Vegetation change based on break-point detection in Fu County, Yan’an City
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摘要:
目的 多项生态工程实施以来,陕北黄土高原地区环境明显改善,探究该区域植被的时空变化,为生态环境建设与差异化决策提供依据。 方法 以陕西省延安市富县为研究对象,利用2000—2019年生长季的Landsat TM/ETM+/OLI影像,运用DBEST算法对归一化植被指数(NDVI)时间序列进行断点检测,并提出多阶段趋势分析方法分析富县植被变化时空特征。 结果 富县植被的断点检测结果表明:66.47%的区域断点NDVI变化幅度小于0.2,主要分布在富县西部和东北地区,植被相对稳定,未发生剧烈变化;33.53%的区域断点NDVI变化幅度大于0.2,其中断点数量多于4个的区域仅占5.88%,集中分布于道路、河流沿线,变化频繁,与人为活动有关。当前阶段趋势分析表明:断点NDVI变化幅度大于0.2的区域,改善的面积占总面积的24.57%,退化面积仅占2.12%;开始时间均在2014年之前,时间分布跨度大,空间异质性强,揭示了富县植被变化的多样性及复杂性;富县有林地、疏林地、灌木林地、未成林造林地4种主要林地均趋于改善,改善面积占林地面积的96.23%。 结论 2000—2019年,富县植被整体呈现改善趋势,生态建设工程取得良好的效果;植被变化的时空特征存在差异,后续决策需因地制宜。图6表1参25 -
关键词:
- 归一化植被指数(NDVI) /
- 时间序列 /
- DBEST /
- 变化检测
Abstract:Objective Given the significant environmental improvement after the implementation of ecological projects in the Loess Plateau of northern Shaanxi, this study is aimed to investigate the effect of ecological projects on vegetation improvement so as to provide reference for ecological construction and development countermeasures. Method With the Landsat TM/ETM+/OLI images in the growing season from 2000 to 2019 collected, the DBEST algorithm was used to detect breakpoints in the normalized difference vegetation index (NDVI) time series before a multi-stage trend analysis method was proposed to analyze the temporal and spatial dynamic of the vegetation change in Fu County, Yan’an City, Shaanxi Province. Result Results of break-point detection showed that 66.47% of the breakpoints showed a change magnitude of NDVI lower than 0.2, mainly distributed in the western and northeastern areas and the vegetation was relatively stable without drastic changes; 33.53% of the breakpoints showed a change magnitude of NDVI higher than 0.2 with areas having more than 4 breakpoints accounting for only 5.88% and they were concentrated on roads and rivers with frequent changes in vegetation due to human activities. Results of trend analysis showed that for areas with a break-point change magnitude of NDVI higher than 0.2, the improved area accounted for 24.57% whereas the degraded area only accounted for 2.12%; Featured with a large time distribution span and a strong spatial heterogeneity, the current trends started before 2014, revealing the diversity and complexity of vegetation changes in Fu County; A trend of improvement was detected in all the four main types of forest land in Fu County, namely wood land, sparse wood land, shrub land and young afforested land with the improved area accounting for 96.23% of the total wood land. Conclusion Recent years have witnessed an overall improvement trend of the vegetation in the study area, showing the positive impact exerted by ecological construction projects and further ecological countermeasures should be formulated taking into consideration in accordance the differences in spatial characteristics of vegetation changes in Fu County from 2000 to 2019 as well as local circumstances. [Ch, 6 fig. 1 tab. 25 ref.] -
Key words:
- normalized difference vegetation index (NDVI) /
