High-resolution urban vegetation coverage estimation based on multi-source remote sensing data fusion
- Vol. 25, Issue 6, Pages: 1216-1226(2021)
Published: 07 June 2021
DOI: 10.11834/jrs.20219178
扫 描 看 全 文
浏览全部资源
扫码关注微信
Published: 07 June 2021 ,
扫 描 看 全 文
皮新宇,曾永年,贺城墙.2021.融合多源遥感数据的高分辨率城市植被覆盖度估算.遥感学报,25(6): 1216-1226
Pi X Y,Zeng Y N and He C Q. 2021. Estimating urban vegetation coverage on the basis of multi-source remote sensing data and temporal mixture analysis. National Remote Sensing Bulletin, 25(6):1216-1226
准确获取城市植被覆盖定量信息对城市生态环境评价,城市规划及可持续城市发展具有重要意义。遥感技术的发展为获取区域及全球植被覆盖信息提供了有效手段,目前基于单传感器、单时相遥感数据的城市植被覆盖度估算方法得到较为广泛的应用。然而,由于城市地表覆盖的复杂性、植被类型的多样性,在一定程度上影响了城市植被覆盖信息提取的精度。为此,本文提出一种基于多源遥感数据与时间混合分析的城市植被覆盖度估算方法。首先,通过时空融合、植被物候特征分析获得最佳时序的GF-1 NDVI数据;其次,基于时间序列的GF-1 NDVI及Landsat 8 SWIR1、SWIR2数据,采用时间混合分析方法以长沙市为例估算城市植被覆盖度。实验研究表明,基于多源遥感数据与时间混合分析方法获得了较高精度的城市植被覆盖度估算(RMSE为0.2485,SE为0.1377,MAE为0.1889),相对于单时相光谱混合分析、传统的像元二分法,本文提出的方法更为稳定,在低、中、高不同植被覆盖区均能获得较高的估算精度,为城市植被覆盖度定量估算提供了有效方法。
The accurate extraction of quantitative information on urban vegetation coverage is of great significance for urban ecological environment assessment
urban planning
and sustainable urban development. With the development of remote sensing technology
effective means for obtaining regional and global vegetation coverage information have emerged. At present
urban vegetation coverage estimation methods based on single-sensor and single-phase remote sensing data are widely used. However
due to the complexity of urban land cover and the diversity of vegetation types
the accuracy of urban vegetation cover information extraction is compromised. In this study
we propose an urban vegetation coverage estimation method based on multi-source remote sensing data and Temporal Mixture Analysis (TMA). First
the best time series GF-1 NDVI data are obtained by using STARFM and vegetation phenomenological analysis. Second
on the basis of time series GF-1 NDVI and Landsat8 SWIR1 and SWIR2 data
TMA is used to estimate the urban vegetation coverage in Changsha City. Results show that the method based on multi-source remote sensing data and TMA can obtain highly accurate urban vegetation coverage estimates (RMSE=0.2485
SE=0.1377
MAE=0.1889). Compared with traditional methods like single-time phase spectral hybrid analysis and dimidiate pixel model
our method is more stable
and can obtain higher estimation accuracy in low
medium
and high vegetation coverage areas. This study provides an effective method for quantitative estimation of urban vegetation coverage.
