Mapping and characterizing mangrove rice growing environments in West-Africa using remote sensing and secondary data

O. Adefurin, M Hamdy, S.J. Zwart

Research output: Contribution to conferencePosterOther research output

44 Downloads (Pure)

Abstract

Rice is one of the major staple foods consumed in Africa and its demand continues to increase as a result of population growth, urbanization and changing diets. Mangrove rice cultivation is of importance along the West-African Atlantic coast from Senegal and Gambia down to Guinea-Bissau, Guinea-Conakry, Sierra Leone and Liberia. Although mangrove rice productivity is low it contributes for a major share to the regional rice production. Sea-level rise and reduction in river discharges, caused by the effects of climate change, lead to salt-water intrusion and are a potential threat to the mangrove rice production and regional food security.
Information about rice areas is crucial to provide informed decision and management with the aim of safeguarding and improving rice production in those areas. However, till date such information is very limited or unavailable at all. Therefore our goal in this study was to map out rice cultivated areas within the mangrove ecosystem stretching from Senegal to Liberia and to characterize those systems in terms of altitude and the rice phenology using secondary data and spatial analysis.
We deployed off-season Landsat 8 Images, 30 meters SRTM Digital elevation data, derived vegetation indices (EVI, SAVI, Tasseled cap Index Wetness) and Google Earth data. Decision tree classification was then applied using the DEM and Tasseled Cap index on Wetness to delineate the Landsat data into uplands and mangrove lowlands. Then, supervised classification using the ‘maximum likelihood’ classifier was applied on the delineated mangrove lowlands to characterize the area into rice and non-rice. The classification result was validated with Google Earth images. The second stage in this work was to investigate the rice phenology using MODIS NDVI time series. Overall, the study shows the potency of using medium resolution satellite images like Landsat for characterizing mangrove rice growing environment.
Original languageEnglish
Publication statusPublished - 2016

Fingerprint

mangrove
rice
remote sensing
Landsat
phenology
West Africa
Shuttle Radar Topography Mission
salt water
image classification
vegetation index
food security
river discharge
spatial analysis
NDVI
MODIS
digital elevation model
population growth
urbanization
time series
diet

Cite this

@conference{061721530fe84a52ad9b9c5d5318e18f,
title = "Mapping and characterizing mangrove rice growing environments in West-Africa using remote sensing and secondary data",
abstract = "Rice is one of the major staple foods consumed in Africa and its demand continues to increase as a result of population growth, urbanization and changing diets. Mangrove rice cultivation is of importance along the West-African Atlantic coast from Senegal and Gambia down to Guinea-Bissau, Guinea-Conakry, Sierra Leone and Liberia. Although mangrove rice productivity is low it contributes for a major share to the regional rice production. Sea-level rise and reduction in river discharges, caused by the effects of climate change, lead to salt-water intrusion and are a potential threat to the mangrove rice production and regional food security.Information about rice areas is crucial to provide informed decision and management with the aim of safeguarding and improving rice production in those areas. However, till date such information is very limited or unavailable at all. Therefore our goal in this study was to map out rice cultivated areas within the mangrove ecosystem stretching from Senegal to Liberia and to characterize those systems in terms of altitude and the rice phenology using secondary data and spatial analysis. We deployed off-season Landsat 8 Images, 30 meters SRTM Digital elevation data, derived vegetation indices (EVI, SAVI, Tasseled cap Index Wetness) and Google Earth data. Decision tree classification was then applied using the DEM and Tasseled Cap index on Wetness to delineate the Landsat data into uplands and mangrove lowlands. Then, supervised classification using the ‘maximum likelihood’ classifier was applied on the delineated mangrove lowlands to characterize the area into rice and non-rice. The classification result was validated with Google Earth images. The second stage in this work was to investigate the rice phenology using MODIS NDVI time series. Overall, the study shows the potency of using medium resolution satellite images like Landsat for characterizing mangrove rice growing environment.",
author = "O. Adefurin and M Hamdy and S.J. Zwart",
year = "2016",
language = "English",

}

Mapping and characterizing mangrove rice growing environments in West-Africa using remote sensing and secondary data. / Adefurin, O.; Hamdy, M; Zwart, S.J.

2016.

Research output: Contribution to conferencePosterOther research output

TY - CONF

T1 - Mapping and characterizing mangrove rice growing environments in West-Africa using remote sensing and secondary data

AU - Adefurin, O.

AU - Hamdy, M

AU - Zwart, S.J.

PY - 2016

Y1 - 2016

N2 - Rice is one of the major staple foods consumed in Africa and its demand continues to increase as a result of population growth, urbanization and changing diets. Mangrove rice cultivation is of importance along the West-African Atlantic coast from Senegal and Gambia down to Guinea-Bissau, Guinea-Conakry, Sierra Leone and Liberia. Although mangrove rice productivity is low it contributes for a major share to the regional rice production. Sea-level rise and reduction in river discharges, caused by the effects of climate change, lead to salt-water intrusion and are a potential threat to the mangrove rice production and regional food security.Information about rice areas is crucial to provide informed decision and management with the aim of safeguarding and improving rice production in those areas. However, till date such information is very limited or unavailable at all. Therefore our goal in this study was to map out rice cultivated areas within the mangrove ecosystem stretching from Senegal to Liberia and to characterize those systems in terms of altitude and the rice phenology using secondary data and spatial analysis. We deployed off-season Landsat 8 Images, 30 meters SRTM Digital elevation data, derived vegetation indices (EVI, SAVI, Tasseled cap Index Wetness) and Google Earth data. Decision tree classification was then applied using the DEM and Tasseled Cap index on Wetness to delineate the Landsat data into uplands and mangrove lowlands. Then, supervised classification using the ‘maximum likelihood’ classifier was applied on the delineated mangrove lowlands to characterize the area into rice and non-rice. The classification result was validated with Google Earth images. The second stage in this work was to investigate the rice phenology using MODIS NDVI time series. Overall, the study shows the potency of using medium resolution satellite images like Landsat for characterizing mangrove rice growing environment.

AB - Rice is one of the major staple foods consumed in Africa and its demand continues to increase as a result of population growth, urbanization and changing diets. Mangrove rice cultivation is of importance along the West-African Atlantic coast from Senegal and Gambia down to Guinea-Bissau, Guinea-Conakry, Sierra Leone and Liberia. Although mangrove rice productivity is low it contributes for a major share to the regional rice production. Sea-level rise and reduction in river discharges, caused by the effects of climate change, lead to salt-water intrusion and are a potential threat to the mangrove rice production and regional food security.Information about rice areas is crucial to provide informed decision and management with the aim of safeguarding and improving rice production in those areas. However, till date such information is very limited or unavailable at all. Therefore our goal in this study was to map out rice cultivated areas within the mangrove ecosystem stretching from Senegal to Liberia and to characterize those systems in terms of altitude and the rice phenology using secondary data and spatial analysis. We deployed off-season Landsat 8 Images, 30 meters SRTM Digital elevation data, derived vegetation indices (EVI, SAVI, Tasseled cap Index Wetness) and Google Earth data. Decision tree classification was then applied using the DEM and Tasseled Cap index on Wetness to delineate the Landsat data into uplands and mangrove lowlands. Then, supervised classification using the ‘maximum likelihood’ classifier was applied on the delineated mangrove lowlands to characterize the area into rice and non-rice. The classification result was validated with Google Earth images. The second stage in this work was to investigate the rice phenology using MODIS NDVI time series. Overall, the study shows the potency of using medium resolution satellite images like Landsat for characterizing mangrove rice growing environment.

M3 - Poster

ER -