Analysis of Land Cover Classification in Arid Environment: A Comparison Performance of Four Classifiers

Mustapha, M. R.; Lim, H. S.; MatJafri, M. Z.

doi:10.5772/33679

Cited by 1 publication

(1 citation statement)

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“…Supervised classification of the Landsat imagery was conducted using Artificial Neural Networks (ANN), which has been shown to produce more accurate results in complex landscapes, such as savanna type environments, compared to conventional classification methods [27][28]. ANN is a multilayer perceptron and trained using the backpropagation method, a commonly used error-minimization technique.…”

Section: Artificial Neural Network Classificationmentioning

confidence: 99%

Spatio-temporal Analysis Dynamics of the Landscape in the Classified Forest of Koulbi, Southwestern of Burkina Faso

Sanon¹,

Hien²,

Tankoano³

et al. 2019

IJNREM

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Most of the protected areas in Burkina Faso are affected by deforestation and forest degradation. This study is done in order to analysis the spatial and temporal dynamics of the classified forest of Koulbi and to identify the drivers of deforestation in the forest. For that, three Landsat images from 1986, 1998 and 2014 were exploited. The supervised classification based on the artificial neural network algorithm, under the ENVI software, was used. A transition matrix was established to analyze land use/land cover changes. The standardized precipitation index of 1985 to 2014 was used to access climate trend. Survey data from riverside villages and field observations were used to assess the human impact. The results show that between 1986 and 2014, the woodlands and tree savannas areas were reduced by 7% and 0.49% per year, respectively. However, substantial yearly increases in the areas of shrub savanna (2.8%) and agricultural fields (1.4%) were observed. During this period, the rainfall had a sharp trend towards aridity, with 18 years of drought against 12 years of humidity. This show that deficit of rainfall is increase. The socioeconomic survey revealed that the classified forest used to be highly occupied and 64% of the inhabitants were yam producers. Cattle breeders and gold diggers have also been observed. We conclude that human activities are the main cause of degradation of the vegetation cover of the Koulbi classified forest. And climatic factors to some extent. It will be necessary to take actions to better conservation.

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Section: Artificial Neural Network Classificationmentioning

confidence: 99%