融合高时空分辨率数据估算植被净初级生产力
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摘要
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Landsat8 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Landsat8 OLI融合数据;然后,基于Landsat8 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30m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.
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.
引文
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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.[DOI:10.1016/j.rse.2012.02.017]
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.(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)[DOI:10.11873/j.issn.1004-0323.2015.5.0884]
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[DOI:10.1016/j.rse.2009.03.007]
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[DOI:10.1016/j.rse.2009.05.011]
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[J].Journal of Central South University of Forestry and Technology,36(5):19-25(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[J].中南林业科技大学学报,36(5):19-25)[DOI:10.14067/j.cnki.1673-923x.2016.05.004]
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[DOI:10.1139/X83-056]
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