气候变化背景下开展山地过渡带植被物候变化规律及区域差异研究对于揭示过渡带对气候变化的响应方式具有重要意义。基于2000-2017年MODIS EVI2数据,反演了秦岭山地植被物候参数并建立了遥感物候数据集,分析了近18年来秦岭山地植被物候...气候变化背景下开展山地过渡带植被物候变化规律及区域差异研究对于揭示过渡带对气候变化的响应方式具有重要意义。基于2000-2017年MODIS EVI2数据,反演了秦岭山地植被物候参数并建立了遥感物候数据集,分析了近18年来秦岭山地植被物候变化的时空特征及其南北差异。结果表明:(1)秦岭山地植被物候变化表现出明显的地形和气候地域分异规律,尤以高海拔区的变化最为显著,全区GSS(物候始期)主要发生于70-130DOY(Day of Year),83.29%的区域呈提前趋势,提前主要集中在0-5d/10a(44.46%)与5-10d/10a(28.60%);GSE(物候末期)主要发生于270-310DOY,50.17%的区域呈推迟趋势,变化趋势不明显;GSL(生长期)集中在150-210d,65.34%的区域呈延长趋势,延长在0-5d/10a(19.28%)、5-10d/10a(20.71%)及10-15d/10a(14.12%)均有分布。(2)秦岭山地GSS对气候变化的响应程度显著大于GSE,南北坡植被物候变化不仅存在区域差异且存在季节差异,GSS北坡较南坡平均约早6.2d且南坡提前趋势较北坡显著,GSE南坡较北坡平均约晚5.8d且北坡推迟趋势较南坡显著,GSL北坡较南坡约长18.7-23.2d。(3)GSS、GSS及GSS变化表现出显著的海拔敏感性,随着海拔上升,GSS逐渐推迟,GSE逐渐提前,GSL逐渐缩短,三者在海拔≤600m及≥2700m地区随海拔变化的波动幅度较大,南北坡三者随海拔的变化亦存在明显的差异,海拔每上升100m,北坡GSS推迟1.76d,GSE提前0.25d,GSL缩短2.01d;南坡GSS推迟1.50d,GSE提前0.44d,GSL缩短1.94d。(4)不同植被垂直带上GSS、GSE及GSL的变化存在明显差异,尤以≤600m植被带上及高山灌丛草甸带上的差异最为明显,且三者在高山灌丛草甸带的发生时间及时长北坡与南坡发生转换,表现为GSS、GSE、GSL北坡较南坡分别平均早3.5d、晚2.9d、长6.4d。展开更多
[Objective] The aim was to explore the feasibility of using single spectrum image to classify crops based on multi-spectral image and Decision Tree Method. [Method] Taking the typical agriculture plantation area in Hu...[Objective] The aim was to explore the feasibility of using single spectrum image to classify crops based on multi-spectral image and Decision Tree Method. [Method] Taking the typical agriculture plantation area in Hulunbeier area, according to field measured spectrum data, the optimum time of main crops, barley, wheat, rapeseed, based on crops spectrum characteristics, by dint of decision-making tree method, and considering spectral matching method, classification of crops was studied such as SAM. [Result] By dint of Landsat TM image gained in the first half of August, based on geographic and atmospheric proof-reading, decision-making tree was constructed. Plantation information about wheat, barley, and rapeseed and plantation grassland was extracted successfully. The general classification accuracy reached 86.90%. Kappa coefficient was 0.831 1. [Conclusion] Taking typical spectrum image as data source, and applying Decision Tree Method to get crops type＇s information had fine application future.展开更多
植被物候遥感产品对全球变化响应、农业生产管理、生态学的应用等多领域研究具有重要意义。但现有植被物候遥感产品还有较多问题,主要包括一方面使用不同参数的时间序列数据以及不同提取算法导致的产品结果差异较大,另一方面在地面验证...植被物候遥感产品对全球变化响应、农业生产管理、生态学的应用等多领域研究具有重要意义。但现有植被物候遥感产品还有较多问题,主要包括一方面使用不同参数的时间序列数据以及不同提取算法导致的产品结果差异较大,另一方面在地面验证中地面观测数据与遥感反演数据的物理含义不一致导致的验证方法的系统性误差。本文以黑河流域为研究区,对比验证基于EVI(Enhanced Vegetation Index)时间序列数据提取的MLCD(MODIS global land cover dynamics product)植被遥感物候产品和基于LAI(Leaf Area Index)时间序列数据提取的UMPM(product by universal multi-life-cycle phenology monitoring method)植被遥感物候产品的有效性及精度等。同时,通过验证分析进一步评估基于EVI和LAI时间序列提取的物候特征的差异及特点,探讨由于地面观测植被物候与遥感提取植被物候的物理意义的不一致问题导致的直接验证结果偏差。结果表明:UMPM产品有效性整体高于MLCD产品,但在以草地和灌木为主的稀疏植被区,由于LAI取值精度的原因,UMPM产品存在较多缺失数据,且时空稳定性较低;基于玉米地面观测数据表明,EVI对植被开始生长的信号比LAI更加敏感,更适合提取生长起点,但植被指数易饱和,峰值起点普遍提前,基于LAI提取的峰值起点更加合理。由于地面观测的物候期在后期更加关注果实生长,遥感观测仅关注叶片的生长,遥感定义的峰值终点和生长终点与玉米的乳熟期和成熟期差异较大。展开更多
