浏览全部资源
扫码关注微信
纸质出版日期： 2018-1 ，
录用日期： 2017-7-25
扫 描 看 全 文
张猛, 曾永年. 2018. 融合高时空分辨率数据估算植被净初级生产力. 遥感学报, 22(1): 143–152
Zhang M and Zeng Y N. 2018. Net primary production estimation by using fusion remote sensing data with high spatial and temporal resolution. Journal of Remote Sensing, 22(1): 143–152
植被净初级生产力NPP(Net Primary Production)遥感估算与分析，有赖于高时空分辨率的遥感数据，但目前中高分辨率的遥感数据受卫星回访周期及天气的影响，在中国南方地区难以获取连续时间序列的数据，从而影响了高精度的区域植被净初级生产力的遥感估算。为此，提出一种基于多源遥感数据时空融合技术与CASA模型估算高时空分辨率NPP的方法。首先，利用多源遥感数据，即Landsat 8 OLI数据与MODIS13Q1数据，采用遥感数据时空融合方法，获得了时间序列的Landsat 8 OLI融合数据；然后，基于Landsat 8 OLI时空融合数据，并采用CASA模型，以长株潭城市群核心区为例，进行区域植被NPP的遥感估算。研究结果表明，基于时间序列Landsat融合数据估算的30 m分辨率的NPP具有良好的空间细节信息，且估算值与实测值的相关系数达0.825，与实测NPP数据保持了较好的一致性。
High-precision and rapidly changing Net Primary Production (NPP) monitoring in regions depends on remote sensing data with high spatial and temporal resolution. However
high-quality remote sensing data with high spatial and temporal resolution are difficult to acquire with a single remote sensor. To solve the problem of missing data
we used the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm in blending MODIS and Landsat data. We obtained high-frequency temporal information from the MODIS data and high-resolution spatial information from the Landsat8 OLI data to predict NPP with high spatial and temporal resolution. Using the STARFM model
we obtained the time series data with 30 m spatial resolution and 16 day temporal resolution. Then
the fused time series NDVI data combined with meteorological data were used as inputs to the Carnegie–Ames–Stanford approach model to estimate the NPP in the Chang–Zhu–Tan urban agglomerations. An area with 500×500 pixels was randomly selected from true Landsat images and fused Landsat images. The correlation coefficient and RMSE between the true Landsat8 NDVI and fused Landsat NDVI image were above 0.7 and 0.08
respectively. This result indicates that the true data and fused data maintained good consistency. The NPP estimated with the fused Landsat NDVI data showed better detailed spatial information than that obtained with the MODIS data. The fused NPP data showed distinct boundaries between water
road
and building
whereas the NPP simulated from the MODIS did not. The mean values of the NPP data obtained with the fused Landsat NDVI and MODIS NDVI data were 323.01 and 260.88 gC·m
–2
·a
–1
respectively. The mean value of the NPP from the fused Landsat NDVI data was higher than that of the NPP from the MODIS data due to the spectral difference between the MODIS and Landsat images. To further validate the fused NPP data
we employed the measured value of NPP. The correlation coefficients
RMSEs
and relative errors of the fused NPP and measured NPP were 0.89
4.83 gC·m
–2
·a
–1
and 9.82%
respectively
which indicated that the fused NPP had a good consistency with the measured data. The fused NPP was consistent with the NPP obtained with the MODIS data in terms of pixels; the vegetation coverage accounted for more than 80% of the mixed pixels. Meanwhile
the fused NPP was significantly higher than the NPP estimated from the MODIS data in terms of pixels; the vegetation coverage accounted for less than 20% of the mixed pixels. At the same time
the fused NPP data retained the time features of the time series MODIS data. With the rapid development of regions
remote sensing data from a single date cannot meet the requirements of the monitoring of dynamic vegetation growth in urban areas. A spatial–temporal fusion technique is an effective way to blend images from different sensors for applications that require high resolutions in both time and space. This technique should be able to support time series remote data with high spatial and temporal resolution for NPP monitoring in regional areas. However
due to the rapid expansion of cities and unreasonable planning of regions
vegetation patches become increasingly fragmented with high spatial heterogeneity. Therefore
on the one hand
we should improve the spatial resolution of remote sensing data. On the other hand
we have to solve spatial heterogeneity
which is the focus of our follow-up research.
