Predicting in situ pasture quality in the Kruger National Park, South Africa, using continuum-removed absorption features

The remote sensing of pasture quality as determined by nitrogen, phosphorous, potassium, calcium and magnesium concentration is critical for a better understanding of wildlife and livestock feeding patterns. Although remote sensing techniques have proved useful for assessing the concentration of fol...

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Vydané v:	Remote sensing of environment Ročník 89; číslo 3; s. 393 - 408
Hlavní autori:	Mutanga, O, Skidmore, A.K, Prins, H.H.T
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York, NY Elsevier Inc 15.02.2004 Elsevier Science
Predmet:	Absorption features Animal, plant and microbial ecology Applied geophysics aviris data Biological and medical sciences Bootstrapping chlorophyll concentration Continuum removal cross-validation Earth sciences Earth, ocean, space Exact sciences and technology Field spectra forest canopy Fundamental and applied biological sciences. Psychology General aspects. Techniques infrared reflectance spectroscopy Internal geophysics leaf reflectance multiple linear-regression nitrogen red edge Savanna rangelands spectral discrimination Teledetection and vegetation maps South Africa Southern Africa Bootstrapping Field spectra Continuum removal Absorption features Savanna rangelands potassium imagery savanna calibration phosphorus wavelength models calcium absorption reflectance nitrogen Africa magnesium remote sensing concentration cartography roots Grazing in situ quality depth correlation regression prediction errors national parks
ISSN:	0034-4257, 1879-0704
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Abstract	The remote sensing of pasture quality as determined by nitrogen, phosphorous, potassium, calcium and magnesium concentration is critical for a better understanding of wildlife and livestock feeding patterns. Although remote sensing techniques have proved useful for assessing the concentration of foliar biochemicals under controlled laboratory conditions, more investigation is required to assess their capabilities in the field, where inconsistent results have been obtained so far. We investigated the possibility of determining the concentration of in situ biochemicals in a savanna rangeland, using the spectral reflectance of five grass species. Canopy spectral measurements were taken in the field using a GER 3700 spectroradiometer. We tested the utility of using four variables derived from continuum-removed absorption features for predicting canopy nitrogen, phosphorus, potassium, calcium and magnesium concentration: (i) continuum-removed derivative reflectance (CRDR), (ii) band depth (BD), (iii) band depth ratio (BDR) and (iv) normalised band depth index (NBDI). Stepwise linear regression was used to select wavelengths from the absorption-feature-based variables. Univariate correlation analysis was also done between the first derivative reflectance and biochemicals. Using a training data set, the variables derived from continuum-removed absorption features could predict biochemicals with R 2 values ranging from 0.43 to 0.80. Results were highest using CRDR data, which yielded R 2 values of 0.70, 0.80, 0.64, 0.50 and 0.68 with root mean square errors (RMSE) of 0.01, 0.004, 0.03, 0.01 and 0.004 for nitrogen, phosphorous, potassium, calcium and magnesium, respectively. Predicting biochemicals on a test data set, using regression models developed from a training data set, resulted in R 2 values ranging from 0.15 to 0.70. The error of prediction (RSE) in the test data set was 0.08 (±10.25% of mean), 0.05 (±5.2% of mean), 0.02 (±11.11% of mean), 0.05 (±11.6% of mean) and 0.03 (±15% of mean) for nitrogen, potassium, phosphorous, calcium and magnesium, respectively, using CRDR. When data was partitioned into species groups, the R 2 increased significantly to >0.80. With high-quality radiometric and geometric calibration of hyperspectral imagery, the techniques applied in this study (i.e. continuum removal on absorption features) may also be applied on data acquired by airborne and spaceborne imaging spectrometers to predict and ultimately to map the concentration of macronutrients in tropical rangelands.