Using three-phase remote sensing images of China-Brazil Earth Resources Satellite 02B (CBERS02B) and Landsat-5 TM, tobacco field was extracted by the analysis of time series image based on the different phenological c...Using three-phase remote sensing images of China-Brazil Earth Resources Satellite 02B (CBERS02B) and Landsat-5 TM, tobacco field was extracted by the analysis of time series image based on the different phenological characteristics between tobacco and other crops. The spectral characteristics of tobacco and corn in luxuriant growth stage are very similar, which makes them difficult to be distinguished using a single-phase remote sensing image. Field film after tobacco seedlings transplanting can be used as significant sign to identify tobacco. Remote sensing interpre- tation map based on the fusion image of TM and CBERS02B's High-Resolution (HR) camera image was used as stan- dard reference material to evaluate the classification accuracy of Spectral Angle Mapper (SAM) and Maximum Like- lihood Classifier (MLC) for time series image based on full samples test method. SAM has higher classification accu- racy and stability than MLC in dealing with time series image. The accuracy and Kappa of tobacco coverage extracted by SAM are 83.4% and 0.692 respectively, which can achieve the accuracy required by tobacco coverage measurement in a large area.展开更多
Using the NDVI ratio method, the authors extracted phenological parameters from NOAA-AVHRR NDVI time-series data （1982-2008）. The start of the growing season （SOS） and the date of maximum NDVI （Peak-t） correlate...Using the NDVI ratio method, the authors extracted phenological parameters from NOAA-AVHRR NDVI time-series data （1982-2008）. The start of the growing season （SOS） and the date of maximum NDVI （Peak-t） correlated significantly with the mean annual precipitation along regional gradients of the steppes. Along the south transect （located at a lower latitude with a higher annual mean temperature） there was a positive correlation between the end of the growing season （EOS） and the mean annual precipitation along precipitation gradients （R2 = 0.709, p 〈 0.0001）. However, along the north transect （located at higher latitude with lower annual mean temperature）, the EOS was slightly negatively related with the mean annual precipitation （R2 = 0.179, p 〈 0.1）. There was positive correlation between the length of the growing season and the annual precipitation along two transects （R2 = 0.876, p 〈 0.0001 for the south transect; R2 = 0.290, p 〈 0.01 for the north transect）. Thus, for the Inner Mongolian steppe, it is precipitation rather than temperature that determines the date of the SOS.展开更多
Near-surface remote sensing(e.g.,digital cameras)has played an important role in capturing plant phenological metrics at either a focal or landscape scale.Exploring the relationship of the digital image-based greennes...Near-surface remote sensing(e.g.,digital cameras)has played an important role in capturing plant phenological metrics at either a focal or landscape scale.Exploring the relationship of the digital image-based greenness index(e.g.,Gcc,green chromatic coordinate)with that derived from satellites is critical for land surface process research.Moreover,our understanding of how well Gcc time series associate with environmental variables at field stations in North American prairies remains limited.This paper investigated the response of grass Gcc to daily environmental factors in 2018,such as soil moisture(temperature),air temperature,and solar