The red-edge bands and their derived vegetation indices play a crucial role in monitoring vegetation health.The Gaofen-6(GF-6)and Sentinel-2A satellites are equipped with two and three red-edge bands,respectively,thus...The red-edge bands and their derived vegetation indices play a crucial role in monitoring vegetation health.The Gaofen-6(GF-6)and Sentinel-2A satellites are equipped with two and three red-edge bands,respectively,thus making them invaluable for monit-oring forest health.To compare the performance of these two satellites’red-edge bands in monitoring forest health,this study selected forests in Liuyang City,Hunan Province and Tonggu County,Jiangxi Province and Hanzhong City,Shaanxi Province in China as study areas and used three commonly used red-edge indices and the Random Forest(RF)algorithm for the comparison.The three selected red-edge indices were the Normalized Difference Red-Edge Index 1(NDRE1),the Missouri emergency resource information system Ter-restrial Chlorophyll Index(MTCI),and the Inverted Red-Edge Chlorophyll Index(IRECI).Through training of sample regions,this study determined the spectral differences among three forest health levels and established classification criteria for these levels.The res-ults showed that GF-6 imagery provided higher accuracy in distinguishing forest health levels than Sentinel-2A,with an average accur-acy of 90.22%versus 76.55%.This difference is attributed to variations in the wavelengths used to construct the red-edge indices between GF-6 and Sentinel-2A.In the RF algorithm,this study employed three distinct band combinations for classification:all bands including red-edge bands,excluding red-edge bands,and only red-edge bands.The results indicated that GF-6 outperformed Sentinel-2A when using the first and second band combinations,yet slightly underperforming with the third.This outcome was closely associ-ated with the importance of each band’s contribution to classification accuracy reveled by the Gini importance score,their sensitivity in detecting forest health conditions,and the total number of bands employed in the classification process.Overall,the NDRE1 derived from GF-6 achieved the highest average accuracy(90.22%).This study provides a scientific basis for selecting appropriate remote sens-ing data and techniques for forest health monitoring,which is of significant importance for the future ecological protection of forests.展开更多
Evolution in geoscientific data provides the mineral industry with new opportunities.A direction of geochemical data generation evolution is towards big data to meet the demands of data-driven usage scenarios that rel...Evolution in geoscientific data provides the mineral industry with new opportunities.A direction of geochemical data generation evolution is towards big data to meet the demands of data-driven usage scenarios that rely on data velocity.This direction is more significant where traditional geochemical data are not ideal,which is the case for evaluating unconventional resources,such as tailing storage facilities(TSFs),because they are not static due to sedimentation,compaction and changes associated with hydrospheric and lithospheric processes(e.g.,erosion,saltation and mobility of chemical constituents).In this paper,we generate big secondary geochemical data derived from Sentinel-2 satellite-remote sensing data to showcase the benefits of big geochemical data using TSFs from the Witwatersrand Basin(South Africa).Using spatially fused remote sensing and legacy geochemical data on the Dump 20 TSF,we trained a machine learning model to predict in-situ gold grades.Subsequently,we deployed the model to the Lindum TSF,which is 3 km away,over a period of a few years(2015-2019).We were able to visualize and analyze the temporal variation in the spatial distributions of the gold grade of the Lindum TSF.Additionally,we were able to infer extraction sequencing(to the resolution of the data),acid mine drainage formation and seasonal migration.These findings suggest that dynamic mineral resource models and live geochemical monitoring(e.g.,of elemental mobility and structural changes)are possible without additional physical sampling.展开更多
