Land Cover Analysis for Agricultural Area in Thailand Using CNN Method

Main Article Content

C. Arnal
S. Bassanetti
H. Corbin
Vissanu Mungkung
Laurent Mezeix


To support agricultural development, increased understanding and data is needed. Performing a land cover enables obtain physical landscapes. The aim of this paper is to propose a land cover for agricultural area in Thailand using CNN method. A four-class dataset is created: forest, palm, rice, and buildings. It is composed of about 1,500,000 with a size of 64x64 pixels. Each class is associated with an optimized CNN model showing an accuracy between 80 and 90 %. After post-processing of each prediction, the land cover is successfully obtained by aggregating the different class predictions. An RGB filter is used to determine the maturation state of the rice and to differentiate the type of palm field. Finally, the estimation of the production of palm and rice can be performed.


Download data is not yet available.

Article Details

How to Cite
C. Arnal, S. Bassanetti, H. Corbin, V. Mungkung, and L. . Mezeix, “Land Cover Analysis for Agricultural Area in Thailand Using CNN Method”, Def. Technol. Acad. J., vol. 5, no. 11, pp. 62–73, Feb. 2023.
Research Articles


M. M. Aburas, S. H. Abdullah, M. F. Ramli and Z. H. Ash’aari, “Measuring Land Cover Change in Seremban, Malaysia Using NDVI Index,” Procedia Environ. Sci., vol. 30, pp. 238-243, 2015.

B. Li, C. Ti and X. Yan, “Estimating rice paddy areas in China using multi-temporal cloud-free normalized difference vegetation index (NDVI) imagery based on change detection,” Pedosphere, vol. 30, no. 6, pp. 734-746, 2020.

H. Chen et al., “A deep learning CNN architecture applied in smart near-infrared analysis of water pollution for agricultural irrigation resources,” Agric. Water Manag., vol. 240, 2020.

C. Yoo, D Han, J. Im and B. Bechtel, “Comparison between convolutional neural networks and random forest for local climate zone classification in mega urban areas using Landsat images,” ISPRS J. Photogramm. Remote Sens., vol. 157, pp.155-170, 2019.

P. Chermprayong, N. Hongkarnjanakul, D. Rouquette, C. Schwob and L. Mezeix, “Convolutional Neural Network for Thailand’s Eastern Economic Corridor (EEC) land cover classification using overlapping process on satellite images,” Remote Sens. Appl.: Soc. Environ., vol. 23, 2021.

M. Cihan, M. Ceylan, H. Soylu and M. Konak. “Fast Evaluation of Unhealthy and Healthy Neonates Using Hyperspectral Features on 700-850 Nm Wavelengths, ROI Extraction, and 3D-CNN,” IRBM, vol. 43, no. 5, pp. 362-371, 2022.

D. Saah et al., “Collect Earth: An online tool for systematic reference data collection in land cover and use applications,” Environ. Model. Softw., vol. 118, pp. 166-171, 2019.

M. Dorber, F. Verones, M. Nakaoka and K. Sudo, “Can we locate shrimp aquaculture areas from space? - A case study for Thailand,” Remote Sens. Appl.: Soc. Environ., vol. 20, 2020.

F. Elmaz, R. Eyckerman, W. Casteels and S. Latré, P. Hellinckx, “CNN-LSTM architecture for predictive indoor temperature modeling,” Build Environ, vol. 206, 2021.

P. Emparanza, N. Hongkarnjanakul, D. Rouquette, C. Schwob and L. Mezeix. “Land cover classification in Thailand's Eastern Economic Corridor (EEC) using convolutional neural network on satellite images,” Remote Sens. Appl.: Soc. Environ., vol. 20, 2020.

F. S.Y. Watanabe et al., “Inland water's trophic status classification based on machine learning and remote sensing data,” Remote Sens. Appl.: Soc. Environ., vol. 19, 2020.

A. C. Fitrianto, K. Tokimatsu and M. S. Wijaya, “Estimation the Amount of Oil Palm Trees Production Using Remote Sensing Technique,” in IOP Conf. Ser.: Earth Environ. Sci., vol. 98, no. 1, Dec. 2017.

D. Fitton, E. Laurens, N. Hongkarnjanakul, C. Schwob and L. Mezeix, “Land cover classification through Convolutional Neur-al Network model assembly: A case study of a local rural area in Thailand,” Remote Sens. Appl.: Soc. Environ., vol. 26, 2022.

