ORCID Profile
0000-0002-0666-2588
Current Organisation
Institut für Zuckerrübenforschung
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Publisher: PeerJ
Date: 26-05-2020
DOI: 10.7717/PEERJ.9209
Abstract: In arid environments, plant primary productivity is generally low and highly variable both spatially and temporally. Resources are not evenly distributed in space and time (e.g., soil nutrients, water), and depend on global (El Niño/ Southern Oscillation) and local climate parameters. The launch of the Sentinel2-satellite, part of the European Copernicus program, has led to the provision of freely available data with a high spatial resolution (10 m per pixel). Here, we aimed to test whether Sentinel2-imagery can be used to quantify the spatial variability of a minor tussock grass ( Enneapogon spp.) in an Australian arid area and whether we can identify different vegetation cover (e.g., grass from shrubs) along different temporal scenarios. Although short-lasting, the Enneapogon grassland has been identified as a key primary food source to animals in the arid environment. If we are able to identify and monitor the productivity of this species remotely, it will provide an important new tool for examining food resource dynamics and subsequent animal responses to them in arid habitat. We combined field vegetation surveys and Sentinel2-imagery to test if satellite spectral data can predict the spatial variability of Enneapogon over time, through GLMMs. Additionally, a cluster analysis (‘gower’ distance, ‘complete’ method), based on Enneapogon seed-productivity, and total vegetation cover in October 2016, identified three clusters: bare ground, grass dominated and shrub dominated. We compared the vegetation indices between these different clusters from October 2016 to January 2017. We found that MSAVI 2 and NDVI correlated with the proportion of Enneapogon with seeds across the landscape and this relationship changed over time. Both vegetation indices (MSAVI 2 and NDVI) were higher in patches with high seed-productivity of Enneapogon than in bare soil, but only in October, a climatically-favorable period during which this dominant grass reached peak seed-productivity. MSAVI 2 and NDVI provided reliable estimates of the heterogeneity of vegetation type across the landscape only when measured in the Austral spring. This means that grass cover is related to seed-productivity and it is possible to remotely and reliably predict food resource availability in arid habitat, but only in certain conditions. The lack of significant differences between clusters in the summer was likely driven by the short-lasting nature of the vegetation in the study and the sparseness of the grass-dominated vegetation, in contrast to the shrub vegetation cluster that was particularly well measured by the NDVI. Overall, our study highlights the potential for Sentinel2-imagery to estimate and monitor the change in grass seed availability remotely in arid environments. However, heterogeneity in grassland cover is not as reliably measured as other types of vegetation and may only be well detected during periods of peak productivity (e.g., October 2016).
Publisher: MDPI AG
Date: 04-2021
Abstract: This study examined the use of hyperspectral profiles for identifying three selected weed species in the alpine region of New South Wales, Australia. The targeted weeds included Orange Hawkweed, Mouse-ear Hawkweed and Ox-eye daisy, which have caused a great concern to regional bio ersity and health of the environment in Kosciuszko National Park. Field surveys using a spectroradiometer were undertaken to measure the hyperspectral profiles of leaves and flowers of the selected weeds and companion native plants. Random Forest (RF) classification was then applied to distinguish which spectral bands would differentiate the weeds from the native plants. Our results showed that an accuracy of 95% was achieved if the spectral profiles of the distinct flowers of the weeds were considered, and an accuracy of 80% was achieved if only the profiles of the leaves were considered. Emulation of the spectral profiles of two multispectral sensors (Sentinel-2 and Parrot Sequoia) was then conducted to investigate whether classification accuracy could potentially be achieved using wider spectral bands.
