Offset-tracking is an essential method for deriving glacier flow rates using optical imagery.Sentinel-2(S2)and Landsat-8/9(L8/9)are popular optical satellites or constellations for polar studies,offering high spatial ...Offset-tracking is an essential method for deriving glacier flow rates using optical imagery.Sentinel-2(S2)and Landsat-8/9(L8/9)are popular optical satellites or constellations for polar studies,offering high spatial resolution with relatively short revisit time,wide swath width,and free accessibility.To evaluate and compare the precision of offset-tracking results yielded with these two kinds of data,in this study S2 and L8/9 imagery observed in Petermann Glacier in Greenland,Karakoram in High-Mountains Asia,and Amery Ice Shelf in the Antarctic are analyzed.Outliers and various systematic error sources in the offset-tracking results including orbital and strip errors were analyzed and eliminated at the pre-process stage.Precision at the off-glacier(bare rock)region was evaluated by presuming that no deformation occurred;then for both glacierized and the off-glacier regions,precision of velocity time series was evaluated based on error propagation theory.The least squares method based on connected components was used to solve flow rates time series based on multi-pair images offset-tracking.The results indicated that S2 achieved slightly higher precision than L8/9 in terms of both single-pair derived displacements and least square solved daily flow rates time series.Generally,the RMSE of daily velocity is 26%lower for S2 than L8/9.Moreover,S2 provided higher temporal resolution for monitoring glacier flow rates.展开更多
Ratoon rice,which refers to a second harvest of rice obtained from the regenerated tillers originating from the stubble of the first harvested crop,plays an important role in both food security and agroecology while r...Ratoon rice,which refers to a second harvest of rice obtained from the regenerated tillers originating from the stubble of the first harvested crop,plays an important role in both food security and agroecology while requiring minimal agricultural inputs.However,accurately identifying ratoon rice crops is challenging due to the similarity of its spectral features with other rice cropping systems(e.g.,double rice).Moreover,images with a high spatiotemporal resolution are essential since ratoon rice is generally cultivated in fragmented croplands within regions that frequently exhibit cloudy and rainy weather.In this study,taking Qichun County in Hubei Province,China as an example,we developed a new phenology-based ratoon rice vegetation index(PRVI)for the purpose of ratoon rice mapping at a 30 m spatial resolution using a robust time series generated from Harmonized Landsat and Sentinel-2(HLS)images.The PRVI that incorporated the red,near-infrared,and shortwave infrared 1 bands was developed based on the analysis of spectro-phenological separability and feature selection.Based on actual field samples,the performance of the PRVI for ratoon rice mapping was carefully evaluated by comparing it to several vegetation indices,including normalized difference vegetation index(NDVI),enhanced vegetation index(EVI)and land surface water index(LSWI).The results suggested that the PRVI could sufficiently capture the specific characteristics of ratoon rice,leading to a favorable separability between ratoon rice and other land cover types.Furthermore,the PRVI showed the best performance for identifying ratoon rice in the phenological phases characterized by grain filling and harvesting to tillering of the ratoon crop(GHS-TS2),indicating that only several images are required to obtain an accurate ratoon rice map.Finally,the PRVI performed better than NDVI,EVI,LSWI and their combination at the GHS-TS2 stages,with producer's accuracy and user's accuracy of 92.22 and 89.30%,respectively.These results demonstrate that the proposed PRVI based on HLS data can effectively identify ratoon rice in fragmented croplands at crucial phenological stages,which is promising for identifying the earliest timing of ratoon rice planting and can provide a fundamental dataset for crop management activities.展开更多
利用卫星影像快速准确地提取湖泊等地表水体范围一直是一个重要的研究课题,其对洪涝灾害监测、水资源管理与利用等具有重要意义。Sentinel-2 MSI和Landsat8 OLI数据是目前主流的开放获取的中高空间分辨率遥感影像。以鄱阳湖区为研究对象...利用卫星影像快速准确地提取湖泊等地表水体范围一直是一个重要的研究课题,其对洪涝灾害监测、水资源管理与利用等具有重要意义。Sentinel-2 MSI和Landsat8 OLI数据是目前主流的开放获取的中高空间分辨率遥感影像。以鄱阳湖区为研究对象,首先,分别使用归一化差异水体指数(normalized difference water index,NDWI)、改进的归一化差异水体指数(modified normalized difference water index,MNDWI)、自动水体提取指数(automated water extraction index,AWEI sh )和基于线性判别分析的水体指数(water index,WI 2015 )等4种常用的水体指数从2种影像中提取湖泊水体的分布信息;然后,分析了在同种水体指数之下2种影像提取结果的差异性和同一幅影像中4种水体指数提取结果的不同;最后,利用同期的高分一号影像目视解译的结果对水体提取结果进行了精度验证。结果表明,对于2种遥感影像,4种水体指数均能成功地提取出研究区的大部分水体;AWEI sh 和WI 2015 的提取精度最高,在Sentinel-2和Landsat8影像上分别达到了98%和94%以上,MNDWI次之,NDWI的提取精度最低;相对而言,Sentinel-2影像提取的水体细部信息更为明显,整体提取效果优于Landsat8影像。展开更多
