| Title: | Non-Linear Height Diameter Models for Forestry |
|---|---|
| Description: | Tree height is an important dendrometric variable and forms the basis of vertical structure of a forest stand. This package will help to fit and validate various non-linear height diameter models for assessing the underlying relationship that exists between tree height and diameter at breast height in case of conifer trees. This package has been implemented on Naslund, Curtis, Michailoff, Meyer, Power, Michaelis-Menten and Wykoff non linear models using algorithm of Huang et al. (1992) <doi:10.1139/x92-172> and Zeide et al. (1993) <doi:10.1093/forestscience/39.3.594>. |
| Authors: | M. Iqbal Jeelani [aut, cre], Fehim Jeelani [aut], Shakeel Ahmad Mir [aut], Syed Naseem Geelani [aut], Mushtaq Ahmad Lone [aut], Asif Ali [aut], Afshan Tabassum [aut], Khalid Ul Islam [aut], Imran Rashid [aut], Md Yeasin [aut] |
| Maintainer: | M. Iqbal Jeelani <[email protected]> |
| License: | GPL-3 |
| Version: | 0.1.0 |
| Built: | 2025-02-13 04:28:56 UTC |
| Source: | https://github.com/cran/ImFoR |
Non-Linear Height Diameter Models for Forestry
ImFoR(data, train_frac = 0.8)ImFoR(data, train_frac = 0.8)
data |
Datasets |
train_frac |
Train-Test fraction |
metrics: Metrics of all applied models
plot: Plot
Jeelani, M.I., Tabassum, A., Rather, K and Gul,M.2023. Neural Network Modeling of Height Diameter Relationships for Himalayan Pine through Back Propagation Approach. Journal of The Indian Society of Agricultural Statistics. 76(3): 169–178
Tabassum, A., Jeelani, M.I., Sharma,M., Rather, K R ., Rashid, I and Gul,M.2022. Predictive Modelling of Height and Diameter Relationships of Himalayan Chir Pine . Agricultural Science Digest - A Research Journal. DOI:10.18805/ag.D-5555
Huang, S., Titus, S.J., and Wiens, D.P. 1992. Comparison of nonlinear height-diameter functiond for major Alberta tree species. Can J. For. Res. 22: 1297-1304. DOI : 10.1139/x92-172
- Zeide, B. 1993. Analysis of growth equations. Forest Science 39(3):594-616. doi:10.1093/forestscience/39.3.594
library("ImFoR") data <- system.file("extdata", "data_test.csv", package = "ImFoR") data_test <- read.csv(data) Model<-ImFoR(data =data_test)library("ImFoR") data <- system.file("extdata", "data_test.csv", package = "ImFoR") data_test <- read.csv(data) Model<-ImFoR(data =data_test)