Remote Sensing, 2022, 6: 100058
[28] Laipelt L, Ruhoff A, Fleischmann AS, et al. Long-term monitoring of evapotranspiration using the SEBAL algorithm and Google Earth Engine cloud computing. ISPRS Journal of Photogrammetry and Remote Sensing, 2021, 178: 81-96
[29] Zhang YF, Liang SL, Zhu ZL, et al. Soil moisture content retrieval from Landsat 8 data using ensemble lear-ning. ISPRS Journal of Photogrammetry and Remote Sensing, 2022, 185: 32-47
[30] Spellman P, Pritt ABC, Salazar N. Tracking changing water budgets across the Bahamian archipelago. Journal of Hydrology, 2021, 598: 126198
[31] Meng XY, Gao X, Li S, et al. Monitoring desertification in Mongolia based on Landsat images and Google Earth Engine from 1990 to 2020. Ecological Indicators, 2021, 129: 107908
[32] 黑龙江省统计局. 黑龙江统计年鉴2021 [EB/OL]. (2022-02-11) [2022-06-03]. http://tjj.hlj.gov.cn/sy/
[33] 国家统计局. 环境统计数据1999—2014 [EB/OL]. (2016-06-04) [2022-06-03]. http://www.stats.gov.cn/ztjc/ztsj/hjtjzl/
[34] Wu YZ, Shi KF, Chen ZQ, 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, 2021, 60: 4407714
[35] Duo A, Zhao WJ, Qu XY, et al. Spatio-temporal variation of vegetation coverage and its response to climate change in North China plain in the last 33 years. International Journal of Applied Earth Observation and Geoinformation, 2016, 53: 103-117
[36] 张月丛, 赵志强, 李双成, 等. 基于SPOT NDVI的华北北部地表植被覆盖变化趋势. 地理研究, 2008, 27(4): 745-754
[37] Carlson TN, Ripley DA. On the relation between NDVI fractional vegetation cover and leaf area index. Remote Sensing of Environment, 1997, 62: 241-252
[38] 李苗苗, 吴炳方, 颜长珍, 等. 密云水库上游植被覆盖度的遥感估算. 资源科学, 2004, 26(4): 157-164
[39] 唐亮, 何明珠, 许华, 等. 基于无人机低空遥感的荒漠植被覆盖度与归一化植被指数验证及其对水热梯度的响应. 应用生态学报, 2020, 31(1): 35-44
[40] Li SJ, Fang HL, Zhang YH, et al. Comprehensive eva-luation of global CI, FVC, and LAI products and their relationships using high-resolution reference data. Science of Remote Sensing, 2022, 6: 100066
[41] Zhang M, Wang JM, Li SJ. Tempo-spatial changes and main anthropogenic influence factors of vegetation fractional coverage in a large-scale opencast coal mine area from 1992 to 2015. Journal of Cleaner Production, 2019, 232: 940-952
[42] 胡琦, 马雪晴, 胡莉婷, 等. Matlab在气象专业教学中的应用——气象要素的M-K检验突变分析. 实验室研究与探索, 2019, 38(12): 48-51
[43] Mao PP, Zhang J, Li M, et al. Spatial and temporal variations in fractional vegetation cover and its driving factors in the Hulun Lake region. Ecological Indicators, 2022, 135: 108490
[44] Xing LT, Huang LX, Chi GY, et al. A dynamic study of a Karst Spring based on wavelet analysis and the Mann-Kendall trend test. Water, 2018, 10: 698
[45] 邵霜霜, 师庆东. 基于FVC的新疆植被覆盖度时空变化. 林业科学, 2015, 51(10): 35-42
[46] He J, Shi XY, Fu YG. Identifying vegetation restoration effectiveness and driving factors on different micro-topographic types of hilly Loess Plateau: From the perspective of ecological resilience. Journal of Environmental Management, 2021, 289: 112562
[47] Li J, Wang JL, Zhang J, et al. Growing-season vegetation coverage patterns and driving factors in the China-Myanmar Economic Corridor based on Google Earth Engine and geographic detector. Ecological Indicators, 2022, 136: 108620
[48] Fan JF, Ma T, Zhou CH, et al. Comparative estimation of urban development in China's cities using socioeconomic and DMSP/OLS night light data. Remote Sensing, 2014, 6: 7840-7856
[49] 陈晋, 卓莉, 史培军, 等. 基于DMSP/OLS数据的中国城市化过程研究——反映区域城市化水平的灯光指数的构建. 遥感学报, 2003, 7(3): 168-175
[50] Anees SA, Zhang XL, Shakeel M, et al. Estimation of fractional vegetation cover dynamics based on satellite remote sensing in Pakistan: A comprehensive study on the FVC and its drivers. Journal of King Saud University-Science, 2022, 34: 101848
[51] 巫清芸, 吴志伟, 林世滔, 等. 赣南地区森林地表死可燃物载量与环境因子的关系. 应用生态学报, 2022, 33(6): 1539-1546
[52] 赵文菲, 曹小玉, 谢政锠, 等. 基于结构方程模型的杉木公益林林分空间结构评价. 林业科学, 2022, 58(8): 76-88
[53] 杨文静, 赵建世, 赵勇, 等. 基于结构方程模型的蒸散发归因分析. 清华大学学报:自然科学版, 2022, 62(3): 581-588
[54] 王子龙, 孙昌鸿, 刘莹, 等. 黑龙江省植被指数时空变化及其对气候因子的响应. 南水北调与水利科技, 2022, 20(4): 737-747
[55] 肖骁, 李京忠, 韩彬, 等. 东北老工业区植被覆盖度时空特征及城市化关联分析. 生态科学, 2017, 36(6): 71-77
[56] 梅晓丹, 田静, 田泽宇, 等. 黑龙江省干旱及植被分布的时空变化分析. 测绘与空间地理信息, 2021, 44(10): 1-5
[57] 石淞, 李文, 丁一书, 等. 东北地区植被时空演变及其对气候变化和人类活动的响应. 中国环境科学, 2022: 1-14
[58] Zheng KY, Tan LS, Sun YW, et al. Impacts of climate change and anthropogenic activities on vegetation change: Evidence from typical areas in China. Ecological Indicators, 2021, 126: 107648
[59] 赵梦溪. 基于Google Earth Engine的黑龙江省植被覆盖时空变化分析. 硕士论文. 哈尔滨: 东北林业大学, 2020
[60] 方利, 王文杰, 蒋卫国, 等. 2000—2014年黑龙江流域(中国)植被覆盖时空变化及其对气候变化的响应. 地理科学, 2017, 37(11): 1745-1754