A Novel Method for Accurate Tree Enumeration in Development Projects Using Canny Gaussian Hysteresis Contour (CGHC)
Keywords:
Computer Vision, Tree Enumeration, Canny Algorithm, Gaussian filter, Hysteresis, Contour AlgorithmAbstract
We provide a new method for counting trees in development projects by utilising cutting-edge image analytics tools to provide accurate and useful information. We use satellite images or aerial photos, convert them to grayscale, and then use Gaussian blurring to minimise noise and improve clarity. By utilising Canny edge detection, we are able to discern tree outlines with remarkable precision, even registering minute differences in plant density. Our method's core is the careful computation of tree area from the contours that were recovered; this yields a numerical representation of the amount of trees in the region that was studied. We use a color-coded visualisation method to improve interpretability, designating areas with high tree density in vivid green, areas with moderate coverage in yellow, and places with sparse distribution in red. Our approach goes beyond simple counting by superimposing tree outlines over the original photos, which helps with spatial analysis and decision-making. Stakeholders may visually evaluate the distribution of trees, spot vegetation gaps or clusters, and make well-informed decisions for land diversion while taking conservation and environmental effect into account. To summarise, our novel methodology provides a thorough approach to exact tree counting in development projects, equipping stakeholders with correct information for sustainable land use and environmental management. Here we depict our project implementation: github.com
Downloads
References
Aluri, Yuva & Kanuri, Naveen & Krishna, Marlapalli & Busi, Rambabu & Chowdary, S & Rani, Bandlamudi. (2021). Framework for Tree Crown Delineation from Panchromatic Images. EAI Endorsed Transactions on Energy Web. 9. 169428. 10.4108/eai.29-4-2021.169428.
Ertel, J., Goldman, L., Spore, J., & Brandt, J. (2023, June 6). Tree Cover Data Comparison. Global Forest Watch. Retrieved from https://www.globalforestwatch.org/blog/data-and-research/tree-cover-data-comparison.
A. Khan et al., "Remote Sensing: An Automated Methodology for Olive Tree Detection and Counting in Satellite Images," in IEEE Access, vol. 6, pp. 77816-77828, 2018, doi: 10.1109/ACCESS.2018.2884199.
M. Waleed, T. -W. Um, A. Khan and Z. Ahmad, "An Automated Method for Detection and Enumeration of Olive Trees Through Remote Sensing," in IEEE Access, vol. 8, pp. 108592-108601, 2020, doi: 10.1109/ACCESS.2020.2999078.
P. Faezah, I. Asmida, M. H. Siti Khairiyah, J. S. Norrizah and C. A. Nuraini, "Diversity and tree species community at Bukit Nanas Forest Reserve, Kuala Lumpur," 2013 IEEE Business Engineering and Industrial Applications Colloquium (BEIAC), Langkawi, Malaysia, 2013, pp. 846-850, doi: 10.1109/BEIAC.2013.6560255.
C. Lin, C. -S. Lo and G. Thomson, "A textural modification of the MMAC algorithm for individual tree delineation in forest stand using aerial bitmap images," 2011 4th International Congress on Image and Signal Processing, Shanghai, 2011, pp. 1604-1608, doi: 10.1109/CISP.2011.6100501.
C. Yu, M. Li and M. Zhang, "Classification of dominant tree species in an urban forest park using the remote sensing image of WorldView-2," 2015 8th International Congress on Image and Signal Processing (CISP), Shenyang, China, 2015, pp. 742-747, doi: 10.1109/CISP.2015.7407976.
Li, H.; Hu, B.; Li, Q.; Jing, L. CNN-Based Individual Tree Species Classification Using High-Resolution Satellite Imagery and Airborne LiDAR Data. Forests 2021, 12, 1697. https://doi.org/10.3390/f12121697 2021.
Kim, D.-H.; Ko, C.-U.; Kim, D.-G.; Kang, J.-T.; Park, J.-M.; Cho, H.-J. Automated Segmentation of Individual Tree Structures Using Deep Learning over LiDAR Point Cloud Data. Forests 2023, 14, 1159. https://doi.org/10.3390/f14061159 2023.
Li, D.; Shen, X.; Yu, Y.; Guan, H.; Li, J.; Zhang, G.; Li, D. Building Extraction from Airborne Multi-Spectral LiDAR Point Clouds Based
on Graph Geometric Moments Convolutional Neural Networks. Remote Sens. 2020, 12, 3186. https://doi.org/10.3390/rs12193186 2020.
J. Yang, Y. He and J. Caspersen, "Individual tree-based species classification for uneven-aged, mixed-deciduous forests using multi-seasonal WorldView-3 images," 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA, 2017, pp. 827-830, doi:10.1109/IGARSS.2017.8127080.
