Final Report
NASA Grant NNX11AJ35G (5/31/2011 – 5/30/2015)
Quantifying changes in agricultural intensification and
expansion for monsoon Asia during 2000-2010
Principal Investigator
Jinwei Dong
University of Oklahoma, Norman, Oklahoma
&
Co-Principal Investigator
Williams Salas
Applied Geosolutions, Inc., Newmarket, New Hampshire
July 30, 2015
(This report reflects activities and results over the period of
6/1/2011 – 5/31/2015)
Table of ContentsPublications3Presentations5Main findings7A.
Unprecedented paddy rice agriculture maps in Monsoon Asia7A.1
MODIS-based paddy rice mapping and relevant products7A.2
Landsat-based paddy rice mapping platform8A.3 Multi-temporal
SAR-based large scale paddy rice mapping9A.4 Evaluations of the
performance of Landsat 8 on paddy rice mapping12B. Plant growing
season, cropping intensity from time series data13B.1. Maps of
plant growing season from MODIS LST data14B.2. Maps of cropping
intensity and crop calendar from MODIS data14B.3. Maps of flooded
areas from MODIS & PALSAR data15C. Capacity building in
community remote sensing and citizen science16C.1. Smartphone app
“Field Photo”16C.2. The Global Geo-Referenced Field Photo
Library17C.3. Integration of field photos and MODIS time series
data18C.4. International training of Global Geo-Referenced Field
Photo Library19C.5. International collaboration and field
surveys20
Publications
2015
1. Dong, J., Xiao, X., Kou, W., Qin, Y., Zhang, G., Li, L., Jin,
C., Zhou, Y., Wang, J., Biradar, C., Liu, J. & Moore Iii, B.
(2015) Tracking the dynamics of paddy rice planting area in
1986–2010 through time series Landsat images and phenology-based
algorithms. Remote Sensing of Environment, 162, 154-168.
2. Jin, C., Xiao, X., Dong, J., Qin, Y. & Wang, Z. 2015.
Mapping paddy rice distribution using multi-temporal Landsat
imagery in the Sanjiang Plain, northeast China. Frontiers of Earth
Science, 1-14.
3. Qin, Y., Xiao, X., Dong, J., Zhou, Y., Zhu, Z., Zhang, G.,
Du, G., Jin, C., Kou, W., Wang, J. & Li, X. (2015) Mapping
paddy rice planting area in cold temperate climate region through
analysis of time series Landsat 8 (OLI), Landsat 7 (ETM+) and MODIS
imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 105,
220-233.
4. Wang, J., Xiao, X., Qin, Y., Dong, J., Zhang, G., Kou, W.,
Jin, C., Zhou, Y. & Zhang, Y. (2015) Mapping paddy rice
planting area in wheat-rice double-cropped areas through
integration of Landsat-8 OLI, MODIS, and PALSAR images. Scientific
Report, 5, 10088.
5. Zhang, G.L., Xiao, X.M., Dong, J.W., Kou, W.L., Jin, C., Qin,
Y.W., Zhou, Y.T., Wang, J. & Biradar, C. (2015) Mapping paddy
rice planting areas through time series analysis of MODIS thermal
and surface reflectance images. ISPRS Journal of Photogrammetry and
Remote Sensing, 106, 157-171.
2014
6. Wang, J., Dong, J., Liu, J., Huang, M., Li, G., Running, S.,
Smith, W., Harris, W., Saigusa, N., Kondo, H., Liu, Y., Hirano, T.,
Gamo, M., Xiao, X. 2014. Comparison of Gross Primary Productivity
derived from GIMMS NDVI3g, GIMMS, and MODIS in Southeast Asia,
Remote Sensing, 6, 2108-2133
2013
7. Zhang, G., Zhang, Y., Dong, J., and Xiao, X., 2013, Green-up
dates in the Tibetan Plateau have continuously advanced from 1982
to 2011. Proceedings of the National Academy of Sciences of the
United States of America, 110(11): 4309-4314.
