Remote Sensing of Pasture Quality

Worldwide, farming systems are undergoing significant changes due to economic, environmental and social drivers. Agribusinesses must increasingly deliver products specified in terms of safety, health and quality. Increasing constraints are being placed on them by the market, the community and by government to achieve a financial benefit within social and environmental limits (Dynes et al. 2003). In order to meet these goals, producers must know the quantity and quality of the inputs into their feeding systems, be able to reliably predict the products and by-products being generated, and have the skills to be able to manage their business accordingly. Easy access to accurate and objective evaluation of forage is the first key component to meeting these objectives in livestock systems (Dynes et al. 2003) and remote sensing has considerable potential to be informative and cost-effective (Pullanagari et al. 2012b)

Identifying Urine Patches on Intensively Managed Grassland Using Aerial Imagery Captured From Remotely Piloted Aircraft Systems

The deposition of livestock urine and feces in grazed fields results in a sizable input of available nitrogen (N) in these soils; therefore significantly increasing potential nitrogen pollution from agricultural areas in the form of nitrous oxide (N2O), ammonia (NH3), and nitrate (NO3−). Livestock deposition events contributes to high spatial variability within the field and generate uncertainties when assessing the contribution that animal waste has on nitrogen pollution pathways. This study investigated an innovative technique for identifying the spatial coverage of urine deposition in grasslands without the need for manual soil measurements. A Remotely Piloted Aircraft System (RPAS) using a twin camera system was used to identify urine patches in a 5 ha field, which had been grazed by sheep 3 weeks previous to measurements. The imagery was processed using Agisoft Photoscan (Agisoft LLC) to produce true and false color orthomosaic imagery of the entire field. Imagery of five areas (225 m2) within the field were analyzed using a custom R script. For a total of 1,125 m2 of grassland, 12.2% of the area consisted of what was classified as urine patch. A simple up-scaling method was applied to these data to calculate N2O emissions for the entire field providing an estimate of 1.3–2.0 kg N2O-N ha−1 emissions from urine and fertilizer inputs

Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods

An unprecedented spectroscopic data stream will soon become available with forthcoming Earth-observing satellite missions equipped with imaging spectroradiometers. This data stream will open up a vast array of opportunities to quantify a diversity of biochemical and structural vegetation properties. The processing requirements for such large data streams require reliable retrieval techniques enabling the spatiotemporally explicit quantification of biophysical variables. With the aim of preparing for this new era of Earth observation, this review summarizes the state-of-the-art retrieval methods that have been applied in experimental imaging spectroscopy studies inferring all kinds of vegetation biophysical variables. Identified retrieval methods are categorized into: (1) parametric regression, including vegetation indices, shape indices and spectral transformations; (2) nonparametric regression, including linear and nonlinear machine learning regression algorithms; (3) physically based, including inversion of radiative transfer models (RTMs) using numerical optimization and look-up table approaches; and (4) hybrid regression methods, which combine RTM simulations with machine learning regression methods. For each of these categories, an overview of widely applied methods with application to mapping vegetation properties is given. In view of processing imaging spectroscopy data, a critical aspect involves the challenge of dealing with spectral multicollinearity. The ability to provide robust estimates, retrieval uncertainties and acceptable retrieval processing speed are other important aspects in view of operational processing. Recommendations towards new-generation spectroscopy-based processing chains for operational production of biophysical variables are given

Assessing the performance of multiple spectral–spatial features of a hyperspectral image for classification of urban land cover classes using support vector machines and artificial neural network

Accurate and spatially detailed mapping of complex urban environments is essential for land managers. Classifying high spectral and spatial resolution hyperspectral images is a challenging task because of its data abundance and computational complexity. Approaches witha combination of spectral and spatial information in a single classification framework haveattracted special attention because of their potential to improve the classification accuracy.We extracted multiple features from spectral and spatial domains of hyperspectral images and evaluated them with two supervised classification algorithms; support vector machines(SVM) and an artificial neural network. The spatial features considered are produced by agray level co-occurrence matrix and extended multiattribute profiles. All of these features were stacked, and the most informative features were selected using a genetic algorithm-based SVM. After selecting the most informative features, the classification model was integrated with a segmentation map derived using a hidden Markov random field. We tested the proposed method on a real application of a hyperspectral image acquired from AisaFENIX and on widely used hyperspectral images. From the results, it can be concluded that the proposed framework significantly improves the results with different spectral and spatial resolutions over different instrumentation

