TITLE:
Spatio-Temporal Dynamics of the Noun Floodplain (Cameroon) Using Remote Sensing
AUTHORS:
Junie Albine Atangana Kenfack, Harold Gwet, Bonaventure Olivier Souley, Rufis Fregue Tagne Tiegam, Paul Tchawa
KEYWORDS:
Cameroon, Land Use, Land Degradation, Remote Sensing, Sustainable Land Management
JOURNAL NAME:
Journal of Geoscience and Environment Protection,
Vol.10 No.10,
October
26,
2022
ABSTRACT: In
Cameroon, the pressure on wetlands appears to be increasing, leading to
desertification and land degradation. This study aims to characterize the
spatial and temporal dynamics of land cover in the Noun floodplain in Cameroon
using multi-date satellite data. To achieve this, the methodology consisted in
using remote sensing and geographical information’s systems techniques to
identify spatial units and detect changes over a 22-year period (1999 to 2021). The land cover maps were
produced from an unsupervised classification with maximum likelihood. The
results identified eight classes: herbaceous savannahs with shrubs, forest
galleries, fields and plantations, herbaceous tan, young fallows, mineralized
and built-up soils, bare soils and surface waters. It appears that in 1999, the
landscape was dominated by natural vegetation (72.6%) located from north to
south of the Noun plain. However, since 2004, the landscape has been dominated
by agricultural areas (56.8%). Natural formations have been progressively
reduced in space over time. The evolution of the Noun floodplain landscape
reveals that 14.3% of the space has remained stable. These are fields and
plantations, young fallows, mineralized soils and surface water. This space has
not migrated to other classes. While about 73.9% of the area has moved to
higher classes, of which 35.6% to herbaceous tans and 26% to fields and
plantations. On the other hand, 72.6% of the area (herbaceous savannahs and
forests gallery) has been heavily degraded. These results show that the
landscape of the Noun floodplain is marked by a progressive agricultural
extension, which would be at the origin of the land degradation. Therefore they
alert the different actors in the territory on the level of advanced land
degradation and suggest sustainable land management on a local scale.