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Comparative analysis of the variation ofin-situ agrometeorological parametersand main indicators estimated fromremote sensing data used for extremeevents monitoring in Region 7 Centre

National Meteorological Administration

18 – 21 August 2015 / Workshop II Sibiu

Project “ Green Path to Sustainable Development ”Program RO 07 – Adapting to Climatic Change 2009-2014

• In the last decades, significant agricultural areas across Romania were affected

by extreme climate events, with multiple negative implications in agriculture,

water resources and ecosystems conservation.

• In this respect, it is particularly important to know the duration, intensity,

frequency and spatio-temporal distribution of the risk factors for agriculture

(drought, heat, excess / deficit of soil water, etc) in order to identify the

agricultural areas with different degrees of vulnerability and elaboration of

strategic decisions for long-term planning such as design, location and

operation of irrigation systems in order to ensure the water needs for plants.

• To have complex agro meteorological information it is necessary to improve the

operational capabilities of monitoring using advanced remote sensing

techniques and Geographic Information Systems (GIS).

Introducere

• Remote sensing techniques play an important role in crop identification,

acreage and production estimation, disease and stress detection, soil and water

resources characterization because they provide spatially explicit information

and access to remote locations. These techniques allow examining the

properties and processes of ecosystems and their inter-annual variability at

multiple scales because remote sensing observations can be obtained over

large areas of interest almost every day.

• Data sets provided by satellite systems can be used in global, regional or local

studies, to obtain input data used to produce various models of energy balance,

water balance, etc.

• From remote sensing data can be extracted biophysical, biological or structural

vegetation parameters: leaf area index (LAI), biomass, photosynthetic active

radiation daily fraction absorbed by vegetation cover (fAPAR), normalized

difference vegetation index (NDVI), normalized difference water index (NDWI).

Introducere

Global Temperature Anomalies averaged from 2009 to 2013

The average temperature in 2013 was 14.6 ˚C, which is 0.6 °C warmer than the mid-20th

century baseline.

The average global temperature has risen about 0.8 °C since 1880, according to the new

analysis.

With the exception of 1998, the 13 warmest years in the 134-year record, all have occurred

since 2000.

(NASA's Goddard Institute for Space Studies 2013 Report; http://svs.gsfc.nasa.gov/).

Introducere

Source: Atlas of mortality and economic losses from weather, climate and water extremes (1970-2012),

WMO No. 1123, 2014

EUROPE

No. of natural disasters

1971-1980 60

1981-1990 246

1991-2000 379

2001-2010 577

Total Europe / 1352

Franta – 123, Italia – 75,

Romania – 71, Spania – 70

▲Increase

Source: Global Assessment Report on Disaster Risk Reduction, UN - 2015. National Risk Profile - Romania

ROMANIA / 2005-2014

Frequency (%) Economic losses(%)

Floods – 49% Earthquake – 52%

Extreme temperature– 22% Floods – 49%

Storms – 11% Drought – 6%

Flash Floods– 7%

Earthquake – 5%

Drought – 3%

DECENIUL SECOLUL XX

ANI EXTREMI SECETOSI ANI EXCESIV PLOIOSI

1901-1910 1907-1908 1910

1911-1920 1917-1918 1911, 1912, 1915, 1919

1921-1930 1923-1924, 1927-1928 1929

1931-1940 1934-1935 1937, 1939, 1940

1941-1950 1945-1946, 1947-1948, 1949-1950 1941, 1944, 1947

1951-1960 1952-1953 1954, 1955, 1957, 1960

1961-1970 1962-1963, 1964-1965 1969, 1970

1971-1980 1973-1974, 1975-1976 1972, 1974, 1975, 1976

1981-1990 1982-1983, 1985-1986, 1987-1988 1981, 1990

1991-2000 1992-1993, 1997-1998, 1999-2000 1991, 1997

SECOLUL XXI

2001-2010 2000-2001, 2001-2002, 2002-2003, 2006-2007, 2008-2009 2005, 2006, 2008, 2010

