Infrastructure Engineering - Research Publications
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ItemNo Preview AvailableTraining sample selection for robust multi-year within-season crop classification using machine learning(Elsevier BV, 2023-07-01)Within-season crop classification using multispectral imagery is an effective way to generate timely crop maps that can support water and crop management; however, developing such models is challenging due to limited satellite imagery and ground truth data available during the season. This study investigated ways to optimize the use of multi-year samples in a within-season crop classification model, aiming to enable accurate within-season crop mapping across years. Our study focused on classifying field-scale corn/maize, cotton, and rice in south-eastern Australia from 2013 to 2019. The crop classification model was based on the random forest and support vector machine algorithms applied to Landsat 8 multispectral bands. We designed four experiments to understand the influences of training sample selection on model accuracy. Specifically, we analyzed how the within-season classification accuracies are affected by 1) training sample size; 2) proportions of classification classes; 3) the inclusion of a non-crop class (e.g., fallow land) in the training sample, and 4) training samples collected from different years. We found that 1) the training sample size should be sufficiently large to ensure within-season classification accuracy; 2) using training samples for each crop type in proportion to their occurrence within the landscape results in more accurate multi-year classification; 3) the inclusion of the non-crop class can reduce the accuracy with which crop types are distinguished, so the proportion of the non-crop class should be maintained at a relatively low level, and 4) predicting the current year with training samples from previous years can lead to a minor decline in accuracy compared to using samples only from the current year. These training sample settings were adopted to develop a final model. We found that the model accuracy continues to improve as more input imagery is added as the cropping season progresses, with a rapid rate of initial improvement which then slows. December, the third month of the summer growing season, is the earliest time that reliable maps were generated, with an overall accuracy of 86 % and user's accuracies for all crops exceeding 80 %. Our proposed experiments are robust and transferable to other regions and seasons to assist the development of within-season crop maps, and can thus be valuable tools to support agricultural management.
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ItemAn analysis framework to evaluate irrigation decisions using short-term ensemble weather forecasts(SPRINGER, 2023-01)Abstract Irrigation water is an expensive and limited resource and optimal scheduling can boost water efficiency. Scheduling decisions often need to be made several days prior to an irrigation event, so a key aspect of irrigation scheduling is the accurate prediction of crop water use and soil water status ahead of time. This prediction relies on several key inputs including initial soil water status, crop conditions and weather. Since each input is subject to uncertainty, it is important to understand how these uncertainties impact soil water prediction and subsequent irrigation scheduling decisions. This study aims to develop an uncertainty-based analysis framework for evaluating irrigation scheduling decisions under uncertainty, with a focus on the uncertainty arising from short-term rainfall forecasts. To achieve this, a biophysical process-based crop model, APSIM (The Agricultural Production Systems sIMulator), was used to simulate root-zone soil water content for a study field in south-eastern Australia. Through the simulation, we evaluated different irrigation scheduling decisions using ensemble short-term rainfall forecasts. This modelling produced an ensemble of simulations of soil water content, as well as ensemble simulations of irrigation runoff and drainage. This enabled quantification of risks of over- and under-irrigation. These ensemble estimates were interpreted to inform the timing of the next irrigation event to minimize both the risks of stressing the crop and/or wasting water under uncertain future weather. With extension to include other sources of uncertainty (e.g., evapotranspiration forecasts, crop coefficient), we plan to build a comprehensive uncertainty framework to support on-farm irrigation decision-making.
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ItemA multi-model approach to assessing the impacts of catchment characteristics on spatial water quality in the Great Barrier Reef catchments(ELSEVIER SCI LTD, 2021-11-01)Water quality monitoring programs often collect large amounts of data with limited attention given to the assessment of the dominant drivers of spatial and temporal water quality variations at the catchment scale. This study uses a multi-model approach: a) to identify the influential catchment characteristics affecting spatial variability in water quality; and b) to predict spatial variability in water quality more reliably and robustly. Tropical catchments in the Great Barrier Reef (GBR) area, Australia, were used as a case study. We developed statistical models using 58 catchment characteristics to predict the spatial variability in water quality in 32 GBR catchments. An exhaustive search method coupled with multi-model inference approaches were used to identify important catchment characteristics and predict the spatial variation in water quality across catchments. Bootstrapping and cross-validation approaches were used to assess the uncertainty in identified important factors and robustness of multi-model structure, respectively. The results indicate that water quality variables were generally most influenced by the natural characteristics of catchments (e.g., soil type and annual rainfall), while anthropogenic characteristics (i.e., land use) also showed significant influence on dissolved nutrient species (e.g., NOX, NH4 and FRP). The multi-model structures developed in this work were able to predict average event-mean concentration well, with Nash-Sutcliffe coefficient ranging from 0.68 to 0.96. This work provides data-driven evidence for catchment managers, which can help them develop effective water quality management strategies.
