Tools for Hazard Monitoring, Assessment, and Response Jesslyn F. Brown, U.S. Geological Survey, Earth Resources Observation and Science, South Dakota, USA

Landsat 8

In the face of hazards, what can people and organizations do to help our situation?Better access to information

Responsive to a changing situationHave the proper content, latency, spatial detail

PlanTake actions to reduce vulnerability

Specific actions to address hazard management within integrated assessment and planning

1. An assessment of the presence and effect of natural events on the goods and services provided by natural resources

2. Estimates of the potential impact of hazard events on development activities3. Include measures to reduce vulnerability in the proposed development activities

Organization of American States

Drought is a kind of climate hazard, and it is one that dramatically effects agriculture. Generally more costly than other hazards especially when we consider the impacts and costs at multiple scales.Multiple tools are needed to access information about hazards

Where, When, How severe?Remote sensing is one tool

Remote Sensing AdvantagesCost-effective and rapid of acquiring up-to-date information over a large geographical areaPractical way to obtain data for inaccessible placesHigh repetition rate and continuous coverage (time and space)Standard methods and techniques

Remote Sensing LimitationsIndirect measure of the phenomenaAtmosphere noise needs to be correctedGeometric and sensor calibration issues

Effect Accuracy

StackingSmoothing

Anomaly DetectionMetrics Calculation(SOS, SG, PASG)

GeoregistrationCompositing

Surface Reflectance

AVHRR

MODIS

User/DecisionSupport System

Satellite Systems

Data/products to partners >> Regular and Quick

Data Services

Data Translation

Vegetation Dynamics

and VegDRIModels

2009>

Existing

EMODIS System

Vegetation Dynamics System

Drought Impacts•Annual direct losses to the U.S. due to drought are, on average, $6-8 billion (FEMA) 2012 $$150 billion•Drought severity can be significantly under- or over-estimated due to inadequate drought observations. This affects Planning, Prediction, Mitigation, and Response.•Impacts are evident at multiple scales (local, regional, national, global) and in multiple sectors

Drought Impacts

Health problemsBankruptcy

ServiceFarm IncomeYields

Migration

Tax BaseState/Regional

Economic Productivity

RegionalProduction

Trade Balance

Inflation

Food PricesDistribution

National Economy

U.S. FoodSupply

Famine

Social Conflict

Food PricesDistribution

Foreign Economies

GlobalFood

Supply

Health problemsBankruptcy

ServiceFarm IncomeYields

Migration

Tax BaseState/Regional

Economic Productivity

RegionalProduction

Trade Balance

Inflation

Food PricesDistribution

National Economy

U.S. FoodSupply

Famine

Social Conflict

Food PricesDistribution

Foreign Economies

GlobalFood

Supply

Local

Regional

National

Global/Multinational

Agricultural Economic SocialWarrick and Bowden, 1981

droughtdrought

drought

Dust Bowldrought

drought

U.S. requirements for operational drought monitoring—geospatial productsProduct coverage National synoptic Product schedule Weekly, circa Monday a.m.Spatial scale 1-4 km2, sub-county detailsProduct latency <24-48 hoursLength of record (providing climatology)

~30 years for climate data, information framed related to climatology or history

Start of SeasonEnd of SeasonLength of SeasonSeasonal “greenness” –cumulative productivity

Multi-year time-series observations support National monitoring of land change

Normalized Difference Vegetation Index (NDVI)

Integrating Satellite and Climate DataSeasonal greenness condition from satellite highlights areas with anomalous vegetation condition

Deficits (compared to average condition) might be caused by drought, flooding, late greennup, land cover conversion, etc. Climate information is needed to provide evidence that enables interpretation of the satellite data anomalies

http://vegdri.cr.usgs.gov/

1) land use/land cover type2) soil available water

capacity (STATSGO)3) ecoregion type4) irrigation status5) elevation

* Models developed from a 20-year historical record (1989 – 2009)

of climate and satellite observations at 3,000+ weather station locations.

Biophysical variables are static over time.

RegressionTree

Model (*)

Satellite Data

1-km2 VegDRI Map

1) Percent Annual Seasonal Greenness (PASG)

2) Start of Season Anomaly (SOSA)

1) Palmer Drought Severity Index (PDSI)

2) Standardized Precipitation Index (SPI)

Climate Data

Biophysical Data

Data Input Variables

1. Historical Database Development

2. Model Development

3. Map Generation

Vegetation Drought Response Index MethodologyBrown, J.F., et al. (2008). The Vegetation Drought Response Index (VegDRI): A new integrated approach for monitoring drought stress in vegetation. GIScience and Remote Sensing, 45, 16-46.

eMODIS Expedited

Terra MODIS

LANCE

T+11hrs

EDOS

MODIS L0 Data

T+3hrs

T+6hrs

MODISL2, L1B Data

MODAPS

LAADS

eMODIS Historical

Input Data Target: Monday 10:30 a.m.

