Abstract
Frequent, region-wide monitoring of changes in pasture quality due to human disturbances or climatic conditions is impossible by field measurements or traditional ecological surveying methods. Remote sensing imagery offers distinctive advantages for monitoring spatial and temporal patterns. The chemical parameters that are widely used as indicators of ecological quality are crude protein (CP) content and neutral detergent fiber (NDF) content. In this study, we investigated the relationship between CP, NDF, and reflectance in the visible–near-infrared–shortwave infrared (VIS–NIR–SWIR) spectral range, using field, laboratory measurements, and satellite imagery (Sentinel-2). Statistical models were developed using different calibration and validation data sample sets: (1) a mix of laboratory and field measurements (e.g., fresh and dry vegetation) and (2) random selection. In addition, we used three vegetation indices (Normalized Difference Vegetative Index (NDVI), Soil-adjusted Vegetation Index (SAVI) and Wide Dynamic Range Vegetation Index (WDRVI)) as proxies to CP and NDF estimation. The best models found for predicting CP and NDF contents were based on reflectance measurements (R2 = 0.71, RMSEP = 2.1% for CP; and R2 = 0.78, RMSEP = 5.5% for NDF). These models contained fresh and dry vegetation samples in calibration and validation data sets. Random sample selection in a model generated similar accuracy estimations. Our results also indicate that vegetation indices provide poor accuracy. Eight Sentinel-2 images (December 2015–April 2017) were examined in order to better understand the variability of vegetation quality over spatial and temporal scales. The spatial and temporal patterns of CP and NDF contents exhibit strong seasonal dependence, influenced by climatological (precipitation) and topographical (northern vs. southern hillslopes) conditions. The total CP/NDF content increases/decrease (respectively) from December to March, when the concentrations reach their maximum/minimum values, followed by a decline/incline that begins in April, reaching minimum values in July.
Highlights
Global changes in land use and climate are causing increasing concern in both developed and developing countries
Chemical composition primarily refers to crude protein (CP) concentration, moisture, lignin and ash content, cell-wall components, neutral detergent fiber (NDF), acid detergent fiber (ADF), digestibility, and metabolic energy concentration [6,7]
All vegetation samples were subjected to laboratory chemical analysis for CP and NDF
Summary
Global changes in land use and climate are causing increasing concern in both developed and developing countries. Neither field methods nor traditional ecological survey methods support frequent or region-wide monitoring of changes in habitat quality due to diverse human or climatic effects. There is a need to develop effective, reliable, and simple methods to estimate ecological quality on high spatial and temporal scales. Ecological monitoring of the pasture quality can be studied by (i) ground-based and (ii) remote sensing methods [5]. In the clipping and field spectrometry methods, the following parameters are widely used as indicators of pasture ecological quality: chemical composition, water content, and nutrient concentration. Ground-based methods are subjective, require a large number of samples, demand specific expertise, are time-consuming and labor intensive, and produce results that are representative only for specific geographical conditions [5]
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