Introduction Acquired and applied MODIS NDVI data to compute near real time “weekly” % change in forest NDVI products, based on 24 day temporal composite.

Slides:



Advertisements
Similar presentations
Examining Vegetation Recovery Time After a Small Scale Disaster using MODIS Data and the OSDC Zac Flamig (University of Oklahoma) Gilbert Warren Cole (University.
Advertisements

USDA Forest Service, Remote Sensing Applications Center, FSWeb: WWW: LANCE User Working Group.
Applications of S-NPP Products for Disaster Response Andrew Molthan Research Meteorologist NASA Marshall Space Flight Center.
EG2234 Earth Observation DISASTER Mitigation and relief.
US Forest Disturbance Trends observed with Landsat Time Series Samuel N. Goward 1 (PI), Jeffrey Masek 2, Warren Cohen 3, Gretchen Moisen 4, Chengquan Huang.
Welcome to NASA Applied Remote Sensing Training (ARSET) Webinar Series Introduction to Remote Sensing Data for Land Management Course Dates: Every Monday,
Assistant Administrator National Oceanic and Atmospheric Administration National Ocean Service September 18, 2008 John H. Dunnigan Toward National Height.
U.S. Department of the Interior U.S. Geological Survey NASA/USDA Workshop on Evapotranspiration April 6, 2011 – Silver Spring, Maryland ET for famine early.
Algorithm Development for Vegetation Change Detection and Environmental Monitoring Louis A. Scuderi 1, Amy Ellwein 2, Enrique Montano 3 and Richard P.
Land Use Change and Effects on Water Quality in the Lake Tahoe Basin: Applications of GIS Christian Raumann Research and Technology Team USGS Western Geographic.
Nancy D. Searby 1, Dan Irwin 2, Ashutosh Limaye 2, 1 NASA HQ Earth Sciences Division Applied Sciences Program 2 NASA MSFC SERVIR Program NOAA Satellite.
Harlan Shannon Meteorologist U.S. Department of Agriculture Office of the Chief Economist World Agricultural Outlook Board Washington D.C., U.S.A. An Overview.
590 Lipoa Parkway, Suite 259 Kihei, Maui, Hawaii (Fax) Pacific Disaster Center.
Landsat and Carbon Assessments COP-15 Copenhagen, Denmark December 11, 2009 Dr. Marcia McNutt Director, U.S. Geological Survey U.S. Department of the Interior.
The Coeur d'Alene Tribe is learning the remote sensing methodology developed by LANDFIRE, and will be attempting to apply the methods to higher resolution.
Understanding Drought
Stennis Space Center Assessing Potential of VIIRS Data for Contribution to a Forest Threat Early Warning System Presented by Joseph P. Spruce NASA Stennis.
DROUGHT MONITORING THROUGH THE USE OF MODIS SATELLITE Amy Anderson, Curt Johnson, Dave Prevedel, & Russ Reading.
Interannual Deforestation Dynamics in Southern Madagascar Humid Forests 2000 to 2005 Jan Dempewolf (1), Ruth DeFries (1), Sandy Andelman (2), Rasolohery.
Satellite Remote Sensing – An Appropriate Technology Solution for the Region. Deanesh Ramsewak, Remote Sensing Officer, Institute of Marine Affairs, Trinidad.
Updating Erosion Hazard Ratings in a Post-fire Assessment A GIS Tool for Soil Scientists.
USDA Forest Service Remote Sensing Applications Center Forest Inventory and Analysis New Technology How is FIA integrating new technological developments.
MODIS: Moderate-resolution Imaging Spectroradiometer National-Scale Remote Sensing Imagery for Natural Resource Applications Mark Finco Remote Sensing.
Assessing Climate Changes in Arctic Sweden and Repercussions on Sami Reindeer Husbandry and Culture: Using a MODIS Image Time Series and Key Informant.
