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Remote Sens. 2015, 7(9), 12076-12102; doi:10.3390/rs70912076

Multi-Temporal Landsat Images and Ancillary Data for Land Use/Cover Change (LULCC) Detection in the Southwest of Burkina Faso, West Africa

1
Department of Civil Engineering, Kwame Nkrumah University of Science and Technology, University Post Office Box PMB, Kumasi, Ghana
2
Remote Sensing Unit at the Institute of Geography and Geology, University of Würzburg, Oswald-Külpe-Weg 86, 97074 Würzburg, Germany
3
Department of Physics, Kwame Nkrumah University of Science and Technology, University Post Office Box PMB, Kumasi, Ghana
4
Department of Geography, University of Ouagadougou, 03 B.P. 7021 Ouagadougou, Burkina Faso
5
Competence Center, West African Science Service Center on Climate Change and Adapted Land Use, Ouagadougou BP 9507, Burkina Faso
*
Author to whom correspondence should be addressed.
Academic Editors: Dengsheng Lu, Guomo Zhou, Conghe Song, Guangxing Wang, Ioannis Gitas and Prasad S. Thenkabail
Received: 1 July 2015 / Revised: 28 August 2015 / Accepted: 7 September 2015 / Published: 18 September 2015
(This article belongs to the Special Issue Carbon Cycle, Global Change, and Multi-Sensor Remote Sensing)
View Full-Text   |   Download PDF [2578 KB, uploaded 18 September 2015]   |  

Abstract

Accurate quantification of land use/cover change (LULCC) is important for efficient environmental management, especially in regions that are extremely affected by climate variability and continuous population growth such as West Africa. In this context, accurate LULC classification and statistically sound change area estimates are essential for a better understanding of LULCC processes. This study aimed at comparing mono-temporal and multi-temporal LULC classifications as well as their combination with ancillary data and to determine LULCC across the heterogeneous landscape of southwest Burkina Faso using accurate classification results. Landsat data (1999, 2006 and 2011) and ancillary data served as input features for the random forest classifier algorithm. Five LULC classes were identified: woodland, mixed vegetation, bare surface, water and agricultural area. A reference database was established using different sources including high-resolution images, aerial photo and field data. LULCC and LULC classification accuracies, area and area uncertainty were computed based on the method of adjusted error matrices. The results revealed that multi-temporal classification significantly outperformed those solely based on mono-temporal data in the study area. However, combining mono-temporal imagery and ancillary data for LULC classification had the same accuracy level as multi-temporal classification which is an indication that this combination is an efficient alternative to multi-temporal classification in the study region, where cloud free images are rare. The LULCC map obtained had an overall accuracy of 92%. Natural vegetation loss was estimated to be 17.9% ± 2.5% between 1999 and 2011. The study area experienced an increase in agricultural area and bare surface at the expense of woodland and mixed vegetation, which attests to the ongoing deforestation. These results can serve as means of regional and global land cover products validation, as they provide a new validated data set with uncertainty estimates in heterogeneous ecosystems prone to classification errors. View Full-Text
Keywords: multi-temporal images; mono-temporal image; ancillary data; LULCC; Burkina Faso; West Africa multi-temporal images; mono-temporal image; ancillary data; LULCC; Burkina Faso; West Africa
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Zoungrana, B.J.-B.; Conrad, C.; Amekudzi, L.K.; Thiel, M.; Da, E.D.; Forkuor, G.; Löw, F. Multi-Temporal Landsat Images and Ancillary Data for Land Use/Cover Change (LULCC) Detection in the Southwest of Burkina Faso, West Africa. Remote Sens. 2015, 7, 12076-12102.

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