Forest and Crop Leaf Area Index Estimation Using Remote Sensing: Research Trends and Future Directions

Leaf area index (LAI) is an important vegetation leaf structure parameter in forest and agricultural ecosystems. Remote sensing techniques can provide an effective alternative to field-based observation of LAI. Differences in canopy structure result in different sensor types (active or passive), platforms (terrestrial, airborne, or satellite), and models being appropriate for the LAI estimation of forest and agricultural systems. This study reviews the application of remote sensing-based approaches across different system configurations (passive, active, and multisource sensors on different collection platforms) that are used to estimate forest and crop LAI and explores uncertainty analysis in LAI estimation. A comparison of the difference in LAI estimation for forest and agricultural applications given the different structure of these ecosystems is presented, particularly as this relates to spatial scale. The ease of use of empirical models supports these as the preferred choice for forest and crop LAI estimation. However, performance variation among different empirical models for forest and crop LAI estimation limits the broad application of specific models. The development of models that facilitate the strategic incorporation of local physiology and biochemistry parameters for specific forests and crop growth stages from various temperature zones could improve the accuracy of LAI estimation models and help develop models that can be applied more broadly. In terms of scale issues, both spectral and spatial scales impact the estimation of LAI. Exploration of the quantitative relationship between scales of data from different sensors could help forest and crop managers more appropriately and effectively apply different data sources. Uncertainty coming from various sources results in reduced accuracy in estimating LAI. While Bayesian approaches have proven effective to quantify LAI estimation uncertainty based on the uncertainty of model inputs, there is still a need to quantify uncertainty from remote sensing data source, ground measurements and related environmental factors to mitigate the impacts of model uncertainty and improve LAI estimation

Artificial Neural Networks in Agriculture

Modern agriculture needs to have high production efficiency combined with a high quality of obtained products. This applies to both crop and livestock production. To meet these requirements, advanced methods of data analysis are more and more frequently used, including those derived from artificial intelligence methods. Artificial neural networks (ANNs) are one of the most popular tools of this kind. They are widely used in solving various classification and prediction tasks, for some time also in the broadly defined field of agriculture. They can form part of precision farming and decision support systems. Artificial neural networks can replace the classical methods of modelling many issues, and are one of the main alternatives to classical mathematical models. The spectrum of applications of artificial neural networks is very wide. For a long time now, researchers from all over the world have been using these tools to support agricultural production, making it more efficient and providing the highest-quality products possible

Flood Forecasting Using Machine Learning Methods

This book is a printed edition of the Special Issue Flood Forecasting Using Machine Learning Methods that was published in Wate

Application of machine learning in operational flood forecasting and mapping

Considering the computational effort and expertise required to simulate 2D hydrodynamic models, it is widely understood that it is practically impossible to run these types of models during a real-time flood event. To allow for real-time flood forecasting and mapping, an automated, computationally efficient and robust data driven modelling engine - as an alternative to the traditional 2D hydraulic models - has been proposed. The concept of computationally efficient model relies heavily on replacing time consuming 2D hydrodynamic software packages with a simplified model structure that is fast, reliable and can robustly retains sufficient accuracy for applications in real-time flood forecasting, mapping and sequential updating. This thesis presents a novel data-driven modelling framework that uses rainfall data from meteorological stations to forecast flood inundation maps. The proposed framework takes advantage of the highly efficient machine learning (ML) algorithms and also utilities the state-of-the-art hydraulic models as a system component. The aim of this research has been to develop an integrated system, where a data-driven rainfall-streamflow forecasting model sets up the upstream boundary conditions for the machine learning based classifiers, which then maps out multi-step ahead flood extents during an extreme flood event. To achieve the aim and objectives of this research, firstly, a comprehensive investigation was undertaken to search for a robust ML-based multi-step ahead rainfall-streamflow forecasting model. Three potential models were tested (Support Vector Regression (SVR), Deep Belief Network (DBN) and Wavelet decomposed Artificial Neural Network (WANN)). The analysis revealed that SVR-based models perform most efficiently in forecasting streamflow for shorter lead time. This study also tested the portability of model parameters and performance deterioration rates. Secondly, multiple ML-based models (SVR, Random Forest (RF) and Multi-layer Perceptron (MLP)) were deployed to simulate flood inundation extents. These models were trained and tested for two geomorphologically distinct case study areas. In the first case of study, of the models trained using the outputs from LISFLOOD-FP hydraulic model and upstream flow data for a large rural catchment (Niger Inland Delta, Mali). For the second case of study similar approach was adopted, though 2D Flood Modeller software package was used to generate target data for the machine learning algorithms and to model inundation extent for a semi-urban floodplain (Upton-Upon-Severn, UK). In both cases, machine learning algorithms performed comparatively in simulating seasonal and event based fluvial flooding. Finally, a framework was developed to generate flood extent maps from rainfall data using the knowledge learned from the case studies. The research activity focused on the town of Upton-Upon-Severn and the analysis time frame covers the flooding event of October-November 2000. RF-based models were trained to forecast the upstream boundary conditions, which were systematically fed into MLP-based classifiers. The classifiers detected states (wet/dry) of the randomly selected locations within a floodplain at every time step (e.g. one hour in this study). The forecasted states of the sampled locations were then spatially interpolated using regression kriging method to produce high resolution probabilistic inundation (9m) maps. Results show that the proposed data centric modelling engine can efficiently emulate the outcomes of the hydraulic model with considerably high accuracy, measured in terms of flood arrival time error, and classification accuracy during flood growing, peak, and receding periods. The key feature of the proposed modelling framework is that, it can substantially reduce computational time, i.e. ~14 seconds for generating flood maps for a flood plain of ~4 km2 at 9m spatial resolution (which is significantly low compared to a fully 2D hydrodynamic model run time)

