Spectral Slope as an Indicator of Pasture Quality

Lugassi, Rachel; Chudnovsky, Alexandra; Zaady, Eli; Dvash, L.; Goldshleger, Naftaly

doi:10.3390/rs70100256

Cited by 18 publications

(24 citation statements)

References 47 publications

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“…NDF CP (1) The conventional method for obtaining these indicators of pasture quality (CP and NDF) is based on in-situ sampling and chemical analysis. However, this conventional methodology involves intensive field work and lengthy laboratory analysis, which are relatively expensive and sometimes belated, making them unpractical for day-to-day farm management [5,6,10,14].…”

Section: Pqdi =mentioning

confidence: 99%

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Monitoring Seasonal Pasture Quality Degradation in the Mediterranean Montado Ecosystem: Proximal versus Remote Sensing

Serrano

Shahidian

Silva

2018

Water

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Montado is an agro-forestry system occupying a large surface in countries of the Mediterranean region. In this system, the natural dryland pasture is the principal source for animal feed in extensive grazing. The climatic seasonality associated with the inter-annual irregularity of precipitation greatly influences the development of pasture and its vegetative cycle. The end of spring is a critical period in terms of animal feed due to the notable reduction in the nutritive value of the plants. The objective of this work was to evaluate, through the correlation between pasture quality indexes (Pasture Quality Degradation Index, PQDI and Normalized Difference Vegetation Index, NDVI), two technological approaches for monitoring the evolution of the quality of a biodiverse pasture in the period of greatest vegetative development (between February and June). The technological approaches consisted of (i) proximal sensing (PS), with the use of an active optical sensor; and (ii) remote sensing (RS), using images captured by a Sentinel-2 satellite. The results of this study show strong and significant correlations between PQDI and NDVI (obtained by PS or RS). These two techniques (PS or RS) can, therefore, be used in a complementary way to identify and anticipate the food supplementation needs for animals and support farmers in decision making.

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Section: Pqdi =mentioning

confidence: 99%

“…Pasture quality can be quantified by certain parameters, such as neutral detergent fiber (NDF) and crude protein (CP) content [10]. It is known that high CP content and low NDF content indicate higher quality pastures [11], which is usually the case when the plants are young.…”

Section: Introductionmentioning

confidence: 99%

Monitoring Seasonal Pasture Quality Degradation in the Mediterranean Montado Ecosystem: Proximal versus Remote Sensing

Serrano

Shahidian

Silva

2018

Water

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“…In a previous study [28], we found that when using slopes across selected spectral ranges of dried and ground vegetation, it was possible to evaluate several pasture quality indicators with high accuracy, such as CP, NDF and metabolic energy concentration. However, that study was restricted to estimates based only on the spectra of dry samples; adjusting the method to fresh vegetation would enable further in-situ analyses, and this was the aim of the present study.…”

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confidence: 99%

Estimating Pasture Quality of Fresh Vegetation Based on Spectral Slope of Mixed Data of Dry and Fresh Vegetation—Method Development

et al. 2015

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Abstract:The main objective of the present study was to apply a slope-based spectral method to both dry and fresh pasture vegetation. Differences in eight spectral ranges were identified across the near infrared-shortwave infrared (NIR-SWIR) that were indicative of changes in chemical properties. Slopes across these ranges were calculated and a partial least squares (PLS) analytical model was constructed for the slopes vs. crude protein (CP) and neutral detergent fiber (NDF) contents. Different datasets with different numbers of fresh/dry samples were constructed to predict CP and NDF contents. When using a mixed-sample dataset with dry-to-fresh ratios of 85%:15% and 75%:25%, the correlations of CP (R 2 = 0.95, in both) and NDF (R 2 = 0.84 and 0.82, respectively) were almost as high as when using only dry samples (0.97 and 0.85, respectively). Furthermore, satisfactory correlations were obtained with a dry-to-fresh ratio of 50%:50% for CP (R 2 = 0.92). The OPEN ACCESSRemote Sens. 2015, 7 8046 results of our study are especially encouraging because CP and NDF contents could be predicted even though some of the selected spectral regions were directly affected by atmospheric water vapor or water in the plants.

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“…Hence, calculating the absorbance area of the water bands (e.g., 1200, 1400, 1780, and 1940 nm) [11,43,44] can be a good indicator of water concentration in the vegetation. The spectral absorption features centered at 1940 nm have been found to be good indicators of plant water content [34,45,46]. Therefore, the area between the straight line and the absorption line ( Figure 2) was calculated (see [35] for more details).…”

Section: Calculating the Water-absorption Areamentioning

confidence: 99%

“…To develop a model to assess Cl and Na content in vegetation based on the slope method [34,35], we used the common CR spectral technique [9,[36][37][38][39][40]. CR normalizes reflectance spectra to a common baseline, enabling the identification of individual absorption features.…”

Section: Slope Calculation and Data Analysesmentioning

confidence: 99%

Studying Vegetation Salinity: From the Field View to a Satellite-Based Perspective

2017

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Salinization of irrigated lands in the semi-arid Jezreel Valley, Northern Israel results in soil-structure deterioration and crop damage. We formulated a generic rule for estimating salinity of different vegetation types by studying the relationship between Cl/Na and different spectral slopes in the visible-near infrared-shortwave infrared (VIS-NIR-SWIR) spectral range using both field measurements and satellite imagery (Sentinel-2). For the field study, the slope-based model was integrated with conventional partial least squares (PLS) analyses. Differences in 14 spectral ranges, indicating changes in salinity levels, were identified across the VIS-NIR-SWIR region (350-2500 nm). Next, two different models were run using PLS regression: (i) using spectral slope data across these ranges; and (ii) using preprocessed spectral reflectance. The best model for predicting Cl content was based on continuum removal reflectance (R 2 = 0.84). Satisfactory correlations were obtained using the slope-based PLS model (R 2 = 0.77 for Cl and R 2 = 0.63 for Na). Thus, salinity contents in fresh plants could be estimated, despite masking of some spectral regions by water absorbance. Finally, we estimated the most sensitive spectral channels for monitoring vegetation salinity from a satellite perspective. We evaluated the recently available Sentinel-2 imagery's ability to distinguish variability in vegetation salinity levels. The best estimate of a Sentinel-2-based vegetation salinity index was generated based on a ratio between calculated slopes: the 490-665 nm and 705-1610 nm. This index was denoted as the Sentinel-2-based vegetation salinity index (SVSI) (band 4 − band 2)/ (band 5 + band 11).

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Spectral Slope as an Indicator of Pasture Quality

Cited by 18 publications

References 47 publications

Monitoring Seasonal Pasture Quality Degradation in the Mediterranean Montado Ecosystem: Proximal versus Remote Sensing

Monitoring Seasonal Pasture Quality Degradation in the Mediterranean Montado Ecosystem: Proximal versus Remote Sensing

Estimating Pasture Quality of Fresh Vegetation Based on Spectral Slope of Mixed Data of Dry and Fresh Vegetation—Method Development

Studying Vegetation Salinity: From the Field View to a Satellite-Based Perspective

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