基于2001和2017年两期MODIS Land Cover(MCD12Q1)产品数据,本文研究了2001-2017年中国土地利用时空变化特征。结果表明,2001-2017年林地变化最大,其次是草地、其他、水域、建设用地和耕地;林地增加的主要来源是草地,草地减少主要是转化...基于2001和2017年两期MODIS Land Cover(MCD12Q1)产品数据,本文研究了2001-2017年中国土地利用时空变化特征。结果表明,2001-2017年林地变化最大,其次是草地、其他、水域、建设用地和耕地;林地增加的主要来源是草地,草地减少主要是转化为林地,其他用地减少主要是转化为草地和水域,水域增加的主要来源是其他用地和草地,建设用地增加的主要来源是草地和耕地,耕地减少主要是转化为草地和林地;林草地、其他用地和水域变化主要发生在西部地区,建设用地和耕地变化主要发生在东部和中部地区。展开更多
Irrespective of several attempts to land use/cover mapping at local, regional, or global scales, mapping of vegetation physiognomic types is limited and challenging. The main objective of the research is to produce an...Irrespective of several attempts to land use/cover mapping at local, regional, or global scales, mapping of vegetation physiognomic types is limited and challenging. The main objective of the research is to produce an accurate nationwide vegetation physiognomic map by using automated machine learning approach with the support of reference data. A time-series of the multi-spectral and multi-indices data derived from Moderate Resolution Imaging Spectroradiometer (MODIS) were exploited along with the land-surface slope data. Reliable reference data of the vegetation physiognomic types were prepared by refining the existing vegetation survey data available in the country. The Random Forests based mapping framework adopted in the research showed high performance (Overall accuracy = 0.82, Kappa coefficient = 0.79) using 148 optimum number of features out of 231 featured used. A nationwide vegetation physiognomic map of year 2013 was produced in the research. The resulted map was compared to the existing MODIS Land Cover Type (MCD12Q1) product of year 2013. A huge difference was found between two maps. Validation with the reference data showed that the MCD12Q1 product did not work satisfactorily in Japan. The outcome of the research highlights the possibility of improving the accuracy of the MCD12Q1 product with special focus on reference data.展开更多
Grasslands are the most dominant terrestrial ecosystem in China, but few national grassland maps have been generated. The grassland resource map produced in the 1980s is widely used as background data, but it has not ...Grasslands are the most dominant terrestrial ecosystem in China, but few national grassland maps have been generated. The grassland resource map produced in the 1980s is widely used as background data, but it has not been updated for almost 40 years.Therefore, a reliable map depicting the current spatial distribution of grasslands across the country is urgently needed. In this study, we evaluated the grassland consistency and accuracy of ten land cover datasets(GLC2000, GlobCover, CCI-LC,MCD12Q1, CLUD, GlobeLand30, GLC-FCS30, CGLS-LC100, CLCD, and FROM-GLC) for 2000, 2010, and 2020 based on extensive fieldwork. We concluded that the area of these ten grassland products ranges from 107.80×10^(4)to 332.46×10^(4)km^(2), with CLCD and MCD12Q1 having the highest area consistency. The spatial and sample consistency is highest in the regions of eastcentral Inner Mongolia, the Qinghai-Tibet Plateau and northern Xinjiang, while the distribution of southern grasslands is scattered and differs considerably among the ten products. MCD12Q1 is significantly more accurate than the other nine products,with an overall accuracy(OA) reaching 77.51% and a kappa coefficient of 0.51;CLCD is slightly less accurate than MCD12Q1(OA=73.02%, kappa coefficient=0.45) and is more conducive to the fine monitoring and management of grassland because of its30-meter resolution. The highest accuracy of grassland was found in the Inner Mongolia-Ningxia region and Qinghai-Tibet Plateau, while the accuracy was worst in the southeastern region. In the future grassland mapping, cartographers should improve the accuracy of the grassland distribution in South China and regions where grassland is confused with forest, cropland and bare land. We specify the availability of valuable data in existing land cover datasets for China’s grasslands and call for researchers and the government to actively produce a new generation of grassland maps.展开更多