Forecasting: theory and practice

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.info:eu-repo/semantics/publishedVersio

Forecasting: theory and practice

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases

Forecasting: theory and practice

Forecasting has always been in the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The lack of a free-lunch theorem implies the need for a diverse set of forecasting methods to tackle an array of applications. This unique article provides a non-systematic review of the theory and the practice of forecasting. We offer a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts, including operations, economics, finance, energy, environment, and social good. We do not claim that this review is an exhaustive list of methods and applications. The list was compiled based on the expertise and interests of the authors. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of the forecasting theory and practice

Multi-sensor and data fusion approach for determining yield limiting factors and for in-situ measurement of yellow rust and fusarium head blight in cereals

The world’s population is increasing and along with it, the demand for food. A novel parametric model (Volterra Non-linear Regressive with eXogenous inputs (VNRX)) is introduced for quantifying influences of individual and multiple soil properties on crop yield and normalised difference vegetation Index. The performance was compared to a random forest method over two consecutive years, with the best results of 55.6% and 52%, respectively. The VNRX was then implemented using high sampling resolution soil data collected with an on-line visible and near infrared (vis-NIR) spectroscopy sensor predicting yield variation of 23.21%. A hyperspectral imager coupled with partial least squares regression was successfully applied in the detection of fusarium head blight and yellow rust infection in winter wheat and barley canopies, under laboratory and on-line measurement conditions. Maps of the two diseases were developed for four fields. Spectral indices of the standard deviation between 500 to 650 nm, and the squared difference between 650 and 700 nm, were found to be useful in differentiating between the two diseases, in the two crops, under variable water stress. The optimisation of the hyperspectral imager for field measurement was based on signal-to-noise ratio, and considered; camera angle and distance, integration time, and light source angle and distance from the crop canopy. The study summarises in the proposal of a new method of disease management through suggested selective harvest and fungicide applications, for winter wheat and barley which theoretically reduced fungicide rate by an average of 24% and offers a combined saving of the two methods of £83 per hectare

A Recursive Update Model for Estimating High-Resolution LAI Based on the NARX Neural Network and MODIS Times Series

Leaf area index (LAI) remote sensing data products with a high resolution (HR) and long time series are in demand in a wide variety of applications. Compared with long time series LAI products with 1 km resolution, LAI products with high spatial resolution are difficult to acquire because of the lack of remote sensing observations in long-term sequences and the lack of estimation methods applicable to highly variable land-cover types. To address these problems, we proposed a recursive update model to estimate 30 m resolution LAI based on the updated Nonlinear Auto-Regressive with Exogenous Inputs (NARX) neural network and MODIS time series. First, we used a variety of HR satellite remote sensing observations to produce HR datasets for recent years. Historical low spatial resolution MODIS products were employed as background information and used to calculate the initial parameters of the NARX neural network for each pixel. Subsequently, one year’s reflectance from the HR dataset was used as the new observation that was input into the NARX model to estimate the HR LAI of that year, and the background and HR data were then used for remodeling to update the NARX model parameters. This procedure was recursively repeated year by year until both MODIS background data and all HR data were involved in the modeling. Finally, we obtained an LAI time series with 30 m resolution. In the cropland study area in Hebei Province, China, the results were compared with LAI measurements from ground sites in 2013 and 2014. A high degree of similarity existed between the results for the two study years (RMSE2013=0.288 and RMSE2014=0.296). The HR LAI estimates showed favorable spatiotemporal continuity and were in good agreement with the multisample ground survey LAI measurements. The results indicated that for data with a rapid revisit cycle and high spatial resolution, the recursive update model based on the NARX neural network has excellent LAI estimation performance and fairly strong fault-tolerance capability

Bioinspired metaheuristic algorithms for global optimization

This paper presents concise comparison study of newly developed bioinspired algorithms for global optimization problems. Three different metaheuristic techniques, namely Accelerated Particle Swarm Optimization (APSO), Firefly Algorithm (FA), and Grey Wolf Optimizer (GWO) are investigated and implemented in Matlab environment. These methods are compared on four unimodal and multimodal nonlinear functions in order to find global optimum values. Computational results indicate that GWO outperforms other intelligent techniques, and that all aforementioned algorithms can be successfully used for optimization of continuous functions