Fine Temporal Resolution Research Articles

A bottom-up modeling approach to quantify cold start emissions from urban road traffic

Cold start emissions, which are released during the first minutes of driving after the vehicle engine is started, may contribute significantly to the urban road traffic emissions. Implementation of bottom-up approaches for emission quantification in urban context is crucial to address the distribution of pollution with fine temporal and spatial resolution and to establish local mitigation measures and plans. In this research a modeling approach to quantify cold start excess emissions from road transport with fine spatial resolution is proposed and applied. In combination with transportation modeling, a new module has been developed to estimate cold start emissions at road segment level and for a trip, while preserving information on Origin-Destination of the trips contributing to those emissions. The methodology was applied to Coimbra case study allowing spatial analysis of cold start emissions in the urban context. The frequency distribution and summary statistics for road segments are provided focusing on different types of roads and seasonal variations. High contribution of cold start emissions is demonstrated with median values above 70% for carbon monoxide and hydrocarbons at local roads, where most people leave or work. The methodology applied in this work highlights the importance of cold start emission quantification for traffic pollution studies.

An integrated framework to assess climate change impacts on water use for thermoelectric power plants

Climate variability and change impact both water demand and water availability for power generation because of the interconnected relationship between water and thermoelectric power generation. To assess the impacts of climate change on water demand by thermoelectric power plants, this study developed a framework that integrates the characteristics of power plants and the results of the Integrated Environmental Control Model (IECM) and the General Circulation Model (GCM) to simulate the water demand. This study contributed a novel tool to assess climate change impacts on water demand based on local climate conditions. This framework was applied to thermoelectric power plants in Illinois to explore the water demand at daily, monthly, and annual temporal resolutions. The results demonstrated that water use is more sensitive at shorter timescales. Thus, it is important to examine the water-energy nexus at a finer temporal resolution. Results also indicated that water consumption is more sensitive to climate change than withdrawal; power plants with a wet cooling tower are more sensitive than ones with a once-through cooling system; and natural gas-fired power plants are more sensitive than coal-fired plants. The case study demonstrated the novel framework is capable to assess climate change impact on water demand of thermoelectric power plants at varying temporal resolutions and the framework is easily applicable to other parts of the world.

Micro-punches versus micro-slices for serial sampling of human dentine: Striking a balance between improved temporal resolution and measuring additional isotope systems.

The last decade has seen a dramatic increase in the application of serial sampling of human dentine in archaeology. Rapid development in the field has provided many improvements in the methodology, in terms of both time resolution as well as the ability to integrate more isotope systems in the analysis. This study provides a comparison of two common sampling approaches, allowing researchers to select the most suitable approach for addressing specific research questions. Two common approaches for sequential sampling of human dentine (micro-punches and micro-slices) are compared in terms of viability and efficacy. Using archaeological deciduous second molars and permanent first molars, this study demonstrates how the two approaches capture aspects of the weaning process in different ways. In addition, different aspects related to the extraction protocols, such as the thickness of the central slide and the solubilisation step, are also evaluated. While both approaches show similar intra-tooth isotopic patterns, the micro-punches approach is preferable for research that requires a very fine temporal resolution, while the micro-slices approach is best for research where δ34 S values are needed, or when the samples are poorly preserved. In addition, the solubilisation step has a large effect on collagen yield, and, to a lesser extent, on isotopic compositions. Therefore, it is important to ensure that only samples that have undergone the same pre-treatment protocol are directly compared. We present the pros and cons of the two micro-sampling approaches and offer possible mitigation strategies to address some of the most important issues related to each approach.

