Modeling diameter distribution of forest stands requires suitable function(s) with the appropriate parameter estimation methods. To date, the parameters of most familiar functions in forestry have been estimated with derivative methods such as moment, percentiles, conditional maximum likelihood, etc. with little emphasis on numerical optimization. Therefore, this study compares the suitability of derivative and numerical methods to estimate diameter distribution of forest stands. The derivative and optimization methods were used to fit six commonly used functions in forestry including Weibull (2 and 3-parameter), Johnson’s SB, beta, generalized beta and gamma. The data comprised 303 and 96 permanent sample plots temperate forest (Eucalyptus globulus and Pinus radiata, respectively) and 65 temporary sample plots from tropical forest (Gmelina arborea). Different indices such as Kolmogorov–Smirnov, Cramer-von Mises and mean squared error were used to assess the methods. The results show that the derivative method by moments provided the best fit for beta and Johnson’s S_B distributions. The optimization methods (“lifereg” and “optim”) were more suitable for the Weibull and gamma distributions. Both methods were appropriate for the generalized beta distribution. Abbreviation: PW2P: Percentile Weibull two-parameter; MW2P: Moments Weibull two-parameter; MLLW2P: Maximum likelihood “lifereg” Weibull two-parameter; MLOW2P: Maximum likelihood “optim” Weibull two-parameter; PW3P: Percentile Weibull three-parameter; MW3P: Moments Weibull three-parameter; MLLW3P: Maximum likelihood “lifereg” Weibull three-parameter; MLOW3P: Maximum likelihood “optim” Weibull three-parameter; CMLJ: Conditional maximum likelihood Johnson’s SB; MJ Moments Johnson’s SB; MLOJ: Maximum likelihood “optim” Johnson’s SB; MB: Moments beta; MLOB Maximum likelihood “optim” beta; MGB: Moments generalized beta; MLOGB Maximum likelihood “optim” generalized beta; MG2P: Moments gamma two-parameter; MLOG2P: Maximum likelihood “optim” gamma two-parameter

Tree height–diameter relationship can be used as an important input component in forest growth and yield models, and description of stand dynamics. Five nonlinear growth functions were fitted to tree height–diameter data of a 21-year-old Gmelina arborea plantation in Ibadan, Nigeria. The data consisted of three sets obtained in 2004, 2008 and 2011 for total tree height and diameter at breast height (DBH). According to the model statistics, the five growth functions fitted the data equally well, but resulted in different asymptote estimates. Modified exponential fit was observed to give the best fit for the three data sets based on least square error, coefficient of determination and significance. The predicted values follow the same nonlinear pattern and formed close to the line of best fit without much outlier. The result of this study revealed that the ability of DBH in determining height is not strong enough based on the model's goodness of fit and the model's ability for predictive purposes. Hence, more variables such as age, crown area and soil fertility were recommended to be incorporated in future prediction of the tree height in the study area.

Vegetation covers is a significant component of biogeochemical cycles. Derived savannah of Ogun State has been affected by vegetation loss and climate change in recent times. There is lack of information on the rate/extent of vegetation loss in the last two decades. This study assessed changes in vegetation cover in derived savanna ecosystem of Ogun State from 2002 to 2023. Landsat images were downloaded from the repository of the United States Geological Survey (USGS). Composites of red, green, blue and near-infra-red spectral bands of study period were obtained and classified using Maximum Likelihood (ML) algorithm into Land Use Land Cover (LULC) categories as follows: bare soil; built-up areas; forest; and grassland. Change in area extent and rate of change of area of classified images were determined for the study period. Confusion matrix of classified images were generated and compared with Google Earth satellite image with accuracy assessed using kappa coefficients. Overall accuracy of the classified images ranged between 79% and 88% with kappa coefficients of between 0.71 and 0.83. Results showed that built-up area increased from 10.3% cover in 2002 to 35.9% cover in 2023. However, there was a significant decline in forest cover from 31.5% to 13.7% for the same period. Significant increase at 4.2 km2 per year in area extent was observed for built-up LULC class while a decline of 2.0 km2 per year in forest cover was recorded for Forest LULC category from 2002 to 2023. The study revealed that urbanization increased as extent of initial forest cover were degraded and replaced with physical infrastructure. Therefore, there is urgent need for policies that promote conservation and sustainable management of forests and grasslands, as well as measures to promote green infrastructure and urban greening initiatives to address the decline in vegetation cover in the derived savanna ecosystem of Ogun State.

Volume estimation is a crucial part of every forest or plantation because it provides the quantity of timber or wood available at a given period and predicts future or expected growth of the forest. Unfortunately, local volume table are not readily available for some–economically important tree species in Nigeria. Hence, it is of paramount importance that local volume tables for economic tree species such as Pinus caribbaea in Area J4, Omo Forest reserve, Ogun state, Nigeria. Aim: The aim of this study is to develop a local volume table for Pinus caribbaea in Area J4, Omo Forest Reserve, Ogun State, Nigeria. Study Design: Simple random sampling was used for this study. Ten Temporary Sample Plots (TSP) of equal size 25m x 25m were randomly located in the selected plantation (P. caribbaea: established in 1997). All the trees with diameter at breast height (dbh) ≥ 10cm in each TSP were enumerated. Place and Duration of Study: The study was carried out in the Pinus caribbaea plantation in Area J4, Omo forest reserve, Ogun State from November 9^th to 20^th, 2023. Methodology: The total tree height, merchantable height, diameters at the top, middle, base, and dbh were measured and used to estimate stand volume. A volume table was developed using five selected models, and the best model was selected using least Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and Root Mean Square Error (RMSE). Data were analyzed using descriptive statistics and regression at α_0.05 with R statistical software. Results: The results obtained in P. caribbaea plantation revealed that the dbh and height were 27.93±6.95 cm and 28.72±3.47 m, respectively, while individual tree volume was 0.83±0.57m³. The combined variable model ( was selected as the best model to develop local volume table for P. caribbaea (AIC = -54.73, BIC = -45.49 and RMSE = 0.20). The statistical metrics implied a suitable selected model which can be use for future predictions with minimal error. Conclusion: The developed volume table for estimating the quantity of wood or timber available in the Pinus caribaea plantation at a given time. The volume model and local volume table developed are to be applied only to the Pinus caribaea plantation in the study area for continuous estimation and valuation of the species.