307
Views
1
CrossRef citations to date
0
Altmetric
Research Article

Brazilian Forest Dataset: A new dataset to model local biodiversity

, , &
Pages 327-354 | Received 26 Feb 2020, Accepted 02 Jan 2021, Published online: 31 Jan 2021

References

  • Bellisario, K. M., Broadhead, T., Savage, D., Zhao, Z., Omrani, H., Zhang, S., Springer, J., & Pijanowski, B. C. (2019). Contributions of mir to soundscape ecology. part 3:tagging and classifying audio features using a multi-labeling k-nearest neighbor approach. Ecological Informatics, 51, 103–111. https://doi.org/10.1016/j.ecoinf.2019.02.010
  • Bishop, C. M. (2006). Pattern recognition and machine learning. Springer.
  • Bosilovich, M. G., Robertson, F. R., Takacs, L., Molod, A., & Mocko, D. (2017). Atmospheric water balance and variability in the merra-2 reanalysis. Journal of Climate, 30(4), 1177–1196. https://doi.org/10.1175/JCLI-D-16-0338.1
  • Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
  • Breiman, L., Friedman, J., Stone, C. J., & Olshen, R. A. (1984). Classification and regression trees. CRC press.
  • Bridle, J. S. (1990). Probabilistic interpretation of feedforward classification network outputs, with relationships to statistical pattern recognition. In Françoise Fogelman Soulié and Jeanny Hérault (Ed.), Neurocomputing (pp. 227–236). Springer Berlin Heidelberg.
  • Bsoul, Q., Al-Shamari, E., Mohd, M., & Atwan, J. (2014). Distance measures and stemming impact on ‎arabic document clustering. In Azizah Jaafar,  Nazlena Mohamad Ali, Shahrul Azman Mohd Noah, Alan F. Smeaton, Peter Bruza, Zainab Abu Bakar, Nursuriati Jamil, and Tengku Mohd Tengku Sembok. (Ed.), Information retrieval technology (pp. 327–339). Springer International Publishing.
  • Burrows, M. T., Schoeman, D. S., Richardson, A. J., Molinos, J. G., Hoffmann, A., Buckley, L. B., Poloczanska. E. S. (2014). Geographical limits to species-range shifts are suggested by climate velocity. Nature, 507(7493), 492. https://doi.org/10.1038/nature12976
  • Carreiras, J., Pereira, J., & Shimabukuro, Y. E. (2006). Land-cover mapping in the brazilian amazon using spot-4 vegetation data and machine learning classification methods. Photogrammetric Engineering and Remote Sensing, 72(8), 897–910. https://doi.org/10.14358/PERS.72.8.897
  • Chianese, E., Camastra, F., Ciaramella, A., Landi, T., Staiano, A., & Riccio, A. (2019). Spatio-temporal learning in predicting ambient particulate matter concentration by multi-layer perceptron. Ecological Informatics, 49, 54–61. https://doi.org/10.1016/j.ecoinf.2018.12.001
  • Coleman, J., & Law, K. (2015). Meteorology. In Reference module in earth systems and environmental sciences. Elsevier. https://doi.org/10.1016/B978-0-12-409548-9.09492-6.
  • Daz, S., Settele, J., Brondizio, E., Ngo, H., Gueze, M., Agard, J., & Chan, K. (2019b). Global assessment report on biodiversity and ecosystem services of the intergovernmental science-policy platform on biodiversity and ecosystem services. UN Paris Fr, 1, 39. https://doi.org/10.5281/zenodo.3553579
  • Diaz, S., Settele, J., Brondzio, E., Ngo, H., Guèze, M., Agard, J., … Zayas, C. 2019a. Summary for policymakers of the global assessment report on biodiversity and ecosystem services of the intergovernmental science-policy platform on biodiversity and ecosystem services. Zenodo.
  • Dietterich, T. G., 2000. Ensemble methods in machine learning, In Proceedings of the first international workshop on multiple classifier systems, Berlin. MCS ‘00, 1–15.
  • Fensholt, R., Sandholt, I., & Rasmussen, M. S. (2004). Evaluation of MODIS LAI, fAPAR and the relation between fAPAR and NDVI in a semi-arid environment using in situ measurements. Remote Sensing of Environment, 91(3–4), 490–507. https://doi.org/10.1016/j.rse.2004.04.009
