Browsing by Author "Simms, Daniel"

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    Dataset for "Automating the derivation of sugarcane growth stages from Earth Observation time series"
    (Cranfield University, 2024-09-12) Joshi, Neha; Simms, Daniel; Burgess, Paul
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    ItemOpen Access
    Emerging resilience metrics in an intensely managed ecological system
    (Elsevier, 2024-03-01) Toumasis, Nikolaos; Simms, Daniel; Rust, Will; Harris, Jim A.; White, John R.; Zawadzka, Joanna Ewa; Corstanje, Ron
    There is growing interest in understanding resilience of ecosystems because of the potential of abrupt and possibly irreversible shifts between alternative ecosystem states. Tipping points are observed in systems with strong positive feedback, providing early warning signals of potential instability. These points can be detected through metrics like critical slowing down (CSD), such as increased recovery time, variance, and autocorrelation. These indicators have been tested in laboratory experiments and field settings, ignoring trait changes. Here we present a long-term temporal analysis of several large, intensely monitored constructed wetlands, the Everglades Stormwater Treatment Areas (STAs), in which sudden changes in plant community composition have been observed. Using wavelet analysis, significant increases and decreases of variance properties (long-term flow data, water quality and nutrient TP loads) across these systems can indicate when and which STAs are less resilient to perturbations. In this study, continuous wavelet transform (CWT) was used to determine the periodicity of any cyclical activity in the data and to determine changes in autocorrelation and variance as measures of CSD. The change detection methods were used to find significant changes in variations and correlations across the time series. By employing these techniques, we were able to spot substantial shifts in model-observed wavelet correlation and model residual wavelet variance and thereby identify where these systems exhibit CSD. Although our analysis is limited to historical data, the proposed approach has practical value in that it identifies STAs that may be vulnerable to perturbation. The study also presents one of the few studies in which CSD is observed in practice rather than modelled in theory.
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    ItemOpen Access
    Hyperspectral imaging for phenotyping plant drought stress and nitrogen interactions using multivariate modeling and machine learning techniques in wheat
    (MDPI, 2024-09-17) Okyere, Frank Gyan; Cudjoe, Daniel Kingsley; Virlet, Nicolas; Castle, March; Riche, Andrew Bernard; Greche, Latifa; Mohareb, Fady; Simms, Daniel; Mhada, Manal; Hawkesford, Malcolm John
    Accurate detection of drought stress in plants is essential for water use efficiency and agricultural output. Hyperspectral imaging (HSI) provides a non-invasive method in plant phenotyping, allowing the long-term monitoring of plant health due to sensitivity to subtle changes in leaf constituents. The broad spectral range of HSI enables the development of different vegetation indices (VIs) to analyze plant trait responses to multiple stresses, such as the combination of nutrient and drought stresses. However, known VIs may underperform when subjected to multiple stresses. This study presents new VIs in tandem with machine learning models to identify drought stress in wheat plants under varying nitrogen (N) levels. A pot wheat experiment was set up in the glasshouse with four treatments: well-watered high-N (WWHN), well-watered low-N (WWLN), drought-stress high-N (DSHN) and drought-stress low-N (DSLN). In addition to ensuring that plants were watered according to the experiment design, photosynthetic rate (Pn) and stomatal conductance (gs) (which are used to assess plant drought stress) were taken regularly, serving as the ground truth data for this study. The proposed VIs, together with known VIs, were used to train three classification models: support vector machines (SVM), random forest (RF), and deep neural networks (DNN) to classify plants based on their drought status. The proposed VIs achieved more than 0.94 accuracy across all models, and their performance further increased when combined with known VIs. The combined VIs were used to train three regression models to predict the stomatal conductance and photosynthetic rates of plants. The random forest regression model performed best, suggesting that it could be used as a stand-alone tool to forecast gs and Pn and track drought stress in wheat. This study shows that combining hyperspectral data with machine learning can effectively monitor and predict drought stress in crops, especially in varying nitrogen conditions.
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    ItemOpen Access
    Very high resolution aerial photography and annotated land cover data of the Peak District National Park
    (Cranfield University, 2023-11-06 17:08) van der Plas, Thijs; Geikie, Simon; Alexander, David; Simms, Daniel
    License Contents of compressed file (zip) from Van der Plas, Geikie, Alexander and Simms, upcoming publication titled Multi-stage semantic segmentation quantifies fragmentation of small habitats at a landscape scale This data set contains the RGB image data and human land cover annotation for 1027 patches of 512 pixels x 512 pixels ( 64 m x 64 m spatial resolution). For more information on how the data can be used, the land cover schema and other details, please see our paper. For code examples of how to use the data, please see the github repository at https://github.com/pdnpa/cnn-land-cover The data is given in two formats: python and tiff. The Python format can be directly loaded by the code in the repository into Pytorch DataLoaders. The tiff format is independent of progamming language and application. This data is released under the CC BY 4.0 license, which means if you use this data set, we ask you to cite along with our paper above.If you use the RGB images, you must acknowledge the following copyright: "Aerial Photography for Great Britain, © Bluesky International Limited and Getmapping Plc [2022]" - README land cover patch data.txt - lc_label_names.json contains mapping from land cover label (integer) to land cover class name - python_format - images_python_all all (train and test) RGB images in .npy format (each of shape (3, 512, 512)) - masks_python_all all (train and test) land cover masks in .npy format (each of shape (512, 512)) - train_test_split_80tiles_2023-03-22-2131.json train/test split in json format - train_test_split_80tiles_2023-03-22-2131.pkl train/test split in pickle format (to be used with the data class in the repository) - tiff_format - images_masks_tiff_train train set only patches, containing both the RGB image (first 3 bands) and the land cover annotation (4th band) (each of shape (4, 512, 512)) - images_masks_tiff_test test set only patches, containing both the RGB image (first 3 bands) and the land cover annotation (4th band) (each of shape (4, 512, 512))