为验证Landsat-8陆地成像仪(operational land imager,OLI)遥感数据与Sentinel-2多光谱成像仪(multispectral imager,MSI)遥感数据监测近海海域叶绿素a浓度可行性,以其为数据源,香港近海海域为研究区域,以半分析模型为方法,挑选与监测...为验证Landsat-8陆地成像仪(operational land imager,OLI)遥感数据与Sentinel-2多光谱成像仪(multispectral imager,MSI)遥感数据监测近海海域叶绿素a浓度可行性,以其为数据源,香港近海海域为研究区域,以半分析模型为方法,挑选与监测点实测叶绿素a浓度采集时间一致且遥感影像云覆盖率小于10%影像清晰的两类遥感影像。对两类遥感影像分别选取2/3的遥感影像数据经预处理后提取其对应实测日期监测点位置遥感反射率进行相关性分析,得到相关性最高的反演因子进行建模,并且利用剩下的1/3数据对其反演回复回归模型进行精度检验,其结果与OCx Ocean Chlorophyll X模型反演结果进行对比效果显著。基于Landsat-8遥感数据建立的最佳反演回归半分析模型决定系数R^(2)为0.906,略高于基于Sentinel-2遥感数据建立的最佳反演回归半分析模型,其R^(2)为0.801。与此同时证明了就香港近海海域叶绿素a浓度反演两类遥感数据的可行性,且两类数据的反演结果均呈现出香港近海海域内部海域叶绿素a浓度高于外部叶绿素a浓度的现象。展开更多
Accurate estimates of forest aboveground biomass(AGB)are critical for supporting strategies of ecosystem conservation and climate change mitigation.The Jiuzhaigou National Nature Reserve,located in Eastern Tibet Plate...Accurate estimates of forest aboveground biomass(AGB)are critical for supporting strategies of ecosystem conservation and climate change mitigation.The Jiuzhaigou National Nature Reserve,located in Eastern Tibet Plateau,has rich forest resources on steep slopes and is very sensitive to climate change but plays an important role in the regulation of regional carbon cycles.However,an estimation of AGB of subalpine forests in the Nature Reserve has not been carried out and whether a global biomass model is available has not been determined.To provide this information,Landsat 8 OLI and Sentinel-2B data were combined to estimate subalpine forest AGB using linear regression,and two machine learning approaches–random forest and extreme gradient boosting,with 54 inventory plots.Regardless of forest type,Observed AGB of the Reserve varied from 61.7 to 475.1 Mg hawith an average of 180.6 Mg ha.Results indicate that integrating the Landsat 8 OLI and Sentinel-2B imagery significantly improved model efficiency regardless of modelling approaches.The results highlight a potential way to improve the prediction of forest AGB in mountainous regions.Modelled AGB indicated a strong spatial variability.However,the modelled biomass varied greatly with global biomass products,indicating that global biomass products should be evaluated in regional AGB estimates and more field observations are required,particularly for areas with complex terrain to improve model accuracy.展开更多
An increase in crop intensity could improve crop yield but may also lead to a series of environmental problems, such as depletion of ground water and increased soil salinity. The generation of high resolution(30 m) cr...An increase in crop intensity could improve crop yield but may also lead to a series of environmental problems, such as depletion of ground water and increased soil salinity. The generation of high resolution(30 m) crop intensity maps is an important method used to monitor these changes, but this is challenging because the temporal resolution of the 30-m image time series is low due to the long satellite revisit period and high cloud coverage. The recently launched Sentinel-2 satellite could provide optical images at 10–60 m resolution and thus improve the temporal resolution of the 30-m image time series. This study used harmonized Landsat Sentinel-2(HLS) data to identify crop intensity. The sixth polynomial function was used to fit the normalized difference vegetation index(NDVI) and enhanced vegetation index(EVI) curves. Then, 15-day NDVI and EVI time series were then generated from the fitted curves and used to generate the extent of croplands. Lastly, the first derivative of the fitted VI curves were used to calculate the VI peaks;spurious peaks were removed using artificially defined thresholds and crop intensity was generated by counting the number of remaining VI peaks. The proposed methods were tested in four study regions, with results showing that 15-day time series generated from the fitted curves could accurately identify cropland extent. Overall accuracy of cropland identification was higher than 95%. In addition, both the harmonized NDVI and EVI time series identified crop intensity accurately as the overall accuracies, producer’s accuracies and user’s accuracies of non-cropland, single crop cycle and double crop cycle were higher than 85%. NDVI outperformed EVI as identifying double crop cycle fields more accurately.展开更多
基金supported by the National Natural Science Foundation of China(Grant no.42371136)the Guangdong Basic and Applied Basic Research Foundation(Grant no.2021B1515020032)the Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)(Grant no.311022003).
