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Three distinct global estimates of historical land-cover change and land-use conversions for over 200 years

Frontiers of Earth Science 2012年第2期6卷 122-139页

作者： Prasanth MEIYAPPAN Atul K. JAIN Department of Atmospheric Sciences University of Illinois Urbana IL 61801 USA

Earth＇s land cover has been extensively transformed over time due to both human activities and natural causes. Previous global studies have focused on developing spatial and temporal patterns of dominant human land-use activities （e.g., cropland, pastureland, urban land, wood harvest）. Process-based modeling studies adopt different strategies to estimate the changes in land cover by using these land-use data sets in combination with a potential vegetation map, and subsequently use this information for impact assessments. However, due to unaccounted changes in land cover （resulting from both indirect anthropogenic and natural causes）, heterogeneity in land-use/cover （LUC） conversions among grid cells, even for the same land use activity, and uncertainty associated with potential vegetation mapping and historical estimates of human land use result in land cover estimates that are substantially different compared to results acquired from remote sensing observations. Here, we present a method to implicitly account for the differences arising from these uncertainties in order to provide historical estimates of land cover that are consistent with satellite estimates for recent years. Due to uncertainty in historical agricultural land use, we use three widely accepted global estimates of cropland and pastureland in combination with common wood harvest and urban land data sets to generate three distinct estimates of historical land-cover change and underlying LUC conversions. Hence, these distinct historical reconstructions offer a wide range of plausible regional estimates of uncertainty and the extent to which different ecosystems have undergone changes. The annual land cover maps and LUC conversion maps are reported at 0.5°×0.5° resolution and describe the area of 28 land- cover types and respective underlying land-use transitions. The reconstructed data sets are relevant for studies addressing the impact of land-cover change on biogeo- physics, biogeochemistry, water cy

关键词： historical land use land-cover change land-use conversions deforestation HYDE Moderate Resolu-tion Imaging Spectroradiometer (MODIS)

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Temporal patterns of land-use change and carbon storage in China and tropical Asia

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Science China(Life Sciences) 2002年第Z1期45卷 10-17页

作者： R. A. Houghton Woods Hole Research Center P.O. Box 296 Woods Hole MA 02543

Evaluating the annual sources and sinks of carbon from land-use changehelps constrain other terms in the global carbon cycle and may help countries choose how to comply with commitments for reduced emissions. This paper presents the results of recent analyses of land-use change in China and tropical Asia. The original forest areas are estimated to have covered 546×106 ha in tropical Asia and 425×106 ha in China. By 1850, 44% of China's forests had been cleared, and another 27% was lost between 1850 and 1980, leaving China with 13% forest cover (29% of the initial forest area). Tropical Asia is estimated to have lost 26%of its initial forest cover before 1850 and another 33% after 1850. The annual emissions of carbon from the two regions reflect the different histories over the last 150 years, with China's emissions peaking in the late 1950s (at 0.2-0.5 Pg C@a-1) and tropical Asia's emissions peaking in 1990s (at 1.0 Pg C@a-1). Despite the fact that most deforestation has been for new agricultural land, the majority of the lands cleared from forests in China are no longer croplands, but fallow or degraded shrublands. Unlike croplands, the origins of these other lands are poorly documented, and thus add considerable uncertainty to estimates of flux before the 1980s. Nevertheless, carbon emissions from China seem to have decreased since the 1960s to nearly zero at present. In contrast, emissions of carbon from tropical Asia were higher in the 1990s than that at any time in the past.

关键词： carbon, China, deforestation, land-use change, tropical Asia.

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Addressing global change challenges for Central Asian socio-ecosystems

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Frontiers of Earth Science 2012年第2期6卷 115-121页

作者： Jiaguo QI Temirbek S. BOBUSHEV Rashid KULMATOV Pavel GROISMAN Garik GUTMAN Michigan State University East Lansing MI 48823 USA American University of Central Asia Bishkek 720040 Kyrgyzstan National University of Uzbekistan Tashkent 100027 Uzbekistan UCAR Project Scientist at NOAA National Climatic Data Center Asheville NC 28801 USA nasa land use and land cover Claange Program Washington DC 20546 USA