8. Zhang, G., Dong, J., Zhou, C., Xu, X., Wang, M., Ouyang H.,
and Xiao, X., 2013, Increasing cropping intensity in response to
climate warming in Tibetan Plateau, China, Field Crops Research,
142: 36-46.
9. Dong J., Liu J., Zhang G., Basara J., Greene S., and Xiao X.,
2013, Climate change affecting temperature and aridity zones: a
case study in Eastern Inner Mongolia, China from 1960–2008 , Theor
Appl Climatol, 113:561-572.
2012
10. Li, P., Feng, Z., Jiang, L., Sheldon, S., Liu, Y., Zhao, Y.,
Liu, Y., and Xiao, X., 2012, A simple method to discriminate rice
cropping system at the field level using Landsat images, Journal of
Applied Remote Sensing, (in press).
11. Sheldon, S. L., Xiao, X., and Biradar, C., 2012, Mapping
evergreen forests in the Brazilian Amazon using MODIS and PALSAR
500-m mosaic imagery, ISPRS Journal of Photogrammetry and Remote
Sensing, 74, 34-40.
12. Dong, J., Xiao, X., Sheldon, S., Biradar, C., Duong, N.,
Hazarika, M., 2012, A comparison of forest cover maps in mainland
Southeast Asia from multiple sources: PALSAR, MERIS, MODIS and FRA.
Remote Sensing of Environment, 127, 60-73.
13. Dong, J., Xiao, X., Sheldon, S., Biradar, C., Liu, J., and
Xie, G, 2012, Mapping tropical forests and rubber plantations in
complex landscapes by integrating PALSAR and MODIS imagery, ISPRS
Journal of Photogrammetry and Remote Sensing, 74, 20-33.
14. Li, P., Feng, Z., Jiang, L., Liu, Y., and Xiao, X., 2012,
Changes in rice cropping systems in the Poyang Lake Region, China,
Journal of Geographical Sciences, 22 (4): 653-668.
15. Zhang, G., Dong, J., Xiao, X., Hu, Z., and Sheldon, S.,
2012, Effectiveness of ecological restoration projects in Horqin
Sandy Land, China, based on SPOT-VGT NDVI data, Ecological
Engineering, 38: 20-29.
2011
16. Xiao, X., Dorovskoy, P., Biradar, C., and Bridge, E., 2011,
A library of geo-referenced photos from the field, AGU EOS, 98
(49): 453-454.
17. Torbick, N, Salas, W., Xiao, X., Ingraham, P, Fearon, M.G.,
Biradar, C., Zhao, D., Liu, Y., Li, P., and Zhao, Y., 2011,
Integrating SAR and optical imagery for regional mapping of paddy
rice attributes in the Poyang Lake watershed, China, Canadian
Journal of Remote Sensing, 37(1): 17-26.
18. Biradar, C., and Xiao, X., 2011, Quantifying the area and
spatial distribution of double- and triple cropping croplands in
India with multi-temporal MODIS imagery in 2005, International
Journal of Remote Sensing, 32(2): 367-386.
Presentations
2015
1. Dong, J., Xiao, X., Menarguez, M., Zhang, G., Qin, Y.,
Mapping paddy rice in northeastern Asia based on Landsat 8 and
Google Earth Engine. 2015 NASA Carbon Cycle & Ecosystems Joint
Science Workshop. College Park, Maryland, 4/20-4/24/2015
(Poster).
2. Qin, Y., Xiao, X., Dong, J., Zhang, G., Shimada, M., Liu, J.,
Li, C., Kou, W., Moore, B., A Comparison of Forest Cover Maps in
China in 2010 from Multi-source: Landsat, PALSAR, MCD12Q1, FAO FRA,
and National Forestry Inventory. 2015 NASA Carbon Cycle &
Ecosystems Joint Science Workshop. College Park, Maryland,
4/20-4/24/2015 (Poster).
