Browsing by Author "Kereszturi G"
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- ItemA physically informed multi-scale deep neural network for estimating foliar nitrogen concentration in vegetation(Elsevier B.V., 2024-05-28) Dehghan-Shoar MH; Kereszturi G; Pullanagari RR; Orsi AA; Yule IJ; Hanly JThis study introduces a Physically Informed Deep Neural Network (PINN) that leverages spectral data and Radiative Transfer Model insights to improve nitrogen concentration estimation in vegetation, addressing the complexities of physical processes. Utilizing a comprehensive spectroscopy dataset from various species across dry/ground (n = 2010), leaf (n = 1512), and canopy (n = 6007) scales, the study identifies 13 spectral bands key for chlorophyll and protein quantification. Key bands at 2276 nm, 755 nm, 1526 nm, 2243 nm, and 734 nm emerged vital for accurate N% prediction. The PINN outperforms partial least squares regression and standard deep neural networks, achieving an R2 of 0.71 and an RMSE of 0.42 (%N) on an independent validation set. Results indicate dry/ground data performed best (R2 = 0.9, RMSE = 0.24 %N), with leaf and canopy data showing lower efficacy (R2 = 0.67, RMSE = 0.45 %N; R2 = 0.65, RMSE = 0.46 %N, respectively). This multi-scale approach provides insights into spectral and N% relationships, enabling precise estimation across vegetation types and facilitating the development of transferable models. The PINN offers a new avenue for analyzing remote sensing data, demonstrating the significant potential for accurate, scale-spanning N% estimation in vegetation. Further enriching our analysis, the inclusion of seasonal data significantly enhanced our model's performance in field spectroscopy, with notable improvements observed across summer, spring, autumn, and winter. This adjustment underlines the model's increased accuracy and predictive capability at the field spectroscopy scale, emphasizing the vital role of integrating environmental factors, including climatic and physiological aspects, in future research.
- ItemCrystal entrainment from cool, low-silica rocks into hot, high-silica melts: diverse primary melt compositions at Taranaki volcano, New Zealand(The Geological Society of London, 2023-05-19) D'Mello N; Zellmer G; Kereszturi G; Ubide T; Procter J; Stewart RThe prevalence of antecrysts in arc volcanic rocks is widely accepted, yet the origin of their carrier melts remains debated. Crystal cargo in lava flows from Taranaki volcano, New Zealand, is dominated by plagioclase, clinopyroxene and amphibole. Except for some crystal rims, mineral phases are in disequilibrium with the melt they are entrained in. Major element chemistry reveals an almost complete compositional overlap between the crystals in the lava and those in xenoliths. The large volume fraction of crystals (35–55 vol%) exerts a strong control on whole-rock compositions, reducing silica by 5–11 wt% compared with the carrier melt. Yet there is no clear relationship between mineral proportion and bulk-rock compositions. Our data are inconsistent with extensive fractional crystallization, commonly invoked as a driver of magma evolution towards silica-rich compositions. Instead, high-temperature, aphyric carrier melts with varied compositions (55–68 wt% SiO2) entrain crystal cargo while ascending through colder, low-silica rocks. Thus, some parental melts at Taranaki volcano are significantly more silica-rich than arc basalts commonly invoked as primary magmas. Further, thermometric and hygrometric constraints preclude a deep crustal hot zone for the source of these melts, which we argue are of subcrustal origin.
- ItemMapping a Cloud-Free Rice Growth Stages Using the Integration of PROBA-V and Sentinel-1 and Its Temporal Correlation with Sub-District Statistics(MDPI (Basel, Switzerland), 2021-04-13) Ramadhani F; Pullanagari R; Kereszturi G; Procter J; Farooque AAMonitoring rice production is essential for securing food security against climate change threats, such as drought and flood events becoming more intense and frequent. The current practice to survey an area of rice production manually and in near real-time is expensive and involves a high workload for local statisticians. Remote sensing technology with satellite-based sensors has grown in popularity in recent decades as an alternative approach, reducing the cost and time required for spatial analysis over a wide area. However, cloud-free pixels of optical imagery are required to pro-duce accurate outputs for agriculture applications. Thus, in this study, we propose an integration of optical (PROBA-V) and radar (Sentinel-1) imagery for temporal mapping of rice growth stages, including bare land, vegetative, reproductive, and ripening stages. We have built classification models for both sensors and combined them into 12-day periodical rice growth-stage maps from January 2017 to September 2018 at the sub-district level over Java Island, the top rice production area in Indonesia. The accuracy measurement was based on the test dataset and the predicted cross-correlated with monthly local statistics. The overall accuracy of the rice growth-stage model of PROBA-V was 83.87%, and the Sentinel-1 model was 71.74% with the Support Vector Machine classifier. The temporal maps were comparable with local statistics, with an average correlation between the vegetative area (remote sensing) and harvested area (local statistics) is 0.50, and lag time 89.5 days (n = 91). This result was similar to local statistics data, which correlate planting and the harvested area at 0.61, and the lag time as 90.4 days, respectively. Moreover, the cross-correlation between the predicted rice growth stage was also consistent with rice development in the area (r > 0.52, p < 0.01). This novel method is straightforward, easy to replicate and apply to other areas, and can be scaled up to the national and regional level to be used by stakeholders to support improved agricultural policies for sustainable rice production.