在快速城市化进程中,我国建设用地急剧扩张,分析建设用地在时间和空间上的变化规律及其原因,对促进我国土地资源的可持续利用具有重要指导意义。基于2001、2006、2011、2017年四期MODIS Land Cover(MCD12Q1)产品数据,结合经济数据和地...在快速城市化进程中,我国建设用地急剧扩张,分析建设用地在时间和空间上的变化规律及其原因,对促进我国土地资源的可持续利用具有重要指导意义。基于2001、2006、2011、2017年四期MODIS Land Cover(MCD12Q1)产品数据,结合经济数据和地貌类型数据,研究中国不同经济区和不同地貌类型区的建设用地扩张的时空变化特征,结果表明:(1)全国建设用地在2001~2006、2006~2011、2011~2017年3时期扩张强度逐渐变弱,扩张速度逐渐变慢。(2)2001~2017年建设用地扩张主要发生在东部地区,该地区扩张强度最强、扩张速度最快。3时期内东部地区建设用地扩张强度逐渐变弱,扩张速度逐渐变慢;中部地区扩张强度逐渐变强,扩张速度逐渐变快;西部地区扩张强度先变弱后趋于稳定,扩张速度逐渐变慢;东北地区扩张强度先变弱后趋于稳定,扩张速度先变快后变慢。(3)2001~2017年建设用地扩张主要发生在平原地区,扩张强度最强,丘陵地区扩张速度最快;平原地区建设用地扩张强度逐渐变弱,扩张速度逐渐变慢;丘陵地区扩张强度先变弱后变强,扩张速度先变慢后变快;山地地区扩张强度逐渐变强,扩张速度先变快后变慢;高原地区扩张强度逐渐变强,扩张速度逐渐变快。从研究结果可知,不同经济区建设用地扩张强度和速度具有差异性,主要受不同时期国家相应的区域政策影响;不同地貌类型对建设用地扩张的时空特征也会产生影响。展开更多
Introduction:Although numerous land cover datasets can act as references for understanding land cover change in China,the inconsistencies between the datasets can also provide understanding.Previous studies on the con...Introduction:Although numerous land cover datasets can act as references for understanding land cover change in China,the inconsistencies between the datasets can also provide understanding.Previous studies on the consistency between land cover datasets have mostly focused on land cover type consistencies and have ignored data consistencies in land cover change.Outcomes:Therefore,we aim to analyse the consistencies in land cover changes through likelihood assessment methods.We compared the spatiotemporal changes in forest,grassland,cropland,and bare land in the Climate Change Initiative land cover dataset(CCI-LC),Moderateresolution Resolution Imaging Spectroradiometer land cover dataset(MCD12Q1),China’s National Land Use and Cover Change(CNLUCC),Globeland30 and Global Land Cover Fine Surface Covering 30(GLC-FCS30)datasets in 2010.The results showed that the percentages and changes in each land cover type in MCD12Q1 were different from those in the other datasets.Discussion:For example,the proportion of grassland in MCD12Q1 was the highest,reaching 48.04%.The places with high consistency were the places where the land cover types were concentrated,and the bare land had the highest consistency.However,the consistency of China’s land cover change was quite low,and the percentage of low consistency was more than 87%from 2000-2018.Comparison of the data with the global artificial impervious area(GAIA)and Hansen-Global Forest Change(Hansen-GFC)datasets showed that the percentage of high construction gain consistency(38.83%)was higher than the forest change consistency,and the percentage forest loss high consistency(8.85%)was lower than the forest gain high consistency(12.76%).Conclusion:The results not only provide a basis for the use of land cover datasets but also give a clearer understanding of the pattern of land cover changes.展开更多
文摘基于2001和2017年两期MODIS Land Cover(MCD12Q1)产品数据,本文研究了2001-2017年中国土地利用时空变化特征。结果表明,2001-2017年林地变化最大,其次是草地、其他、水域、建设用地和耕地;林地增加的主要来源是草地,草地减少主要是转化为林地,其他用地减少主要是转化为草地和水域,水域增加的主要来源是其他用地和草地,建设用地增加的主要来源是草地和耕地,耕地减少主要是转化为草地和林地;林草地、其他用地和水域变化主要发生在西部地区,建设用地和耕地变化主要发生在东部和中部地区。