2011-2020 2011-2012 , …2013, 2014 …

Cresterea frecventei anilor secetosi incepand din 1981

Perioade cu precipitatii abundente pe secvente scurte de timp generatoare de

viituri rapide si inundatii (ex. 2004-2005, primavara si vara 2006, vara 2008 si 2010,

primavara si toamna 2013, primavara si vara 2014)

Ani secetoşi / ploioşi în România, perioada 1901-2014

Analiza climatologica

OBSERVED SHIFTS IN THE

COURSE OF THE MEAN ANNUAL

AIR TEMPERATURE IN ROMANIA

7.0

7.5

8.0

8.5

9.0

9.5

10.0

10.5

11.0

19

61

19

62

19

63

19

64

19

65

19

66

19

67

19

68

19

69

19

70

19

71

19

72

19

73

19

74

19

75

19

76

19

77

19

78

19

79

19

80

19

81

19

82

19

83

19

84

19

85

19

86

19

87

19

88

19

89

19

90

19

91

19

92

19

93

19

94

19

95

19

96

19

97

19

98

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

ºCMean annual air temperature trend in Romania

over 1961-2014 period

The warmest 5 years in Romania / 1961-2014 (1961-1990 / 8.8C)

Annual air temperature Deviation

1. 2007 10.6C 1.8C

2. 1994 10.4C 1.6C

3. 2009 10.3C 1.5C

4. 2000, 2008, 2014

10.2C 1.4C

5. 2002, 2013 10.1C 1.3C

1961-1990 / 8.8ºC

1981-2014 / 9.3ºC

+0.5ºC

RainfallSeptember 1st – October 31 (mm)Sowing period for winter wheat

1961-2014

1981-20101961-1990

Rainfall regime classesdrymoderate dryoptimalrainy

RainfallNovember 1st – March 31 (mm)

1961-2014


1961-1990 1981-2010

Zoning of the precipitation duringthe dormant period (November

1st – March 31)

Precipitatii medii multianuale

BLAJ

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

I II III IV V VI VII VIII IX X XI XII

luna

l/m

p

media 1961-1990

media 1981-2010

Multiannual rainfall regime Blaj 1961-2014

Month

mm

Rainfall (annual precipitation)June 1st – August 31(mm)

Summer period


1961-1990 1981-2010

1961-2014

Rainfall (annual precipitation)June 1st – August 31(mm)

Summer period

Rainfall (annual precipitation)September 1st – August 31(mm)

agricultural year

1961-2014

1981-20101961-1990

Rainfall regime classes

moderate dryoptimalrainy

excessive rainy



excessive rainy



excessive rainy

Precipitatii medii multianuale

LACAUTI

0.0

20.0

40.0

60.0

80.0

100.0

120.0

140.0

160.0


luna

l/m

p

media 1961-1990

media 1981-2010

Month

Multiannual rainfall regime Lacauti 1961-2014

mm

Multiannual rainfall regime Lacauti 1961-2014

Zoning of the precipitation duringthe agricultural year (September 1st -

August 31)

- 15.5% rainfall

Evolution of the mean annual air temperature recorded in Region 7 Centre,

over 1961-1990 and 1981-2010 intervals

Temperaturi medii multianuale

JOSENI

-10.0

-8.0

-6.0

-4.0

-2.0

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

18.0


luna

grade

media 1963-1990

media 1981-2010


MIERCUREA CIUC

-10.0

-8.0

-6.0

-4.0

-2.0

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

18.0

20.0


luna

grade

media 1961-1990

media 1981-2010

+ 0.6C + 0.6C

Evolution of the mean annual airtemperature recorded in Region 7 Centre,over 1961-1990 and 1981-2010 intervals


BLAJ

-6.0

-4.0

-2.0

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

18.0

20.0

22.0


luna

grade

media 1961-1990

media 1981-2010


SIBIU

-6.0

-4.0

-2.0

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

18.0

20.0

22.0


luna

grade

media 1961-1990

media 1981-2010

IntervalMonthly mean air temperature (C) – Region 7 Centre


1961-1990 -5.0 -3.0 1.6 7.0 12.0 14.8 16.5 16.0 12.4 7.1 1.8 -2.5

1981-2010 -4.0 -3.0 1.5 7.2 12.6 15.6 17.4 17.0 12.4 7.5 1.9 -2.7

Deviation +1.0 0 +0.1 +0.2 +0.6 +0.8 +0.9 +1.0 0 +0.4 +0.1 -0.2

Evolution of the mean annual airtemperature recorded in Region 7 Centre,over 1961-1990 and 1981-2010 intervals


