7/24/02 MODIS Science Meeting Seasonal Variability Studies Across the Amazon Basin with MODIS Vegetation Indices Alfredo Huete 1, Kamel Didan 1, Piyachat.

Slides:



Advertisements
Similar presentations
Lab for Remote Sensing Hydrology and Spatial Modeling Dept. of Bioenvironmental Systems Engineering, NTU Satellite Remote Sensing for Land-Use/Land-Cover.
Advertisements

Beyond Spectral and Spatial data: Exploring other domains of information GEOG3010 Remote Sensing and Image Processing Lewis RSU.
Session 7: Land Applications Burned Area RENATA LIBONATI Instituto Nacional de Pesquisas Espaciais (INPE) Brazil EUMETRAIN.
MODIS The MODerate-resolution Imaging Spectroradiometer (MODIS ) Kirsten de Beurs.
Scaling Biomass Measurements for Examining MODIS Derived Vegetation Products Matthew C. Reeves and Maosheng Zhao Numerical Terradynamic Simulation Group.
Xiangming Xiao Department of Botany and Microbiology, College of Arts and Sciences Center for Spatial Analysis, College of Atmospheric.
III LBA Scientific Conference, July 27-29, 2004 SEASONAL ANALYSIS OF THE MOD13A2 (NDVI / EVI) AND MOD15A2 (LAI / fAPAR) PRODUCTS FOR THE CERRADO REGION.
Active Remote Sensing Systems March 2, 2005 Spectral Characteristics of Vegetation Temporal Characteristics of Agricultural Crops Vegetation Indices Biodiversity.
Satellite Remote Sensing and Applications in Hydrometeorology Xubin Zeng Dept of Atmospheric Sciences University of Arizona Tucson, AZ
Carbon Cycle and Ecosystems Important Concerns: Potential greenhouse warming (CO 2, CH 4 ) and ecosystem interactions with climate Carbon management (e.g.,
MODIS Science Team Meeting Silver Spring, MD - April 15-16, 2013 MODIS VI Product Suite Status From C5 to C6 Kamel Didan & Armando Barreto Work inherited.
 Floods and droughts are the most important hydrological disturbances in intermittent streams.  The concept of hydrological disturbance is strongly.
Green Vegetation Fraction (GVF) derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor onboard the SNPP satellite Zhangyan Jiang 1,2,
Remote Sensing of Hydrological Variables over the Red Arkansas Eric Wood Matthew McCabe Rafal Wojcik Hongbo Su Huilin Gao Justin Sheffield Princeton University.
The Role of Models in Remotely Sensed Primary Production Estimates
Giant Kelp Canopy Cover and Biomass from High Resolution SPOT Imagery for the Santa Barbara Channel Kyle C Cavanaugh, David A Siegel, Brian P Kinlan, Dan.
Landscape-scale forest carbon measurements for reference sites: The role of Remote Sensing Nicholas Skowronski USDA Forest Service Climate, Fire and Carbon.
Jorge Peña Arancibia, Francis Chiew, Tim McVicar, Yongqiang Zhang, Albert Van Dijk, Mohammed Mainuddin and others 29 April 2014 CSIRO LAND AND WATER Dynamic.
Radiometric and biophysical measures of global vegetation from multi-dimensional MODIS data Ramakrishna Nemani NTSG.
Nara Luísa Reis de Andrade, Luciana Sanches, Peter Zeilhofer, Segundo Durval Rezende Pereira, José de Souza Nogueira Environmental Physics Research Group.
Global NDVI Data for Climate Studies Compton Tucker NASA/Goddard Space Fight Center Greenbelt, Maryland
Real-time integration of remote sensing, surface meteorology, and ecological models.
Assessment of Regional Vegetation Productivity: Using NDVI Temporal Profile Metrics Background NOAA satellite AVHRR data archive NDVI temporal profile.
A METHODOLOGY TO SELECT PHENOLOGICALLY SUITABLE LANDSAT SCENES FOR FOREST CHANGE DETECTION IGARSS 2011, Jul, 27, 2011 Do-Hyung Kim, Raghuram Narashiman,
Observing Kalahari ecosystems at local to regional scales: a remote sensing perspective Nigel Trodd Coventry University.
Land degradation & change detection of biophysical products using multi temporal SPOT NDVI image data : a case in Blue Nile river basin, Ethiopia Taye.
Review of Statistics and Linear Algebra Mean: Variance:
The role of remote sensing in Climate Change Mitigation and Adaptation.
Slide #1 Emerging Remote Sensing Data, Systems, and Tools to Support PEM Applications for Resource Management Olaf Niemann Department of Geography University.
Karnieli: Introduction to Remote Sensing
Canada Centre for Remote Sensing Field measurements and remote sensing-derived maps of vegetation around two arctic communities in Nunavut F. Zhou, W.
Remote Sensing of Vegetation. Vegetation and Photosynthesis About 70% of the Earth’s land surface is covered by vegetation with perennial or seasonal.
Dec 15, 2004 AGUMolly E. Brown, PhD1 Inter-Sensor Validation of NDVI time series from AVHRR, SPOT-Vegetation, SeaWIFS, MODIS, and LandSAT ETM+ Molly E.
ORNL DAAC MODIS Subsetting and Visualization tools Tools and services to access subsets of MODIS data Suresh K. Santhana Vannan National Aeronautics and.