High resolution satellite images are becoming increasingly available for urban multi-temporal semantic understanding.However,few datasets can be used for land-use/land-cover(LULC)classification,binary change detection...High resolution satellite images are becoming increasingly available for urban multi-temporal semantic understanding.However,few datasets can be used for land-use/land-cover(LULC)classification,binary change detection(BCD)and semantic change detection(SCD)simultaneously because classification datasets always have one time phase and BCD datasets focus only on the changed location,ignoring the changed classes.Public SCD datasets are rare but much needed.To solve the above problems,a tri-temporal SCD dataset made up of Gaofen-2(GF-2)remote sensing imagery(with 11 LULC classes and 60 change directions)was built in this study,namely,the Wuhan Urban Semantic Understanding(WUSU)dataset.Popular deep learning based methods for LULC classification,BCD and SCD are tested to verify the reliability of WUSU.A Siamese-based multi-task joint framework with a multi-task joint loss(MJ loss)named ChangeMJ is proposed to restore the object boundaries and obtains the best results in LULC classification,BCD and SCD,compared to the state-of-the-art(SOTA)methods.Finally,a large spatial-scale mapping for Wuhan central urban area is carried out to verify that the WUsU dataset and the ChangeMJ framework have good application values.展开更多
A physical method,based on the simplification of surface radiation terms in remote sensing equations, has been suggested to retrieve the surface temperature,vertical temperature profile and surface emissivity from the...A physical method,based on the simplification of surface radiation terms in remote sensing equations, has been suggested to retrieve the surface temperature,vertical temperature profile and surface emissivity from the first eight channel observations of TIROS-N/HIRS2.Analyses of several examples indicate that this method can obtain much more accurate temperatures in the lower atmosphere than a statistical technique, and that the surface temperature and emissivity retrieved are also reasonable.展开更多
SiB2(simple biosphere model Version 2)是用来模拟生态系统通量较为理想的国外模型,为了探讨其在我国黄河灌区的适用性及利用遥感数据驱动模型的可行性,并用其来研究该地区农田能量收支情况,以位山灌区为研究试点,利用位山实验站1a左...SiB2(simple biosphere model Version 2)是用来模拟生态系统通量较为理想的国外模型,为了探讨其在我国黄河灌区的适用性及利用遥感数据驱动模型的可行性,并用其来研究该地区农田能量收支情况,以位山灌区为研究试点,利用位山实验站1a左右的观测数据对模型进行了验证分析,模拟结果表明:SiB2模型能够较好地模拟位山试验站农田的能量通量、CO2通量及地表温度,净辐射、潜热通量、感热通量、CO2通量与地表温度的模拟值与观测值吻合较好,线性相关系数R分别为0.988,0.714,0.607,0.677与0.933,其中净辐射模拟效果最好,感热通量偏差较大。另外,利用遥感MODIS LAI数据驱动SiB2模型表明,除净辐射外,模拟效果很差,因此在站点尺度遥感LAI(叶面积指数,leaf area index)产品不适合驱动SiB2模型。展开更多
The return of crop residues to cultivated fields has numerous agronomic and soil quality benefits and,therefore,the areal extent of crop residue cover(CRC)could provide a rapid measure of the sustainability of agricul...The return of crop residues to cultivated fields has numerous agronomic and soil quality benefits and,therefore,the areal extent of crop residue cover(CRC)could provide a rapid measure of the sustainability of agricultural production systems in a region.Recognizing the limitations of traditional CRC methods,a new method is proposed for estimating the spatial and temporal distribution of maize residue cover(MRC)in the Jilin Province,NE China.The method used random forest(RF)algorithms,13 tillage indices and 9 textural feature indicators derived from Sentinel-2 data.The tillage indices with the best predictive performance were STI and NDTI(R^(2) of 0.85 and 0.84,respectively).Among the texture features,the bestfitting was Band8AMean-5*5(R^(2) of 0.56 and 0.54 for the line-transect and photographic methods,respectively).Based on MSE and InNodePurity,the optimal combination of RF algorithm for the linetransect method was STI,NDTI,NDI7,NDRI5,SRNDI,NDRI6,NDRI7 and Band3Mean-3*3.Likewise,the optimal combination of RF algorithm for the photographic method was STI,NDTI,NDI7,SRNDI,NDRI6,NDRI5,NDRI9 and Band3Mean-3*3.Regional distribution of MRC in the Jilin Province,estimated using the RF prediction model,was higher in the central and southeast sections than in the northwest.That distribution was in line with the spatial heterogeneity of maize yield in the region.These findings showed that the RF algorithm can be used to map regional MRC and,therefore,represents a useful tool for monitoring regional-scale adoption of conservation agricultural practices.展开更多