Publisher: Springer Science and Business Media LLC
Date: 28-04-2022
Publisher: Frontiers Media SA
Date: 27-03-2015
Publisher: Springer Science and Business Media LLC
Date: 19-07-2019
Publisher: Wiley
Date: 13-02-2018
DOI: 10.1111/PPA.12830
Publisher: MDPI AG
Date: 10-03-2023
DOI: 10.3390/RS15061524
Abstract: Over the last two decades, UAVs have become an indispensable acquisition platform in the remote sensing community. Meanwhile, advanced lightweight sensors have been introduced in the market, including LiDAR scanners with multiple beams and hyperspectral cameras measuring reflectance using many different narrow-banded filters. To date, however, few fully fledged drone systems exist that combine different sensing modalities in a way that complements the strengths and weaknesses of each. In this paper, we present our multimodal drone payload and sensor fusion pipeline, which allows multispectral point clouds to be generated at subcentimeter accuracy. To that end, we combine high-frequency navigation outputs from a professional-grade GNSS with photogrammetric bundle adjustment and a dedicated point cloud registration algorithm that takes full advantage of LiDAR’s specifications. We demonstrate that the latter significantly improves the quality of the reconstructed point cloud in terms of fewer ghosting effects and less noise. Finally, we thoroughly discuss the impact of the quality of the GNSS/INS system on the structure from the motion and LiDAR SLAM reconstruction process.
Publisher: MDPI AG
Date: 07-03-2019
Abstract: Disease management in agriculture often assumes that pathogens are spread homogeneously across crops. In practice, pathogens can manifest in patches. Currently, disease detection is predominantly carried out by human assessors, which can be slow and expensive. A remote sensing approach holds promise. Current satellite sensors are not suitable to spatially resolve in idual plants or lack temporal resolution to monitor pathogenesis. Here, we used multispectral imaging and unmanned aerial systems (UAS) to explore whether myrtle rust (Austropuccinia psidii) could be detected on a lemon myrtle (Backhousia citriodora) plantation. Multispectral aerial imagery was collected from fungicide treated and untreated tree canopies, the fungicide being used to control myrtle rust. Spectral vegetation indices and single spectral bands were used to train a random forest classifier. Treated and untreated trees could be classified with high accuracy (95%). Important predictors for the classifier were the near-infrared (NIR) and red edge (RE) spectral band. Taking some limitations into account, that are discussedherein, our work suggests potential for mapping myrtle rust-related symptoms from aerial multispectral images. Similar studies could focus on pinpointing disease hotspots to adjust management strategies and to feed epidemiological models.
Publisher: Burleigh Dodds Science Publishing
Date: 28-06-2022
Abstract: Plant diseases pose a significant threat to agriculture. Precise and appropriately timed detection and identification of plant diseases is crucial for disease management, and for the selection of resistant and tolerant varieties. The detection of plant diseases by the human eye is dependent on the experience of the expert and on external influences such as environmental conditions. New sensor systems, artificial intelligence, and robotics – summarised under the term “digital technologies" – can help make complex assessments more efficient. There has been immense progress in the field of digital plant disease detection in the last two decades, through interdisciplinary research and technological advances. Understanding plant-pathogen interactions and visualising the underlying biochemical and biophysical processes with optical sensors enables plant disease detection and characterization. These innovative digital tools contribute to an objective and automated assessment of crop traits and are helping shape the future of smart farming and plant phenotyping.
Publisher: Verlag Dr. Albert Bartens KG
Date: 16-06-2022
DOI: 10.36961/SI28804
Abstract: Weed scoring is crucial to test the efficacy of herbicides and other weed management methods but has proven to be labor intense and variable across scoring in iduals and time. This article provides a comparison of new approaches of weed scoring based on digital tools to conventional visual scoring. The first method collected aerial imagery that was then scored manually on a computer screen. The second method is a machine learning approach, automatically detecting and counting weeds in aerial imagery. The reference method is a conventional visual scoring by human raters. Across all scoring methods, the results show similar patterns, but the total number of scored plants differs. In comparison to the digital approaches, in field scoring by human raters estimated a higher weed infestation. Possible reasons for this, as well as the advantages and disadvantages of each method are discussed to explore new modes of weed scoring. A clear benefit of digital scoring is the potential to automate the procedure and its objective, repeatable nature.
Publisher: Wiley
Date: 15-02-2019
DOI: 10.1111/PPA.12996
Publisher: Springer Science and Business Media LLC
Date: 19-05-2022
Publisher: MDPI AG
Date: 15-12-2015
DOI: 10.3390/RS71215862
No related grants have been discovered for Rene Heim.