文摘Offset-tracking is an essential method for deriving glacier flow rates using optical imagery.Sentinel-2(S2)and Landsat-8/9(L8/9)are popular optical satellites or constellations for polar studies,offering high spatial resolution with relatively short revisit time,wide swath width,and free accessibility.To evaluate and compare the precision of offset-tracking results yielded with these two kinds of data,in this study S2 and L8/9 imagery observed in Petermann Glacier in Greenland,Karakoram in High-Mountains Asia,and Amery Ice Shelf in the Antarctic are analyzed.Outliers and various systematic error sources in the offset-tracking results including orbital and strip errors were analyzed and eliminated at the pre-process stage.Precision at the off-glacier(bare rock)region was evaluated by presuming that no deformation occurred;then for both glacierized and the off-glacier regions,precision of velocity time series was evaluated based on error propagation theory.The least squares method based on connected components was used to solve flow rates time series based on multi-pair images offset-tracking.The results indicated that S2 achieved slightly higher precision than L8/9 in terms of both single-pair derived displacements and least square solved daily flow rates time series.Generally,the RMSE of daily velocity is 26%lower for S2 than L8/9.Moreover,S2 provided higher temporal resolution for monitoring glacier flow rates.
基金supported by the National Natural Science Foundation of China(42271360 and 42271399)the Young Elite Scientists Sponsorship Program by China Association for Science and Technology(CAST)(2020QNRC001)the Fundamental Research Funds for the Central Universities,China(2662021JC013,CCNU22QN018)。
文摘Ratoon rice,which refers to a second harvest of rice obtained from the regenerated tillers originating from the stubble of the first harvested crop,plays an important role in both food security and agroecology while requiring minimal agricultural inputs.However,accurately identifying ratoon rice crops is challenging due to the similarity of its spectral features with other rice cropping systems(e.g.,double rice).Moreover,images with a high spatiotemporal resolution are essential since ratoon rice is generally cultivated in fragmented croplands within regions that frequently exhibit cloudy and rainy weather.In this study,taking Qichun County in Hubei Province,China as an example,we developed a new phenology-based ratoon rice vegetation index(PRVI)for the purpose of ratoon rice mapping at a 30 m spatial resolution using a robust time series generated from Harmonized Landsat and Sentinel-2(HLS)images.The PRVI that incorporated the red,near-infrared,and shortwave infrared 1 bands was developed based on the analysis of spectro-phenological separability and feature selection.Based on actual field samples,the performance of the PRVI for ratoon rice mapping was carefully evaluated by comparing it to several vegetation indices,including normalized difference vegetation index(NDVI),enhanced vegetation index(EVI)and land surface water index(LSWI).The results suggested that the PRVI could sufficiently capture the specific characteristics of ratoon rice,leading to a favorable separability between ratoon rice and other land cover types.Furthermore,the PRVI showed the best performance for identifying ratoon rice in the phenological phases characterized by grain filling and harvesting to tillering of the ratoon crop(GHS-TS2),indicating that only several images are required to obtain an accurate ratoon rice map.Finally,the PRVI performed better than NDVI,EVI,LSWI and their combination at the GHS-TS2 stages,with producer's accuracy and user's accuracy of 92.22 and 89.30%,respectively.These results demonstrate that the proposed PRVI based on HLS data can effectively identify ratoon rice in fragmented croplands at crucial phenological stages,which is promising for identifying the earliest timing of ratoon rice planting and can provide a fundamental dataset for crop management activities.