- time series /
- DBEST /
- change detection
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表 1 富县林地类型趋势情况统计
Table 1. Statistics on trend of forest land types in Fu County
林地类型 改善/% 基本不变/% 退化/% 有林地 96.98 0.86 2.16 疏林地 94.32 1.91 3.77 灌木林地 94.41 1.81 3.78 未成林造林地 92.97 1.91 5.12 其他用地 86.66 2.50 10.84 -
[1] HEIMANN M, REICHSTEIN M. Terrestrial ecosystem carbon dynamics and climate feedbacks [J]. Nature, 2008, 451(7176): 289 − 292. [2] 徐涵秋. 城市遥感生态指数的创建及其应用[J]. 生态学报, 2013, 33(24): 7853 − 7862. XU Hanqiu. A remote sensing urban ecological index and its application [J]. Acta Ecol Sin, 2013, 33(24): 7853 − 7862. [3] 许文鑫, 周玉科, 梁娟珠. 基于变化点的青藏高原植被时空动态变化研究[J]. 遥感技术与应用, 2019, 34(3): 667 − 676. XU Wenxin, ZHOU Yuke, LIANG Juanzhu. A breakpoints based spatio-temporal analysis of Tibetan Plateau vegetation [J]. Remote Sens Technol Appl, 2019, 34(3): 667 − 676. [4] CAO Qian, WU Jianguo, YU Deyang, et al. The biophysical effects of the vegetation restoration program on regional climate metrics in the Loess Plateau, China [J]. Agric For Meteorol, 2019, 268: 169 − 180. [5] HANSEN M C, POTAPOV P V, MOORE R, et al. High-resolution global maps of 21st-century forest cover change [J]. Science, 2013, 342(6160): 850 − 853. [6] 孙雷刚, 刘剑锋, 徐全洪. 河北坝上地区植被覆盖变化遥感时空分析[J]. 国土资源遥感, 2014, 26(1): 167 − 172. SUN Leigang, LIU Jianfeng, XU Quanhong. Remote Sensing based temporal and spatial analysis of vegetation cover changes in Bashang area of Hebei Province [J]. Remote Sens Land Resour, 2014, 26(1): 167 − 172. [7] 刘咏梅, 马黎, 黄昌, 等. 基于MODIS-Landsat时空融合的陕北黄土高原植被覆盖变化研究[J]. 西北大学学报(自然科学版), 2019, 49(1): 62 − 70. LIU Yongmei, MA Li, HUANG Chang, et al. Study on the change of vegetation coverage of Loess Plateau in northern Shaanxi Province based on MODIS-Landsat fusion data [J]. J Northwest Univ Nat Sci Ed, 2019, 49(1): 62 − 70. [8] QAMER F M, SHEHZAD K, ABBAS S, et al. Mapping deforestation and forest degradation patterns in western Himalaya, Pakistan [J]. Remote Sens, 2016, 8(385): 1 − 17. [9] 沈文娟, 李明诗, 黄成全. 长时间序列多源遥感数据的森林干扰监测算法研究进展[J]. 遥感学报, 2018, 22(6): 1005 − 1022. SHEN Wenjuan, LI Mingshi, HUANG Chengquan. Review of remote sensing algorithms for monitoring forest disturbance from time series and multi-source data fusion [J]. J Remote Sens, 2018, 22(6): 1005 − 1022. [10] 刘宝柱, 方秀琴, 何祺胜, 等. 基于MODIS数据和BFAST方法的植被变化监测[J]. 国土资源遥感, 2016, 28(3): 146 − 153. LIU Baozhu, FANG Xiuqin, HE Qisheng, et al. Monitoring the changes of vegetation based on MODIS data and BFAST methods [J]. Remote Sens Land Resour, 2016, 28(3): 146 − 153. [11] FORKEL M, CARVALHAIS N, VERBESSELT J, et al. Trend change detection in NDVI time series: effects of inter-annual variability and methodology [J]. Remote Sens, 2013, 5(5): 2113 − 2144. [12] STOW D, PETERSEN A, HOPE A, et al. Greenness trends of Arctic tundra vegetation in the 1990s: comparison of two NDVI data sets from NOAA AVHRR systems [J]. Int J Remoting Sens, 2007, 28(21): 4807 − 4822. [13] 李斌. 青藏高原植被时空分布规律及其影响因素研究[D]. 北京: 中国地质大学, 2016. LI Bin. Study of Vegetation’s Spatial and Temporal Distribution and Influencing Factors on the Tibetan Plateau[D]. Beijing: China University of Geosciences, 2016. [14] 海月, 杨广斌, 李若男, 等. 