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Spectrometric Models to estimate Forage Provision variables (green Biomass, metabolisable Energy) from hyperspectral Reflectance of Vegetation Canopies

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These models enable the user to estimate forage quantity (green biomass, gBM) and forage quality (metabolisable energy, ME) from hyperspectral reflectance data of vegetation canopies measured using an ASD FieldSpec 3 (or higher) Hi-Res Portable Spectroradiometer. The models were calibrated in R statistical software and were provided as RData-Files.

The models were calibrated using spectral reflectance as well as forage provision data from several sites within the study area, comprising a steep south-north gradient of climatic aridity reaching from northern Ghana to central Burkina Faso between 9.0°N and 13.5°N latitude and 0.1°W and 2.0°W longitude. It is located in the southern and northern Sudanian zone of West Africa’s savanna belt, capturing a precipitation range of 600 mm to 1200 mm corresponding to UNEP aridity indices of 0.31 (semi-arid) to 0.69 (humid).

Data collection took place at 21 sites during the rainy season 2012 (June-September). Our sampling design intended to cover diverse vegetation types and a wide range of land-use intensities (including protected and degraded areas). We stratified sampling at sites by topographic position (upslope, footslope and lowland).

Vegetation samples were oven-dried (60°C, > 48 h) to obtain gBM. Dried samples were ground for analysis of in vitro gas production (GP) using the Hohenheim gas test (HGT). Crude protein (CP) content was determined by LUFA NRW using Kjeldahl´s method (method 4.1.1). The ME was calculated using the following equation:

ME (MJ kg-1 dry matter, DM) = 2.20 + 0.1357 GP + 0.0057 CP + 0.0002859 CP²,

where GP is expressed as ml 200 mg-1 DM and CP is expressed as g kg-1 DM.

A partial least-squares regression was used to model the relations between spectral data and target variables (ME and gBM). We used the PLSR implementation in the R package 'autopls'. During PLSR, we also performed multiplicative scatter correction and brightness normalization of reflectance spectra.

Before the models can be applied, spectra have to be smoothed using Savitzky-Golay smoothing filter and noisy regions (bands 940:1170, 1350:1700, 2001:5000) have to be excluded from model application.

Further information:

Ferner, J., Linstädter, A., Südekum, K. H., & Schmidtlein, S. (2015). Spectral indicators of forage quality in West Africa’s tropical savannas. International Journal of Applied Earth Observation and Geoinformation, 41, 99-106.

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FieldValue
Modified
2024-03-08
Release Date
2019-09-18
Identifier
9dcb6908-014b-4575-ade5-e711d03a522b
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Temporal Coverage
Friday, January 20, 2017 (All day)
Language
English
Contact Name
Jessica Ferner
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Jessica Ferner
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Restricted
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Data Citation
Ferner, J., Linstädter, A., Südekum, K. H., & Schmidtlein, S. (2015). Spectral indicators of forage quality in West Africa’s tropical savannas. International Journal of Applied Earth Observation and Geoinformation, 41, 99-106. https://wascal-dataportal.org/geonetwork/apps/search/?uuid=0029ab7f-d227-4f57-84b4-adcdb931886f
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