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ItemEnhancing the Accuracy and Temporal Transferability of Irrigated Cropping Field Classification Using Optical Remote Sensing Imagery(MDPI, 2022-02)Mapping irrigated areas using remotely sensed imagery has been widely applied to support agricultural water management; however, accuracy is often compromised by the in-field heterogeneity of and interannual variability in crop conditions. This paper addresses these key issues. Two classification methods were employed to map irrigated fields using normalized difference vegetation index (NDVI) values derived from Landsat 7 and Landsat 8: a dynamic thresholding method (method one) and a random forest method (method two). To improve the representativeness of field-level NDVI aggregates, which are the key inputs in our methods, a Gaussian mixture model (GMM)-based filtering approach was adopted to remove noncrop pixels (e.g., trees and bare soils) and mixed pixels along the field boundary. To improve the temporal transferability of method one we dynamically determined the threshold value to account for the impact of interannual weather variability based on the dynamic range of NDVI values. In method two an innovative training sample pool was designed for the random forest modeling to enable automatic calibration for each season, which contributes to consistent performance across years. The irrigated field mapping was applied to a major irrigation district in Australia from 2011 to 2018, for summer and winter cropping seasons separately. The results showed that using GMM-based filtering can markedly improve field-level data quality and avoid up to 1/3 of omission errors for irrigated fields. Method two showed superior performance, exhibiting consistent and good accuracy (kappa > 0.9) for both seasons. The classified maps in wet winter seasons should be used with caution, because rainfall alone can largely meet plant water requirements, leaving the contribution of irrigation to the surface spectral signature weak. The approaches introduced are transferable to other areas, can support multiyear irrigated area mapping with high accuracy, and significantly reduced model development effort.
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ItemParsimonious Gap-Filling Models for Sub-Daily Actual Evapotranspiration Observations from Eddy-Covariance Systems(MDPI, 2022-03)Missing data and low data quality are common issues in field observations of actual evapotranspiration (ETa) from eddy-covariance systems, which necessitates the need for gap-filling techniques to improve data quality and utility for further analyses. A number of models have been proposed to fill temporal gaps in ETa or latent heat flux observations. However, existing gap-filling approaches often use multi-variate models that rely on relationships between ETa and other meteorological and flux variables, highlighting a critical lack of parsimonious gap-filling models. This study aims to develop and evaluate parsimonious approaches to fill gaps in ETa observations. We adapted three gap-filling models previously used for other meteorological variables but never applied to infill sub-daily ETa or flux observations from eddy-covariance systems before. All three models are solely based on the observed diurnal patterns in the ETa data, which infill gaps in sub-daily data with sinusoidal functions (Sinusoidal), smoothing functions (Smoothing) and pattern matching (MaxCor) approaches, respectively. We presented a systematic approach for model evaluation, considering multiple patterns of data gaps during different times of the day. The three gap-filling models were evaluated together with another benchmarking gap-filling model, mean diurnal variation (MDV) that has been commonly used and has similar data requirement. We used a case study with field measurements from an EC system over summer 2020–2021, at a maize field in southeastern Australia. We identified the MaxCor model as the best gap-filling model, which informs the diurnal pattern of the day to infill by using another day with similar temporal patterns and complete data. Following the MaxCor model, the MDV and the Sinusoidal models show comparable performances. We further discussed the infilling models in terms of their dependence on data availability and their suitability for different practical situations. The MaxCor model relies on high data availability for both days with complete data and the available records within each day to infill. The Sinusoidal model does not rely on any day with complete data, which makes it the ideal choice in situations where days with complete records are limited.
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ItemA Bayesian approach to understanding the key factors influencing temporal variability in stream water quality - a case study in the Great Barrier Reef catchments(COPERNICUS GESELLSCHAFT MBH, 2021-05-20)Abstract. Stream water quality is highly variable both across space and time. Water quality monitoring programmes have collected a large amount of data that provide a good basis for investigating the key drivers of spatial and temporal variability. Event-based water quality monitoring data in the Great Barrier Reef catchments in northern Australia provide an opportunity to further our understanding of water quality dynamics in subtropical and tropical regions. This study investigated nine water quality constituents, including sediments, nutrients and salinity, with the aim of (1) identifying the influential environmental drivers of temporal variation in flow event concentrations and (2) developing a modelling framework to predict the temporal variation in water quality at multiple sites simultaneously. This study used a hierarchical Bayesian model averaging framework to explore the relationship between event concentration and catchment-scale environmental variables (e.g. runoff, rainfall and groundcover conditions). Key factors affecting the temporal changes in water quality varied among constituent concentrations and between catchments. Catchment rainfall and runoff affected in-stream particulate constituents, while catchment wetness and vegetation cover had more impact on dissolved nutrient concentration and salinity. In addition, in large dry catchments, antecedent catchment soil moisture and vegetation had a large influence on dissolved nutrients, which highlights the important effect of catchment hydrological connectivity on pollutant mobilisation and delivery.
