1887

Abstract

Summary

The provided materials address the remote identification and monitoring of land structure utilizing GIS technologies. They highlight the application of the OBIA method, a prominent plugin in QGIS, particularly valuable for land management on agricultural and forestry lands, among other domains. Through the course of this work, optimal parameters for classifying and segmenting spatial images within the study area were determined. The findings reveal that within the Dmytriv territorial community, the majority of the land (138.9 km2) is attributed to urban areas (35.1%), followed by agricultural land (33.3%), forests (29.5%), and water bodies (2.1%). The classification process achieved an accuracy rate of 77.8%. Utilizing the OBIA plugin in conjunction with the SCP controlled classification method significantly streamlines and enhances land structure monitoring procedures.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.2023520035
2023-11-07
2025-04-26
Loading full text...

Full text loading...

/deliver/fulltext/2214-4609/2023/monitoring'2023/Mon23-035.html?itemId=/content/papers/10.3997/2214-4609.2023520035&mimeType=html&fmt=ahah

References

  1. Azeez, O. S., Shafri, H. Z., Alias, A. H., & Haron, N. A. (2022). Integration of Object-Based Image Analysis and Convolutional Neural Network for the Classification of High-Resolution Satellite Image: A Comparative Assessment.Applied Sciences, 12(21), 10890.
    [Google Scholar]
  2. Bennett, R., Oosterom, P. van, Lemmen, C., & Koeva, M. (2020). Remote Sensing for Land Administration.Remote Sensing, 12(15), 2497. MDPI AG. Retrieved from http://dx.doi.org/10.3390/rs12152497
    [Google Scholar]
  3. Johnson, B., Jozdani, S. (2018). Identifying Generalizable Image Segmentation Parameters for Urban Land Cover Mapping through Meta-Analysis and Regression Tree Modeling Remote Sens., 10 (2), p. 73, URL: 10.3390/rs10010073
    https://doi.org/10.3390/rs10010073 [Google Scholar]
  4. Ma, L., Fu, T., Blaschke, T., Li, M., Tiede, D., Zhou, Z., Ma, X., Chen, D. (2017) Evaluation of Feature Selection Methods for Object-Based Land Cover Mapping of Unmanned Aerial Vehicle Imagery Using Random Forest and Support Vector Machine Classifiers ISPRSInt. J. Geo-Inf., 6 (2), p. 51, URL: 10.3390/ijgi6020051
    https://doi.org/10.3390/ijgi6020051 [Google Scholar]
  5. Ma, L., Cheng, L., Li, M., Liu, Y., Ma, X. (2015) Training Set Size, Scale, and Features in Geographic Object-Based Image Analysis of Very High Resolution Unmanned Aerial Vehicle Imagery ISPRS J.Photogramm. Remote Sens., 102 pp. 14–27, URL: 10.1016/j.isprsjprs.2014.12.026
    https://doi.org/10.1016/j.isprsjprs.2014.12.026 [Google Scholar]
  6. Zaki, A., Buchori, I., Sejati, A. W., Liu, Y. (2022). An object-based image analysis in QGIS for image classification and assessment of coastal spatial planning.The Egyptian Journal of Remote Sensing and Space Science, 25(2), 349–359.
    [Google Scholar]
/content/papers/10.3997/2214-4609.2023520035
Loading
/content/papers/10.3997/2214-4609.2023520035
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error