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This study presents change detection analysis using object-based image analysis in Dong Ra Nang national forest, Kalasin province. This national forest was previously cleared and used for agricultural purposes for an extended period of time according to media reports published between 2007-2015. In order to perform a LU/LC examination, a time series of LANDSAT 7 ETM+ images acquired from 3 dates (25/11/1999, 28/11/2007 and 21/11/2015) were used for image segmentation and LU/LC classification. Furthermore, a CART algorithm and crucial image band ratios, such as MSAVI and NDVI, including mean of image layer bands, were used to improve image classification of degradation of the forest. The information from three thematic layers sampling points that had been derived from visual interpretations was used for CART training, applying and classifying the satellite images into 6 LU/LC classes, namely, (1) dense forest, (2) light forest, (3) bare land, (4) agricultural area, (5) plantation area, and (6) bodies of water on hierarchical image networks. Prior to the deforestation detection analysis, each image scene was classified individually using a CART algorithm. Then, the classified images were synchronized with the main map for performing land use/ land cover changes analysis focused on deforestation using image hierarchical image network by relation to image objects in vertical and horizontal aspects. The results indicated that the forest areas decreased dramatically by 50% from 1999-2007. On the other hand, there was a slight increase in bare land by an area of 38.68 sq.km. The majority of the area was used for farm land according to the report of the Forest Management Office, Khon Kaen province. The vegetation area emerged in the central area surrounding by bare land and agricultural area. In 2007-2015, the vegetation rapidly decreased by 30.89 sq.km., and the area tended to be bare land and agricultural area.
Keywords: OBIA; remote sensing; classification and regression tree; CART
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 Lang, S., 2008. Object-based image analysis for remote sensing applications: modeling reality - dealing with complexity. In: T. Blaschke, S. Lang and G.J. Hay, eds. Object-Based Image Analysis: Spatial Concepts for Knowledge-Driven Remote Sensing Applications. Heidelberg: Springer; pp. 3-27.
 Karimi, H., Jafarnezhad, J. and Kakhani, A., 2020. Landsat time-series for land use change detection using support vector machine: Case study of Javanrud District, Iran. In: 2020 International Conference on Computer Science and Software Engineering (CSASE), 2020. 12831.
 Martha, T.R., Kerle, N., van Westen, C.J., Jetten, V. and Kumar, K.V., 2011. Segment optimization and data-driven thresholding for knowledge-based landslide detection by object-based image analysis. IEEE Transactions on Geoscience and Remote Sensing, 49(12), 4928-4943.
 Heumann, B.W., 2011. An object-based classification of mangroves using a hybrid decision tree-support vector machine approach. Remote Sensing, 3(11), 2440-2460.
 Hussain, M., Chen, D., Cheng, A., Wei, H. and Stanley, D., 2013. Change detection from remotely sensed images: From pixel-based to object-based approaches. ISPRS Journal of Photogrammetry and Remote Sensing, 80, 91-106.
 Ma, C., Ai, B., Zhao, J., Xu, X. and Huang, W., 2019. Change detection of mangrove forests in coastal Guangdong during the past three decades based on remote sensing data. Remote Sensing, 11(8), 921, https://doi.org/10.3390/rs11080921 https://doi.org/10.3390/rs11080921
 Liu, X., Chen, Y., Li, S., Cheng, L. and Li, M., 2019. Hierarchical classification of urban ALS data by using geometry and intensity information. Sensors, 19(20), 4583, https://doi.org/ 10.3390/s19204583
 Song, A., Kim, Y. and Han, Y. 2020. Uncertainty analysis for object-based change detection in very high-resolution satellite images using deep learning network. Remote Sensing, 12(15), 2345, https://doi.org/10.3390/rs12152345
 Pereira-Pires, J.E., Aubard, V., Silva, J.M.N., Ribeiro, R.A., Pereira, J.M.C., Fonseca, J.M. and Andre, M., 2020. Pixel-based and object-based change detection methods for assessing fuel break maintenance. In: 2020 International Young Engineers Forum (YEF-ECE), July, 2020, 49-54.
 Drăguţ, L., Eisank, C. and Strasser, T., 2011. Local variance for multi-scale analysis in geomorphometry. Geomorphology, 130(3), 162-172.
 Qin, R., Huang, X., Gruen, A. and Schmitt, G., 2015. Object-based 3-D building change detection on multitemporal stereo images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(5), 2125-2137.
