Geomatics and Environmental Engineering, vol. 17, no. 1

Issue

Vol. 17 No. 1 (2023): Geomatics and Environmental Engineering

Date Log

Submitted
May 18, 2022
Accepted
Oct 5, 2022
Published
Dec 11, 2022
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Integrating Vegetation Indices and Spectral Features for Vegetation Mapping from Multispectral Satellite Imagery Using AdaBoost and Random Forest Machine Learning Classifiers

https://doi.org/10.7494/geom.2023.17.1.57
Rashmi Saini
Department of CSE, G. B. Pant Institute of Engineering and Technology, Pauri Garhwal, India
https://orcid.org/0000-0002-9802-7089

Corresponding Author(s) : Rashmi Saini

2rashmisaini@gmail.com

Geomatics and Environmental Engineering, Vol. 17 No. 1 (2023): Geomatics and Environmental Engineering

Abstract

Vegetation mapping is an active research area in the domain of remote sensing. This study proposes a methodology for the mapping of vegetation by integrating several vegetation indices along with original spectral bands. The Land Use Land Cover classification was performed by two powerful Machine Learning techniques, namely Random Forest and AdaBoost. The Random Forest algorithm works on the concept of building multiple decision trees for the final prediction. The other Machine Learning technique selected for the classification is AdaBoost (adaptive boosting), converts a set of weak learners into strong learners. Here, multispectral satellite data of Dehradun, India, was utilised. The results demonstrate an increase of 3.87% and 4.32% after inclusion of selected vegetation indices by Random Forest and AdaBoost respectively. An Overall Accuracy (OA) of 91.23% (kappa value of 0.89) and 88.59% (kappa value of 0.86) was obtained by means of the Random Forest and AdaBoost classifiers respectively. Although Random Forest achieved greater OA as compared to AdaBoost, interestingly AdaBoost provided better class-specific accuracy for the Shrubland class compared to Random Forest. Furthermore, this study also evaluated the importance of each individual feature used in the classification. Results demonstrated that the NDRE, GNDVI, and RTVIcore vegetation indices, and spectral bands (NIR, and Red-Edge), obtained higher importance scores.

