The application of GIS and

remote sensing on icipe’s

R&D paradigms

Elfatih M. Abdel-Rahman

Gladys Mosomtai

Tobias Landmann

David Makori

icipe-African Insect Science for Food and Health

KENYATTA UNIVERSITY GIS DAY – 18th Nov. 2014

icipe (Overview)

More than 40 years, icipe (International

Centre for Insect Physiology and Ecology) has

been the principal insect and arthropod

research institute for Africa.

„Research and development thrust for icipe’s

4-H paradigm;

- Plant health

- Animal health

- Human health

- Environmental heath

icipe (Overview)

Earth Observation Unit within icipe

It‟s part of the Adaptation to Climate Change

and Ecosystems Services (ACCES) cluster

Works closely with icipe scientists from all 4-H

to provide geospatial services to various

projects

Involved in training students and staff

from icipe as well as collaborating partner

institutions on GIS and remote sensing.

Thematic areas for EOU

Food Security - mapping of crops and

cropping systems.

Biodiversity (BD) indicators - “Incidences of

Ecosystem Failure”, “Habitat dynamics,”

“Degradation.”

Integrated Land Use & Ecosystem

Services - mapping the floral cycle to quantify

pollination effects and understand bee health.

Thematic areas for EOU

Disease mapping - involved in mapping the

vector habitat as a proxy for disease

occurrence (than mapping the disease itself)

Food security

1. As part of risks assessment for agriculture: Predicting the

Impacts of climate change on future pest and disease

outbreaks (e.g., Diamondback moth infestation in Taita)

Current 2013 Future 2055

Methodology

1. Species distribution

modelling tools e.g

MAXENT, GARP

2. Data – worldclim data

Africlim data

RS variables

LST, NDVI

Data source

http://www.worldclim.org/download

https://webfiles.york.ac.uk/KITE/AfriClim/GeoTIFF_30s/baseline_worldclim/

Food security

2. Crop and cropping patterns mapping for pests and

disease prediction (e.g., Stem borers in Maize)

this work is under progress (First field visit 10 – 13

November 2014)

Food security

Multi-sensor time series remotely sensed data (RapidEgye,

Landsat 8, Sentinel-2 and SAR)

- Crop mask

- Crop types (patterns)

- Seasonality (phenology)

- Surrounded areas

- Use of machine-learning classification algorithms (e.g.,

Random forest)

Food security

Developing java-based RF for agricultural land

use classification tool

The tool does

automatic

classification

using random

forest algorithm

Red- set directory

Green- upload

image to classify

Set parameter of

the algorithm

Biodiversity (BD) indicators

Land productivity decline mapping (2001-2012):

Approach

Multi-sensor approach

for human-induced land

productivity mapping

(2001-2012)*

*Landmann & Dubovyk (2014) Spatial analysis of human-induced vegetation

productivity decline over eastern Africa using a decade (2001-2011) of medium

resolution MODIS time-series data. Int. J. Applied Earth Observation and

Geoinformation 33: 76-82 – published paper

Data

MODIS NDVI – MOD 13Q

Product

TRMM - rainfall data

Sources

http://earthexplorer.usgs.gov/

Software used

1. IDRISI

2. ENVI

3. ARCMAP

Biodiversity (BD) indicators

Moderate decline

“severe” decline

Rainfall corrected (normalized) Normalized

Difference Vegetation Index (NDVI) time-series

data, at 250-meter resolution, is used to map

human-induced change between 2000 and 2012

Flower mapping using airborne hyperspectral data

Knowledge about the floral cycle and the abundance and

distribution of flowering of mellipherous plants in the

landscape

Bee hive productivity and bee health studies as well as

pollination

The study site is located in the Mwingi Central Sub County,

Kitui County in Kenya

AisaEAGLE imaging spectrometer (Feb 2013 and Jan. 2014)

Ecosystem services quantification

Reference data collection (Flowering trees, flowers color)

Bee hive productivity and bee health studies as well as

pollination

Flowering, green trees and soil endmembers

Ecosystem services quantification

0

20

40

60

408

.4

443

.2

479

.2

515

.2

551

.4

588

.6

625

.9

663

.3

700

.6

738

.2

776

.1

814

.3

852

.4

890

.5

928

.7

966

.9

Ref

lact

ance

(%

)

