Optimal scale is one of the important issues in ecology and geography.Based on land-use data of the Tarim River Basin in Xinjiang of China in the 1950s,regarding the area of land use types as the parameter in scale se...Optimal scale is one of the important issues in ecology and geography.Based on land-use data of the Tarim River Basin in Xinjiang of China in the 1950s,regarding the area of land use types as the parameter in scale selecting,the histograms of the patches in area are charted.Then,by reinforcing the normalized scale variances(NSV) with 3 landscape indi-ces,the scale characteristics of land use in the Tarim River Basin can be summarized.(1) NSV in the Tarim River up to a maximum at scale of 1:50,000 which is considered appropriate for the Tarim River.(2) Diversity indices of saline land are consistent with NSV's.Diversity indices and NSV of sandy land showed that the appropriate scale is in the same scale domain.There is a significant difference between diversity indices and NSV of forestland and shrub-land.(3) Fractal dimension of sandy land and saline land showed a hierarchical structure at a scale of 1:10,000.Fractal dimension of forestland and shrubland are distributed under the same hierarchical structure in the region.展开更多
The quality change of cultivated land resources requires the scientific deployment of monitoring areas.In this study,Yuzhou City is taken as an example to divide the quality monitoring control areas through the study ...The quality change of cultivated land resources requires the scientific deployment of monitoring areas.In this study,Yuzhou City is taken as an example to divide the quality monitoring control areas through the study of natural quality,utilization level,income level,and utilization characteristics,etc.Compared with the traditional administrative unit division or cultivated land quality division method,the regional homogeneity of this study is stronger,and there are quality differences between regions.It provides precondition and precision guarantee for the monitoring of cultivated land quality.展开更多
Spatiotemporal data fusion technologies have been widely used for land surface phenology(LSP)monitoring since it is a low-cost solution to obtain fine-resolution satellite time series.However,the reliability of fused ...Spatiotemporal data fusion technologies have been widely used for land surface phenology(LSP)monitoring since it is a low-cost solution to obtain fine-resolution satellite time series.However,the reliability of fused images is largely affected by land surface heterogeneity and input data.It is unclear whether data fusion can really benefit LSP studies at fine scales.To explore this research question,this study designed a sophisticated simulation experiment to quantify effectiveness of 2 representative data fusion algorithms,namely,pair-based Spatial and Temporal Adaptive Reflectance Fusion Model(STARFM)and time series-based Spatiotemporal fusion method to Simultaneously generate Full-length normalized difference vegetation Index Time series(SSFIT)by fusing Landsat and Moderate Resolution Imaging Spectroradiometer(MODIS)data in extracting pixel-wise spring phenology(i.e.,the start of the growing season,SOS)and its spatial gradient and temporal variation.Our results reveal that:(a)STARFM can improve the accuracy of pixel-wise SOS by up to 74.47%and temporal variation by up to 59.13%,respectively,compared with only using Landsat images,but it can hardly improve the retrieval of spatial gradient.For SSFIT,the accuracy of pixel-wise SOS,spatial gradient,and temporal variation can be improved by up to 139.20%,26.36%,and 162.30%,respectively;(b)the accuracy improvement introduced by fusion algorithms decreases with the number of available Landsat images per year,and it has a large variation with the same number of available Landsat images,and(c)this large variation is highly related to the temporal distributions of available Landsat images,suggesting that fusion algorithms can improve SOS accuracy only when cloud-free Landsat images cannot capture key vegetation growth period.This study calls for caution with the use of data fusion in LSP studies at fine scales.展开更多
