Vegetation variability, malnutrition, and armed conflict in the Horn - - PowerPoint PPT Presentation

Vegetation variability, malnutrition, and armed conflict in the Horn of Africa

Pedram Rowhani (McGill University) Olivier Degomme (CRED – UCLouvain) Debarati Guha‐Sapir (CRED – UCLouvain) Eric Lambin (UCLouvain & Stanford)

KlimaCampus, Hamburg University, November 20, 2009

Climate change‐famine‐war

Study area

4 million km² supporting 56 million people 3 of the world’s poorest countries hit by conflicts and famines (> 6 million IDP)

• Sudan (states‐wilayat):

Southern Sudan since early 1980’s; Darfur, war opposing nomad Arab militia to different local non‐Arab groups

• Ethiopia (zones):

The Ogaden province; Border dispute with Eritrea; Involved in insurgency in Somalia

• Somalia (regions‐gobollada):

No permanent national government

Data

• Complex Emergency Database (CE‐DAT): Global Acute Malnutrition

(GAM) represents the proportion of children below 2 standard deviation from the average ratio weight‐over‐height

• MODIS: Vegetation variability (SCV), production (iEVI), land

degradation (decreases in iEVI)

• Armed Conflicts database, version 3‐2005: Conflicts with at least 25

battle deaths/year, LAT/LONG and Radius

• Gridded Gross Domestic Product (GDP)
• Road density

Global Acute Malnutrition (GAM)

The nutritional status of a population is one of the basic indicators to assess the severity of a humanitarian crisis. The weight and height of children between 6 and 59 months are measured and the results are used as a proxy indicator for the general health of the entire population. Global Acute Malnutrition (GAM) = weight‐for‐height index less than ‐2 standard deviations from the mean weight of a reference population of children of the same height and/or having oedema. Thresholds: <5% = acceptable 5% to 9.9% = poor 10% to 14.9% = serious >15% = critical

First day of data collected by MODIS on the TERRA platform

Remote sensing

MODerate resolution Imaging Spectroradiometer (MODIS)

• MODIS data

– Geo‐location ~ 50 m – 250 – 1000 m resolution – Enhanced radiometric quality – Real‐time atmospheric correction – Designed for vegetation monitoring

• MOD43B4

– Feb 2000 – DEC 2006 – 1 km resolution – 16‐day composites – BRDF corrected reflectance products (Schaaf et al. 2002)

Global MODIS Enhanced Vegetation Index

L C C G EVI

blue 2 red 1 nir red nir

       ρ ρ ρ ρ ρ

Vegetation variability (1)

• Change Vector

– Difference of annual profile vectors

Change Vector EVI Profiles

1 2

I I CV    

I1 I2 CV

Vegetation variability (2)

Sum of the absolute values of the Change Vector (SCV)

Linderman et al., 2005





 

n i ref i year i

I I

1

SCV(year)

• Changes in amount of EVI
• Changes in phenology

and/or timing of activity

Vegetation variability (3)

Mean SCV 2000‐2006 period at 1km resolution (SCV/iEVI(ref))

Vegetation availability / Degradation

integrated EVI (iEVI)

Six year profile

1000 1500 2000 2500 3000 3500 4000 4500 1 9 17 25 33 41 49 57 65 73 81 89 97 105 113 121 129 137 Time EVI







23 1 j j

EVI iEVI

Land degradation (iEVI)

Method

Logistic regressions: 1.Malnutrition ~ f(Conflict) 2.Malnutrition~f(SCV,iEVI,degradation,GCP,Roads) 3.Conflict~f(SCV,iEVI,degradation,GCP,Roads) Administrative & Village level

Results (1) malnutrition ~ conflict

Logistic regression model results measuring the relationship between GAM and conflict (pseudo‐R2 = 0.19, model chi‐square = 0.0008, area under ROC = 0.74, AIC = 46.506)

In the Horn of Africa, GAM values over 15 are 11.7 times more likely to be found in conflict areas.

Results (2) Malnutrition

• Regions

with unpredictable vegetation productivity have an increased likelihood

• f acute malnutrition.
• Malnutrition

is 15.4 times more likely to be found in a poor area.

• The likelihood
• f observing

malnutrition decreases with road density.

Multivariate logistic regression model results analyzing the spatial distribution of conflict (pseudo‐R² = 0.23/0.04, area under ROC = 0.81/0.64)

Variable Parameter estimate Standard error P-value Odds ratio

• admin. unit village admin. unit Village admin. Unit village admin. unit village

Intercept 22.61 7.36 9.85 2.06 0.0217 0.0003

• log10

GCP

• 3.57
• 0.87

1.36 0.258 0.0089 0.0007 0.0283 0.419 Roads

• 53.55

25.28 0.0341 5.5·10-24 SCV 17.38 8.66 0.0446 3.55·1007

Results (3) Armed conflict

Multivariate logistic regression model results analyzing the spatial distribution of conflict (pseudo‐R2 = 0.08/0.13, area under ROC = 0.71/0.74)

• Better economic situation reduces likelihood of armed conflict.
• Armed conflicts are more likely in regions with

more vegetation.

• Interannual variability in

vegetation and land degradation do not explain the presence of conflict in the Horn of Africa.

Variable Parameter estimate Standard error P-value Odds ratio

• Admin. unit

village

• admin. unit

Village

• admin. unit

village

• admin. unit village

Intercept 7.235 2.108 4.08 2.63 0.0762 0.01 1387.06 8.23 log10 GCP

• 1.089

0.548 0.0471 0.3366 iEVI 3.6·10-05 5.73·10-05 1.50·10-05 1.16·10-05 0.0163 8.04·10-07 1.00004 1.00007

Conclusions

• Malnutrition and armed conflict are closely related
• Direct association between interannual variability in vegetation

productivity and malnutrition

• Vegetation variability indirectly associated with conflict
• Short‐term land degradation not related to malnutrition and

armed conflict

• Better economic situation found in peaceful areas with low

levels of malnutrition (scale independent)

• High vegetation production associated with conflict
• Different processes at different scales
• Data quality/availability!!!

Thank you!

Pedram.rowhani‐ardekani@mcgill.ca