Ran Huang and Talat Odman Remote Sensing and Measurements

Slides:



Advertisements
Similar presentations
1 1. FY09 GOES-R3 Project Proposal Title Page Title: Trace Gas and Aerosol Emissions from GOES-R ABI Project Type: GOES-R algorithm development project.
Advertisements

Fire Modeling Protocol MeetingBoise, IDAugust 31 – September 1, 2010 Applying Fire Emission Inventories in Chemical Transport Models Zac Adelman
Georgia Chapter of the Air & Waste Management Association Annual Conference: Improved Air Quality Modeling for Predicting the Impacts of Controlled Forest.
Global Fire Emissions and Fire Effects on Biophysical Properties and the Associated Radiative Forcing Yufang Jin 1, James Randerson 1, G. R. van der Werf.
Piedmont National Wildlife Refuge February 14, 2006 Smoke Simulation Analysis Gary L. Achtemeier USDA Forest Service Athens, GA.
Remote Sensing Forest Fires: Before and After Rob Gaboy & Aimee Treutlein.
Trans-Pacific transport of Asian dust and pollution: Accumulation of biomass burning CO in subtropics and dipole structure of transport Junsang Nam 1,
Effects of Siberian forest fires on regional air quality and meteorology in May 2003 Rokjin J. Park with Daeok Youn, Jaein Jeong, Byung-Kwon Moon Seoul.
Talat Odman and Yongtao Hu, Georgia Tech Zac Adelman, Mohammad Omary and Uma Shankar, UNC James Boylan and Byeong-Uk Kim, Georgia DNR.
1 NOAA’s National Climatic Data Center April 2005 Climate Observation Program Blended SST Analysis Changes and Implications for the Buoy Network 1.Plans.
1 Sean Raffuse, Dana Sullivan, Lyle Chinkin, Daniel Pryden, and Neil Wheeler Sonoma Technology, Inc. Petaluma, CA Sim Larkin and Robert Solomon U.S. Forest.
Talat Odman, Aditya Pophale, Rushabh Sakhpara, Yongtao Hu, Michael Chang and Ted Russell Georgia Institute of Technology AQAST 9 at Saint Louis University.
Operational Air Quality and Source Contribution Forecasting in Georgia Georgia Institute of Technology Yongtao Hu 1, M. Talat Odman 1, Michael E. Chang.
Project Idea Fire potential models can help stratify and reduce the number of false positive fire ‘detections’ by assigning probability levels to the landscape.
Aircraft spiral on July 20, 2011 at 14 UTC Validation of GOES-R ABI Surface PM2.5 Concentrations using AIRNOW and Aircraft Data Shobha Kondragunta (NOAA),
Victoria Naipal Max-Planck Institute for Meteorology Land Department; Vegetation Modelling Group Supervisor: Ch.Reick CO-Supervisor: J.Pongratz EGU,
Developing a High Spatial Resolution Aerosol Optical Depth Product Using MODIS Data to Evaluate Aerosol During Large Wildfire Events STI-5701 Jennifer.
Estimates of Biomass Burning Particulate Matter (PM2.5) Emissions from the GOES Imager Xiaoyang Zhang 1,2, Shobha Kondragunta 1, Chris Schmidt 3 1 NOAA/NESDIS/Center.
Sensitivity of top-down correction of 2004 black carbon emissions inventory in the United States to rural-sites versus urban-sites observational networks.
Simulating prescribed fire impacts for air quality management Georgia Institute of Technology M. Talat Odman, Yongtao Hu, Fernando Garcia-Menendez, Aika.
Using MODIS fire count data as an interim solution for estimating biomass burning emission of aerosols and trace gases Mian Chin, Tom Kucsera, Louis Giglio,
Remote Sensing and Modeling of the Georgia 2007 Fires Eun-Su Yang, Sundar A. Christopher, Yuling Wu, Arastoo P. Biazar Earth System Science Center University.
DATA ANALYSIS PROJECT SIMONE PHILLIPS 24 APRIL 2014 EAS 4480.
Impacts of Biomass Burning Emissions on Air Quality and Public Health in the United States Daniel Tong $, Rohit Mathur +, George Pouliot +, Kenneth Schere.
MONTHLY CO AND FIRE COUNTS IN THREE NORTHERN HEMISPHERE REGIONS AND IN THREE LOW LATITUDE REGIONS  With MOPITT CO data and ATSR fire count data, CO emission.
2002 FIRE INVENTORY VISTAS States – First Draft Oct 2003 National RPO meeting Nov 5, 2003.
NASA Earth Observing System Visualization Tools ARSET - AQ Applied Remote SEnsing Training – Air Quality A project of NASA Applied Sciences Introduction.