P. Griffiths, C. Nendel and P. Hostert, “Intra-annual reflectance composites from Sentinel-2 and Landsat for national-scale crop and land cover mapping,” Remote Sens. Environ., vol. 220, pp.135-151, 2019.

R. Hernawati, K. Wikantika and S. Darmawan, “Modeling of oil palm phenology based on remote sensing data: opportunities and challenges,” J. Appl. Rem. Sens., vol. 16, no. 2, 2022. doi: 10.1117/1.JRS.16.021501.

J. Kong et al., “Evaluation of four image fusion NDVI products against in-situ spectral-measurements over a heterogeneous rice paddy landscape,” Agric. For. Meteorol., vol. 297, 2021.

W. Li, C. Chen, M. Zhang, H. Li and Q. Du. “Data Augmentation for Hyperspectral Image Classification With Deep CNN,” in IEEE Geoscience and Remote Sensing Letters, vol. 16, no. 4, pp. 593-597, April 2019, doi: 10.1109/LGRS.2018.2878773.

W. Li, R. Dong, H. Fu and L. Yu. “Large-Scale Oil Palm Tree Detection from High-Resolution Satellite Images Using Two-Stage Convolutional Neural Networks,” Remote Sens., vol. 11 no, 1, 2019.

W. Li, H. Fu, L. Yu and A. Cracknell. “Deep Learning Based Oil Palm Tree Detection and Counting for High-Resolution Remote Sensing Images,” Remote Sens., vol. 9, no. 1, pp. 22, 2017.

L. Ma, Y. Liu, X. Zhang, Y. Ye, G. Yin and B. A. Johnson, “Deep learning in remote sensing applications: a meta-analysis and review,” ISPRS J. Photogramm. Remote Sens., vol. 152, pp. 166-177, 2019.

M. Panahi, N. Sadhasivam, H. R. Pourghasemi, F. Rezaie and S. Lee, “Spatial prediction of groundwater potential mapping based on convolutional neural network (CNN) and support vector regression (SVR),” J. Hydrol., vol. 588, 2020.

M. Mentet, N. Hongkarnjanakul, C. Schwob and L. Mezeix. “Method to apply and visualize physical models associated to a land cover performed by CNN: A case study of vegetation and water cooling effect in Bangkok Thailand,” Remote Sens. Appl.: Soc. Environ., vol. 28, 2022.

D. L. Olson and D. Delen, Advanced Data Mining Techniques. Heidelberg: Springer-Verlag,. 2008, pp. 151-167.

ONU Info. “La population mondiale atteindra 8 milliards d’habitants en novembre (ONU).” (accessed Jul. 11, 2022).

FertiGlobal. “Paddy Rice.” (accessed Aug. 10, 2022).

V. Palazzi, S. Bonafoni, F. Alimenti, P. Mezzanotte and L. Roselli. “Feeding the World With Microwaves: How Remote and Wireless Sensing Can Help Precision Agriculture,” IEEE Microw. Mag., vol. 20, pp. 72-86, 2019.

H. Ritchie and M. Roser. “Palm Oil.” (accessed Aug. 11, 2022).

D. Saah et al., “Land Cover Mapping in Data Scarce Environments: Challenges and Opportunities,” Front. Environ. Sci., vol. 7, pp. 150-160, 2019.

D.P. Shrestha, M. Suriyaprasit and S. Prachansri, “Assessing soil erosion in inaccessible mountainous areas in the tropics: The use of land cover and topographic parameters in a case study in Thailand,” Catena, vol. 121, pp. 40-52, 2014.

J. Soriano-González, E. Angelats, M. Martínez-Eixarch and C. Alcaraz, “Monitoring rice crop and yield estimation with Sentinel-2 data,” Field Crops Res., vol. 281, 2022.

J. Xie, K. Hu, Y. Guo, Q. Zhu and J. Yu, “On loss functions and CNNs for improved bioacoustic signal classification,” Ecol. Inform., vol. 64, 2021.

L. Yu et al., “Meta-discoveries from a synthesis of satellite-based land-cover mapping research,” Int. J. Remote Sens., vol. 35, no. 13, pp. 4573–4588, 2014.

Y. Zhang, D. Xiao, Y. Liu and H. Wu, “An algorithm for automatic identification of multiple developmental stages of rice spikes based on improved Faster R-CNN,” Crop J., Vol. 10, no. 5, pp. 1323-1333, 2022.