Kandula, Ashok Reddy, R. Sathya, And S. Narayana. "Comparative Analysis Of Machine Learning Techniques On Genetic Mutation-Based Cancer Diagnosis Data." Journal Of Theoretical And Applied Information Technology 100.6 (2022) https://api.semanticscholar.org/CorpusID:265675372.
Hou, C.; Liu, Z.; Chen, Y.; Wang, S.; Liu, A. Tree Species Classification from Airborne Hyperspectral Images Using Spatial–Spectral Network. Remote Sens. 2023, 15, 5679. https://doi.org/10.3390/rs15245679 2023.
Kandula, Ashok Reddy, R. Sathya, and S. Narayana. "Multivariate Analysis on Personalized Cancer Data using a Hybrid Classification Model using Voting Classifier." International Journal of Intelligent Systems and Applications in Engineering 11.1 (2023): 354-362 https://ijisae.org/index.php/IJISAE/article/view/2546.
Tanya V. Olegario; Renann G. Baldovino; Nilo T. Bugtai; (2020). A Decision Tree-based Classification of Diseased Pine and Oak Trees Using Satellite Imagery. 2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (), – doi:10.1109/hnicem51456.2020.9400002 .
A. Gupta, J. Byrne, D. Moloney, S. Watson and H. Yin, "Tree Annotations in LiDAR Data Using Point Densities and Convolutional Neural Networks," in IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 2, pp. 971-981, Feb. 2020, doi: 10.1109/TGRS.2019.2942201. using Deep Learning Techniques to Identify Pneumonia," 2023 Second International Conference on Augmented Intelligence and
A. Reddy Kandula, S. D. Gangiredla, K. P. Tirukkovalluri, S. Yallamilli, N. Tummalapalli and S. Talluri, "X-Ray Image Analysis Sustainable Systems (ICAISS), Trichy, India, 2023, pp. 674-679, doi: 10.1109/ICAISS58487.2023.10250543.
K. Yilong, T. Junsnan, Z. Huaiqmg, J. Xian, L. Kangnmg and Z. Ning, "Research on forest visualization system based on data cube," 2012 IEEE International Conference on Computer Science and Automation Engineering (CSAE), Zhangjiajie, China, 2012, pp. 769-773, doi: 10.1109/CSAE.2012.6272879.
A. R. Kandula, S. Kalyanapu, L. S. Gottipati, H. Kamma, M. Munagala and P. Ramachandran, "Revolutionizing Malaria Diagnosis: A Deep Learning Approach," 2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS), Coimbatore, India, 2023, pp. 63-66, doi: 10.1109/ICSCSS57650.2023.10169586.
D. I. Moody et al., "Land cover classification in fused multisensor multispectral satellite imagery," 2016 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI), Santa Fe, NM, USA, 2016, pp. 85-88, doi: 10.1109/SSIAI.2016.7459181.
S. Kalyanapu, A. R. Kandula, P. Madhuri, P. Ganta, H. N. Sai Sri Vattikuti and U. Darapu, "Enhancing Breast Cancer Prediction Through SVM-Based Analysis," 2023 Annual International Conference on Emerging Research Areas: International Conference on Intelligent Systems (AICERA/ICIS), Kanjirapally, India, 2023, pp. 1-6, doi: 10.1109/AICERA/ICIS59538.2023.10420106.
E. -s. Kim, J. -s. Lee, G. -e. Park and J. -h. Lim, "Change Analysis of Subalpine Coniferous Forest Area over the Last 20 Years using Time-series Landsat Images," IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, 2018, pp. 5240-5242, doi: 10.1109/IGARSS.2018.8519000.
Q. Tan and J. Wang, "Mapping urban tree coverage using object-oriented image analysis method: A case study," 2009 IEEE International Geoscience and Remote Sensing Symposium, Cape Town, South Africa, 2009, pp. III-342-III-345, doi: 10.1109/IGARSS.2009.5417772.
W. Huang, H. Li and G. Lin, "Classifying forest stands based on multi-scale structure features using Quickbird image," 2015 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services (ICSDM), Fuzhou, China, 2015, pp. 202-208, doi: 10.1109/ICSDM.2015.7298054.
T. V. Olegario, R. G. Baldovino and N. T. Bugtai, "A Decision Tree-based Classification of Diseased Pine and Oak Trees Using Satellite Imagery," 2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), Manila, Philippines, 2020, pp. 1-4, doi: 10.1109/HNICEM51456.2020.9400002.
A. R. Kandula, M. Tadiparthi, P. Yakkala, S. Pasupuleti, P. Pagolu and S. M. Chandrika Potharlanka, "Design and Implementation of a Chatbot for Automated Legal Assistance using Natural Language Processing and Machine Learning," 2023 Annual International Conference on Emerging Research Areas: International Conference on Intelligent Systems (AICERA/ICIS), Kanjirapally, India, 2023, pp. 1-6, doi: 10.1109/AICERA/ICIS59538.2023.10420298
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
