Estimating tropical pasture quality at canopy level using band depth analysis with continuum removal in the visible domain

Mutanga, Onisimo; Skidmore, Andrew K.; Kumar, Lalit; Ferwerda, J.G.

doi:10.1080/01431160512331326738

Cited by 115 publications

(80 citation statements)

References 29 publications

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“…From this detailed spectral data, the variation in plant traits of grassland, caused by the type and the amount of fertilization ( Figure 5), correspond to a relative small variability in reflectance values ( Figure 6). Thus, in order to identify this variation, the narrow-band vegetation indices were calculated instead of broad vegetation indices, as shown in previous studies [14,15,[24][25][26][27][28]. Narrow-band vegetation indices improve the results in processing the hyperspectral images since they allow us to exploit all of the information contained in the spectrum [14,15,[24][25][26][27][28].…”

Section: Discussionmentioning

confidence: 99%

“…For these reasons, various studies have applied the combination of UAVs and hyperspectral sensors in grassland mapping and monitoring and have documented its great potential for detecting water stress [48] and for estimating grassland biophysical traits [14,15,[23][24][25][26][27][28]36,37].…”

Section: State Of the Art Of Uavs Applicationsmentioning

confidence: 99%

“…Although additional vegetation indices present in previous studies [5,[12][13][14][15][24][25][26][27][28]37] were analyzed, in this paper, only the indices that gave the best results are shown. In particular, the MERIS Terrestrial Chlorophyll Index (MTCI) [56], the ratio of the Modified Chlorophyll Absorption in Reflectance and Optimized Soil-Adjusted Vegetation Index (MCARI/OSAVI) modified by Wu [57], the Red-edge Chlorophyll Index (CIred-edge) [58,59] and the Normalized Difference Red Edge (NDRE) [60] were selected for the estimation of structural and biochemical traits.…”

Section: The Narrow-band Vegetation Indicesmentioning

confidence: 99%

“…Nevertheless, when comparing the results of the two different approaches for computing the same index, some studies have shown that the use of narrow-band indices results in improved statistical relationships between biochemical and biophysical traits and vegetation indices [15,23]. Moreover, earlier studies have shown that narrow-band information of hyperspectral images is important for providing information for the estimation of biochemical and biophysical vegetation variables by applying the vegetation indices [14,15,[23][24][25][26][27][28]. However, in this way, it is not possible to fully exploit the hyperspectral data potential of the large number of spectral bands available.…”

Section: Introductionmentioning

confidence: 99%

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Estimating Plant Traits of Grasslands from UAV-Acquired Hyperspectral Images: A Comparison of Statistical Approaches

Capolupo

Kooistra

Berendonk³

et al. 2015

IJGI

121

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Grassland ecosystems cover around 40% of the entire Earth's surface. Therefore, it is necessary to guarantee good grassland management at field scale in order to improve its conservation and to achieve optimal growth. This study identified the most appropriate statistical strategy, between partial least squares regression (PLSR) and narrow vegetation indices, for estimating the structural and biochemical grassland traits from UAV-acquired hyperspectral images. Moreover, the influence of fertilizers on plant traits for grasslands was analyzed. Hyperspectral data were collected from an experimental field at the farm Haus Riswick, near Kleve in Germany, for two different flight campaigns in May and October. The collected image blocks were geometrically and radiometrically corrected for surface reflectance. Spectral signatures extracted for the plots were adopted to derive grassland traits by computing PLSR and the following narrow vegetation indices: the MERIS Terrestrial Chlorophyll Index (MTCI), the ratio of the Modified Chlorophyll Absorption in Reflectance and Optimized Soil-Adjusted Vegetation Index (MCARI/OSAVI) modified by Wu, the Rededge Chlorophyll Index (CIred-edge), and the Normalized Difference Red Edge (NDRE). PLSR showed promising results for estimating grassland structural traits and gave less satisfying outcomes for the selected chemical traits (crude ash, crude fiber, crude protein, Na, K, metabolic energy). Established relations are not influenced by the type and the amount of fertilization, while they are affected by the grassland health status. PLSR is found to be the best strategy, among the approaches analyzed in this paper, for exploring structural and biochemical features of grasslands. Using UAV-based hyperspectral sensing allows for the highly detailed assessment of grassland experimental plots.