多源遥感数据GF-1时空融合时间混合分析植被覆盖度城市
multi-source satellite remote sensing dataGF-1spatiotemporal fusiontemporal mixture analysisvegetation coverageurban area
Andersen H E, McGaughey R J and Reutebuch S E. 2005. Estimating forest canopy fuel parameters using LIDAR data. Remote Sensing of Environment, 94(4): 441-449 [DOI: 10.1016/j.rse.2004.10.013http://dx.doi.org/10.1016/j.rse.2004.10.013]
Chen Y H, Li X B and Shi P J. 2002. Landscape spatial-temporal pattern analysis on change in the fraction of green vegetation based on remotely sensed data: a case study in Haidian District, Beijin. Acta Ecologica Sinica, 22(10): 1581-1586
陈云浩, 李晓兵, 史培军. 2002. 基于遥感的植被覆盖变化景观分析——以北京海淀区为例. 生态学报, 22(10): 1581-1586 [DOI: 10.3321/j.issn:1000-0933.2002.10.002http://dx.doi.org/10.3321/j.issn:1000-0933.2002.10.002]
Cui T X, Gong Z N, Zhao W J, Zhao Y L and Lin C. 2013. Research on estimating wetland vegetation abundance based on spectral mixture analysis with different endmember model: a case study in Wild Duck Lake wetland, Beijing. Acta Ecologica Sinica, 33(4): 1160-1171
崔天翔, 宫兆宁, 赵文吉, 赵雅莉, 林川. 2013. 不同端元模型下湿地植被覆盖度的提取方法——以北京市野鸭湖湿地自然保护区为例. 生态学报, 33(4): 1160-1171 [DOI: 10.5846/stxb201204270604http://dx.doi.org/10.5846/stxb201204270604]
Degerickx J, Roberts D A and Somers B. 2019. Enhancing the performance of Multiple Endmember Spectral Mixture Analysis (MESMA) for urban land cover mapping using airborne lidar data and band selection. Remote Sensing of Environment, 221: 260-273 [DOI: 10.1016/j.rse.2018.11.026http://dx.doi.org/10.1016/j.rse.2018.11.026]
Gao F, Masek J, Schwaller M and Hall F. 2006. On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance. IEEE Transactions on Geoscience and Remote Sensing, 44(8): 2207-2218 [DOI: 10.1109/tgrs.2006.872081http://dx.doi.org/10.1109/tgrs.2006.872081]
Gao Y G and Xu H Q. 2017. Estimation of multi-scale urban vegetation coverage based on multi-source remote sensing images. Journal of Infrared and Millimeter Waves, 36(2): 225-234
高永刚, 徐涵秋. 2017. 基于多源遥感影像的多尺度城市植被覆盖度估算. 红外与毫米波学报, 36(2): 225-234 [DOI: 10.11972/j.issn.1001-9014.2017.02.017http://dx.doi.org/10.11972/j.issn.1001-9014.2017.02.017]
Georgescu M, Morefield P E, Bierwagen B G and Weaver C P. 2014. Urban adaptation can roll back warming of emerging megapolitan regions. Proceedings of the National Academy of Sciences of the United States of America, 111(8): 2909-2914 [DOI: 10.1073/pnas.1322280111http://dx.doi.org/10.1073/pnas.1322280111]
Grahn P and Stigsdotter U A. 2003. Landscape planning and stress. Urban Forestry and Urban Greening, 2(1): 1-18 [DOI: 10.1078/1618-8667-00019http://dx.doi.org/10.1078/1618-8667-00019]
Green A A, Berman M, Switzer P and Craig M D. 1988. A transformation for ordering multispectral data in terms of image quality with implications for noise removal. IEEE Transactions on Geoscience and Remote Sensing, 26(1): 65-74 [DOI: 10.1109/36.3001http://dx.doi.org/10.1109/36.3001]
Grimm N B, Faeth S H, Golubiewski N E, Redman C L, Wu J G, Bai X M and Briggs J M. 2008. Global change and the ecology of cities. Science, 319(5864): 756-760 [DOI: 10.1126/science.1150195http://dx.doi.org/10.1126/science.1150195]
Hu J B, Chen W, Li X Y and He X Y. 2009. Urban vegetation coverage change inside the third-ring road of Shenyang city, China: a study with linear spectral unmixing technique. Chinese Journal of Applied Ecology, 20(5): 1140-1146
胡健波, 陈玮, 李小玉, 何兴元. 2009. 基于线性混合像元分解的沈阳市三环内城市植被盖度变化. 应用生态学报, 20(5): 1140-1146 [DOI: 10.13287/j.1001-9332.2009.0187http://dx.doi.org/10.13287/j.1001-9332.2009.0187]
Hu S J, Hu D Y and Zhao W J. 2010. Extract urban vegetation coverage based on LSMM and improved FCM: a case study in Haidian district. Acta Ecologica Sinica, 30(4): 1018-1024
胡姝婧, 胡德勇, 赵文吉. 2010. 基于LSMM和改进的FCM提取城市植被覆盖度——以北京市海淀区为例. 生态学报, 30(4): 1018-1024