Landsat 8 OLIMODIS时空融合高时空分辨率净初级生产力遥感估算
Landsat 8 OLIMODISdata fusionhigh spatial and temporalnet primary productionremote sensing estimation
陈晓玲, 曾永年. 2016. 亚热带山地丘陵区植被NPP时空变化及其与气候因子的关系——以湖南省为例. 地理学报, 71(1): 35–48
Chen X L and Zeng Y N. 2016. Spatial and temporal variability of the net primary production (NPP) and its relationship with climate factors in subtropical mountainous and hilly regions of China: a case study in Hunan province. Acta Geographica Sinica, 71(1): 35–48 (
邓楚雄, 谢炳庚, 李晓青, 刘利科, 向云波. 2013. 基于投影寻踪法的长株潭城市群地区耕地集约利用评价. 地理研究, 32(11): 2000–2008
Deng C X, Xie B G, Li X Q, Liu L K and Xiang Y B. 2013. Evaluation of intensive cultivated land use based on a projection pursuit model in Changsha-Zhuzhou-Xiangtan urban agglomeration. Geographical Research, 32(11): 2000–2008 (
杜江, 刘渝. 2008. 城市化与环境污染: 中国省际面板数据的实证研究. 长江流域资源与环境, 17(6): 825–830
Du J and Liu Y. 2008. Urbanization and environmental pollution: empirical study based on provincial panel data of China. Resources and Environment in the Yangtze Basin, 17(6): 825–830 (
Field C B, Randerson J T and Malmströem C M. 1995. Global net primary production: combining ecology and remote sensing. Remote Sensing of Environment, 51(1): 74–88
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
高清竹, 万运帆, 李玉娥, 盛文萍, 江村旺扎, 王宝山, 李文福. 藏北高寒草地NPP变化趋势及其对人类活动的响应. 生态学报 2007. 藏北高寒草地NPP变化趋势及其对人类活动的响应. 生态学报, 27(11): 4612–4619
Gao Q Z, Wan Y F, Li Y E, Sheng W P, Jiangcun W Z, Wang B S and Li W F. 2007. Trends of grassland NPP and its response to human activity in northern Tibet. Acta Ecologica Sinica, 27(11): 4612–4619 (
Gitelson A A, Peng Y, Masek J G, Rundquist D C, Verma S, Suyker A, Baker J M, Hatfield J L and Meyers T. 2012. Remote estimation of crop gross primary production with Landsat data. Remote Sensing of Environment, 121: 404–414
管小彬, 沈焕锋, 甘文霞, 张良培. 2015. 基于Landsat TM/ETM+影像的武汉市冬季NPP估算及其时空变化分析. 遥感技术与应用, 30(5): 884–890
Guan X B, Shen H F, Gan W X and Zhang L P. 2015. Estimation and spatiotemporal analysis of Winter NPP in Wuhan based on Landsat TM/ETM+ images. Remote Sensing Technology and Application, 30(5): 884–890 (
Hilker T, Wulder M A, Coops N C, Linke J, McDermid G, Masek J G, Gao F and White J C. 2009. A new data fusion model for high spatial-and temporal-resolution mapping of forest disturbance based on Landsat and MODIS. Remote Sensing of Environment, 113(8): 1613–1627
Hiker H, Wulder M A, Coops N C, Seitz N, White J C, Gao F, Masek J G and Stenhouse G. 2009. Generation of dense time series synthetic Landsat data through data blending with MODIS using a spatial and temporal adaptive reflectance fusion model. Remote Sensing of Environment, 113(9): 1988–1999
胡小飞, 唐宪, 胡月明, 樊舒迪, 王璐. 2016. 广州市城市森林净初级生产力遥感估算. 中南林业科技大学学报, 36(5): 19–25
Hu X F, Tang X, Hu Y M, Fan S D and Wang L. 2016. Estimation of net primary productivity of urban forest ecological system in Guangzhou city. Journal of Central South University of Forestry and Technology, 36(5): 19–25 (
