NASA LCLUC Program and MuSLI Activities...Brochure E-newsletter Webinar series 200 projects since...
Transcript of NASA LCLUC Program and MuSLI Activities...Brochure E-newsletter Webinar series 200 projects since...
NASA LCLUC Program and MuSLI Activities
Garik Gutman, LCLUC Program Manager, Landsat Program Scientist
NASA Headquarters Washington, DC
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Land-Cover/Land-Use Change Program
LCLUC is an interdisciplinary scientific theme within NASA’s Earth Science
program. The ultimate vision of this program is to develop the capability
for periodic global inventories of land use and land cover from space,
to develop the scientific understanding and models necessary to
simulate the processes taking place, and to evaluate the consequences
of observed and predicted changes
• Drivers of LCLUC
• Natural Drivers
• Anthropogenic Drivers
• Socio-Economic Drivers
• Landscape Modification
• Impacts of LCLUC
• Carbon Cycle
• Surface Hydrology
• Atmosphere
Carbon Cycle
and Ecosystems
Focus Research
Area
Terrestrial Ecosystems
Program
Ocean Biology
Program
Biodiversity
Program
Applications
Program Carbon Management
Coastal Management
Water Management
Agri. Management
Land-Cover/Land-Use
Change Program
Water and Energy Cycle
Focus Research Area
Terrestrial Hydrology
Internal NASA
Programmatic
Linkages
Atmospheric
Composition
Focus Area
Radiation
Science
Vuln./ Adapt.
4%
Synthesis Studies 5%
Climate Variability
and Change 6%
Water and Energy Cycle Impacts
7%
Ecosystems and Biodiversity Impacts
8%
Observations and Data/ Detection and
Monitoring of LCLUC 27%
Drivers of Change 11%
Predictive Land Use Modeling
14%
Carbon and Biogeochemical Cycle
Impacts 18%
LCLUC Program Content
Impacts - 33% (Carbon+Water+Eco) Monitoring – 27% LU Modeling – 14% Drivers – 11% LCLUC- Climate interactions - 6% Synthesis – 5% Vulnerability/Adaptation – 4% http://lcluc.hq.nasa.gov
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Brochure
E-newsletter
Webinar series
200 projects since Program’s inception Each year ~40 3-yr projects Total in the Program >200 researchers
Solicitations in Time • LCLUC-08
– Climate change impacts on land use and contributions to large international non-NASA projects(“small” proposals)
– 66 step-1 received, 48 were encouraged to submit step-2 proposals, 42 were submitted, 18 were selected (9 impacts, 9 “small”)
• LCLUC-09
– Detecting/quantifying and explaining/attributing agricultural or urban land use
– 135 step-1 received, 67 were invited to submit step-2 proposals, 62 were submitted, 13 were selected (3 urban, 10 agriculture)
• LCLUC-10
– Land use in wetland regions (vulnerability, impacts, and adaptation) + LCLUC Synthesis
– 49 step-1 received, 29 were invited to submit step-2 proposals, 25 were submitted, 7 were selected (2 synthesis, 5 wetlands)
• LCLUC-11
– Early career scientists, land-use across political borders
– 90 step-1 received, 26 were invited to submit step-2 proposals, 26 were submitted, 10 were selected
• LCLUC-12
• Continental mapping of industrial forests from Landsat observations + LCUC Synthesis
• 24 step-1 received, 15 were invited to submit step-2 proposals, 16 were submitted, 10 were selected (6 synthesis, 4 industrial forests)
• LCLUC-13
• Changes in land use and land cover in mountainous areas +Synthesis
• 71 step-1 received, 33 invited, 31 submitted, 9 selected (3 Synthesis, 6 mountain land use)
• LCLUC-14 – Multi-source Land Imaging (MuSLI)
• LCLUC-15
• Changes in Agriculture and Urban land use in South Asia + LCLUC Synthesis (69 step-1 received)
• LCLUC-16
• Changes in Forest, Agriculture and Urban land use in Southeast Asia, Caucasus + LCLUC Synthesis
• LCLUC-17 – Multi-source Land Imaging (MuSLI)
Multi-Source Land Imaging (MuSLI) Landsat-Sentinel Team under LCLUC
• Sentinel-1a: launched in Apr
2014 • Sentinel-1b: launched in Apr
2016 • Sentinel-2a: 1.5 yr good
functioning • Sentinel-2b: to be launched in
spring of 2017 • Sentinel-3a: launched Feb
2016 • Landsat-7 & - 8 – nominal
operations
MuSLI NASA Project Scientist Jeff Masek, NASA Landsat-9 Project Scientist
•Prior efforts to synergistically use Sentinel data along with Landsat-8
•Joint NASA-UMD-CNES/CESBIO project •Sentinel-2 NASA Data Use Preparation team •Cross-Calibration (GSFC)
MuSLI ESA Project Scientist Benjamin Koetz, ESA Earth Observations Engineer
Salas: Rice crops product prototype
(South Asia)/Torbick, AG
Applied Geosolution, LLC
International Collaborators:
Hoekman, Wageningen U.