Huang B and Zhao Y Q. 2017. Research status and prospect of spatiotemporal fusion of multi-source satellite remote sensing imagery. Acta Geodaetica et Cartographica Sinica, 46(10): 1492-1499
黄波, 赵涌泉. 2017. 多源卫星遥感影像时空融合研究的现状及展望. 测绘学报, 46(10): 1492-1499 [DOI: 10.11947/j.AGCS.2017.20170376http://dx.doi.org/10.11947/j.AGCS.2017.20170376]
Huang Y Y, Feng L, Zhu L J, Liu H and Li C X. 2015. Vegetation information extraction based on HJ-1 satellite time-series images by pixel unmixing. Journal of Nanjing University (Natural Sciences), 51(5): 1058-1067
黄银友, 冯莉, 朱榴骏, 刘寒, 李成蹊. 2015. 基于混合像元分解的HJ-1卫星时间序列影像城市植被信息提取. 南京大学学报(自然科学), 51(5): 1058-1067 [DOI: 10.13232/j.cnki.jnju.2015.05.017http://dx.doi.org/10.13232/j.cnki.jnju.2015.05.017]
Jia K, Liang S L, Gu X F, Baret F, Wei X Q, Wang X X, Yao Y J, Yang L Q and Li Y W. 2016. Fractional vegetation cover estimation algorithm for Chinese GF-1 wide field view data. Remote Sensing of Environment, 177: 184-191 [DOI: 10.1016/j.rse.2016.02.019http://dx.doi.org/10.1016/j.rse.2016.02.019]
Johnson B, Tateishi R and Kobayashi T. 2012. Remote sensing of fractional green vegetation cover using spatially-interpolated endmembers. Remote Sensing, 4(9): 2619-2634 [DOI: 10.3390/rs4092619http://dx.doi.org/10.3390/rs4092619]
Knight J and Voth M. 2011. Mapping impervious cover using multi-temporal MODIS NDVI data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 4(2): 303-309 [DOI: 10.1109/jstars.2010.2051535http://dx.doi.org/10.1109/jstars.2010.2051535]
Kuang W H. 2019. Mapping global impervious surface area and green space within urban environments. Scientia Sinica Terrae, 49(7): 1151-1168
匡文慧. 2019. 全球城市人居环境不透水面与绿地空间特征制图. 中国科学: 地球科学, 49(7): 1151-1168 [DOI: 10.1360/N072018-00164http://dx.doi.org/10.1360/N072018-00164]
Li W L and Wu C S. 2014. Phenology-based temporal mixture analysis for estimating large-scale impervious surface distributions. International Journal of Remote Sensing, 35(2): 779-795 [DOI: 10.1080/01431161.2013.873147http://dx.doi.org/10.1080/01431161.2013.873147]
Liu W J, Zeng Y N and Zhang M. 2018. Mapping rice paddy distribution by using time series HJ blend data and phenological parameters. Journal of Remote Sensing, 22(3): 381-391
柳文杰, 曾永年, 张猛. 2018. 融合时间序列环境卫星数据与物候特征的水稻种植区提取. 遥感学报, 22(3): 381-391 [DOI: 10.11834/jrs.20187298http://dx.doi.org/10.11834/jrs.20187298]
Lu M, Chen J, Tang H J, Rao Y H, Yang P and Wu W B. 2016. Land cover change detection by integrating object-based data blending model of Landsat and MODIS. Remote Sensing of Environment, 184: 374-386 [DOI: 10.1016/j.rse.2016.07.028http://dx.doi.org/10.1016/j.rse.2016.07.028]
Lu Y H, Coops N C and Hermosilla T. 2017. Estimating urban vegetation fraction across 25 cities in pan-Pacific using Landsat time series data. ISPRS Journal of Photogrammetry and Remote Sensing, 126: 11-23 [DOI: 10.1016/j.isprsjprs.2016.12.014http://dx.doi.org/10.1016/j.isprsjprs.2016.12.014]
Ma M L, Zhu Y, Li W L, Yao X, Cao W X and Tian Y C. 2012. Extracting area information of paddy rice based on stratified multiple endmember spectral mixture analysis. Transactions of the CSAE, 28(2): 154-159
马孟莉, 朱艳, 李文龙, 姚霞, 曹卫星, 田永超. 2012. 基于分层多端元混合像元分解的水稻面积信息提取. 农业工程学报, 28(2): 154-159 [DOI: 10.3969/j.issn.1002-6819.2012.02.027http://dx.doi.org/10.3969/j.issn.1002-6819.2012.02.027]
Meusburger K, Bänninger D and Alewell C. 2010. Estimating vegetation parameter for soil erosion assessment in an alpine catchment by means of QuickBird imagery. International Journal of Applied Earth Observation and Geoinformation, 12(3): 201-207 [DOI: 10.1016/j.jag.2010.02.009http://dx.doi.org/10.1016/j.jag.2010.02.009]
Nowak D J, Rowntree R A, McPherson E G, Sisinni S M, Kerkmann E R and Stevens J C. 1996. Measuring and analyzing urban tree cover. Landscape and Urban Planning, 36(1): 49-57 [DOI: 10.1016/S0169-2046(96)00324-6http://dx.doi.org/10.1016/S0169-2046(96)00324-6]