2014
3. Xiao, X., Zhang, G., Dong, J., Menarguez, M., Kou, W., Jin,
C., Qin, Y., Zhou, Y., Wang, J., Moore, B. Changes of paddy rice
planting areas in Northeastern Asia from 1986 to 2014 based on
Landsat data. GC31B: Environmental, Socioeconomic, and Climatic
Changes in Northern Eurasia and Their Feedbacks to the Global Earth
System I Posters (GC31B-0473). The American Geophysical Union
(AGU), Fall Meeting, San Francisco, 12/15-12/19/2014. (Poster)
4. Qin, Y., Xiao, X., Dong, J., Zhang, G. Comparison of Forest
Cover Maps in China in 2010 from Multi-source: Landsat, PALSAR,
MCD12Q1, FAO FRA, and National Forestry Inventory. B51B: Global
Forest Dynamics and Interactions with a Changing Climate I Posters
(B51B-0024A) The American Geophysical Union (AGU), Fall Meeting,
San Francisco, 12/15-12/19/2014. (Poster)
5. Jinwei Dong, Xiangming Xiao, Weili Kou, Yuanwei Qin, Geli
Zhang, and Chandra Biradar. A Landsat-based paddy rice mapping
platform (Landsat-RICE) based on time series Landsat images and
phenology-based algorithms. NASA Land-Cover and Land-Use Change
Science Team Meeting, Rockville, Maryland, 04/23-04/25/2014.
(Poster)
6. Qin, Y., Xiao, X., Dong, J., Zhou Y., Zhu, Z., Zhang G., Du
G., Jin C., Kou W., Wang J and Li X. 2014, Evaluate the potential
of Landsat 8 imagery for mapping paddy rice planting area, LCLUC
Spring Science Team Meeting 2014 - Focus on Urban LCLUC.
(Poster)
7. Torbick, N., Salas, W. 2014. Mapping agricultural and
forested landscapes to support food security and carbon cycle
science. Kyoto and Carbon Phase 3 Report. 21st Science Team
meeting, Kyoto Research Park, Japan. 12/3/2014 – 12/5/2014.
8. Torbick, N., Salas, W. Xiangming, X. 2014. Mapping rice
agriculture across Monsoon Asia using multiscale imagery. IGARSS
& Canadian Symposium on Remote Sensing. Quebec Canada. July
13-18, 2014.
9. Torbick et al. 2014. Mapping rice in the USA with Earth
Observations in real time. The American Geophysical Union (AGU),
Fall Meeting, San Francisco, 12/15-12/19/2014. (Poster)
2013
10. Cui Jin; Xiangming Xiao; Jinwei Dong; Zongming Wang; and
Kaishan Song. Mapping paddy rice using multi-temporal Landsat
imagery in the Sanjiang Plain, Northeast China. B41A. Earth
Observations for Global Agricultural Monitoring II Posters
(B41A-0389). The American Geophysical Union (AGU), Fall Meeting,
San Francisco, 12/08-12/13/2013. (Poster)
11. Jinwei Dong, Xiangming Xiao, Chandrashekhar Biradar,
Bangqian Chen, Nathan Torbick, and Guishui Xie. A simple and robust
automated approach to map rubber plantations from PALSAR, Landsat
and MODIS. NASA Land-Cover and Land-Use Change Science Team
Meeting, Rockville, Maryland, 04/02-04/04/2013. (Poster)
2012
12. Jinwei Dong, Xiangming Xiao, Sage Sheldon, Chandrashekhar
Biradar. A comparison of forest cover maps in Mainland Southeast
Asia from multiple sources: PALSAR, MERIS, MODIS and FRA, GC51B:
Spatiotemporal Change Detection and the Data Infrastructure of
Environmental Observatories I Posters The American Geophysical
Union (AGU), Fall Meeting, San Francisco, 12/03-12/07/2012
(Poster).