Le Toan, CESBIO
Operational algorithms and products for near real time maps of rice extent and rice crop growth stage using multi-source remote sensing
Huang: Inundation product prototype
(Americas)/Jones, USGS/ Lang, Fish
and Wildlife Service
University of Maryland
International Collaborators:
Creed, Western U., Canada
Towards Near Daily Monitoring of Inundated Areas over North
America through Multi-Source Fusion of Optical and Radar Data
Small: Global Urban Product/ Greg
Yetman, Columbia U./Nghiem, JPL
Columbia U. and JPL
International Collaborators:
Esch, DLR
Multi-source imaging of infrastructure and urban growth using
Landsat, Sentinel and SRTM
Friedl: Global veg. phenology
product prototype/ Gray, BU
Melaas, BU
Boston University
International Collaborator:
Eklundh, Sweden
Multisource Imaging of Seasonal Dynamics in Land Surface
Phenology: A Fusion Approach Using Landsat and Sentinel-2
Roy: Fire Burned Areas
(Africa)/Kovalskyy, SDSU/ Boschetti,
U. Idaho
South Dakota State U.
International collaborators:
Chuvieco, Spain; Tansey, UK
Prototyping a Landsat-8 Sentinel-2 global burned area product
Hansen: Global Crops Product
Prototype (Type and Area)/Potapov,
UMD
University of Maryland
International collaborators:
Defourny, Belgium
Integrating Landsat 7, 8 and Sentinel 2 data in improving crop type
identification and area estimation
Townshend: Global Tree cover and
water bodies product
prototype/Sexton, UMD / Feng, UMD/
Channan, UMD
University of Maryland
International Collaborators:
Schmullius, Germany
Multi-source imaging of time-serial tree and water cover at
continental to global scales
NASA LCLUC Multi-Source Land Imaging Projects: Mid-term
MuSLI Science Team: 40+ members
Salas , Applied Geosolutions
Torbick, AG
Lang, U Maryland
Jones, USGS
Huang, UMD (new PI)
Small, Columbia U.
Nghiem, JPL
Greg Yetman, Columbia U.
Friedl, Boston U.
Gray, BU
Melaas, BU
Roy, South Dakota State U.
Kovalskyy, SDSU
Boschetti, U. Idaho
Hansen, U. Maryland
Potapov, UMD
Townshend, U. Maryland
Sexton, UMD
Feng, UMD
Channan, UMD
Hoekman, Wageningen U.
Le Toan, CESBIO
Creed, Western U., Canada
Esch, DLR
Eklundh, Sweden
Chuvieco, Spain
Tansey, UK
Defourny, Belgium
Schmullius, Germany
Masek, NASA, MSLI Project
Scientist
Markham, NASA, calibration team
Helder, SDSU
Czapla-Myers (U. Az)
Schott, RIT
DIRSIG model, LST
Vermote, NASA GSFC
Atm. Corr. Team Claverie, U. MD
Woodcock, Boston U.,
clouds (USPI +1)
Dungan, NASA Ames, NEX
Ganguly, NASA Ames, NEX
PI and CO-Is Int. Collaborators Landcover Project Science Office
Koetz, ESA,
Sentinel-2 Projects
Coordinator
Dedieu & Hagolle,
CESBIO
Operational algorithms and products for near real time maps of rice extent and rice crop growth stage using multi-source remote
sensing PI: W. Salas, N. Torbick, AGS
Thuy Le Toan CESBIO, Dirk Hoekman, Wageningen
• Fuse SAR-optical for mapping agricultural conditions; technology transfer with partners
• S. Asia • Sentinel-1, PALSAR-2, Landsat-8,
Sentinel-2, Radarsat-2 • Fusion provides more and better
information; the approach strives to extract strengths of any / each satellite to complement another platform
• Pilot sites have beta products; begun cal / val coordination with ESA, regional partners (AsiaRice, IRRI, VAST, SERVIR, Ministries,…)
Example Tonle Sap, Cambodia Sentinel-1A Rice Inundation Dynamics Time Series
• Goals:
– Develop automated inundation mapping algorithms for Landsat, Sentinel-2 and Sentinel-1
– Generate near daily inundation products over US and southern Canada
• Progress:
– Algorithm for the automated quantification sub-pixel water fraction (SWF) developed for optical data streams (Landsat-5/7/8 and Sentinel-2) and tested over several sites
– Algorithm for automated classification of water for Sentinel-1 developed and tested over several sites
• Benefits of MuSLI inundation approaches:
– Provide needed spatial-temporal details: small water bodies, areas inundated for short periods, rapid inundation changes
– Enable advances in understanding aquatic systems: connectivity, function, carbon, and biodiversity
Towards Near Daily Monitoring of Inundated Areas over North America through Multi-Source Fusion of Optical and Radar Data
