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
Poyang Lake is the largest freshwater lake in China. This paper conducted a digital and rapid investigation of the lake’s wetland vegetation biomass using Landsat ETM data acquired on April 16, 2000. First, utilizing...Poyang Lake is the largest freshwater lake in China. This paper conducted a digital and rapid investigation of the lake’s wetland vegetation biomass using Landsat ETM data acquired on April 16, 2000. First, utilizing the false color composite derived from the ETM data as one of the main references, the authors designed a reasonable sampling route for field measurement of the biomass, and carried it out on April 18–28, 2000. Then after both the sampling data and the ETM data were geometrically corrected to an equal-area projection of Albers, linear relationships among the sampling data and some transformed data derived from the ETM data and the ETM 4 were calculated. The results show that the sampling data is best relative to the band 4 data with a high correlation coefficient of 0.86, followed by the DVI and NDVI data with 0.83 and 0.80 respectively. Therefore, a linear regression model, which was based on the field data and band 4 data, was used to estimate the total biomass of entire Poyang Lake, and then the map of the biomass distribution was compiled.展开更多
The world’s largest mangrove ecosystem,the Sunderbans is experiencing multidimensional threats of degradation.The present study was aimed to understand these problems and search for proper remedies by applying suitab...The world’s largest mangrove ecosystem,the Sunderbans is experiencing multidimensional threats of degradation.The present study was aimed to understand these problems and search for proper remedies by applying suitable remote sensing technologies.South-western parts of Indian Sunderbans Biosphere Reserve had been chosen for assessment of land use/land cover changes in between 1975 and 2006 by using multitemporal Landsat data.Results indicated considerable reduction of open mangrove stands and associated biodiversity mainly in the forest-habitation interference zones of Sunderbans.On the contrary,increase in the coverage of dense mangroves in the reserved forests had been observed indicating the existence of proper centralized management regimes.Overall,a cumulative loss of approximately 0.42%of its original mangrove cover in between 1975 and 2006 had been estimated for this part of the Sunderbans which was at parity with the findings of other studies in the Sunderbans or similar mangrove ecosystems of the tropics.Expansion of non agricultural lands in the last two decades was found to be related with the growth of new settlements,tourism infrastructure,and facilities.This transformation was attributed to the shifting of local peoples’interest from traditional forestry and subsistence farming towards alternative occupations like shrimp culture,coastal tourism,and commercial fishing although environmentally hazardous livelihood activities like collection of prawn seeds along the riverbanks were still persistent.展开更多
Most lakes have undergone significant changes on the Tibetan Plateau in recent decades,affecting water resources on the Tibetan Plateau and its surrounding areas.In this paper,we investigated the variations of 25 lake...Most lakes have undergone significant changes on the Tibetan Plateau in recent decades,affecting water resources on the Tibetan Plateau and its surrounding areas.In this paper,we investigated the variations of 25 lakes in five sub-regions on the Tibetan Plateau from 1972 to 2019 based on SRTM DEM data and Landsat imagery.We used a method to derive lake-levels based on DEM and lake boundaries delineated from Landsat imagery,and then calculated the changes in lake area,level,and volume in 1972 to 2019.We also analyzed the potential impacts of temperature,precipitation,glacial and permafrost melting in lake changes during this period.The results show that the lakes tended to shrink until 2010 in southern and western plateau,after which they began to expand gradually but the overall trend is still shrinking.Limited meltwater from glaciers and permafrost and low precipitation are the main reasons for their shrinkage.The lakes in the central plateau,northwest plateau and northeast plateau tend to expand overall.The reason for the expansion of the lakes is not only precipitation but also the melting of glaciers and permafrost.Overall,the lake changes have gone through 3 phases,namely a slight decrease during 1972-2000,a rapid increase during 2000-2010,and a slowdown in the last decade(2010-2019).Multiple factors such as temperature,precipitation,the state of glaciers and permafrost have contributed to the changes in the lake.展开更多