Development of Wildland Fire Emission Inventories with the BlueSky Smoke Modeling Framework Sean Raffuse, Erin Gilliland, Dana Sullivan, Neil Wheeler,
Modeling Wildfire Emissions Using Geographic Information Systems (GIS) Technology and Satellite Data STI-3009 Presented by Neil J. M. Wheeler Sonoma Technology,
Continued improvements of air quality forecasting through emission adjustments using surface and satellite data & Estimating fire emissions: satellite.
OVERVIEW OF ATMOSPHERIC PROCESSES: Daniel J. Jacob Ozone and particulate matter (PM) with a global change perspective.
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.
Georgia Institute of Technology Comprehensive evaluation on air quality forecasting ability of Hi-Res in southeastern United States Yongtao Hu 1, M. Talat.
DETERMINING REGIONAL CARBON EMISSIONS UNDER VARIABLE FIRE REGIMES IN CENTRAL SIBERIA D.J. McRae 1, S.G. Conard 2, G.A. Ivanova 3, S.P. Baker 4, A.I. Sukhinin.
Impact of the changes of prescribed fire emissions on regional air quality from 2002 to 2050 in the southeastern United States Tao Zeng 1,3, Yuhang Wang.
Uncertainties in Wildfire Emission Estimates Workshop on Regional Emissions & Air Quality Modeling July 30, 2008 Shawn Urbanski, Wei Min Hao, Bryce Nordgren.
Impacts of Landuse Management and Climate Change on Landslides Susceptibility over the Olympic Peninsula of Washington State Muhammad Barik and Jennifer.
This report presents analysis of CO measurements from satellites since 2000 until now. The main focus of the study is a comparison of different sensors.
AEROCOM AODs are systematically smaller than MODIS, with slightly larger/smaller differences in winter/summer. Aerosol optical properties are difficult.
Fire, Smoke & Air Quality: Tools for Data Exploration & Analysis : Data Sharing/Processing Infrastructure This project integrates.
Figures from “The ECMWF Ensemble Prediction System”
Eun-Su Yang and Sundar A. Christopher Earth System Science Center University of Alabama in Huntsville Shobha Kondragunta NOAA/NESDIS Improving Air Quality.
CMAQ PM 2.5 Forecasts Adjusted to Errors in Model Wind Fields Eun-Su Yang 1, Sundar A. Christopher 2 1 Earth System Science Center, UAHuntsville 2 Department.
Yuqiang Zhang1, Owen R, Cooper2,3, J. Jason West1
15th Annual CMAS Conference
5th International Conference on Earth Science & Climate Change
Use of Satellite Data for Georgia’s Air Quality Planning Activities Tao Zeng and James Boylan Georgia EPD – Air Protection Branch TEMPO Applications.
Adverse Effects of Drought on Air Quality in the US
Xiquan Dong, Baike Xi, Erica Dolinar, and Ryan Stanfield
Orin J. Hutchinson1 with Dr. Ramesh Sivanpillai2
1st HAQAST Stakeholder Meeting
Implications of burned area approaches in emission inventories for modeling wildland fire pollution in the contiguous U.S Regional smoke layer? August.
2017 Annual CMAS conference, October 24, 2017
Soo-Hyun Yoo and Pingping Xie
Forecasting the Impacts of Wildland Fires
17th Annual CMAS Conference Sadia Afrin and Fernando Garcia Menendez
Evaluating Land-Use Classification Methodology Using Landsat Imagery
Forecasting Exposures to Prescribed Fire Smoke for Health Predictions in Southeastern USA Talat Odman, Ha Ai, Yongtao Hu, Armistead.
Jeff Key*, Aaron Letterly+, Yinghui Liu+
A Novel Approach for Identifying Dust Storms with Hourly Surface Air Monitors in the Western United States Barry Baker1,2 and Daniel Tong1,2 1National.
Hands on Satellite Data to Monitor Biomass
Monika Kopacz, Daniel Jacob, Jenny Fisher, Meghan Purdy
Estimating PM2.5 using MODIS and GOES aerosol optical depth retrievals in IDEA Hai Zhang , Raymond M. Hoff1 , Shobha Kondragunta2.
+ = Climate Responses to Biomass Burning Aerosols over South Africa
A case study in the local estimation of shear-wave logs
The Aqua-MODIS calibration transfer using DCC
Lin Zhang, Daniel J. Jacob, Jennifer A
Georgia Institute of Technology
Evaluation of IRI-2012 by comparison with JASON-1 TEC and incoherent scatter radar observations during the solar minimum period Eun-Young Ji,
Title Why do we underestimate Elemental Carbon in PM?
Presentation transcript:

Ran Huang and Talat Odman Remote Sensing and Measurements Burned Area Comparisons between Prescribed Burning Permits in Southeastern USA and two Satellite-derived Products three Good morning! I’m so glad today to give you the talk about pb burned area comparison between permit records and satellite-derived products in the southeast. Ran Huang and Talat Odman 2017 CMAS Conference Remote Sensing and Measurements October 25, 2017

Acknowledgements Frank Sorrels, Daniel Chan John K. Fish Mark Ruminski, Shobha Kondragunta, Xiaoyang Zhang First, I would like to thank those who provide us with the data and help us. Yongtao Hu, Armistead Russell James Randerson, Yang Chen

Prescribed Burning Fire Emissions (Tons) Prescribed burning (PB) is a land management tool used to improve native vegetation and wildlife habitat, control insects and disease, and reduce wildfire risk in the USA. According to US EPA 2014 NEI: 12.5% of PM2.5 emissions in the US are attributable to prescribed burning; 35% of PM2.5 emissions from prescribed burning originate from the southeastern US; Prescribed burning is the largest source of PM2.5 (24%) in the southeastern US PB, is a land management tool.. According to 2014 NEI, under the fire section, 45% of fire emissions comes from PB. 12.5% … From this figure we can see, Southeast US is the most active PB area in the US. Fire Emissions (Tons)

Questions we meet … There is no unified record of prescribed burning information for the Southeast U.S. There is no post-burn information A permit is not an actual fire Satellite-derived products are popular tools to estimate the burned area. PB is usually small fires to keep them under control. Satellite-derived products may have large uncertainty when used in estimating burn area of those small fires. (Hoelzemann 2004; Randerson et al. 2012; Kukavskaya et al. 2013; Mouillot et al. 2014) The fire emission usually estimated based on burned area, permit records and satellite-derived products are the major data sources. However, there is no unified record of prescribed burning information for the Southeastern U.S. . Also, there is no post-burn information which means that a permit is not an actual fire. What about satellite-derived products? It’s really popular tools. But, there’s only a few products continues to be available publicly at this time to provide burned area information. PB is usually small fires to keep them under control. Some previous researchers show that satellite-derived products have large uncertainty when used in estimating burn area of those small fires

Our Analysis GA and FL Jan. – Apr. of 2015 and 2016 Permit records compared to satellite-derived burned areas 3 satellite products BBEP GFED4s BAECV Our analysis focuses on GA and FL, because they have the most complete PB permit records. .