文摘Irrespective of several attempts to land use/cover mapping at local, regional, or global scales, mapping of vegetation physiognomic types is limited and challenging. The main objective of the research is to produce an accurate nationwide vegetation physiognomic map by using automated machine learning approach with the support of reference data. A time-series of the multi-spectral and multi-indices data derived from Moderate Resolution Imaging Spectroradiometer (MODIS) were exploited along with the land-surface slope data. Reliable reference data of the vegetation physiognomic types were prepared by refining the existing vegetation survey data available in the country. The Random Forests based mapping framework adopted in the research showed high performance (Overall accuracy = 0.82, Kappa coefficient = 0.79) using 148 optimum number of features out of 231 featured used. A nationwide vegetation physiognomic map of year 2013 was produced in the research. The resulted map was compared to the existing MODIS Land Cover Type (MCD12Q1) product of year 2013. A huge difference was found between two maps. Validation with the reference data showed that the MCD12Q1 product did not work satisfactorily in Japan. The outcome of the research highlights the possibility of improving the accuracy of the MCD12Q1 product with special focus on reference data.
基金supported by the Major Consulting Research Project of the Chinese Academy of Engineering(2020-XZ-29,2021-HZ-5,2022-HZ-09)the Fundamental Research Funds for the Central Universities,Lanzhou University(lzujbky-2020-kb29,lzujbky-2021-kb13)+3 种基金China Agriculture Research System of MOF(Ministry of Finance)and MARA(Ministry of Agriculture and Rural Affairs)Gansu Province 2021 Outstanding Graduate Student“Innovation Star”Project(2021CXZX-040)Key Laboratory of Grassland Livestock Industry Innovation,Ministry of Agriculture and Rural Affairs,China“Top Innovative Talents”Training Program(CMSYS2020-5)the 111 Project(B12002)。
文摘Grasslands are the most dominant terrestrial ecosystem in China, but few national grassland maps have been generated. The grassland resource map produced in the 1980s is widely used as background data, but it has not been updated for almost 40 years.Therefore, a reliable map depicting the current spatial distribution of grasslands across the country is urgently needed. In this study, we evaluated the grassland consistency and accuracy of ten land cover datasets(GLC2000, GlobCover, CCI-LC,MCD12Q1, CLUD, GlobeLand30, GLC-FCS30, CGLS-LC100, CLCD, and FROM-GLC) for 2000, 2010, and 2020 based on extensive fieldwork. We concluded that the area of these ten grassland products ranges from 107.80×10^(4)to 332.46×10^(4)km^(2), with CLCD and MCD12Q1 having the highest area consistency. The spatial and sample consistency is highest in the regions of eastcentral Inner Mongolia, the Qinghai-Tibet Plateau and northern Xinjiang, while the distribution of southern grasslands is scattered and differs considerably among the ten products. MCD12Q1 is significantly more accurate than the other nine products,with an overall accuracy(OA) reaching 77.51% and a kappa coefficient of 0.51;CLCD is slightly less accurate than MCD12Q1(OA=73.02%, kappa coefficient=0.45) and is more conducive to the fine monitoring and management of grassland because of its30-meter resolution. The highest accuracy of grassland was found in the Inner Mongolia-Ningxia region and Qinghai-Tibet Plateau, while the accuracy was worst in the southeastern region. In the future grassland mapping, cartographers should improve the accuracy of the grassland distribution in South China and regions where grassland is confused with forest, cropland and bare land. We specify the availability of valuable data in existing land cover datasets for China’s grasslands and call for researchers and the government to actively produce a new generation of grassland maps.