Synthetic aperture radar(SAR)records important information about the interaction of electromagnetic waves with the Earth’s surface.However,long-term and high-resolution backscatter coefficient data are still lacking ...Synthetic aperture radar(SAR)records important information about the interaction of electromagnetic waves with the Earth’s surface.However,long-term and high-resolution backscatter coefficient data are still lacking in many urban studies(e.g.,building height estimation).Here,we proposed a framework to reconstruct the 1-km backscatter coefficient in 1990-2022 utilizing the Sentinel-1 Ground Range Detected data and Landsat time series data in the Jing-Jin-Ji(JJJ)region.First,we developed a regression model to convert the optical signals from Landsat into backscatter coefficients as the Sentinel-1 data,using observations from 2015 to 2022.Then,we reconstructed backscatter coefficients from 1990 to 2022 using the long-term Landsat data.Using the reconstructed backscatter coefficients,we analyzed the dynamic patterns of building height over the past decades.The proposed approach performs well on estimating the backscatter coefficient and its spatial pattern,with the annual mean absolute error,root mean square error,and R^(2) of 1.10 dB,1.50 dB,and 0.64,respectively.The temporal trends revealed from the reconstructed backscatter data are reliable compared with satellite observations at a relatively coarse resolution,with Pearson’s coefficients above 0.92 in 6 sample cities.The derived building height from the reconstructed SAR data indicates that the JJJ region experienced a noticeable upward expansion in 1990-2022,e.g.,Beijing has the fastest growth rate of 0.420 km^(3)/decade regarding the total building volumes.The proposed framework of reconstructing SAR data from optical satellite images provides a new insight to complement the long-term and high-resolution backscatter from local to global scales.展开更多
Understanding wetland change is critical to establishing and implementing international conservation and management conventions.With such knowledge,supporting sustainable development,making management decisions,improv...Understanding wetland change is critical to establishing and implementing international conservation and management conventions.With such knowledge,supporting sustainable development,making management decisions,improving policies,and conducting scientific research become possible.However,consistent information on changes in Chinese wetlands has been unavailable.We applied the hybrid object-based and hierarchical classification approach to∼53,000 scenes of Landsat images acquired between 1980 and 2020 and created a national wetland mapping product(China_Wetlands)for six periods(e.g.,1980,1990,2000,2010,2015,and 2020).China_Wetlands revealed diverse changes in Chinese wetlands and their trajectories in response to climate change and human impacts over the past four decades.Specifically,there was a substantial shrinkage in wetland area before 2015,with a small rebound between 2015 and 2020.The net loss was∼60.9×10^(3)km^(2),which represents 12%of the area in 1980.However,the loss of natural wetlands was hidden by human-made wetland gain with an offset of 15.6×10^(3)km^(2).Additionally,the expansion of surface water extent approximately 14.0×10^(3)km^(2)obscured the loss of vegetated wetlands.Wetland loss in hotspot areas(e.g.,Sanjiang Plain and Yangtze River Delta)should not be neglected.The sustainable management and effective conservation of wetlands in China should target wetland areas,landscape structures,and small wetlands delivering important ecosystem services.Moreover,the conversion of wetland types and the invasion of alien species need to be monitored and regulated.China_Wetlands will be a critical wetland dataset for ecological research and the assessment of national and global environmental objectives(e.g.,the United Nation’s sustainable development goals).展开更多
Analysis of the nexus between vegetation dynamics and climatic parameters like surface temperature is essential in environmental and ecological studies and for monitoring of the natural resources. This study explored ...Analysis of the nexus between vegetation dynamics and climatic parameters like surface temperature is essential in environmental and ecological studies and for monitoring of the natural resources. This study explored the spatio-temporal distribution of land surface temperature(LST) and Normalized Difference Vegetation Index(NDVI) and the relationship between them in the Andassa watershed from 1986 to 2016 periods using Landsat data. Monthly average air temperature data of three meteorological sites were used for validating the results. The findings of the study showed that the LST of the Andassa watershed has increased during the study periods. Overall, average LST has been rising with an increasing rate of 0.081?C per year. Other results of this study also showed that there has been a dynamic change in vegetation cover of the watershed in all seasons. There was also a negative correlation between LST and NDVI in all the studied years. From this study we can understand that there has been degradation of vegetation and intensification of LST from 1986 to 2016.展开更多