Burned area Permit Records Satellite-derived Products State Period Location Info GA 2015, 2016: Jan. – Apr. Address FL Lat/Lon BBEP GFED4s BAECV (2015) Daily Monthly Annual Blended Polar Geo Biomass Burning Emissions Product (BBEP): GOES-East, GOES-West, MODIS and the AVHRR, hourly. (Burned area, fuel loading, combustion factor, and emission factor.) Global Fire Emissions Database (GFED4s): Small fires included, combining 1-km thermal anomalies (active fires) from Terra and Aqua and 500 m burned area observations from MODIS. Burned Area Essential Climate Variable (BAECV) Landsat 4, 5 and 7 sense;  Using a gradient boosted regression model to estimate the probability that a pixel (30 × 30m) had burned, followed by a thresholding process to generate a binary burned | unburned classification. Only annual result is available because of Landsat satellite characteristic (16 days repeat cycle). In order to check the uncertainty of the available permit records and satellite-derived product. We chose GA and FL as our research domain because they have the most complete PB permit records. We chose BBEP, GFED4s and BAECV as satellite-derived products. BBEP is …; GFED4s is …; BAECV is … * All following results are based on first four months data except BAECV related.

How accurate are the permit burned areas? Phone call survey: Here’s the results. The each point here represents the total burned area of one person for that year. 2016 and 2017 survey results both show good agreement between permit records and actual burn. 2017 actual burns has better agreement with the permit. The regression has closer to 1 slope and smaller intercept.

How trustable are the permit records? Tall Timbers Research Station, Tallahassee, FL Permit: 26 Days Actual Burn: 16 Days Actual Permit (Points) Ask permit, but not burn: 264 (11%) They typically ask for a few more acres than they end up burning. Weather is the reason they would have pulled a permit and not burned. Permit Asked: 2330 Then we also did a case study in Tall Timbers Research Station. It located in the middle of panhandle area of FL and has very complete burned area records.. The left figure shows the actual burned area of TT in 2015 from their records. The middle on shows the permits they ask. They asked permit for 26 day but only burned on 16 days. The actual burned acres is about 58% of the permit they have asked. Ask permit and burn: 2066 (89%) Actual burn: 1354 2015 PB records from Tall Timbers 58% Unit: Acres

2015 Tall Timbers Satellite results Actual burned: 1354 acres Red polygons (BAECV): 84 acres Points in red circle (BBEP): 70 acres Only 5~6% of burned acres has been captured by the Satellite-derived products. Then we also compared the satellite derived products with the actual burns.

State total burned acres from all datasets Let’s take a look at the overall situation of Georgia and Florida pb. This is the first four months state total burned acres, In GA, GFED4s only accounts for 8% and 11% of the burned areas in permit records while BBEP accounts for 15% and 44% in the first four months of 2015 and 2016, In FL, GFED4s’ state total burned areas are 19% and 16% of those in permit records in 2015 and 2016, respectively. BBEP’s are 15% and 75% of burned areas in permit records.

Total burned acres by county in GA and FL Here’s the spatial figure of total burned acres by county in GA and FL. For GA, most of the burn activity is in the southwest of Georgia. In Florida, the largest burn areas are in the panhandle and the southcentral counties The legend range here of all three satellite-related figures are 10 times smaller than the permit. GFED4s, BBEP and BAECV can't capture the level of burned area in permit records

Comparison of county-level total burned areas of 2015 in Georgia : Permit vs. BAECV, GFED4s, BBEP Here’s the comparison of county-level total burned areas. It showed that permit are correlated with all satellite-derived products BBEP and BAECV are in better agreement than GFED4s with permits. The R2 are 0.4 and 0.48 respectively. But all the slopes are less than 0.1. R2 = 0.29 R2 = 0.40 R2 = 0.48 *Annual results

Comparison of daily state total burned areas of 2015 and 2016 in Georgia (top row) and Florida (bottom row): Permit vs. BBEP Here’s the daily analysis in GA and FL. In 2015, r2 equals to 0.57 and 0.48, respectively. However, they are not correlated as strongly in 2016 (r2 = 0.29 and 0.14) Note that permit records only account for prescribed burns while satellites cannot differentiate prescribed burns from wildfires. However, our investigations showed no records of wildfire incidences on those days.    

