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

Sustainability

Jingwei Song¹  Xinyuan Wang¹  Ying Liao¹  Jing Zhen¹  Natarajan Ishwaran²  Huadong Guo¹  Ruixia Yang¹  Chuansheng Liu¹  Chun Chang¹ 

[1] Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China; E-Mails:;International Centre on Space Technologies for National and Cultural Heritage under the Auspices of UNESCO, Chinese Academy of Sciences and UNESCO, Beijing 100094, China; E-Mail:

关键词: habitat assessment;    artificial neural network;    geographic information system;    remote sensing;    giant panda;   

DOI  :  10.3390/su6074059

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.