  • Gobron N., B. A. S., Knorr, W., & B., P. (2010). Fraction of Absorbed Photosynthetically Active Radiation (FAPAR). Bulletin of the American Meteorological Society, 91(7), S50–S51. https://doi.org/10.1175/BAMS-91-7-StateoftheClimate
  • Gobron N., P. B., Belward, A. S., & W., K. (2010). Monitoring biosphere vegetation 1998-2009. Geophysical Research Letters, 37(L15402), 6. https://doi.org/10.1029/2010GL043870
  • Gobron, N., Pinty, B., Verstraete, M., & Widlowski, J. L. (2000). Advanced vegetation indices optimized for up-coming sensors: design, performance, and applications. IEEE Transactions on Geoscience and Remote Sensing, 38(6), 2489–2505. https://doi.org/10.1109/36.885197
  • Haldar, S. (2013). Chapter 4 - exploration geochemistry. In S. Haldar (Ed.), Mineral exploration (pp. 55–71). Elsevier.
  • Haykin, S. (1994). Neural networks: A comprehensive foundation (1st ed.). Prentice Hall PTR.
  • Hethcoat, M., Edwards, D., Carreiras, J., Bryant, R., França, F., & Quegan, S. (2019). A machine learning approach to map tropical selective logging. Remote Sensing of Environment, 221, 569–582. https://doi.org/10.1016/j.rse.2018.11.044
  • Hill, S. L., Arnell, A., Butchart, S. H., Hilton-Taylor, C., Ciciarelli, C., Davis, C., Dinerstein, E., Purvis, A., & Burgess, N. D. (2019). Measuring forest biodiversity status and changes globally. Frontiers in Forests and Global Change, 2, 70. https://doi.org/10.3389/ffgc.2019.00070
  • Huang, J., & Gao, J. (2017). An ensemble simulation approach for artificial neural network: An example from chlorophyll a simulation in lake poyang, china. Ecological Informatics, 37, 52–58. https://doi.org/10.1016/j.ecoinf.2016.11.012
  • Jeawak, S. S., Jones, C. B., & Schockaert, S. (2020). Predicting environmental features by learning spatiotemporal embeddings from social media. Ecological Informatics, 55, 101031. https://doi.org/10.1016/j.ecoinf.2019.101031
  • Liu, L., & Lei, Y. (2018). An accurate ecological footprint analysis and prediction for beijing based on svm model. Ecological Informatics, 44, 33–42. https://doi.org/10.1016/j.ecoinf.2018.01.003
  • Mahecha, M. D., Furst, L. M., Gobron, N., & Lange, H. (2010). Identifying multiple spatiotemporal patterns: A refined view on terrestrial photosynthetic activity. Pattern Recognition Letters, 31(14), 2309–2317. https://doi.org/10.1016/j.patrec.2010.06.021
  • Masson-Delmotte, V., Zhai, P., Portner, H. O., Roberts, D., Skea, J., Shukla, P., Pirani, A., Moufouma-Okia, W., Péan, C., Pidcock, R., Connors, S., Matthews, J., Chen, Y., Zhou, X., Gomis, M., Lonnoy, E., Maycock, T., Tignor, M., ., & Waterfield, T., 2019. IPcc, 2018: global warming of 1.5c. an ipcc special report on the impacts of global warming of 1.5C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty, 2019 Intergovernmental Panel on Climate Change.
  • Matthews, B. (1975). Comparison of the predicted and observed secondary structure of t4 phage lysozyme. Biochimica Et Biophysica Acta (BBA) - Protein Structure, 405(2), 442–451. https://doi.org/10.1016/0005-2795(75)90109-9
  • Mavroforakis, M. E., & Theodoridis, S., 2005. Support vector machine (svm) classification through geometry, in: 2005 13th european signal processing conference, IEEE, 1–4.
  • McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics, 5(4), 115–133. https://doi.org/10.1007/BF02478259
  • Mello, R. F. D., & Ponti, M. A. (2018). Machine learning: A practical approach on the statistical learning theory. Springer.
  • Mitchell, T. M. (1997). Machine learning. McGraw-Hill.
  • Molod, A., Takacs, L., Suarez, M., & Bacmeister, J. (2015). Development of the geos-5 atmospheric general circulation model: Evolution from merra to merra2. Geoscientific Model Development, 8(5), 1339–1356. https://doi.org/10.5194/gmd-8-1339-2015
