Browsing by Browse by FOR 2020 "300206 Agricultural spatial analysis and modelling"

Rice yield depends on factors including variety, weather, field management, nutrient and water availability. We analyzed important drivers of yield variability at the field scale, and developed yield forecast models for crops in the temperate irrigated rice growing region of Australia. We fused a time-series of Sentinel1 and Sentinel-2 satellite remote sensing imagery, spatial weather data and field management information. Rice phenology was predicted using previously reported models. Higher yields were associated with early flowering, higher chlorophyll indices and higher temperatures around flowering. Successive rice cropping in the same field was associated with lower yield (p<0.001). After running a series of leave-one-year-out cross validation experiments, final models were trained using 2018–2022 data, and were applied to predicting the yield of 1580 fields (43,700 hectares) from an independent season with challenging conditions (2023). Models which aggregated remote sensing and weather time-series data to phenological periods provided more accurate predictions than models that aggregated these predictors to calendar periods. The accuracy of forecast models improved as the growing season progressed, reaching RMSE=1.6 t/ha and Lin’s concordance correlation coefficient (LCCC) of 0.67 30 days after flowering at the field level. Explainability was provided using the SHAP method, revealing the likely drivers of yield variability overall, and of individual fields.

Changes in climate and changes in land use and land cover (LULC) are the two most pressing threats to sustainability and productivity in Social-Ecological Systems (SESs).

The Village Tank Cascade Systems (VTCSs) in Sri Lanka are among the oldest SESs in the world. Human populations began developing these systems during the 4th century BCE (Before the Common Era) to sustain their livelihoods and well-being in the face of climatic uncertainties in the dry zone of Sri Lanka. For more than two millennia, VTCSs operated in harmony with nature, preserving unique biodiversity and ecosystem services. Over time, further human interventions saw the VTCSs evolve into sustainable agroecosystems. In the 21st century, VTCSs are vulnerable to adverse global environmental changes. Ecological health in the system has deteriorated as a result, threatening the food security and livelihood of rural farming communities. Currently, about 16,500 village tanks are operational. Approximately 90% of these form part of clusters called VTCSs.

Measures to build resilience and adaptive capacity to the threats posed by impacts of climate and LULC change stressors on sustainability and productivity in VTCSs are urgently required. However, little is known or understood about the spatial and temporal aspects of changes in climate and LULC in VTCSs. Research is required to better understand how to manage critically-threatened VTCS landscapes. This study quantifies climate and LULC changes in VTCSs and evaluates the risk these changes pose to the sustainability and productivity of VTCSs. The study incorporates transdisciplinary approaches combining various research methods, tools and techniques: i) biophysical assessment using quantitative modelling techniques for climate and LULC data, and ii) community perception assessment using standardised participatory tools and techniques within the community and with other stakeholders and experts involved in the management of the VTCSs.

The thesis consists of nine chapters. An introductory chapter is followed by a review of current research into SESs (Chapter Two), focussing specifically on VTCSs in Sri Lanka. An extensive and in-depth review of research literature was carried out to identify existing knowledge with respect to sustainability and productivity dimensions of VTCSs. This was then subjected to bibliometric analysis and knowledge mapping to assess the status, statistical trends, terminological clusters and emerging concepts of VTCS research. It also offers some insight into the research hotspots and neglected areas of research into VTCSs.

Chapter Three presents a comprehensive, logical framework for analysing causal factors and processes that result in reduced system productivity. It seeks to identify factors that reduce the ecological productivity of VTCSs by examining the social-ecological nexus between system elements, the way these factors impact productivity and the challenges for restoration. The study makes use of i) spatial data and analysis techniques to differentiate spatial components and identify significant spatial patterns, and ii) participatory field assessment and key informant interview data to determine the social-ecological nexus.

Chapter Four investigates trends of climate variability and their potential impacts on VTCSs especially with respect to future paddy cultivation. Paddy is the staple food in Sri Lanka and paddy cultivation is the main livelihood of people in VTCS areas. Rainfall and temperature data of the VTCS area were used to evaluate past variability trends (1970 to 2020). Modelled data was used to project future trends (from the present to 2100). The modelled data indicates that future rainfall and temperature variability are likely to deviate considerably from past trends across the VTCS zones.

In addition, modelled data projects a continuous warming trend. Warming is likely to increase variability in rainfall resulting in longer/shorter seasons and the increased likelihood of extreme weather events in the future. The area of land suitable for paddy farming is also likely to change over time in response to future climate change scenarios. The changes projected in this study are likely to negatively impact paddy farming in the future.