Chengquan Huang (PI), Ben DeVries, Wenli Huang, University of Maryland Megan Lang (Co-I), US Fish and Wildlife Service National Wetland Inventory; John Jones (Co-I), US Geological Survey
Irena Creed (International Collaborator), University of Western Ontario, London, ON, Canada
Time series sub-pixel water fraction (SWF) maps derived using Sentinel-1 (S1), Sentinel-2A (S2) and Landsat-8 (L8) imagery over the Everglades
Inundation change within above area
5 km
Multi-source imaging of infrastructure and urban growth using Landsat, Sentinel and SRTM
C. Small - Columbia Univ. USA S. Nghiem – NASA/JPL USA T. Esch – DLR Germany
• Develop continuous index to map built/impervious land cover
• Map changes in settlement extent between 2000 and 2015
• Global Extent – 20 urban-rural gradients in different biomes
• Optical: Landsat + Sentinel 2
• Radar: SRTM + Sentinel 1
• Multi-season – Spectral Stability
• Multi-source – Reflectance + Backscatter
• Multi-decade – Robust WRT sensor & time
• Developed spectral stability index
• Now characterizing reflectance-backscatter relations across biome
Stable Reflectance = Impervious
surfaces
High Backscatter = Corner reflectors
Both together = High Infrastructure Density
Multisource Imaging of Seasonal Dynamics in Land Surface Phenology: A Fusion Approach Using Landsat and Sentinel-2
Mark Friedl, PI; Josh Gray, Co-I; Boston University Lars Eklundh, Lund University, Patrick Hostert, Humboldt University, International Collaborators
• Goals:
– To quantify the timing and magnitude of land surface phenology events
(“phenometrics”) at moderate spatial resolution, and
– To generate gap-filled time series of spectral vegetation indices that
characterize the entire seasonal cycle of land surface phenology at fixed
time steps.
• Geographic area: Global (but focused on study sites)
• Data used: Landsat 8, S2 ( supplemented by Landsat 5, 7 & MODIS)
• Advantage of MuSLI approach: Time series density
• Up-to-date progress:
– Ongoing international collaboration with Lund University and Humboldt
University, Berlin (BU Team visiting Berlin & Lund in Nov., 2016)
– Manuscript in prep., AGU presentation, analyzing phenology algorithms
– Data set development focused on Cal/Val data for core test sites.
– Manuscript in prep describing Kalman Filter algorithm to fuse OLI and MSI.
– Analysis of phenology algorithms based on HLS data (see figure at right)
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45
65
85SOS (days since Oct. 8, 2015)
0 5 10 15km
Above: Estimated day of year for SOS (start of season) based on HLS time series for scene near Lahore, Pakistan. Below: sample HLS time series for a multi-cropped pixel; estimated phenology dates identified by vertical lines.
David Roy, Haiyan Huang, Lin Yan, Hankui Zhang, Jian Li, (GSCE, South Dakota State University, USA, Luigi Boschetti (Idaho, USA) International Collaborators: Jose Gómez-Dans & Philip Lewis (UCL, London, U.K), Emilio Chuvieco (Alcala, Spain), Kevin Tansey (Leicester, U.K)
• Goal: Prototype global 30m burned area product to meet user needs for – improved carbon budget accounting – greenhouse gas and aerosols emissions – environmental management – post-fire assessment and remediation – people - environment - climate - fire research
• Geographic area: Africa + global sample
• Data used: Landsat-8 and Sentinel-2A/B
• MuSLI advantage:
– Landsat-8 and Sentinel-2A/B together provide needed temporal resolution for time series burn change detection
– Landsat-8 has improved quantization, signal/noise characteristics, and acquisition coverage over heritage Landsat missions
– Sentinel-2 has Landsat-8 like bands at 10m & 20m with higher acquisition coverage than Landsat
• Progress: Automated sensor-agnostic burned area algorithm for WELD processed time series prototyped
• Progress: Sentinel-2 processing under global WELD processing, and Landsat-8 to Sentinel-2 registration, developed and implemented
Landsat-8
global WELD tile
hh19vv12h3v2
Cape Town
Sentinel-2
global WELD tile
hh19vv12h3v2
Cape Town
• Progress: Four publications
1. Huang, H., Roy, D.P., Boschetti, B., Zhang, H.K., Yan, L., Kumar, S.S., Gomez-Dans, J., Li, J., 2016, Separability analysis of Sentinel-2A multi-spectral instrument (MSI) data for burned area discrimination, Remote Sensing, In Press.
