File Download
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.3390/rs10050739
- Scopus: eid_2-s2.0-85047540273
- WOS: WOS:000435198400081
Supplementary
- Citations:
- Appears in Collections:
Article: Developing an integrated remote sensing based biodiversity index for predicting animal species richness
Title | Developing an integrated remote sensing based biodiversity index for predicting animal species richness |
---|---|
Authors | |
Keywords | Biodiversity Metric comparison Metric integration Remote sensing Species richness |
Issue Date | 2018 |
Citation | Remote Sensing, 2018, v. 10, n. 5, article no. 739 How to Cite? |
Abstract | Many remote sensingmetrics have been applied in large-scale animal speciesmonitoring and conservation. However, the capabilities of these metrics have not been well compared and assessed. In this study, we investigated the correlation of 21 remote sensing metrics in three categories with the global species richness of three different animal classes using several statistical methods. As a result, we developed a new index by integrating several highly correlated metrics. Of the 21 remote sensing metrics analyzed, evapotranspiration (ET) had the greatest impact on species richness on a global scale (explained variance: 52%). Themetricswith a high explained variance on the global scaleweremainly in the energy/productivity category. Themetrics in the texture category exhibited higher correlation with species richness at regional scales. We found that radiance and temperature had a larger impact on the distribution of bird richness, compared to their impacts on the distributions of both amphibians and mammals. Threemachine learningmodels (i.e., support vectormachine, randomforests, and neural networks) were evaluated formetric integration, and the randomforestmodel showed the best performance. Our newly developed index exhibited a 0.7 explained variance for the three animal classes' species richness on a global scale, with an explained variance that was 20% higher than any of the univariatemetrics. |
Persistent Identifier | http://hdl.handle.net/10722/321791 |
ISI Accession Number ID |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Wu, Jinhui | - |
dc.contributor.author | Liang, Shunlin | - |
dc.date.accessioned | 2022-11-03T02:21:28Z | - |
dc.date.available | 2022-11-03T02:21:28Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | Remote Sensing, 2018, v. 10, n. 5, article no. 739 | - |
dc.identifier.uri | http://hdl.handle.net/10722/321791 | - |
dc.description.abstract | Many remote sensingmetrics have been applied in large-scale animal speciesmonitoring and conservation. However, the capabilities of these metrics have not been well compared and assessed. In this study, we investigated the correlation of 21 remote sensing metrics in three categories with the global species richness of three different animal classes using several statistical methods. As a result, we developed a new index by integrating several highly correlated metrics. Of the 21 remote sensing metrics analyzed, evapotranspiration (ET) had the greatest impact on species richness on a global scale (explained variance: 52%). Themetricswith a high explained variance on the global scaleweremainly in the energy/productivity category. Themetrics in the texture category exhibited higher correlation with species richness at regional scales. We found that radiance and temperature had a larger impact on the distribution of bird richness, compared to their impacts on the distributions of both amphibians and mammals. Threemachine learningmodels (i.e., support vectormachine, randomforests, and neural networks) were evaluated formetric integration, and the randomforestmodel showed the best performance. Our newly developed index exhibited a 0.7 explained variance for the three animal classes' species richness on a global scale, with an explained variance that was 20% higher than any of the univariatemetrics. | - |
dc.language | eng | - |
dc.relation.ispartof | Remote Sensing | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | Biodiversity | - |
dc.subject | Metric comparison | - |
dc.subject | Metric integration | - |
dc.subject | Remote sensing | - |
dc.subject | Species richness | - |
dc.title | Developing an integrated remote sensing based biodiversity index for predicting animal species richness | - |
dc.type | Article | - |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.3390/rs10050739 | - |
dc.identifier.scopus | eid_2-s2.0-85047540273 | - |
dc.identifier.volume | 10 | - |
dc.identifier.issue | 5 | - |
dc.identifier.spage | article no. 739 | - |
dc.identifier.epage | article no. 739 | - |
dc.identifier.eissn | 2072-4292 | - |
dc.identifier.isi | WOS:000435198400081 | - |