Chapter Five examines changes in land use and land cover (LULC) as well as landscape patterns and the impacts of these changes on the productivity of VTCS zones. Satellite imagery and GIS techniques were used to map LULC in VTCS landscapes in 1994 and 2021. These maps were then used to create a LULC change matrix and calculate spatial pattern metrics to examine LULC and landscape pattern change dynamics over the time period. The results reveal transformations of the LULC and gradual changes of landscape patterns from natural forest to agriculture. Landscape patterns were analysed at both the landscape and LULC class levels. At the landscape level, the structure became more complex and fragmented, while at the class level, increased fragmentation of forest habitats was observed. The study concludes that VTCSs are undergoing a gradual loss of environmental sustainability as a result of these changes. Assessment of LULC, including fragmentation, can help to monitor the spatial pattern impacts. Thus, the study provides scientific guidance for the ecological restoration of degraded VTCSs.

Chapter Six of the thesis assesses ecosystem services in VTCSs using a participatory research approach, involving the integration of local knowledge, experts’ judgments and land use system attribute data. The study develops an ecosystem services supply and demand matrix model. This model shows that the current overall ecosystem services demand for regulating and supporting ecosystem services exceeds supply. The model highlights that land degradation and biodiversity deterioration reduce the capacity to provide ecosystem services. The study concludes that ecological restoration of VTCSs depends on the extent to which integrated effort addresses the levels of ecological complexity, as well as the social engagement of communities and other stakeholders. The study provides a scientific basis to guide future land use decisionmaking in VTCSs.

In Chapter Seven, the thesis describes a household survey that was conducted in a VTCS to investigate farmers’ perceptions of the impacts of changing climate and land use on food production. The majority of farmers felt that the climate of the VTCS areas had changed over time. They reported a notable increase in the variability of rainfall patterns. The increased cost of production, damage caused by wildlife, and land degradation were regarded as the factors that most impacted food production. Farmers rated deforestation and land clearing—encroachment of natural habitats as the most impactful on LULC. Farmers’ perceptions of the severity of both climate and land use changes were influenced by the following determinants: level of training, household size, farm size, level of income/profit, level of adaptation and the location of the farm. The study findings will help to formulate localised land use policies and climate change adaptation strategies in VTCSs with a combination of both top-down and bottom-up approaches.

Chapter Eight undertakes a comprehensive assessment and characterisation of underutilised, neglected, and wild food plants found in Sri Lanka, with special attention to SESs. Plant species were identified and documented on the basis of six plant-food groups. The work involved: field surveys, field observations, community interviews, herbarium records, key informant interviews and expert validation. Particular attention was paid to the ecosystem services provided by these food plants in the SESs of SriLanka. The chapter also provides data concerning the nutritional values of these plants. Policies to enhance climate resilience and adaptive capacity of VTCS food systems are recommended

In conclusion (Chapter Nine), the findings of this research demonstrate that climate and LULC changes significantly impact the productivity and sustainability of the VTCSs in Sri Lanka, particularly with respect to ecosystem health and food production. Through a participatory approach, the study integrates community perceptions with other aspects of the SES phenomenon of tank cascades (VTCSs), including ecosystem services, spatial changes, vulnerability, adaptive capacity, resilience, and restoration strategies, decision-making, and plant life. In so doing, it offers a range of new insights into the ways in which these challenges can be met. Although the findings of this study are specific to the VTCSs of Sri Lanka, they may be applicable to SESs in other tropical regions with similar climatic and LULC conditions.

This dataset was created during a study assessing the impacts of climate change on climate/land suitability and the quality of tea in Sri Lanka, specifically Camellia sinensis (L) O. Kuntze. Data for chemical analysis of tea biochemicals were collected through sample collection during field visits over the years from 2018 to 2020 and subsequent chemical analysis. Data for climate modelling were collected from online databases, Departments of Climate and Meteorology of Sri Lanka. Data for geospatial analysis were gathered from shapefiles, Department of Agriculture Sri Lanka. This data was then used to model climate/land and tea quality.

