Crack development is a clear indicator of the durability of concrete bridges. Traditional bridge inspections that rely inspectors to climb on bridges with lift cars are unsafe for inspectors and also time- and labor-consuming. Therefore, this research proposes a solution that applies unmanned aerial vehicles (UAV) and high-resolution digital cameras to measure concrete bridge cracks. An experiment was conducted on an Ai-He concrete bridge located in Yangmei District, Taoyuan City, Taiwan. Two types of images were taken. Close-up images observed cracks more clearly, and long-range images covered the ground control points. We registered these two types of images to establish the absolute coordinate system with ground control points and tie points through block triangulation. This research examines three approaches of generating tie points: (1) manually select tie points with features on the bridge such as nails and dots, (2) randomly input tie points generated from Scale-Invariant Feature Transform (SIFT), and (3) randomly input tie points generated from SIFT as the initial tie points and perform automatic tie generation with the ERDAS Leica Photogrammetry Suite (LPS) image matching module (automatic tie generation). Afterwards, close-up images were processed into orthorectified images with 0.1 mm pixel size for crack size measurements. Crack sizes were determined by a manual measurement approach and an inflection point approach for comparison. This research established a workflow for UAV bridge inspection that locates and measures cracks in concrete bridges, which consequently provides a safe and cost-efficient concrete bridge crack monitoring solution with acceptable accuracies.

Food sustainability has become a major global concern in recent years. Multiple complimentary strategies to deal with this issue have been developed; one of these approaches is regenerative farming. The identification and analysis of crop type phenology are required to achieve sustainable regenerative faming. Earth Observation (EO) data have been widely applied to crop type identification using supervised Machine Learning (ML) and Deep Learning (DL) classifications, but these methods commonly rely on large amounts of ground truth data, which usually prevent historical analysis and may be impractical in very remote, very extensive or politically unstable regions. Thus, the development of a robust but intelligent unsupervised classification model is attractive for the long-term and sustainable prediction of agricultural yields. Here, we propose FastDTW-HC, a combination of Fast Dynamic Time Warping (DTW) and Hierarchical Clustering (HC), as a significantly improved method that requires no ground truth input for the classification of winter food crop varieties of barley, wheat and rapeseed, in Norfolk, UK. A series of variables is first derived from the EO products, and these include spectral indices from Sentinel-2 multispectral data and backscattered amplitude values at dual polarisations from Sentinel-1 Synthetic Aperture Radar (SAR) data. Then, the phenological patterns of winter barley, winter wheat and winter rapeseed are analysed using the FastDTW-HC applied to the time-series created for each variable, between Nov 2019 and June 2020. Future research will extend this winter food crop mapping analysis using FastDTW-HC modelling to a regional scale.

Annual meteorological variations and the impact of climate change in recent years impacted on agricultural production and distribution. Since wheat is a main food resource and the most widely grown crop in the world, it is essential to ensure the sustainability of its production. Therefore, accurate wheat mapping is essential for agricultural production forecasts. Multi-spectral satellite image analysis, including supervised machine learning (ML) methods, has been applied to wheat and other crop mapping, but such passive, optical imaging approaches are strongly influenced by weather conditions and cloud cover, whilst the supervised ML algorithms are highly reliant on manual labelling and ground control data. To avoid the limitation of weather and cloud, this research integrates Sentinel-1 Synthetic Aperture Radar (SAR) data with Sentinel-2 multi-spectral image products to achieve more reliable and accurate winter wheat mapping. Normalised Difference Vegetation Index (NDVI) retrieved from multi-spectral imagery and Sentinel-1’s dual-polarisation radar signals, VV, VH, acquired in different time series, are used as key inputs to an unsupervised ML model based on Dynamic Time Warping (DTW) and hierarchical clustering to prevent time-consuming manual labelling. The chosen study area lies in Norfolk, UK. The result of winter wheat classification with NDVI time series data in this study reaches 72% accuracy, but the improved classification integrating NDVI, VH, VV and VH/VV values achieves 86% accuracy. Future research will focus on optimizing the ML model with multi data integration in addition additional research sites will test more complicated scenarios and multiple crop classification.

