Urbanization and Other Land Use Land Cover Change Assessment in the Greater Kumasi Area of Ghana

Urbanization posits the expression of urban expanse expansion due to population growth, rise in built-up areas, high population density and its correspondingly urban way of life. Unrestrained impetus of development and land use land cover change (LULCC) portent several issues such as unlawful urban sprawl, loss of agricultural land, forest loss and other associated complications. This study analyzed the dynamics of urbanization and other LULCC in Ghana’s Greater Kumasi area via Landsat images (TM 1986, OLI 2013 and OLI 2023) using ERDAS Imagine, Idrisi and ArcGIS software. Implementing supervised classification technique, the Maximum Likelihood Classifier (MLC) procedure was employed to categories the study area into five LULC classes. Accuracy assessment undertaken on the resultant LULC maps was deemed very satisfactory. The results from 1986-2023 pointed to an upsurge in a built-up extent as of 8% to 41%, a decrease in Closed Forest from 9% to 4%, another decrease in Open Forests from 64% to 33%, a slight increase from 16% to 20% in farmlands and a stable level of water share. Further analysis indicated that the study area had undergone LULCC within the periods 1986-2013 and 2013-2023 at 60% and 37% respectively. The findings showed uncontrolled urban sprawling along major roads and forest loss as deforestation outside protected areas and degradation in protected forest. The monitoring of urbanization and other LULCC is important for local, and national governments and other bodies charged with the implementation of programs and policies that manage and utilize natural resources. Development adapts to mitigate the effect on the environment.

Assessment of Land Use and Land Cover Change in Southwest Mauritania, Remote Sensing and GIS Approach

Most countries’ land use and land cover (LULC) are changing dramatically today. Most of these changes are related to the way humans and the environment interact. Various methodologies and data sources have been used in conjunction with remote sensing (RS) to categorize and map changes in LULC. This study used RS and Geographic Information System (GIS) tools to analyze LULC change and transitions from 1984 to 2022 in a tropical forested landscape in southwest Mauritania. Using a suitable and high-quality collection of Landsat satellite images. For the classification and creation of LULC maps for the selected periods, the supervised technique using a maximum likelihood classifier was used. The results indicated that there was a remarkable change in all classes of LULC, with an increase in all classes, except barren land, which had a tremendous decrease of −68.58% for the total study area. Therefore, for the total study area, an increase in agricultural land (221%), water bodies (118.46%), vegetation (57.50%), and built-up areas (14.65%) was observed. We believe that by informing policymakers, environmental managers, and the general public about the current changes, our study will help the region to establish appropriate land use rules that may lead to policy document development.

Multilocus Phylogeny of Lycodon and the Taxonomic Revision of Oligodon multizonatum

Classification of the Asian snake genera Lycodon and Oligodon has proven challenging. We conducted a molecular phylogenetic analysis to estimate the phylogenetic relationships in the genus of Lycodon and clarify the taxonomic status of Oligodon multizonatum using mitochondrial（cyt b, ND4） and nuclear（c-mos） genes. Phylogenetic trees estimated using Maximum Likelihood and Bayesian Inference indicated that O. multizonatum is actually a species of Lycodon. Comparing morphological data from O. multizonatum and its closest relatives also supported this conclusion. Our results imply that a thorough review of the evolutionary relationships in the genus of Lycodon is strong suggested.

Effects of atmospheric correction and pansharpening on LULC classification accuracy using WorldView-2 imagery

Changes of Land Use and Land Cover(LULC)affect atmospheric,climatic,and biological spheres of the earth.Accurate LULC map offers detail information for resources management and intergovernmental cooperation to debate global warming and biodiversity reduction.This paper examined effects of pansharpening and atmospheric correction on LULC classification.Object-Based Support Vector Machine(OB-SVM)and Pixel-Based Maximum Likelihood Classifier(PB-MLC)were applied for LULC classification.Results showed that atmospheric correction is not necessary for LULC classification if it is conducted in the original multispectral image.Nevertheless,pansharpening plays much more important roles on the classification accuracy than the atmospheric correction.It can help to increase classification accuracy by 12%on average compared to the ones without pansharpening.PB-MLC and OB-SVM achieved similar classification rate.This study indicated that the LULC classification accuracy using PB-MLC and OB-SVM is 82%and 89%respectively.A combination of atmospheric correction,pansharpening,and OB-SVM could offer promising LULC maps from WorldView-2 multispectral and panchromatic images.