Remote Sensing and Image Processing: 4 Dr. Hassan J. Eghbali.
Ceilometer Observation of Seasonal and Diurnal Variation in Cloud Cover Fraction, Cloud Base Height, and Visual Range in the Eastern Amazon Region Matthew.
Variations in Continental Terrestrial Primary Production, Evapotranspiration and Disturbance Faith Ann Heinsch, Maosheng Zhao, Qiaozhen Mu, David Mildrexler,
BIOPHYS A PHYSICALLY-BASED CONTINUOUS FIELDS ALGORITHM and Climate and Carbon Models FORREST G. HALL, FRED HUEMMRICH Joint Center for Earth Systems Technology.
Assessing the Phenological Suitability of Global Landsat Data Sets for Forest Change Analysis The Global Land Cover Facility What does.
Scaling Up Above Ground Live Biomass From Plot Data to Amazon Landscape Sassan S. Saatchi NASA/Jet Propulsion Laboratory California Institute of Technology.
ORNL DAAC: Introduction Bob Cook ORNL DAAC Environmental Sciences Division Oak Ridge National Laboratory.
Goal: to understand carbon dynamics in montane forest regions by developing new methods for estimating carbon exchange at local to regional scales. Activities:
Measuring Vegetation Characteristics
A MODIS-Derived Photochemical Reflectance Index to Detect Inter-Annual Variations in the Photosynthetic Light-Use Efficiency of a Boreal Deciduous Forest.
Beyond Spectral and Spatial data: Exploring other domains of information: 3 GEOG3010 Remote Sensing and Image Processing Lewis RSU.
Characterizing Seasonal Patterns of Leaf Phenology in Central Amazon Várzea Forest with the MODIS Enhanced Vegetation Index L. Hess, P. Ratana, A. Huete,
By Harish Anandhanarayanan Mentor: Dr. Alfredo Huete.
Xiangming Xiao Institute for the Study of Earth, Oceans and Space University of New Hampshire, USA The third LBA Science Conference, July 27, 2004, Brasilia,
Vegetation Indices AVHRR to MODIS to VIIRS Kamel Didan 1, Compton Tucker 2, Armando Barreto 1, Jorge Pinzon 3 Nikolay Shabanov * 1 University of Arizona,
Figure 1. (A) Evapotranspiration (ET) in the equatorial Santarém forest: observed (mean ± SD across years of eddy fluxes, K67 site, blue shaded.
0 0 Robert Wolfe NASA GSFC, Greenbelt, MD GSFC Hydrospheric and Biospheric Sciences Laboratory, Terrestrial Information System Branch (614.5) Carbon Cycle.
A Remote Sensing Approach for Estimating Regional Scale Surface Moisture Luke J. Marzen Associate Professor of Geography Auburn University Co-Director.
Mark Friedl 1, Xiaoyang Zhang 2 1 Department of Geography and Environment, Boston University 2 ERT at NOAA/NESDIS/STAR NASA MEASURES #NNX08AT05A Science.
1/13 Development of high level biophysical products from the fusion of medium resolution sensors for regional to global applications: the CYCLOPES project.
Data Processing Flow Chart Start NDVI, EVI2 are calculated and Rank SDS are incorporated Integrity Data Check: Is the data correct? Data: Download a) AVHRR.
MODIS Science Team Meeting Columbia, MD - April 29-May 1, 2014 MODIS VI Product Suite Status Kamel Didan & Armando Barreto The University of Arizona.
Global Vegetation Monitoring Unit Problems encountered using Along Track Scanning Radiometer data for continental mapping over South America Requirement.
Monitoring land use and land cover changes in oceanic and fragmented lanscapes with reconstructed MODIS time series R. Lecerf, T. Corpetti, L. Hubert-Moy.
References: 1)Ganguly, S., Samanta, A., Schull, M. A., Shabanov, N. V., Milesi, C., Nemani, R. R., Knyazikhin, Y., and Myneni, R. B., Generating vegetation.
Science Review Panel Meeting Biosphere 2, Tucson, AZ - January 4-5, 2011 Vegetation Phenology and Vegetation Index Products from Multiple Long Term Satellite.
REMOTE SENSING FOR VEGETATION AND LAND DEGRADATION MONITORING AND MAPPING Maurizio Sciortino, Luigi De Cecco, Matteo De Felice, Flavio Borfecchia ENEA.
Kamel Didan (UA), Miura Tomoaki (UH), Friedl Mark (BU), Xioyang Zhang (NOAA), Czapla-Myers Jeff (UA), Van Leeuwen Willem(UA), Jenkerson Calli (LP-DAAC),
What is PEN (Phenological Eyes Network) ? It is a network of ground observation sites for long-term continuous validation of terrestrial ecological remote-sensing.
Temporal Classification and Change Detection
Vegetation Indices Radiometric measures of the amount, structure, and condition of vegetation, Precise monitoring tool phenology inter-annual variations.
Vegetation Indices Radiometric measures of the amount, structure, and condition of vegetation, Precise monitoring tool phenology inter-annual variations.
Department of Hydrology and Water Resources
Data Analysis, Version 1 VIP Laboratory May 2011.
Phenology Images (5-Year Long Term Average)
Presentation transcript:

