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:: Volume 9, Issue 18 (10-2021) ::
PEC 2021, 9(18): 93-114 Back to browse issues page
Assessing the Ability of supervised Classification of Landsat 8 Satellite Images in Mapping Rangelands Plant Community (Case Study: Rangelands of Southern Yazd Province)
Hadi Zare khormizi *, Hamid Reza Ghafarian Malamiri
Tehran University , hadi.zarekh@gmail.com
Abstract:   (177 Views)
Investigating spatial and temporal changes in the composition of plant species and communities is an essential step in assessing pasture health conditions, understanding the evolutionary processes of the local ecosystem, and developing rangeland management strategies. The aim of this study is to evaluate the ability of supervised classification of Landsat 8 satellite images in mapping the plant communities in the southern rangelands of Yazd province. To do so, we selected and sampled 90 training samples from areas that showed a homogeneous composition of plant species up to a minimum radius of 60 meters from the central point in 2015, and then; plant communities were separated based on the prevalence of the percentage of cover crown. A Landsat 8 satellite image of OLI sensor on May 29, 2015 was used after geometric, atmospheric and radiometric corrections. In the present study, the final accuracy of six supervised classification algorithms including parallel classification algorithms, the minimum distance, the Mahalanobis distance, the maximum likelihood, the neural network and support vector machine with radial kernel were examined in the separation and determination of the range of the plant community. Based on the results, the maximum likelihood algorithm and the neural network had the highest accuracy with the final accuracy of 96.4% and 84.8% and Kappa coefficient of 0.95 and 0.82, respectively. In general, the results of this study showed that in order to differentiate and classify different plant communities in the study area, the maximum likelihood algorithm has good results and combining field studies with remote sensing is a very suitable capability in segregating and classifying the plant types and communities.
Keywords: Landsat 8 satellite, Maximum likelihood algorithm, Plant community, Supervised Classification, Yazd
Full-Text [PDF 1461 kb]   (67 Downloads)    
Type of Study: Research | Subject: General
Received: 2020/04/15 | Accepted: 2020/12/21 | Published: 2021/09/28
1. Breunig, F.M., Galvao, L.S., Formaggio, A.R., Epiphanio, J.C. 2011. Classification of soybean varieties using different techniques: case study with Hyperion and sensor spectral resolution simulations. Journal of Applied Remote Sensing, 5 (1): 053533.
2. Černá, L., Chytrý, M. 2005. Supervised classification of plant communities with artificial neural networks. Journal of vegetation Science, 16 (4): 407-414.
3. Chuang, C.W., Lin, C.Y., Chienn, C.H., Chou, W.C. 2011. Application of Markov-chain model for vegetation restoration assessment at landslide areas caused by a catastrophic earthquake in Central Taiwan. Ecological Modelling, 222 (3): 835-845.
4. Dixon, B., Candade, N., 2008. Multispectral landuse classification using neural networks and support vector machines: one or the other, or both? International Journal of Remote Sensing 29 (4), 1185-1206.
5. Eidvidge, C.D., 1990. Visible and Near Infrared Reflectance Charactristics of Dry Plant Materials. International Journal of Remote Sensing, 11(10), PP. 1775 - 1795.
6. Hagen, S.C., Heilman, P., Marsett, R., Torbick, N., Salas, W., Ravensway, J., Qi, J. 2012. apping Total Vegetation Cover Across Western angelands With Moderate-Resolution Imaging Spectroradiometer Data. Rangeland Ecology & Management, 65 (5): 456-467.
7. Marcinkowska-Ochtyra, A., Zagajewski, B., Ochtyra, A., Jarocińska, A., Wojtuń, B., Rogass, C., ... & Lavender, S. 2017. Subalpine and alpine vegetation classification based on hyperspectral APEX and simulated EnMAP images. International journal of remote sensing, 38 (7): 1839-1864.
8. Martínez-López, J., Carreño, M.F., Palazón-Ferrando, J.A., Martínez-Fernández, J., Esteve, M.A. 2014. Remote sensing of plant communities as a tool for assessing the condition of semiarid Mediterranean saline wetlands in agricultural catchments. International Journal of Applied Earth Observation and Geoinformation, 26: 193-204.
9. Richards, J.A. 2013. Remote sensing digital image analysis, fifth edition, springer, 494 pp.
10. Tian, S., Zhang, X., Tian, J., Sun, Q. 2016. Random forest classification of wetland landcovers from multi-sensor data in the arid region of Xinjiang, China. Remote Sensing, 8 (11): 954.
11. Wachendorf, M., Fricke, T., Möckel, T. 2018. Remote sensing as a tool to assess botanical composition, structure, quantity and quality of temperate grasslands. Grass and forage science, 73(1): 1-14.
12. Wang, F., Qin, Z., Song, C., Tu, L., Karnieli, A., Zhao, S. 2015. An improved mono-window algorithm for land surface temperature retrieval from Landsat 8 thermal infrared sensor data. Remote Sensing, 7 (4): 4268-4289.
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Zare khormizi H, Ghafarian Malamiri H R. Assessing the Ability of supervised Classification of Landsat 8 Satellite Images in Mapping Rangelands Plant Community (Case Study: Rangelands of Southern Yazd Province). PEC. 2021; 9 (18) :93-114
URL: http://pec.gonbad.ac.ir/article-1-687-en.html

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Volume 9, Issue 18 (10-2021) Back to browse issues page
مجله حفاظت زیست بوم گیاهان Journal of Plant Ecosystem Conservation
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