Next Article in Journal
Bilateral Filter Regularized L2 Sparse Nonnegative Matrix Factorization for Hyperspectral Unmixing
Next Article in Special Issue
Physical Retrieval of Land Surface Emissivity Spectra from Hyper-Spectral Infrared Observations and Validation with In Situ Measurements
Previous Article in Journal
Assessment of Forest Above-Ground Biomass Estimation from PolInSAR in the Presence of Temporal Decorrelation
Previous Article in Special Issue
Modeling the Distributions of Brightness Temperatures of a Cropland Study Area Using a Model that Combines Fast Radiosity and Energy Budget Methods
Article Menu
Issue 6 (June) cover image

Export Article

Open AccessArticle

Application of Thermal and Phenological Land Surface Parameters for Improving Ecological Niche Models of Betula utilis in the Himalayan Region

1
Center for Earth System Research and Sustainability, Institute of Geography, University of Hamburg, Bundesstraße 55, 20146 Hamburg, Germany
2
Biodiversity, Evolution and Ecology of Plants (BEE), Biocenter Klein Flottbek and Botanical Garden, University of Hamburg, Ohnhorststr. 18, 22609 Hamburg, Germany
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(6), 814; https://doi.org/10.3390/rs10060814
Received: 24 April 2018 / Revised: 18 May 2018 / Accepted: 22 May 2018 / Published: 24 May 2018
  |  
PDF [1982 KB, uploaded 24 May 2018]
  |  

Abstract

Modelling ecological niches across vast distribution ranges in remote, high mountain regions like the Himalayas faces several data limitations, in particular nonavailability of species occurrence data and fine-scale environmental information of sufficiently high quality. Remotely sensed data provide key advantages such as frequent, complete, and long-term observations of land surface parameters with full spatial coverage. The objective of this study is to evaluate modelled climate data as well as remotely sensed data for modelling the ecological niche of Betula utilis in the subalpine and alpine belts of the Himalayan region covering the entire Himalayan arc. Using generalized linear models (GLM), we aim at testing factors controlling the species distribution under current climate conditions. We evaluate the additional predictive capacity of remotely sensed variables, namely remotely sensed topography and vegetation phenology data (phenological traits), as well as the capability to substitute bioclimatic variables from downscaled numerical models by remotely sensed annual land surface temperature parameters. The best performing model utilized bioclimatic variables, topography, and phenological traits, and explained over 69% of variance, while models exclusively based on remotely sensed data reached 65% of explained variance. In summary, models based on bioclimatic variables and topography combined with phenological traits led to a refined prediction of the current niche of B. utilis, whereas models using solely climate data consistently resulted in overpredictions. Our results suggest that remotely sensed phenological traits can be applied beneficially as supplements to improve model accuracy and to refine the prediction of the species niche. We conclude that the combination of remotely sensed land surface temperature parameters is promising, in particular in regions where sufficient fine-scale climate data are not available. View Full-Text
Keywords: Betula utilis; Chelsa; ecological niche model; Enhanced Vegetation Index; Himalaya; MODIS (Moderate-resolution Imaging Spectroradiometer) Land Cover Dynamics; MODIS Land Surface Temperature; plant phenology; remote sensing; treeline ecotone Betula utilis; Chelsa; ecological niche model; Enhanced Vegetation Index; Himalaya; MODIS (Moderate-resolution Imaging Spectroradiometer) Land Cover Dynamics; MODIS Land Surface Temperature; plant phenology; remote sensing; treeline ecotone
Figures

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Supplementary material

SciFeed

Share & Cite This Article

MDPI and ACS Style

Bobrowski, M.; Bechtel, B.; Böhner, J.; Oldeland, J.; Weidinger, J.; Schickhoff, U. Application of Thermal and Phenological Land Surface Parameters for Improving Ecological Niche Models of Betula utilis in the Himalayan Region. Remote Sens. 2018, 10, 814.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top