7/24/02 MODIS Science Meeting Seasonal Variability Studies Across the Amazon Basin with MODIS Vegetation Indices Alfredo Huete 1, Kamel Didan 1, Piyachat Ratana 1, Laerte Ferreira 2, Yosio Shimabokuro 3, Tomoaki Miura 1 Gao Xiang 1 MOD13 *Terrestrial Biophysics and Remote Sensing Lab University of Arizona 1 University of Arizona, Tucson, Arizona USA 2 Universidade Federal de Goiás – UFG 3 Instituto Nacional de Pesquisas Espaciais - INPE

7/24/02 MODIS Science Meeting Validation Validation concerns the outputs or the intended uses of the VI’s so as to help the user community understand the reliability, credibility, and limitations of the products. MOD09 (SR)---> MOD13(VI) Phenology (seasonality) Change detection (Interannual) Biophysical (growth models)

7/24/02 MODIS Science Meeting MODIS Vegetation Indices ARVI Atmosphere Resistance SAVI Canopy Background Correction Soil-adjusted Vegetation Index NDVI Normalized Difference Vegetation Index EVI Enhanced Vegetation Index

7/24/02 MODIS Science Meeting Long term, time series AVHRR-NDVI data (Pathfinder 8km -yearly averaged) Accurate and stable time series data is needed for studies on interannual variation of vegetation in response to climate and for characterization of vegetation anomalies at continental and regional scales. Pinatubu Sensor degradation El Niño

7/24/02 MODIS Science Meeting *20 year averaged monthly AVHRR - NDVI in Brazil (Pathfinder 8 km) Seasonality & Phenology Role

7/24/02 MODIS Science Meeting Objectives ˚Evaluate the initial two years of MODIS Vegetation Index (VI) time series data over the Amazon Basin and surrounding regions of Brazil, ˚Examine the usefulness of MODIS data in characterizing seasonality along a climate-based ecological transect from the Brazilian cerrado to the seasonal tropical rainforests, ˚Examine the usefulness of MODIS data in discriminating land use/conversion patterns and in characterizing the resulting changes in seasonality.