Crop Monitoring Using Sentinel-2 and UAV Multispectral Imagery: A Comparison Case Study in Northeastern Germany

Monitoring within-field crop variability at fine spatial and temporal resolution can assist farmers in making reliable decisions during their agricultural management; however, it traditionally involves a labor-intensive and time-consuming pointwise manual process. To the best of our knowledge, few studies conducted a comparison of Sentinel-2 with UAV data for crop monitoring in the context of precision agriculture. Therefore, prospects of crop monitoring for characterizing biophysical plant parameters and leaf nitrogen of wheat and barley crops were evaluated from a more practical viewpoint closer to agricultural routines. Multispectral UAV and Sentinel-2 imagery was collected over three dates in the season and compared with reference data collected at 20 sample points for plant leaf nitrogen (N), maximum plant height, mean plant height, leaf area index (LAI), and fresh biomass. Higher correlations of UAV data to the agronomic parameters were found on average than with Sentinel-2 data with a percentage increase of 6.3% for wheat and 22.2% for barley. In this regard, VIs calculated from spectral bands in the visible part performed worse for Sentinel-2 than for the UAV data. In addition, large-scale patterns, formed by the influence of an old riverbed on plant growth, were recognizable even in the Sentinel-2 imagery despite its much lower spatial resolution. Interestingly, also smaller features, such as the tramlines from controlled traffic farming (CTF), had an influence on the Sentinel-2 data and showed a systematic pattern that affected even semivariogram calculation. In conclusion, Sentinel-2 imagery is able to capture the same large-scale pattern as can be derived from the higher detailed UAV imagery; however, it is at the same time influenced by management-driven features such as tramlines, which cannot be accurately georeferenced. In consequence, agronomic parameters were better correlated with UAV than with Sentinel-2 data. Crop growers as well as data providers from remote sensing services may take advantage of this knowledge and we recommend the use of UAV data as it gives additional information about management-driven features. For future perspective, we would advise fusing UAV with Sentinel-2 imagery taken early in the season as it can integrate the effect of agricultural management in the subsequent absence of high spatial resolution data to help improve crop monitoring for the farmer and to reduce costs.

Design of AC-coupled low gain avalanche diodes (AC-LGADs): a 2D TCAD simulation study

AC-Coupled Low Gain Avalanche Diodes (AC-LGADs) are a new type of silicon sensors which conceived for experiments at future colliders. As an evolution of the standard Low Gain Avalanche Diodes (LGADs), AC-LGADs have similar fast timing performance. The innovation is that AC-LGADs are available to provide fine temporal and spatial resolution simultaneously, thanks to two key features: AC-coupled readout through the dielectric layer and a continuous resistive n+ implant. The Institute of High Energy Physics (IHEP) High-Granularity Timing Detector group is developing its first version of AC-LGAD sensors. This paper present the impact of following parameters on AC-LGAD sensor performance: n+ dose, dielectric material and thickness, pad/pitch ratio and the distance between pad and cathode. All data are obtained from 2D Technology Computer-Aided Design (TCAD) simulations. The simulations will benefit future design and optimization.

Observational Investigation of the Low‐Level Jets in the Metropolitan Region of São Paulo, Brazil

AbstractThe main features of low‐level jet (LLJ) in the metropolitan region of São Paulo (MRSP), Brazil, are assessed using rawinsondes carried out: (a) every 3‐hr during 10‐consecutive days in summer and winter field campaigns of the MCITY BRAZIL Project in 2013, (b) at 0900 and 2100 local time, from September 2009 to August 2013. These observations indicate that the LLJ is a typical feature of the MRSP, observed in 85% of the 20 days of the field‐campaigns and 77.6% of the 1,446 days of regular rawinsonde period. The fine temporal and spatial resolution soundings indicate that most of the LLJs occur during nighttime and early in the morning, with mean intensity of 8.5 ± 0.3 m s−1, height of 539 ± 26 m, and mostly (52.5%) from east and north. The coarse resolution soundings indicate the LLJ display a seasonal variation with maximum intensity in October (8.6 ± 0.3 m s−1) and a minimum in February (7.1 ± 0.2 m s−1), a maximum height in March (703 ± 151 m) and a minimum in June (577 ± 151 m). During MCITY campaigns about 76.4% of LLJ events show inertial oscillation and 35.3% of them are combined with sea breeze. Only 17.6% of LLJ events are associated with cold fronts and post‐frontal high pressure system. The urban heat island intensity, surface inversion layer strength, particulate matter 2.5 and carbon monoxide concentrations are negatively correlated with the LLJ intensity, suggesting the jet‐induced turbulent mixing may contribute to reduce them.

