Semi-Automatic Classification of Rotating Crops in Northern Thailand by Using Temporal LANDSAT Images
DOI:
https://doi.org/10.13052/jmm1550-4646.18317Keywords:
Feature classification, satellite images, temporal, supervised classification, semi-automaticAbstract
This work focuses on rotating crops in the forest conservation areas in the
northern region of Thailand which always cause false detection for forest
encroachment and deforestation. Therefore, this work establishes a database
of rotating crop areas in the northern region of Thailand and additionally
develops a semi-automatic classification approach to help facilitate the classification
process. LANDSAT images ranging from 1987 to 2018 are used
as the input data for classifying the rotating crop areas. The semi-automatic
classification approach is comprised of the automatic supervised classification
and the manual classification by visual interpretation, respectively. The
automatic and manual classification procedures are explained, and the results
are verified by using ground truth locations distributed over the study region
which gives 81.72% accuracy.
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References
Lillesand, T., Kiefer, R.W. and Chipman, J., 2015. Remote sensing and image interpretation. John Wiley & Sons, 2015.
Dong, J., Xiao, X., Sheldon, S., Biradar, C. and Xie, G., “Mapping tropical forests and rubber plantations in complex landscapes by integrating PALSAR and MODIS imagery,” ISPRS Journal of Photogrammetry and remote sensing, vol. 74, pp. 20–33, 2012.
Li, Z. and Fox, J.M., “Mapping rubber tree growth in mainland Southeast Asia using time-series MODIS 250 m NDVI and statistical data,” Applied Geography, vol. 32(2), pp. 420–432, 2012.
Li, Z. and Fox, J.M., “Integrating Mahalanobis typicalities with a neural network for rubber distribution mapping,” Remote Sensing Letters, vol. 2(2), pp. 157–166, 2011.
Beckschäfer, P., “Obtaining rubber plantation age information from very dense Landsat TM & ETM+ time series data and pixel-based image compositing,” Remote Sensing of Environment, vol. 196, pp. 89–100, 2017.
Fan, H., Fu, X., Zhang, Z. and Wu, Q., “Phenology-based vegetation index differencing for mapping of rubber plantations using Landsat OLI data,” Remote Sensing, vol. 7(5), pp. 6041–6058, 2015.
Kou, W., Xiao, X., Dong, J., Gan, S., Zhai, D., Zhang, G., Qin, Y. and Li, L., “Mapping deciduous rubber plantation areas and stand ages with PALSAR and Landsat images,” Remote Sensing, vol. 7(1), pp. 1048–1073, 2015.
Li, Z. and Fox, J.M., “Rubber tree distribution mapping in northeast Thailand,” International journal of Geosciences, vol. 2(04), p. 573, 2011.
Hu, H., Liu, W. and Cao, M., “Impact of land use and land cover changes on ecosystem services in Menglun, Xishuangbanna, Southwest China,” Environmental Monitoring and Assessment, vol. 146(1–3), pp. 147–156, 2008.
Suratman, M.N., Bull, G.Q., Leckie, D.G., Lemay, V.M., Marshall, P.L. and Mispan, M.R., “Prediction models for estimating the area, volume, and age of rubber (Hevea brasiliensis) plantations in Malaysia using Landsat TM data,” International Forestry Review, vol. 6(1), pp. 12–12, 2004.
Chen, B., Cao, J., Wang, J., Wu, Z., Tao, Z., Chen, J., Yang, C. and Xie, G., “Estimation of rubber stand age in typhoon and chilling injury afflicted area with Landsat TM data: a case study in Hainan Island, China,” Forest ecology and management, vol. 274, pp. 222–230, 2012.
Li, H., Ma, Y., Aide, T.M. and Liu, W., “Past, present and future land-use in Xishuangbanna, China and the implications for carbon dynamics,” Forest Ecology and Management, vol. 255(1), pp. 16–24, 2008.
Crist, E.P., “A TM tasseled cap equivalent transformation for reflectance factor data. Remote Sensing of Environment”, vol. 17(3), pp. 301–306, 1985.
Han, T., Wulder, M.A., White, J.C., Coops, N.C., Alvarez, M.F. and Butson, C., “An efficient protocol to process Landsat images for change detection with tasselled cap transformation”. IEEE Geoscience and Remote Sensing Letters, vol. 4(1), pp. 147–151, 2007.