USGS Drought Monitoring

NDMC Vegetation Drought Response Index

NIDIS Drought Portal

U.S. Drought Monitor

VegDRI

VegDRI Flow: Providing Near-real time Delivery of Information

8-day Cumulative ETa AnomalyApr 1– Sep 12, 2012

Courtesy: G. Senay

2001, 2006 National Land Cover Datasets

20 LC ClassesOnly 2 Agricultural Classes

~78% Overall Accuracy

Information for decision-makers through combinations of Information/data

Crop Type Classification of the U.S. Great Plains: An Application of Regression Tree Modeling using Remote Sensing and Environmental Data

• Spatial coverage: U.S. Great Plains• Temporal coverage: 2000 – 2011• Spatial resolution: 250 meter• Overall model classification

accuracy: 87%• Linear agreement with county

results from the USDA NASS Survey Program: R2 = 0.76

D. Howard, B. Wylie

Global Agricultural Monitoring System of Systems:

GEOSS ActivitySatellite observations, in-situ observations and model outputs are needed. Standardization and coordination among countriesCombined information (seasonal forecast models, agro-meterological data [precip, temp, humidity], in-situ obs, satellite obs at various scales, optical, thermal, microwave…Planted area, crop condition, crop type, crop yield

SummaryRemotely sensed observations (spatial detail and synoptic coverage) are a critical tool for monitoring hazardsNeed to understand limitations and accuracyNeed for continued development of tools that fit national needs, and also enhance a global systemInternational cooperation and technical advancement is being supported through GLAM

Thank You! Muchos Gracias!

Questions? Preguntas?

Extra slides

U.S. Department of the InteriorU.S. Geological Survey

2006 Satellite Vegetation Phenology for the Conterminous U.S.

April 2, 2006 April 30, 2006 May 28, 2006

June 25, 2006 July 23, 2006 August 20, 2006

September 17, 2006 October 15, 2006 October 29, 2006

March 2007

Earth Observing Systems

VIIRS optical band widths will be similar to MODIS

1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

NOAA-11 AVHRR(1:30pm at launch, 5:00pm at the end)NOAA-14 AVHRR(1:30pm at launch, 5:00pm at the end)NOAA-16 AVHRR(2:00pm)NOAA-17 AVHRR(10:00am)SPOT-4 VEGETATION(10:30am)Terra MODIS(10:30am)Aqua MODIS(1:30pm)NPP VIIRS(1:30pm)Landsat-5 TM(10:00am)Landsat-7 ETM+(10:00am)

Jan 1995 Sep 2001

Jan 2001

Aug 2002

Feb 2000

Jun 2002

Apr 1998

Sep 2011

Sep 2003*

Dec 2002

Jan 1989 Sep 1994

Not Recommended

(Operating as Secondary)

(Operating as Secondary)

Since March 1984

April 1999 May 2003SLC-of fSLC-on

Courtesy: D. Wilhite

Integrated Assessment and Planning

Examples of Federal Policy Decisions

Allocation of Federal Emergency Relief Funds

U.S. Drought Monitor used as primary tool in allocating federal emergency drought relief

Bureau of Reclamation for American Indian Nations of the Southwest

Use of US Drought Monitor to trigger several key drought mitigation programs (including livestock assistance under the non-Fat Dry Milk Initiative and the Conservation Reserve Program)

Examples of Agribusiness Decisions

Federal Drought Information in Agribusiness

Used by Farmers and Ranchers in making purchasing decisions

Making crop planting decisionsIdentifying areas of greatest demand for hayEvaluating feed supplies and potential for shortages

Kansas Association of Wheat Growers and other agriculture-related groups rely on US Drought Monitor products

Examples of Agribusiness DecisionsFutures pricing of U.S. Commodities

Kansas City and Chicago Boards of Trade use federal drought monitoring and forecast information in futures pricing for U.S. CommoditiesOther Futures brokering companies (e.g., Allendale Inc.) use the USDM for assessing impacts to grain futures and for providing advisories to customers

Examples of Water Management Decisions•Bonneville Power Administration with $3B in annual revenues depends on water supply forecasts/drought information for hydro power management decisions•Critical water management decisions that affect agriculture and wildlife resources in Klamath Basin are heavily dependent on accurate and timely drought information

Examples of Energy DecisionsFederal Energy Regulatory Commission

Use Drought Monitor information for the oversight of energy marketsHold drought workshops to improve and maintain coordination and cooperation among licensees, agencies, stakeholders, and Commission staff during developing drought

State and Local DecisionsState and local drought planning

Requires higher spatial resolution and improved method for communicating drought classification categoriesEnhanced coverage of surface observing networks along with improvements in reporting frequency and timeliness will support decision-making at the county level