ADT: The automatic weather station data tool

Climate data are essential in an array of climate research and applications. Climate data also provide the foundation for the provision of climate services. However, in many parts of Africa, weather stations are sparse, and their numbers have been declining over the last half-century. Moreover, the distribution of existing meteorological stations is uneven, with most weather stations located in towns and cities along the main roads. To address these data gaps, efforts over the last decade, largely driven by external donor funding, have focused on expanding meteorological observation networks in many parts of Africa, mainly through the provision of Automatic Weather Stations (AWS) to National Meteorological Services (NMS). While AWS offer a number of advantages over the conventional ones, which include automated reporting at a very fine temporal resolution (15 min, on average), they also have several disadvantages and accompanying challenges to their use. Some of these well-known challenges are the high maintenance requirements and associated costs that arise from the need to procure replacement parts that may not be available locally. However, another major, under-discussed challenge confronting NMS is the disparities between the different station types provided by different donors that has given rise to barriers to pragmatically using the plethora of data collected by AWS in decision-making processes. These disparities include major differences in the way the data from various AWS types are formatted and stored, which result in poorly coordinated, fragmented, and unharmonized datasets coming from different AWS networks. The end result is that while top-of-the-line AWS networks may systematically be collecting highly needed data, the inability of NMS to efficiently, combine, synchronize, and otherwise integrate these data coherently in their databases limits their use. To address these challenges, a free web-based application called Automatic Weather Station Data Tool (ADT) with an easy-to-use graphical user interface was developed to help NMS to access, process, perform quality control, and visualize data from different AWS networks in one place. Now implemented in five African countries (Ethiopia, Ghana, Kenya, Rwanda, and Zambia), ADT also enables real-time monitoring of stations to see which ones are working and which ones are offline. This tool emerged from a wider climate services approach, the Enhancing National Climate Services (ENACTS), recognizing that availability of high-quality climate data does not automatically translate to ease of access or effective use.

Countrywide classification of permanent grassland habitats at high spatial resolution

AbstractEuropean grasslands face strong declines in extent and quality. Many grassland types are priority habitats for national and European conservation strategies. Countrywide, high spatial resolution maps of their distribution are often lacking. Here, we modelled the spatial distribution of 20 permanent grassland habitats at the level of phytosociological alliances across Switzerland at 10x10 m resolution. First, we applied ensemble models to provide distribution maps of the individual habitat types, using training data from various sources. Copernicus Sentinel satellite imagery and variables describing climate, soil and topography were used as predictors. The performance of these models was assessed based on the true skill statistics with a split‐sampling of the data. Second, the individual maps were combined into countrywide maps of the most and second most likely habitat type, respectively, using an expert‐based weighting approach. The performance of the combined map for the most likely habitat type was assessed via an independent testing dataset and a comparison of the predicted habitat‐type proportions with extrapolations from field surveys. Most individual maps had useful to excellent predictive performance (TSS ≥ 0.6). For most grid cells in the combined maps, the most and second most likely habitat types were either ecologically closely related or representing two grassland types along a nutrient gradient. The same was true for omission errors. We found good agreement between the predicted and estimated proportions from field surveys. The area of raised bogs appears to be underestimated, while dry grasslands showed highest agreement. This work highlights the potential of earth observation data at fine spatial and temporal resolution to map habitats at broad scales, thereby providing the foundation for diverse conservation applications. A particular challenge remains in capturing the transition from nutrient‐poor to nutrient‐rich grasslands, which is highly important for biodiversity conservation.

Spatial and temporal variations of the flow characteristics at a tidal stream power site: A high-resolution numerical study

The upcoming commercial development of tidal stream power requires a profound knowledge of tidal streams. In this article, the spatial and temporal variations of the flow characteristics are investigated through numerical simulations, at fine spatial and temporal resolutions. The study highlights the necessity of local site characterisation prior to turbine installation, due to the high spatial variability of the flow characteristics over distances of less than 100 m. The high variability in space and time of the flow characteristics at the scale of a turbine and its impact on the blade angle of attack underscore the relevance of a good blade pitch control strategy. The study of extreme events in term of maximal flow velocity and variations in the flow direction gives insight for the structural dimensioning of future tidal stream power devices.

