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Deciphering Tropical Tree Communities Using Earth Observation Data and Machine Learning


Affiliations
1 Indian Institute of Remote Sensing (ISRO), Dehradun 248 001, India
2 National Remote Sensing Centre (ISRO), Hyderabad 500 037, India
 

Publicly available EO datasets offer new possibilities to generate biodiversity information at the community composition level, an essential biodiversity variable, beyond forest type. We demonstrated the potential of Sentinel-2, GEDI LiDAR canopy height and ALOSDEM in discriminating and classifying tropical tree communities in the Western Himalayas, India. For this, tree communities were first identified based on the ordination of field data and subsequently classified using satellite data applying machine learning, i.e. random forest (RF). From the three forest types in the study area, eight distinct tree communities were identified for which classification accuracy increased from single date (75.17%) to multi-date images (85.33%) and further by applying feature selection (88.17%). Whereas the best classification accuracy of 94.66% was achieved when canopy height and topographic variables were also considered. The findings suggest that RF is suitable for mapping tree communities by combining Sentinel-2 with GEDI and DEM parameters.

Keywords

Biodiversity, Canopy Height, Machine Learning, Remote Sensing, Tropical Forest.
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  • Giam, X., Global biodiversity loss from tropical deforestation. Proc. Natl. Acad. Sci. USA, 2017, 114, 5775–5777.

  • Barlow, J. et al., Anthropogenic disturbance in tropical forests can double biodiversity loss from deforestation. Nature, 2016, 535, 144–147.

  • Imai, N. et al., Tree community composition as an indicator in biodiversity monitoring of REDD+. For. Ecol. Manage., 2014, 313, 169–179.

  • Ferrier, S., Mapping spatial pattern in biodiversity for regional conservation planning: where to from here? Syst. Biol., 2002, 51, 331–363.

  • Pettorelli, N. et al., Framing the concept of satellite remote sensing essential biodiversity variables: challenges and future directions. Remote Sensing Ecol. Conserv., 2016, 2, 122–131.

  • Vihervaara, P. et al., How essential biodiversity variables and remote sensing can help national biodiversity monitoring. Global Ecol. Conserv., 2017, 10, 43–59.

  • Erinjery, J. J., Singh, M. and Kent, R., Mapping and assessment of vegetation types in the tropical rainforests of the Western Ghats using multispectral Sentinel-2 and SAR Sentinel-1 satellite imagery. Remote Sensing Environ., 2018, 216, 345–354.

  • Macintyre, P., van Niekerk, A. and Mucina, L., Efficacy of multi-season Sentinel-2 imagery for compositional vegetation classification. Int. J. Appl. Earth Obs. Geoinf., 2020, 85, 101980.

  • Breidenbach, J. et al., National mapping and estimation of forest area by dominant tree species using Sentinel-2 data. Can. J. For. Res., 2021, 51, 365–379.

  • Young, B., Yarie, J., Verbyla, D., Huettmann, F. and Herrick, K. C. F., Modeling and mapping forest diversity in the boreal forest of interior Alaska. Landsc. Ecol., 2017, 32, 397–413.

  • Nordkvist, K., Granholm, A. H., Holmgren, J., Olsson, H. and Nilsson, M., Combining optical satellite data and airborne laser scanner data for vegetation classification. Remote Sensing Lett., 2012, 3, 393–401.

  • Rees, H., Nyström, M., Nordkvist, K. and Olsson, H., Combining airborne laser scanning data and optical satellite data for classification of alpine vegetation. Int. J. Appl. Earth Obs. Geoinf., 2014, 27, 81–90.

  • Abdollahnejad, A., Panagiotidis, D., Joybari, S. S. and Surovỳ, P., Prediction of dominant forest tree species using QuickBird and environmental data. Forests, 2017, 8, 42.

  • Hościło, A. and Lewandowska, A., Mapping forest type and tree species on a regional scale using multi-temporal Sentinel-2 data. Remote Sensing, 2019, 11, 929.

  • Dorigo, W., Lucieer, A., Podobnikar, T. and Čarni, A., Mapping invasive Fallopia japonica by combined spectral, spatial, and temporal analysis of digital orthophotos. Int. J. Appl. Earth Obs. Geoinf., 2012, 19, 185–195.

  • Khatami, R., Mountrakis, G. and Stehman, S. V., A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: general guidelines for practitioners and future research. Remote Sensing Environ., 2016, 177, 89–100.

  • Peel, M. C., Finlayson, B. L. and Mcmahon, T. A., Hydrology and earth system sciences updated world map of the Köppen–Geiger climate classification. Hydrol. Earth Syst. Sci., 2007, 11, 1633–1644.