文摘在快速城市化进程中,我国建设用地急剧扩张,分析建设用地在时间和空间上的变化规律及其原因,对促进我国土地资源的可持续利用具有重要指导意义。基于2001、2006、2011、2017年四期MODIS Land Cover(MCD12Q1)产品数据,结合经济数据和地貌类型数据,研究中国不同经济区和不同地貌类型区的建设用地扩张的时空变化特征,结果表明:(1)全国建设用地在2001~2006、2006~2011、2011~2017年3时期扩张强度逐渐变弱,扩张速度逐渐变慢。(2)2001~2017年建设用地扩张主要发生在东部地区,该地区扩张强度最强、扩张速度最快。3时期内东部地区建设用地扩张强度逐渐变弱,扩张速度逐渐变慢;中部地区扩张强度逐渐变强,扩张速度逐渐变快;西部地区扩张强度先变弱后趋于稳定,扩张速度逐渐变慢;东北地区扩张强度先变弱后趋于稳定,扩张速度先变快后变慢。(3)2001~2017年建设用地扩张主要发生在平原地区,扩张强度最强,丘陵地区扩张速度最快;平原地区建设用地扩张强度逐渐变弱,扩张速度逐渐变慢;丘陵地区扩张强度先变弱后变强,扩张速度先变慢后变快;山地地区扩张强度逐渐变强,扩张速度先变快后变慢;高原地区扩张强度逐渐变强,扩张速度逐渐变快。从研究结果可知,不同经济区建设用地扩张强度和速度具有差异性,主要受不同时期国家相应的区域政策影响;不同地貌类型对建设用地扩张的时空特征也会产生影响。
基金This work was supported by the National Natural Science Foundation of China[42101287]Qufu Normal University Dissertation Research and Innovation Fund[LWCXS202121]+2 种基金Bayannur Ecological Governance and Green Development Academician Expert Workstation[YSZ2018-1]Shandong Provincial Natural Science Foundation[ZR2019BD045]the Science and Technology Project of Inner Mongolia Autonomous Region,China[NMKJXM202109].
文摘Introduction:Although numerous land cover datasets can act as references for understanding land cover change in China,the inconsistencies between the datasets can also provide understanding.Previous studies on the consistency between land cover datasets have mostly focused on land cover type consistencies and have ignored data consistencies in land cover change.Outcomes:Therefore,we aim to analyse the consistencies in land cover changes through likelihood assessment methods.We compared the spatiotemporal changes in forest,grassland,cropland,and bare land in the Climate Change Initiative land cover dataset(CCI-LC),Moderateresolution Resolution Imaging Spectroradiometer land cover dataset(MCD12Q1),China’s National Land Use and Cover Change(CNLUCC),Globeland30 and Global Land Cover Fine Surface Covering 30(GLC-FCS30)datasets in 2010.The results showed that the percentages and changes in each land cover type in MCD12Q1 were different from those in the other datasets.Discussion:For example,the proportion of grassland in MCD12Q1 was the highest,reaching 48.04%.The places with high consistency were the places where the land cover types were concentrated,and the bare land had the highest consistency.However,the consistency of China’s land cover change was quite low,and the percentage of low consistency was more than 87%from 2000-2018.Comparison of the data with the global artificial impervious area(GAIA)and Hansen-Global Forest Change(Hansen-GFC)datasets showed that the percentage of high construction gain consistency(38.83%)was higher than the forest change consistency,and the percentage forest loss high consistency(8.85%)was lower than the forest gain high consistency(12.76%).Conclusion:The results not only provide a basis for the use of land cover datasets but also give a clearer understanding of the pattern of land cover changes.
