Underground coal fires are one of the most common and serious geohazards in most coal producing countries in the world. Monitoring their spatio-temporal changes plays an important role in controlling and preventing th...Underground coal fires are one of the most common and serious geohazards in most coal producing countries in the world. Monitoring their spatio-temporal changes plays an important role in controlling and preventing the effects of coal fires, and their environmental impact. In this study, the spatio-temporal changes of underground coal fires in Khanh Hoa coal field(North-East of Viet Nam) were analyzed using Landsat time-series data during the 2008-2016 period. Based on land surface temperatures retrieved from Landsat thermal data, underground coal fires related to thermal anomalies were identified using the MEDIAN+1.5×IQR(IQR: Interquartile range) threshold technique. The locations of underground coal fires were validated using a coal fire map produced by the field survey data and cross-validated using the daytime ASTER thermal infrared imagery. Based on the fires extracted from seven Landsat thermal imageries, the spatiotemporal changes of underground coal fire areas were analyzed. The results showed that the thermalanomalous zones have been correlated with known coal fires. Cross-validation of coal fires using ASTER TIR data showed a high consistency of 79.3%. The largest coal fire area of 184.6 hectares was detected in 2010, followed by 2014(181.1 hectares) and 2016(178.5 hectares). The smaller coal fire areas were extracted with areas of 133.6 and 152.5 hectares in 2011 and 2009 respectively. Underground coal fires were mainly detected in the northern and southern part, and tend to spread to north-west of the coal field.展开更多
Using the NASA Earth Exchange platform,the North American Forest Dynamics(NAFD)project mapped forest history wall-to-wall,annually for the contiguous US(1986–2010)using the Vegetation Change Tracker algorithm.As with...Using the NASA Earth Exchange platform,the North American Forest Dynamics(NAFD)project mapped forest history wall-to-wall,annually for the contiguous US(1986–2010)using the Vegetation Change Tracker algorithm.As with any effort to identify real changes in remotely sensed time-series,data gaps,shifts in seasonality,misregistration,inconsistent radiometry and cloud contamination can be sources of error.We discuss the NAFD image selection and processing stream(NISPS)that was designed to minimize these sources of error.The NISPS image quality assessments highlighted issues with the Landsat archive and metadata including inadequate georegistration,unreliability of the pre-2009 L5 cloud cover assessments algorithm,missing growing-season imagery and paucity of clear views.Assessment maps of Landsat 5–7 image quantities and qualities are presented that offer novel perspectives on the growing-season archive considered for this study.Over 150,000+Landsat images were considered for the NAFD project.Optimally,one high quality cloud-free image in each year or a total of 12,152 images would be used.However,to accommodate data gaps and cloud/shadow contamination 23,338 images were needed.In 220 specific path-row image years no acceptable images were found resulting in data gaps in the annual national map products.展开更多
Mountains are undergoing widespread changes caused by human activities and climate change.Given the importance of mountains,the protection and sustainable development of mountain ecosys-tems have been listed as the go...Mountains are undergoing widespread changes caused by human activities and climate change.Given the importance of mountains,the protection and sustainable development of mountain ecosys-tems have been listed as the goals of the United Nations 2030 Sustainable Development Agenda.As one of the indicators,the Mountain Green Cover Index(MGCI)datasets can provide consis-tent and comparable status of green vegetation in mountainous areas,which can support the mapping of heterogeneous mountain ecosystem health and monitoring changes over time.The produc-tion of explicitly high-spatial-resolution MGCI datasets is therefore urgently needed to support the protection measures at subnational and multitemporal scales.In this paper,the MGCI datasets with 500-meter spatial resolutions,covering the economic corridors of the Belt and Road Initiative(BRI),were developed for 2010 to 2019 based on all available Landsat-8 data and the Google Earth Engine cloud computing platform.The validation of green vegeta-tion cover with the ground-truth samples indicated that the data-sets can achieve an overall accuracy of 94.06%,with well-detailed spatial and temporal variations.The archived datasets include the MGCI of each BRI economic corridor,matched to a geospatial layer denoting the economic corridor boundaries.The essential informa-tion of the datasets and their limitations,along with the production flow,were described in this paper.展开更多
To understand the mechanism of wetland cover change with both moderate spatial resolution and high temporal frequency,this research evaluates the applicability of a spatiotemporal reflectance blending model in the Poy...To understand the mechanism of wetland cover change with both moderate spatial resolution and high temporal frequency,this research evaluates the applicability of a spatiotemporal reflectance blending model in the Poyang Lake area,China,using 9 time-series Landsat-5 Thematic Mapper images and 18 time-series Terra Moderate Resolution Imaging Spectroradiometer images acquired between July 2004 and November 2005.The customized blending model was developed based on the enhanced spatial and temporal adaptive reflectance fusion model(ESTARFM).Reflectance of the moderate-resolution image pixels on the target dates can be predicted more accurately by the proposed customized model than the original ESTARFM.Water level on the input image acquisition dates strongly affected the accuracy of the blended reflectance.It was found that either of the image sets used as prior or posterior inputs are required when the difference of water level between the prior or posterior date and target date at Poyang Hydrological Station is<2.68 m to achieve blending accuracy with a mean average absolute difference of 4%between the observed and blended reflectance in all spectral bands.展开更多
基金funded by the Ministry-level Scientific and Technological Key Programs of Ministry of Natural Resources and Environment of Viet Nam "Application of thermal infrared remote sensing and GIS for mapping underground coal fires in Quang Ninh coal basin" (Grant No. TNMT.2017.08.06)
文摘Underground coal fires are one of the most common and serious geohazards in most coal producing countries in the world. Monitoring their spatio-temporal changes plays an important role in controlling and preventing the effects of coal fires, and their environmental impact. In this study, the spatio-temporal changes of underground coal fires in Khanh Hoa coal field(North-East of Viet Nam) were analyzed using Landsat time-series data during the 2008-2016 period. Based on land surface temperatures retrieved from Landsat thermal data, underground coal fires related to thermal anomalies were identified using the MEDIAN+1.5×IQR(IQR: Interquartile range) threshold technique. The locations of underground coal fires were validated using a coal fire map produced by the field survey data and cross-validated using the daytime ASTER thermal infrared imagery. Based on the fires extracted from seven Landsat thermal imageries, the spatiotemporal changes of underground coal fire areas were analyzed. The results showed that the thermalanomalous zones have been correlated with known coal fires. Cross-validation of coal fires using ASTER TIR data showed a high consistency of 79.3%. The largest coal fire area of 184.6 hectares was detected in 2010, followed by 2014(181.1 hectares) and 2016(178.5 hectares). The smaller coal fire areas were extracted with areas of 133.6 and 152.5 hectares in 2011 and 2009 respectively. Underground coal fires were mainly detected in the northern and southern part, and tend to spread to north-west of the coal field.