BRASOV

-8.0

-6.0

-4.0

-2.0

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

18.0

20.0


luna

gra

de

media 1961-1990

media 1981-2010


BARAOLT

-8.0

-6.0

-4.0

-2.0

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

18.0

20.0


luna

grade

media 1961-1990

media 1981-2010


DUMBRAVENI

-6.0

-4.0

-2.0

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

18.0

20.0

22.0


luna

grade

media 1961-1990

media 1981-2010


FAGARAS

-8.0

-6.0

-4.0

-2.0

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

18.0

20.0

22.0


luna

grade

media 1961-1990

media 1981-2010

y = 0.0031x + 8.6497

6.0

7.0

8.0

9.0

10.0

11.0

12.0

190

1

190

5

190

9

191

3

191

7

192

1

192

5

192

9

193

3

193

7

194

1

194

5

194

9

195

3

195

7

196

1

196

5

196

9

197

3

197

7

198

1

198

5

198

9

199

3

199

7

200

1

200

5

200

9

201

3

Mean annual air temperature - Sibiu / 1901-

2013

ºC

y = 0.0055x + 7.3392

4.0

6.0

8.0

10.0

190

1

190

5

190

9

191

3

191

7

192

1

192

5

192

9

193

3

193

7

194

1

194

5

194

9

195

3

195

7

196

1

196

5

196

9

197

3

197

7

198

1

198

5

198

9

199

3

199

7

200

1

200

5

200

9

201

3

ºC Mean annual air temperature - Brasov / 1901-

2013

y = 0.0082x + 8.3773

6.0

7.0

8.0

9.0

10.0

11.0

190

1

190

5

190

9

191

3

191

7

192

1

192

5

192

9

193

3

193

7

194

1

194

5

194

9

195

3

195

7

196

1

196

5

196

9

197

3

197

7

198

1

198

5

198

9

199

3

199

7

200

1

200

5

200

9

201

3

ºCMean annual air temperature - Tg. Mures /1901-

2013

Observed shifts in the course of

the mean annual air temperature

SIBIU

1961-1990 8.5ºC

1991-2013 9.2ºC, +0.7ºC

BRASOV

1961-1990 7.5ºC

1991-2013 8.1ºC, +0.6ºC

TG. MURES

1961-1990 8.8ºC

1991-2013 9.4ºC, +0.6ºC

y = -0.2501x + 659.02

0.0

500.0

1000.0

1500.0

1…1…1…1…1…1…1…1…1…1…1…1…1…1…1…1…1…1…1…1…1…1…1…1…1…2…2…2…2…

mmAnnual precipitation amounts trend - Sibiu /1901-

2013

y = -1.9722x + 787.460

500

1000

1500

190

1

190

5

190

9

191

3

191

7

192

1

192

5

192

9

193

3

193

7

194

1

194

5

194

9

195

3

195

7

196

1

196

5

196

9

197

3

197

7

198

1

198

5

198

9

199

3

199

7

200

1

200

5

200

9

201

3

mm Annual precipitation amounts trend - Brasov /1901-

y = -0.5954x + 639.48

0

200

400

600

800

1000

1200

190

1

190

5

190

9

191

3

191

7

192

1

192

5

192

9

193

3

193

7

194

1

194

5

194

9

195

3

195

7

196

1

196

5

196

9

197

3

197

7

198

1

198

5

198

9

199

3

199

7

200

1

200

5

200

9

201

3

Annual precipitation amounts trend - Tg. Mures /1901-m

m

Observed shifts in the course of the

annual precipitation amounts (mm)

