What question did this study set out to answer?

To assess deforestation extent and devise policies to mitigate its impact using remote sensing data.

May 16, 2026Open Access

Deforestation Rate Analysis in Bayelsa State From Remotely Sensed Data

Key Points

To assess deforestation extent and devise policies to mitigate its impact using remote sensing data.
Analyzed spatial changes in deforestation from 2008 to 2018 using remote sensing techniques.
Employed Maximum Likelihood Classification (MLC) for land cover analysis with high classification accuracy.
Data obtained from the US Geological Surveys (USGS) database and processed using ArcGIS 10.6.
Vegetation area decreased from 5610.1 km² in 2008 to 5414.0 km² in 2018, representing a 3.49% loss.
Change detection methods indicated significant impacts on the ecosystem, including increased flooding and soil erosion.
High classification accuracy was achieved: 96.48% in 2008 and 94.53% in 2018.

Abstract

Deforestation has been noticed of recent as one of the most intense ecological challenges in the Niger Delta Nigeria where Bayelsa has been at the centre stage. This study area with a human population of 1,704,515 according to National Population census 2006 considered the extent and degree of deforestation between 2008 and 2018 using remote sensing technique. The objectives of the study include: To demonstrate how remotely sensed data can be used to perform spatial, change detection and analysis, to come up with proper policies that can reduce deforestation rate in the study area. This article provides a spatial analytical framework of the rate of deforestation within 10 years in the area using satellite imageries that were geo-referenced and geo-processed in ArcGIS 10.6 to world coordinate system. Land use/land cover change analysis was carried out to know how the land, vegetation, water and other land covers have changed within the period of study. Data sources were obtained from the United States Geological Surveys (USGS) database. Change detection was used to analyze the rate of deforestation in the area by integrating the analytical tools of geographic information systems (GIS) with remote sensing. A very high classification accuracy of 96.48% in 2008 imagery and 94.53% in 2018 was recorded. Training samples were used and Maximum Likelihood Classification (MLC) in order to estimate the percentage area of these features. The total area of vegetation in 2008(imagery band 7,4,3) was 5610.1km2 while in 2018(imagery band 7,5,3) decreases to 5414.0km2 with 191.1km2 (which is about 3.49% ) vegetation lost within the ten (10) years in the study area indicating deforestation. The impact of this on the ecosystem has been increased flooding, soil erosion as well as habitat loss. Accurate land use planning seems to be an efficient measure to preserve the mangrove swamp and coastal environment. The study therefore amongst others recommended ecosystem management strategies by policy makers to ameliorate the situation.

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