Proximal sensing techniques to monitor pasture quality and quantity on dairy farms : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Soil Science at Massey University, Manawatu, New Zealand

Regular and timely measurements of pasture quality and quantity allow dairy farmers to make effective decisions ensuring an adequate supply of nutrients to animals, efficient utilization of pasture, manipulation of stocking rates, management grazing intervals, and optimisation of input resources (e.g. nitrogen fertilisers) which results in more economic, environmentally aware, sustainable grazing systems. The objectives of this research were to investigate the potential of proximal sensing tools to estimate pasture quality parameters (crude protein, CP; acid detergent fibre, ADF; neutral detergent fibre, NDF; ash, dietary cation-anion difference, DCAD; lignin, lipid, metabolisable energy, ME and organic matter digestibility, OMD) in mixed pastures. Three proximal sensors, ASD FieldSpec® Pro FR spectroradiometer (hyperspectral), Cropscan™ (multispectral) and Crop Circle™ (multispectral), were employed in this study. In the hyperspectral study, the spectral reflectance measurements of pasture samples were acquired using an ASD FieldSpec® Pro FR spectroradiometer which has a spectral range of 350-2500 nm and attached with canopy pasture probe (CAPP) to ensure ambient light conditions. The acquired spectral data were pre-processed by various procedures: spectral averaging, smoothing and derivative transformation, then partial least squares regression was applied to regress against the corresponding measured values. The regression model was validated with an external dataset to evaluate the reliability and robustness of the model. The performance of both calibration and validation models were more or less similar. The validation model predicted the pasture quality parameters CP, ADF, NDF, ash, DCAD, lignin, ME and OMD with reasonable accuracy (0.65 ≤ R2 ≤ 0.83; 1.70 ≤ RPD ≤ 2.48; 0.64 ≤ NSE ≤ 0.83) and the lipid was predicted with lower accuracy (R2-0.55; RPD-1.44; NSE-0.50). Cropscan relies on sunlight for its energy source and measures reflectance in 16 broad wavebands; it was evaluated for its potential to assess pasture quality parameters that are collected in one season. The relationship between spectral reflectance measured using the Cropscan and pasture quality parameters were established using single wavebands, new vegetation indices and stepwise multiple linear regression (SMLR) and the models were validated with an external dataset. Of all the models, the new non-linear new combination of RDVI index models were performed satisfactory results (0.65 ≤ R2 ≤ 0.85) for predicting CP, DCAD, ME and OMD. CP, ash, DCAD, lipid, ME and OMD were estimated with moderate accuracy (0.60 ≤ R2 ≤ 0.80) using the SMLR model. The Cropscan instrument was also used to test the potential for predicting pasture quality in different seasons (autumn, spring and summer). Improved accuracy was observed with season-specific models as compared to the combined season dataset models. A three channel active optical sensor, Crop Circle™ was used to estimate herbage biomass and standing crude protein (SCP) using various indices. The results showed that the three channel based pasture index proved a reliable index for estimating biomass (R2 = 0.69; RMSE = 518 kg ha-1) and SCP (R2 = 0.77; RMSE = 110 kg ha-1) with moderate accuracy. Based on the calibration of PI, spatial analysis was assessed for biomass in ten dairy fields. In spatial analysis, semivariograms revealed the spatial dependency for biomass was moderate to strong and varied between the fields. This study indicates that proximal sensors have considerable potential for real-time in situ assessment of pasture quality and quantity in mixed pastures. The results indicate that spectral resolution and number of wavelengths used in the sensor are crucial for determining pasture quality with high accuracy which would allow future research to develop proximal sensors with an optimal number of wavelengths and spectral resolution

The Behavioural Responses of Beef Cattle (Bos taurus) to Declining Pasture Availability and the Use of GNSS Technology to Determine Grazing Preference

