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

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 OPEN ACCESSRemote Sens. 2015, 7 12077 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.

Predicting the Potential Impact of Climate Change on Carbon Stock in Semi-Arid West African Savannas

“…The classification technique used to derive the LULC map for the year 2017 was the random forest (RF) algorithm. This algorithm has been used, e.g., by Forkuor [33], Knauer et al [57] and Zoungrana et al [58], in the Sudanian Savanna of West Africa to analyze LULC dynamics. Previous studies [51,[59][60][61] have compared the performances of different machine learning algorithms (MLAs), such as support vector machines (SVM), regression trees (RTs), Artificial Neural Networks (ANN), stochastic gradient boosting (SGB), and RF.…”

Impact of the Accuracy of Land Cover Data Sets on the Accuracy of Land Cover Change Scenarios in the Mono River Basin, Togo, West Africa

“…Nevertheless it is important to know the validity of LCM outputs based on local expertise (Zoungrana et al, 2015).…”

Using Multi-Temporal Landsat Images and Support Vector Machine to Assess the Changes in Agricultural Irrigated Areas in the Mogtedo Region, Burkina Faso

“…The classification of land use/land cover (LULC) was performed using unsupervised ISOCLASS clustering K-means and decision-tree algorithms [13]. Similarly, Zoungrana et al [15] and Knauer et al [16] used multi-date or multi-temporal Landsat and MODIS images for the detection of changes in LULC (including agricultural lands and irrigated areas) for regions in southwest Burkina Faso in the former study, and for entire Burkina Faso in the latter. In both studies, the classification was carried out using random forest classification approach [15,16].…”

“…Similarly, Zoungrana et al [15] and Knauer et al [16] used multi-date or multi-temporal Landsat and MODIS images for the detection of changes in LULC (including agricultural lands and irrigated areas) for regions in southwest Burkina Faso in the former study, and for entire Burkina Faso in the latter. In both studies, the classification was carried out using random forest classification approach [15,16]. Traoré et al [17] used a different classification technique (i.e., maximum likelihood classifier, MLC) with Landsat imagery and aerial photographs to assess the evolution over time of the irrigated areas in the Kou watershed, Burkina Faso.…”