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Using single- and multi-target regression trees and ensembles to model a compound index of vegetation condition

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  • Kocev, Dragi
  • Džeroski, Sašo
  • White, Matt D.
  • Newell, Graeme R.
  • Griffioen, Peter

Abstract

An important consideration in conservation and biodiversity planning is an appreciation of the condition or integrity of ecosystems. In this study, we have applied various machine learning methods to the problem of predicting the condition or quality of the remnant indigenous vegetation across an extensive area of south-eastern Australia—the state of Victoria. The field data were obtained using the ‘habitat hectares’ approach. This rapid assessment technique produces multiple scores that describe the condition of various attributes of the vegetation at a given site. Multiple sites were assessed and subsequently circumscribed with GIS and remote-sensed data.

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  • Kocev, Dragi & Džeroski, Sašo & White, Matt D. & Newell, Graeme R. & Griffioen, Peter, 2009. "Using single- and multi-target regression trees and ensembles to model a compound index of vegetation condition," Ecological Modelling, Elsevier, vol. 220(8), pages 1159-1168.
    • Handle: RePEc:eee:ecomod:v:220:y:2009:i:8:p:1159-1168
    DOI: 10.1016/j.ecolmodel.2009.01.037
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    References listed on IDEAS

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    1. R. J. Scholes & R. Biggs, 2005. "A biodiversity intactness index," Nature, Nature, vol. 434(7029), pages 45-49, March.
    2. C. R. Margules & R. L. Pressey, 2000. "Systematic conservation planning," Nature, Nature, vol. 405(6783), pages 243-253, May.
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    2. Meenakshi Sharma & Prashant Kaushik & Aakash Chawade, 2021. "Frontiers in the Solicitation of Machine Learning Approaches in Vegetable Science Research," Sustainability, MDPI, vol. 13(15), pages 1-14, August.
    3. Jeongsub Choi & Mengmeng Zhu & Jihoon Kang & Myong K. Jeong, 2024. "Convolutional neural network based multi-input multi-output model for multi-sensor multivariate virtual metrology in semiconductor manufacturing," Annals of Operations Research, Springer, vol. 339(1), pages 185-201, August.
    4. Jagannath Aryal & Chiranjibi Sitaula & Sunil Aryal, 2022. "NDVI Threshold-Based Urban Green Space Mapping from Sentinel-2A at the Local Governmental Area (LGA) Level of Victoria, Australia," Land, MDPI, vol. 11(3), pages 1-21, February.
    5. Moung-Jin Lee & Wonkyong Song & Saro Lee, 2015. "Habitat Mapping of the Leopard Cat ( Prionailurus bengalensis ) in South Korea Using GIS," Sustainability, MDPI, vol. 7(4), pages 1-21, April.
    6. Sinclair, Steve J. & Avirmed, Otgonsuren & White, Matthew D. & Batpurev, Khorloo & Griffioen, Peter A. & Liu, Canran & Jambal, Sergelenkhuu & Sime, Hayley & Olson, Kirk A., 2021. "Rangeland condition assessment in the Gobi Desert: A quantitative approach that places stakeholder evaluations front and Centre," Ecological Economics, Elsevier, vol. 181(C).
    7. Everaert, Gert & Boets, Pieter & Lock, Koen & Džeroski, Sašo & Goethals, Peter L.M., 2011. "Using classification trees to analyze the impact of exotic species on the ecological assessment of polder lakes in Flanders, Belgium," Ecological Modelling, Elsevier, vol. 222(14), pages 2202-2212.
    8. Holguin-Gonzalez, Javier E. & Boets, Pieter & Alvarado, Andres & Cisneros, Felipe & Carrasco, María C. & Wyseure, Guido & Nopens, Ingmar & Goethals, Peter L.M., 2013. "Integrating hydraulic, physicochemical and ecological models to assess the effectiveness of water quality management strategies for the River Cuenca in Ecuador," Ecological Modelling, Elsevier, vol. 254(C), pages 1-14.
    9. Schmid, Lena & Gerharz, Alexander & Groll, Andreas & Pauly, Markus, 2023. "Tree-based ensembles for multi-output regression: Comparing multivariate approaches with separate univariate ones," Computational Statistics & Data Analysis, Elsevier, vol. 179(C).
    10. Yujing Zhou & Dubo He, 2024. "Multi-Target Feature Selection with Adaptive Graph Learning and Target Correlations," Mathematics, MDPI, vol. 12(3), pages 1-24, January.
    11. Shijie Li & Zuoqin Qian & Ji Liu, 2024. "Multi-Output Regression Algorithm-Based Non-Dominated Sorting Genetic Algorithm II Optimization for L-Shaped Twisted Tape Insertions in Circular Heat Exchange Tubes," Energies, MDPI, vol. 17(4), pages 1-22, February.
    12. Kocev, Dragi & Naumoski, Andreja & Mitreski, Kosta & Krstić, Svetislav & Džeroski, Sašo, 2010. "Learning habitat models for the diatom community in Lake Prespa," Ecological Modelling, Elsevier, vol. 221(2), pages 330-337.
    13. Mannan Karim & Jiqiu Deng & Muhammad Ayoub & Wuzhou Dong & Baoyi Zhang & Muhammad Shahzad Yousaf & Yasir Ali Bhutto & Muhammad Ishfaque, 2023. "Improved Cropland Abandonment Detection with Deep Learning Vision Transformer (DL-ViT) and Multiple Vegetation Indices," Land, MDPI, vol. 12(10), pages 1-24, October.
    14. Wen Song & Wei Song & Haihong Gu & Fuping Li, 2020. "Progress in the Remote Sensing Monitoring of the Ecological Environment in Mining Areas," IJERPH, MDPI, vol. 17(6), pages 1-17, March.
    15. Choi, Jong-Kuk & Oh, Hyun-Joo & Koo, Bon Joo & Ryu, Joo-Hyung & Lee, Saro, 2011. "Crustacean habitat potential mapping in a tidal flat using remote sensing and GIS," Ecological Modelling, Elsevier, vol. 222(8), pages 1522-1533.
    16. Meyer, Angela, 2021. "Multi-target normal behaviour models for wind farm condition monitoring," Applied Energy, Elsevier, vol. 300(C).

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