An analysis of the characteristics, causes, and trends of spatio-temporal changes in vegetation in the Nuomuhong alluvial fan based on Google Earth Engine
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摘要: Google Earth Engine(GEE)平台使植被遥感监测突破了数据获取难、本地存储量大和处理效率低的限制。基于GEE平台,利用空间分辨率为30 m的Landsat卫星数据和空间分辨率为250 m的MODIS卫星数据,结合温度和降水气象数据研究2000—2017年间青海省诺木洪洪积扇地表植被的时空变化趋势及持续性,并分析不同时代洪积扇上枸杞种植园和盐碱化区的植被关系及未来变化。结果表明: ①2000—2017年间最大化合成归一化差异植被指数年均值从0.029上升到0.054,增幅为0.025,最大增强植被指数(enhanced vegetation index,EVI)年均值从0.633上升到0.771,增幅为0.138,多年EVI最大化的均值结果显示峰值区间在每年的5—10月; ②对最大EVI均值与温度、降水量数据进行相关分析和偏相关分析,最大EVI均值与温度相关系数为0.839,表现为强相关性,与降水量相关系数为0.457,表现为弱相关性,且最大EVI均值与温度、降水均存在显著的正相关关系; ③在18 a内枸杞种植园植被改善较快,而盐碱化区植被有所衰减; ④未来枸杞种植区与盐碱化区植被变化均具有强持续性,枸杞种植区植被增长对盐碱化区植被有一定的制约效应,且在未来一段时间会持续存在。
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关键词:
- 诺木洪洪积扇 /
- 植被变化 /
- 植被指数 /
- Hurst指数 /
- Google Earth Engine
Abstract: Google Earth Engine enables some limitations in the remote sensing monitoring of vegetation to be overcome, including difficult data acquisition, large local storage capacity, and low processing efficiency. Using GEE, the data of satellites Landsat and MODIS (spatial resolution: 30 m and 250 m, respectively), and temperature and precipitation data, this study investigated the spatio-temporal change trends and sustainability of the vegetation in the Nuomuhong alluvial fan in Qinghai Province during 2000—2017. Moreover, this study analyzed the relationships of vegetation between wolfberry plantations and salinized areas on the alluvial fans of different eras and the future changes of the relationships. The results are as follows: ① The average annual maximum synthetic normalized difference vegetation index (NDVI) increased from 0.029 to 0.054 during 2000—2017, with an increased amplitude of 0.025. The average annual enhanced vegetation index (EVI) increased from 0.633 to 0.771 during these years, with an increased amplitude of 0.138. The multiyear average maximum EVI showed that EVI peaks occurred from May to October each year. ② A correlation analysis and a partial correlation analysis were conducted between the average maximum EVI and the temperature and precipitation data. According to the analytical results, the correlation coefficient between the average maximum EVI and temperature was 0.839, indicating a strong positive correlation. Meanwhile, the correlation coefficient between the average maximum EVI and precipitation amount was 0.457, indicating a weak positive correlation. ③ Over 18 years, the vegetation in wolfberry plantations was rapidly improved, while the vegetation in the salinized area degenerated. ④ The future changes in the vegetation in wolfberry planting areas and salinized areas will have strong sustainability. The vegetation growth in wolfberry planting areas will continuously restrict the vegetation in the salinized area to a certain extent in a period in the future.-
Key words:
- Nuomuhong alluvial fan /
- vegetation change /
- vegetation index /
- Hurst index /
- Google Earth Engine
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[1] 张学珍, 郑景云, 何凡能, 等. 1982—2006年中国东部秋季植被覆盖变化过程的区域差异[J]. 自然资源学报, 2013, 28(1):28-37.[1] Zhang X Z, Zheng J Y, He F N, et al. Spatial differences of variations in autumn fractional vegetation coverage in Eastern China during 1982—2006[J]. Journal of Natural Resources, 2013, 28(1):28-37.[2] Wang X, Hua T, Lang L, et al. Spatial differences of aeolian desertification responses to climate in arid Asia[J]. Global and Planetary Change, 2017, 148:22-28. [3] Yang L M, Bruce K W, Larry L T, et al. An analysis of relationships among climate forcing and time-integrated NDVI of grasslands over the U.S. Northern and Central Great Plains[J]. Remote Sensing of Environment, 1998, 65(1):25-37. [4] Yang W, Yang Z, Merchant J W. An assessment of AVHRR NDVI eco-climatological relations in Nebraska,USA[J]. International Journal of Remote Sensing, 1997, 18(10):2161-2180. [5] 陈发虎, 傅伯杰, 夏军, 等. 近70年来中国自然地理与生存环境基础研究的重要进展与展望[J]. 中国科学:地球科学, 2019, 49(11):1659-1696.[5] Chen F H, Fu B J, Xia J, et al. Major advances in studies of the physical geography and living environment of China during the past 70 years and future prospects[J]. Science China:Earth Sciences, 2019, 49(11):1659-1696.[6] 徐浩杰, 杨太保. 柴达木盆地植被生长时空变化特征及其对气候要素的响应[J]. 自然资源学报, 2014, 29(3):398-407.[6] Xu H J, Yang T B. Spatial-temporal variations of vegetation activities and its responses to climatic factors in the Qaidam Basin[J]. Journal of Natural Resources, 2014, 29(3):398-407.