Land cover products provide critical information for monitoring and analyzing land surface changes.However,notable disagreement and incompatible classification systems among existing land cover products bring challeng...Land cover products provide critical information for monitoring and analyzing land surface changes.However,notable disagreement and incompatible classification systems among existing land cover products bring challenges in using them.Here,we developed a hierarchical International Geosphere-Biosphere Programme(IGBP)classification system and integrated four widely used land cover products(i.e.,MODIS-IGBP,ESA-CCI,GlobeLand30,and GLC_FCS30)based on their accuracy against a collection of global reference samples.We generated a hybrid global annual land cover product(HYBMAP)with~1 km(1/120°,30″)spatial resolution from 2000 to 2020.The HYBMAP integrates information from the four products of high-and medium-resolution and reduces the disagreement between them by up to 20.1%.The overall accuracy of the HYBMAP is 75.5%,which is higher than the best of the four products(MODIS-IGBP,70.9%).HYBMAP also integrates the temporal change information from the four products and identifies a faster growth of built-up lands.The HYBMAP provides more consistent and reliable global land cover time series data for global change research.It is free to access at https://doi.org/10.5281/zenodo.10488191.展开更多
Land cover mapping plays a critical role in monitoring land system changes.Despite advancements in remote sensing technologies,traditional satellite-based approaches are often constrained by cloud cover,coarse tempora...Land cover mapping plays a critical role in monitoring land system changes.Despite advancements in remote sensing technologies,traditional satellite-based approaches are often constrained by cloud cover,coarse temporal resolution,and limitations in capturing fine-scale landscape dynamics,leading to gaps in continuous and real-time monitoring.Near-surface cameras offer a solution by providing high-frequency,ground-level observations that bridge temporal gaps and enhance spatial detail.Therefore,this study makes substantial contributions by pioneering the integration of near-surface camera observations with satellite imagery,addressing key challenges in imaging perspective differences and limited coverage of ground-based observations for enhanced land cover monitoring at a 30-m/10-m scale.A key innovation lies in leveraging near-surface cameras to reconstruct dense satellite data time series and capture daily land cover dynamics,addressing critical temporal gaps in traditional satellite-based approaches.The research further advances the field by implementing state-of-the-art deep learning techniques,particularly the Segment Anything Model(SAM),to achieve precise parcel-level delineation and reduce classification noise at a high-resolution(meter-and submeter level)scale.Furthermore,the framework’s ability to synthesize multimodal data sources(near-surface cameras,Sentinel-1/2,and high-resolution imagery)represents a methodological breakthrough in space and surface sensor integration for real-time land cover change detection,enabling time-sensitive applications and early warning systems for land system changes.展开更多
基金National Natural Science Foundation of China,No.40571030No.40730633
文摘Optimal scale is one of the important issues in ecology and geography.Based on land-use data of the Tarim River Basin in Xinjiang of China in the 1950s,regarding the area of land use types as the parameter in scale selecting,the histograms of the patches in area are charted.Then,by reinforcing the normalized scale variances(NSV) with 3 landscape indi-ces,the scale characteristics of land use in the Tarim River Basin can be summarized.(1) NSV in the Tarim River up to a maximum at scale of 1:50,000 which is considered appropriate for the Tarim River.(2) Diversity indices of saline land are consistent with NSV's.Diversity indices and NSV of sandy land showed that the appropriate scale is in the same scale domain.There is a significant difference between diversity indices and NSV of forestland and shrub-land.(3) Fractal dimension of sandy land and saline land showed a hierarchical structure at a scale of 1:10,000.Fractal dimension of forestland and shrubland are distributed under the same hierarchical structure in the region.
基金Supported by Basic Scientific Research Project of Henan Academy of Sciences(220601065)Henan Youth Science Fund Project(212300410168)。
文摘The quality change of cultivated land resources requires the scientific deployment of monitoring areas.In this study,Yuzhou City is taken as an example to divide the quality monitoring control areas through the study of natural quality,utilization level,income level,and utilization characteristics,etc.Compared with the traditional administrative unit division or cultivated land quality division method,the regional homogeneity of this study is stronger,and there are quality differences between regions.It provides precondition and precision guarantee for the monitoring of cultivated land quality.
基金supported by the National Natural Science Foundation of China(Project Nos.42271331 and 42022060)The Hong Kong Polytechnic University(Project Nos.4-ZZND and Q-CDBP).