Material Culture Studies in the Age of Big Data: Digital Excavation of Homemade Face-Mask Production during the COVID-19 Pandemic

This manuscript presents a novel approach to the study of contemporary material culture using digital data. Scholars interested in the materiality of past and contemporary societies have been limited to information derived from assemblages of excavated, collected, or physically observed materials; they have yet to take full advantage of large or complex digital datasets afforded by the internet. To demonstrate the power of this approach and its potential to disrupt our understanding of the material world, we present a study of an ongoing global health crisis, the COVID-19 pandemic. In particular, we focus on face-mask production during the pandemic across the United States in 2020 and 2021. Scraping information on homemade face-mask characteristics at multimonth intervals—including location and materials—we analyze the production of masks and their change over time. We demonstrate that this new methodology, coupled with a sociopolitical examination of mask use according to state policies and politicization, provides an unprecedented avenue to understand the changing distributions and social significances of material culture. Our study of mask making elucidates a clear linkage between partisan politics and decreasing disease mitigation effectiveness. We further reveal how time-averaged asssemblages drown out the political meanings of artifacts otherwise visible with finer temporal resolution.

Deep learning models of cognitive processes constrained by human brain connectomes

Decoding cognitive processes from recordings of brain activity has been an active topic in neuroscience research for decades. Traditional decoding studies focused on pattern classification in specific regions of interest and averaging brain activity over many trials. Recently, brain decoding with graph neural networks has been shown to scale at fine temporal resolution and on the full brain, achieving state-of-the-art performance on the human connectome project benchmark. The reason behind this success is likely the strong inductive connectome prior that enables the integration of distributed patterns of brain activity. Yet, the nature of such inductive bias is still poorly understood. In this work, we investigate the impact of the inclusion of multiple path lengths (through high-order graph convolution), the homogeneity of brain parcels (graph nodes), and the type of interactions (graph edges). We evaluate the decoding models on a large population of 1200 participants, under 21 different experimental conditions, acquired from the Human Connectome Project database.Our findings reveal that the optimal choice for large-scale cognitive decoding is to propagate neural dynamics within empirical functional connectomes and integrate brain dynamics using high-order graph convolutions. In this setting, the model exhibits high decoding accuracy and robustness against adversarial attacks on the graph architecture, including randomization in functional connectomes and lesions in targeted brain regions and networks. The trained model relies on biologically meaningful features for the prediction of cognitive states and generates task-specific graph representations resembling task-evoked activation maps. These results demonstrate that a full-brain integrative model is critical for the large-scale brain decoding. Our study establishes principles of how to effectively leverage human connectome constraints in deep graph neural networks, providing new avenues to study the neural substrates of human cognition at scale.

Daily Carbon Assessment Framework: Towards Near Real-Time Building Carbon Emission Benchmarking for Operative and Design Insights

The energy consumption and its related carbon emission of non-domestic complex buildings in an urban context are complicated due to their wide variety of functions and services. A detailed assessment of the carbon emission of such buildings can contribute to decision making for in-operation building management and schematic designs of future proposals. Concurrently, advances in smart meter data analytics and sensor-enabled operational data streams offer the opportunity to investigate this problem at a finer temporal resolution. This research developed a daily carbon emission benchmarking system of a mixed-use building in a UK university. The research period was set at an annual range from 1 January 2019 to 31 December 2019 and was segmented by strategic periods in line with the operation schedule of the building. The daily benchmark revealed the fluctuation of the building’s energy consumption and associated carbon emissions. Based on this, a digital twin framework was developed to identify the possible time periods when the building is less carbon efficient and potential building characters that can lead to increased carbon emission in the operational stage compared with what originally expected at the design stage. We discuss how these insights can offer actionable knowledge for user groups such as asset managers and architects.