2. Storey, J., Roy, D.P., Masek, J., Gascon, F., Dwyer, J., Choate, M., 2016, A note on the temporary mis-registration of Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multi Spectral Instrument (MSI) imagery, Remote Sensing of Environment, 186, 121-122.
3. Roy, D.P., Li, J., Zhang, H.K., Yan, L., 2016, Best practices for the reprojection and resampling of Sentinel-2 Multi Spectral Instrument Level 1C data, Remote Sensing Letters, 7(11), 1023-1032.
4. Yan, L., Roy, D.P., Zhang, H.K., Li, J., Huang, H., 2016, An automated approach for sub-pixel registration of Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multi Spectral Instrument (MSI) imagery, Remote Sensing, 8(6), 520.
Date 1
Date 2 Mapped burned
Prototyping a Landsat-8 Sentinel-2 Global Burned Area Product
South America 2015-2016 growing season soybean cover
Integrating Landsat 7, 8 and Sentinel 2 data in improving crop type identification and area estimation
PIs: M. Hansen, P. Potapov, University of Maryland International collaborators: Pierre Defourny, UCL and Carlos Di Bella, INTA
Goals • Develop and implement a system for defining
the required phenological sampling for mapping crop types.
• Determine if Landsat and/or Sentinel 2 acquisitions are sufficient in meeting required sampling frequencies for selected commodity crop type mapping (required best individual date image inputs).
• Compare the performance of single date images and seasonal metrics in mapping crop type at local (per sample block) and regional (all sample blocks at once) scales.
• Given sufficient temporal richness, validate area estimation of crop type for large scale industrial and fine scale smallholder cropping systems.
Scale - National
Data - Primarily Landsat 7 and 8 and Sentinel 2A
Advantage - Crops require more detailed phenologic profiles for characterization
Progress – Testing use of data in Tanzania for corn mapping, waiting for systematic acquisitions over large commodity crop landscapes
Multi-source imaging of time-serial tree and water cover at continental to global scales
PI: John Townshend, Global Land Cover Facility, Department of Geographical Sciences, University of Maryland Co-Is: Joseph O. Sexton, Min Feng, Saurabh Channan, Global Land Cover Facility, Department of Geographical
Sciences, University of Maryland International collaborator: Christiane Schmullius, University of Jena, ESA GLOBBIOMASS Project
• Goal: Develop methodologies for fusing optical and radar imagery into data streams of tree- and water-cover estimates
• Geographic area: Global • Data used: Landsat TM, ETM+ & OLI; Sentinel
1 & 2; PALSAR • Advantage: long-term and globally consistent
estimates without gaps due to clouds or sensor failure
• Progress: – Global estimates of tree cover based on Landsat
(2010) – Development of online portal for collaborative
data visualization and progress-reporting – Successful first test of incorporating Sentinel-2:
filled cloud gaps – Unsuccessful first test of incorporating Sentinel-1:
weak tree-cover signal
Global, Landsat-based tree-cover estimates for the 2010 GLS epoch visualized through an online portal for collaboration based on shared geospatial datasets.
Outstanding Issues
• Higher level products depend on preprocessing – Calibration
– Cloud detection
– Registration
– Atmospheric correction
• Errors in TOA reflectance due to cloud/cloud shadow contamination propagate to higher level products
• Mis-registration is an obstacle in many applications which use pixel-to-pixel colocation
Towards Higher Level Products
From Landsat and Sentinel Data
• Sensor reflectance products – assumed to be available to MuSLI PI’s – Are the L8 and S2 surface reflectance products and/or algorithms
available?
– Object: a seamless, sensor-independent reflectance record at high spatial, and high-temporal resolution to support land science
– Compatible atmospheric correction & cloud/shadow detection
– Corrections for BRDF (solar, view angle), band pass differences
– Adjustment to common frame, resolution (~30m), compositing period (~5 day) • Absolute geolocation of MSI L1C products may differ from Landsat-8 L1T products
by up to ~37m (2s), due to errors in the Landsat GLS2000 reference
• What is the status for each algorithm as compared to what’s done in Europe?
• 39 tests sites (22 from MusLI ST) were selected – Time series will be constructed
• 368 S2 tile and 360 Landsat path/row
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Coordination and collaboration of MuSLI and Landsat Science Teams
• New call for MuSLI is coming out in Feb (NOI by Apr 1; Proposals by June 1 – one step only, no social science required)
• MuSLI team members will participate in at least one of Landsat ST meetings during the year
• MuSLI team members who are not on the Landsat team will be encouraged to share their results with Landsat team members
• Landsat team members will be invited to international MuSLI team meetings
Thank You