SIBIU

1901-1980 653.3 mm

1981-2013 624.1 mm

BRASOV

1901-1980 711.2 mm

1981-2013 587.3 mm

TG. MURES

1901-1980 617.2 mm

1981-2013 572.2 mm

0

20

40

60

80

100

120


l/m

p

luna

Precipitatii medii multianuale (l/mp) 1961-1990, 1981-2010 si 1961-2014 / SIBIU

media 1961-1990

media 1981-2010

media 1961-2014

Interval / Month May June July August

1961-1990 78,2 99,0 86,4 68,1

1981-2010 69,1 92,9 92,6 76,8

1961-2014 77,9 96,4 93,5 72,1

SignificanceOPTIMAL

pluviometric regime

Multiannual averages of

rainfall (l / m2) fallen in 24 h,

recorded at the

meteorological station Sibiu

Month Value / Date

(1961-1990)

Value / Date

(1981-2010)

Value / Date

(1961-2014)

Value / Date

(2015)

Mai 69,9 l/mp /

10.05.1973

35,3 l/mp /

30.05.1998

69,9 l/mp /

10.05.1973

18,5 l/mp /

07.05.2015

Iunie 45,2 l/mp /

22.06.1979

70,4 l/mp /

18.06.1998

70,4 l/mp /

18.06.1998

44,9 l/mp /

26.06.2015

Iulie 57,0 l/mp /

19.07.1974

53,2 l/mp /

28.07.2004

57,0 l/mp /

19.07.1974

3,9 l/mp / 28.07.2015

August 65,8 l/mp /

25.08.1977

46,6 l/mp /

05.08.2005

65,8 l/mp /

25.08.1977

0 l/mp / 01-

13.08.2015

Maximum Precipitation (l / m) fallen in 24 h, recorded at the meteorological

station Sibiu

DateSoil moisture

(mc/ha)

% Cau (% from usable water

capacity of the soil)Significance

31.05.2015 810 mc/ha 51 %CAu Satisfactory supply


DateSoil moisture

(mc/ha)




31.07.2015 465 mc/ha 29 %CAu Strong pedological drought

15.08.2015 331 mc/ha 21 %CAu Strong pedological drought

Soil moisture (mc / ha) on the 0-100 cm soil profile, for winter wheat, during the peak

demand of water for the plants (May-June) to agrometeorological station Sibiu

Soil moisture (mc / ha) on the 0-100 cm soil profile, for maize, during the peak demand of

water for the plants (June - August) to agrometeorological station Sibiu

Legend (% CAu - % from usable water capacity of the soil)

0 – 20%CAu : Extreme pedological dorught(SE)

20 – 35%CAu : Strong pedological drought(SP)

35 – 50%CAu : Moderate pedological drought(SM)

50 – 70%CAu : Satisfactory supply(AS)

70 – 85%CAu : Almost optimum supply (ApO)

85 – 100%CAu : Optimal supply(AO)

> 100%CAu : Excess (E)

Interval / Month May June July August

1961-1990 74,6 78,0 83,6 63,8

1981-2010 65,7 84,8 77,0 65,6

1961-2014 72,9 83,7 80,4 65,2

SignificanceOPTIMAL

pluviometric regime

Multiannual averages of

rainfall (l / m2) fallen in 24 h,

recorded at the

meteorological station Targu

Mures

Precipitatii medii multianuale (l/mp) 1961-1990, 1981-2010 si 1961-2014 / TG. MURES

0

10

20

30

40

50

60

70

80

90


luna

l/m

p

media 1961-1990

media 1981-2010

media 1961-2014

Maximum Precipitation (l / m) fallen in 24 h, recorded at the meteorological

station Targu Mures

Month Value / Date

(1961-1990)

Value / Date

(1981-2010)

Value / Date

(1961-2014)

Value / Date

(2015)

Mai 51.1 l/mp / 13.05.1970 29 l/mp / 14.05.1984 51.1 l/mp / 13.05.1970 23,4 l/mp / 14.05.2015

Iunie 53.5 l/mp / 12.06.1974 49.2 l/mp / 18.06.1998 56,8 l/mp / 10.06.2011 27,2 l/mp / 26.06.2015

Iulie 67.8 l/mp / 02.07.1975 59.6 l/mp / 18.07.1985 67.8 l/mp / 02.07.1975 9,8 l/mp / 09.07.2015

August 38,8 l/mp / 05.08.1962 49,6 l/mp / 24.08.200549,6 l/mp / 24.08.2005 25,6 l/mp / 06.08.2015

