142 research outputs found

    Digital agriculture: research, development and innovation in production chains.

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    Digital transformation in the field towards sustainable and smart agriculture. Digital agriculture: definitions and technologies. Agroenvironmental modeling and the digital transformation of agriculture. Geotechnologies in digital agriculture. Scientific computing in agriculture. Computer vision applied to agriculture. Technologies developed in precision agriculture. Information engineering: contributions to digital agriculture. DIPN: a dictionary of the internal proteins nanoenvironments and their potential for transformation into agricultural assets. Applications of bioinformatics in agriculture. Genomics applied to climate change: biotechnology for digital agriculture. Innovation ecosystem in agriculture: Embrapa?s evolution and contributions. The law related to the digitization of agriculture. Innovating communication in the age of digital agriculture. Driving forces for Brazilian agriculture in the next decade: implications for digital agriculture. Challenges, trends and opportunities in digital agriculture in Brazil

    Consumer-grade UAV imagery facilitates semantic segmentation of species-rich savanna tree layers

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    Conventional forest inventories are labour-intensive. This limits the spatial extent and temporal frequency at which woody vegetation is usually monitored. Remote sensing provides cost-effective solutions that enable extensive spatial coverage and high sampling frequency. Recent studies indicate that convolutional neural networks (CNNs) can classify woody forests, plantations, and urban vegetation at the species level using consumer-grade unmanned aerial vehicle (UAV) imagery. However, whether such an approach is feasible in species-rich savanna ecosystems remains unclear. Here, we tested whether small data sets of high-resolution RGB orthomosaics suffice to train U-Net, FC-DenseNet, and DeepLabv3 + in semantic segmentation of savanna tree species. We trained these models on an 18-ha training area and explored whether models could be transferred across space and time. These models could recognise trees in adjacent (mean F1-Score = 0.68) and distant areas (mean F1-Score = 0.61) alike. Over time, a change in plant morphology resulted in a decrease of model accuracy. Our results show that CNN-based tree mapping using consumer-grade UAV imagery is possible in savanna ecosystems. Still, larger and more heterogeneous data sets can further improve model robustness to capture variation in plant morphology across time and space

    Ten simple rules for working with high resolution remote sensing data

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    Researchers in Earth and environmental science can extract incredible value from high- resolution (sub-meter, sub-hourly or hyper-spectral) remote sensing data, but these data can be difficult to use. Correct, appropriate and competent use of such data requires skills from remote sensing and the data sciences that are rarely taught together. In practice, many researchers teach themselves how to use high-resolution remote sensing data with ad hoc trial and error processes, often resulting in wasted effort and resources. In order to implement a consistent strategy, we outline ten rules with examples from Earth and environmental science to help academic researchers and professionals in industry work more effectively and competently with high-resolution data

    USING UNPILOTED AERIAL VEHICLE STRUCTURE FROM MOTION AND SNOWMODEL TO MAP SPATIAL DISTRIBUTION OF WIND DEPOSITED SNOW IN MOUNT WASHINGTON, NH AVALANCHE TERRAIN

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    East of the Rocky Mountains, United States avalanche terrain is almost exclusive to Mount Washington, New Hampshire. Mount Washington’s east-aspect glacial cirques are subject to frequent wind slab avalanche problems due to high winds and ample snowfall in fetch areas above the cirques. Quantification of these slabs’ location, extent, and depth is an integral part of avalanche forecasting and risk assessment. This research used SnowModel, a spatially distributed snow-evolution model, to simulate wind slab depth maps using Mount Washington Observatory meteorologic station data on a 1 m grid. SnowModel’s SnowTran-3D, a snow redistribution by wind algorithm, is tested for one of the first times in the Eastern United States. Snowpack seasonal evolution and accumulation event-based model performance is calibrated and validated using 15 snow depth maps collected throughout the winter of 2021-2022. Snow depth maps were constructed via Structure from Motion (SfM) analysis photogrammetry. SfM maps were derived from optical imagery collected using an Unpiloted Aerial Vehicle (UAV) and were able to quantify wind slab depth with a 5 cm spatial resolution. Limited ground validation showed UAV SfM values are accurate with a 30 cm RMSE on the 2/01/2022 sample date. Total snow depth and snow depth change map time series of each study location consistently show wind-transported snow accumulation and erosion patterns on Mount Washington. SnowModel can capture Mount Washington’s widespread snow redistribution trends but fails to quantify the magnitude and distribution of wind slabs as the UAV SfM can. SnowModel-derived snow depth was compared to Landsat 8’s Normalized Difference Snow Index (NDSI) and shows a significant signal in snow depth increase when NDSI exceeds 0.4. This study provides the first of its kind approach for capturing Mount Washington’s winter snowpack evolution using UAV SfM and a physically based snow evolution model

    Digital agriculture: research, development and innovation in production chains.