[7] 王林林, 刘普幸, 王允. 近14年来柴达木盆地NDVI时空变化及其影响因素[J]. 生态学杂志, 2015, 34(6):1713-1722.[7] Wang L L, Liu P X, Wang Y. Spatial and temporal variations of NDVI and its influencing factors in Qaidam Basin in the last 14 years[J]. Chinese Journal of Ecology, 2015, 34(6):1713-1722.[8] 张斯琦, 陈辉, 宋家明, 等. 2000—2015年柴达木盆地植被覆盖度时空变化及其与环境因子的关系[J]. 干旱区地理, 2019, 42(5):1124-1131.[8] Zhang S Q, Chen H, Song J M, et al. Spatial and temporal variation of fractional vegetation cover and its relationship with environmental factors in the Qaidam Basin during 2000—2015[J]. Arid Land Geography, 2019, 42(5):1124-1131.[9] 李艳丽, 杨太保, 曾彪. 基于MODIS数据的柴达木盆地南缘绿洲土地覆盖动态变化研究[J]. 中国沙漠, 2011, 31(1):34-41.[9] Li Y L, Yang T B, Zeng B. Land cover change of southern fringe oasis in the Qaidam Basin from 2000 to 2009 based on MODIS data[J]. Journal of Desert Research, 2011, 31(1):34-41.[10] Gorelick N, Hancher M, Dixon M, et al. Google Earth Engine:Planetary-scale geosptial analysis for everyone[J]. Remote Sensing of Environment, 2017, 202:18-27. [11] Goldblatt R, You W, Hanson G, et al. Detection the boundaries of urban areas in India:A dataset for pixel-based image classification in Google Earth Engine[J]. Remote Sensing, 2016, 8(8):634. [12] Dong J, Xiao X, Menarguez M A, et al. Mapping paddy rice planting area in northeastern Asia with Landsat8 image,phenology-based algorithm and Google Earth Engine[J]. Remote Sensing of Environment, 2016, 185:142-154. [13] Fu Y, Chen H, Niu H, et al. Spatial and temporal variation of vegetation phenology and its response to climate changes in Qaidam Basin from 2000 to 2015[J]. Journal of Geographical Sciences, 2018, 28(4):400-414. [14] 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]. 自然资源遥感, 2014, 26(1):167-172.doi: 10.6046/gtzyyg.2014.01.28. [14] Sun L G, Liu J F, Xu Q H. Remote sensing based temporal and spatial analysis of vegetation over changes in Bashang area of Hebei Province[J]. Remote Sensing for Land and Resources, 2014, 26(1):167-172.doi: 10.6046/gtzyyg.2014.01.28. [15] 徐嘉昕, 方世波, 张廷斌, 等. 2000—2016年三江源区植被生长季NDVI变化及其对气候因子的响应[J]. 自然资源遥感, 2020, 32(1):237-246.doi: 10.6046/gtzyyg.2020.01.32. [15] Xu J X, Fang S B, Zhang T B, et al. NDVI changes and its correlation with climate factors of the Three River-Headwater region in growing seasons during 2000—2016[J]. Remote Sensing for Land and Resources, 2020, 32(1):237-246.doi: 10.6046/gtzyyg.2020.01.32. [16] 蔡博峰, 于嵘. 基于遥感的植被长时序趋势特征研究进展及评价[J]. 遥感学报, 2009, 13(6):1170-1186.[16] Cai B F, Yu R. Advance and evaluation in the long time series vegetation trends research based on remote sensing[J]. Journal of Remote Sensing, 2009, 13(6):1170-1186.[17] Hurst H E. Long term storage capacity of reservoirs[J]. American Society of Civil Engineers Transactions, 1951, 116(776):770-808. [18] Granero M A S, Segovia J E T, Perez J G. Some comments on Hurst exponent and the long memory processes on capital markets[J]. Physica A:Statistical Mechanics and Its Applications, 2008, 387(22):5543-5551. [19] 刘明霞, 刘友存, 陈明, 等. 2000—2018赣江上游植被覆盖度时空演化及其对气候变化的响应[J]. 水土保持通报, 2020, 40(5):284-290.[19] Liu M X, Liu Y C, Chen M, et al. Spatiotemporal evolution of vegetation coverage and its response to climate change in upper reaches of Ganjiang River basin during 2000—2018[J]. Bulletin of Soil and Water Conservation, 2020, 40(5):284-290.[20] 徐浩杰, 杨太保. 1981—2010年柴达木盆地气候要素变化特征及湖泊和植被响应[J]. 地理科学进展, 2013, 32(6):868-875. [20] Xu H J, Yang T B. Climate factors change and its impact on lake area and vegetation growth in the Qaidam Basin during 1981—2010[J]. Progress in Geography, 2013, 32(6):868-875.[21] 梁守真, 禹定峰, 王猛, 等. 应用遥感时序数据研究植被变化与气候因子的关系——以环渤海地区为例[J]. 自然资源遥感, 2015, 27(3):114-121.doi: 10.6046/gtzyyg.2015.03.19. [21] Liang S Z, Yu D F, Wang M, et al. Application of remote sensing time-series data to investigate the relationship between vegetation change and climatic factors:A case study of Circum-Bohai Sea area[J]. Remote Sensing for Land and Resources, 2015, 27(3):114-121.doi: 10.6046/gtzyyg.2015.03.19. [22] 张华, 安徽敏. 基于GEE的1987—2019年民勤绿洲NDVI变化特征及趋势分析[J]. 中国沙漠, 2021, 41(1):28-36.[22] Zhang H, An H M. Analysis of NDVI variation characteristics and trend of Minqin Oasis from 1987 to 2019 based on GEE[J]. Journal of Desert Research, 2021, 41(1):28-36.[23] 常纯, 王心源, 杨瑞霞, 等. 基于DEM-NDVI的高山植被带定量刻划[J]. 地理研究, 2015, 34(11):2113-2123.
[23] Chang C, Wang X Y, Yang R X, et al. A quantitative characterization method for alpine vegetation zone based on DEM and NDVI[J]. Geographical Research, 2015, 34(11):2113-2123.
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