文摘利用卫星影像快速准确地提取湖泊等地表水体范围一直是一个重要的研究课题,其对洪涝灾害监测、水资源管理与利用等具有重要意义。Sentinel-2 MSI和Landsat8 OLI数据是目前主流的开放获取的中高空间分辨率遥感影像。以鄱阳湖区为研究对象,首先,分别使用归一化差异水体指数(normalized difference water index,NDWI)、改进的归一化差异水体指数(modified normalized difference water index,MNDWI)、自动水体提取指数(automated water extraction index,AWEI sh )和基于线性判别分析的水体指数(water index,WI 2015 )等4种常用的水体指数从2种影像中提取湖泊水体的分布信息;然后,分析了在同种水体指数之下2种影像提取结果的差异性和同一幅影像中4种水体指数提取结果的不同;最后,利用同期的高分一号影像目视解译的结果对水体提取结果进行了精度验证。结果表明,对于2种遥感影像,4种水体指数均能成功地提取出研究区的大部分水体;AWEI sh 和WI 2015 的提取精度最高,在Sentinel-2和Landsat8影像上分别达到了98%和94%以上,MNDWI次之,NDWI的提取精度最低;相对而言,Sentinel-2影像提取的水体细部信息更为明显,整体提取效果优于Landsat8影像。
基金supported financially by the Specialized Fund for the Post-Disaster Reconstruction and Heritage Protec-tion in Sichuan Province(5132202019000128)the Everest Scientific Research Program of Chengdu University of Technology(80000-2021ZF11410)+3 种基金the Second Tibetan Plateau Scientific Expedition and Research Program(STEP,2019QZKK0307)the State Key Laborato-ry of Geohazard Prevention and Geoenvironment Protection Independent Research Project(SKLGP2018Z004)the key technologies of Mountain rail transit green construction in ecologically sensitive region based on Mountain rail transit from Dujiangyan to Mt.Siguniang anti-poverty project(2018-zl-08)Study on risk identification and countermeasures of Sichuan-Tibet Railway Major Projects(2019YFG0460)。
文摘Accurate estimates of forest aboveground biomass(AGB)are critical for supporting strategies of ecosystem conservation and climate change mitigation.The Jiuzhaigou National Nature Reserve,located in Eastern Tibet Plateau,has rich forest resources on steep slopes and is very sensitive to climate change but plays an important role in the regulation of regional carbon cycles.However,an estimation of AGB of subalpine forests in the Nature Reserve has not been carried out and whether a global biomass model is available has not been determined.To provide this information,Landsat 8 OLI and Sentinel-2B data were combined to estimate subalpine forest AGB using linear regression,and two machine learning approaches–random forest and extreme gradient boosting,with 54 inventory plots.Regardless of forest type,Observed AGB of the Reserve varied from 61.7 to 475.1 Mg hawith an average of 180.6 Mg ha.Results indicate that integrating the Landsat 8 OLI and Sentinel-2B imagery significantly improved model efficiency regardless of modelling approaches.The results highlight a potential way to improve the prediction of forest AGB in mountainous regions.Modelled AGB indicated a strong spatial variability.However,the modelled biomass varied greatly with global biomass products,indicating that global biomass products should be evaluated in regional AGB estimates and more field observations are required,particularly for areas with complex terrain to improve model accuracy.
基金supported by the China Postdoctoral Science Foundation (2017M620075 and BX201700286)the National Natural Science Foundation of China (NSFC-61661136006)
文摘An increase in crop intensity could improve crop yield but may also lead to a series of environmental problems, such as depletion of ground water and increased soil salinity. The generation of high resolution(30 m) crop intensity maps is an important method used to monitor these changes, but this is challenging because the temporal resolution of the 30-m image time series is low due to the long satellite revisit period and high cloud coverage. The recently launched Sentinel-2 satellite could provide optical images at 10–60 m resolution and thus improve the temporal resolution of the 30-m image time series. This study used harmonized Landsat Sentinel-2(HLS) data to identify crop intensity. The sixth polynomial function was used to fit the normalized difference vegetation index(NDVI) and enhanced vegetation index(EVI) curves. Then, 15-day NDVI and EVI time series were then generated from the fitted curves and used to generate the extent of croplands. Lastly, the first derivative of the fitted VI curves were used to calculate the VI peaks;spurious peaks were removed using artificially defined thresholds and crop intensity was generated by counting the number of remaining VI peaks. The proposed methods were tested in four study regions, with results showing that 15-day time series generated from the fitted curves could accurately identify cropland extent. Overall accuracy of cropland identification was higher than 95%. In addition, both the harmonized NDVI and EVI time series identified crop intensity accurately as the overall accuracies, producer’s accuracies and user’s accuracies of non-cropland, single crop cycle and double crop cycle were higher than 85%. NDVI outperformed EVI as identifying double crop cycle fields more accurately.
