提出融入中高空间分辨率遥感影像精确地类识别信息,以改进传统的Chen NDVI尺度转换模型的方法,并基于两个模型共同进行MODIS 250m 16D合成植被指数产品MOD13 Q1(MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid)真实性...提出融入中高空间分辨率遥感影像精确地类识别信息,以改进传统的Chen NDVI尺度转换模型的方法,并基于两个模型共同进行MODIS 250m 16D合成植被指数产品MOD13 Q1(MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid)真实性检验。研究以地类丰富的厦门市作为研究区主体,并以30m Landsat8陆地成像仪OLI(Operational Land Imager)影像作为验证数据,实践了上述方法。实验结果表明:MOD13 Q1产品总体质量较好,但是存在偏高估计的问题,尤其是对人工地物更为明显,在实际应用中应予以关注;融入精细地类信息的改进ChenNDVI模型相比较融入粗略地类信息的传统Chen NDVI模型,升尺度转换结果无显著差异,但是前者在精细、定量刻画“不同地类对NDVI尺度效应影响”方面更有优势,这对遥感地表参数尺度效应研究具有重要的启示意义。展开更多
To evaluate and provide an appropriate theoretical direction for research into climate-vegetation interactions using meteorological station data at different time scales,we examined differences between the Normalized ...To evaluate and provide an appropriate theoretical direction for research into climate-vegetation interactions using meteorological station data at different time scales,we examined differences between the Normalized Difference Vegetation Index (NDVI)and Enhanced Vegetation Index (EVI)and their responses to climate factors. We looked for correlations between'data extracted from MOD13Q1 remote sensing images and meteorological station data for the two indexes.The results showed that even though NDVI and EVI are derived from the same remote sensing image,their response to climate factors was significantly different.In the same meteorological station,the correlation coefficients for NDVI,EVI and climate factors were different;correlation coefficients between NDVI,EVI and climate factors varied with meteorological station.In addition,there was a lag effect for responses of NDVI to average minimum temperature,average temperature,average vapor pressure,minimum relative humidity, extreme wind speed,maximum wind speed,average wind speed and average station air-pressure.EVI had a lag only for average minimum temperature,average vapor pressure,extreme wind speed,maximum wind speed and average station air-pressure.The lag period was variable,but most were in the -3 period.Different vegetation types had different sensitivities to climate.The correlation between meteorological stations and vegetation requires more attention in future research.展开更多
文摘提出融入中高空间分辨率遥感影像精确地类识别信息,以改进传统的Chen NDVI尺度转换模型的方法,并基于两个模型共同进行MODIS 250m 16D合成植被指数产品MOD13 Q1(MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid)真实性检验。研究以地类丰富的厦门市作为研究区主体,并以30m Landsat8陆地成像仪OLI(Operational Land Imager)影像作为验证数据,实践了上述方法。实验结果表明:MOD13 Q1产品总体质量较好,但是存在偏高估计的问题,尤其是对人工地物更为明显,在实际应用中应予以关注;融入精细地类信息的改进ChenNDVI模型相比较融入粗略地类信息的传统Chen NDVI模型,升尺度转换结果无显著差异,但是前者在精细、定量刻画“不同地类对NDVI尺度效应影响”方面更有优势,这对遥感地表参数尺度效应研究具有重要的启示意义。
基金National Key Research and Development Program of China(2016YFC0501003)
文摘To evaluate and provide an appropriate theoretical direction for research into climate-vegetation interactions using meteorological station data at different time scales,we examined differences between the Normalized Difference Vegetation Index (NDVI)and Enhanced Vegetation Index (EVI)and their responses to climate factors. We looked for correlations between'data extracted from MOD13Q1 remote sensing images and meteorological station data for the two indexes.The results showed that even though NDVI and EVI are derived from the same remote sensing image,their response to climate factors was significantly different.In the same meteorological station,the correlation coefficients for NDVI,EVI and climate factors were different;correlation coefficients between NDVI,EVI and climate factors varied with meteorological station.In addition,there was a lag effect for responses of NDVI to average minimum temperature,average temperature,average vapor pressure,minimum relative humidity, extreme wind speed,maximum wind speed,average wind speed and average station air-pressure.EVI had a lag only for average minimum temperature,average vapor pressure,extreme wind speed,maximum wind speed and average station air-pressure.The lag period was variable,but most were in the -3 period.Different vegetation types had different sensitivities to climate.The correlation between meteorological stations and vegetation requires more attention in future research.
