{"help":"Return the metadata of a dataset (package) and its resources. :param id: the id or name of the dataset :type id: string","success":true,"result":[{"id":"9dcb6908-014b-4575-ade5-e711d03a522b","name":"spectrometric-models-estimate-forage-provision-variables-green-biomass-metabolisable-energy","title":"Spectrometric Models to estimate Forage Provision variables (green Biomass, metabolisable Energy) from hyperspectral Reflectance of Vegetation Canopies","author":"Jessica Ferner","author_email":"ferner@uni-bonn.de","maintainer":"WASCAL Scientific Research Data Catalog ","maintainer_email":"sysadmin@wascal.org","license_title":"https:\/\/opendatacommons.org\/licenses\/odbl\/1.0\/","notes":"\u003Cp\u003EThese 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.\u003C\/p\u003E\n\u003Cp\u003EThe 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\u00b0N and 13.5\u00b0N latitude and 0.1\u00b0W and 2.0\u00b0W longitude. It is located in the southern and northern Sudanian zone of West Africa\u2019s 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).\u003C\/p\u003E\n\u003Cp\u003EData 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).\u003C\/p\u003E\n\u003Cp\u003EVegetation samples were oven-dried (60\u00b0C, \u0026gt; 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\u00b4s method (method 4.1.1). The ME was calculated using the following equation:\u003C\/p\u003E\n\u003Cp\u003EME (MJ kg-1 dry matter, DM) = 2.20 + 0.1357 GP + 0.0057 CP + 0.0002859 CP\u00b2,\u003C\/p\u003E\n\u003Cp\u003Ewhere GP is expressed as ml 200 mg-1 DM and CP is expressed as g kg-1 DM.\u003C\/p\u003E\n\u003Cp\u003EA 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 \u0027autopls\u0027. During PLSR, we also performed multiplicative scatter correction and brightness normalization of reflectance spectra.\u003C\/p\u003E\n\u003Cp\u003EBefore 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.\u003C\/p\u003E\n\u003Cp\u003EFurther information:\u003C\/p\u003E\n\u003Cp\u003EFerner, J., Linst\u00e4dter, A., S\u00fcdekum, K. H., \u0026amp; Schmidtlein, S. (2015). Spectral indicators of forage quality in West Africa\u2019s tropical savannas. International Journal of Applied Earth Observation and Geoinformation, 41, 99-106.\u003C\/p\u003E\n","url":"https:\/\/www.wascal-dataportal.org\/?q=dataset\/spectrometric-models-estimate-forage-provision-variables-green-biomass-metabolisable-energy","state":"Active","log_message":"Update to resource \u0027Spectrometric Models to estimate Forage Provision variables (green Biomass, metabolisable Energy) from hyperspectral Reflectance of Vegetation Canopies \u003Cstrong\u003E (Please contact by email the distributor to get access to the data) \u003C\/strong\u003E\u0027","private":true,"revision_timestamp":"Mon, 03\/31\/2025 - 13:43","metadata_created":"Wed, 09\/18\/2019 - 10:59","metadata_modified":"Mon, 03\/31\/2025 - 13:43","creator_user_id":"486d993f-80bb-47e9-87d0-0ec4da756df0","type":"Dataset","resources":[{"id":"7fc903c6-808c-4292-867a-10d8ed13fbe8","revision_id":"","url":"","description":"\u003Cp\u003EThese 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.\u003C\/p\u003E\n\u003Cp\u003EThe 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\u00b0N and 13.5\u00b0N latitude and 0.1\u00b0W and 2.0\u00b0W longitude. It is located in the southern and northern Sudanian zone of West Africa\u2019s 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).\u003C\/p\u003E\n\u003Cp\u003EData 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).\u003C\/p\u003E\n\u003Cp\u003EVegetation samples were oven-dried (60\u00b0C, \u0026gt; 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\u00b4s method (method 4.1.1). The ME was calculated using the following equation:\u003C\/p\u003E\n\u003Cp\u003EME (MJ kg-1 dry matter, DM) = 2.20 + 0.1357 GP + 0.0057 CP + 0.0002859 CP\u00b2,\u003C\/p\u003E\n\u003Cp\u003Ewhere GP is expressed as ml 200 mg-1 DM and CP is expressed as g kg-1 DM.\u003C\/p\u003E\n\u003Cp\u003EA 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 \u0027autopls\u0027. During PLSR, we also performed multiplicative scatter correction and brightness normalization of reflectance spectra.\u003C\/p\u003E\n\u003Cp\u003EBefore 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.\u003C\/p\u003E\n\u003Cp\u003EFurther information:\u003C\/p\u003E\n\u003Cp\u003EFerner, J., Linst\u00e4dter, A., S\u00fcdekum, K. H., \u0026amp; Schmidtlein, S. (2015). Spectral indicators of forage quality in West Africa\u2019s tropical savannas. International Journal of Applied Earth Observation and Geoinformation, 41, 99-106.\u003C\/p\u003E\n","format":"csv","state":"Active","revision_timestamp":"Mon, 03\/31\/2025 - 13:43","name":"Spectrometric Models to estimate Forage Provision variables (green Biomass, metabolisable Energy) from hyperspectral Reflectance of Vegetation Canopies \u003Cstrong\u003E (Please contact by email the distributor to get access to the data) \u003C\/strong\u003E","mimetype":"csv","size":"","created":"Wed, 09\/18\/2019 - 11:02","resource_group_id":"","last_modified":"Date changed  Mon, 03\/31\/2025 - 13:43"}],"tags":[{"id":"3890912b-fbd0-49ea-a87b-98ad7f5266d6","vocabulary_id":"2","name":"Atmosphere"}]}]}