radiation.Thereafter,using a derivative-based phenology extraction method,we evaluated the correspondence between key phenological events(mainly including start,end and length of growing season,and date with maximum greenness value)derived from Gcc,MODIS and VIIRS NDVI(EVI)for the period 2015–2018.The results showed that daily Gcc was in good agreement with ground-level environmental variables.Additionally,multivariate regression analysis identified that the grass growth in the study area was mainly affected by soil temperature and solar radiation,but not by air temperature.High frequency Gcc time series can respond immediately to precipitation events.In the same year,the phenological metrics retrieved from digital cameras and multiple satellites are similar,with spring phenology having a larger relative difference.There are distinct divergences between changing rates in the greenup and senescence stages.Gcc also shows a close relationship with growing degree days(GDD)derived from air temperature.This study evaluated the performance of a digital camera for monitoring vegetation phenological metrics and related climatic factors.This research will enable multiscale modeling of plant phenology and grassland resource management of temperate prairie ecosystems.展开更多
文摘气候变化背景下开展山地过渡带植被物候变化规律及区域差异研究对于揭示过渡带对气候变化的响应方式具有重要意义。基于2000-2017年MODIS EVI2数据,反演了秦岭山地植被物候参数并建立了遥感物候数据集,分析了近18年来秦岭山地植被物候变化的时空特征及其南北差异。结果表明:(1)秦岭山地植被物候变化表现出明显的地形和气候地域分异规律,尤以高海拔区的变化最为显著,全区GSS(物候始期)主要发生于70-130DOY(Day of Year),83.29%的区域呈提前趋势,提前主要集中在0-5d/10a(44.46%)与5-10d/10a(28.60%);GSE(物候末期)主要发生于270-310DOY,50.17%的区域呈推迟趋势,变化趋势不明显;GSL(生长期)集中在150-210d,65.34%的区域呈延长趋势,延长在0-5d/10a(19.28%)、5-10d/10a(20.71%)及10-15d/10a(14.12%)均有分布。(2)秦岭山地GSS对气候变化的响应程度显著大于GSE,南北坡植被物候变化不仅存在区域差异且存在季节差异,GSS北坡较南坡平均约早6.2d且南坡提前趋势较北坡显著,GSE南坡较北坡平均约晚5.8d且北坡推迟趋势较南坡显著,GSL北坡较南坡约长18.7-23.2d。(3)GSS、GSS及GSS变化表现出显著的海拔敏感性,随着海拔上升,GSS逐渐推迟,GSE逐渐提前,GSL逐渐缩短,三者在海拔≤600m及≥2700m地区随海拔变化的波动幅度较大,南北坡三者随海拔的变化亦存在明显的差异,海拔每上升100m,北坡GSS推迟1.76d,GSE提前0.25d,GSL缩短2.01d;南坡GSS推迟1.50d,GSE提前0.44d,GSL缩短1.94d。(4)不同植被垂直带上GSS、GSE及GSL的变化存在明显差异,尤以≤600m植被带上及高山灌丛草甸带上的差异最为明显,且三者在高山灌丛草甸带的发生时间及时长北坡与南坡发生转换,表现为GSS、GSE、GSL北坡较南坡分别平均早3.5d、晚2.9d、长6.4d。
基金Supported by the Open Subject of Key Lab of Resources Remote-sensing and Digital Agriculture in Agricultural Department(RDA1008)~~
文摘[Objective] The aim was to explore the feasibility of using single spectrum image to classify crops based on multi-spectral image and Decision Tree Method. [Method] Taking the typical agriculture plantation area in Hulunbeier area, according to field measured spectrum data, the optimum time of main crops, barley, wheat, rapeseed, based on crops spectrum characteristics, by dint of decision-making tree method, and considering spectral matching method, classification of crops was studied such as SAM. [Result] By dint of Landsat TM image gained in the first half of August, based on geographic and atmospheric proof-reading, decision-making tree was constructed. Plantation information about wheat, barley, and rapeseed and plantation grassland was extracted successfully. The general classification accuracy reached 86.90%. Kappa coefficient was 0.831 1. [Conclusion] Taking typical spectrum image as data source, and applying Decision Tree Method to get crops type＇s information had fine application future.
