High resolution land cover datasets integration and application based on Landsat and Globcover data from 1975 to 2010 in Siberia

Liu, Tingxiang; Zhang, Shuwen; Xu, Xinliang; Bu, Kun; Jia, Ning; Chang, Liping

doi:10.1007/s11769-016-0819-9

Cited by 5 publications

(4 citation statements)

References 26 publications

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“…A total of ten GEE clusters were identified, and these included papers related to tidal flats, Landsat time series, land surface temperature, wetlands, the recursive hierarchical segmentation method (RHSEG), central Asia, land-use change, nighttime lights, the NDVI, and yield estimation. The clustering results suggested that GE was widely applied as a virtual Earth in studies on ship detection [77][78][79][80][81][82][83], land-conversion mapping [84][85][86], glacial geomorphology [6,[87][88][89][90][91], and the Earth's lithosphere [92].…”

Section: Knowledge Base Analysismentioning

confidence: 99%

Progress and Trends in the Application of Google Earth and Google Earth Engine

Zhao

et al. 2021

Remote Sensing

142

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Earth system science has changed rapidly due to global environmental changes and the advent of Earth observation technology. Therefore, new tools are required to monitor, measure, analyze, evaluate, and model Earth observation data. Google Earth (GE) was officially launched by Google in 2005 as a ”geobrowser”, and Google Earth Engine (GEE) was released in 2010 as a cloud computing platform with substantial computational capabilities. The use of these two tools or platforms in various applications, particularly as used by the remote sensing community, has developed rapidly. In this paper, we reviewed the applications and trends in the use of GE and GEE by analyzing peer-reviewed articles, dating up to January 2021, in the Web of Science (WoS) core collection using scientometric analysis (i.e., by using CiteSpace) and meta-analysis. We found the following: (1) the number of articles describing the use of GE or GEE increased substantially from two in 2006 to 530 in 2020. The number of GEE articles increased much faster than those concerned with the use of GE. (2) Both GE and GEE were extensively used by the remote sensing community as multidisciplinary tools. GE articles covered a broader range of research areas (e.g., biology, education, disease and health, economic, and information science) and appeared in a broader range of journals than those concerned with the use of GEE. (3) GE and GEE shared similar keywords (e.g., “land cover”, “water”, “model”, “vegetation”, and “forest”), which indicates that their application is of great importance in certain research areas. The main difference was that articles describing the use of GE emphasized its use as a visual display platform, while those concerned with GEE placed more emphasis on big data and time-series analysis. (4) Most applications of GE and GEE were undertaken in countries, such as the United States, China, and the United Kingdom. (5) GEE is an important tool for analysis, whereas GE is used as an auxiliary tool for visualization. Finally, in this paper, the merits and limitations of GE and GEE, and recommendations for further improvements, are summarized from an Earth system science perspective.

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Section: Knowledge Base Analysismentioning

confidence: 99%

Progress and Trends in the Application of Google Earth and Google Earth Engine

Zhao

et al. 2021

Remote Sensing

142

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“…We use the quality assessment (QA) flag to select the best quality pixels in each of the MODIS products. The land cover maps of Siberia in the 1970s and in 2010s are based on the previous work of our research group [38], which were extracted from the global land cover data and the Landsat TM and MSS data through interactive interpretation. All of the products used in the study were transformed to the Albers Equal Area projection at a resolution of 1 km.…”

Section: Study Area and Data Processingmentioning

confidence: 99%

Temporal and Spatial Changes in Snow Cover and the Corresponding Radiative Forcing Analysis in Siberia from the 1970s to the 2010s

Liu

Zhang

2017

Advances in Meteorology

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In the context of global climate change, the extent of snow cover in Siberia has significantly decreased since the 1970s, especially in spring. The changes of snow cover at middle and high latitudes have significant impacts on the meteorological and hydrological processes because the snow cover can affect the surface energy, water balance, and the development of the atmospheric boundary layer. In this paper, the temporal and spatial changes in snow cover were firstly estimated based on a long time series of remote sensing snow cover data, both showing a decreased trend. Based on this, we estimated the radiative forcing caused by the snow cover changes from the 1970s to the 2010s and compared it with the radiative forcing caused by the vegetation cover changes over the same time period in Siberia, indicating that the snow cover changes in Siberia can accelerate climate warming and the vegetation cover changes here have the opposite effect. Furthermore, the snow cover changes may play a more important role than the vegetation cover changes in regulating the surface radiation balance in Siberia on the regional scale.

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“…To avoid the impact of land cover changes on the phenology variation, only the unchanged forest during 1992 to 2015 was analyzed in this paper. Due to fewer human activities, the land use and land cover changes were not significant from 1975 to 2010 in Siberia [33]. Although the land use changes were significant in Northeast China from the 1980s to 2000, the forests changes mainly concentrated on deforestation [34].…”

Section: Datamentioning

confidence: 99%

Forest Phenology Shifts in Response to Climate Change over China–Mongolia–Russia International Economic Corridor

Yan

Zhang

2020

Forests

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Vegetation phenology is a sensitive indicator of climate change. With the intensification of global warming, the changes in growing seasons of various vegetation types have been widely documented across the world. However, as one of the most vulnerable regions in response to the global climate change, the phenological responses and associated mechanisms in mid–high latitude forests are still not fully understood. In this study, long-term changes in forest phenology and the associated relationship with the temperature and snow water equivalent in the China–Mongolia–Russia International Economic Corridor were examined by analyzing the satellite-measured normalized difference vegetation index and the meteorological observation data during 1982 to 2015. The average start date of the growing season (SOS) of the forest ecosystem in our study area advanced at a rate of 2.5 days/decade, while the end date of the growing season (EOS) was delayed at a rate of 2.3 days/decade, contributing to a growing season that was approximately 15 days longer in the 2010s compared to that in 1980s. A higher April temperature is beneficial to the advance in the SOS, and a higher summer temperature has the potential to extend the EOS in the forest ecosystem. However, our results also suggest that a single temperature cannot fully explain the advance of the SOS, as well as the delay in the EOS. The preseason Snow Water Equivalent (SWE) is also an essential factor in influencing the growing season. A higher SWE in February and March and lower SWE in April tend to advance the SOS, while higher SWE in pre-year December and lower SWE in current year October are beneficial to the extension of the EOS.

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High resolution land cover datasets integration and application based on Landsat and Globcover data from 1975 to 2010 in Siberia

Cited by 5 publications

References 26 publications

Progress and Trends in the Application of Google Earth and Google Earth Engine

Progress and Trends in the Application of Google Earth and Google Earth Engine

Temporal and Spatial Changes in Snow Cover and the Corresponding Radiative Forcing Analysis in Siberia from the 1970s to the 2010s

Forest Phenology Shifts in Response to Climate Change over China–Mongolia–Russia International Economic Corridor

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