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| 1 | A global,high-resolution(30-m)inland water body dataset for 2000:first results of a topographic-spectral classification algorithm显示文摘The science and management of terrestrial ecosystems require accurate,high-resolution mapping of surface water.We 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 masks.The 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 errors<4%and omission errors<14%relative to each.Most of these were accounted for in the seasonality of water cover,snow and ice,and clouds–effects which were compounded by differences in image acquisition date relative to reference datasets.The Global Land Cover Facility(GLCF)inland surface water dataset is available for open access at the GLCF website(http://gffzzc4da8a9a1d6d4c7ahf0vkfoc99nwx69np.ffgz.tsg.suse.edu.cn). | Min Feng Joseph O.Sexton Saurabh Channan John R.Townshend | 2016 | International Journal of Digital Earth2016，9，2： | 13 |
| 2 | Global characterization and monitoring of forest cover using Landsat data: opportunities and challenges显示文摘The compilation of global Landsat data-sets and the ever-lowering costs of computing now make it feasible to monitor the Earth’s land cover at Landsat resolutions of 30 m.In this article,we describe the methods to create global products of forest cover and cover change at Landsat resolutions.Nevertheless,there are many challenges in ensuring the creation of high-quality products.And we propose various ways in which the challenges can be overcome.Among the challenges are the need for atmospheric correction,incorrect calibration coefficients in some of the data-sets,the different phenologies between compila-tions,the need for terrain correction,the lack of consistent reference data for training and accuracy assessment,and the need for highly automated character-ization and change detection.We propose and evaluate the creation and use of surface reflectance products,improved selection of scenes to reduce phenological differences,terrain illumination correction,automated training selection,and the use of information extraction procedures robust to errors in training data along with several other issues.At several stages we use Moderate Resolution Spectro-radiometer data and products to assist our analysis.A global working prototype product of forest cover and forest cover change is included. | John R.Townshend Jeffrey G.Masek Chengquan Huang Eric.F.Vermote Feng Gao Saurabh Channan Joseph O.Sexton Min Feng Raghuram Narasimhan Dohyung Kim Kuan Song Danxia Song Xiao-Peng Song Praveen Noojipady Bin Tan Matthew C.Hansen Mengxue Li Robert E.Wolfe | 2012 | International Journal of Digital Earth2012，5，5： | 10 |
| 3 | Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error显示文摘We developed a global,30-m resolution dataset of percent tree cover by rescaling the 250-m MOderate-resolution Imaging Spectroradiometer(MODIS)Vegetation Continuous Fields(VCF)Tree Cover layer using circa-2000 and 2005 Landsat images,incorporating the MODIS Cropland Layer to improve accuracy in agricultural areas.Resulting Landsat-based estimates maintained consistency with the MODIS VCF in both epochs(RMSE=8.6%in 2000 and 11.9%in 2005),but showed improved accuracy in agricultural areas and increased discrimination of small forest patches.Against lidar measurements,the Landsat-based estimates exhibited accuracy slightly less than that of the MODIS VCF(RMSE=16.8%for MODIS-based vs.17.4%for Landsat-based estimates),but RMSE of Landsat estimates was 3.3 percentage points lower than that of the MODIS data in an agricultural region.The Landsat data retained the saturation artifact of the MODIS VCF at greater than or equal to 80%tree cover but showed greater potential for removal of errors through calibration to lidar,with post-calibration RMSE of 9.4%compared to 13.5%in MODIS estimates.Provided for free download at the Global Land Cover Facility(GLCF)website(www.landcover.org),the 30-m resolution GLCF tree cover dataset is the highest-resolution multi=temporal depiction of Earth’s tree cover available to the Earth science community. | Joseph O.Sexton Xiao-Peng Song Min Feng Praveen Noojipady Anupam Anand Chengquan Huang Do-Hyung Kim Kathrine M.Collins Saurabh Channan Charlene DiMiceli John R.Townshend | 2013 | International Journal of Digital Earth2013，6，5： | 5 |
| 4 | Integrating global land cover products for improved forest cover characterization: an application in North America显示文摘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 cover.Employing 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 America.Evaluated 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,respectively.Although demonstrated for forest,this approach based on integration of multiple products has potential for improved characterization of other land cover types as well. | Xiao-Peng Song Chengquan Huang Min Feng Joseph O.Sexton Saurabh Channan John R.Townshend | 2014 | International Journal of Digital Earth2014，7，9： | 1 |