Johnson W C and Sharpe D M. 1983. The ratio of total to merchantable forest biomass and its application to the global carbon budget. Canadian Journal of Forest Research, 13(3): 372–383
李梦桃, 周忠学. 2016. 西安市城市景观的正负生态系统服务测算及空间格局. 地理学报, 71(7): 1215–1230
Li M T, Zhou Z X. 2016. Positive and negative ecosystem services evaluation and its spatial pattern analysis on urban landscape: a case study of Xi’an City. Acta Geographica Sinica, 71(7): 1215–1230 (
刘敏, 伏玉玲, 杨芳. 2014. 基于涡度相关技术的城市碳通量研究进展. 应用生态学报, 25(2): 611–619
Liu M, Fu Y L and Yang F. 2014. Research progress on urban carbon fluxes based on eddy covariance technique. Chinese Journal of Applied Ecology, 25(2): 611–619 (
刘新, 刘寿东, 赵小艳, 王咏薇. 2013. 南京城市化进程与热岛效应的初步研究. 长江流域资源与环境, 22(12): 1543–1549
Liu X, Liu S D, Zhao X Y and Wang Y W. 2013. A preliminary research of urbanization process and heat island effect in Nanjing. Resources and Environment in the Yangtze Basin, 22(12): 1543–1549 (
Matsushita B and Tamura M. 2002. Integrating remotely sensed data with an ecosystem model to estimate net primary productivity in East Asia. Remote Sensing of Environment, 81(1): 58–66
牛忠恩, 闫慧敏, 黄玫, 胡云锋, 陈静清. 2016. 基于MODIS-OLI遥感数据融合技术的农田生产力估算. 自然资源学报, 31(5): 875–885
Niu Z E, Yan H M, Huang M, Hu Y F and Chen J Q. 2016. Agricultural productivity estimation with MODIS-OLI fusion data. Journal of Natural Resources, 31(5): 875–885 (
Singh D. 2011. Generation and evaluation of gross primary productivity using Landsat data through blending with MODIS data. International Journal of Applied Earth Observation and Geoinformation, 13(1): 59–69
孙昌龙, 靳诺, 张小雷, 杜宏茹. 2013. 城市化不同演化阶段对碳排放的影响差异. 地理科学, 33(3): 266–272
Sun C L, Jin N, Zhang X L and Du H R. 2013. The impact of urbanization on the CO2 emission in the various development stages. Scientia Geographica Sinica, 33(3): 266–272 (
孙鸿烈, 郑度, 姚檀栋, 张镱锂. 2012. 青藏高原国家生态安全屏障保护与建设. 地理学报, 67(1): 3–12
Sun H L, Zheng D, Yao T D and Zhang Y L. 2012. Protection and construction of the national ecological security shelter zone on Tibetan Plateau. Acta Geographica Sinica, 67(1): 3–12 (
孙佩军, 张锦水, 潘耀忠, 谢登峰, 袁周米琪. 2016. 构建时空融合模型进行水稻遥感识别. 遥感学报, 20(2): 328–343
Sun P J, Zhang J S, Pan Y Z, Xie D F and Yuan Z M Q. 2016. Temporal-spatial-fusion model for area extraction of paddy rice using multi-temporal remote sensing images. Journal of Remote Sensing, 20(2): 328–343 (
孙政国, 杨齐, 李建龙. 2012. 土地利用与土地覆盖变化对中小城市净初级生产力的影响. 中国农学通报, 28(15): 291–296
Sun Z G, Yang Q and Li J L. 2012. The influence of land use and cover change on net primary productivity of middle-sized and small cities. Chinese Agricultural Science Bulletin, 28(15): 291–296 (
孙泽祥, 刘志锋, 何春阳, 邬建国. 2016. 中国快速城市化干燥地区的生态系统服务权衡关系多尺度分析——以呼包鄂榆地区为例. 生态学报, 36(15): 4881–4891
Sun Z X, Liu Z F, He C Y and Wu J G. 2016. Multi-scale analysis of ecosystem service trade-offs in urbanizing drylands of China: a case study in the Hohhot-Baotou-Ordos-Yulin region. Acta Ecologica Sinica, 36(15): 4881–4891 (
Turner D P, Ritts W D, Cohen W B, Gower S T, Running S W, Zhao M S, Costa M H, Kirschbaum A A, Ham J M, Saleska S R and Ahl D E. 2006. Evaluation of MODIS NPP and GPP products across multiple biomes. Remote Sensing of Environment, 102(3/4): 282–292