The impacts of climate change on tea production systems may be very variable, at both the national and global levels. In particular, Sri Lanka is considered vulnerable to climate fluctuations due to a variety of geographic, socioeconomic, and political factors. The predicted effects of climate change could have serious and irreversible consequences for tea production, quality, and habitats. Therefore, the consequences of climate change on Sri Lanka's tea industry should be extensively researched to determine its impact on production and quality, which in turn related to export revenues and employment for rural populations. However, information is exiguous on how climate change could affect climate/land suitability and tea quality under rainfed conditions in Sri Lanka. To narrow this gap, this study aimed at evaluating the effects of climate change on climate/land suitability for tea and its quality using a case study of Sri Lanka, a well-known high-quality black tea producer, as a classic example of a susceptible region. The study used species distribution techniques, geographic information system (GIS), remote sensing (RS)–based applications, and chemical analysis of tea leaves. The systematic review suggested that the impacts of the current and future climate on tea production systems outweigh the beneficial impacts, having multidimensional and multifaceted consequences. Tea yield increases when CO₂ levels rise, but this positive effect could be hindered by rising temperatures. Further, tea yield would be negatively impacted by drought, uneven rainfall, and extreme weather events. For tea quality attributes, climate change can serve as both a boon and a bane, leaving questions and giving research priority to quantifying the thresholds of biochemicals to define tea quality, according to customer satisfaction. Climate change affects tea habitats by causing losses, gains, and shifts in climate suitability. Further review suggested the scarcity of appropriate method to model impacts of future climate changes on tea quality and for determining climate suitability for tea. It also highlighted the importance of implementation of adaptive and mitigation measures in tea production to alleviate the undesirable impacts of climate change. At regional scale climate modelling for Sri Lanka's tea sector, indicated that precipitation seasonality, annual mean temperature and annual precipitation are the three most important bioclimatic variables of tea habitat distribution in Sri Lanka. Land suitability classes for tea cultivation comprised of low suitability (42.1%), unsuitable (28.5%), moderate (12.4%), highly suitable (13.9%), and very highly suitable (2.5%). There is a chance of decrease in optimal and medium suitability areas in low-elevation regions in the future, with overall decline assessed to be between 8-17% for all suitability areas. This indicating that climate change will have a negative effect on the habitat suitability of tea in Sri Lanka by 2050 and 2070. Further, the refinement in land suitability classification through inclusion of other climatic and environmental variables (solar radiation, temperature, rainfall, topographic and soil) in climate model made two suggestions namely (1) there is a noticeable difference between tea- and non-tea-growing areas in terms of all above factors" (2) under future climate change scenario, tea-growing regions in Sri Lanka could expand to a range of locales, if some key variables are carefully managed.

For tea quality assessment, model showed a significant interaction effect of weather conditions, cultivar, and geographical location over the concentrations of major tea quality biochemicals (total polyphenol content (TPC), free sugar, protein, and theanine) in tea leaves. The bioclimatic variables present seasonality (monthly range in temperature and precipitation), monthly trends (mean monthly temperature, monthly total precipitation), and extreme environmental variables (temperature of the coldest and warmest month, and precipitation of the wettest and driest months). They particularly caused changes in the four tested biochemicals of tea. The thresholds of all tested biochemicals are likely to increase with future climate change as temperatures and rainfall intensities are likely to increase. The distribution class with "very high" concentrations of TPC and theanine is expected to increase by 10% and 14%, respectively, in the future, while protein and free sugar classes are expected to decrease by 14% and 12%, respectively. For tea quality assessment, model showed a significant interaction effect of weather conditions, cultivar, and geographical location over the concentrations of major tea quality biochemicals (total polyphenol content (TPC), free sugar, protein, and theanine) in tea leaves. The bioclimatic variables present seasonality (monthly range in temperature and precipitation), monthly trends (mean monthly temperature, monthly total precipitation), and extreme environmental variables (temperature of the coldest and warmest month, and precipitation of the wettest and driest months). They particularly caused changes in the four tested biochemicals of tea. The thresholds of all tested biochemicals are likely to increase with future climate change as temperatures and rainfall intensities are likely to increase. The distribution class with "very high" concentrations of TPC and theanine is expected to increase by 10% and 14%, respectively, in the future, while protein and free sugar classes are expected to decrease by 14% and 12%, respectively.