Long-Term Variability of Wind Speed and Direction in the Mediterranean Basin

In this work, the ERA5 reanalysis dataset, with its fine spatial and temporal resolution, is used to study the wind speed and direction characteristics in the Mediterranean basin from 1979 to 2020. Their variability, trend and mean values, as well as their association, are examined in the monthly, annual and interannual/decadal time scales. The long-term variability of the wind direction is assessed using the angular variance, while the mean annual and interannual variability are used for the wind speed. The most characteristic and constant flow systems appear in the Gulf of Lion (Mistral) and the Aegean Sea during the summer (Etesian winds). The mean annual variability maximizes in the northern part of the basin, while the western part appears to have the most variable wind directions, while the fastest increasing wind speeds appear in the southwestern Levantine sub-basin. The long-term linear trend for the mean, the 95th and 99th quantiles of annual wind speed and the number of occurrences of extreme wind events have been also assessed. For particular areas, there is an increasing tendency in both intensity and frequency of occurrence of extreme wind events. Τhe linear-circular correlation coefficient has been implemented in order to quantify the linear association between the wind direction and the wind speed. This parameter demonstrates higher annual values for the Mistral wind in the Gulf of Lion and the Etesian winds in the Aegean Sea during the summer. Finally, the comparison of the results of ERA5 with the results of ERA-Interim highlighted significant differences in the Mediterranean wind speed and direction characteristics.

Exploring the spatial resolution of TMS-EEG coupling on the sensorimotor region

The use of TMS-EEG coupling as a neuroimaging tool for the functional exploration of the human brain recently gained strong interest. If this tool directly inherits the fine temporal resolution from EEG, its spatial counterpart remains unknown. In this study, we explored the spatial resolution of TMS-EEG coupling by evaluating the minimal distance between two stimulated cortical sites that would significantly evoke different response dynamics. TMS evoked responses were mapped on the sensorimotor region in twenty participants. The stimulation grid was composed of nine targets separated between 10 and 15 mm on average. The dynamical signatures of TMS evoked activity were extracted and compared between sites using both local and remote linear regression scores and spatial generalized mixed models. We found a significant effect of the distance between stimulated sites on their dynamical signatures, neighboring sites showing differentiable response dynamics. Besides, common dynamical signatures were also found between sites up to 25-30 mm from each other. This overlap in dynamical properties decreased with distance and was stronger between sites within the same Brodmann area. Our results suggest that the spatial resolution of TMS-EEG coupling might be at least as high as 10 mm. Furthermore, our results reveal an anisotropic spatial resolution that was higher across than within the same Brodmann areas, in accordance with the TMS induced E-field modeling. Common cytoarchitectonic leading to shared dynamical properties within the same Brodmann area could also explain this anisotropy. Overall, these findings suggest that TMS-EEG benefits from the spatial resolution of TMS, which makes it an accurate technique for meso-scale brain mapping.

How will hydro-energy generation of the Nepalese Himalaya vary in the future? A climate change perspective

Despite being one of the proven clean-energy technologies, hydroelectricity is losing attention in global research. Hydroelectricity is extremely important for countries possessing the required water resources, already heavily reliant on it and those lacking the financial capacity to invest in other expensive energy technologies. This study assessed the possible impact of climate change (CC) on hydro-energy generation in the Nepalese Himalaya (possessing eight peaks out of 14 over 8000 m) with a tremendous hydropower potential (∼50,000 MW). A planned 1200 MW storage type Budhigandaki Hydroelectricity Project is taken as a case. We estimated the energy generation for the baseline as well as 10 CC scenarios considering RCPs 4.5 and 8.5 at monthly, seasonal, and annual temporal scales for the mid-century. Results show that energy generation is highly dependent on the reservoir operating rule. The average annual energy generation is expected to vary within −5 to +12% of the base case in the mid-century, with significant variations across the months. We also infer that designing hydro-projects based on ensembled climate values could lead to a “rosy” but less probable and risky picture of energy generation in the future. Therefore, assessment of a wide spectrum of plausible CC scenarios are recommended. Storage type projects with provision of flexible operating rules considering finer temporal resolution and allocation to competing users (in case of multipurpose projects) supported by appropriate policies are desirable for climate resiliency. Complementing the existing energy generation mix with other technologies in areas where hydroelectricity is expected to undergo adverse impacts of CC is warranted for attaining future energy security and environmental safeguarding. Possibility of additional energy due to CC is a strong motivation for this region to focus on hydroelectricity development in the future.

