mapping rice and rice growing environments in west-africa using remote sensing and spatial modelling...

Mapping rice and rice growing

environments in West-Africa using

remote sensing and spatial

modelling tools

Sander Zwart

AS El-Vilaly, JF Djagba, S Steinbach, F Holecz

4th International Rice CongressBangkok, Thailand, Oct 27 – Nov 1, 2014

1. Importance of rice production for food security

in West-Africa

2. Rice production environments

3. Strategy for rice mapping in West-Africa

4. First results

5. Challenges for rice mapping


Rice and food security in West-Africa

Rice production and consumption in Africa (1970-2010) Source: FAOSTAT, AfricaRice



Contribution of various staple crops in diets in West-Africa

(1961-2010)Source: FAOSTAT, AfricaRice



Challenges for food security:

(West-)Africa is by far not self-sufficient and

depends on international markets.

Climate change is impacting W-Africa strongly;

less rainfall and more erratic and intense rainfall

events, lower river discharges, floods.



Why do we need creating maps and statistics of

rice?

• (sub-)national rice statistics are very unreliable

or absent in Africa

• Understanding where rice is for efficient

targeting of technologies, interventions and

actions


Rice production environments

Rainfed upland

and lowland

Smallholder fields

Intercropping

Fragmented landscape

Inland valleys / wetlands

Very dynamic

One rice crop

Irrigated rice

Large-scale

systems

Gradual expansion

Two seasons

Mangrove rice

Cleared lands in forested areas

Stable systems

One season only


Rice production environments

Differences between Asian and African rice

landscapes

Asia Africa

Irrigated rice (80%) upland rainfed

lowland rainfed

lowland irrigated (~10%)

Stable area Dynamic & expanding

30% of arable land 4% of arable land

Contiguous rice areas Fragmented

Paddy land preparation Dry land preparation

High fertilizer inputs Low fertilizer inputs


Strategy for rice mapping in West-Africa

Rainfed upland

and lowland

Radar RS

Spatial modelling

Random Forest

Irrigated rice

Optical RS

Radar RS

Mangrove rice

off-season Landsat

GoogleEarth interpretation


Strategy for rice mapping in West-Africa

An assessment of the rice growing areas in

planned using data no older than 5 years.

Irrigated rice: Landsat 8 imagery, supervised

classification

Use of radar imagery planned

Mangrove rice: Landsat 8 imagery (off-season)

GoogleEarth interpretation

Rainfed systems: spatial modelling

Random Forest

Radar imagery4th International Rice CongressBangkok, Thailand, Oct 27 – Nov 1, 2014

First results – irrigated rice

Pilot testing of radar remote sensing in two hubs:

Cosmo-SkyMed imagery is acquired every 16

days during rice season

Spatial resolution of 3m

Senegal: irrigated rice conditions (July-December)

Benin: upland and lowland rice (June-december)

Goals: mapping rice and assessing crop

phenology dates (SoS and harvest)

Field validation collected (500 points)


First results – irrigated rice

Preliminary results December 2013 (green is rice)


First results – mangrove rice

1. Visual interpretation and digitization in

GoogleEarth (2010-2014 high resolution

satellite images)

2. Remove water and mangrove forest patches

using off-season NDVI maps derived from

Landsat 8 imagery

Implemented in Senegal, The Gambia, Guinea-

Bissau, Guinea-Conakry, Sierra Leone, Liberia

Total of 11 Landsat scenes


First results – mangrove rice


First results – mapping inland valleys / wetlands

Inland valley or wetlands (irrigated and rainfed

lowland)

• Areas suitable for rice production due to

favorable hydrological conditions

• Important for current and future rice production



stream

20 202121

23 2324

25m2524 altitude (m)

30m

Digital Elevation Model(2-dimensional)


Selected inland valley bottom

1 m threshold

Spatial analysis – Mapping inland valleys

Remote Sensing – Beyond Images14-15 December 2013, Mexico City


Validation

Omission/comission errors, accuracy and area

estimation/comparison



Currently only 10% cultivated (official stats)

Mapping rice in the inland valleys using remote

sensing classification is (currently) impossible:

• valley size

• heterogenous agricultural landscape

• image resolution

• extent

• dynamics



Random Forest

Machine learning technique based on the

construction of decision trees that can be used for

regression or classification purposes

Predict the presence of rice cultivation in the inland

valleys.



• Collection of data on inland valleys and

presence or non-presence of rice or agriculture

• Building geo-spatial data bases containing:

Road networks, villages, travel distance, markets

(inputs and outputs), population density, inland

valleys, soil types, water availability, rainfall (remote

sensing), etc.

• Implementation in Sierra Leone, Liberia, Benin

and Mali


Challenges for rice mapping

• Rainfed upland agriculture might be too

fragmented, too small scall-scale, too dynamic,

to be able to identify.

• Skilled person-power

- Very few young people are educated in

GIS/RS

- (Almost) no experience with radar remote

sensing.


Thank you! Merci!

Center of Excellence for Rice Research


mapping rice and rice growing environments in west-africa using remote sensing and spatial modelling...

Presentations & Public Speaking

mapping rice

statistics of rice

westafrica rice production

importance of rice production

rice seasonspatial resolution

subnational rice statistics

westafrica challenges

westafrica rainfed upland