Central Asia is one of the most vulnerable regions on the planet earth to global climate change, depending on very fragile natural resources. The Soviet legacy has left the five countries （Kazakhstan, Tajikistan, Kyrgyzstan, Turkmenistan, and Uzbekistan） with a highly integrated system but they are facing great challenges with tensions that hinder regional coordination of food and water resources. With increasing climate variability and warming trend in the region, food and water security issues become even more crucial now and, if not addressed properly, could affect the regional stability. The long-term drivers of these two most critical elements, food and water, are climate change; the immediate and probably more drastic factors affecting the food and water security are land uses driven by institutional change and economic incentives. As a feedback, changes in land use and land cover have directly implications on water uses, food production, and lifestyles of the rural community in the region. Regional and international efforts have been made to holistically understand the cause, extent, rate and societal implications of land use changes in the region. Much of these have been understood, or under investiga- tion by various projects, but solutions or research effort to develop solutions, to these urgent regional issues are lacking. This article, serves as an introduction to the special issue, provides a brief overview of the challenges facing the Central Asian countries and various interna- tional efforts in place that resulted in the publications of this special issue.

关键词： Central Asia climate change land use andland cover change water and food security

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Leveraging the use of labeled benchmark datasets for urban area change mapping and area estimation: a case study of the Washington DC–Baltimore region

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International Journal of Digital Earth 2022年第1期15卷 1169-1186页

作者： Yiming Zhang Sergii Skakun Michael Oluwatosin Adegbenro Qing Ying Department of Geographical Sciences University of MarylandCollege ParkMDUSA nasa Goddard Space Flight Center Code 619 GreenbeltMDUSA Earth System Science Interdisciplinary Center University of MarylandCollege ParkMDUSA

Worldwide economic development and population growth have led to unprecedented changes in urban land use in the twenty-first century. As satellite data become available at higher spatial (3–10 m) and temporal (1–3 days) resolution,new opportunities arise to map and quantify urban area changes. While deep learning (DL) models have recently shown great performance when dealing with satellite data,their training requires a lot of labeled data which are not necessarily available at global scale. Satellite benchmark datasets,commonly used to advance methods,provide labeled data,but are rarely used for mapping and area estimation outside the training data. In this study,we aim to utilize the Sentinel-2-based benchmark dataset,Onera Satellite change Detection (OSCD),to train a DL model and analyze its performance at local scale to map urban land use changes,estimate area of changes and provide characterization of changes. We apply the model over the Washington DC–Baltimore area for 2018–2019. We show that in just one year almost 1% of the total urban area underwent changes with the majority coming from the construction of commercial buildings,followed by residential buildings. Almost 10% of changes were attributed to the construction of new or renovation of existing schools.

关键词： Sentinel-2 urban area change detection deep learning

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North Carolina’s forest disturbance and timber production assessed using time series landsat observations

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International Journal of Digital Earth 2015年第12期8卷 947-969页

作者： Chengquan Huang Pui-Yu Ling Zhiliang Zhu Department of Geographical Sciences University of MarylandCollege ParkMDUSA U.S.Geological Survey RestonVAUSA

Wood products provide a relatively long-term carbon storage *** to lack of consistent datasets on these products,however,it is difficult to determine their carbon *** main purpose of this study was to quantify forest disturbance and timber product output(TPO)using time series landsat observations for North *** results revealed that North Carolina had an average forest disturbance rate of 178,000 ha per year from 1985 to *** derived disturbance products were found to be highly correlated with TPO survey data,explaining up to 87%of the total variance of county level industrial roundwood *** level TPO estimates derived using the landsat-based disturbance products tracked those derived from ground-based survey data *** TPO modeling approach developed in this study complements the ground-based TPO surveys conducted by the US Forest *** allows derivation of TPO estimates for the years that did not have TPO survey data,and may be applicable in other regions or countries where at least some ground-based survey data on timber production are available for model development and dense time series landsat observations exist for developing annual forest disturbance products.

关键词： timber products output remote sensing vegetation change tracker

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A global,high-resolution(30-m)inland water body dataset for 2000:first results of a topographic-spectral classification algorithm

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International Journal of Digital Earth 2016年第2期9卷 113-133页

作者： Min Feng Joseph O.Sexton Saurabh Channan John R.Townshend Global land cover Facility Department of Geographical SciencesUniversity of MarylandCollege ParkMDUSA

The science and management of terrestrial ecosystems require accurate,high-resolution mapping of surface *** produced a global,30-m-resolution inland surface water dataset with an automated algorithm using landsat-based surface reflectance estimates,multispectral water and vegetation indices,terrain metrics,and prior coarse-resolution water *** dataset identified 3,650,723 km2 of inland water globally–nearly three quarters of which was located in North America(40.65%)and Asia(32.77%),followed by Europe(9.64%),Africa(8.47%),South America(6.91%),and Oceania(1.57%).Boreal forests contained the largest portion of terrestrial surface water(25.03%of the global total),followed by the nominal‘inland water’biome(16.36%),tundra(15.67%),and temperate broadleaf and mixed forests(13.91%).Agreement with respect to the Moderate-resolution Imaging Spectroradiometer water mask and landsat-based national land-cover datasets was very high,with commission errorscover,snow and ice,and clouds–effects which were compounded by differences in image acquisition date relative to reference *** Global land cover Facility(GLCF)inland surface water dataset is available for open access at the GLCF website(http://***).