Wavelength (nm)

2013 Base Soil Green Trees

Flowering Trees

0

10

20

30

40

408

.4

443

.2

479

.2

515

.2

551

.4

588

.6

625

.9

663

.3

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.6

738

.2

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.1

814

.3

852

.4

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.5

928

.7

966

.9

Ref

lect

ancy

(%

)

Wavelength (nm)

2014 Green Trees Base Soil

Flowering Trees Brown Trees

Spectral linear unmixing

Change vector analysis (CVA)

Ecosystem services quantification

Air- and space-borne imaging

& in situ observations

Ecosystem services quantification

Accuracy assessment

Ecosystem services quantification

Class

Ground Truth

Data Accuracy

(%) 2013 2014

Flowering in

February 2013

71 34 67.60

Flowering in

January 2014

27 78 74.30

Overall 70.95

Disease mapping

Animal tracking to understand their migration routes in order to

understand rift valley fever (RVF) outbreak

Seasonal pattern of livestock, disease vectors and ecological

variables enhanced our understanding of how, where and why

certain diseases occur and spread

At the end something like “Income increase and food

insecurity reduced through support to economic growth”

Disease mapping

Animal tracking to understand their migration routes in order

to understand RVF outbreak

Land dynamics

- Land cover and land use

- Vegetation seasonality

- Population and stocking

density

- Fodder availability and quality

- Flooding patterns

- Water body density and

distribution

Livestock Degradation

Constraints & Challenges: data availability, secondary or primary,

feature behavior

Climate, weather

Factors that determine occurrence, vector trajectories,

animal migration as qualitative and quantitative data linkages

Land form

Land dynamics

Cropland and irrigation expansion

Vegetation production and trends

Vegetation variables: amplitudes, point

of „greening‟

Drought indicators

Flooding regime

Livestock Degradation

Constraints & Challenges: data availability, secondary or primary,

feature behavior

Climate, weather

Factors that determine occurrence, vector trajectories, animal

migration as qualitative and quantitative data linkages

Land form

GPS positions

and movements

of two herds

(orange and red)

overlaid on a

satellite derived

map showing

inundation

patterns for 2012

near to permanently flooded

4-6 months of flooding /year

< 4 months of flooding /year

Current work in NE Kenya

Disease mapping

Vector diversity, animal

serological sampling points and

animal migration routes

Vector sampling sites, ecological

variables and animal migration

routes

Mapping land use dynamics and RVF in Baringo, Isiolo

and Garisa districts

• Understanding dynamic drivers of disease is vital in

understanding how to manage outbreaks, new niches that are

developing and coming up with preventive measures

• According et al. (1992) and Linthicum et al. (2001) RVF occurs

in:

– soil types-solonetz, solanchaks, planosols

– Elevation-less than 1100m asl

– Vector-Aedes,Culicine, and others

– In cycles of 5 to 15 years of heavy rainfall and flooding

especially in arid and semi-arid low lying flat landscape

areas with accumulation of flood water in depressions

known as „dambos‟and has been connected to El

Niño/Southern Oscillation

– Dense vegetation cover persistent for 3 months –NDVI

greater than 0.1

Data used

Spread of RVF outbreak over the years

1912 -1931 1951-1954

1961-1964 2006-2007

Mapping RVF niches using RS

According to Hightower et al.

2012 solonetz, calcisols,

solonchaks and planosols area

associated with RFV because of

its ability to retain water for

long hence providing breeding

ground for mosquitoes

Garisa is largely covered by

solonetz soil type

-Hydro tools was used to

generate the drainage pattern

of the study area using 30m

DEM

-Rivers developed from

200,000m2 drainage area

-Sinks indicate area where

water can collect e.g dambos

-Generated using Hammond

landform formula

-Garisa and Isiolo is largely

flat

-Baringo varies from plains

with high mountains to nearly

flat plains

Case study of Somaliland

Conclusions

Successful applications of GIS/ RS (Spatial

modeling) in addressing icipe‟s R&D themes

The results/ outputs of these projects (e.g.,

land productivity and flower maps) could be

use to assist researchers and policy makers in

taking informed decisions regarding issues of

food security that uplifting livelihood of people

Acknowledgements

Thank you