The presence of green spaces within city centres has been recognized as a valuable component of the city landscape.Vegetation provides a variety of benefits including energy saving,improved air quality,reduced noise p...The presence of green spaces within city centres has been recognized as a valuable component of the city landscape.Vegetation provides a variety of benefits including energy saving,improved air quality,reduced noise pollution,decreased ambient temperature and psychological restoration.Evidence also shows that the amount of vegetation,known as‘greenness’,in densely populated areas,can also be an indicator of the relative wealth of a neighbourhood.The‘grey-green divide’,the contrast between built-up areas with a dominant grey colour and green spaces,is taken as a proxy indicator of sustainable management of cities and planning of urban growth.Consistent and continuous assessment of greenness in cities is therefore essential for monitoring progress towards the United Nations Sustainable Development Goal 11.The availability of multi-temporal greenness information from Landsat data archives together with data derived from the city centres database of the Global Human Settlement Layer(GHSL)initiative,offers a unique perspective to quantify and analyse changes in greenness across 10,323 urban centres all around the globe.In this research,we assess differences between greenness within and outside the built-up area for all the urban centres described by the city centres database of the GHSL.We also analyse changes in the amount of green space over time considering changes in the built-up areas in the periods 1990,2000 and 2014.The results show an overall trend of increased greenness between 1990 and 2014 in most cities.The effect of greening is observed also for most of the 32 world megacities.We conclude that using simple yet effective approaches exploiting open and free global data it is possible to provide quantitative information on the greenness of cities and its changes over time.This information is of direct interest for urban planners and decision-makers to mitigate urban related environmental and social impacts.展开更多
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.
基金The Knowledge Innovation Project of CAS, No. KZCX1-Y-02,No. KZCX2-310 The key project of Ninth Five-Year+3 种基金 Plan of CAS, No.KZ951-A1-102-01 The National Ninth Five-Year Plan Project,No.96-b02-01
文摘Poyang Lake is the largest freshwater lake in China. This paper conducted a digital and rapid investigation of the lake’s wetland vegetation biomass using Landsat ETM data acquired on April 16, 2000. First, utilizing the false color composite derived from the ETM data as one of the main references, the authors designed a reasonable sampling route for field measurement of the biomass, and carried it out on April 18–28, 2000. Then after both the sampling data and the ETM data were geometrically corrected to an equal-area projection of Albers, linear relationships among the sampling data and some transformed data derived from the ETM data and the ETM 4 were calculated. The results show that the sampling data is best relative to the band 4 data with a high correlation coefficient of 0.86, followed by the DVI and NDVI data with 0.83 and 0.80 respectively. Therefore, a linear regression model, which was based on the field data and band 4 data, was used to estimate the total biomass of entire Poyang Lake, and then the map of the biomass distribution was compiled.
文摘The world’s largest mangrove ecosystem,the Sunderbans is experiencing multidimensional threats of degradation.The present study was aimed to understand these problems and search for proper remedies by applying suitable remote sensing technologies.South-western parts of Indian Sunderbans Biosphere Reserve had been chosen for assessment of land use/land cover changes in between 1975 and 2006 by using multitemporal Landsat data.Results indicated considerable reduction of open mangrove stands and associated biodiversity mainly in the forest-habitation interference zones of Sunderbans.On the contrary,increase in the coverage of dense mangroves in the reserved forests had been observed indicating the existence of proper centralized management regimes.Overall,a cumulative loss of approximately 0.42%of its original mangrove cover in between 1975 and 2006 had been estimated for this part of the Sunderbans which was at parity with the findings of other studies in the Sunderbans or similar mangrove ecosystems of the tropics.Expansion of non agricultural lands in the last two decades was found to be related with the growth of new settlements,tourism infrastructure,and facilities.This transformation was attributed to the shifting of local peoples’interest from traditional forestry and subsistence farming towards alternative occupations like shrimp culture,coastal tourism,and commercial fishing although environmentally hazardous livelihood activities like collection of prawn seeds along the riverbanks were still persistent.