关键词： inland water landsat global high resolution MODIS

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Annual Maps of Forests in Australia from Analyses of Microwave and Optical Images with FAO Forest Definition

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Journal of Remote Sensing 2021年第1期2021卷 134-144页

作者： Yuanwei Qin Xiangming Xiao Jean-Pierre Wigneron Philippe Ciais Josep G.Canadell Martin Brandt Xiaojun Li Lei Fan Xiaocui Wu Hao Tang Ralph Dubayah Russell Doughty Qing Chang Sean Crowell Bo Zheng Kevin Neal Jorge A.Celis Berrien MooreⅢ Department of Microbiology and Plant Biology Center for Earth Observation and ModelingUniversity of OklahomaNormanOK 73019USA ISPA UMR 1391INRA Nouvelle-AquitaineBordeaux Villenave d’OrnonFrance Laboratoire des Sciences du Climat et de l'Environnement LSCE/IPSLCEA-CNRS-UVSQUniversitéParis-Saclay91191 Gif-sur-YvetteFrance CSIRO Oceans and Atmosphere CanberraACTAustralia Department of Geosciences and Natural Resource Management University of CopenhagenCopenhagenDenmark Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station School of Geographical SciencesSouthwest UniversityChongqing 400715China Department of Geography National University of Singapore1 Arts LinkKent RidgeSingapore 117570 Department of Geographical Sciences University of MarylandCollege ParkMDUSA Division of Geological and Planetary Sciences California Institute of TechnologyPasadenaCA 91125USA O’Neill School of Public and Environmental Affairs Indiana University Bloomington702 N Walnut Grove AveBloomingtonIN 47405USA College of Atmospheric and Geographic Sciences University of OklahomaNormanOK 73019USA Institute of Environment and Ecology Tsinghua Shenzhen International Graduate SchoolTsinghua UniversityShenzhen 518055China

The Australian governmental agencies reported a total of 149 million ha forest in the Food and Agriculture Organization of the United Nations(FAO)in 2010,ranking sixth in the world,which is based on a forest definition with tree height>2 ***,we report a new forest cover data product that used the FAO forest definition(tree cover>10%and tree height>5 meters at observation time or mature)and was derived from microwave(Phased Array type L-band Synthetic Aperture Radar,PALSAR)and optical(Moderate Resolution Imaging Spectroradiometer,MODIS)images and validated with very high spatial resolution images,Light Detection and Ranging(LiDAR)data from the Ice,Cloud,and land Elevation Satellite(ICESat),and in situ field survey *** new PALSAR/MODIS forest map estimates 32 million ha of forest in 2010 over ***/MODIS forest map has an overall accuracy of~95%based on the reference data derived from visual interpretation of very high spatial resolution images for forest and nonforest cover *** with the canopy height and canopy coverage data derived from ICESat LiDAR strips,PALSAR/MODIS forest map has 73%of forest pixels meeting the FAO forest definition,much higher than the other four widely used forest maps(ranging from 36%to 52%).PALSAR/MODIS forest map also has a reasonable spatial consistency with the forest map from the National Vegetation Information *** new annual map of forests in Australia could support cross-country comparison when using data from the FAO Forest Resource Assessment Reports.

关键词： forest Forest PALSAR

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Global operational land imager landsat-8 reflectance-based active fire detection algorithm

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International Journal of Digital Earth 2018年第2期11卷 154-178页

作者： S.S.Kumar D.P.Roy Plant and Environmental Sciences New Mexico State UniversityLas CrucesNMUSA Geospatial Sciences Center of Excellence South Dakota State UniversityBrookingsSDUSA

A global operational land imager(GOLI)landsat-8 daytime active fire detection algorithm is *** utilizes established contextual active fire detection approaches but takes advantage of the significant increase in fire reflectance in landsat-8 band 7(2.20μm)relative to band 4(0.66μm).The detection thresholds are fixed and based on a statistical examination of 39 million non-burning landsat-8 ***-temporal tests based on band 7 reflectance and relative changes in normalized difference vegetation index in the previous six months are used to reduce commissions *** probabilities of active fire detection for the GOLI and two recent landsat-8 active fire detection algorithms are simulated to provide insights into their performance with respect to the fire size and *** algorithms are applied to 11 landsat-8 images that encompass a range of burning conditions and *** and omission errors are assessed by visual interpretation of detected active fire locations and by examination of the landsat-8 images and higher spatial resolution Google Earth *** GOLI algorithm has lower omission and comparable commission errors than the recent landsat-8 active fire detection *** GOLI algorithm has demonstrable potential for global application and is suitable for implementation with other landsat-like reflective wavelength sensors.