Piwowar J M, Peddle D R and Ledrew E F. 1998. Temporal mixture analysis of arctic sea ice imagery: a new approach for monitoring environmental change. Remote Sensing of Environment, 63(3): 195-207 [DOI: 10.1016/S0034-4257(97)00105-3http://dx.doi.org/10.1016/S0034-4257(97)00105-3]
Ridd M K. 1995. Exploring a V-I-S (vegetation-impervious surface-soil) model for urban ecosystem analysis through remote sensing: comparative anatomy for cities. International Journal of Remote Sensing, 16(12): 2165-2185 [DOI: 10.1080/01431169508954549http://dx.doi.org/10.1080/01431169508954549]
Roberts D A, Gardner M, Church R, Ustin S, Scheer G and Green R O. 1998. Mapping chaparral in the santa monica mountains using multiple endmember spectral mixture models. Remote Sensing of Environment, 65(3): 267-279 [DOI: 10.1016/s0034-4257(98)00037-6http://dx.doi.org/10.1016/s0034-4257(98)00037-6]
Tyrväinen L, Pauleit S, Seeland K and de Vries S. 2005. Benefits and uses of urban forests and trees//Konijnendijk C, Nilsson K, Randrup T and Schipperijn J, eds. Urban Forests and Trees: A Reference Book. Berlin, Heidelberg: Springer: 81-114 [DOI: 10.1007/3-540-27684-x_5http://dx.doi.org/10.1007/3-540-27684-x_5]
Van de Voorde T, Vlaeminck J and Canters F. 2008. Comparing different approaches for mapping urban vegetation cover from Landsat ETM+data: a case study on Brussels. Sensors, 8(6): 3880-3902 [DOI: 10.3390/s8063880http://dx.doi.org/10.3390/s8063880]
Wang B, Jia K, Liang S L, Xie X H, Wei X Q, Zhao X, Yao Y J and Zhang X T. 2018. Assessment of sentinel-2 MSI spectral band reflectances for estimating fractional vegetation cover. Remote Sensing, 10(12): 1927 [DOI: 10.3390/rs10121927http://dx.doi.org/10.3390/rs10121927]
Wang Q M and Atkinson P M. 2018. Spatio-temporal fusion for daily Sentinel-2 images. Remote Sensing of Environment, 204: 31-42 [DOI: 10.1016/j.rse.2017.10.046http://dx.doi.org/10.1016/j.rse.2017.10.046]
Xiao J F and Moody A. 2005. A comparison of methods for estimating fractional green vegetation cover within a desert-to-upland transition zone in central New Mexico, USA. Remote Sensing of Environment, 98(2/3): 237-250 [DOI: 10.1016/j.rse.2005.07.011http://dx.doi.org/10.1016/j.rse.2005.07.011]
Yang F, Matsushita B, Fukushima T and Yang W. 2012. Temporal mixture analysis for estimating impervious surface area from multi-temporal MODIS NDVI data in Japan. ISPRS Journal of Photogrammetry and Remote Sensing, 72: 90-98 [DOI: 10.1016/j.isprsjprs.2012.05.016http://dx.doi.org/10.1016/j.isprsjprs.2012.05.016]
Yang L Q, Jia K, Liang S L, Wei X Q, Yao Y J and Zhang X T. 2017. A robust algorithm for estimating surface fractional vegetation cover from landsat data. Remote Sensing, 9(8): 857 [DOI: 10.3390/rs9080857http://dx.doi.org/10.3390/rs9080857]
Zhang X W, Wu B F, Ling F, Zeng Y, Yan N N and Yuan C. 2010. Identification of priority areas for controlling soil erosion. CATENA, 83(1): 76-86 [DOI: 10.1016/j.catena.2010.06.012http://dx.doi.org/10.1016/j.catena.2010.06.012]
Zhu X L and Liu D S. 2015. Improving forest aboveground biomass estimation using seasonal Landsat NDVI time-series. ISPRS Journal of Photogrammetry and Remote Sensing, 102: 222-231 [DOI: 10.1016/j.isprsjprs.2014.08.014http://dx.doi.org/10.1016/j.isprsjprs.2014.08.014]
Zhu Y S, Zeng Y N and Zhang M. 2017. Extract of land use/cover information based on HJ satellites data and object-oriented classification. Transactions of the Chinese Society of Agricultural Engineering, 33(14): 258-265
朱永森, 曾永年, 张猛. 2017. 基于HJ卫星数据与面向对象分类的土地利用/覆盖信息提取. 农业工程学报, 33(14): 258-265 [DOI: 10.11975/j.issn.1002-6819.2017.14.035http://dx.doi.org/10.11975/j.issn.1002-6819.2017.14.035]
相关文章
相关作者
相关机构