01-13.08.2015

DateSoil moisture

(mc/ha)





DateSoil moisture

(mc/ha)






Soil moisture (mc / ha) on the 0-100 cm soil profile, for winter wheat, during the peak

demand of water for the plants (May-June) to agrometeorological station Targu Mures

Soil moisture (mc / ha) on the 0-100 cm soil profile, for maize, during the peak demand of

water for the plants (June - August) to agrometeorological station Targu Mures

Legend (% CAu - % from usable water capacity of the soil)

0 – 20%CAu : Extreme pedological dorught(SE)

20 – 35%CAu : Strong pedological drought(SP)

35 – 50%CAu : Moderate pedological drought(SM)

50 – 70%CAu : Satisfactory supply(AS)

70 – 85%CAu : Almost optimum supply (ApO)

85 – 100%CAu : Optimal supply(AO)

> 100%CAu : Excess (E)

GIS database

•The GIS database contains

info-layers in a relational

structure, that are: sub-basins

and basin limits; land

topography (15m cell size

DEM); hydrographic and

canal networks; transport

network (roads, railways);

localities; administrative

boundaries; agro –

meteorological stations; land

cover/land use, updated from

satellite images

GIS Database

CLC 2012

GIS Database

Remote sensimg data used

• In order to monitor the vegetation statement, the medium and high resolution satellite

images have been used to obtain the dedicated vegetation indexes. These indexes are

good indicators of drought and they are used also by the scientific community (European

Drought Observatory).

• TERRA – AQUA/MODIS Surface Reflectance 8-Day L3 Global 500 m products

(MOD09A1): provides bands 1–7 at 500 m resolution in an 8-day gridded level-3 product

in the sinusoidal projection. Science Data Sets provided for this product include

reflectance values for Bands 1–7, quality assessment, and the day of the year for the

pixel along with solar, view, and zenith angles.

• TERRA MODIS 8 – day LAI/fAPAR product (1 km spatial resolution): is composited every

8 days at 1-kilometer resolution on a Sinusoidal grid. Science Data Sets provided in the

MOD15A2 include LAI, FPAR, a quality rating, and standard deviation for each variable.

• Medium resolution satellite data: 2000 - present

Remote sensimg data

• The LANDSAT 5 TM data: 7 spectral bands, with 30 m spatial resolution (thermal band (6)

has 120 m spatial resolution). (200-2013).

• Landsat 7 ETM+ data: the main features are: a panchromatic band with 15 m spatial

resolution (band 8); visible bands in the spectrum of blue, green, red, near-infrared (NIR),

and mid-infrared (MIR) with 30 m spatial resolution (bands 1-5, 7); a thermal infrared

channel with 60 m spatial resolution (band 6). (2000-present).

• Landsat 8 OLI data: the main features are: a panchromatic band with 15 m spatial

resolution (band 8); visible bands in the spectrum of blue, green, red, near-infrared (NIR),

and mid-infrared (MIR) with 30 m spatial resolution (bands 1-9); two thermal infrared

channels with 100 m spatial resolution (bands 10 and 11). (2014 – present)

• SPOT 7 data.

• Image product resolution: 6 m for multispectral and 1.5 for panchromatic.

• Spectral bands, with simultaneous panchromatic and multispectral

acquisitions: Panchromatic (450 – 745 nm); Blue (450 – 525 nm); Green

(530 – 590 nm); Red (625 – 695 nm); Near-infrared (760 – 890 nm).

https://en.wikipedia.org/wiki/Panchromatic

https://en.wikipedia.org/wiki/Near-infrared

Vegetation indices

• The Normalized Difference Vegetation Index (NDVI) is a non-linear transformation of

visible bands (Red) and near infrared (NIR), being defined as the difference between these

two bands divided by their sum:

NDVI = (NIR-RED) / (NIR + RED).

• NDVI is a "measure" of development and vegetation density and is associated with

biophysical parameters as: biomass, leaf area index (LAI), used widely in crop growth

models, the percentage of vegetation cover of the land, photosynthetic activity of

vegetation.

• NDVI values range from -1.0 to 1.0, with negative values indicating clouds and water,

positive values near zero indicating bare soil, and higher positive values of NDVI ranging

from sparse vegetation (0.1 - 0.5) to dense green vegetation (0.6 and above).