13. Jinwei Dong, Xiangming Xiao, Sage Sheldon, Chandrashekhar
Biradar. A comparison of forest cover maps in Mainland Southeast
Asia from multiple sources: PALSAR, MERIS, MODIS and FRA. NASA
Land-Cover and Land-Use Change Science Team Meeting, Rockville,
Maryland, 04/03-04/05/2012. (Poster)
2011
14. Jinwei Dong, Xiangming Xiao, Sage Sheldon, Chandrashekhar
Biradar. Mapping croplands and rubber forests in complex topography
landscapes based on PALSAR 50m mosaic image, Landsat and MODIS: a
case study in Hainan Island, China. NASA Land-Cover and Land-Use
Change Science Team Meeting, Adelphi, Maryland, 03/28-03/30/2011.
(Poster)
Main findingsA. Unprecedented paddy rice agriculture maps in
Monsoon Asia
With the support of this project, we have been making efforts on
mapping the paddy rice agriculture in Monsoon Asia, the source of
over 90% global rice production. Our works can be divided in to
four aspects: 1) a MODIS-based annual paddy rice mapping efforts,
including an automatic paddy rice mapping platform (MODIS-RICE) and
annual paddy rice maps in Monsoon Asia from 2000 to 2014 (Zhang et
al., ISPRS P&RS, 2015; Zhang et al., In Preparation). 2) A
Landsat-based paddy rice mapping efforts, including an automatic
paddy rice mapping platform (Landsat-RICE) and epoch-based paddy
rice maps in hotspots from 1980s to 2010s (Dong et al., RSE, 2015).
3) Multi-temporal SAR-based large scale paddy rice mapping efforts
in tropical regions (Torbick et al., In Preparation). 4)
Evaluations of the performance of Landsat 8 on paddy rice hotspots
regions (Qin et al., ISPRS P&RS, 2015; Wang et al., SR, 2015;
Zhou et al., IJAEOG, In Revision).
A.1 MODIS-based paddy rice mapping and relevant products
Based on the paddy rice mapping algorithm which originated from
Xiao et al. (2005, 2006), we have extended the mapping spatially to
the whole Monsoon Asia and temporarily to 2000-2014. Fig. 1
showcases one year of map in 2010. A detailed description of the
products is available in recent publications (Zhang et al., ISPRS
P&RS, 2015, and Zhang et al., In Preparation).
Fig. 1 Paddy rice map in 2010 for Monsoon Asia by using time
series analysis of MODIS 8-day reflectance and land surface
temperature data
A.2 Landsat-based paddy rice mapping platform
We developed an automated Landsat-based paddy rice mapping
platform (Landsat-RICE) that uses time series Landsat images to
track changes in paddy rice areas over decades. A pilot study was
completed for one case region in northeastern Asia where single
paddy rice dominates and a lot of in-situ field data are available
(Fig. 2, Dong et al., RSE, 2015). We reported expansion of paddy
rice at five-year interval (1986-1990, 1991-1995, 1996-2000,
2001-2005, 2006-2010) in the study area. A rapid paddy rice field
expansion occurred there, and we provided satellite-based evidence
for the agricultural land expansion in higher latitude temperate
regions. Regional analyses was also carried out in several hot
spots including within Vietnam (Fig. 3)
Fig. 2 A schematic diagram of the automated Landsat-based paddy
rice mapping platform
Fig. 3 Paddy rice and other land use map in Thanh Hoa and Nghe
An of Vietnam.
