Drought Monitoringusing Satellite Remote
Sensing Datain Java Island, Indonesia
Parwati, Orbita Roswintiarti, and Nanin Anggraini
Indonesian National Institute of Aeronautics and Space (LAPAN)
Presented at The Fourth GEOSS Asia‐Pacific Symposium “Towards a Global Earth Observation System of Systems that supports the Societal Benefit Areas of Climate and Biodiversity”
Bali, 10‐12 March 2010
Contents
Background and Objective1
Data2
Methods3
Results and Discussion4
Conclusions5
BackgroundDrought is a serious natural hazard characterized by lower than normal precipitation that when extended over a longer period of time is insufficient to meet the demands of human activities and the environment.
Types of drought:Meteorological drought: Precipitation deficitAgricultural drought: Soil water storage deficit Hydrological drought: Reduction of streamflow or subsurface water supplies
Background (Cont.)Timely information about the onset of drought, its extent, intensity, duration, and impacts could limitdrought‐related losses of life, minimize human suffering, and reduce damage to the economy and environment.Satellite remote sensing data with their:
consistencymulti‐temporal coverage of large areas in real‐time and at frequent intervalsmapping at a regular spatial resolution
could provide drought‐related information with cost‐effective.
ObjectiveObjective: To conduct detailed analysis of drought dynamics in Java islandto identify the spatio‐temporal drought patterns in meteorological and vegetation aspects during the 2009 ENSO.
In Indonesia, drought and severe vegetation stress were closely related with El Niño/Southern Oscillation (ENSO) events.
DataData:
Tropical Rainfall Measuring Mission (TRMM)• Period for calculating long‐term mean rainfall: Jan 1998 to Dec 2008
Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS)• Period for calculating Min/Max: Jan 2007 to Dec 2008
TRMM
MODIS
Example of accumulated rainfall from TRMM
Spatial resolution: 27 km
How MODIS detects vegetative chlorophyll
Enhanced Vegetation Index (EVI):
C1=6.0, C2=7.5, L=1.0, G=2.5
Healthy vegetation (more chlorophyll): absorbs most of the visible light that hits itreflects a large portion of the near‐infrared light.
Unhealthy or sparse vegetation (less chlorophyll):
reflects more visible lightreflects less near‐infrared light.
xGLrCrCr
rrEVI
BluedNIR
dNIR
+−+−
=2Re1
Re
Example of EVI distribution
Enhanced Vegetation Index (13-20 August 2009)
Spatial resolution: 250 meter
For the area without vegetation, the index characterizes surface conditions.
How MODIS detects Land Surface Temperature
Land Surface Temperature (LST) is derived from the split‐window LST algorithm:
Δε=(ε31‐ε32) and ε=0.5(ε31+ε32) are the difference and mean of surface emissivities in MODIS band 31 and 32.
T31 and T32 are the brightness temperatures in these two split‐window bands.
The coefficients A1, A2, A3, B1, B2, B3, C are given by interpolation on asset of multi‐dimensional look‐up tables (LUT).
CTTBBBTTAAALST +−Δ
+−
+++Δ
+−
+=2
)1(2
)1( 32312321
32312321 ε
εεε
εε
εε
Example of LST distribution
Land Surface Temperature (13-20 August 2009)
Spatial resolution: 1000 meter
Methods
Meteorological drought
Agricultural drought
Meteorological DroughtStandardized Precipitation Index (SPI):
Xij: rainfall at the ith location and jth time, Xim: climatologicalrainfall at the ith location, and σ: its standard deviation.
σimij XX
SPI−
=
Agricultural DroughtVegetation Condition Index (VCI) associates with moisture condition of vegetation:
Temperature Condition Index (TCI) associates with thermal condition of vegetation:
Vegetation Health Index (VHI) represents overall vegetation health:
minmax
min100EVIEVI
EVIEVIxVCI
−−
=
minmax
max100LSTLSTLSTLSTxTCI
−−
=
)(5.0 TCIVCIVHI +=
Classes of Agricultural drought‐based VHI
SPI (June – November 2009)
Calculated based on TRMM Data 1998 - 2008
VCI (June – November 2009)
For the area without vegetation, the index characterizes surface conditions. Calculated based on MODIS Data 2007 - 2008
TCI (June – November 2009)
For the area without vegetation, the index characterizes surface conditions. Calculated based on MODIS Data 2007 - 2008
VHI (June – November 2009)
For the area without vegetation, the index characterizes surface conditions. Calculated based on MODIS Data 2007 - 2008
SPI and VHI Results in ENSO Condition
El Nino
La Nina
Field checking on paddy condition areas (3‐5 Nov 2009)
Jatibarang, Indramayu Tukdana, Indramayu
Pagaden, Subang Ciwaringin, Cirebon
ConclusionsSatellite remote sensing data and information have been used to monitor drought‐vulnerable areas over Java island during June‐November 2009 of ENSO based on SPI and VHI indices.
The consistency and timely information of drought‐based remote sensing should be complemented with some verification and validation in the fields. In addition, attemps should be done to increase the long‐term mean and downscale the results for the local purposes.