SERVIR-AFRICA: an overview André Kooiman International workshop on higher resolution Land cover mapping for the African continent UNEP, 27 June 2013.
Spatially Complete Global Surface Albedos Derived from MODIS Data
Mortality as an early indicator of forest health issues. A case study using EAB. Andrew D. Hill Kirk M. Stueve Paul Sowers.
EG2234 Earth Observation DISASTER Mitigation and relief.
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,
Getting Ready for the Future Woody Turner Earth Science Division NASA Headquarters May 7, 2014 Biodiversity and Ecological Forecasting Team Meeting Sheraton.
MODIS-Based Techniques for Assessing of Fire Location and Timing in the Alaskan Boreal Forest Nancy H.F. French 1, Lucas Spaete 1, Elizabeth Hoy 2, Amber.
UW-Milwaukee Geography Phenological Monitoring: A key approach to assessing the impact of spring starting earlier Mark D. Schwartz Department of Geography.
Forest Health Monitoring. Aerial survey Annual ‘first cut’ at detecting and assessing forest health disturbancesAnnual ‘first cut’ at detecting and assessing.
May 16-18, 2005MultTemp 2005, Biloxi, MS1 Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data James C. Tilton Mail Code 606*
Page 1. U.S. Department of the Interior U.S. Geological Survey Landsat at 40: The Nation’s oldest Earth-observing satellite program Landsat at 40: The.
Analysis of Forest Fire Disturbance in the Western US Using Landsat Time Series Images: Haley Wicklein Advisors: Jim Collatz and Jeff Masek,
Translation to the New TCO Panel Beverly Law Prof. Global Change Forest Science Science Chair, AmeriFlux Network Oregon State University.
Jake F. Weltzin Mark D. Schwartz In-situ validation of land- surface phenology A framework for involvement with USA National Phenology Network.
Causes of Haze Assessment Update for Fire Emissions Joint Forum -12/9/04 Meeting Marc Pitchford.
Vegetation Index Date (time step 16 days) 2010 Season 2005 (average yield season) 2003 (high yield season) Mean NDVI curve ( ) Vegetation Index.
U.S. Department of the Interior U.S. Geological Survey Entering A New Landsat Era – The Future is Now Tom Loveland U.S. Geological Survey Earth Resources.
Ecosystem Function and Health Program Problem Area: Quantify and predict ecosystem responses to environmental stressors (e.g. climate change). Develop.
A National Hazards Information Strategy (NHIS) Helen M. Wood Director, Office of Satellite Data Processing & Distribution “A coordinated approach for using.
NASA Earth Observing System Visualization Tools ARSET - AQ Applied Remote SEnsing Training – Air Quality A project of NASA Applied Sciences Introduction.
EGSC WaterSMART-irrigation water use research John W. Jones USGS Eastern Geographic Science Center March 08, 2012 Input for the ACF WaterSMART Stakeholders.
Assessing the Phenological Suitability of Global Landsat Data Sets for Forest Change Analysis The Global Land Cover Facility What does.
Assessing pine bark beetle mortality in Southern CA Forests Presented by California Department of Forestry Mark Rosenberg Rich Walker Bill Stewart Visit.
Pollutant Emissions from Large Wildfires in the Western United States Shawn P. Urbanski, Matt C. Reeves, W. M. Hao US Forest Service Rocky Mountain Research.
Goal: to understand carbon dynamics in montane forest regions by developing new methods for estimating carbon exchange at local to regional scales. Activities:
E X P E R I E N C E Y O U R A M E R I C A A.T. MEGA-Transect National Park Service U.S. Department of the Interior Northeast Temperate Network Fred Dieffenbach.
Satellite-Based Shift in Vegetation Seasonality Xiaoyang Zhang ERT at NOAA/NESDIS/STAR.
Condition of Forests in San Diego County: Recent Conifer Tree Mortality and the Institutional Response Presented by California Department of Forestry Mark.
