HEIGHT-DIAMETER MODELS FOR PREDICTION AND FOREST COVER CHANGE IN AN UNEVEN-AGED STAND IN UNIVERSITY OF BENIN, EDO STATE

ABSTRACT

Forest cover change is a critical environmental issue globally, and Nigeria is no exception, rapid urbanization, agricultural expansion, and logging activities have led to significant deforestation and degradation of forest ecosystems. This study was carried out to show the application of height-diameter models to predict forest structure and forest cover change in an uneven-aged stand at the University of Benin. The main objective of this research project is to identify and apply suitable height-diameter models for prediction and to determine the forest cover change in the arboretum over a period of 32 years for the effective management of the mixed forest stand in University of Benin. While the specific objective include to ascertain the distribution pattern of the trees using nearest neighbor analysis. One hundred and seventy three (173) trees were enumerated and subjected for analysis in this study. Total enumeration of the trees was done due to the size of the study area and trees with diameter greater than 10cm were used for this study. From the data collected, Newton’s volume and the basal area was calculated. Five (5) height -diameter regression models and six (6) volume models were used for this study. The data were analyzed using descriptive statistics, ArcGIS and Excel Data Analysis tool. The result indicated that the Curtis model was fit poorly while the Wykoff model emerged as the best fit for predicting tree height based on diameter at breast height (DBH). For the Normalized Difference Vegetation Index (NDVI) analysis, it revealed a significant increase in vegetation density and biomass in the arboretum, suggesting positive forest growth over the period of 32 years (1991 to 2002 to 2023). 2indicating an increase in biomass and vegetation content. While for the Nearest Neighbor Analysis indicated a dispersed spatial distribution of trees, potentially reflecting human influence on stand development. A larger dataset should be used for this study to achieve better predictive performance and reduces estimation variance. Also collaboration should be encouraged with other research institutions, universities and forestry organizations to expand the scope of research and address emerging challenges in forest ecology and management.