Remotely sensing images are now available for monitoring vegetation dynamics over large areas.In this paper,an improved logistic model that combines double logistic model and global function was developed.Using this m...Remotely sensing images are now available for monitoring vegetation dynamics over large areas.In this paper,an improved logistic model that combines double logistic model and global function was developed.Using this model with SPOT/NDVI data,three key vegetation phenology metrics,the start of growing season (SOS),the end of growing season (EOS) and the length of growing season (LOS),were extracted and mapped in the Changbai Mountains,and the relationship between the key phenology metrics and elevation were established.Results show that average SOS of forest,cropland and grassland in the Changbai Mountains are on the 119th,145th,and 133rd day of year,respectively.The EOS of forest and grassland are similar,with the average on the 280th and 278th,respectively.In comparison,average EOS of the cropland is relatively earlier.The LOS of forest is mainly from the 160th to 180th,that of the grassland extends from the 140th to the 160th,and that of cropland stretches from the 110th to the 130th.As the latitude increases for the same land cover in the study area,the SOS significantly delays and the EOS becomes earlier.The SOS delays approximately three days as the elevation increases 100 m in the areas with elevation higher than 900 m above sea level (a.s.l.).The EOS is slightly earlier as the elevation increases especially in the areas with elevation below 1200 m a.s.l.The LOS shortens approximately four days as the elevation increases 100 m in the areas with elevation higher than 900 m a.s.l.The relationships between vegetation phenology metrics and elevation may be greatly influenced by the land covers.Validation by comparing with the field data and previous research results indicates that the improved logistic model is reliable and effective for extracting vegetation phenology metrics.展开更多
Accurate estimation of rice phenology is of critical importance for agricultural practices and studies. However, the accuracy of phenological parameters extracted by remote sensing data cannot be guaranteed because of...Accurate estimation of rice phenology is of critical importance for agricultural practices and studies. However, the accuracy of phenological parameters extracted by remote sensing data cannot be guaranteed because of the influence of climate, e.g. the monsoon season, and limited available remote sensing data. In this study, we integrate the data of H J-1 CCD and Landsat-8 operational land imager （OLI） by using the ordinary least-squares （OLS） and construct higher temporal resolution vegetation indices （VIs） time-series data to extract the phenological param- eters of single-cropped rice. Two widely used VIs, namely the normalized difference vegetation index （NDVI） and 2-band enhanced vegetation index （EVI2）, were adopted to minimize the influence of environmental factors and the intrinsic difference between the two sensors. Savitzky-Golay （S-G） filters were applied to construct continuous VI profiles per pixel. The results showed that, compared with NDVI, EVI2 was more stable and comparable between the two sensors. Compared with the observed phenological data of the single-cropped rice, the integrated VI time-series had a relatively low root mean square error （RMSE）, and EVI2 showed higher accuracy compared with NDVI. We also demonstrate the application of phenology extraction of the single-cropped rice in a spatial scale in the study area. While the work is of general value, it can also be extrapolated to other regions where qualified remote sensing data are the bottleneck but where complementary data are occasionally available.展开更多
文摘基于2010年中分辩率成像光谱仪(moderate-resolution imaging specroradiometer,MODIS)数据,以江苏省为研究区域,采用不同信号滤波方法,对水稻物候期的提取方法进行了分析和研究。在获得水稻的增强型植被指数(enhanced uegetatisn index,EVI)数据基础上,进行了HANTS(harmonic analysis of time series)滤波和小波变换滤波的对比分析,使用小波滤波重构后的数据结合Matlab软件进行了水稻物候期的格点化提取,并验证了结果的准确性。结论如下:二者都能较好的去除噪声,还原原始信息,但对云噪声污染较严重地区,小波滤波相比HANTS滤波效果更好,新的移栽期提取方法和小波滤波的物候期提取方法能够较为准确的反应真实的水稻物候情况,经站点数据检验,能很好地反应真实水稻物候情况。
基金Under the auspices of Major State Basic Research Development Program of China (No.2009CB426305)Cultivation Foundation of Science and Technology Innovation Platform of Northeast Normal University (No.106111065202)
文摘Remotely sensing images are now available for monitoring vegetation dynamics over large areas.In this paper,an improved logistic model that combines double logistic model and global function was developed.Using this model with SPOT/NDVI data,three key vegetation phenology metrics,the start of growing season (SOS),the end of growing season (EOS) and the length of growing season (LOS),were extracted and mapped in the Changbai Mountains,and the relationship between the key phenology metrics and elevation were established.Results show that average SOS of forest,cropland and grassland in the Changbai Mountains are on the 119th,145th,and 133rd day of year,respectively.The EOS of forest and grassland are similar,with the average on the 280th and 278th,respectively.In comparison,average EOS of the cropland is relatively earlier.The LOS of forest is mainly from the 160th to 180th,that of the grassland extends from the 140th to the 160th,and that of cropland stretches from the 110th to the 130th.As the latitude increases for the same land cover in the study area,the SOS significantly delays and the EOS becomes earlier.The SOS delays approximately three days as the elevation increases 100 m in the areas with elevation higher than 900 m above sea level (a.s.l.).The EOS is slightly earlier as the elevation increases especially in the areas with elevation below 1200 m a.s.l.The LOS shortens approximately four days as the elevation increases 100 m in the areas with elevation higher than 900 m a.s.l.The relationships between vegetation phenology metrics and elevation may be greatly influenced by the land covers.Validation by comparing with the field data and previous research results indicates that the improved logistic model is reliable and effective for extracting vegetation phenology metrics.
基金supported by the National High-Tech R&D Program(863)of China(No.2012AA12A30703)the Fundamental Research Funds for the Central Universities,China
文摘Accurate estimation of rice phenology is of critical importance for agricultural practices and studies. However, the accuracy of phenological parameters extracted by remote sensing data cannot be guaranteed because of the influence of climate, e.g. the monsoon season, and limited available remote sensing data. In this study, we integrate the data of H J-1 CCD and Landsat-8 operational land imager （OLI） by using the ordinary least-squares （OLS） and construct higher temporal resolution vegetation indices （VIs） time-series data to extract the phenological param- eters of single-cropped rice. Two widely used VIs, namely the normalized difference vegetation index （NDVI） and 2-band enhanced vegetation index （EVI2）, were adopted to minimize the influence of environmental factors and the intrinsic difference between the two sensors. Savitzky-Golay （S-G） filters were applied to construct continuous VI profiles per pixel. The results showed that, compared with NDVI, EVI2 was more stable and comparable between the two sensors. Compared with the observed phenological data of the single-cropped rice, the integrated VI time-series had a relatively low root mean square error （RMSE）, and EVI2 showed higher accuracy compared with NDVI. We also demonstrate the application of phenology extraction of the single-cropped rice in a spatial scale in the study area. While the work is of general value, it can also be extrapolated to other regions where qualified remote sensing data are the bottleneck but where complementary data are occasionally available.
