Linking remotely sensed forage quality estimates from WorldView-2 multispectral data with cattle distribution in a savanna landscape

Zengeya, Fadzai M.; Mutanga, Onisimo; Murwira, Amon

doi:10.1016/j.jag.2012.07.008

Cited by 52 publications

(48 citation statements)

References 43 publications

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“…However, VIP values indicated the importance of the yellow wavelength region. This is consistent to some extent with a recent study conducted by Zengeya et al [97], who found that the WorldVidw-2 yellow band improved the detection of low nitrogen level within savanna grasses especially at the beginning of the dry season, while the red-edge band was weakly correlated with low nitrogen levels. Up to 75% of the leaf nitrogen is in the chloroplasts that contains chlorophyll [111], and the sensitivity of the yellow band to a low level of nitrogen could be attributed to subtle changes in chlorophyll concentration at the beginning of the dry season, thus G s .…”

Section: Discussionsupporting

confidence: 92%

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Early Detection of Plant Physiological Responses to Different Levels of Water Stress Using Reflectance Spectroscopy

et al. 2017

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Early detection of water stress is critical for precision farming for improving crop productivity and fruit quality. To investigate varying rootstock and irrigation interactions in an open agricultural ecosystem, different irrigation treatments were implemented in a vineyard experimental site either: (i) nonirrigated (NIR); (ii) with full replacement of evapotranspiration (FIR); or (iii) intermediate irrigation (INT, 50% replacement of evapotranspiration). In the summers 2014 and 2015, we collected leaf reflectance factor spectra of the vineyard using field spectroscopy along with grapevine physiological parameters. To comprehensively analyze the field-collected hyperspectral data, various band combinations were used to calculate the normalized difference spectral index (NDSI) along with 26 various indices from the literature. Then, the relationship between the indices and plant physiological parameters were examined and the strongest relationships were determined. We found that newly-identified NDSIs always performed better than the indices from the literature, and stomatal conductance (G s ) was the plant physiological parameter that showed the highest correlation with NDSI(R 603 ,R 558 ) calculated using leaf reflectance factor spectra (R 2 = 0.720). Additionally, the best NDSI(R 685 ,R 415 ) for non-photochemical quenching (NPQ) was determined (R 2 = 0.681). G s resulted in being a proxy of water stress. Therefore, the partial least squares regression (PLSR) method was utilized to develop a predictive model for G s . Our results showed that the PLSR model was inferior to the NDSI in G s estimation (R 2 = 0.680). The variable importance in the projection (VIP) was then employed to investigate the most important wavelengths that were most effective in determining G s . The VIP analysis confirmed that the yellow band improves the prediction ability of hyperspectral reflectance factor data in G s estimation. The findings of this study demonstrate the potential of hyperspectral spectroscopy data in motoring plant stress response.

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Section: Discussionsupporting

confidence: 92%

“…Furthermore, both green and yellow bands are known to be sensitive to subtle changes in photosynthetic pigments [58,95,96]. In addition, the yellow band highlights the negative change in slope from the green peak around 550 nm [97]. Figure 8 depicts the effects of irrigation treatments on leaf reflectance factor spectra.…”

Section: Discussionmentioning

confidence: 99%

Early Detection of Plant Physiological Responses to Different Levels of Water Stress Using Reflectance Spectroscopy

et al. 2017

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“…From the 64 possible two band combinations of eight WV2 spectral bands to compute vegetation ratios [38,39], the five most reliable combinations that were recommended by Pu and Landry [2] are shown in Table 3.…”

Section: Study Areamentioning

confidence: 99%

High Spatial Resolution WorldView-2 Imagery for Mapping NDVI and Its Relationship to Temporal Urban Landscape Evapotranspiration Factors

et al. 2014

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Evapotranspiration estimation has benefitted from recent advances in remote sensing and GIS techniques particularly in agricultural applications rather than urban environments. This paper explores the relationship between urban vegetation evapotranspiration (ET) and vegetation indices derived from newly-developed high spatial resolution WorldView-2 imagery. The study site was Veale Gardens in Adelaide, Australia. Image processing was applied on five images captured from February 2012 to February 2013 using ERDAS Imagine. From 64 possible two band combinations of WorldView-2, the most reliable one (with the maximum median differences) was selected. Normalized Difference Vegetation Index (NDVI) values were derived for each category of landscape cover, namely trees, shrubs, turf grasses, impervious pavements, and water bodies. Urban landscape evapotranspiration rates for Veale Gardens were estimated through field monitoring using observational-based landscape coefficients. The relationships between remotely sensed NDVIs for the entire Veale Gardens and for individual NDVIs of different vegetation covers were compared with field measured urban landscape evapotranspiration rates. The water stress conditions experienced in January

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“…The results achieved here are comparable with that of Ramoelo et al [16] where red edge based indices-from RapidEye images were used to regionally map leaf N, (see also Figure 4. Zengeya et al [48] and Ramoelo et al [18]) and indicated that WorldView-2, has the potential to estimate leaf N with comparable accuracy. The main disadvantage of using RapidEye and WorldView data is the cost, and it might not be affordable for most developing countries.…”

Section: Leaf N Predictive Modelsmentioning

confidence: 99%

Explaining Leaf Nitrogen Distribution in a Semi-Arid Environment Predicted on Sentinel-2 Imagery Using a Field Spectroscopy Derived Model

Ramoelo

Cho

2018

Remote Sensing

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Leaf nitrogen concentration (leaf N, %) is an essential component for understanding biogeochemical cycling. Leaf N is a good indicator of grass or forage quality, which is important for understanding the movements and feeding patterns of herbivores. Leaf N can be used as input for rangeland carrying capacity and stocking rate models. The estimation of leaf N has been successful using hyperspectral and commercial high spatial resolution satellite data such as WorldView-2 and RapidEye. Empirical methods have been used successfully to estimate leaf N, on the basis that it correlates with leaf chlorophyll. As such, leaf N was estimated using red edge based indices. The new Sentinel-2 sensor has two red edge bands, is freely available, and could further improve the estimation of leaf N at a regional scale. The objective of this study is to develop red edge based Sentinel-2 models derived from an analytical spectral device (ASD) spectrometer to map and monitor leaf N using Sentinel-2 images. Field work for leaf N and ASD data were collected in 2014 (December) in and around Kruger National Park, South Africa. ASD data were resampled to the Sentinel-2 spectral configuration using the spectral response function. The Sentinel-2 data for various dates were acquired from the European Space Agency (ESA) portal. The Sentinel-2 atmospheric correction (Sen2Cor) process was implemented. Simple empirical regression was used to estimate leaf N. High leaf N prediction accuracy was achieved at the ASD level and the best model was inverted on Sentinel-2 images to explain leaf N distribution at a regional scale over time. The spatial distribution of leaf N is influenced by the underlying geological substrate, fire frequency and other environmental variables. This study is a demonstration of how ASD data can be used to calibrate Sentinel-2 for leaf N estimation and mapping.

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Linking remotely sensed forage quality estimates from WorldView-2 multispectral data with cattle distribution in a savanna landscape

Cited by 52 publications

References 43 publications

Early Detection of Plant Physiological Responses to Different Levels of Water Stress Using Reflectance Spectroscopy

Early Detection of Plant Physiological Responses to Different Levels of Water Stress Using Reflectance Spectroscopy

High Spatial Resolution WorldView-2 Imagery for Mapping NDVI and Its Relationship to Temporal Urban Landscape Evapotranspiration Factors

Explaining Leaf Nitrogen Distribution in a Semi-Arid Environment Predicted on Sentinel-2 Imagery Using a Field Spectroscopy Derived Model

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