In-Depth In Vivo Crosslinking in Minutes by a Compact, Membrane-Permeable, and Alkynyl-Enrichable Crosslinker.

Chemical crosslinking coupled with mass spectrometry (CXMS) has emerged as a powerful technique to obtain the dynamic conformations and interaction interfaces of protein complexes. Limited by the poor cell membrane permeability, chemical reactivity, and biocompatibility of crosslinkers, in vivo crosslinking to capture the dynamics of protein complexes with finer temporal resolution and higher coverage is attractive but challenging. In this work, a trifunctional crosslinker bis(succinimidyl) with propargyl tag (BSP), involving compact size, proper amphipathy, and enrichment capacity, was developed to enable better cell membrane permeability and efficient crosslinking in 5 min without obvious cellular interference. Followed by a two-step enrichment method based on click chemistry at the peptide level, 13,098 crosslinked peptides (5068 inter-crosslinked peptides and 8030 intra-crosslinked peptides) were identified under the data threshold of peptide-spectrum matches (PSMs) ≥2 on the basic of the FDR control of 1%, which was the most comprehensive dataset for homo species cells by a non-cleavable crosslinker. Besides, the interactome network comprising 1519 proteins connected by 2913 interaction edges in various intracellular compartments, as well as 80S ribosome structural dynamics, were characterized, showing the great potential of our in vivo crosslinking approach in minutes. All these results demonstrated that our developed BSP could provide a valuable toolkit for the in-depth in vivo analysis of protein-protein interactions (PPIs) and protein architectures with finer temporal resolution.

A mathematical perspective on edge-centric brain functional connectivity

Edge time series are increasingly used in brain imaging to study the node functional connectivity (nFC) dynamics at the finest temporal resolution while avoiding sliding windows. Here, we lay the mathematical foundations for the edge-centric analysis of neuroimaging time series, explaining why a few high-amplitude cofluctuations drive the nFC across datasets. Our exposition also constitutes a critique of the existing edge-centric studies, showing that their main findings can be derived from the nFC under a static null hypothesis that disregards temporal correlations. Testing the analytic predictions on functional MRI data from the Human Connectome Project confirms that the nFC can explain most variation in the edge FC matrix, the edge communities, the large cofluctuations, and the corresponding spatial patterns. We encourage the use of dynamic measures in future research, which exploit the temporal structure of the edge time series and cannot be replicated by static null models.

Near real-time surface water extraction from GOES-16 geostationary satellite ABI images by constructing and sharpening the green-like band

Continuous monitoring of water bodies is critical for a range of applications including water resource management, natural hazard assessment and climate change analysis. GOES-16 geostationary satellite Advanced Baseline Imager (ABI) imagery has a very fine (per 10 min) temporal resolution, which is essential for frequent monitoring of water body changes. However, GOES-16 ABI imagery has not been applied for water body mapping, as it lacks a green waveband, essential for the construction of water index images used for surface water extraction. Moreover, the spatial resolution of GOES-16 ABI imagery varies from 0.5 km to 2 km, with only the red band having a spatial resolution of 0.5 km. To solve these problems, we constructed a green-like band and applied component substitution (CS), multiresolution analysis (MRA) and geostatistical-based pansharpening to increase the spatial resolution of the original GOES-16 ABI imagery. The results show that the addition of the constructed green-like band increased significantly the accuracy of water body mapping with water indices such as the NDWI and MNDWI. Sharpening the 1 km bands using the 0.5 km red band of GOES-16 ABI imagery further increased the accuracy of water body mapping with greater spatial detail. Notably, experiments performed in this research demonstrated that the order of the two steps (i.e. green-like band construction and multi-spectral image pansharpening), also influences the accuracy of water body mapping. Due to the different ability to preserve spectral information, the strategy of pansharpening-then-construction was more suitable for MRA-based pansharpening, while the opposite was true for CS-based methods, with no significant difference for geostatistical-based methods. An additive wavelet luminance proportion (AWLP) approach in the pansharpening-then-construction strategy was applied to produce 0.5 km time-series water body maps every 10 min within a day for the main part of the Amazon River, as it produced a water body map with the greatest accuracy. The downscaled GOES-16 ABI imagery, with its constructed and sharpened green-like band, has great potential for near real-time fine-scale mapping of surface water bodies.

