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Remote Sens. 2017, 9(1), 81; doi:10.3390/rs9010081

Distinguishing Intensity Levels of Grassland Fertilization Using Vegetation Indices

1
Center for Remote Sensing of Land Surfaces, University of Bonn, Walter-Flex-Str. 3, Bonn NRW 53113, Germany
2
Institute of Crop Science and Resource Conservation, University of Bonn, Auf dem Hügel 6, Bonn NRW 53121, Germany
*
Author to whom correspondence should be addressed.
Academic Editors: Roberto Colombo and Prasad S. Thenkabail
Received: 8 December 2016 / Revised: 30 December 2016 / Accepted: 10 January 2017 / Published: 16 January 2017
View Full-Text   |   Download PDF [2316 KB, uploaded 16 January 2017]   |  

Abstract

Monitoring the reaction of grassland canopies on fertilizer application is of major importance to enable a well-adjusted management supporting a sustainable production of the grass crop. Up to date, grassland managers estimate the nutrient status and growth dynamics of grasslands by costly and time-consuming field surveys, which only provide low temporal and spatial data density. Grassland mapping using remotely-sensed Vegetation Indices (VIs) has the potential to contribute to solving these problems. In this study, we explored the potential of VIs for distinguishing five differently-fertilized grassland communities. Therefore, we collected spectral signatures of these communities in a long-term fertilization experiment (since 1941) in Germany throughout the growing seasons 2012–2014. Fifteen VIs were calculated and their seasonal developments investigated. Welch tests revealed that the accuracy of VIs for distinguishing these grassland communities varies throughout the growing season. Thus, the selection of the most promising single VI for grassland mapping was dependent on the date of the spectra acquisition. A random forests classification using all calculated VIs reduced variations in classification accuracy within the growing season and provided a higher overall precision of classification. Thus, we recommend a careful selection of VIs for grassland mapping or the utilization of temporally-stable methods, i.e., including a set of VIs in the random forests algorithm. View Full-Text
Keywords: field spectroscopy; classification; random forests; vegetation index; grassland; multitemporal; ground-based sensors field spectroscopy; classification; random forests; vegetation index; grassland; multitemporal; ground-based sensors
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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).

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MDPI and ACS Style

Hollberg, J.L.; Schellberg, J. Distinguishing Intensity Levels of Grassland Fertilization Using Vegetation Indices. Remote Sens. 2017, 9, 81.

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