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遥感技术与应用  2021, Vol. 36 Issue (6): 1425-1435    DOI: 10.11873/j.issn.1004-0323.2021.6.1425
遥感应用     
遥感数据融合下的雄安新区NPP时空格局分析
张容容1(),曾靖宇1,3,吴晓萍1,周晓桢2,任斌裕2,唐佳1,3,王前锋1,2,4()
1.福州大学环境与安全工程学院,福建 福州 350116
2.数字中国研究院(福建),福建 福州 350116
3.北京师范大学地理科学学部,北京 100101
4.太平洋西北国家实验室联建全球变化研究所,美国 马里兰 20740
Spatial and Temporal Pattern Analysis of NPP in Xiong’an New Area based on Remote Sensing Data Fusion
Rongrong Zhang1(),Jingyu Zeng1,3,Xiaoping Wu1,Xiaozhen Zhou2,Yubin Ren2,Jia Tang1,3,Qianfeng Wang1,2,4()
1.College of Environmental and Safety Engineering,Fuzhou University,Fuzhou 350116,China
2.The Academy of Digital China (Fujian),Fuzhou 350116,China
3.Department of Geography,Beijing Normal University,Beijing 100101,China
4.Joint Global Change Research Institute,Pacific Northwest National Laboratory and University of Maryland,College Park MD 20740,US
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摘要:

高时空分辨率数据对实现植被生产力动态监测和生态环境评估具有重要意义。以雄安新区为研究区,基于改进的ESTARFM融合模型构建高时空分辨率NDVI数据集,结合改进的CASA模型,模拟和分析了2000~2018年区域植被NPP的时空变化特征,并探讨气温与降水对NPP的影响。结果表明:①改进的ESTARFM融合模型预测结果性能较好;②研究区NPP的分布在空间上与土地覆被密切相关;③NPP在2000~2018年的变化趋势并不显著,但有明显的阶段性波动特征,主要是受到城镇化发展与农业技术水平提高等作用的影响;④由于区域气候的变化引起植被水分胁迫,降水对植被NPP的影响较气温更为显著。该研究能为雄安新区及其他区域的可持续发展评估提供一定的科学依据和借鉴意义。

关键词: 雄安新区改进的ESTARFM固碳改进的CASANPP    
Abstract:

High spatial and temporal resolution data is of great significance for dynamic monitoring of vegetation productivity and ecological environment assessment. In this paper, Xiong’an New Area was taken as the research area to build a high spatial-temporal resolution NDVI data set based on our improved ESTARFM fusion model. Combined with the improved CASA model, the spatial-temporal variation characteristics of regional vegetation NPP from 2000 to 2018 were simulated and analyzed, and the impacts of temperature and precipitation on NPP were discussed. The results showed that :(1) the improved ESTARFM fusion model predicted better performance. (2) The distribution of NPP in the study area was spatially closely related to land cover. (3) The change trend of NPP from 2000 to 2018 was not significant, but it had obvious characteristics of periodic fluctuation, which is mainly affected by urbanization development and improvement of agricultural technology level. (4) As the regional climate change causes vegetation water stress, precipitation had a more significant impact on vegetation NPP than air temperature. In short, the improved ESTARFM fusion method performs well in Xiong’an new area. The improved CASA model, which takes into account the change of vegetation cover in different periods, can simulate the NPP in the study area relatively accurately. This research can provide some scientific basis and reference significance for the sustainable development assessment of Xiong’an New Area and other similar areas.

Key words: Xiong’an New Area    Improved ESTARFM    Carbon sequestration    Improved CASA    NPP
收稿日期: 2020-09-21 出版日期: 2022-01-26
ZTFLH:  Q948  
基金资助: 国家自然科学基金项目(41601562)
通讯作者: 王前锋     E-mail: zrr9695@163.com;wangqianfeng@fzu.edu.cn
作者简介: 张容容(1996-),女,福建宁德人,硕士研究生,主要从事资源生态遥感研究。E?mail: zrr9695@163.com
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引用本文:

张容容,曾靖宇,吴晓萍,周晓桢,任斌裕,唐佳,王前锋. 遥感数据融合下的雄安新区NPP时空格局分析[J]. 遥感技术与应用, 2021, 36(6): 1425-1435.

Rongrong Zhang,Jingyu Zeng,Xiaoping Wu,Xiaozhen Zhou,Yubin Ren,Jia Tang,Qianfeng Wang. Spatial and Temporal Pattern Analysis of NPP in Xiong’an New Area based on Remote Sensing Data Fusion. Remote Sensing Technology and Application, 2021, 36(6): 1425-1435.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2021.6.1425        http://www.rsta.ac.cn/CN/Y2021/V36/I6/1425

图1  研究区气象站点和土地利用分类图 审图号:GS(2019)3333(a)22个气象站点空间分布图 (b)雄安新区土地利用分类图
图2  IDW插值结果与CRU的温度、降雨数据之间的相关关系
类型最大光能利用率(gC/MJ)SRmax
农用地0.5424.46
林地0.4855.17
草地0.5424.46
灌木丛0.4294.49
湿地0.5424.46
建设用地0.5424.46
裸地0.5424.46
表1  各植被类型最大光能利用率与SR最大值
图3  NDVI空间和散点图
图4  2000~2018年雄安新区年均NPP空间格局
年份雄安新区安新县容城雄县
2000~20050.53%0.55%0.65%0.43%
2005~2010-0.51%0.06%-0.68%-1.22%
2010~20151.03%0.75%0.90%1.54%
2015~2018-3.65%-4.22%-3.71%-2.77%

平均值

(g·C/m2/a)

705.62708.2707.15701.03
表2  2000~2018年各区县年NPP及其增长率
土地利用类型2000~20052005~20102010~20152015~2018

平均值

(g·C/m2/a)

农田3.15%-0.77%3.30%-10.86%740.27
林地7.35%-0.02%2.97%-23.92%719.43
草地2.71%-6.38%8.00%-11.35%700.91
灌木丛1.16%4.72%-0.27%-32.15%724.16
湿地6.88%3.52%-5.12%-9.93%722.85
水体8.62%1.83%-4.90%-4.82%697.50
不透水面-1.70%-11.89%18.45%-11.52%678.40
裸地-3.55%-20.16%17.24%-12.17%625.79
表3  2000~2018年各土地利用类型年NPP及其增长率
图5  雄安新区NPP空间趋势(P<0.05) ((a)NPP的空间趋势分布(g·C/m2/a), (b)NPP空间趋势的显著性)
图6  温度及降雨与NPP之间相关关系
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