Target‐oriented habitat and wildlife management: estimating forage quantity and quality of semi‐natural grasslands with Sentinel‐1 and Sentinel‐2 data-Reference-Cited by-同舟云学术

Target‐oriented habitat and wildlife management: estimating forage quantity and quality of semi‐natural grasslands with Sentinel‐1 and Sentinel‐2 data

Published:2020-02-24 Issue:3 Volume:6 Page:381-398
ISSN:2056-3485
Container-title:Remote Sensing in Ecology and Conservation
language:en
Short-container-title:Remote Sens Ecol Conserv

Author:

Raab Christoph¹²^ORCID,Riesch Friederike¹²^ORCID,Tonn Bettina¹²^ORCID,Barrett Brian³^ORCID,Meißner Marcus⁴,Balkenhol Niko²⁴⁵,Isselstein Johannes¹²⁴

Affiliation:

1. Grassland Science Faculty of Agricultural Science University of Goettingen Von‐Siebold‐Str. 8 37075Goettingen Germany

2. Centre of Biodiversity and Sustainable Land Use (CBL) University of Goettingen Buesgenweg 1 37077Goettingen

3. School of Geographical and Earth Sciences University of Glasgow Scotland United Kingdom

4. Institut für Wildbiologie Göttingen und Dresden e.V. Buesgenweg 3 37077Goettingen Germany

5. Wildlife Sciences Faculty of Forest Sciences and Forest Ecology University of Goettingen 37077Goettingen Germany

Funder

Landwirtschaftliche Rentenbank

Publisher

Wiley

Subject

Nature and Landscape Conservation,Computers in Earth Sciences,Ecology,Ecology, Evolution, Behavior and Systematics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/rse2.149

Reference105 articles.

1. Measuring chemical composition and nutritive value in forages.

2. Satellite remote sensing of grasslands: from observation to management

3. Application of Repeat-Pass TerraSAR-X Staring Spotlight Interferometric Coherence to Monitor Pasture Biophysical Parameters: Limitations and Sensitivity Analysis

4. Modeling Managed Grassland Biomass Estimation by Using Multitemporal Remote Sensing Data—A Machine Learning Approach

5. Assessment of multi-temporal, multi-sensor radar and ancillary spatial data for grasslands monitoring in Ireland using machine learning approaches

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