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An Improved Neural Network for Regional Giant Panda Habitat Suitability Mapping: A Case Study in Ya’an Prefecture

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  • Jingwei Song

    (Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
    These authors contributed equally to this work.)

  • Xinyuan Wang

    (Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China)

  • Ying Liao

    (Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China)

  • Jing Zhen

    (Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China)

  • Natarajan Ishwaran

    (International Centre on Space Technologies for National and Cultural Heritage under the Auspices of UNESCO, Chinese Academy of Sciences and UNESCO, Beijing 100094, China
    These authors contributed equally to this work.)

  • Huadong Guo

    (Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China)

  • Ruixia Yang

    (Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China)

  • Chuansheng Liu

    (Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China)

  • Chun Chang

    (Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China)

  • Xin Zong

    (Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China)

Abstract

Expert knowledge is a combination of prior information and subjective opinions based on long-experience; as such it is often not sufficiently objective to produce convincing results in animal habitat suitability index mapping. In this study, an animal habitat assessment method based on a learning neural network is proposed to reduce the level of subjectivity in animal habitat assessments. Based on two hypotheses, this method substitutes habitat suitability index with apparent density and has advantages over conventional ones such as those based on analytical hierarchy process or multivariate regression approaches. Besides, this method is integrated with a learning neural network and is suitable for building non-linear transferring functions to fit complex relationships between multiple factors influencing habitat suitability. Once the neural network is properly trained, new earth observation data can be integrated for rapid habitat suitability monitoring which could save time and resources needed for traditional data collecting approaches through extensive field surveys. Giant panda ( Ailuropoda melanoleuca ) natural habitat in Ya’an prefecture and corresponding landsat images, DEM and ground observations are tested for validity of using the methodology reported. Results show that the method scores well in key efficiency and performance indicators and could be extended for habitat assessments, particularly of other large, rare and widely distributed animal species.

Suggested Citation

  • Jingwei Song & Xinyuan Wang & Ying Liao & Jing Zhen & Natarajan Ishwaran & Huadong Guo & Ruixia Yang & Chuansheng Liu & Chun Chang & Xin Zong, 2014. "An Improved Neural Network for Regional Giant Panda Habitat Suitability Mapping: A Case Study in Ya’an Prefecture," Sustainability, MDPI, vol. 6(7), pages 1-18, June.
  • Handle: RePEc:gam:jsusta:v:6:y:2014:i:7:p:4059-4076:d:37560
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    References listed on IDEAS

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    1. Lu, Chun Yan & Gu, Wei & Dai, Ai Hua & Wei, Hai Yan, 2012. "Assessing habitat suitability based on geographic information system (GIS) and fuzzy: A case study of Schisandra sphenanthera Rehd. et Wils. in Qinling Mountains, China," Ecological Modelling, Elsevier, vol. 242(C), pages 105-115.
    2. Thiago Nunes Kehl & Viviane Todt & Mauricio Roberto Veronez & Silvio César Cazella, 2012. "Amazon Rainforest Deforestation Daily Detection Tool Using Artificial Neural Networks and Satellite Images," Sustainability, MDPI, vol. 4(10), pages 1-8, October.
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    Cited by:

    1. Romaan Hayat Khattak & Liwei Teng & Shakeel Ahmad & Fathul Bari & Ejaz Ur Rehman & Altaf Ali Shah & Zhensheng Liu, 2022. "In Pursuit of New Spaces for Threatened Mammals: Assessing Habitat Suitability for Kashmir Markhor ( Capra falconeri cashmeriensis) in the Hindukush Range," Sustainability, MDPI, vol. 14(3), pages 1-15, January.
    2. 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.
    3. Jing Zhen & Xinyuan Wang & Qingkai Meng & Jingwei Song & Ying Liao & Bo Xiang & Huadong Guo & Chuansheng Liu & Ruixia Yang & Lei Luo, 2018. "Fine-Scale Evaluation of Giant Panda Habitats and Countermeasures against the Future Impacts of Climate Change and Human Disturbance (2015–2050): A Case Study in Ya’an, China," Sustainability, MDPI, vol. 10(4), pages 1-19, April.

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