Keywords

ensemble classifiers Machine Learning Random Forest AdaBoost vegetation mapping vegetation indices
Saini, R. (2022). Integrating Vegetation Indices and Spectral Features for Vegetation Mapping from Multispectral Satellite Imagery Using AdaBoost and Random Forest Machine Learning Classifiers. Geomatics and Environmental Engineering, 17(1), 57–74. https://doi.org/10.7494/geom.2023.17.1.57
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX
References
  1. Chan J.C.-W., Paelinckx D.: Evaluation of Random Forest and AdaBoost treebased ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery. Remote Sensing of Environment, vol. 112(6), 2008, pp. 999–3011. https://doi.org/10.1016/j.rse.2008.02.011.
  2. Liu J., Feng Q., Gong J., Zhou J., Liang J., Li Y.: Winter wheat mapping using a random forest classifier combined with multi-temporal and multi-sensor data. International Journal of Digital Earth, vol. 11(8), 2018, pp. 783-802. https://doi.org/10.1080/17538947.2017.1356388.
  3. Adam E., Mutanga O., Odindi J., Abdel-Rahman E.M.: Land-use / cover classification in a heterogeneous coastal landscape using RapidEye imagery: evaluating the performance of random forest and support vector machines classifiers. International Journal of Remote Sensing, vol. 35, 2014, pp. 3440–3458. https://doi.org/10.1080/01431161.2014.903435.
  4. Tigges J., Lakes T., Hostert P.: Urban vegetation classification: Benefits of multitemporal RapidEye satellite data. Remote Sensing of Environment, vol. 136, 2013, pp. 66–75. https://doi.org/10.1016/j.rse.2013.05.001.
  5. Micheletti N., Foresti L., Robert S., Leuenberger M., Pedrazzini A., Jaboyedof M., Kanevski M.: Machine Learning feature selection methods for landslide susceptibility mapping. Mathematical Geosciences, vol. 46, 2014, pp. 33–57. https://doi.org/10.1007/s11004-013-9511-0.
  6. Saini R., Verma S.K., Gautam A.: Implementation of Machine Learning classifiers for built-up extraction using textural features on Sentinel-2 data. [in:] 2021 IEEE 7th International Conference on Advanced Computing and Communication Systems (ICACCS), Vol. 1, IEEE, Piscataway 2021, pp. 1394–1399. https://doi.org/10.1109/ICACCS51430.2021.9441713.
  7. Saini R., Ghosh S.K.: Exploring capabilities of Sentinel-2 for vegetation mapping using Random Forest. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLII-3, 2018, pp. 1499–1502. https://doi.org/10.5194/isprs-archives-XLII-3-1499-2018.
  8. Saini R., Ghosh S.K.: Crop classification in a heterogeneous agricultural environment using ensemble classifiers and single-date Sentinel-2A imagery. Geocarto International, vol. 36(19), 2019, pp. 2141–2159. https://doi.org/10.1080/10106049.2019.1700556.
  9. Lu D., Weng Q.: A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, vol. 28(5), 2007, pp. 823–870. https://doi.org/10.1080/01431160600746456.
  10. Breiman L.: Random Forests. Machine Learning, vol. 45(1), 2001, pp. 5–32. https://doi.org/10.1023/A:1010933404324.
  11. Belgiu M., Drăguţ L.: Random forest in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, vol. 114, 2016, pp. 24–31. https://doi.org/10.1016/j.isprsjprs.2016.01.011.
  12. Zhang C., Xie Z.: Data fusion and classifier ensemble techniques for vegetation mapping in the coastal Everglades. Geocarto International, vol. 29(3), 2013, pp. 228–243. https://doi.org/10.1080/10106049.2012.756940.
  13. Freund Y., Schapire R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, vol. 55(1), 1997, pp. 119–139. https://doi.org/10.1006/jcss.1997.1504.
  14. Colkesen I., Kavzoglu T.: Ensemble-based canonical correlation forest (CCF) for land use and land cover classification using Sentinel-2 and Landsat OLI imagery. Remote Sensing Letters, vol. 8(11), 2017, pp. 1082–1091. https://doi.org/10.1080/2150704X.2017.1354262.
  15. Schuster C., Förster M., Kleinschmit B.: Testing the red edge channel for improving land-use classifications based on high-resolution multi-spectral satellite data. International Journal of Remote Sensing, vol. 33(17), 2012, pp. 5583–5599. https://doi.org/10.1080/01431161.2012.666812.
  16. Inglada J., Arias M., Tardy B., Hagolle O., Valero S., Morin D., Dedieu G. et al.: Assessment of an operational system for crop type map production using high temporal and spatial resolution satellite optical imagery. Remote Sensing, vol. 7(9), 2015, pp. 12356–12379. https://doi.org/10.3390/rs70912356.
  17. Sonobe R., Yamaya Y., Tani H., Wang X., Kobayashi N., Mochizuki K.I.: Evaluating metrics derived from Landsat 8 OLI imagery to map crop cover. Geocarto International, vol. 34(8), 2019, pp. 839–855. https://doi.org/10.1080/10106049.2018.1425739.
  18. Frampton W.J., Dash J., Watmough G., Milton E.J.: Evaluating the capabilities of Sentinel-2 for quantitative estimation of biophysical variables in vegetation. ISPRS Journal of Photogrammetry and Remote Sensing, vol. 82, 2013, pp. 83–92. https://doi.org/10.1016/j.isprsjprs.2013.04.007.
  19. Rotjanakusol T., Laosuwan T.: An Investigation of Drought around Chi Watershed during Ten-Year Period Using Terra/Modis Data. Geographia Technica, vol. 14(2), 2019, pp. 74–83. https://doi.org/10.21163/GT_2019.142.07.
  20. Uttaruk Y., Laosuwan T.: Drought detection by application of remote sensing technology and vegetation phenology. Journal of Ecological Engineering, vol. 18(6), 2017, pp. 115–121. https://doi.org/10.12911/22998993/76326.
  21. Rouse J.W., Haas R.H., Schell J.H.: Monitoring the Vernal Advancement and Retrogradation (Greenwave Effect) of Natural Vegetation. Texas A & M University, College Station, 1974.
  22. Gitelson A., Merzlyak M.N.: Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimation. Journal of Plant Physiology, vol. 143(3), 1994, pp. 286–292. https://doi.org/10.1016/S0176-1617(11)81633-0.
  23. Gitelson A.A., Kaufman Y.J., Merzlyak M.N.: Use of a green channel inremote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment, vol. 58(3), 1996, pp. 289–298. https://doi.org/10.1016/S0034-4257(96)00072-7.
  24. Haboudane D., Miller J.R., Pattey E., Zarco-Tejada P.J. Strachan I.B.: Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sensing of Environment, vol. 90(3), 2004, pp. 337–352. https://doi.org/10.1016/j.rse.2003.12.013.
  25. Chen P.F., Tremblay N., Wang J.H.., Vigneaulta P.: New index for crop canopyfresh biomass estimation. Spectroscopy and Spectral Analysis, vol. 30(2), 2010, pp. 512–517 [in Chinese]. https://doi.org/10.3964/j.issn.1000-0593(2010)02-0512-06.
  26. Forkuor G., Dimobe K., Serme I., Tondoh J.E.: Landsat-8 vs. Sentinel-2: examining the added value of sentinel-2’s red-edge bands to land-use and land-cover mapping in Burkina Faso. GIScience & Remote Sensing, vol. 55(3), 2018, pp. 331–354. https://doi.org/10.1080/15481603.2017.1370169.
  27. Kim H.-O., Yeom J.-M.: Sensitivity of vegetation indices to spatial degradation of RapidEye imagery for paddy rice detection: a case study of South Korea. GIScience & Remote Sensing, vol. 52(1), 2015, pp. 1–17. https://doi.org/10.1080/15481603.2014.1001666.
  28. Ustuner M., Sanli F.B., Abdikan S., Esetlili M.T., Kurucu Y.: Crop type classification using vegetation indices of RapidEye imagery. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XL-7, 2014, pp. 195–198. https://doi.org/10.5194/isprsarchives-XL-7-195-2014.
  29. Otunga C., Odindi J., Mutanga O., Adjorlolo C.: Evaluating the potential of the red edge channel for C3 (Festuca spp.) grass discrimination using Sentinel-2 and Rapid Eye satellite image data. Geocarto International, vol. 34(10), 2019, pp. 1123–1143. https://doi.org/10.1080/10106049.2018.1474274.
  30. Peng L., Liu K., Cao J., Zhu Y., Li F., Liu L.: Combining GF-2 and RapidEye satellite data for mapping mangrove species using ensemble machine-learning methods. International Journal of Remote Sensing, vol. 41(3), 2020, pp. 813–838. https://doi.org/10.1080/01431161.2019.1648907.
Read More
Author biographies is not available.
Download this PDF file
PDF
Statistic
Read Counter : 494 Download : 371

Downloads

Download data is not yet available.