  • Mueller-Dombois, D. (2001). Island biogeography. In S. A. Levin (Ed.), Encyclopedia of biodiversity (pp. 565–580). Elsevier.
  • Nguyen, U., Glenn, E. P., Dang, T. D., & Pham, L. T. (2019). Mapping vegetation types in semi-arid riparian regions using random forest and object-based image approach: A case study of the colorado river ecosystem, grand canyon, arizona. Ecological Informatics, 50, 43–50. https://doi.org/10.1016/j.ecoinf.2018.12.006
  • O’Connor, B., Bojinski, S., Röösli, C., & Schaepman, M. E. (2020). Monitoring global changes in biodiversity and climate essential as ecological crisis intensifies. Ecological Informatics, 55, 101033. https://doi.org/10.1016/j.ecoinf.2019.101033
  • Paul, W., Stackhouse, T. Z., Westberg, D., Barnett, A. J., Bristow, T., Macpherson, B., & Hoell, J. M., 2018. POWER release 8 (with GIS applications) methodology (data parameters, sources, and validation) - data version 8.0.1, tech. rep., NASA, Last access: Nov 22, 2019.
  • Rienecker, M. M., Suarez, M. J., Gelaro, R., Todling, R., Bacmeister, J., Liu, E., Bosilovich, M. G., Schubert, S. D., Takacs, L., Kim, G. K., Bloom, S., Chen, J., Collins, D., Conaty, A., da Silva, A., Gu, W., Joiner, J., Koster, R. D., Lucchesi, R., Molod, A., … Woollen, J. (2011). Merra: nasa’s modern-era retrospective analysis for research and applications. Journal of Climate, 24(14), 3624–3648. https://doi.org/10.1175/JCLI-D-11-00015.1
  • Rios, R., Nogueira, T., Palma, G., & Mello, R., 2019. Brazilian forest dataset.
  • Rosenblatt, F., 1961. Principles of neurodynamics. perceptrons and the theory of brain mechanisms, tech. rep., CORNELL AERONAUTICAL LAB INC BUFFALO NY.
  • Schepaschenko, D., Chave, J., Phillips, O. L., Lewis, S. L., Davies, S. J., Réjou-Méchain, M., … Zo-Bi, I. (2019). The forest observation system, building a global reference dataset for remote sensing of forest biomass. Scientific Data, 6(1), 1–11. https://doi.org/10.1038/s41597-019-0196-1
  • Scholkopf, B., & Smola, A. J. (2001). Learning with kernels: support vector machines, regularization, optimization, and beyond., cambridge: MIT Press.
  • Srinet, R., Nandy, S., & Patel, N. (2019). Estimating leaf area index and light extinction coefficient using random forest regression algorithm in a tropical moist deciduous forest, india. Ecological Informatics, 52, 94–102. https://doi.org/10.1016/j.ecoinf.2019.05.008
  • Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15, 1929–1958. http://jmpapers/papers/v15/srivastava14a.html
  • Subhashini, R., & Kumar, V. J. S., 2010. Evaluating the performance of similarity measures used in document clustering and information retrieval, in: International conference on integrated intelligent computing, Bangalore, India, 27–31.
  • Vapnik, V. N. (1998). Statistical learning theory. 1st ed.
  • von Luxburg, U., & Schölkopf, B. (2011). Statistical learning theory: models, concepts, and results. In D. M. Gabbay, S. Hartmann, & J. Woods (Eds.), Inductive logic (Vol. 10, pp. 651–706). North-Holland, Handbook of the History of Logic.
  • Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data mining: practical machine learning tools and techniques (pp. 1–621). Morgan Kaufmann.
  • Zappi, D. C., Filardi, F. L. R., Leitman, P.,Souza, V. C., Walter, B. T. M., Pirani, J. R., … Zickel, C. S. (2015). Growing knowledge: An overview of seed plant diversity in Brazil. Rodriguesia, 66(4), 1085–1113. https://doi.org/10.1590/2175-7860201566411
  • Zhang, L., Huettmann, F., Liu, S., Sun, P., Yu, Z., Zhang, X., & Mi, C. (2019). Classification and regression with random forests as a standard method for presence-only data sdms: A future conservation example using china tree species. Ecological Informatics, 52, 46–56. https://doi.org/10.1016/j.ecoinf.2019.05.003

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.