文摘Spatiotemporal data fusion technologies have been widely used for land surface phenology(LSP)monitoring since it is a low-cost solution to obtain fine-resolution satellite time series.However,the reliability of fused images is largely affected by land surface heterogeneity and input data.It is unclear whether data fusion can really benefit LSP studies at fine scales.To explore this research question,this study designed a sophisticated simulation experiment to quantify effectiveness of 2 representative data fusion algorithms,namely,pair-based Spatial and Temporal Adaptive Reflectance Fusion Model(STARFM)and time series-based Spatiotemporal fusion method to Simultaneously generate Full-length normalized difference vegetation Index Time series(SSFIT)by fusing Landsat and Moderate Resolution Imaging Spectroradiometer(MODIS)data in extracting pixel-wise spring phenology(i.e.,the start of the growing season,SOS)and its spatial gradient and temporal variation.Our results reveal that:(a)STARFM can improve the accuracy of pixel-wise SOS by up to 74.47%and temporal variation by up to 59.13%,respectively,compared with only using Landsat images,but it can hardly improve the retrieval of spatial gradient.For SSFIT,the accuracy of pixel-wise SOS,spatial gradient,and temporal variation can be improved by up to 139.20%,26.36%,and 162.30%,respectively;(b)the accuracy improvement introduced by fusion algorithms decreases with the number of available Landsat images per year,and it has a large variation with the same number of available Landsat images,and(c)this large variation is highly related to the temporal distributions of available Landsat images,suggesting that fusion algorithms can improve SOS accuracy only when cloud-free Landsat images cannot capture key vegetation growth period.This study calls for caution with the use of data fusion in LSP studies at fine scales.
基金supported by the National Natural Science Foundation of China(42271104)the Shenzhen Funda-mental Research Program(GXWD20201231165807007-20200814213435001)+1 种基金the Shenzhen Science and Tech nology Program(JCYJ20220531093201004)Shenzhen Science and Technology Program(KQTD20221101093604016).
文摘Land cover products provide critical information for monitoring and analyzing land surface changes.However,notable disagreement and incompatible classification systems among existing land cover products bring challenges in using them.Here,we developed a hierarchical International Geosphere-Biosphere Programme(IGBP)classification system and integrated four widely used land cover products(i.e.,MODIS-IGBP,ESA-CCI,GlobeLand30,and GLC_FCS30)based on their accuracy against a collection of global reference samples.We generated a hybrid global annual land cover product(HYBMAP)with~1 km(1/120°,30″)spatial resolution from 2000 to 2020.The HYBMAP integrates information from the four products of high-and medium-resolution and reduces the disagreement between them by up to 20.1%.The overall accuracy of the HYBMAP is 75.5%,which is higher than the best of the four products(MODIS-IGBP,70.9%).HYBMAP also integrates the temporal change information from the four products and identifies a faster growth of built-up lands.The HYBMAP provides more consistent and reliable global land cover time series data for global change research.It is free to access at https://doi.org/10.5281/zenodo.10488191.
基金supported by the National Key R&D Program of China(2024YFF1307600)supported by the Investigation Research Program between Ecological Environment and Human Health in Wuyi Mountain(20242120035)+5 种基金the open project of State Key Laboratory of Efficient Utilization of Arable Land in China,the Institute of Agricultural Resources and Regional Planning,Chinese Academy of Agricultural Sciences(No.EUAL-2025-03)the Chebaling National Nature Reserve Phenology Monitoring Network Construction and Application Project(CBLHT-2025050)the Xizang Science and Technology Plan Project(XZ202403ZY0018)the National Key Scientific and Technological Infrastructure project“Earth System Science Numerical Simulator Facility”(EarthLab)supported by the National Natural Science Foundation of China(42201367)the Fundamental Research Funds for the Central Universities under grant DUT23RC(3)064.
文摘Land cover mapping plays a critical role in monitoring land system changes.Despite advancements in remote sensing technologies,traditional satellite-based approaches are often constrained by cloud cover,coarse temporal resolution,and limitations in capturing fine-scale landscape dynamics,leading to gaps in continuous and real-time monitoring.Near-surface cameras offer a solution by providing high-frequency,ground-level observations that bridge temporal gaps and enhance spatial detail.Therefore,this study makes substantial contributions by pioneering the integration of near-surface camera observations with satellite imagery,addressing key challenges in imaging perspective differences and limited coverage of ground-based observations for enhanced land cover monitoring at a 30-m/10-m scale.A key innovation lies in leveraging near-surface cameras to reconstruct dense satellite data time series and capture daily land cover dynamics,addressing critical temporal gaps in traditional satellite-based approaches.The research further advances the field by implementing state-of-the-art deep learning techniques,particularly the Segment Anything Model(SAM),to achieve precise parcel-level delineation and reduce classification noise at a high-resolution(meter-and submeter level)scale.Furthermore,the framework’s ability to synthesize multimodal data sources(near-surface cameras,Sentinel-1/2,and high-resolution imagery)represents a methodological breakthrough in space and surface sensor integration for real-time land cover change detection,enabling time-sensitive applications and early warning systems for land system changes.