A.3 Multi-temporal SAR-based large scale paddy rice mapping
A large effort was carried out coordinating a research agenda
between NASA and JAXA and the use of PALSAR (Figs. 4-6). As part of
the JAXA science team we helped build an L-band acquisition
strategy to enable the development of large-area products with
systematic observations for wetland regions as part of the Japan
Aerospace Exploration Agency Kyoto & Carbon Initiative. One
focus application of the initiative was the mapping of rice
agriculture to support global change science and food security. In
this research application an operational approach was developed to
utilize multiscale Phased Array type L-band Synthetic Aperture
Radar (PALSAR) to map rice extent across Indochina. Multitemporal
finebeam (12.5m), Mosaic (50m), and ScanSAR Strip (50m) imagery
circa 2009 was integrated using a Classification and Regression
Tree approach. Fine-beam imagery and geo-referenced field photos
were used to develop training data to classify dual pol (hh:hv)
mosaic imagery using the randomForest algorithm. Multitemporal
PALSAR Strips collected during rice growth stages (inundation,
sowing, tillering, harvest) further refined rice maps across
Indochina at 50m scale. Comparison against government crop surveys
show moderate to strong agreement (R2: 0.73-0.89) with dense rice
regions across the Mekong and Red River Deltas, Chao Phraya and
I-San Plateau, and coastal regions. The maps and operational
technique have potential to improve continuity of rice mapping,
monitoring, and systems modeling across Indochina in a cost
effective manner.
Fig. 4 FBD (hh:hv) 50m Mosaic at 34.3 incidence angle during
June 12th – October 15th, 2009 (R:HH, G:HV, B:HH/HV) for portion of
Mekong Delta, Vietnam showing RSP paths during different flood and
growth stages with evident stripping from adjacent ascending orbits
that create processing challenges. Multitemporal HH image
(R:9/21/11, G:2/3/11, B:11/6/11) over An Giang and Kien Giang
Provincial area shows varying phenologies of paddies and greater
detail at 12.5m ground resolution.
Fig. 5 Example CART iteration for Cambodia derived from PALSAR
Mosaics & ScanSAR.
Fig. 6 Fractional cover % at 10km (left) and % rice alnd use
(right) by administrative district for Indochina. The major growing
areas are well captured by the multitemporal imagery with dense
rice regions mapped across the Mekong and Red River Deltas, Chao
Phraya and I-San Plateau, and coastlines.
A.4 Evaluations of the performance of Landsat 8 on paddy rice
mapping
Since the launch of Landsat 8 in 2013, images from Operational
Land Imaging (OLI) and the Thermal Infrared Sensor (TIRS) are
freely available to the public. We carried out a series of pilot
study to evaluate the potential of Landsat 8 images to map paddy
rice. We selected a number of sites along the latitudinal gradient
and diverse cropping systems, where we have access to large number
of in-situ data, ranging from single paddy rice area (Sanjiang
Plain, Panjin Plain in Northeastern China), winter wheat and paddy
rice rotation area (Yangtze river delta, Southern China), double
rice area (Poyang Lake, China; Thailand), and triple rice area
(Mekong Delta, Vietnam). Recently, we have completed initial
analyses for single paddy rice area, and two papers have been
published (Qin et al., ISPRAS P&RS, 2015; Wang et al., SR,
2015) and one is in revision (Zhou et al., IJAEOG, In Revision).
For example, for Landsat 8 path/row (114/27), we analyzed 12
Landsat 8 OLI images, and the resultant map of paddy rice has a
promising accuracy (Qin et al., 2015, Fig. 7).
Fig. 7 . Spatial distribution of paddy rice area within 1-km
gridcell from (a) paddy rice map at 30-m spatial resolution from
Landsat 8 imagery in 2013, (b) NLCD-2010 dataset and (c) the
difference between (a) and (b), and (d) frequency distribution of
(c).
B. Plant growing season, cropping intensity from time series
data
As part of effort to develop a multi-sensor approach to map
agriculture in monsoon Asia, we use time series MODIS data to
develop three datasets that are used as input for paddy rice
agriculture analysis: (1) maps of plant growing seasons, (2) maps
of cropping intensity and crop calendar at annual interval, (3)
flooded/inundated area. These data products will help us to define
timing windows for selection of Landsat images at individual
path/row of Landsat images. Also, these products are standalone
products.
