Research on temporal and spatial variation of fractional vegetation cover in Golden Camellia National Nature Reserve using linear fusion method

doi:10.13474/j.cnki.11-2246.2021.328

Abstract

Abstract: When using high spatial resolution images to study FVC changes in a small area, due to the influence of sensors, imaging conditions, and cloud cover, continuous long time series image data is missing or unavailable. At the same time, the low time resolution of the image also greatly limits the study of continuous time series FVC changes in small areas. In response to this problem, this paper uses a linear fusion method to fuse continuous long time series FVC images, which solves the problem of the lack of continuous time series data and low time resolution of Landsat images due to cloud cover and banding when studying FVC spatiotemporal changes. And using Sen+Mann-Kendall trend analysis, it is found that the FVC of the Golden Camellia Natural Reserve showes an overall increase from 2000 to 2016. The area with a significant increase in FVC accounted for about 37.32%, and the area with no significant increase accounted for about 58.56%. The FVC image obtained by the linear fusion method can finely characterize the changes of the surface FVC, and better solve the limitation of the lack of continuous time series data of the high spatial resolution FVC image, which is conducive to the study of long time series spatiotemporal changes of FVC in small areas.

Key words: linear fusion method, fractional vegetation cover, Golden Camellia National Nature Reserve, Sen trend estimation method, Mann-Kendall test

CLC Number:

P237

CHEN Wei, WANG Zhe, ZHAO Haimeng, LI Lihe, ZHANG Xuepeng, LI Guangchao. Research on temporal and spatial variation of fractional vegetation cover in Golden Camellia National Nature Reserve using linear fusion method[J]. Bulletin of Surveying and Mapping, 2021, 0(11): 1-6.

References

[1] XU G C, ZHANG J X, LI P, et al. Vegetation restoration projects and their influence on runoff and sediment in China[J]. Ecological Indicators, 2018, 95:233-241.
[2] GITELSON A A, KAUFMAN Y J, STARK R, et al. Novel algorithms for remote estimation of vegetation fraction[J]. Remote Sensing of Environment, 2002, 80(1):76-87.
[3] 张喜旺, 吴炳方. 基于中高分辨率遥感的植被覆盖度时相变换方法[J]. 生态学报, 2015, 35(4):1155-1164.
[4] 杜金鸿, 刘方正, 周越,等. 自然保护地生态系统服务价值评估研究进展[J]. 环境科学研究, 2019, 32(9):1475-1482.
[5] 邱成, 胡金明, 杨飞龄. 基于NDVI的云南省自然保护区保护成效分析[J]. 生态学报, 2020, 40(20):7312-7322.
[6] RODRIGUES A S L, ANDELMAN S J, BAKARR M I, et al. Effectiveness of the global protected area network in representing species diversity[J]. Nature, 2004, 428(6983):640-643.
[7] 李广超, 赵海盟, 伍毅,等. 2000-2016年广西防城金花茶自然保护区关键生态环境参数时空变化[J]. 测绘通报, 2021(2):13-17.
[8] LEPRIEUR C, KERR Y H, MASTORCHIO S, et al. Monitoring vegetation cover across semi-arid regions:comparison of remote observations from various scales[J]. International Journal of Remote Sensing, 2000, 21(2):281-300.
[9] 李苗苗. 植被覆盖度的遥感估算方法研究[D].北京:中国科学院研究生院(遥感应用研究所), 2003.
[10] 张学玲, 张莹, 牛德奎,等. 基于TM NDVI的武功山山地草甸植被覆盖度时空变化研究[J]. 生态学报, 2018, 38(7):2414-2424.
[11] 刘家琰,谢宗强,申国珍,等.基于SPOT-VEGETATION数据的神农架林区1998-2013年植被覆盖度格局变化[J]. 生态学报, 2018, 38(11):3961-3969.
[12] HUANG B, SONG H H. Spatiotemporal reflectance fusion via sparse representation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(10):3707-3716.
[13] SONG H H, HUANG B. Spatiotemporal satellite image fusion through one-pair image learning[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(4):1883-1896.
[14] HUANG B, ZHANG H K. Spatio-temporal reflectance fusion via unmixing:accounting for both phenological and land-cover changes[J]. International Journal of Remote Sensing, 2014, 35(16):6213-6233.
[15] 原宝东, 宋宜娟. 广西自然保护区资源分布现状[J].国土与自然资源研究, 2009(2):63-64.
[16] JIA K, LIANG S L, LIU S H, et al. Global land surface fractional vegetation cover estimation using general regression neural networks from MODIS surface reflectance[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(9):4787-4796.
[17] 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[J]. 资源科学, 2004,26(4):153-159.
[18] ZHANG H K, HUANG B, ZHANG M, et al. A gener-alization of spatial and temporal fusion methods for remotely sensed surface parameters[J]. International Journal of Remote Sensing, 2015, 36(17):4411-4445.
[19] 黄波, 赵涌泉. 多源卫星遥感影像时空融合研究的现状及展望[J]. 测绘学报, 2017, 46(10):1492-1499.
[20] DE BEURS K M, HENEBRY G M. A statistical framework for the analysis of long image time series[J]. International Journal of Remote Sensing, 2005, 26(8):1551-1573.