0029ab7f-d227-4f57-84b4-adcdb931886f Jessica Ferner ferner@uni-bonn.de 2017-05-24T12:19:20 ISO 19115:2003/19139 1.0 Spectrometric Models to estimate Forage Provision variables (green Biomass, metabolisable Energy) from hyperspectral Reflectance of Vegetation Canopies 2017-01-20T11:00:00 1 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. Estimation of forage provision variables (gBM, ME) from hyperspectral reflectance of vegetation canopies Jessica Ferner Center for Remote Sensing of Land Surfaces (ZFL), University of Bonn Junior Researcher +49 (0)228-73-1831 Bonn 53113 Germany ferner@uni-bonn.de https://wascal-dataportal.org/geonetwork/srv/eng/resources.get?uuid=0029ab7f-d227-4f57-84b4-adcdb931886f&fname=type_applications.png large_thumbnail png grazing living environment livestock farming livestock environment overgrazing fodder plant fodder vegetation animal nutrition animal husbandry biomass feeding of animals GEMET - Concepts, version 2.4 2010-01-13 geonetwork.thesaurus.external.theme.gemet forage quality forage quantity field spectroscopy reflectance partial least-squares regression modelling Ghana Burkina Faso Region 2006-09-22 geonetwork.thesaurus.external.place.regions Nothern Ghana Southern Burkina The models can be used by WASCAL members. A redistrubution is not allowed. 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. environment -2.285119598419064 -0.3075805361943132 8.696843390387354 13.524923718849402 R script for model application available upon request rdata 2017 Jessica Ferner Center for Remote Sensing of Land Surfaces (ZFL), University of Bonn ferner@uni-bonn.de https://wascal-dataportal.org/geonetwork/srv/eng/file.disclaimer?uuid=0029ab7f-d227-4f57-84b4-adcdb931886f&fname=final_BM_model_slim-.rdata&access=private WWW:DOWNLOAD-1.0-http--download final_BM_model_slim-.rdata final_BM_model_slim https://wascal-dataportal.org/geonetwork/srv/eng/file.disclaimer?uuid=0029ab7f-d227-4f57-84b4-adcdb931886f&fname=final_ME_model_slim-.rdata&access=private WWW:DOWNLOAD-1.0-http--download final_ME_model_slim-.rdata final_ME_model_slim https://www.researchgate.net/profile/Anja_Linstaedter/publication/276853081_Spectral_indicators_of_forage_quality_in_West_Africas_tropical_savannas/links/556d639908aeab777223266b.pdf WWW:LINK-1.0-http--link Scientific paper 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. Data quality of forage variables modelled from hyperspectral reflectance is: Model adjR² nRMSE [%] metabolisable enery 0.56 11.7 green biomass 0.64 11.3