Modeling the Volume of Residual Stand Using Environmental Data and Remote Sensing: An Application of Artificial Neural Network and Multiple Linear Regression

Faramarzi, Hassn; Shabani, Saeid; Ahmadi, Akram

[Home ] [Archive]

[ فارسی ]

Main Menu

Home

Journal Information

Articles archive

For Authors

For Reviewers

Registration

Contact us

Site Facilities

Search in website

Receive site information

Volume 10, Issue 21 (12-2022)

PEC 2022, 10(21): 155-167

Back to browse issues page

Modeling the Volume of Residual Stand Using Environmental Data and Remote Sensing: An Application of Artificial Neural Network and Multiple Linear Regression

Hassn Faramarzi ^*

, Saeid Shabani²

, Akram Ahmadi²

Faculty of Natural Resources and Marine Sciences, Noor, Mazandaran, Faculty of Natural Resources and Marine Sciences, TMU , Faramarzi.hassan@yahoo.com
2- Research Department of Natural Resources, Golestan Agricultural and Natural Resources Research and Education Center

Abstract: (1562 Views)

In order to sustainably manage and optimize forest utilization, it is crucial to have information on the volume of standing mass. This study aimed to model the standing mass of the educational and research forest of Darabkola Sari using remote sensing data and artificial neural network and multiple linear regression methods. 186 sample plots of 10 R were randomly and systematically collected, and using topographic maps and LISS-III images from the IRS-P6 satellite, the vegetation characteristics of the area were prepared. Modeling and accuracy evaluation of the models were done using these variables and collected data. The multiple linear model showed higher accuracy with an R2 of 0.75 and RMSE of 0.3 compared to the artificial neural network model in the region. The results can be utilized in management planning and as a factor in the design of logging routes and forest roads to cover areas with standing trees. The influential indicators were TVI and DVI. The results of the present research can be used in management planning and as one of the effective factors in the design of logging routes and forest roads, so that the areas with the volume of standing trees are covered more.

Article number: 12

Keywords: Vegetation Indicators, Multilayer Perceptron, Darabkola Educational Research Forest, Standing Mass Volume Modeling.

Full-Text [PDF 743 kb] (326 Downloads)

Type of Study: Research | Subject: Special
Received: 2022/07/21 | Accepted: 2022/10/18 | Published: 2023/04/22

References

1. Afrin, S., Gupta, A., Farjad, B., Ahmed, M.R., Achari, G., Hassan, Q.K. 2019. Development of land-use/land-cover maps using landsat-8 and MODIS data, and their integration for hydro-ecological applications. Sensors, 19: 4891.

2. Ahmad, M.W., Mourshed, M., Rezgui, Y. 2017. Trees vs Neurons: Comparison between random forest and ANN for high-resolution prediction of building energy consumption. Energy and Buildings 147: 77-89.

3. de Freitas, E.C.S., de Paiva, H.N., Neves, J.C.L., Marcatti, G.E., Leite, H.G. 2020. Modeling of eucalyptus productivity with artificial neural networks. Industrial Crops and Products, 146, 112149.

4. Devotta, S., Chelanib, A., Vonsild, A. 2021. Prediction of flammability classifications of refrigerants by artificial neural network and random forest modelPrévision des classifications d'inflammabilité des frigorigènes par un réseau neuronal artificiel et un modèle de forêt d'arbres décisionnels. International Journal of Refrigeration, 131: 947-955.

5. Dong, L., Bettinger, P., Liu, Z. 2022. Optimizing neighborhood-based stand spatial structure: Four cases of boreal forests. Forest Ecology and Management, 506: 119965.

6. Gasparri, N.I., Parmuchi, M.G., Bono, J., Karszenbaum, H., Montenegro, C.L. 2010. Assessing multi-temporal Landsat 7 ETM+ images for estimating above-ground biomass in subtropical dry forests of Argentina. Journal of Arid Environments 74: 1262-1270 .

7. Hollaus, M., Wagner, W., Maier, B., Schadauer, K. 2007. Airborne Laser Scanning of Forest Stem Volume in a Mountainous Environment. Sensors 7: 1559-1577 .