基金contributes to the North American Carbon Program,with grant support from NASA’s Carbon Cycle Science and Applied Sciences Programs[NNX11AJ78G]Previous NASA NACP grants[NNG05GE55G][NNX08AI26G]were critical in developing the foundations of the current NISPS.
文摘Using the NASA Earth Exchange platform,the North American Forest Dynamics(NAFD)project mapped forest history wall-to-wall,annually for the contiguous US(1986–2010)using the Vegetation Change Tracker algorithm.As with any effort to identify real changes in remotely sensed time-series,data gaps,shifts in seasonality,misregistration,inconsistent radiometry and cloud contamination can be sources of error.We discuss the NAFD image selection and processing stream(NISPS)that was designed to minimize these sources of error.The NISPS image quality assessments highlighted issues with the Landsat archive and metadata including inadequate georegistration,unreliability of the pre-2009 L5 cloud cover assessments algorithm,missing growing-season imagery and paucity of clear views.Assessment maps of Landsat 5–7 image quantities and qualities are presented that offer novel perspectives on the growing-season archive considered for this study.Over 150,000+Landsat images were considered for the NAFD project.Optimally,one high quality cloud-free image in each year or a total of 12,152 images would be used.However,to accommodate data gaps and cloud/shadow contamination 23,338 images were needed.In 220 specific path-row image years no acceptable images were found resulting in data gaps in the annual national map products.
基金was funded by the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant number XDA19030303)the National Key Research and Development Program of China(No.2020YFA0608700)the Youth Innovation Promotion Association of CAS(Grant 2019365).
文摘Mountains are undergoing widespread changes caused by human activities and climate change.Given the importance of mountains,the protection and sustainable development of mountain ecosys-tems have been listed as the goals of the United Nations 2030 Sustainable Development Agenda.As one of the indicators,the Mountain Green Cover Index(MGCI)datasets can provide consis-tent and comparable status of green vegetation in mountainous areas,which can support the mapping of heterogeneous mountain ecosystem health and monitoring changes over time.The produc-tion of explicitly high-spatial-resolution MGCI datasets is therefore urgently needed to support the protection measures at subnational and multitemporal scales.In this paper,the MGCI datasets with 500-meter spatial resolutions,covering the economic corridors of the Belt and Road Initiative(BRI),were developed for 2010 to 2019 based on all available Landsat-8 data and the Google Earth Engine cloud computing platform.The validation of green vegeta-tion cover with the ground-truth samples indicated that the data-sets can achieve an overall accuracy of 94.06%,with well-detailed spatial and temporal variations.The archived datasets include the MGCI of each BRI economic corridor,matched to a geospatial layer denoting the economic corridor boundaries.The essential informa-tion of the datasets and their limitations,along with the production flow,were described in this paper.
基金This work was supported by the Ministry of Science and Technology,China,National Research Program[2010CB530300,2012AA12A407,2012CB955501,2013AA122003]the National Natural Science Foundation of China[41271099].
文摘To understand the mechanism of wetland cover change with both moderate spatial resolution and high temporal frequency,this research evaluates the applicability of a spatiotemporal reflectance blending model in the Poyang Lake area,China,using 9 time-series Landsat-5 Thematic Mapper images and 18 time-series Terra Moderate Resolution Imaging Spectroradiometer images acquired between July 2004 and November 2005.The customized blending model was developed based on the enhanced spatial and temporal adaptive reflectance fusion model(ESTARFM).Reflectance of the moderate-resolution image pixels on the target dates can be predicted more accurately by the proposed customized model than the original ESTARFM.Water level on the input image acquisition dates strongly affected the accuracy of the blended reflectance.It was found that either of the image sets used as prior or posterior inputs are required when the difference of water level between the prior or posterior date and target date at Poyang Hydrological Station is<2.68 m to achieve blending accuracy with a mean average absolute difference of 4%between the observed and blended reflectance in all spectral bands.