基金Under the auspices of National Natural Science Foundation of China(No.31971639)National Natural Science Foundation of Fujian Province,China(No.2023J01225)。
文摘The red-edge bands and their derived vegetation indices play a crucial role in monitoring vegetation health.The Gaofen-6(GF-6)and Sentinel-2A satellites are equipped with two and three red-edge bands,respectively,thus making them invaluable for monit-oring forest health.To compare the performance of these two satellites’red-edge bands in monitoring forest health,this study selected forests in Liuyang City,Hunan Province and Tonggu County,Jiangxi Province and Hanzhong City,Shaanxi Province in China as study areas and used three commonly used red-edge indices and the Random Forest(RF)algorithm for the comparison.The three selected red-edge indices were the Normalized Difference Red-Edge Index 1(NDRE1),the Missouri emergency resource information system Ter-restrial Chlorophyll Index(MTCI),and the Inverted Red-Edge Chlorophyll Index(IRECI).Through training of sample regions,this study determined the spectral differences among three forest health levels and established classification criteria for these levels.The res-ults showed that GF-6 imagery provided higher accuracy in distinguishing forest health levels than Sentinel-2A,with an average accur-acy of 90.22%versus 76.55%.This difference is attributed to variations in the wavelengths used to construct the red-edge indices between GF-6 and Sentinel-2A.In the RF algorithm,this study employed three distinct band combinations for classification:all bands including red-edge bands,excluding red-edge bands,and only red-edge bands.The results indicated that GF-6 outperformed Sentinel-2A when using the first and second band combinations,yet slightly underperforming with the third.This outcome was closely associ-ated with the importance of each band’s contribution to classification accuracy reveled by the Gini importance score,their sensitivity in detecting forest health conditions,and the total number of bands employed in the classification process.Overall,the NDRE1 derived from GF-6 achieved the highest average accuracy(90.22%).This study provides a scientific basis for selecting appropriate remote sens-ing data and techniques for forest health monitoring,which is of significant importance for the future ecological protection of forests.
基金supported by a Department of Science and Innovation(DSI)-National Research Foundation(NRF)Thuthuka Grant(Grant UID:121973)and DSI-NRF CIMERA.
文摘Evolution in geoscientific data provides the mineral industry with new opportunities.A direction of geochemical data generation evolution is towards big data to meet the demands of data-driven usage scenarios that rely on data velocity.This direction is more significant where traditional geochemical data are not ideal,which is the case for evaluating unconventional resources,such as tailing storage facilities(TSFs),because they are not static due to sedimentation,compaction and changes associated with hydrospheric and lithospheric processes(e.g.,erosion,saltation and mobility of chemical constituents).In this paper,we generate big secondary geochemical data derived from Sentinel-2 satellite-remote sensing data to showcase the benefits of big geochemical data using TSFs from the Witwatersrand Basin(South Africa).Using spatially fused remote sensing and legacy geochemical data on the Dump 20 TSF,we trained a machine learning model to predict in-situ gold grades.Subsequently,we deployed the model to the Lindum TSF,which is 3 km away,over a period of a few years(2015-2019).We were able to visualize and analyze the temporal variation in the spatial distributions of the gold grade of the Lindum TSF.Additionally,we were able to infer extraction sequencing(to the resolution of the data),acid mine drainage formation and seasonal migration.These findings suggest that dynamic mineral resource models and live geochemical monitoring(e.g.,of elemental mobility and structural changes)are possible without additional physical sampling.
基金supported by National Key Research and Development Program of China under grant number 2022YFB3903404National Natural Science Foundation of China under grant number 42325105,42071350LIESMARS Special Research Funding.