关键词： Earth observation remote sensing visible/infrared remote sensing

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Integrating global land cover products for improved forest cover characterization: an application in North America

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International Journal of Digital Earth 2014年第9期7卷 709-724页

作者： Xiao-Peng Song Chengquan Huang Min Feng Joseph O.Sexton Saurabh Channan John R.Townshend Global land cover Facility Department of Geographical SciencesUniversity of MarylandCollege ParkMDUSA

Six widely used coarse-resolution global land cover data-sets–Global land cover Characterization(GLCC),Global land cover 2000(GLC2000),Globcover land cover product(Globcover),MODIS land cover product(MODIS LC),the University of Maryland land cover product(UMD LC),and the MODIS Vegetation Continuous Fields tree cover layer(MODIS VCF)disagree substantially in their estimates of forest *** a regression tree model trained on higher-resolution,landsat-based data,these multisource multiresolution maps were integrated for an improved characterization of forest cover over North *** using a withheld test sample,the integrated percent forest cover(IPFC)data-set has a root mean square error of 11.75%–substantially better than the 17.37% of GLCC,17.61% of GLC2000,17.96% of Globcover,15.23% of MODIS LC,19.25%of MODIS VCF,and 15.15%of UMD LC,*** demonstrated for forest,this approach based on integration of multiple products has potential for improved characterization of other land cover types as well.

关键词： land cover forest data fusion regression tree North America

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Do green spaces affect the spatiotemporal changes of PM_(2.5) in Nanjing?

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Ecological Processes 2016年第1期5卷 78-90页

作者： Jiquan Chen Liuyan Zhu Peilei Fan Li Tian Raffaele Lafortezza International Center for Ecology Meteorologyand EnvironmentNanjing University of Information Science and TechnologyNanjing 210044China CGCEO/Geography Michigan State UniversityEast LansingMI 48824USA School of Planning Designand ConstructionMichigan State UniversityEast LansingMI 48824USA Institute of Geographic Sciences and Natural Resources Research Chinese Academy of SciencesBeijing 100101China Department of Scienze Agro-Ambientali e Territoriali University of BariBari 70126Italy Department of Geography Michigan State University1405 S.Harrison RoadEast LansingMI 48823USA

Introduction:Among the most dangerous pollutants is PM_(2.5),which can directly pass through human lungs and move into the blood *** use of nature-based solutions,such as increased vegetation cover in an urban landscape,is one of the possible solutions for reducing PM_(2.5) *** study objective was to understand the importance of green spaces in pollution ***:Daily PM_(2.5) concentrations were manually collected at nine monitoring stations in Nanjing over a 534-day period from the air quality report of the China National Environmental Monitoring Center(CNEMC)to quantify the spatiotemporal change of PM_(2.5) concentration and its empirical relationship with vegetation and landscape structure in ***:The daily average,minimum,and maximum PM_(2.5) concentrations from the nine stations were 74.0,14.2,and 332.0μg m^(−3),*** of the 534 days,the days recorded as“excellent”and“good”conditions were found mostly in the spring(30.7%),autumn(25.6%),and summer(24.5%),with only 19.2% of the days in the *** PM_(2.5) concentrations exceeding the safe standards of the CNEMC were recorded predominately during the winter(39.3-100.0%).Our hypothesis that green vegetation had the potential to reduce PM_(2.5) concentration was accepted at specific seasons and *** PM_(2.5) concentration appeared very highly correlated(R2>0.85)with green cover in spring at 1-2 km scales,highly correlated(R2>0.6)in autumn and winter at 4 km scale,and moderately correlated in summer(R2>0.4)at 2-,5-,and 6-km ***,a non-significant correlation between green cover and PM_(2.5) concentration was found when its level was>75μg m^(−3).Across the Nanjing urban landscape,the east and southwest parts had high pollution ***:Although the empirical models seemed significant for spring only,one should not devalue the importance of green vegetation in other seasons because the regulations are often complicated by vegetation,me

关键词： PM_(2.5) Green space Edge density Nanjing Pollution control Seasonal variation Nature-Based Solution(NBS)

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