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Net primary production estimation by using fusion remote sensing data with high spatial and temporal resolution

  • role： First author第一作者
  • Email：251zhangmeng@sina.com
  • Introduction：张猛(1987—　)，男，博士研究生，研究方向为环境遥感。E-mail：251zhangmeng@sina.com
Meng ZHANG,  
  • role： Corresponding author通信作者
  • Email：ynzeng@mail.csu.edu.cn
  • Introduction：曾永年(1959—　)，男，教授，研究方向为遥感与地理信息系统及其环境变化。E-mail：ynzeng@mail.csu.edu.cn
Yongnian ZENG*

resumen

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.

palabra clave

Landsat 8 OLI;MODIS;data fusion;high spatial and temporal;net primary production;remote sensing estimation

References

  1. 1.
    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 (
  2. 2.
    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 (
  3. 3.
    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 (
  4. 4.
    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
  5. 5.
    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
  6. 6.
    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 (
  7. 7.
    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
  8. 8.
    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 (
  9. 9.
    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
  10. 10.
    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
  11. 11.
    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 (
  12. 12.
    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
  13. 13.
    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 (
  14. 14.
    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 (
  15. 15.
    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 (
  16. 16.
    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
  17. 17.
    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 (
  18. 18.
    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
  19. 19.
    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 (
  20. 20.
    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 (
  21. 21.
    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 (
  22. 22.
    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 (
  23. 23.
    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 (
  24. 24.
    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
  25. 25.
    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 (
  26. 26.
    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
  27. 27.
    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 (
  28. 28.
    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 (
  29. 29.
    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 (
  30. 30.
    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 (
  31. 31.
    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
  32. 32.
    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 (
  33. 33.
    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
  34. 34.
    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 (
  35. 35.
    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

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