Combining technologies for monitoring spatial behaviour of livestock with technologies that monitor pasture availability, offers the opportunity to improve the management and welfare of extensively produced beef cattle. The aims of the study were to investigate changes to beef cattle behaviour as pasture availability changed, and to determine whether Global Navigation Satellite System (GNSS) technology could determine livestock grazing preference and hence improve pasture management and paddock utilisation. Data derived from GNSS collars included distance travelled and location in the paddock. The latter enabled investigation of individual animal interactions with the underlying Normalised Difference Vegetation Index (NDVI) and pasture biomass of the paddock. As expected, there was a significant temporal decrease in NDVI during the study and an increase in distance travelled by cattle (P < 0.001; r2 = 0.88). The proportion of time budget occupied in grazing behaviour also increased (P < 0.001; r2 = 0.71). Cattle showed a partial preference for areas of higher pasture biomass/NDVI, although there was a large amount of variation over the course of the study. In conclusion, cattle behaviour changed in response to declining NDVI, highlighting how technologies that monitor these two variables may be used in the future as management tools to assist producers better manage cattle, to manipulate grazing intensity and paddock utilisation

Integrating Airborne Hyperspectral, Topographic, and Soil Data for Estimating Pasture Quality Using Recursive Feature Elimination with Random Forest Regression

Accurate and efficient monitoring of pasture quality on hill country farm systems is crucial for pasture management and optimizing production. Hyperspectral imaging is a promising tool for mapping a wide range of biophysical and biochemical properties of vegetation from leaf to canopy scale. In this study, the potential of high spatial resolution and airborne hyperspectral imaging for predicting crude protein (CP) and metabolizable energy (ME) in heterogeneous hill country farm was investigated. Regression models were developed between measured pasture quality values and hyperspectral data using random forest regression (RF). The results proved that pasture quality could be predicted with hyperspectral data alone; however, accuracy was improved after combining the hyperspectral data with environmental data (elevation, slope angle, slope aspect, and soil type) where the prediction accuracy for CP was R2CV (cross-validated coefficient of determination) = 0.70, RMSECV (cross-validated root mean square error) = 2.06%, RPDCV (cross-validated ratio to prediction deviation) = 1.82 and ME: R2CV = 0.75, RMSECV = 0.65 MJ/kg DM, RPDCV = 2.11. Interestingly, the accuracy was further out-performed by considering important hyperspectral and environmental variables using RF combined with recursive feature elimination (RFE) (CP: R2CV = 0.80, RMSECV = 1.68%, RPDCV = 2.23; ME: R2CV = 0.78, RMSECV = 0.61 MJ/kg DM, RPDCV = 2.19). Similar performance trends were noticed with validation data. Utilizing the best model, spatial pasture quality maps were created across the farm. Overall, this study showed the potential of airborne hyperspectral data for producing accurate pasture quality maps, which will help farm managers to optimize decisions to improve environmental and economic benefits

Mapping a Cloud-Free Rice Growth Stages Using the Integration of PROBA-V and Sentinel-1 and Its Temporal Correlation with Sub-District Statistics

Monitoring rice production is essential for securing food security against climate change threats, such as drought and flood events becoming more intense and frequent. The current practice to survey an area of rice production manually and in near real-time is expensive and involves a high workload for local statisticians. Remote sensing technology with satellite-based sensors has grown in popularity in recent decades as an alternative approach, reducing the cost and time required for spatial analysis over a wide area. However, cloud-free pixels of optical imagery are required to produce accurate outputs for agriculture applications. Thus, in this study, we propose an integration of optical (PROBA-V) and radar (Sentinel-1) imagery for temporal mapping of rice growth stages, including bare land, vegetative, reproductive, and ripening stages. We have built classification models for both sensors and combined them into 12-day periodical rice growth-stage maps from January 2017 to September 2018 at the sub-district level over Java Island, the top rice production area in Indonesia. The accuracy measurement was based on the test dataset and the predicted cross-correlated with monthly local statistics. The overall accuracy of the rice growth-stage model of PROBA-V was 83.87%, and the Sentinel-1 model was 71.74% with the Support Vector Machine classifier. The temporal maps were comparable with local statistics, with an average correlation between the vegetative area (remote sensing) and harvested area (local statistics) is 0.50, and lag time 89.5 days (n = 91). This result was similar to local statistics data, which correlate planting and the harvested area at 0.61, and the lag time as 90.4 days, respectively. Moreover, the cross-correlation between the predicted rice growth stage was also consistent with rice development in the area (r > 0.52, p < 0.01). This novel method is straightforward, easy to replicate and apply to other areas, and can be scaled up to the national and regional level to be used by stakeholders to support improved agricultural policies for sustainable rice production