VEGETATION

A STUDY OF THE INTEGRATION OF VEGETATION IN RESIDENTIAL BUILDINGS IN BENIN CITY, EDO STATE

Year of Publication
upload
Publication Type
Abstract
The earliest vertical gardens extend back 2000 years to the Mediterranean region, and ornamental roof gardens were first created by the civilizations that inhabited the basins of the Tigris and Euphrates rivers (the most famous examples of which were the Hanging Gardens of Babylon in the seventh and eight centuries B. C.). Northern European regions, such as Norway's sod roofs or the Mediterranean basin's climbing plants for shading vertical surfaces, have a number of examples of green roofs and façades dating to the 18th and 19th centuries. To support sustainable construction practises, modern building envelopes also contain cutting-edge materials and other technologies.
Supervisor(s)
co-supervisor

SPATIO-TEMPORAL CHANGE DETECTION ANALYSIS OF VEGETATION COVER IN EDO STATE

Year of Publication
Publication Type
Abstract
The degradation of vegetated lands due to modernization, agricultural expansion, and climate change has become a growing environmental concern in Edo State, Nigeria. Vegetation plays a critical role in sustaining biodiversity, regulating local climate, reducing soil erosion, and supporting livelihoods through agriculture and forest resources. However, rapid population growth, increasing demand for land, and infrastructural development have intensified pressure on natural vegetation across the state. This study aims to map and monitor the spatio-temporal dynamics of vegetated lands in Edo State using satellite remote sensing data within the Google Earth Engine (GEE) platform. Multi-temporal satellite imagery of Edo State was acquired and preprocessed using Moderate Resolution Imaging Spectroradiometer (MODIS) data as the primary source. Vegetation indices, particularly the Normalized Difference Vegetation Index (NDVI), were computed to classify and map vegetated areas and to evaluate vegetation health and density over time. Time-series analysis and pixel-based classification techniques were applied to assess vegetation patterns and to detect changes in vegetation cover between 2015, 2020, and 2025. The NDVI-derived vegetation classes were categorized into dense vegetation, sparse vegetation, and non-vegetated or built-up surfaces to enable clearer interpretation of vegetation transformation.
Supervisor(s)
co-supervisor