• Indirectly, NDVI is used to estimate the effects of rainfall over a period of time, to estimate

the state of vegetation for different crops, and environmental quality as habitat for various

animals, pests and diseases.

Vegetation indices (cont.)

The NDVI spatial distribution obtained from MODIS data (MOD09A1): 05.08-

05.09.2007 (droughty year)

05-12.08.2007

13-20.08.2007

21-28.08.2007

29.08-

05.09.2007

NDVI is an indicator

of presence, density

and health of

vegetation

compared to a pixel

(1km2); the positive

values are colored in

shades of green to

dark green and

negative values are

colored in shades

from yellow to

brown, indicating a

lack of vegetation or

bad health.


The NDVI spatial distribution obtained from MODIS data (MOD09A1): 05.08-

05.09.2014

05-12.08.2014

13-20.08.2014

21-28.08.2014

29.08-

05.09.2014

NDVI is an indicator

of presence, density

and health of

vegetation

compared to a pixel

(1km2); the positive

values are colored in

shades of green to

dark green and

negative values are

colored in shades

from yellow to

brown, indicating a

lack of vegetation or

bad health.


The NDVI spatial distribution obtained from SPOT 7 data

NDVI is an indicator of

presence, density and

health of vegetation

compared to a pixel

(1km2); the positive values

are colored in shades of

green to dark green and

negative values are

colored in shades from

yellow to brown, indicating

a lack of vegetation or bad

health.


• The Normalized Difference Water Index (NDWI) is a satellite-derived index from the Near-

Infrared (NIR) and Short Wave Infrared (SWIR) reflectance channels:

𝑁𝐷𝑊𝐼=(𝑁𝐼𝑅 −𝑆𝑊𝐼𝑅)/(𝑁𝐼𝑅+𝑆𝑊𝐼𝑅)

• where SWIR and NIR are spectral reflectance from short wave infrared band and near-

infrared regions, respectively.

• NDWI values range from -1.0 to 1.0. The common range for green vegetation is -0.1 to

0.4. This index increases with vegetation water content or from dry soil to free water.

• NDWI index is a good indicator of water content of leaves and is used for detecting and

monitoring the humidity of the vegetation cover. It is well known that during dry periods,

the vegetation is affected by water stress, which influence plant development and can

cause damage to crops. Because it is influenced by plants dehydration and wilting, NDWI

may be a better indicator for drought monitoring than NDVI. By providing near real-time

data related to plant water stress to the users can be improved water management,

particularly by irrigating agricultural areas affected by drought, according to water needs.


The NDWI spatial distribution obtained from MODIS data (MOD09A1): 05.08-


05-12.08.2007

13-20.08.2007

21-28.08.2007

29.08-

05.09.2007

NDWI index is a

good indicator of

water content of

leaves; the positive

values (NDWI > 0.3)

are colored in

shades of green to

dark blue and

negative values

(NDWI < 0.2) are

colored in shades

from light green to

brown, indicating

vegetation affected

by water stress.


The NDWI spatial distribution obtained from MODIS data (MOD09A1): 05.08-

05.09.2014

05-12.08.2014

13-20.08.2014

21-28.08.2014

29.08-

05.09.2014

NDWI index is a

good indicator of

water content of

leaves; the positive

values (NDWI > 0.3)

are colored in

shades of green to

dark blue and

negative values

(NDWI < 0.2) are

colored in shades

from light green to

brown, indicating

vegetation affected

by water stress.


• The Normalized Difference Drought Index (NDDI) NDDI is a relatively new superior drought

indicator. It is calculated as the ratio of the difference between the normalized difference

vegetation index and normalized difference water index and their sum:

NDDI = (NDVI - NDWI) / (NDVI + NDWI)

• It combines information from visible, NIR, and SWIR channel. NDDI can offer an

appropriate measure of the dryness of a particular area, because it combines information

on both vegetation and water.

• NDDI had a stronger response to summer drought conditions than a simple difference

between NDVI and NDWI, and is therefore a more sensitive indicator of drought.