AbstractList	The remote sensing of pasture quality as determined by nitrogen, phosphorous, potassium, calcium and magnesium concentration is critical for a better understanding of wildlife and livestock feeding patterns. Although remote sensing techniques have proved useful for assessing the concentration of foliar biochemicals under controlled laboratory conditions, more investigation is required to assess their capabilities in the field, where inconsistent results have been obtained so far. We investigated the possibility of determining the concentration of in situ biochemicals in a savanna rangeland, using the spectral reflectance of five grass species. Canopy spectral measurements were taken in the field using a GER 3700 spectroradiometer. We tested the utility of using four variables derived from continuum-removed absorption features for predicting canopy nitrogen, phosphorus, potassium, calcium and magnesium concentration: (i) continuum-removed derivative reflectance (CRDR), (ii) band depth (BD), (iii) band depth ratio (BDR) and (iv) normalised band depth index (NBDI). Stepwise linear regression was used to select wavelengths from the absorption-feature- based variables. Univariate correlation analysis was also done between the first derivative reflectance and biochemicals. Using a training data set, the variables derived from continuum-removed absorption features could predict biochemicals with R super(2) values ranging from 0.43 to 0.80. Results were highest using CRDR data, which yielded R super(2) values of 0.70, 0.80, 0.64, 0.50 and 0.68 with root mean square errors (RMSE) of 0.01, 0.004, 0.03, 0.01 and 0.004 for nitrogen, phosphorous, potassium, calcium and magnesium, respectively. Predicting biochemicals on a test data set, using regression models developed from a training data set, resulted in R super(2) values ranging from 0.15 to 0.70. The error of prediction (RSE) in the test data set was 0.08 (+/-10.25% of mean), 0.05 (+/-5.2% of mean), 0.02 (+/-11.11% of mean), 0.05 (+/-11.6% of mean) and 0.03 (+/-15% of mean) for nitrogen, potassium, phosphorous, calcium and magnesium, respectively, using CRDR. When data was partitioned into species groups, the R super(2) increased significantly to >0.80. With high-quality radiometric and geometric calibration of hyperspectral imagery, the techniques applied in this study (i.e. continuum removal on absorption features) may also be applied on data acquired by airborne and spaceborne imaging spectrometers to predict and ultimately to map the concentration of macronutrients in tropical rangelands. The remote sensing of pasture quality as determined by nitrogen, phosphorous, potassium, calcium and magnesium concentration is critical for a better understanding of wildlife and livestock feeding patterns. Although remote sensing techniques have proved useful for assessing the concentration of foliar biochemicals under controlled laboratory conditions, more investigation is required to assess their capabilities in the field, where inconsistent results have been obtained so far. We investigated the possibility of determining the concentration of in situ biochemicals in a savanna rangeland, using the spectral reflectance of five grass species. Canopy spectral measurements were taken in the field using a GER 3700 spectroradiometer. We tested the utility of using four variables derived from continuum-removed absorption features for predicting canopy nitrogen, phosphorus, potassium, calcium and magnesium concentration: (i) continuum-removed derivative reflectance (CRDR), (ii) band depth (BD), (iii) band depth ratio (BDR) and (iv) normalised band depth index (NBDI). Stepwise linear regression was used to select wavelengths from the absorption-feature-based variables. Univariate correlation analysis was also done between the first derivative reflectance and biochemicals. Using a training data set, the variables derived from continuum-removed absorption features could predict biochemicals with R2 values ranging from 0.43 to 0.80. Results were highest using CRDR data, which yielded R2 values of 0.70, 0.80, 0.64, 0.50 and 0.68 with root mean square errors (RMSE) of 0.01, 0.004, 0.03, 0.01 and 0.004 for nitrogen, phosphorous, potassium, calcium and magnesium, respectively. Predicting biochemicals on a test data set, using regression models developed from a training data set, resulted in R2 values ranging from 0.15 to 0.70. The error of prediction (RSE) in the test data set was 0.08 (±10.25% of mean), 0.05 (±5.2% of mean), 0.02 (±11.11% of mean), 0.05 (±11.6% of mean) and 0.03 (±15% of mean) for nitrogen, potassium, phosphorous, calcium and magnesium, respectively, using CRDR. When data was partitioned into species groups, the R2 increased significantly to >0.80. With high-quality radiometric and geometric calibration of hyperspectral imagery, the techniques applied in this study (i.e. continuum removal on absorption features) may also be applied on data acquired by airborne and spaceborne imaging spectrometers to predict and ultimately to map the concentration of macronutrients in tropical rangelands The remote sensing of pasture quality as determined by nitrogen, phosphorous, potassium, calcium and magnesium concentration is critical for a better understanding of wildlife and livestock feeding patterns. Although remote sensing techniques have proved useful for assessing the concentration of foliar biochemicals under controlled laboratory conditions, more investigation is required to assess their capabilities in the field, where inconsistent results have been obtained so far. We investigated the possibility of determining the concentration of in situ biochemicals in a savanna rangeland, using the spectral reflectance of five grass species. Canopy spectral measurements were taken in the field using a GER 3700 spectroradiometer. We tested the utility of using four variables derived from continuum-removed absorption features for predicting canopy nitrogen, phosphorus, potassium, calcium and magnesium concentration: (i) continuum-removed derivative