Food sustainability has become a major global concern in recent years. Multiple complimentary strategies to deal with this issue have been developed, one of these approaches is regenerative farming. The identification and analysis of crop type phenology are required to achieve sustainable regenerative faming. Earth Observation (EO) data has been widely applied to crop type identification using supervised Machine Learning (ML) and Deep Learning (DL) classifications, but these methods commonly rely on large amounts of ground truth data, which usually prevents historical analysis and maybe impractical in very remote, very extensive or politically unstable regions. Thus, the development of a robust but intelligent unsupervised classification model is attractive for the long term and sustainable prediction of agricultural yields. Here we propose FastDTW-HC, the combination of Fast Dynamic Time Warping (DTW) and Hierarchical Clustering (HC), as a significantly improved method for the classification of winter food crop varieties of barley, wheat and rapeseed, in Norfolk, UK, that requires no ground truth input. A series of variables is first derived from the EO products and these include spectral indices from the Sentinel-2 multispectral data and backscattered amplitude values at dual polarisations from Sentinel-1 Synthetic Aperture Radar (SAR) data. Then the phenological patterns of winter barley, winter wheat and winter rapeseed are analysed using FastDTW-HC applied to the time-series created for each variable, between Nov 2019 and June 2020. This research demonstrates the results from multiple combinational analyses, at a small test site in Norfolk, where the best outcome reaches a Kappa coefficient of 0.54, accuracies of 76% for winter barley, 72% for winter wheat and 91% for winter rapeseed. Future research will extend this winter food crop mapping analysis using FastDTW-HC modelling to a regional scale and will incorporate meteorological data, to investigate the potential for monitoring of winter food crop yield and biomass.

This study is based on the best crop classification result generated by the proposed unsupervised Machine Learning (ML) method in Li et al., 2025a, using the spectral indices calculated by the formula with spectral bands from Sentinel-2 image products, Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), Enhanced Vegetation Index (EVI) and Normalized Difference Moisture Index (NDMI). The patterns and characteristics of these spectral indices, across arable fields with different crop types following the winter growing seasons, have not yet been analyzed in detail. This research aims to provide a comprehensive study of each input spectral index and its impact on the crop classification model. Each spectral index is analyzed across a series of crop fields, using Sentinel-2 images, carefully selected to follow the patterns of winter crop phenology, and the results of unsupervised classification for each crop type in Norfolk, UK are successfully generated and analyzed. The different growing rates between winter barley and wheat have been classified found on a monthly basis using Sentinel-2 RGB images and thus the images during the harvest time, May and June, can support crop classifications. Wild grasses or other plants on the fields led to some crop misclassification from November to March in the Sentinel-2 RGB images. Similarity between winter barley and wheat and the different sowing time among the same type of crop also led to misclassification. In future these misclassifications could be avoided through better understanding of the relation between spectral indices and crop planting cycles.

Food insecurity occurs due to the impact of climate change and intense global conditions. Thus, understanding the crop farming plans and monitoring crop yields have become major tasks for decision makers. Previous work has applied remote sensing techniques and empirical methods to predict the yields and analyse the relationships between spectral indices and historical crop yield data. However, a limitation of these studies is that they do not extract the values of spectral indices by crop types, which can cause inaccurate results when investigating the correlations between the yield and the spectral indices. This research develops a yield prediction framework with historical crop maps by unsupervised classification with zero ground truth using Sentinel-2 imagery to retrieve the values of spectral indices of winter barley. The extracted spectral indices, the meteorological and historical yield data in North Norfolk, UK are implemented in 1D Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) and CNN-LSTM for winter barley yield predictions. LSTM with Soil-Adjusted Vegetation Index (SAVI) has outstanding performance overall and the best result approaches a Mean Square Error (MSE) of 0.21 kg/hectare and a Mean Absolute Error (MAE) of 13.63 kg/hectare. SAVI is the best predictor due to the strong positive correlation with the yield of winter barley. The developed framework with unsupervised crop classification and LSTM-SAVI can be applied on multiple crop types and in different regions using opensource datasets, the historical yields, the spectral indices and the meteorological data. Correlations between these datasets indicate that higher maximum and minimum SAVI and temperature and sun hours at the germination and seedling growth stages increase the yields of winter barley but excess rainfall with higher NDMI at the tillering stage and sun hours at the stem elongation, flowering and grain filling stages lead to a decline in the yields of winter barley.