Forest canopy density analysis of Sokpomba Forest Reserve, Edo State

Forest is a dynamic landscape especially in the tropics as a result of high anthropogenic activities. This study therefore, attempts to evaluate the changes in forest canopy density sequel to the interaction between man and forest ecosystem in Sokpomba Forest Reserve from 1990 to 2020. Relevant Remote Sensing and GIS algorithms were used at different levels of this study. Landsat images formed the major input data for the analysis. In addition to the satellite images, ground control points (GCP) picked with the aid of Global Positioning System (GPS) were used to calculate the accuracy assessment of the Forest Canopy Density (FCD) analysis. The high canopy density (HD) decreased from 320.82km2 in 1990 to 292.82km2 in 2020. Conversely, the low canopy density (LD) increased from 171.12km2 in 1990 to 282.82km2 in 2020. The transitioning of the different Forest Canopy Densities from one category to another was also captured in this study. For instance between 2005 and 2020, about 37 km² changed from low density (LD) to no forest (NF). The accuracy assessment shows that the image classification is good in the sense that the Overall Accuracy figures are 69% (1990), 84% (2005) and 85% (2020). This forest modeling technique is very apt when it comes to the monitoring of forest cover dynamics, forest disturbance and ways of mitigating them. Key words: Geographic Information System, Remote sensing, Forest changes, Landsat, FCD, classification, anthropogenic and  urbanization

Assessment of Land Use/Cover Change Using Remote Sensing and GIS Techniques: A Case of Osogbo and Its Peripheral Areas in Nigeria

The importance of accurate and timely information describing the nature and extent of land resources and changes over time is increasing, especially in rapidly growing city areas. Landsat satellite imageries of three different time periods, i.e., Landsat Thematic Mapper (TM) of 1982, 2000 and 2018 were acquired by Global Land Cover Facility Site (GLCF) and earth explorer site, quantify the changes in the Osogbo and its peripheral areas from 1982 to 2018 over a period of 36 years. These data sets were imported in ArcGIS 10.3, ERDAS Imagine and IDRIS Selva, satellite image processing softwares to create a false colour composite (FCC), supervised classification methodology was employed using maximum likelihood technique. The images of the study area were categorized into four different classes namely Core-urban, Peri-urban, Vegetation, water body. The results indicate that during the last thirty-six (36) years, Core-Urban land and water body have been increased by 2.74% (38.20 km2) and 0.98% (13.69 km2) while Peri-Urban land, and vegetation cover have decreased by 0.35% (5.00 km2), and 3.36 % (46.87 km2), respectively. The results quantify the land cover change patterns in the city and its peripheral area and demonstrate the potential of multitemporal Landsat data to provide an accurate, economical means to map and analyse changes in land cover over time that can be used as inputs to land management and policy decisions

Remote sensing and GIS forest landscape assessment for sustainable forest management

This study set out to assess the dynamic characteristics of the Ikere forest reserve landscape between 1985 and 2017 using remote sensing data and spatial metrics. Landscape of the study area maintained complex patterns of spatial heterogeneity over the years. Forest cover loss to other land cover types results in new large non-forest area at increasing rate. As at the year 2017, the changes in land cover types were not yet at equilibrium, thus the need to determine the future forest cover extent using a three-way markov Chain model. The decrease in number of patches of forest land (NumP) with increase in its mean patch size (MPS) shows that the forest is becoming a single unit probably due to clearing of existing patches of forest trees. The decrease in class diversity and evenness (SDI and SEI) of the general landscape over the years strengthens this assertion. The findings of this study would be very helpful to government and other stakeholders responsible for ensuring sustainable forest and general environment. Keyword: Landscape, Spatial metrics, sustainable forest and Environmen