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Section: Discussionmentioning

confidence: 99%

Section: State Of the Art Of Uavs Applicationsmentioning

confidence: 99%

Section: The Narrow-band Vegetation Indicesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Estimating Plant Traits of Grasslands from UAV-Acquired Hyperspectral Images: A Comparison of Statistical Approaches

Capolupo

Kooistra

Berendonk³

et al. 2015

IJGI

121

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“…Continuum removal was developed to enhance the absorption features along the spectrum by Kokaly and Clark (1999). This method has been used to estimate foliar chemical concentrations using spectra collected in the laboratory, field and from the canopy using airborne sensors [30,[41][42][43].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Macronutrients at the Canopy Level Using Spaceborne Imaging Spectroscopy and LiDAR Data in a Mixedwood Boreal Forest

et al. 2015

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Information on foliar macronutrients is required in order to understand plant physiological and ecosystem processes such as photosynthesis, nutrient cycling, respiration and cell wall formation. The ability to measure, model and map foliar macronutrients (nitrogen (N), phosphorus (P), potassium (K), calcium (Ca) and magnesium (Mg)) at the forest canopy level provides information on the spatial patterns of ecosystem processes (e.g., carbon exchange) and provides insight on forest condition and stress. Imaging spectroscopy (IS) has been used particularly for modeling N, using airborne and satellite imagery mostly in temperate and tropical forests. However, there has been very little research conducted at these scales to model P, K, Ca, and Mg and few studies have focused on boreal forests. We report results of a study of macronutrient modeling using spaceborne IS and airborne light OPEN ACCESSRemote Sens. 2015, 7 9046 detection and ranging (LiDAR) data for a mixedwood boreal forest canopy in northern Ontario, Canada. Models incorporating Hyperion data explained approximately 90% of the variation in canopy concentrations of N, P, and Mg; whereas the inclusion of LiDAR data significantly improved the prediction of canopy concentration of Ca (R 2 = 0.80). The combined used of IS and LiDAR data significantly improved the prediction accuracy of canopy Ca and K concentration but decreased the prediction accuracy of canopy P concentration. The results indicate that the variability of macronutrient concentration due to interspecific and functional type differences at the site provides the basis for the relationship observed between the remote sensing measurements (i.e., IS and LiDAR) and macronutrient concentration. Crown closure and canopy height are the structural metrics that establish the connection between macronutrient concentration and IS and LiDAR data, respectively. The spatial distribution of macronutrient concentration at the canopy scale mimics functional type distribution at the site. The ability to predict canopy N, P, K, Ca and Mg in this study using only IS, only LiDAR or their combination demonstrates the excellent potential for mapping these macronutrients at canopy scales across larger geographic areas into the next decade with the launch of new IS satellite missions and by using spaceborne LiDAR data.

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Hyperspectral Remote Sensing: Use in Detecting Abiotic Stresses in Agriculture

Das

Mahajan

Singh

2018

Advances in Crop Environment Interaction

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Estimating tropical pasture quality at canopy level using band depth analysis with continuum removal in the visible domain

Cited by 115 publications

References 29 publications

Estimating Plant Traits of Grasslands from UAV-Acquired Hyperspectral Images: A Comparison of Statistical Approaches

Estimating Plant Traits of Grasslands from UAV-Acquired Hyperspectral Images: A Comparison of Statistical Approaches

Prediction of Macronutrients at the Canopy Level Using Spaceborne Imaging Spectroscopy and LiDAR Data in a Mixedwood Boreal Forest

Hyperspectral Remote Sensing: Use in Detecting Abiotic Stresses in Agriculture

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