文摘High resolution satellite images are becoming increasingly available for urban multi-temporal semantic understanding.However,few datasets can be used for land-use/land-cover(LULC)classification,binary change detection(BCD)and semantic change detection(SCD)simultaneously because classification datasets always have one time phase and BCD datasets focus only on the changed location,ignoring the changed classes.Public SCD datasets are rare but much needed.To solve the above problems,a tri-temporal SCD dataset made up of Gaofen-2(GF-2)remote sensing imagery(with 11 LULC classes and 60 change directions)was built in this study,namely,the Wuhan Urban Semantic Understanding(WUSU)dataset.Popular deep learning based methods for LULC classification,BCD and SCD are tested to verify the reliability of WUSU.A Siamese-based multi-task joint framework with a multi-task joint loss(MJ loss)named ChangeMJ is proposed to restore the object boundaries and obtains the best results in LULC classification,BCD and SCD,compared to the state-of-the-art(SOTA)methods.Finally,a large spatial-scale mapping for Wuhan central urban area is carried out to verify that the WUsU dataset and the ChangeMJ framework have good application values.
文摘A physical method,based on the simplification of surface radiation terms in remote sensing equations, has been suggested to retrieve the surface temperature,vertical temperature profile and surface emissivity from the first eight channel observations of TIROS-N/HIRS2.Analyses of several examples indicate that this method can obtain much more accurate temperatures in the lower atmosphere than a statistical technique, and that the surface temperature and emissivity retrieved are also reasonable.
文摘SiB2(simple biosphere model Version 2)是用来模拟生态系统通量较为理想的国外模型,为了探讨其在我国黄河灌区的适用性及利用遥感数据驱动模型的可行性,并用其来研究该地区农田能量收支情况,以位山灌区为研究试点,利用位山实验站1a左右的观测数据对模型进行了验证分析,模拟结果表明:SiB2模型能够较好地模拟位山试验站农田的能量通量、CO2通量及地表温度,净辐射、潜热通量、感热通量、CO2通量与地表温度的模拟值与观测值吻合较好,线性相关系数R分别为0.988,0.714,0.607,0.677与0.933,其中净辐射模拟效果最好,感热通量偏差较大。另外,利用遥感MODIS LAI数据驱动SiB2模型表明,除净辐射外,模拟效果很差,因此在站点尺度遥感LAI(叶面积指数,leaf area index)产品不适合驱动SiB2模型。
基金jointly supported by the National Key Research and Development Program of China(2021YFD1500103)the Science and Technology Project for Black Soil Granary(XDA28080500)the National Science&Technology Fundamental Resources Investigation Program of China(2018FY100300).
文摘The return of crop residues to cultivated fields has numerous agronomic and soil quality benefits and,therefore,the areal extent of crop residue cover(CRC)could provide a rapid measure of the sustainability of agricultural production systems in a region.Recognizing the limitations of traditional CRC methods,a new method is proposed for estimating the spatial and temporal distribution of maize residue cover(MRC)in the Jilin Province,NE China.The method used random forest(RF)algorithms,13 tillage indices and 9 textural feature indicators derived from Sentinel-2 data.The tillage indices with the best predictive performance were STI and NDTI(R^(2) of 0.85 and 0.84,respectively).Among the texture features,the bestfitting was Band8AMean-5*5(R^(2) of 0.56 and 0.54 for the line-transect and photographic methods,respectively).Based on MSE and InNodePurity,the optimal combination of RF algorithm for the linetransect method was STI,NDTI,NDI7,NDRI5,SRNDI,NDRI6,NDRI7 and Band3Mean-3*3.Likewise,the optimal combination of RF algorithm for the photographic method was STI,NDTI,NDI7,SRNDI,NDRI6,NDRI5,NDRI9 and Band3Mean-3*3.Regional distribution of MRC in the Jilin Province,estimated using the RF prediction model,was higher in the central and southeast sections than in the northwest.That distribution was in line with the spatial heterogeneity of maize yield in the region.These findings showed that the RF algorithm can be used to map regional MRC and,therefore,represents a useful tool for monitoring regional-scale adoption of conservation agricultural practices.