8. Latifi, H., Fassnacht, F.E., Hartig, F., Berger, C., Hernández, J., Corvalán, P., Koch, B. 2015. Stratified aboveground forest biomass estimation by remote sensing data. International Journal of Applied Earth Observation and Geoinformation 38: 229-241 .

9. Lee, S., Ryu, J.-H., Lee, M.-J., Won, J.-S. 2003. Use of an artificial neural network for analysis of the susceptibility to landslides at Boun, Korea .Environmental Geology 44: 820-833 .

10. Li, W., Buitenwerf, R., Munk, M., Bøcher, P.K., Svenning, J.C. 2020. Deep-learning based high-resolution mapping shows woody vegetation densification in greater Maasai Mara ecosystem. Remote Sensing of Environment 247: 111953.

11. Liu, J., Wang, X., Wang, T. 2019. Classification of tree species and stock volume estimation in ground forest images using Deep Learning. Computers and Electronics in Agriculture, 166: 105012 .

12. Lu, X., 2005. Spatial variability and temporal change of water discharge and sediment flux in the lower Jinsha tributary: impact of environmental changes. River Research and Applications, 21: 229-243 .

13. McRoberts, R.E., Tomppo, E.O., 2007. Remote sensing support for national forest inventories. Remote sensing of environment, 110: 412-419.

14. Pham, T.A., Ly, H.-B., Tran, V.Q., Giap, L.V., Vu, H.-L.T., Duong, H.-A.T., 2020. Prediction of pile axial bearing capacity using artificial neural network and random forest. Applied Sciences, 10: 1871 .

15. Rencher, A.C., Christensen, W.F. 2012. Chapter 10, Multivariate regression–Section 10.1, Introduction. Methods of multivariate analysis. Wiley Series in Probability and Statistics, 709: 19 .

16. Santoro, M., Eriksson, L., Askne, J., Schmullius, C. 2006. Assessment of stand‐wise stem volume retrieval in boreal forest from JERS‐1 L‐band SAR backscatter. International Journal of Remote Sensing, 27: 3425-3454 .

17. Seyhan, I. 2004. RS & GIS (Remote Sensing & Geographical Information Systems) .

18. Sylvain, J.-D., Drolet, G., Brown, N. 2019. Mapping dead forest cover using a deep convolutional neural network and digital aerial photography. ISPRS Journal of Photogrammetry and Remote Sensing 156: 14-26.

19. Triepke, F.J., 2017. Fuzzy classification of vegetation for ecosystem mapping, Mapping Forest Landscape Patterns, Springer, pp. 63-103 .

20. Wang, L., Liu, J., Xu, S., Dong, J., Yang, Y. 2017. Forest above ground biomass estimation from remotely sensed imagery in the mount tai area using the RBF ANN algorithm. Intelligent Automation & Soft Computing, 1 -8.

21. Wang, Y., Zhang, X., Guo, Z. 2021. Estimation of tree height and aboveground biomass of coniferous forests in North China using stereo ZY-3, multispectral Sentinel-2, and DEM data. Ecological Indicators 126: 107645 .

22. Xu, C., Manley, B., Morgenroth, J. 2018. Evaluation of modelling approaches in predicting forest volume and stand age for small-scale plantation forests in New Zealand with RapidEye and LiDAR, Int J Appl Earth Obs Geoinformation 73: 386–396.

23. Zekić-Sušac, M., Has, A., Knežević, M. 2021. Predicting energy cost of public buildings by artificial neural networks, CART, and random forest, Neurocomputing, 439: 223-233.

Send email to the article author

Add your comments about this article

Mendeley

Zotero

RefWorks

Faramarzi H, Shabani S, Ahmadi A. Modeling the Volume of Residual Stand Using Environmental Data and Remote Sensing: An Application of Artificial Neural Network and Multiple Linear Regression. PEC 2022; 10 (21) : 12
URL: http://pec.gonbad.ac.ir/article-1-871-en.html

Rights and permissions
	This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Volume 10, Issue 21 (12-2022)

Back to browse issues page

Persian site map - English site map - Created in 0.06 seconds with 37 queries by YEKTAWEB 4710