7/24/02 MODIS Science Meeting MODIS EVI Seasonality ( )

7/24/02 MODIS Science Meeting

7/24/02 MODIS Science Meeting EVI NDVI Histograms of VI’s at 250 m, 500 m, and 1 km resolutions South America (August 12 to August 27, 2000)

7/24/02 MODIS Science Meeting

7/24/02 MODIS Science Meeting Brasilia National Park Blue = ASD Yellow = Digital Images

7/24/02 MODIS Science Meeting MQUALS and MODIS (Global) MODIS

7/24/02 MODIS Science Meeting EVI EVI Histogram of Brasilia Tile (Cerrado + conversions) Dry 8/00 8/01 Wet 3/02 4/01

7/24/02 MODIS Science Meeting

7/24/02 MODIS Science Meeting Cerrado Physiognomies

7/24/02 MODIS Science Meeting Cangaçu & Santana do Araguaia Red = ASD Yellow = Digital Images

7/24/02 MODIS Science Meeting MODIS VI Seasonal Profiles of Land Converted Areas biomass: to t / ha LAI: 5.61 to 7.06 biomass: ~1.3 t / ha LAI: ~2.82 biomass: 6.85 to t / ha LAI: 4.11 to 6.27 Primary Forest (“High”) Pasture site Regeneration site MODIS 250m EVI

7/24/02 MODIS Science Meeting Cerrado Forest Converted Pastures Land Conversion at Santana do Araguaia & Cangaçu (Forest - Cerrado Transition)

7/24/02 MODIS Science Meeting

7/24/02 MODIS Science Meeting Dry 11/00 11/01 Wet 7/01 4/ EVI EVI Histogram of Tapajos Tile (Seasonal Forest)

7/24/02 MODIS Science Meeting

7/24/02 MODIS Science Meeting

7/24/02 MODIS Science Meeting Litterfall Seasonal Dynamics (Tapajos) (Woods Hall/ LBA/ )

7/24/02 MODIS Science Meeting

7/24/02 MODIS Science Meeting Forest Cerrado open closed

7/24/02 MODIS Science Meeting Forest Cerrado open closed

7/24/02 MODIS Science Meeting

7/24/02 MODIS Science Meeting Conclusions (Brazil) We found MODIS to be useful in characterizing the spatial and temporal dynamics of the Amazon Basin, Multitemporal profiles of the MODIS data revealed well- defined seasonal patterns in the cerrado region with decreasing dry-wet seasonal patterns in the transitional areas near Santana do Araguaia, Seasonality was observed to a small and uncertain extent at the Tapajos National Forest site, however, it was unclear whether this was associated with seasonal changes in forest leaf area or temporal changes in understory vegetation, We further found MODIS VI seasonal patterns to significantly vary in land converted areas.

7/24/02 MODIS Science Meeting

7/24/02 MODIS Science Meeting Algorithm summary Composite result

7/24/02 MODIS Science Meeting

7/24/02 MODIS Science Meeting

7/24/02 MODIS Science Meeting Clouds, cloud shadow, and BRDF induce the largest uncertainties.

7/24/02 MODIS Science Meeting Snow Problem in VI’s NDVIEVI Snow effects: Blue > Red > NIR NDVI gives false negative signal EVI gives false positive signal

7/24/02 MODIS Science Meeting EVI & SAVI Relationships for Snow EVI RT-Model MODIS Data ( )

7/24/02 MODIS Science Meeting NDVI & EVI Relationships RT-Model MODIS Data ( )

7/24/02 MODIS Science Meeting Biophysical Validation

7/24/02 MODIS Science Meeting Trac Fpar MODIS NDVI Trac SZA 10° to 30° Trac SZA 30º to 50º Trac SZA 50º to 75º Huemmrich and Privette

7/24/02 MODIS Science Meeting Conclusions VI products are provisionally validated from radiometric, seasonal, interannual and biophysical perspectives, product accuracy has been assessed by a number of independent measurements, at a number of locations or times representative of conditions portrayed by the product. Residual cloud, cloud shadow, BRDF, topography, and snow induce the largest uncertainties in the VI’s, Assessment of feasibility of using snow product and BRDF products. VI product accuracy varies with QA.