Tree height prediction models for two forest reserves in Nigeria using mixed-effects approach

Abstract

Height-diameter models for predicting tree height are essential for routine forest inventory. These models can be developed using fixed-or mixed-effects approach. Few studies have applied the mixed-effect approach to developed height prediction model for the natural forest in Nigeria. Therefore, in this study, the mixed-effect modelling approach was used to develop height prediction models for Ikrigon and Cross River South (CRS) Forest Reserves, Nigeria. Data consist of 776 and 438 height-diameter pairs from Ikrigon and CRS Forest Reserves, respectively. Five 2-parameters and five 3-parameters height-diameter models were evaluated including Nalund, Wykoff, Curtis, Meyer, Michaelis-Menten, Chapman-Richards, Ratkowsky, Korf, Logistic and Gompertz. Model fitting was done in two stages: Fixed-effect approach was used in the first stage wherein candidate models were selected and refitted in the second stage using mixed-effect approach. Adjusted coefficient of determination, root mean square error, mean absolute bias, Akaike information and Bayesian information criterion were used to assess the models. The results showed that Gompertz and Meyer models were more consistent. Gompertz and Meyer had adjusted coefficient of determination, root mean square error, mean absolute bias, Akaike information criterion and Bayesian information criterion of 0.

NONLINEAR MIXED-EFFECT HEIGHT-DIAMETER MODEL FOR Pinus pinaster AIT. AND Pinus radiata D. DON

“…These models include: Bertalanffy, Curtis, Meyer, Michailoff, Michaelis-Menten (MM), Naslund, Power, Wykoff, Chapman-Richards (Richards), Gompertz, Korf, Logistic, Prodan, Ratkowsky and Weibull models (Table 2). These models have been consistently used in forestry including recent work by Corral-Rivas et al (2019) and Ogana (2019a). The models were first fitted to the height-diameter data (fitting and validation) of P. pinaster and P. radiata using ordinary non-linear least square (ONLS) method, implemented in the 'nls' function in R (R Core Team, 2017).…”

“…Total height and diameter at breast height (D at 1.3 m above the ground) are fundamental tree variables that are routinely measured in forest inventory. They are required for the assessment of non-spatial structure of forest stands and estimation of volume (Adame et al, 2008;Gómez-García et al, 2014), basal area and determination of the competitive position of a tree in forest stand (West, 2015;Ogana, 2019a) and assessment of site productivity (Jayaraman and Lappi, 2001;West, 2015). Measurement of tree diameter at breast height is relatively simple, accurate and with low cost (Ferraz-Filho et al, 2018;Corral-Rivas et al, 2019).…”

“…Single-variable mixed-effect H-D models have been consistently used in the recent times (e.g. Sharma and Parton, 2007;Budhathoki et al, 2008;Zhang et al, 2016;Kalbi et al, 2017;Ogana, 2019a). Corral-Rivas et al (2019), asserted that modelling H-D relationships with single-variable (one predictor) [D] may not be adaptable to different stand dynamics and silvicultural conditions; and as such, may not possibly estimate all H-D relationships in the stands.…”

Modelling height-diameter relationships in complex tropical rain forest ecosystems using deep learning algorithm

“…Several nonlinear single predictor height-diameter functions have been used to describe tree height and diameter relationships in both even-aged and uneven-aged stands (Mehtätalo et al 2015;Corral-Rivas et al 2019;Bronisz and Mehtätalo 2020;Ciceu et al 2020;Ercanli 2020a;Xie et al 2020), and in complex natural forests (Feldpausch et al 2011;Temesgen et al 2014;Kearsley et al 2017;Ogana 2019;Chenge 2021). To select the base model for the complex tropical forests, 18 single predictor h-d models were initially evaluated.…”

“…Feldpausch et al (2011) developed regional h-d allometry models for tropical forest ecosystems using the ordinary least square technique. A similar approach was used by Ogana (2019) to fit h-d models in tropical mixed forests in Nigeria. However, procedures that do not take into consideration species-specific variability may not give precise predictions of height (Temesgen et al 2014).…”