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ItemA data-based predictive model for spatiotemporal variability in stream water quality(COPERNICUS GESELLSCHAFT MBH, 2020-02-24)Abstract. Our current capacity to model stream water quality is limited – particularly at large spatial scales across multiple catchments. To address this, we developed a Bayesian hierarchical statistical model to simulate the spatiotemporal variability in stream water quality across the state of Victoria, Australia. The model was developed using monthly water quality monitoring data over 21 years and across 102 catchments (which span over 130 000 km2). The modeling focused on six key water quality constituents: total suspended solids (TSS), total phosphorus (TP), filterable reactive phosphorus (FRP), total Kjeldahl nitrogen (TKN), nitrate–nitrite (NOx) and electrical conductivity (EC). The model structure was informed by knowledge of the key factors driving water quality variation, which were identified in two preceding studies using the same dataset. Apart from FRP, which is hardly explained (19.9 %), the model explains 38.2 % (NOx) to 88.6 % (EC) of the total spatiotemporal variability in water quality. Across constituents, the model generally captures over half of the observed spatial variability; the temporal variability remains largely unexplained across all catchments, although long-term trends are well captured. The model is best used to predict proportional changes in water quality on a Box–Cox-transformed scale, but it can have substantial bias if used to predict absolute values for high concentrations. This model can assist catchment management by (1) identifying hot spots and hot moments for waterway pollution; (2) predicting the effects of catchment changes on water quality, e.g., urbanization or forestation; and (3) identifying and explaining major water quality trends and changes. Further model improvements should focus on the following: (1) alternative statistical model structures to improve fitting for truncated data (for constituents where a large amount of data fall below the detection limit); and (2) better representation of nonconservative constituents (e.g., FRP) by accounting for important biogeochemical processes.
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ItemCharacterisation of spatial variability in water quality in the Great Barrier Reef catchments using multivariate statistical analysis(PERGAMON-ELSEVIER SCIENCE LTD, 2018-12)Water quality monitoring is important to assess changes in inland and coastal water quality. The focus of this study was to improve understanding of the spatial component of spatial-temporal water quality dynamics, particularly the spatial variability in water quality and the association between this spatial variability and catchment characteristics. A dataset of nine water quality constituents collected from 32 monitoring sites over a 11-year period (2006-2016), across the Great Barrier Reef catchments (Queensland, Australia), were evaluated by multivariate techniques. Two clusters were identified, which were strongly associated with catchment characteristics. A two-step Principal Component Analysis/Factor Analysis revealed four groupings of constituents with similar spatial pattern and allowed the key catchment characteristics affecting water quality to be determined. These findings provide a more nuanced view of spatial variations in water quality compared with previous understanding and an improved basis for water quality management to protect nearshore marine ecosystem.
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ItemKey Factors Affecting Temporal Variability in Stream Water Quality(AMER GEOPHYSICAL UNION, 2019-01)Abstract Understanding the factors that influence temporal variability in water quality is critical for designing water quality management strategies. In this study, we explore the key factors that affect temporal variability in stream water quality across multiple catchments using a Bayesian hierarchical model. We apply this model to a case study data set consisting of monthly water quality measurements obtained over a 20‐year period from 102 water quality monitoring sites in the state of Victoria (Southeast Australia). We investigate six water quality constituents: total suspended solids, total phosphorus, filterable reactive phosphorus, total Kjeldahl nitrogen, nitrate‐nitrite (NOx), and electrical conductivity. We find that same‐day streamflow has the greatest effect on water quality variability for all constituents. Additional important predictors include soil moisture, antecedent streamflow, vegetation cover, and water temperature. Overall, the models do not explain a large proportion of temporal variation in water quality, with Nash‐Sutcliffe coefficients lower than 0.49. However, when considering performance on a site‐by‐site basis, we see high model performance in some locations, with Nash‐Sutcliffe coefficients of up to 0.8 for NOx and electrical conductivity. The effect of the temporal predictors on water quality varies between sites, which should be explored further for potential spatial patterns in future studies. There is also potential for further extension of these temporal variability models into a predictive spatiotemporal model of riverine constituent concentrations, which will be a useful tool to inform decision making for catchment water quality management.