  • Champion, H. G. and Seth, S. K., A Revised Survey of the Forest Types of India, Manager of Publication, 1968.

  • Chacko, V. J., A Manual on Sampling Techniques for Forest Surveys, Manager of Publications, Government of India, New Delhi, 1965.

  • http://www.rainfor.org/ (accessed on 2 April 2019).

  • Hill, M. O., A FORTRAN Program for arranging multivariate data in an ordered two-way table by classification of the individuals and attributes. Twinspan, 1979.

  • https://scihub.copernicus.eu/ (accessed on 16 December 2019).

  • Breiman, L., Statistical modeling: the two cultures. Stat. Sci., 2001, 16, 199–215.

  • Rapinel, S., Rossignol, N., Hubert-Moy, L., Bouzillé, J. B. and Bonis, A., Mapping grassland plant communities using a fuzzy approach to address floristic and spectral uncertainty. Appl. Veg. Sci., 2018, 21, 678–693.

  • Ramoelo, A., Cho, M., Mathieu, R. and Skidmore, A., Potential of Sentinel-2 spectral configuration to assess rangeland quality. J. Appl. Remote Sensing, 2015, 9, 094096.

  • Immitzer, M., Vuolo, F. and Atzberger, C., First experience with Sentinel-2 data for crop and tree species classifications in central Europe. Remote Sensing, 2016, 8, 166.

  • Clerici, N., Valbuena Calderón, C. A. and Posada, J. M., Fusion of Sentinel-1A and Sentinel-2A data for land cover mapping: a case study in the lower Magdalena region, Colombia. J. Maps, 2017, 13, 718–726.

  • Zarco-Tejada, P. J., Miller, J. R., Noland, T. L., Mohammed, G. H. and Sampson, P. H., Scaling-up and model inversion methods with narrowband optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data. IEEE Trans. Geosci. Remote Sensing, 2001, 39, 1491–1507.

  • Dorren, L. K. A., Maier, B. and Seijmonsbergen, A. C., Improved landsat-based forest mapping in steep mountainous terrain using object-based classification. For. Ecol. Manage., 2003, 183, 31–46.

  • Rouse, J. W., Haas, R. H., Schell, J. A. and Deering, D. W., Monitoring vegetation systems in the Great Plains with ERTS, NASA Special Publication, USA, 1974, vol. 351(1), p. 309.

  • Jiang, Z., Huete, A. R., Didan, K. and Miura, T., Development of a two-band enhanced vegetation index without a blue band. Remote Sensing Environ., 2008, 112, 3833–3845.

  • Gitelson, A. and 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. J. Plant Physiol., 1994, 143, 286–292.

  • Gitelson, A. A., Gritz, Y. and Merzlyak, M. N., Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. J. Plant Physiol., 2003, 160, 271–282.

  • Tucker, C. J., Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing Environ., 1979, 8, 127–150.


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  • Deciphering Tropical Tree Communities Using Earth Observation Data and Machine Learning

Abstract Views: 276  |  PDF Views: 145

Authors

Rahul Bodh
Indian Institute of Remote Sensing (ISRO), Dehradun 248 001, India
Hitendra Padalia
Indian Institute of Remote Sensing (ISRO), Dehradun 248 001, India
Divesh Pangtey
Indian Institute of Remote Sensing (ISRO), Dehradun 248 001, India
Ishwari Datt Rai
Indian Institute of Remote Sensing (ISRO), Dehradun 248 001, India
Subrata Nandy
Indian Institute of Remote Sensing (ISRO), Dehradun 248 001, India
C. Sudhakar Reddy
National Remote Sensing Centre (ISRO), Hyderabad 500 037, India

Abstract


Publicly available EO datasets offer new possibilities to generate biodiversity information at the community composition level, an essential biodiversity variable, beyond forest type. We demonstrated the potential of Sentinel-2, GEDI LiDAR canopy height and ALOSDEM in discriminating and classifying tropical tree communities in the Western Himalayas, India. For this, tree communities were first identified based on the ordination of field data and subsequently classified using satellite data applying machine learning, i.e. random forest (RF). From the three forest types in the study area, eight distinct tree communities were identified for which classification accuracy increased from single date (75.17%) to multi-date images (85.33%) and further by applying feature selection (88.17%). Whereas the best classification accuracy of 94.66% was achieved when canopy height and topographic variables were also considered. The findings suggest that RF is suitable for mapping tree communities by combining Sentinel-2 with GEDI and DEM parameters.

Keywords


Biodiversity, Canopy Height, Machine Learning, Remote Sensing, Tropical Forest.

References





DOI: https://doi.org/10.18520/cs%2Fv124%2Fi6%2F704-712