| 5 | Assessment of the three factors affecting Myanmar’s forest cover change using Landsat and MODIS vegetation continuous fields data显示文摘Long-term observation of the earth is essential for studying the factors affecting global environmental changes.Digital earth technology can facilitate the monitoring of global environmental change with its ability to process vast amounts of information.In this study,we map the forest cover change of Myanmar from 2000 to 2005 using a training data automation procedure and support vector machines algorithm.Our results show that Myanmar’s forests have declined 0.68%annually over this six-year period.We validated our derived change results and found the overall accuracy to be greater than 88%.We also assessed forest loss from protected areas,areas close to roads,and areas subject to fire,which were most likely to lose forested area.The results revealed the main reasons for forest losses in some hotspots to be increased agricultural conversion,fire,and the construction of highways.This information is useful for identifying the driving forces behind forest changes and to support environmental policy development in Myanmar. | Fu-Jiang Liu Chengquan Huang Yong Pang Mengxue Li Dan-Xia Song Xiao-Peng Song Saurabh Channan Joseph O.Sexton Die Jiang Ping Zhang Yan Guo Yao-Feng Li John R.Townshend | 2016 | International Journal of Digital Earth2016，9，6： | 1 |
| 6 | Isolating type-specific phenologies through spectral unmixing of satellite time series显示文摘Vegetation phenology is commonly studied using time series of multispectral vegetation indices derived from satellite imagery.Differences in reflectance among land-cover and/or plant functional types are obscured by sub-pixel mixing,and so phenological analyses have typically sought to maximize the compositional purity of input satellite data by increasing spatial resolution.We present an alternative method to mitigate this‘mixed-pixel problem’and extract the phenological behavior of individual land-cover types inferentially,by inverting the linear mixture model traditionally used for sub-pixel land-cover mapping.Parameterized using genetic algorithms,the method takes advantage of the discriminating capacity of calibrated surface reflectance measurements in red,near infrared,and short-wave infrared wavelengths,as well as the Normalized Difference Vegetation Index(NDVI)and the Normalized Difference Water Index.In simulation,the unmixing procedure reproduced the reflectances and phenological signals of grass,crop,and deciduous forests with high fidelity(RMSE<0.007 NDVI);and in empirical tests,the algorithm extracted the phenological characteristics of evergreen trees and seasonal grasses in a semi-arid savannah.The approach shows potential for a wide range of ecological applications,including detection of differential responses to climate,soil,or other factors among vegetation types. | Jyoteshwar R.Nagol Joseph O.Sexton Anupam Anand Ritvik Sahajpal Thomas C.Edwards | 2018 | International Journal of Digital Earth2018，11，3： | 0 |
| 7 | Improved modeling and analysis of the patch size–frequency distribution of forest disturbances in China based on a Landsat forest cover change product显示文摘Forest disturbances have been altering the ecological properties of ecosystems;meanwhile,disturbance events of varying sizes create different structures and functions for a forest landscape.Therefore,size and frequency are important attributes of disturbances,and their relationship should be studied.We present a hierarchical method through the modeling of the overall trend of the size–frequency distribution and the characterization of the non-constant variances of disturbance sizes at each frequency level.This method was demonstrated to accurately model the sizes as well as the corresponding frequencies;thus,the total disturbed area and number of disturbance patches were both accurately estimated.By applying the method to 13 provinces in China,consistent patterns were revealed by the modeling results and remote-sensing-based product,showing that between 2000 and 2005,forests in most provinces were dominated by moderate disturbances(10 ha1000 ha)occurred in the northeastern and northwestern provinces.This study concludes that the proposed method can improve the representation of the size–frequency distribution of forest disturbances. | Dan-Xia Song Chengquan Huang Tao He Joseph O.Sexton Ainong Li Sike Li Hao Wu John R.Townshend | 2021 | International Journal of Digital Earth2021，14，2： | 0 |
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