基金This work is supported by the National Key Research and Development Program of China[grant number 2018YFD1100104]the National Key Research and Development Program of China[grant number 2020YFC1521900].
文摘Most lakes have undergone significant changes on the Tibetan Plateau in recent decades,affecting water resources on the Tibetan Plateau and its surrounding areas.In this paper,we investigated the variations of 25 lakes in five sub-regions on the Tibetan Plateau from 1972 to 2019 based on SRTM DEM data and Landsat imagery.We used a method to derive lake-levels based on DEM and lake boundaries delineated from Landsat imagery,and then calculated the changes in lake area,level,and volume in 1972 to 2019.We also analyzed the potential impacts of temperature,precipitation,glacial and permafrost melting in lake changes during this period.The results show that the lakes tended to shrink until 2010 in southern and western plateau,after which they began to expand gradually but the overall trend is still shrinking.Limited meltwater from glaciers and permafrost and low precipitation are the main reasons for their shrinkage.The lakes in the central plateau,northwest plateau and northeast plateau tend to expand overall.The reason for the expansion of the lakes is not only precipitation but also the melting of glaciers and permafrost.Overall,the lake changes have gone through 3 phases,namely a slight decrease during 1972-2000,a rapid increase during 2000-2010,and a slowdown in the last decade(2010-2019).Multiple factors such as temperature,precipitation,the state of glaciers and permafrost have contributed to the changes in the lake.
基金supported by the National Natural Science Foundation of China(42101418 and 42371413)the National Natural Science Foundation of China/RGC Joint Research Scheme(42361164614 and N_HKU722/23)+1 种基金the NSFC Excellent Young Scientists Fund(Overseas)the Chinese University Scientific Fund.
文摘Synthetic aperture radar(SAR)records important information about the interaction of electromagnetic waves with the Earth’s surface.However,long-term and high-resolution backscatter coefficient data are still lacking in many urban studies(e.g.,building height estimation).Here,we proposed a framework to reconstruct the 1-km backscatter coefficient in 1990-2022 utilizing the Sentinel-1 Ground Range Detected data and Landsat time series data in the Jing-Jin-Ji(JJJ)region.First,we developed a regression model to convert the optical signals from Landsat into backscatter coefficients as the Sentinel-1 data,using observations from 2015 to 2022.Then,we reconstructed backscatter coefficients from 1990 to 2022 using the long-term Landsat data.Using the reconstructed backscatter coefficients,we analyzed the dynamic patterns of building height over the past decades.The proposed approach performs well on estimating the backscatter coefficient and its spatial pattern,with the annual mean absolute error,root mean square error,and R^(2) of 1.10 dB,1.50 dB,and 0.64,respectively.The temporal trends revealed from the reconstructed backscatter data are reliable compared with satellite observations at a relatively coarse resolution,with Pearson’s coefficients above 0.92 in 6 sample cities.The derived building height from the reconstructed SAR data indicates that the JJJ region experienced a noticeable upward expansion in 1990-2022,e.g.,Beijing has the fastest growth rate of 0.420 km^(3)/decade regarding the total building volumes.The proposed framework of reconstructing SAR data from optical satellite images provides a new insight to complement the long-term and high-resolution backscatter from local to global scales.
基金funded by the National Natural Science Foundation of China(42222103,42171379,and 42101379)the Open Research Program of the International Research Center of Big Data for Sustainable Development Goals(CBAS2023ORP04)the Youth Innovation Promotion Association of Chinese Academy of Sciences(2017277).