B.1. Maps of plant growing season from MODIS LST data
The growing season of natural vegetation differ substantially
from that of crops. For example, the emergence of new leaves (or
green-up) of natural vegetation (deciduous trees and shrubs, annual
grass) often starts when air temperature is above 0 oC in the
spring. The planting dates of crops often occur much later, after
air temperature reaches above 5 oC or 10 oC in the spring. The
thermal range and timing determine the cropping intensity (number
of crops) and crop calendar.
We used MODIS Land Surface Temperature data (8-day composite,
MOD11A2 and MYD11A2) to map the starting dates and ending date of
LST > 0 oC, 5 oC and 10 oC in spring, and fall seasons,
respectively (Fig. 8). Given the local pastime of Aqua satellite
(1:30pm and 1:30am), we first used MODIS LST data from Aqua
satellite. For those years without Aqua satellite, we used MODIS
LST data from Terra satellite (10:30am and 10:30pm).
Fig. 8. Spatial distribution of the starting dates and ending
dates of LST > 0 oC, 5 oC and 10 oC in spring and autumn by
using time series MODIS land surface temperature data.
B.2. Maps of cropping intensity and crop calendar from MODIS
data
In the past we used MODIS 8-day surface reflectance data
(MOD09A1) from 2001-2010 to generate three vegetation indices
(NDVI, EVI and LSWI), and applied a phenology-based algorithm to
map cropping intensity, crop calendar and paddy rice (Biradar et
al., 2012).
In an effort to simplify the algorithm for mapping cropping
intensity, we currently used the TIMESAT software to process
MODIS-based time series NDVI and EVI data in an effort to extract
phenology and temporal development of vegetation, including the
starting dates and ending dates of plant growing season, amplitude,
integrated values).
We continued to evaluate the MODIS-based maps of cropping
intensity and crop calendar (e.g., Fig. 9). A paper is in
preparation to show the results (Zhang et al., In Preparation). The
resultant MODIS-based cropping intensity and crop calendar maps
will be used as input data layer in the RICE-MODIS and RICE-Landsat
analysis platforms, which helps us to choose appropriate time
window for paddy rice transplanting.
Fig. 9. Cropping intensity map in Bangladesh, eastern India, and
Nepal by using MODIS data and the TIMESAT software.
B.3. Maps of flooded areas from MODIS & PALSAR data
We have used time series data of NDVI, EVI and LSWI from MODIS
8-day surface reflectance (MOD09A1) to map flooded/inundated areas.
We used the same algorithm reported in our previous studies (Xiao
et al., 2005, 2006) and applied it to the time windows defined by
the MODIS LST. We have generated maps of flooded/inundated areas
from 2000 to 2014. We also generated paddy rice maps annually for
Monsoon Asia (Zhang et al., 2014, in preparation). In addition we
used ~monthly ScanSAR data to map inundation dynamics and paddy
rice across Monsoon Asia (e.g., Fig. 10). We are finalizing
comparison to MODIS maps and are prepping a publication (Torbick et
al., In Prep).
Fig. 10. Illustration of rice growth stages for a double crop
system, PALSAR sigma nought (dB) response at 34.3 incidence angle,
and key paddy attributes used to operationally map rice extent.
C. Capacity building in community remote sensing and citizen
science
Our capacity building activities include (1) EOMF data portal
and tools for community remote sensing and citizen science and (2)
international collaboration and training.