reflectance (CRDR), (ii) band depth (BD), (iii) band depth ratio (BDR) and (iv) normalised band depth index (NBDI). Stepwise linear regression was used to select wavelengths from the absorption-feature-based variables. Univariate correlation analysis was also done between the first derivative reflectance and biochemicals. Using a training data set, the variables derived from continuum-removed absorption features could predict biochemicals with R 2 values ranging from 0.43 to 0.80. Results were highest using CRDR data, which yielded R 2 values of 0.70, 0.80, 0.64, 0.50 and 0.68 with root mean square errors (RMSE) of 0.01, 0.004, 0.03, 0.01 and 0.004 for nitrogen, phosphorous, potassium, calcium and magnesium, respectively. Predicting biochemicals on a test data set, using regression models developed from a training data set, resulted in R 2 values ranging from 0.15 to 0.70. The error of prediction (RSE) in the test data set was 0.08 (±10.25% of mean), 0.05 (±5.2% of mean), 0.02 (±11.11% of mean), 0.05 (±11.6% of mean) and 0.03 (±15% of mean) for nitrogen, potassium, phosphorous, calcium and magnesium, respectively, using CRDR. When data was partitioned into species groups, the R 2 increased significantly to >0.80. With high-quality radiometric and geometric calibration of hyperspectral imagery, the techniques applied in this study (i.e. continuum removal on absorption features) may also be applied on data acquired by airborne and spaceborne imaging spectrometers to predict and ultimately to map the concentration of macronutrients in tropical rangelands. The remote sensing of pasture quality as determined by nitrogen, phosphorous, potassium, calcium and magnesium concentration is critical for a better understanding of wildlife and livestock feeding patterns. Although remote sensing techniques have proved useful for assessing the concentration of foliar biochemicals under controlled laboratory conditions, more investigation is required to assess their capabilities in the field, where inconsistent results have been obtained so far. We investigated the possibility of determining the concentration of in situ biochemicals in a savanna rangeland, using the spectral reflectance of five grass species. Canopy spectral measurements were taken in the field using a GER 3700 spectroradiometer. We tested the utility of using four variables derived from continuum-removed absorption features for predicting canopy nitrogen, phosphorus, potassium, calcium and magnesium concentration: (i) continuum-removed derivative reflectance (CRDR), (ii) band depth (BD), (iii) band depth ratio (BDR) and (iv) normalised band depth index (NBDI). Stepwise linear regression was used to select wavelengths from the absorption-feature- based variables. Univariate correlation analysis was also done between the first derivative reflectance and biochemicals. Using a training data set, the variables derived from continuum-removed absorption features could predict biochemicals with R super 2 values ranging from 0.43 to 0.80. Results were highest using CRDR data, which yielded R super 2 values of 0.70, 0.80, 0.64, 0.50 and 0.68 with root mean square errors (RMSE) of 0.01, 0.004, 0.03, 0.01 and 0.004 for nitrogen, phosphorous, potassium, calcium and magnesium, respectively. Predicting biochemicals on a test data set, using regression models developed from a training data set, resulted in R super 2 values ranging from 0.15 to 0.70. The error of prediction (RSE) in the test data set was 0.08 (+/-10.25 percent of mean), 0.05 (+/-5.2 percent of mean), 0.02 (+ /-11.11 percent of mean), 0.05 (+/-11.6 percent of mean) and 0.03 (+/-15 percent of mean) for nitrogen, potassium, phosphorous, calcium and magnesium, respectively, using CRDR. When data was partitioned into species groups, the R super 2 increased significantly to > 0.80. With high-quality radiometric and geometric calibration of hyperspectral imagery, the techniques applied in this study (i.e. continuum removal on absorption features) may also be applied on data acquired by airborne and spaceborne imaging spectrometers to predict and ultimately to map the concentration of macronutrients in tropical rangelands.
Author	Mutanga, O Skidmore, A.K Prins, H.H.T
Author_xml	– sequence: 1 givenname: O surname: Mutanga fullname: Mutanga, O email: Mutanga@itc.nl organization: International Institute for Geo-Information Science and Earth Observation (ITC), Hengelosestraat 99, P.O. Box 6, Enschede 7500 AA, The Netherlands – sequence: 2 givenname: A.K surname: Skidmore fullname: Skidmore, A.K organization: International Institute for Geo-Information Science and Earth Observation (ITC), Hengelosestraat 99, P.O. Box 6, Enschede 7500 AA, The Netherlands – sequence: 3 givenname: H.H.T surname: Prins fullname: Prins, H.H.T organization: Department of Tropical Nature Conservation and Vertebrate Ecology, Wageningen Agricultural University, Bornsesteeg, 69, Wageningen 6708 PD, The Netherlands
BackLink	http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=15425150$$DView record in Pascal Francis
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Snippet	The remote sensing of pasture quality as determined by nitrogen, phosphorous, potassium, calcium and magnesium concentration is critical for a better...
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SubjectTerms	Absorption features Animal, plant and microbial ecology Applied geophysics aviris data Biological and medical sciences Bootstrapping chlorophyll concentration Continuum removal cross-validation Earth sciences Earth, ocean, space Exact sciences and technology Field spectra forest canopy Fundamental and applied biological sciences. Psychology General aspects. Techniques infrared reflectance spectroscopy Internal geophysics leaf reflectance multiple linear-regression nitrogen red edge Savanna rangelands spectral discrimination Teledetection and vegetation maps
Title	Predicting in situ pasture quality in the Kruger National Park, South Africa, using continuum-removed absorption features
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