文摘Understanding wetland change is critical to establishing and implementing international conservation and management conventions.With such knowledge,supporting sustainable development,making management decisions,improving policies,and conducting scientific research become possible.However,consistent information on changes in Chinese wetlands has been unavailable.We applied the hybrid object-based and hierarchical classification approach to∼53,000 scenes of Landsat images acquired between 1980 and 2020 and created a national wetland mapping product(China_Wetlands)for six periods(e.g.,1980,1990,2000,2010,2015,and 2020).China_Wetlands revealed diverse changes in Chinese wetlands and their trajectories in response to climate change and human impacts over the past four decades.Specifically,there was a substantial shrinkage in wetland area before 2015,with a small rebound between 2015 and 2020.The net loss was∼60.9×10^(3)km^(2),which represents 12%of the area in 1980.However,the loss of natural wetlands was hidden by human-made wetland gain with an offset of 15.6×10^(3)km^(2).Additionally,the expansion of surface water extent approximately 14.0×10^(3)km^(2)obscured the loss of vegetated wetlands.Wetland loss in hotspot areas(e.g.,Sanjiang Plain and Yangtze River Delta)should not be neglected.The sustainable management and effective conservation of wetlands in China should target wetland areas,landscape structures,and small wetlands delivering important ecosystem services.Moreover,the conversion of wetland types and the invasion of alien species need to be monitored and regulated.China_Wetlands will be a critical wetland dataset for ecological research and the assessment of national and global environmental objectives(e.g.,the United Nation’s sustainable development goals).
文摘Analysis of the nexus between vegetation dynamics and climatic parameters like surface temperature is essential in environmental and ecological studies and for monitoring of the natural resources. This study explored the spatio-temporal distribution of land surface temperature(LST) and Normalized Difference Vegetation Index(NDVI) and the relationship between them in the Andassa watershed from 1986 to 2016 periods using Landsat data. Monthly average air temperature data of three meteorological sites were used for validating the results. The findings of the study showed that the LST of the Andassa watershed has increased during the study periods. Overall, average LST has been rising with an increasing rate of 0.081?C per year. Other results of this study also showed that there has been a dynamic change in vegetation cover of the watershed in all seasons. There was also a negative correlation between LST and NDVI in all the studied years. From this study we can understand that there has been degradation of vegetation and intensification of LST from 1986 to 2016.
文摘The presence of green spaces within city centres has been recognized as a valuable component of the city landscape.Vegetation provides a variety of benefits including energy saving,improved air quality,reduced noise pollution,decreased ambient temperature and psychological restoration.Evidence also shows that the amount of vegetation,known as‘greenness’,in densely populated areas,can also be an indicator of the relative wealth of a neighbourhood.The‘grey-green divide’,the contrast between built-up areas with a dominant grey colour and green spaces,is taken as a proxy indicator of sustainable management of cities and planning of urban growth.Consistent and continuous assessment of greenness in cities is therefore essential for monitoring progress towards the United Nations Sustainable Development Goal 11.The availability of multi-temporal greenness information from Landsat data archives together with data derived from the city centres database of the Global Human Settlement Layer(GHSL)initiative,offers a unique perspective to quantify and analyse changes in greenness across 10,323 urban centres all around the globe.In this research,we assess differences between greenness within and outside the built-up area for all the urban centres described by the city centres database of the GHSL.We also analyse changes in the amount of green space over time considering changes in the built-up areas in the periods 1990,2000 and 2014.The results show an overall trend of increased greenness between 1990 and 2014 in most cities.The effect of greening is observed also for most of the 32 world megacities.We conclude that using simple yet effective approaches exploiting open and free global data it is possible to provide quantitative information on the greenness of cities and its changes over time.This information is of direct interest for urban planners and decision-makers to mitigate urban related environmental and social impacts.