Citation: Moskal., L. M. and D. M. Styers, Land use/land cover (LULC) from high-resolution near infrared aerial imagery: costs and applications.
U.S. Department of the Interior U.S. Geological Survey October 22, 2015 EROS Fire Science Understanding a Changing Earth.
USDA Forest Service, Remote Sensing Applications Center, FSWeb: WWW: National Geospatial Fire.
Fire Products NASA ARSET-AQ Links Updated November 2013 ARSET Applied Remote SEnsing Training A project of NASA Applied Sciences.
South East Strategic Regional Coastal Monitoring Programme – Annual Review Meeting, November 2009 Satellite Data for Coastal Monitoring Trevor Burton Fugro-BKS.
Stennis Space Center Using MODIS Data to Detect Historical Gypsy Moth Defoliation Joseph P. Spruce Science Systems and Applications, Inc. John C. Stennis.
Stennis Space Center Phenological Parameters Estimation Tool Presented by Jerry Gasser Lockheed Martin Mission Services John C. Stennis Space Center USDA.
بسم الله الرحمن الرحيم In the Name of God In the Name of God
USGS EROS LCMAP System Status Briefing for CEOS
VEGA-GEOGLAM Web-based GIS for crop monitoring and decision support in agriculture Evgeniya Elkina, Russian Space Research Institute The GEO-XIII Plenary.
NASA JPL Drought Project Kickoff Meeting
Developing the Vegetation Drought Response Index (VegDRI): Monitoring Vegetation Stress from a Local to National Scale Dr. Brian Wardlow National Drought.
VegDRI History, Current Status, and Related Activities
An Introduction to VegDRI
Orin J. Hutchinson1 with Dr. Ramesh Sivanpillai2
Flood Potential in Africa
REMOTE SENSING ANALYSIS OF URBAN SPRAWL IN BIRMINGHAM, ALABAMA:
Presentation transcript:

Introduction Acquired and applied MODIS NDVI data to compute near real time “weekly” % change in forest NDVI products, based on 24 day temporal composite products that are refreshed every 8 days. Historical MODIS NDVI products were derived from temporal processing and fusion of MODIS Aqua and Terra NDVI. Current eMODIS NDVI data was used to compute current NDVI and MOD13 NDVI products were used to compute historical baselines. Historical NDVI baseline products were computed for 2010, and time frames. Computed forest % NDVI change products for each 24 day analysis window, comparing current year NDVI to that from each baseline. Change products are posted in ForWarn, a near real time on forest threat EWS tool that includes the U.S. Forest Change Assessment Viewer (FCAV) – see (Figures 1-8). Also supplied forest change products to USFS FHTET Forest Disturbance Mapper (FDM) tool to assist 2011 aerial disturbance detection surveys. Assessed apparent disturbances compared to available reference data (Landsat, aerial, and ground-based geospatial data) and through communications with federal and state agency forest health specialists. This poster discusses near real time (NRT) 2011 MODIS forest disturbance detection products within the ForWarn system for forest change recognition and tracking. This system is being developed by the U.S. Forest Service (USFS) Eastern and Western Threat Centers with help from NASA, ORNL, and the USGS (Hargrove et al., 2009). Forests occur on approximately 751 million acres of the United States, representing almost a third of the entire U.S. land base. Such forests can be negatively impacted or threatened by disturbances from biotic and abiotic factors, some of which are associated with climate change (e.g., drought, fire, insect attacks). Current geospatial information on forest disturbances is a crucial component of ForWarn as part of a national forest threat EWS. Temporally processed daily MODIS satellite data was used in the EWS to monitor 2011 forest disturbances at broad regional to CONUS scales. The 2011 national forest disturbance detection products build upon previous work since 2006, as discussed by Hargrove et al. (2009). These products use USGS eMODIS expedited NDVI data for 2011 and pre 2011 MOD13 NDVI products for computing historical NDVI baselines. Methods Results and Discussion Conclusions and Future Work References Example 2011 Forest Disturbance Detections Using MODIS Satellite Data Products Resident to the US Forest Service ForWarn System W.H. Hargrove 1 ; J. Spruce 2 ; G. Gasser 3 ; J. Smoot 2 ; P. Kuper 2 1. Eastern Forest Environmental Threat Assessment Center, USDA Forest Service, Asheville, NC, USA 2. Computer Science Corporation, Contractor to NASA Applied Science and Technology Project Office, Stennis Space Center, MS, USA 3. Lockheed Martin Civil Programs, Stennis Space Center, MS, USA POC MODIS-based NRT and other geospatial visualization products were posted each week on the EWS’s FCAV that depicted regionally evident 2011 forest disturbances (e.g., damage from insects, storms, and fire). Computed forest change products from multiple baseline enabled many current, recent, and longer term disturbances to be assessed (Figures 1, 3-6, and 8). More severe disturbance (e.g., mortality from fire and beetles) usually showed higher NDVI drops than ephemeral disturbance (e.g., caterpillar defoliation). The 24 day compositing period and the fusion of MODIS Aqua and Terra data helped to mitigate cloud contamination effects, though product sometimes showed false NDVI drops apparently due to frequent bad weather. The use of rainbow color tables on the % NDVI change products within the FCAV aided the visualization of regionally evident forest disturbances. Effective NRT regional products were posted typically with low latencies of 1 -2 days after last collection date. The results for 2011 included multiple examples in which MODIS NRT forest change products showed regionally evident forest disturbances The on-line posting of refreshed NRT geospatial products into the FCAV enabled end-users with a means to view and track regionally evident forest disturbance. The MODIS NRT % change in forest NDVI products enabled many extensive forest disturbances to be viewed and tracked during the 2011 growing season. Forest change products based on 3 baselines was useful for assessing forest disturbance vintage and persistence, both for single and multiyear events. Additional research is being done to assess whether MODIS phenology products such as these could be further applied to identify and quantify the areal extent of forest disturbance across CONUS. Future work will include efforts to further automate forest disturbance detection and attribution. GIS techniques are being used to assess disturbance persistence and the year in which a given disturbance initially occurred. These products also have potential for aiding climate change studies, such as research on forest mortality associated with prolonged drought. Several other MODIS phenological products are being assessed for aiding forest disturbance monitoring needs of ForWarn, including cumulative NDVI products. Hargrove, William W., Joseph P. Spruce, Gerald E. Gasser, and Forrest M. Hoffman (2009). Toward a national early warning system for forest disturbances using remotely sensed canopy phenology. Photogrammetric Engineering & Remote Sensing, 75: Figure 1. ForWarn forest change product showing coastal Louisiana spring 2011 swamp forest defoliation (hot colors) for date ending 5/8/2011, incorporating a baseline. Figure 2. ForWarn circa 2006 land cover map from the National Land Cover Database for southeastern Louisiana. This product is resampled to the MODIS meter cell size. Figure 3. ForWarn forest change product showing Louisiana spring 2010 swamp forest defoliation in hot colors for date ending 5/8/2010, incorporating baseline. Figure 8. ForWarn MODIS forest change product view of the Uinta Mountains, showing change versus a baseline. This product is for date ending 9/5/2011. Figure 6. ForWarn forest change product for the Uinta Mountains showing fresh change in bark beetle damaged forests. This map is for date ending 9/5/2011, using a 2010 baseline. Figure 7. ForWarn circa 2001 high specificity land cover map from the GAP/Landfire programs. This product shows forest types for disturbed areas in Figures 6 and 8. Figure 5. ForWarn circa 2001 high specificity land cover map from the GAP/Landfire programs. This shows the forest types for disturbed areas in Figure 4. Figure 4. ForWarn forest change product for Oregon’s Blue Mountains showing NDVI drops in area defoliated by pine butterflies. For date ending 9/5/2011, product uses a 2010 baseline.