China s Global Land Cover Mapping at 30 M Resolution

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1 Geospatial World Forum 2015 China s Global Land Cover Mapping at 30 M Resolution Jun Chen1,2 1 National Geomatics Center, 2ISPRS Lisbon, Portugal, May 28,2015

2 GlobeLand30 Video

3 Contents Introduction Introduction 1 POK_based operational mapping Data product and service Further works

4 Land Cover and Change Information Tools/Services Data Specific to Global Disasters Climate Adaptation Forest Carbon Carbon Energy Ecosystem/Biodiversity Water/Ocean Urban Biodiversity Impacts Pollution Global Land Cover/Weather Health Water Agriculture [GEO 2013] Natural attributes /characteristics of a variety of material types covering the surface of the globe Requested by all the nine SBAs as identified by GEO

5 Global Land Cover Data Products Level Data Products Spatial Resolution Temporal Resolution Global USGS UMD 1km 1km One year One year BU 1km One year GLC2000 1km One year GLC m One year Regional EU- Corine National USGS 1:100,000/ 100m 30m China 30m Spain Coarse spatial and temporal resolution Low spatial accuracy and consistency

6 Global Land Cover Mapping at 30 m Resolution Landsat MODIS 30m Imagery (2000) HJ FY-3 30m Imagery (2010) Image classification GlobeLand30 (2000/2010)

7 GlobeLand3030m Global Land Cover Data Sets Chen Jun, et.al., 2014, China: Open access to Earth land-cover map, Nature, 514:434, 23 Oct. 2014

8 Contents Introduction Introduction 2 POK_based operational mapping Data product-globeland30 Further works

9 Experiential/ Operational Mapping Experimental and operational classifications are two different approaches for large area land cover mapping and monitoring (Hansen and Loveland, 2012). Operational : development and delivery of reliable data products within a pre-defined time schedule. Experimental development and performance testing of novel algorithms and models Challenges raised by the Global Scale 30m GLC mapping project Data products for 2010 and

10 Three Major Challenges from Global Scale Local Scale Data Acquisition Easy access of remotely Difficult access of sensed data with full remotely sensed data with coverage global coverage Data Processing Consistent acquisition season, being easy for geometric and radiometric correction Land Cover Classification Sampling and Validation Classification scheme Global Scale Single classifier is enough Various acquisition season, arising difficulty in geometric and radiometric correction Single classifier is not widely applicable Low cost for acquiring High cost for sampling, training and test samples and samples are generally incomplete Available scheme could Not only satisfy the be selected from existing requirement of global schemes under specific change research, but also requirement be crosswalkable to existing schemes Major Challenges 1. Geometric and radiometric reconstruction for global 30m imagery coverage 2. Appropriate classifiers for handling spectral confusion and diversity 3. Assurance of data product quality

11 POK based Operational Approach RS imagery Global coverage of qualified 30m imagery Reduction of omission and commission errors Optimal Earth surface Ancillary data Processing multi-type imagery Reliability Web-based ancillary data and service Integrating pixel and object classifiers Knowledge -based quality controlling Operational Mapping Approach feasible suitability Solving the problems related to e characterization of complex landscapes Chen Jun, et/.al., Global Land Cover Mapping at 30m Resolution: a POK-based Operational Approach ISPRS Journal of Photogramme try and Remote Sensing, 103 (2015): 7-27

12 Integrating Pixel OK-based Classification Approach Per-class extraction Pixel-based classification Object-based Identification Knowledge-based verification Decomposing into per-class classification and then integrating the results Combining computer classification and expert knowledge for handling spectral confusion and diversity across the globe

13 Chen et.al., ISPRS J., 2015(6)

14 Contents Introduction Introduction 3 POK_based operational mapping Data product and service Further works

15 Four Characteristics Spatial resolution: 10 times higher Temporal dimension: 2000/2010 Accuracy: 83% given by 3rd party Open Access: donated to UN and

16 1st 30m Global Land Cover Data Product Level Data Products Spatial Resolution Temporal Resolution Global USGS UMD 1km 1km One year One year BU 1km One year GLC2000 1km One year GLC m One year GlobalLand30 30m GlobeLand30 30m Regional EU- Corine 1:100,000/ 100m National USGS 30m China 30m Spain 2000/ / m permits detection of land change at the scale of most human activity [Loveland, 2010]

17 Beijing- 2010

18 Beijing- 2000

19 Beijing

20 Beijing

21 Shanghai-2000

22 Shanghai

23 Shanghai

24 Accuracy Assessment Map sheets and smapling Map sheets selected: 80 Total samples: 154,070 Region Map smaple sheet s s Asia Europe Africa Americ a Oceani c Total

25 Accuracy Assessment 2010 Class croplands forest grass shrub wetland water artificial bareland Ice User acc % 89.00% 76.88% 72.52% 79.63% 92.09% 86.97% 77.33% Area % % Total accur % ±0.18%

26 Donated to United Nations Sept , New York, donation ceremony of GlobeLand30 UN SG Ban Ki-moon said: The World needs solid, science-based information for making wise decisions for sustainable development. These detailed data sets will help us to better understand, monitor and manage changes in land cover and land use all over our planet

27 On Line Service

28 Nature Published a Letter (514:434, 23 Oct. 2014) China: Open access to Earth land-cover map The map, known as GlobeLand30, comprises data sets collected at 30metreresolution more than ten times that of previous data sets. The GlobeLand30 data sets are freely available and comprise ten types of land cover, including forests, artificial surfaces and wetlands, for the years 2000 and 2010.

29 Downloading- Up to May 25, 2015 南美洲 ; 6% 大洋洲 ; 1% 非洲 ; 8% 北美洲 ; 14% 亚洲 ; 55% More than 5000 users from 90 countries 120, 0,000 map sheets downloaded 欧洲 ; 17% 国家 申请请 次数 请 下请请请 幅 中国 China 美国 USA 加拿大 Canada 蒙古 Mongilia 肯尼 亚 Kenya 意大利 Italy 巴西 Brasil 巴基斯坦 Pakistan 英国 UK 荷 亚 Neitherlands

30 Example of Users Users UN systems NGO GO Research institutes Universities Name of Orgnaisations FAO UNEP UNHabitat UNMIS UNDESA ESCAP UN Unit in Mali UNESCO Islamabad, WWF TNC, The Nature Conservancy) Conservation International, NASA GSFC USGS European Commission, JRC DFZ IIASA INPE Indian Institute of Science Space Research Institute of Ukraine IERSD/NOA, Harvard Yale Un. Maryland Glombia Uni.,

31 Contents Introduction Introduction 4 POK_based operational mapping Data product and service Further works

32 Supporting Post-2015 Agenda and Future Earth Ecolog accounting Dyna monitoring Users Future Earth/Post-2015 Agenda Application Measure Monitoring Manage Big data analysis Geospat statistics Open source software UN Service Platform Service Data Service Push service Processing service Data Supporting GlobeLand30 Servers Standards Refinement Updating Validation Integrating with Socio-economic environmental information

33 Continuous Updating and Refinement ④ Old imagery 1990s 1980 s 1970 s 2015 ② ① 2nd level classification (for certain classes) Change detection New Imagery Higher R. Imagery Globalland Refinement Finer Resol. LC ③(i.e.,10m) Globalland Refinement 2nd level Classes ① ② Globalland ③ Finer resolution (10m) mapping (hot spot areas) ④Historical mapping (backward)

34 Thanks for Your Attention!

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