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    Digital transformation in the field towards sustainable and smart agriculture. Digital agriculture: definitions and technologies. Agroenvironmental modeling and the digital transformation of agriculture. Geotechnologies in digital agriculture. Scientific computing in agriculture. Computer vision applied to agriculture. Technologies developed in precision agriculture. Information engineering: contributions to digital agriculture. DIPN: a dictionary of the internal proteins nanoenvironments and their potential for transformation into agricultural assets. Applications of bioinformatics in agriculture. Genomics applied to climate change: biotechnology for digital agriculture. Innovation ecosystem in agriculture: Embrapa?s evolution and contributions. The law related to the digitization of agriculture. Innovating communication in the age of digital agriculture. Driving forces for Brazilian agriculture in the next decade: implications for digital agriculture. Challenges, trends and opportunities in digital agriculture in Brazil.Translated by Beverly Victoria Young and Karl Stephan Mokross

    Land Surface Monitoring Based on Satellite Imagery

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    This book focuses attention on significant novel approaches developed to monitor land surface by exploiting satellite data in the infrared and visible ranges. Unlike in situ measurements, satellite data provide global coverage and higher temporal resolution, with very accurate retrievals of land parameters. This is fundamental in the study of climate change and global warming. The authors offer an overview of different methodologies to retrieve land surface parameters— evapotranspiration, emissivity contrast and water deficit indices, land subsidence, leaf area index, vegetation height, and crop coefficient—all of which play a significant role in the study of land cover, land use, monitoring of vegetation and soil water stress, as well as early warning and detection of forest fires and drought

    Climate resilient and sustainable forest management : IBFRA conference 28-31 August 2023. Book of abstracts

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    The 20th IBFRA (The International Boreal Forest Research Association) conference held in Helsinki Finland 28-31 August 2023 brings together researchers, companies, policy makers and members of the civil society. The conference main theme is Climate resilient and sustainable forest management. The abstracts of the conference are in this publication

    LIPIcs, Volume 277, GIScience 2023, Complete Volume

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    LIPIcs, Volume 277, GIScience 2023, Complete Volum

    Integrating Technology Into Wildlife Surveys

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    Technology is rapidly improving and being incorporated into field biology, with survey methods such as machine learning and uncrewed aircraft systems (UAS) headlining efforts. UAS paired with machine learning algorithms have been used to detect caribou, nesting waterfowl and seabirds, marine mammals, white-tailed deer, and more in over 19 studies within the last decade alone. Simultaneously, UAS and machine learning have also been implemented for infrastructure monitoring at wind energy facilities as wind energy construction and use has skyrocketed globally. As part of both pre-construction and regulatory compliance of newly constructed wind energy facilities, monitoring of impacts to wildlife is assessed through ground surveys following the USFWS Land-based Wind Energy Guidelines. To streamline efforts at wind energy facilities and improve efficiency, safety, and accuracy in data collection, UAS platforms may be leveraged to not only monitor infrastructure, but also impacts to wildlife in the form of both pre- and post-construction surveys. In this study, we train, validate, and test a machine learning approach, a convolutional neural network (CNN), in the detection and classification of bird and bat carcasses. Further, we compare the trained CNN to the currently accepted and widely used method of human ground surveyors in a simulated post-construction monitoring scenario. Last, we establish a baseline comparison of manual image review of waterfowl pair surveys with currently used ground surveyors that could inform both pre-construction efforts at energy facilities, along with long-standing federal and state breeding waterfowl surveys. For the initial training of the CNN, we collected 1,807 images of bird and bat carcasses that were split into 80.0% training and 20.0% validation image sets. Overall detection was extremely high at 98.7%. We further explored the dataset by evaluating the trained CNN’s ability to identify species and the variables that impacted identification. Classification of species was successful in 90.5% of images and was associated with sun angle and wind speed. Next, we performed a proof of concept to determine the utility of the trained CNN against ground surveyors in ground covers and with species that were both used in the initial training of the model and novel. Ground surveyors performed similar to those surveying at wind energy facilities with 63.2% detection, while the trained CNN fell short at 28.9%. Ground surveyor detection was weakly associated with carcass density within a plot and strongly with carcass size. Similarly, detection by the CNN was associated with carcass size, ground cover type, visual obstruction of vegetation, and weakly with carcass density within a plot. Finally, we examined differences in breeding waterfowl counts between ground surveyors and UAS image reviewers and found that manual review of UAS imagery yielded similar to slightly higher counts of waterfowl. Significant training, testing, and repeated validation of novel image data sets should be performed prior to implementing survey methods reliant upon machine learning algorithms. Additionally, further research is needed to determine potential biases of counting live waterfowl in aerial imagery, such as bird movement and double counting. While our initial results show that UAS imagery and machine learning can improve upon current techniques, extensive follow-up is strongly recommended in the form of proof-of-concept studies and additional validation to confirm the utility of the application in new environments with new species that allow models to be generalized. Remotely sensed imagery paired with machine learning algorithms have the potential to expedite and standardize monitoring of wildlife at wind energy facilities and beyond, improving data streams and potentially reducing costs for the benefit of both conservation agencies and the energy industry
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