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Subject: search.filters.subject.0909 Geomatic Engineering

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Now showing 1 - 6 of 6
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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.
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    Evaluation of Point Hyperspectral Reflectance and Multivariate Regression Models for Grapevine Water Status Estimation
    (MDPI AG, 12/08/2021) Wei H-E; Grafton M; Bretherton M; Irwin M; Sandoval E
    Monitoring and management of plant water status over the critical period between flower-ing and veraison, plays a significant role in producing grapes of premium quality. Hyperspectral spectroscopy has been widely studied in precision farming, including for the prediction of grapevine water status. However, these studies were presented based on various combinations of transformed spectral data, feature selection methods, and regression models. To evaluate the performance of different modeling pipelines for estimating grapevine water status, a study spanning the critical period was carried out in two commercial vineyards at Martinborough, New Zealand. The modeling used six hyperspectral data groups (raw reflectance, first derivative reflectance, second derivative reflectance, continuum removal variables, simple ratio indices, and vegetation indices), two variable selection methods (Spearman correlation and recursive feature elimination based on cross-validation), an ensemble of selected variables, and three regression models (partial least squares regression, random forest regression, and support vector regression). Stem water potential (used as a proxy for vine water status) was measured by a pressure bomb. Hyperspectral reflectance was undertaken by a handheld spectroradiometer. The results show that the best predictive performance was achieved by applying partial least squares regression to simple ratio indices (R2 = 0.85; RMSE = 110 kPa). Models trained with an ensemble of selected variables comprising multicombination of transformed data and variable selection approaches outperformed those fitted using single combinations. Although larger data sizes are needed for further testing, this study compares 38 modeling pipelines and presents the best combination of procedures for estimating vine water status. This may lead to the provision of rapid estimation of vine water status in a nondestructive manner and highlights the possibility of applying hyperspectral data to precision irrigation in vineyards.
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    Evaluation of the use of UAV-derived Vegetation Indices and Environmental Variables for Grapevine Water Status Monitoring based on Machine Learning Algorithms and SHAP Analysis
    (1/06/2022) Wei H-E; Grafton MC; Bretherton M; Irwin M; Sandoval E; Horne, D; Singh, R
    Monitoring and management of grapevine water status (GWS) over the critical period between flowering and veraison plays a significant role in producing grapes of premium quality. The spatial and temporal variation of GWS, is conventionally characterized by using a pressure bomb. This is laborious and time-consuming, which limits the number of samples. Although unmanned aerial vehicles (UAVs) can provide mapping across the entire vineyards efficiently, most commercial UAV-based multispectral sensors do not contain a shortwave infrared band which makes the monitoring of GWS problematic. As GWS is an integrated response to vegetation characteristics, temporal trends, weather conditions, and soil/terrain data, it is assumed that these ancillary variables have the potential to enhance the capability of UAVs for GWS monitoring. The goal of this study is to explore whether and which of these ancillary variables may improve the accuracy of GWS estimation using UAV, and provide insights into the contribution that each selected variable contributes to the variation in GWS. A UAV was flown over two Pinot Noir vineyards in New Zealand to generate aerial images with 4.3 cm resolution, and 18 vegetation indices (VIs) were computed for every sampled grapevine. The strongly correlated VIs were used as the core input for later GWS modeling. Ancillary data included soil/terrain, weather, and temporal variables. Slope and elevation were extracted from a digital elevation model, and apparent electrical conductivity (ECa) was obtained from an EM38 survey. A local weather station provided continuous air temperature, humidity, rainfall, wind speed, and irradiance data, which were computed as variables at weekly and daily intervals. Day of the year (DOY) was used to represent the temporal trend along the growing season. Three machine learning algorithms (elastic net, random forest regression, and support vector regression) were used to regress the predictors against stem water potential (Ψstem), measured by a pressure bomb and used as a proxy for GWS. Shapley Additive exPlanations (SHAP) analysis (a statistical tool that weighs the importance of each variable in a model) was used to assess the relationship between selected variables and Ψstem. The results show that Transformed Chlorophyll Absorption Reflectance Index (TCARI) and Excess Green Index (ExG) are the best correlated VIs, but their correlation with Ψstem is poor (rTCARI = 0.6; rExG = 0.58). The coefficient of determination (R2) of the TCARI-based model increased from 0.35 to 0.7 when DOY and elevation were included as ancillary inputs. R2 of the ExG-based model increased from 0.3 to 0.74 when DOY, elevation, slope, ECa, and daily average windspeed, were included as ancillary inputs. Support vector regression was the best model to describe the relationship between Ψstem and selected predictors. This study has provided proof of the concept of developing GWS estimation models that potentially enhance the monitoring capacities of UAVs for GWS, as well as provide individual GWS mapping at the vineyard scale. This may enable growers to improve irrigation management, leading to controlled vegetative growth and optimized berry quality.
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    Predicting the distribution of oxytropis ochrocephala bunge in the source region of the yellow river (China) based on uav sampling data and species distribution model
    (1/12/2021) Zhang X; Yuan Y; Zhu Z; Ma Q; Yu H; Li M; Ma J; Yi S; He XZ; Sun Y
    Oxytropis ochrocephala Bunge is an herbaceous perennial poisonous weed. It severely affects the production of local animal husbandry and ecosystem stability in the source region of Yellow River (SRYR), China. To date, however, the spatiotemporal distribution of O. ochrocephala is still unclear, mainly due to lack of high-precision observation data and effective methods at a regional scale. In this study, an efficient sampling method, based on unmanned aerial vehicle (UAV), was proposed to supply basic sampling data for species distribution models (SDMs, BIOMOD in this study). A total of 3232 aerial photographs were obtained, from 2018 to 2020, in SRYR, and the potential and future distribution of O. ochrocephala were predicted by an ensemble model, consisting of six basic models of BIOMOD. The results showed that: (1) O. ochrocephala mainly distributed in the southwest, middle, and northeast of the SRYR, and the high suitable habitat of O. ochrocephala accounted for 3.19%; (2) annual precipitation and annual mean temperature were the two most important factors that affect the distribution of O. ochrocephala, with a cumulative importance of 60.45%; and (3) the distribution probability of O. ochrocephala tends to increase from now to the 2070s, while spatial distribution ranges will remain in the southwest, middle, and northeast of the SRYR. This study shows that UAVs can potentially be used to obtain the basic data for species distribution modeling; the results are both beneficial to establishing reasonable management practices and animal husbandry in alpine grassland systems.
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    Assessing the Leaf Blade Nutrient Status of Pinot Noir Using Hyperspectral Reflectance and Machine Learning Models
    (MDPI AG, 8/03/2023) Lyu H; Grafton MC; Ramilan T; Irwin M; Sandoval - Cruz E; Díaz-Varela, RA