Decoding the inconsistency of six cropland maps in China

Accurate cropland information is critical for agricultural planning and production,especially in foodstressed countries like China.Although widely used medium-to-high-resolution satellite-based cropland maps have been developed from various remotely sensed data sources over the past few decades,considerable discrepancies exist among these products both in total area and in spatial distribution of croplands,impeding further applications of these datasets.The factors influencing their inconsistency are also unknown.In this study,we evaluated the consistency and accuracy of six cropland maps widely used in China in circa 2020,including three state-of-the-art 10-m products(i.e.,Google Dynamic World,ESRI Land Cover,and ESA WorldCover)and three 30-m ones(i.e.,GLC_FCS30,GlobeLand 30,and CLCD).We also investigated the effects of landscape fragmentation,climate,and agricultural management.Validation using a ground-truth sample revealed that the 10-m-resolution WorldCover provided the highest accuracy(92.3%).These maps collectively overestimated Chinese cropland area by up to 56%.Up to 37%of the land showed spatial inconsistency among the maps,concentrated mainly in mountainous regions and attributed to the varying accuracy of cropland maps,cropland fragmentation and management practices such as irrigation.Our work shed light on the promotion of future cropland mapping efforts,especially in highly inconsistent regions.

The future of global land change monitoring

Land change science co-evolves with remote sensing technology.The world has witnessed an exponential growth in Earth observation satellites since 1972,and concurrently,land change research has experienced transformative advancement.This review summarizes the major milestones in global land cover and change mapping in a chronological order,from the pioneering efforts in the 1980s to the latest innovations at present,illustrating the tremendous progress in monitoring global land change from space.The second part of the review presents a critical synopsis of the recent progress in land change research,focusing on the technical aspects of temporal trends characterization,change mapping and area estimation,as well as the applied aspects of driver attribution and the complex consequences to the Earth system and human society.The last part of the article offers insights in the strategic directions of land change monitoring,including generation of analysis ready data,application of artificial intelligence algorithms,reconstruction of historical land change records,and near-real-time land change monitoring.Land change science will continue to play a vital role in addressing a wide range of global challenges,including climate change and carbon sequestration,food security,sustainable energy transition,natural disaster relief and environmental change in conflicted societies.

Global characterization and monitoring of forest cover using Landsat data: opportunities and challenges

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.

Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error

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.

小P值并不能保证可靠结果:以帕金森病的静息态脑功能成像元分析为例

Thousands of resting state functional magnetic resonance imaging(RS-f MRI)articles have been published on brain disorders.For precise localization of abnormal brain activity,a voxel-level comparison is needed.Because of the large number of voxels in the brain,multiple comparison correction(MCC)must be performed to reduce false positive rates,and a smaller P value(usually including either liberal or stringent MCC)is widely recommended[1].

Assessment of the three factors affecting Myanmar’s forest cover change using Landsat and MODIS vegetation continuous fields data

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.

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

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.

Development of S-NPP VIIRS global surface type classification map using support vector machines

With the launch of the Joint Polar Satellite System(JPSS)/Suomi National Polar-orbiting Partnership(S-NPP)satellite in October 2011,many of the terrestrial remote sensing products generated from Moderate Resolution Imaging Spectroradiometer(MODIS),such as the global land cover map,have been inherited and expanded into the JPSS/S-NPP mission using the new Visible Infrared Imaging Radiometer Suite(VIIRS)data.In this study,an improved algorithm including the use of a new classifier support vector machines(SVM)classifier was proposed to produce VIIRS surface type maps.In addition to the new classification algorithm,a new post-processing strategy involving the use of new ancillary data to refine the classification output is implemented.As a result,the new global International Geosphere-Biosphere Programme(IGBP)map based on the 2014 VIIRS surface reflectance data was generated with a 78.5±0.6% overall classification accuracy.The new map was compared to a previously delivered VIIRS surface type map,and to the MODIS land cover product.Validation results including the error matrix,overall accuracy,and the user’s and producer’s accuracy suggest the new global surface type map provides similar classification accuracy compared to the old VIIRS surface type map,with higher accuracy achieved in agricultural types.

Improving global land cover characterization through data fusion

Global-scale land cover characterization has advanced from a spatial resolution of 1×1°in the mid-1990s to 30×30 m resolution to date.However,some mapping challenges exist persistently regardless of the increasing spatial resolution.Data fusion has been proved as an effective way of improving land cover characterization.Here we applied a machine learning-based data integration approach for improving global-scale forest cover characterization.The approach employed six coarse-resolution(250-1000 m)global land cover maps as input and various regional,higher-resolution land cover data-sets as reference to build regression tree models per continent.The average error of 10-fold cross validation of the regression tree models varied between 7.70 and 15.68% forest cover and the r2 varied between 0.76 and 0.94,indicating the robustness of the trained models.As a result of data fusion,the synthesized global forest cover map was more accurate than any input global product.We also showed that other major vegetative land cover types such as cropland,woodland,grassland,and wetland all exhibit similar magnitude of discrepancies as forest among existing land cover maps.Our developed method,because of its type-and scale-invariant feature,can be implemented for other land cover types for improving their global characterization.The ensemble approach can also be internalized for improving data quality when generating a global land cover product,where multiple versions can be produced and subsequently integrated.