Browsing by Author "Tran DX"

Now showing 1 - 3 of 3
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    Effects of spatial data resolution on the modelling and mapping of soil organic carbon content in hill country grassland landscapes
    (John Wiley and Sons Ltd on behalf of British Society of Soil Science, 2024-01-19) Tran DX; Dominati E; Lowry J; Mackay A; Vibart R; Pearson D; Devantier B; Noakes E
    Limited use has been made of spatially explicit modelling of soil organic carbon (SOC) in highly complex farmed landscapes to advance current mapping efforts. This study aimed to address this gap in knowledge by evaluating the spatial prediction of SOC content in the 0–75 mm soil depth in hill country landscapes in New Zealand (NZ) using point-based training data, along with topographic covariates and Sentinel 2 spectral band ratios using an automated set of machine learning (AutoML) tools in ArcGIS. Subsequently, it also focused on quantifying the effects of spatial data resolution (i.e., 1, 8, 15, and 25 m) in terms of predicted map accuracy. Farmlets with contrasting phosphorus fertilizer and sheep grazing histories located at the Ballantrae Hill Country Research Station, NZ were selected to conduct the research. Six candidate algorithms incorporated in the AutoML tools (i.e., XGBoost, LightGBM, linear regression, decision trees, extra trees, random forest) and ensemble model were utilized to model the spatial pattern of SOC content. The results show that the ensemble model that combine predictions of various algorithms applied for 1 m data resolution enables the highest performance and accuracy (i.e., R2 =.76, RMSE = 0.66%). Among the predictive variables used in the model, slope, wetness, and topographic position indices were found to be the most important topographical features that explain SOC patterns in the study area. Inclusion of spectral indices derived from remote sensing, including surface soil moisture and clay minerals ratio, made further improvement to the SOC content prediction. The study reveals that a decrease in the resolution of the geospatial data does not substantively affect the mean SOC content estimation of a farm-scale modelling. However, using coarser resolution data reduces the ability of the model to predict changes in the spatial pattern of SOC content across a hill country grassland landscape.
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    Integrating ecosystem services with geodesign to create multifunctional agricultural landscapes: A case study of a New Zealand hill country farm
    (Elsevier Ltd, 2023-02) Tran DX; Pearson D; Palmer A; Dominati EJ; Gray D; Lowry J
    An ecosystem-based management approach (EBM) is suggested as one solution to help to tackle environmental challenges facing worldwide farming systems whilst ensuring socio-economic demands are met. Despite its usefulness, the application of this approach at the farm-scale presents several implementation problems, including the difficulty of (a) incorporating the concept of ecosystem services (ES) into agricultural land use decision-making and (b) involving the farmer in the planning process. This study aims to propose a solution to overcome these challenges by utilising a geodesign framework and EBM approach to plan and design a sustainable multifunctional agricultural landscape at the farm scale. We demonstrate how the proposed approach can be applied to plan and design multifunctional agricultural landscapes that offer improved sustainability, using a New Zealand hill country farm as a case study. A geodesign framework is employed to generate future land use and management scenarios for the study area, visualize changes, and assess the impacts of future land use on landscape multifunctionality and the provision of associated ES and economic outcomes. In this framework, collaboration with the farmer was carried out to obtain farm information and co-design the farmed landscapes. The results from our study demonstrate that farmed landscapes where multiple land use/ land cover types co-exist can provide a wide range of ES and therefore, meet both economic and environmental demands. The assessment of impacts for different land use change scenarios demonstrates that land use change towards increasing landscape diversity and complexity is a key to achieving more sustainable multifunctional farmed landscapes. The integration of EBM and geodesign, is a transdisciplinary approach that can help farmers target land use and management decisions by considering the major ES that are, and could be, provided by the landscapes in which these farm systems are situated, therefore maximising the potential for beneficial outcomes.
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    Spatiotemporal analysis of forest cover change and associated environmental challenges: a case study in the Central Highlands of Vietnam
    (2022-01-01) Tran DX; Tran TV; Pearson D; Myint SW; Lowry J; Nguyen TT
    Spatiotemporal regression combining Theil-Sen median trend and Man-Kendall tests was applied to MODIS time-series data to quantify the trend and rate of change to forest cover in the Central Highlands, Vietnam from 2001 to 2019. Several MODIS data products, including Percent Tree Cover (PTC), Evapotranspiration (ET), Land Surface Temperature (LST), and Gross Primary Productivity (GPP) were selected as indicators for forest cover and climate and carbon cycle patterns. Emerging hot spot analysis was applied to identify patterns of long-term deforestation. Spatial regression analysis using Geographically Weighted Regression (GWR) was performed to understand variations in the relationship between vegetation changes and trends in LST, ET, and GPP. Our analysis reveals that deforestation occurred significantly in the study area with a total decrease of 14.5% in PTC and a total of 7314 deforestation hot spots were identified. Results indicate that forest cover loss explains 72.9%, 67.7%, and 89.4% of the changes in ET, GPP, and LST, respectively, and the levels of influence are heterogenous across space and dependent on the types of deforestation hot spots. The approach introduced in our study can be performed worldwide to address complex research questions about environmental challenges that emerge from deforestation.