Mapping rice and rice growing environments in West-Africa using remote sensing and spatial modelling tools

Mapping rice and rice growing

environments in West-Africa using

remote sensing and spatial

modelling tools

Sander Zwart

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

Rice and food security in West-Africa

Rice production and consumption in Africa (1970-2010)

Rice and food security in West-Africa

Contribution of various staple crops in diets in West-Africa (1961-2010)

Rice and food security in West-Africa

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.

Rice and food security in West-Africa

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

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 imagery

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)

First results – irrigated 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 – mapping inland valleys / wetlands

Inland valley or wetlands (irrigated and rainfed

lowland)

• Areas suitable for rice production due to favorable hydrological conditions

First results – mapping inland valleys / wetlands

stream

Digital Elevation Model

(2-dimensional)

Selected inland valley bottom

First results – mapping inland valleys / wetlands

Validation

Omission/comission errors, accuracy and area estimation/comparison

First results – mapping inland valleys / wetlands

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

First results – mapping inland valleys / wetlands

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

First results – mapping inland valleys / wetlands

• 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!