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)

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

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

Propagation of atmospheric condition parameter uncertainty in measurements of landscape heterogeneity

Remote sensing products are widely used in ecology and environmental science to understand how surfaces are composed and configured in a landscape and how they change through time. Land cover maps that describe the nature of habitats available to organisms have become critical tools in the study of anthropogenic impacts, such as fragmentation, on biodiversity and related ecological processes. Despite the increasing importance and widespread use of remote sensing derived landscape pattern indices measured using landscape composition and configuration metrics via land cover maps, these indices may conceal substantial and potentially problematic concerns about the uncertainty of reflectance data. This uncertainty derives from the presence of varying atmospheric conditions between the earth and the sensor and is often unreported. We asked how the magnitudes of measured landscape composition and configuration change and the dispersion in the changes caused by reflectance uncertainty compare. Using a Monte Carlo framework, we estimated multiple realizations of reflectance images of a simulated hyperspectral dataset depicting a peri-urban area. These estimates were influenced by uncertainty originating from atmospheric condition parameters (column water vapour, aerosol optical depth, and aerosol type). We generated multiple classified maps using Support Vector Machines and quantified standard landscape composition and configuration metrics. High dispersion in composition and configuration metrics caused by reflectance uncertainty strongly impacted our ability to detect and compare changes of the kind that natural scientists typically derive from such maps. We conclude that the propagation of uncertainty limits the extent to which current landscape patterns and Land Use and Land Cover change (LULC) analyses can be used for fine-scale decision-making for landscape management

The classification of hill country vegetation from hyperspectral imagery

Remotely sensed hyperspectral data provides the possibility to categorise and quantify the farm landscape in great detail, supplementing local expert knowledge and adding confidence to decisions. This paper examines the novel use of hyperspectral aerial imagery to classify various components of the hill country farming landscape. As part of the Ravensdown / MPI PGP project, “Pioneering to Precision”, eight diverse farms, five in the North and three in the South Island were sampled using the AisaFENIX hyperspectral imager. The resulting images had a 1m spatial resolution (approx.) with 448 spectral bands from 380 – 2500 nm. The primary aim of the PGP project is to develop soil fertility maps from spectral information. Images were collected in tandem with ground sampling and timed to coincide with spring and autumn seasons. Additional classification of the pasture components of two farms are demonstrated using various data pre-processing and classification techniques to ascertain which combination would provide the best accuracy. Classification of pasture with Support Vector Machines (SVM) achieved 99.59% accuracy. Classification of additional landscape components on the same two farms is demonstrated. Components classified as non-pasture ground cover included; water, tracks/soil, Manuka, scrub, gum, poplar and other tree species. The techniques were successfully used to classify the components with high levels of accuracy. The ability to classify and quantify landscape components has numerous applications including; fertiliser and farm operational management, rural valuation, strategic farm management and planning.fals