Land Use Landcover Change Monitoring and Projection in the Dano Catchment, Southwest Burkina Faso

Gloria C. Okafor, Thompson Annor, Samuel N. Odai, Isaac Larbi (doi:


Burkina Faso has long been experiencing intense land-use landcover changes (LULCC) which have resulted in widespread land degradation. Hence, the need to obtain LULC information for improved land-use planning and sustainable management of land-resources cannot be overemphasized. This study examined the historical LULCC in the Dano catchment and projected the situation in 2050 for business-as-usual (BAU) and afforestation scenarios. Multitemporal Landsat images of 1990, 2000, 2010 and 2016 were classified with an overall accuracy of more than 90%. The Cellular-Automata Markov approach was used to project the future LULC pattern after identifying major driving forces of LULCC. The results revealed a substantial expansion in settlement and cropland area of about 62% and 6% respectively, which triggered a 15% decrease in forest cover, thus paving the way for severe soil degradation. The increase in cropland, settlement area, water bodies, and the decrease of forest were at an annual rate of 3.8%, 10.5%, 6.97% and 2.53% respectively within the past 26 years. The projected LULC under the BAU scenario revealed further forest loss from 46.72% in 2016 to 38.54%, owing to an extension in agriculture from 38.51% to 46.69%. The afforestation scenario projected a potential increase in forest by 2.13% and a decrease in cropland by 2.09% in the future relative to 2016. This study illustrates the accelerated land degradation and the challenges on ecosystem sustainability of the Dano landscape, hence, appropriate interventions like reforestation, protection measures and policy option in strategic land-use planning are needed to resolve the further loss of forest cover.


Land degradation; Dano catchment; Markov‐cellular automata; Projection; Remote sensing

Full Text: PDF


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.

*2016 Journal Impact Factor was established by dividing the number of articles published in 2014 and 2015 with the number of times they are cited in 2016 based on Google Scholar, Google Search and the Microsoft Academic Search. If ‘A’ is the total number of articles published in 2014 and 2015, and ‘B’ is the number of times these articles were cited in indexed publications during 2016 then, journal impact factor = A/B. To know More: (