王伦澈, 龚威, 张淼, 马盈盈. 2013. 武汉地区植被NPP动态监测研究. 武汉大学学报(信息科学版), 38(5): 548–552
Wang L C, Gong W, Zhang M and Ma Y Y. 2013. Dynamic monitoring of vegetation NPP in Wuhan based on MODIS. Geomatics and Information Science of Wuhan University, 38(5): 548–552 (
Wu M Q, Niu Z, Wang C Y, Wu C Y and Wang L. 2012. Use of MODIS and Landsat time series data to generate high-resolution temporal synthetic Landsat data using a spatial and temporal reflectance fusion model. Journal of Applied Remote Sensing, 6(1): 063507
张猛, 曾永年. 2015. 基于多时相Landsat数据融合的洞庭湖区水稻面积提取. 农业工程学报, 31(13): 178–185
Zhang M and Zeng Y N. 2015. Mapping paddy fields of Dongting lake area by fusing Landsat and MODIS data. Transactions of the Chinese Society of Agricultural Engineering, 31(13): 178–185 (
张猛, 曾永年, 朱永森. 2017. 面向对象方法的时间序列MODIS数据湿地信息提取——以洞庭湖流域为例. 遥感学报, 21(3): 479–492
Zhang M, Zeng Y N and Zhu Y S. 2017. Wetland mapping of Dongting Lake Basin based on time-series MODIS data and object-oriented method. Journal of Remote Sensing, 21(3): 479–492 (
张骞, 高明, 杨乐, 陈成龙, 孙宇, 王金柱. 2017. 1988–2013年重庆市主城九区生态用地空间结构及其生态系统服务价值变化. 生态学报, 37(2): 566–575
Zhang Q, Gao M, Yang L, Chen C L, Sun Y and Wang J Z. 2017. Changes in the spatial structure of ecological land and ecosystem service values in nine key districts of Chongqing City over the past 25 years. Acta Ecologica Sinica, 37(2): 566–575 (
张镱锂, 祁威, 周才平, 丁明军, 刘林山, 高俊刚, 摆万奇, 王兆锋, 郑度. 2014. 青藏高原高寒草地净初级生产力(NPP)时空分异. 地理学报, 68(9): 1197–1211
Zhang Y L, Qi W, Zhou C P, Ding M J, Liu L S, Gao J G, Bai W Q, Wang Z F and Zhen D. 2014. Spatial and temporal variability in the net primary production (NPP) of alpine grassland on Tibetan Plateau from 1982 to 2009. Acta Geographica Sinica, 68(9): 1197–1211 (
Zhao F, Xu B, Yang X C, Jin Y X, Li J Y, Xia L, Chen S and Ma H L. 2014. Remote sensing estimates of grassland aboveground biomass based on MODIS net primary productivity (NPP): a case study in the Xilingol grassland of Northern China. Remote Sensing, 6(6): 5368–5386
朱士华, 艳燕, 胡云锋, 张弛. 2016. 干旱区城市化对生态系统碳库的影响—以乌鲁木齐市为例. 自然资源学报, 31(7): 1086–1099
Zhu S H, Yan Y, Hu Y F and Zhang C. 2016. The impact of urbanization on ecosystem carbon storage in arid area. Journal of Natural Resources, 31(7): 1086–1099 (
朱文泉. 2005. 中国陆地生态系统植被净初级生产力遥感估算及其与气候变化关系的研究. 北京: 北京师范大学
Zhu W Q. 2005. Estimation of net primary productivity of Chinese terrestrial vegetation based on remote sensing and its relationship with global climate change. Beijing: Beijing Normal University
朱文泉, 潘耀忠, 龙中华, 陈云浩, 李京, 扈海波. 2005. 基于GIS和RS的区域陆地植被NPP估算——以中国内蒙古为例. 遥感学报, 9(3): 300–307
Zhu W Q, Pan Y Z, Long Z H, Chen Y H, Li J and Hu H B. 2005. Estimating net primary productivity of terrestrial vegetation based on GIS and RS: a case study in Inner Mongolia, China. Journal of Remote Sensing, 9(3): 300–307 (
Zhu X L, Chen J, Gao F, Chen X H and Masek J G. 2010. An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions. Remote Sensing of Environment, 114(11): 2610–2623
相关作者
相关机构
京公网安备11010802024621