- ItemMapping nutrient concentration in pasture using hyperspectral imaging(2015) Yule IJ; Pullanagari RR; Irwin M; McVeagh P; Kereszturi G; White M; Manning M
- ItemQuantifying location error to define uncertainty in volcanic mass flow hazard simulations(Copernicus Publications on behalf of the European Geosciences Union, 2021-08-20) Mead SR; Procter J; Kereszturi GThe use of mass flow simulations in volcanic hazard zonation and mapping is often limited by model complexity (i.e. uncertainty in correct values of model parameters), a lack of model uncertainty quantification, and limited approaches to incorporate this uncertainty into hazard maps. When quantified, mass flow simulation errors are typically evaluated on a pixel-pair basis, using the difference between simulated and observed ("actual") map-cell values to evaluate the performance of a model. However, these comparisons conflate location and quantification errors, neglecting possible spatial autocorrelation of evaluated errors. As a result, model performance assessments typically yield moderate accuracy values. In this paper, similarly moderate accuracy values were found in a performance assessment of three depth-averaged numerical models using the 2012 debris avalanche from the Upper Te Maari crater, Tongariro Volcano, as a benchmark. To provide a fairer assessment of performance and evaluate spatial covariance of errors, we use a fuzzy set approach to indicate the proximity of similarly valued map cells. This "fuzzification"of simulated results yields improvements in targeted performance metrics relative to a length scale parameter at the expense of decreases in opposing metrics (e.g. fewer false negatives result in more false positives) and a reduction in resolution. The use of this approach to generate hazard zones incorporating the identified uncertainty and associated trade-offs is demonstrated and indicates a potential use for informed stakeholders by reducing the complexity of uncertainty estimation and supporting decision-making from simulated data.
- ItemRemote exploration and monitoring of geothermal sources: A novel method for foliar element mapping using hyperspectral (VNIR-SWIR) remote sensing(Elsevier Ltd, 2023-06) Rodriguez-Gomez C; Kereszturi G; Jeyakumar P; Pullanagari R; Reeves R; Rae A; Procter JNHyperspectral remote sensing is an emerging technique to develop new cost- and time-effective geophysical mapping methods. To overcome challenges introduced by plant cover in geothermal systems globally, we hypothesise that foliage can be used as a proxy to map underlying surface geothermal activity and heat-flux due to their capability on elemental uptake from geothermal fluids and host rock/soil. This study shows for the first time that foliar elemental mapping can be used to image geothermal systems using both high-resolution airborne and satellite hyperspectral images. This study has specifically targeted kanuka shrub (kunzea ericoides var. microflora) as a proxy media to develop air- and spaceborne hyperspectral solutions to monitor inaccessible, biologically and culturally sensitive geothermal areas. Using high resolution airborne AisaFENIX and PRISMA hyperspectral data, foliar element maps for Ag, As, Ba and Sb have been developed using Kernel Partial Least Squares Regression and Random Forest classification models to track their foliar distribution and develop a conceptual model for metal and thermal induced changes in plants. Our study shows evidence that the created foliar element maps are in concordance with independent LiDAR-retrieved canopy structure and height as well as temperature effects of the underlying geothermal field. This study has proven air- and spaceborne hyperspectral sensors can indeed capture critical information within the VNIR and SWIR regions (e.g. ∼452, ∼500, ∼670, ∼820, ∼970, ∼1180, ∼1400 and ∼2000 nm) that can be used to identify metal and thermal-induced spectral changes in plants reliably (overall accuracy of 0.41–0.66) with remotely sensed imagery. Our non-invasive method using hyperspectral remote sensing can complement existing practices for exploration and management of renewable geothermal resources through timely monitoring from air- and spaceborne platforms.