• This index can be an optimal complement to in-situ based indicators or for other indicators

based on remote sensing data.


The NDDI spatial distribution obtained from MODIS data (MOD09A1): 05.08-


05-12.08.2007

13-20.08.2007

21-28.08.2007

29.08-

05.09.2007

NDDI had a stronger

response to summer

drought; NDDI > 0.4

values, colored in

shades from yellow

to red, are indicating

vegetation affected

drought.


The NDDI spatial distribution obtained from MODIS data (MOD09A1): 05.08-

05.09.2014

05-12.08.2014

13-20.08.2014

21-28.08.2014

29.08-

05.09.2014

NDDI had a stronger

response to summer

drought; NDDI > 0.4

values, colored in

shades from yellow

to red, are indicating

vegetation affected

drought.

MODIS NDVI over Covasna county

21.07 - 13.08.2013

21.07 – 28.07.2013 29.07 – 5.08.2013 6.08 – 13.08.2013

Date Soil moisture

(mc/ha)

% CAu (Soil water supply

capacity)

Classes

10.07.2013 1216 76 %CAu Close to the optimal supply

20.07.2013 883 55 %CAu Satisfactory supply

31.07.2013 695 43 %CAu Moderate pedological drought

10.08.2013 548 34 %CAu Strong pedological drought


The soil moisture and soil water supply capacity values recorded at

the agrometeorological station Sfantu Gheorghe.

MODIS NDVI evolution over Covasna county 29.07 - 5.08.2010; 2011; 2012 and 2013

Land cover/land use map

over Covasna Study Area

29.07 – 5.08.2012Moderate pedological drought

29.07 – 5.08.2010Close to the optimal supply

29.07 – 5.08.2011Satisfactory supply

29.07 – 5.08.2013Moderate pedological drought

MODIS NDVI-13.08.2012

Date Soil moisture

(mc/ha)

% CAu (Soil water

supply capacity)

Classes



31.07.2012 679 mc/ha 42 %CAu Moderate pedological drought



21.07 – 28.07.2012 29.07 – 5.08.2012 6.08 – 13.08.2012

NDWI

NDDI

Date Soil moisture (mc/ha) % CAu (Soil water supply capacity) Classes

10.07.2013 1216 76 %CAu Close to the optimal supply

20.07.2013 883 55 %CAu Satisfactory supply


10.08.2013 548 34 %CAu Strong pedological drought


21.07 – 28.07.2013 29.07 – 5.08.2013 6.08 – 13.08.2013

NDWI

NDDI

MODIS NDWI and NDDI over Covasna county on 21.07

-13.08.2013

The minimum values of NDVI NDWI and NDII are recorded in the period 20.07-

15.08.2013, due to the lack of precipitation and decrease of the in-soil moisture

reserve. For this period NDDI has maximum values between 0.2 - 0.4.

Analysis of vegetation state evolution with satellite-based indices

in Sfantu Gheorghe area on 24 April – 3 Dec. 2013

Using remote sensing data for drought monitoring

Conclusions

• The vegetation indexes extracted from satellite images, correlated with meteorological and

agrometeorological information, are good indicators of vegetation condition, in this case are

relevant for monitoring the beginning, duration and intensity of drought.

• Remote sensing techniques can enhance and improve the drought analysis, especially

considering the scarce availability of measured ground truth data.

• The advantage of multi-annual imagery availability allows the overlay and cross-checking of

doughty, normal or rainy years.

• GIS technologies offer the possibility of crossed-analysis between various data sources

such as vegetation indexes and CORINE land-cover classes.

• Referring to the entire image without offering information on how vegetation indices reflects

the behavior of various land-cover classes under drought stress.

Thank you for your attention

!

Argentina NERTAN

Remote Sensing & GIS Department

Email : [email protected]

Telefon : +40-21-318.32.40 ext. 163

Fax : +40-21-316.21.39

Oana Alexandra OPREA

Agrometeorological Laboratory


Telefon : +40-21-318.32.40 ext. 107

Fax : +40-21-316.21.39

Alexandru Dumitrescu

Depatment of Climatology


Telefon : +40-21-318.32.40 ext. 135

Fax : +40-21-316.21.39

mailto:[email protected]



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