Daily Analysis: 03/17/2016 We also did day-by-day analysis between permit and BBEP. Here’s an example. For 17 March 2016, although BBEP and permit record burn areas are almost the same statewide in Florida (~1.40×104 acres), the locations of the fires are quite different. In particular, BBEP fires do not capture the permitted burns in the panhandle area of FL. And BBEP are not agree with the small burns permitted in South Florida there are only a few small fires detected by the satellite and their locations are different from those of the permitted burns. In addition, larger fires seen by the satellite in North Central Florida are in different locations, even different counties. BBEP and permit record burned areas are almost the same statewide in Florida (~1.40×104 acres).

Daily count of fires: Permit vs BBEP 1/8 2015 R = 0.72 GA 2016 1/7 R = 0.73 Daily count of fires: Permit vs BBEP 2015 1/3 R = 0.66 In Georgia, although the correlations between the daily counts are good (0.72 for 2015 and 0.73 for 2016), BBEP fire counts are only about one-eighth (2015) and one-seventh of permit record burn counts In Florida, the average daily number of burns from permit record,124, is three times larger than the average daily number of fires from BBEP in 2015. In 2016, the average daily count from permit records,132, is three times larger than that from BBEP FL 2016 1/3 R = 0.67

Comparison of monthly state total burned areas of 2015 and 2016 in Georgia (top row) and Florida (bottom row): Permit vs. GFED4s Considering GFED4s only has the monthly total burn areas, there are only four points in the comparisons between permit records and GFED4s. There is strong correlation. However, considering there’re only four points in the comparison, this strong correlation result is not that reliable.

Sugarcane burn Glades(yellow), Hendry(orange) and Palm Beach(brown) Considering the openness, burn area  and high frequency of sugarcane burns, and that they are conducted in open fields, satellites should be efficient in detecting this type of burns.   Glades(yellow), Hendry(orange) and Palm Beach(brown)

Sugarcane burn: Permit VS BBEP BBEP comparison show only a slight improvement compared to other types of burn. Here the sugarcane burn comparison inform that it’s really important to have post-burn situation collecting system. We believe that people burn that much of sugarcane burn, but they may not burn on the day they had a permit. The red dot in the comparisons of county-wise total burn areas on figure that represent Palm Beach County.

Conclusions Current satellite-derived products have limitations in estimating the burned areas of small fires. Three satellite-derived products all underestimate the burned areas compared to permit recorded data about 7% ~ 75% according to different years, states and products. BAECV is more accurate than the other two but it cannot give the daily or even monthly burned areas. Satellite-derived products can capture a cluster of fires better than isolated fires, but may misinterpret those small fires together as one big fire, as shown in our specific days’ analysis in Florida. Considering the openness, wide area and high frequency the sugarcane burn, it should be detected more efficiency. However, BBEP comparison show only a slight improvement compared to other types of burn.

Georgia Institute of Technology Questions? Ran Huang Georgia Institute of Technology ranhuang2014@gatech.edu

GA and FL county-level burned area comparison

Special days-03/18/2016 For 18 March 2016, there is an overlap of permitted burns and BBEP-detected fire locations; however, BBEP detects one very large fire (~10,000 acres) while the permit database implies many small fires in that area. The county total burn area for the satellite-detected fires is much larger than the one for permitted burns in cHARLOTTE  County.   BBEP may be interpreting several small fires as one big fire