Mapping land-use intensity of grasslands in Germany with machine learning and Sentinel-2 time series

Information on grassland land-use intensity (LUI) is crucial for understanding trends and dynamics in biodiversity, ecosystem functioning, earth system science and environmental monitoring. LUI is a major driver for numerous environmental processes and indicators, such as primary production, nitrogen deposition and resilience to climate extremes. However, large extent, high resolution data on grassland LUI is rare. New satellite generations, such as Copernicus Sentinel-2, enable a spatially comprehensive detection of the mainly subtle changes induced by land-use intensification by their fine spatial and temporal resolution. We developed a methodology quantifying key parameters of grassland LUI such as grazing intensity, mowing frequency and fertiliser application across Germany using Convolutional Neural Networks (CNN) on Sentinel-2 satellite data with 20 m × 20 m spatial resolution. Subsequently, these land-use components were used to calculate a continuous LUI index. Predictions of LUI and its components were validated using comprehensive in situ grassland management data. A feature contribution analysis using Shapley values substantiates the applicability of the methodology by revealing a high relevance of springtime satellite observations and spectral bands related to vegetation health and structure. We achieved an overall classification accuracy of up to 66% for grazing intensity, 68% for mowing, 85% for fertilisation and an r2 of 0.82 for subsequently depicting LUI. We evaluated the methodology's robustness with a spatial 3-fold cross-validation by training and predicting on geographically distinctly separated regions. Spatial transferability was assessed by delineating the models' area of applicability. The presented methodology enables a high resolution, large extent mapping of land-use intensity of grasslands.

Integrating the Strength of Multi-Date Sentinel-1 and -2 Datasets for Detecting Mango (Mangifera indica L.) Orchards in a Semi-Arid Environment in Zimbabwe

Generating tree-specific crop maps within heterogeneous landscapes requires imagery of fine spatial and temporal resolutions to discriminate among the rapid transitions in tree phenological and spectral features. The availability of freely accessible satellite data of relatively high spatial and temporal resolutions offers an unprecedented opportunity for wide-area land use and land cover (LULC) mapping, including tree crop (e.g., mango; Mangifera indica L.) detection. We evaluated the utility of combining Sentinel-1 (S1) and Sentinel-2 (S2) derived variables (n = 81) for mapping mango orchard occurrence in Zimbabwe using machine learning classifiers, i.e., support vector machine and random forest. Field data were collected on mango orchards and other LULC classes. Fewer variables were selected from ‘All’ combined S1 and S2 variables using three commonly utilized variable selection methods, i.e., relief filter, guided regularized random forest, and variance inflation factor. Several classification experiments (n = 8) were conducted using 60% of field datasets and combinations of ‘All’ and fewer selected variables and were compared using the remaining 40% of the field dataset and the area underclass approach. The results showed that a combination of random forest and relief filter selected variables outperformed (F1 score > 70%) all other variable combination experiments. Notwithstanding, the differences among the mapping results were not significant (p ≤ 0.05). Specifically, the mapping accuracy of the mango orchards was more than 80% for each of the eight classification experiments. Results revealed that mango orchards occupied approximately 18% of the spatial extent of the study area. The S1 variables were constantly selected compared with the S2-derived variables across the three variable selection approaches used in this study. It is concluded that the use of multi-modal satellite imagery and robust machine learning classifiers can accurately detect mango orchards and other LULC classes in semi-arid environments. The results can be used for guiding and upscaling biological control options for managing mango insect pests such as the devastating invasive fruit fly Bactrocera dorsalis (Hendel) (Diptera: Tephritidae).