The rise in global production of horticultural tree crops over the past few decades is driving technology-based innovation and research to promote productivity and efficiency. Although mango production is on the rise, application of the remote sensing technology is generally limited and the available study on retrieving mango phenology stages specifically, was focused on the application of optical data. We therefore sought to answer the questions; (1) can key phenology stages of mango be retrieved from radar (Sentinel-1) particularly due to the cloud related limitations of optical satellite remote sensing in the tropics? and (2) does weather have any effect on phenology? The study was conducted on a mango farm in the Yilo Krobo Municipal Area of Ghana. Time series analysis for radar vegetation index (RVI) values for 2018 – 2021 was used to retrieve three key phenology stages of mango namely; Start of Season (SoS), Peak of Season (PoS) and End of Season (EoS). Characteristic annual peaks (in April/May for the major season and October/November for the minor season) and troughs (in June/July for the major season and December/January for the minor season) in the phenology trend of mango were identified. Rainfall and temperature explained less than 2% and 14% of the variability respectively in mango phenology. The application of radar remote sensing provides a cutting edge technology in the assessment of mango phenology, particularly in the tropics where cloud cover is a big challenge. This study offers an opportunity for production efficiency in the mango value chain as understanding of the crop's phenology allows growers to manage farm and post-harvest operations.

There is currently no operational method of managing irrigation in Australia's sugar industry on the basis of systematic, direct monitoring of sugar plant physiology. Satellite remote sensing systems, having come a long way in the past 10 years now offer the potential to apply the current ground-based 'FAO' or 'crop coefficient (K_c)' approach in a way that offers a synoptic view of crop water status across fields. In particular, multi-constellation satellite remote sensing, utilising a combination of freely available Landsat and Sentinel 2 imagery, supplemented by paid-for imagery from other existing satellite systems is capable of providing the necessary spatial resolution and spectral bands and revisit frequency. The significant correlations observed between K_c and spectral vegetation indices (VIs), such as the widely used normalised difference vegetation index (NDVI) in numerous other crops bodes well for the detection and quantification of the spatial difference in evapotranspiration (ET_c) in sugar which is necessary for irrigation scheduling algorithms. Whilst the NDVI may not serve as the appropriate index for sugarcane, given the potential of the NDVI to saturate at the high leaf area index observed in fully developed cane canopies, other VIs such as the Green-NDVI (GNDVI) may provide the response required. In practise, with knowledge of an appropriate K_c-VI relationship, K_c obtained from time-series (weekly) remotely sensed data, integrated with local agrometeorological data to provide ET_o, would provide estimates of ET_c from which site-specific irrigated water requirements (IWR) could be estimated. The use of UAVs equipped with multispectral sensors, even active optical sensors (AOS), to 'fill the gaps' in optical data acquisition due to cloud cover is conceivable. Cross calibration of any passive imaging system, as with the multi-constellation satellite data is essential. The use of radar images (microwave remote sensing) (for example, Sentinel 1&2 C-SAR, 5m) offers all weather, day-and-night capabilities although further work is necessary to understand the link between the radar back scatter, which is responding to surface texture, and evapotranspiration (and K_c). Further R&D in ascertaining the K_c-VI relationships during crop growth is necessary, as is the testing of multi-sensor cross-calibration and the relationship between radar remote sensing and K_c. Existing irrigation advisory delivery systems in Australia such as IrriSAT should be investigated for their applicability to the sugar industry. The estimated season cost to a user for a sugarcane irrigation advisory service in Australia, based on the use of data from existing optical satellite imaging systems and utilising the K_c approach, is likely to be of the order of US$2-3/ha.

Early tree crop yield forecasts are valuable to industry and to growers, as they inform improved harvest logistics, forward selling, insurance and marketing strategies. Previous work has demonstrated the utility of weather and particularly remote sensing data to forecast tree crop yield at the orchard block scale. In this work, such data were aggregated spatially to block boundaries, and temporally at quarterly intervals. Yield prediction models were trained with a large set of grower-supplied yield data (more than 10 years, 20 orchards, 200 blocks across the Australian growing regions, for a total of 1156 yield records). Yields were forecast three months before harvest begins, and were compared to actual yields. Errors were typically around 10% and 23% at the regional and block levels respectively. Errors in 2020 were higher in non-irrigated regions due to an extreme drought in east Australia. Models were able to describe much of the variability of yields even for orchards not included in the training data, but block-level prediction errors increased by 4.1% in this case. Bootstrap sampling was used to investigate data requirements. At least 400-500 training data points was needed to minimize prediction errors. Weather data alone did not produce satisfactory accuracy, fusing weather and remote sensing data produced the best results. Including predictor data from all 8 quarterly periods from the 2 years before harvest proved a good strategy. These results demonstrate the potential of tree crop forecasting using public spatio-temporal datasets, give guidance on data requirements and identify areas for further work.