文摘植被物候遥感产品对全球变化响应、农业生产管理、生态学的应用等多领域研究具有重要意义。但现有植被物候遥感产品还有较多问题,主要包括一方面使用不同参数的时间序列数据以及不同提取算法导致的产品结果差异较大,另一方面在地面验证中地面观测数据与遥感反演数据的物理含义不一致导致的验证方法的系统性误差。本文以黑河流域为研究区,对比验证基于EVI(Enhanced Vegetation Index)时间序列数据提取的MLCD(MODIS global land cover dynamics product)植被遥感物候产品和基于LAI(Leaf Area Index)时间序列数据提取的UMPM(product by universal multi-life-cycle phenology monitoring method)植被遥感物候产品的有效性及精度等。同时,通过验证分析进一步评估基于EVI和LAI时间序列提取的物候特征的差异及特点,探讨由于地面观测植被物候与遥感提取植被物候的物理意义的不一致问题导致的直接验证结果偏差。结果表明:UMPM产品有效性整体高于MLCD产品,但在以草地和灌木为主的稀疏植被区,由于LAI取值精度的原因,UMPM产品存在较多缺失数据,且时空稳定性较低;基于玉米地面观测数据表明,EVI对植被开始生长的信号比LAI更加敏感,更适合提取生长起点,但植被指数易饱和,峰值起点普遍提前,基于LAI提取的峰值起点更加合理。由于地面观测的物候期在后期更加关注果实生长,遥感观测仅关注叶片的生长,遥感定义的峰值终点和生长终点与玉米的乳熟期和成熟期差异较大。
基金Under the auspices of China Postdoctoral Science Foundation (No. 20080430586, 20070420018)National Natural Science Foundation of China (No. 40801161, 40801172)Sino US International Cooperation in Science and Technology (No. 2007DFA20640)
文摘Using three-phase remote sensing images of China-Brazil Earth Resources Satellite 02B (CBERS02B) and Landsat-5 TM, tobacco field was extracted by the analysis of time series image based on the different phenological characteristics between tobacco and other crops. The spectral characteristics of tobacco and corn in luxuriant growth stage are very similar, which makes them difficult to be distinguished using a single-phase remote sensing image. Field film after tobacco seedlings transplanting can be used as significant sign to identify tobacco. Remote sensing interpre- tation map based on the fusion image of TM and CBERS02B's High-Resolution (HR) camera image was used as stan- dard reference material to evaluate the classification accuracy of Spectral Angle Mapper (SAM) and Maximum Like- lihood Classifier (MLC) for time series image based on full samples test method. SAM has higher classification accu- racy and stability than MLC in dealing with time series image. The accuracy and Kappa of tobacco coverage extracted by SAM are 83.4% and 0.692 respectively, which can achieve the accuracy required by tobacco coverage measurement in a large area.
基金jointly supported by the National Basic Research Program of China[973 Program,2012CB956202]the Collaborative Innovation Center of Research and Development on Tibetan Characteristic Agricultural and Animal Husbandry Resources
文摘Using the NDVI ratio method, the authors extracted phenological parameters from NOAA-AVHRR NDVI time-series data （1982-2008）. The start of the growing season （SOS） and the date of maximum NDVI （Peak-t） correlated significantly with the mean annual precipitation along regional gradients of the steppes. Along the south transect （located at a lower latitude with a higher annual mean temperature） there was a positive correlation between the end of the growing season （EOS） and the mean annual precipitation along precipitation gradients （R2 = 0.709, p 〈 0.0001）. However, along the north transect （located at higher latitude with lower annual mean temperature）, the EOS was slightly negatively related with the mean annual precipitation （R2 = 0.179, p 〈 0.1）. There was positive correlation between the length of the growing season and the annual precipitation along two transects （R2 = 0.876, p 〈 0.0001 for the south transect; R2 = 0.290, p 〈 0.01 for the north transect）. Thus, for the Inner Mongolian steppe, it is precipitation rather than temperature that determines the date of the SOS.
基金National Natural Science Foundation of China(41601478)National Key Research and Development Program of China(2018YFB0505301,2016YFC0500103)
文摘Near-surface remote sensing(e.g.,digital cameras)has played an important role in capturing plant phenological metrics at either a focal or landscape scale.Exploring the relationship of the digital image-based greenness index(e.g.,Gcc,green chromatic coordinate)with that derived from satellites is critical for land surface process research.Moreover,our understanding of how well Gcc time series associate with environmental variables at field stations in North American prairies remains limited.This paper investigated the response of grass Gcc to daily environmental factors in 2018,such as soil moisture(temperature),air temperature,and solar radiation.Thereafter,using a derivative-based phenology extraction method,we evaluated the correspondence between key phenological events(mainly including start,end and length of growing season,and date with maximum greenness value)derived from Gcc,MODIS and VIIRS NDVI(EVI)for the period 2015–2018.The results showed that daily Gcc was in good agreement with ground-level environmental variables.Additionally,multivariate regression analysis identified that the grass growth in the study area was mainly affected by soil temperature and solar radiation,but not by air temperature.High frequency Gcc time series can respond immediately to precipitation events.In the same year,the phenological metrics retrieved from digital cameras and multiple satellites are similar,with spring phenology having a larger relative difference.There are distinct divergences between changing rates in the greenup and senescence stages.Gcc also shows a close relationship with growing degree days(GDD)derived from air temperature.This study evaluated the performance of a digital camera for monitoring vegetation phenological metrics and related climatic factors.This research will enable multiscale modeling of plant phenology and grassland resource management of temperate prairie ecosystems.