 Zhang, H., Eziz, A., Xiao, J., Tao, S., Wang, S., Tang, Z., Zhu, J. and Fang, J., 2019. High-resolution vegetation mapping using extreme gradient boosting based on extensive features. Remote Sensing, 11(12), 1505, https://doi.org/10.3390/rs11121505
 Qi, J., Chehbouni, A., Huete, A.R., Kerr, Y.H. and Sorooshian, S. 1994. A modified soil adjusted vegetation index. Remote Sensing of Environment, 48(2), 119, https//doi.org/10.1016/0034-4257(94)90134-1
 Ren, H. and Feng, G., 2014. Are soil‐adjusted vegetation indices better than soil‐unadjusted vegetation indices for above‐ground green biomass estimation in arid and semi‐arid grasslands? Grass and Forage Science, 70(4), https//:doi.org/10.1111/gfs.12152
 Huo, L.-Z., Boschetti, L. and Sparks, A.M., 2019. Object-based classification of forest disturbance types in the conterminous United States. Remote Sensing, 11(5), 477, https://doi.org/10.3390/rs11050477
 Zhan, Q., Molenaar, M. and Tempfli, K., 2002. Hierarchical image object-based structural analysis toward urban land use classification using high-resolution imagery and airborne LIDAR data. In: Proceedings of the 3rd International Symposium on Remote Sensing of Urban Area's 2002, pp. 251-258.
 Shao, Y. and Lunetta, R.S. 2012. Comparison of support vector machine, neural network, and CART algorithms for the land-cover classification using limited training data points. ISPRS Journal of Photogrammetry and Remote Sensing, 70, 78-87.
 Denison, D.G.T., Mallick, B.K. and Smith, A.F.M., 1998. A Bayesian CART algorithm | Biometrika, 85(2), 363-377.
 Ma, H., Liu, Y., Ren, Y., Wang. D., Yu, L. and Yu, J., 2020. Improved CNN classification method for groups of buildings damaged by earthquake, based on high resolution remote sensing images. Remote Sensing, 12(2), 260, https//doi.org/10.3390/rs12020260
 Hong, L. and Zhang, M., 2020. Object-oriented multiscale deep features for hyperspectral image classification. International Journal of Remote Sensing, 41(14), 5549-5572.
 Rahimizadeh, N., Kafaky, S.B., Sahebi, M.R. and Mataji, A. 2019. Forest structure parameter extraction using SPOT-7 satellite data by object- and pixel-based classification methods. Environmental Monitoring and Assessment, 192(1), 43, https://doi.org/10.1007/s10661-019-8015-x
 Fallatah, A., Jones, S. and Mitchell, D. 2020. Object-based random forest classification for informal settlements identification in the Middle East: Jeddah a case study. International Journal of Remote Sensing, 41(11), 4421-445.
 Ghasemain, B., Asl, D.T., Pham, B.T., Avand, M., Nguyen, H.D. and Janizadeh, S., 2020. Shallow landslide susceptibility mapping: A comparison between classification and regression tree and reduced error pruning tree algorithms. Vietnam Journal of Earth Sciences, 42(3), 208-227.
 Benedetti, A., Picchiani, M., Del Frate, F., 2018. Sentinel-1 and sentinel-2 data fusion for urban change detection. IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, 2018, 1962-1965.
 Lin, Y., Zhang, L., Wang, N., Zhang, X., Cen, Y. and Sun, X., 2020. A change detection method using spatial-temporal-spectral information from Landsat images. International Journal of Remote Sensing, 41(2), 772-793.
 Ai, J., Zhang, C., Chen, L. and Li, D., 2020. Mapping annual land use and land cover changes in the Yangtze estuary region using an object-based classification framework and landsat time series data. Sustainability, 12(2), 659, https://doi.org/10.3390/su12020659
 Zhang, X., Xiao, P. and Feng, X. 2020. Object-specific optimization of hierarchical multiscale segmentations for high-spatial resolution remote sensing images. ISPRS Journal of Photogrammetry and Remote Sensing, 159, 308-321.
 Krauß, T. and Tian, J., 2020. Automatic change detection from high-resolution satellite imagery. In: D.G. Hadjimitsis, K. Themistocleous, B. Cuca, A. Agapiou, V. Lysandrou, R. Lasaponara, N. Masini and G. Schreier, eds. Remote Sensing for Archaeology and Cultural Landscapes: Best Practices and Perspectives Across Europe and the Middle East. Cham: Springer International Publishing, pp. 47-58.
 Ghosh, D. and Chakravortty, S., 2020. Change detection of tropical mangrove ecosystem with subpixel classification of time series hyperspectral imagery. In: D.J. Hemanth, ed. Artificial Intelligence Techniques for Satellite Image Analysis. Cham: Springer International Publishing, pp. 189-211.
 Tejenaki, S.A., Ebadi, H. and Mohammadzadeh, A. 2020. Automatic road detection and extraction from MultiSpectral images using a new hierarchical object-based method. Journal of Geomatics Science and Technology, 9(3), 13-27.