基于时序突变检测的植被空间变化特征识别方法研究——以海河北部山区为例[J]. 生态学报, 2020, 40(24): 1 − 10. HAI Yue, YANG Guangbin, LI Ruonan, et al. Recognition of vegetation spatial variation based on time-series mutation detection: a case study of the mountainous area of Northern Haihe River Basin [J]. Acta Ecol Sin, 2020, 40(24): 1 − 10. [15] KENNEDY R E, YANG Zhiqiang, COHEN W B. Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr-Temporal segmentation algorithms [J]. Remote Sens Environ, 2010, 114(12): 2897 − 2910. [16] VERBESSELT J, HYNDMAN R, NEWNHAM G, et al. Detecting trend and seasonal changes in satellite image time series [J]. Remote Sens Environ, 2010, 114(1): 106 − 115. [17] JAMALI S, JONSSON P, EKLUNDH L, et al. Detecting changes in vegetation trends using time series segmentation [J]. Remote Sens Environ, 2015, 156: 182 − 195. [18] 刘吉彤, 安如. 基于DBEST的2003—2012年济南市植被变化研究[J]. 甘肃科学学报, 2018, 30(4): 17 − 23. LIU Jitong, AN Ru. Study on vegetation change in Ji’nan during 2003−2012 based on DBEST [J]. J Gansu Sci, 2018, 30(4): 17 − 23. [19] 王恩鲁, 汪小钦, 陈芸芝. 时间序列植被覆盖度断点检测方法研究[J]. 地球信息科学学报, 2017, 19(10): 1355 − 1363. WANG Enlu, WANG Xiaoqin, CHEN Yunzhi. The breakpoints detection method using time series of vegetation fractional coverage [J]. J Geo-Inf Sci, 2017, 19(10): 1355 − 1363. [20] SHEN Xiaoji, AN Ru, FENG Li, et al. Vegetation changes in the Three-River Headwaters Region of the Tibetan Plateau of China [J]. Ecol Indic, 2018, 93: 804 − 812. [21] 王颖洁, 刘良云, 王志慧. 基于时序Landsat数据的三江平原植被地表类型变化遥感探测研究[J]. 遥感技术与应用, 2015, 30(5): 959 − 968. WANG Yingjie, LIU Liangyun, WANG Zhihui. Land covermapping based on Landsat time-series stacks in Sanjiang Plain [J]. Remote Sens Technol Appl, 2015, 30(5): 959 − 968. [22] 贾铎, 王藏姣, 牟守国, 等. 基于NDVI时间序列轨迹的草原露天矿区植被时空动态特征[J]. 应用生态学报, 2017, 28(6): 1808 − 1816. JIA Duo, WANG Cangjiao, MU Shouguo, et al. Vegetation spatial and temporal dynamic characteristics based on NDVI time series trajectories in grassland opencast coal mining [J]. Chin J Appl Ecol, 2017, 28(6): 1808 − 1816. [23] 陈昀琳. 基于Landsat和MODIS NDVI时序数据的青海湖流域植被覆盖度提取及其变化分析[D]. 北京: 中国地质大学, 2019. CHEN Yunlin. Fractional Vegetation Cover in Qinghai Lake Watershed Extraction and Change Analysis based on Time-series NDVI Data of Landsat and MODIS[D]. Beijing: China University of Geosciences, 2019. [24] 李杰, 张军, 刘陈立, 等. 基于MODIS-NDVI的中老缅交界区近16年植被覆盖时空变化特征[J]. 林业科学, 2019, 55(8): 9 − 18. LI Jie, ZHANG Jun, LIU Chenli, et al. Spatiotemporal variation of vegetation coverage in recent 16 years in the border region of China, Laos, and Myanmar based on MODIS-NDVI [J]. Sci Silv Sin, 2019, 55(8): 9 − 18. [25] 宋怡, 马明国. 基于SPOT VEGETATION数据的中国西北植被覆盖变化分析[J]. 中国沙漠, 2007, 27(1): 89 − 94. SONG Yi, MA Mingguo. Study on vegetation cover change in Northwest China based on SPOT VEGETATION data [J]. J Desert Res, 2007, 27(1): 89 − 94. -
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https://zlxb.zafu.edu.cn/article/doi/10.11833/j.issn.2095-0756.20210457

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