C.1. Smartphone app “Field Photo”
Millions of smartphones are used in the world, and it has the
capacity to capture photos of land cover and land use in the field
with accurate latitude and longitude information. This project
contributed substantially to the development of smartphone app
“Field Photo”. We first developed and released smartphone app
“Field Photo” for iPhone in October 2013, and then developed and
released smartphone app “Field Photo” for Android-based smartphone
in February 2014. The smartphone app is freely available to the
public in Apple Store and Google Play Store (Fig. 11). The Field
Photo app allows students, researchers and citizens to take photos
during field trips, geotag them and add metadata and field notes to
the photos. The field photos are then uploaded to the Global
Geo-Referenced Field Photo Library at the University of Oklahoma
(http://www.eomf.ou.edu/photos), where people share, visualize and
archive field photos that document land use and land cover change,
flood, drought, fire, and so on. These photos and associated
thematic databases are also used to support satellite image
analyses for monitoring the dynamics of Planet Earth, where now
more than seven billions of people live. The integration of mobile
phone, Geo-Referenced Field Photo Library and satellite images
allows us to socialize the pixels in the images.
Fig. 11. Information on the smartphone app “Field Photo”
C.2. The Global Geo-Referenced Field Photo Library
Geo-referenced field photos are an important component in field
surveys, and can be used as in-situ reference or ground-truth
datasets to support image interpretation, classification and data
product evaluation. This project contributes substantially to the
development and operation of the Global Geo-Referenced Field Photo
Library (http://www.eomf.ou.edu/photos/) at the OU (Fig. 12). We
published the 1st paper that introduces the Field Photo Library in
AGU EOS newsletter in December 2011 (Xiao et al., 2011), and that
specific EOS issue was widely distributed at the AGU Fall 2012
Annual Meeting in San Francisco. The Field Photo Library has been
well received by the research communities and citizens. It now
hosts 140,000+ field photos in the world.
The geo-referenced field photos are now used in conjunction with
Google Earth to digitalize and delineate the Regions of Interest
(ROIs) for various land cover types, and the resultant ROIs are
then used for supervised classification, including the use of
decision tree method. We have completed initial evaluation of the
workflow from geo-referenced field photos to ROIs, and the results
were reported in two papers (Dong et al., 2012, 2013).
Fig. 12. A flyer on the Global Geo-Referenced Field Photo
Library
C.3. Integration of field photos and MODIS time series data
Our EOMF data portal hosts a number of MODIS standard data
products, including surface reflectance (MOD09A1). It now
integrates field photos with the MODIS data, so that a user can
extract time series MODIS data from 2000 to present to study land
use and land cover change at the pixel containing the field photo
(Fig. 13).
Fig. 13. Integration between field photos and MODIS time series
data in the EOMF data portal
C.4. International training of Global Geo-Referenced Field Photo
Library
Dr. Xiao and graduate student Michael Menarguez carried out a
training session in the 2nd Global Land Project meeting in 2014,
Berlin, Germany. The training section was led by Dr. Xiao at OU,
and collaborated with a group from the International Institute of
Applied System Analysis (IIASA) in Vienna, Austria, and a group
from University of Tokyo. It had three training components. The 1st
component is to introduce mobile device and app that allow people
to use smartphones (iPhone, Android phones) to collect
geo-referenced field photos. The 2nd component is to introduce the
EOMF Field Photo Library. The third component is to introduce the
procedure how to use field photos to support evaluation of land
cover maps, example the Geo-Wiki project (www.geo-wiki.org), a
global network of volunteers to help improve the quality of global
land cover maps. More than 30 participants participated in the
training session in the afternoon of March 20, 2014, and 50 flyers
on the smartphone app and field photo library were distributed to
participants of the GLP 2014 conference.
C.5. International collaboration and field surveys
In June 2013, Dr. Xiao visited Dr. Lam Dao Nguyen at the HCMC
Institute of Resources Geography, Vietnam Academy of Science and
Technology, and carried out a 3-day field trip with his research
group in the Mekong delta, with a focus on paddy rice agriculture.
A large number of geo-referenced field photos were collected in
that region and have been uploaded into the Global Geo-Referenced
Field Photo Library at OU.
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