Although herbicide drifts are known worldwide and recognized as one of the major risks for crop security in the agriculture sector, the traditional assessment of damage in cotton crops caused by herbicide drifts has several limitations. The aim of this study was to assess proximal sensor and modelling techniques in the detection of phenoxy herbicide dosage in cotton crops. In situ hyperspectral data (400-900 nm) were collected at four different times after ground-based spraying of cotton crops in a factorial randomized complete block experimental design with dose and timing of exposure as factors. Three chemical doses: nil, 5% and 50% of the recommended label rate of the herbicide 2,4-D were applied to cotton plants at specific growth stages (i.e. 4-5 nodes, 7-8 nodes and 11-12 nodes). Results have shown that yield had a significant correlation (p-values <0.05) to the green peak (~550 nm) and NIR range, as the pigment and cell internal structure of the plants are key for the assessment of damage. Prediction models integrating raw spectral data for the prediction of dose have performed well with classification accuracy higher than 80% in most cases. Visible and NIR range were significant in the classification. However, the inclusion of the green band (around 550 nm) increased the classification accuracy by more than 25%. This study shows that hyperspectral sensing has the potential to improve the traditional methods of assessing herbicide drift damage.

To establish an indirect method for estimating and partitioning pasture evapotranspiration, it is vital to develop a direct reference method that aligns with the required temporal and spatial resolution. An evapotranspiration chamber offers an effective solution as it is easy to deploy and operates at an appropriate measurement scale. In this study, we prepared and tested a closed hemispherical chamber for on-site measurements of evaporation and/ or transpiration. Advanced data monitoring and logging techniques were integrated to enhance the precision and reliability of direct in-field evapotranspiration measurements. During laboratory testing, vapor accumulation within the chamber was monitored to identify the best representative segment of the vapor accumulation curve. Results indicated that the instrument stabilizes its readings within 5 to 10 s post-deployment in laboratory settings. The subsequent 15 s produce stable readings that best represent actual vapor accumulation. The optimal fan speed, producing an air speed of 5.36 ms^{− 1} at the vicinity of the fan within the chamber, paired with a wire mesh above the vapor-producing surface, yielded the best results. The study established a calibration factor (C) of 1.02 based on the actual water loss and vapor accumulation readings from the sensors at this fan speed. Advanced data analytics were applied to derive the calibration factor and to calculate the values of evapotranspiration from the changing microclimate within the chamber. Direction towards complete automation and the limitations of the chamber in field measurement are provided. The chamber was also tested under field conditions, and the paper examines its practical application and necessary adjustments for field measurements.

This study presents a comprehensive evaluation of seasonal, locational, and varietal variations in canopy reflectance responses in 315 commercial citrus blocks from three major growing regions in Australia. The dataset includes three different citrus types (Mandarin, Navel, Valencia) and 26 varieties. The aim is to utilize this combined information to better understand yield variation and develop improved forecasting models. Landsat satellite data spanning from October 2006 to February 2021 (1419 tiles) were used to derive reflectance values, and calculate four vegetation indices (NDVI, GNDVI, LSWI, and GCVI), for each citrus block. These indices were then analyzed alongside corresponding yield data, which consisted of 3660 individual yield records dating back to 2007. Two temporal resolutions were incorporated as predictors: spatio-temporal vegetation index time series (TS) aggregated every two months and annual time series of historical block-yield records. Six statistical and machine learning algorithms were calibrated using a leave-one-year-out cross-validation approach (LOYO CV) and validated for one-year forward prediction over a five-year period (2017–2021). The results highlight significant yield variations across years, alternate bearing patterns, and spatio-temporal changes in reflectance profiles influenced by seasonal conditions, varietal characteristics, and locations. The support vector machine (SVM) algorithm with a radial basis function kernel consistently outperformed other algorithms, indicating a non-linear relationship between citrus yield and predictors. The SVM model achieved an RMSE of 15.5 T ha⁻¹ , R² of 0.88, MAE of 12.1 T ha⁻¹ , and MAPE of 29% in predicting block-yield across farms, varieties, and seasons. These prediction accuracy metrics demonstrate an improvement over current forecasting methods. Notably, the proposed approach utilizes freely available imagery, provides forecasts between two to nine months before harvest, and eliminates the need for infield counting of fruit load for image calibration. This approach provides an improved method for understanding seasonal yield variation and quantifying citrus block-yield, offering valuable insights for growers in harvest logistics, labor allocation, and resource management.

Regional maps of rice fields provided early in each growing season facilitate production estimates, planning around harvest logistics, marketing and targeted agronomic recommendations. This work develops maps of all irrigated rice fields in New South Wales, Australia. Classification models were trained on reference maps from the 2019 and 2020 harvest seasons. Model predictions were tested against a reference rice map from the 2021 harvest season, covering 60,000 km ² . The random forest algorithm was used, with features from aggregated time-series of Sentinel-2 imagery. A sequence of maps were generated at intervals of 15 days, from early to late in the growing season, with accuracy assessed at each time. The maps achieved 95% overall accuracy against point samples at 16 January 2021 ( ≈80 days after sowing). Pixel-based F1-scores against the reference map were above 80% for the 1, 16 and 31 January classified maps.

The performance of a multi-view machine vision method was documented at an orchard level, relative to packhouse count. High repeatability was achieved in night-time imaging, with an absolute percentage error of 2% or less. Canopy architecture impacted performance, with reasonable estimates achieved on hedge, single leader and conventional systems (3.4, 5.0, and 8.2 average percentage error, respectively) while fruit load of trellised orchards was over-estimated (at 25.2 average percentage error). Yield estimations were made for multiple orchards via: (i) human count of fruit load on ~5% of trees (FARM), (ii) human count of 18 trees randomly selected within three NDVI stratifications (CAL), (iii) multi-view counts (MV-Raw) and (iv) multi-view corrected for occluded fruit using manual counts of CAL trees (MV-CAL). Across the nine orchards for which results for all methods were available, the FARM, CAL, MV-Raw and MV-CAL methods achieved an average percentage error on packhouse counts of 26, 13, 11 and 17%, with SD of 11, 8, 11 and 9%, respectively, in the 2019–2020 season. The absolute percentage error of the MV-Raw estimates was 10% or less in 15 of the 20 orchards assessed. Greater error in load estimation occurred in the 2020–2021 season due to the time-spread of flowering. Use cases for the tree level data on fruit load was explored in context of fruit load density maps to inform early harvesting and to interpret crop damage, and tree frequency distributions based on fruit load per tree.

Phytophthora root rot disease (PRR) is a major threat in avocado orchards, causing extensive production loss and tree death if left unmanaged. PRR infects the roots of avocado trees, resulting in reduced uptake of water and nutrients, showing canopy decline, defoliation and, if not managed, tree mortality. Although the Australian avocado industry has implemented several preventative strategies in orchards for managing the disease spread, assessment of disease severity in orchards remains a challenge. Commercially, PRR severity can be assessed visually by a ‘Ciba-Geigy’ canopy health ranking method, where the degree of canopy decline exhibited by infected trees is compared to a series of calibration photos. A rating of 0 signifies a healthy canopy, whilst a rating of 10 indicates total leaf loss and tree death. This method is highly subjective, labour inefficient, non- scalable and generally only provides a positive diagnosis of infected trees once a high severity of decline has occurred. As an alternative, this study evaluated a range of remote sensing technologies that may offer a non-invasive surrogate to the visual ‘Ciba-Geigy’ method. Red, green and blue (R,G, B), multispectral and thermal imagery were acquired from a range of commercially available sensors to evaluate the performance against PRR-induced canopy decline (assessed using ‘Ciba-Geigy’ method) within a commercial avocado orchard. RGB images acquired with a smartphone mounted FLIR ONE camera were able to quantify the canopy decline via canopy porosity associated with PRR infection (R² =0.98, RMSE 0.32). Worldview-3 (WV-3) satellite imagery, more specifically the simple ratio vegetation index (SRVI), produced the highest coefficient of determination in quantifying canopy decline as defined by the ‘Ciba-Geigy’ method (R² =0.96, RMSE 0.38). A measure of stomatal conductance derived from the proximal measure of canopy temperature (by FLIR B250 hand held camera) from the sunlit and shaded side of tree was found to be strongly correlated with canopy porosity associated with PRR (R² > 0.91). Additionally, this study developed a new analytical ‘histogram method’ for segregating thermal data associated with canopy to that non- canopy related. By offering an alternative to the commonly used ‘wet’ and ‘dry’ reference panel method, this output offers significant benefit for the future automated processing of many thermal images, such as that required for large commercial orchards. This thermal procedure was also found to detect canopy decline pre-visually (at ‘Ciba-Geigy’ ranking ‘2’). This research has clearly demonstrated the potential of remote sensing imagery acquired from a range of sensors, as a useful surrogate for assessing PRR induced canopy decline in avocado orchards. These approaches can significantly improve the scalability and efficiency of PRR assessment under commercial production.