High-resolution Intensity Research Articles

A long-term high-resolution dataset of grasslands grazing intensity in China

Grazing is a significant anthropogenic disturbance to grasslands, impacting their function and composition, and affecting carbon budgets and greenhouse gas emissions. However, accurate evaluations of grazing impacts are limited by the absence of long-term high-resolution grazing intensity data (i.e., the number of livestock per unit area). This study utilized census livestock data and a satellite-based vegetation index to develop the first Long-term High-resolution Grazing Intensity (LHGI) dataset of grassland in seven pastoral provinces in western China from 1980 to 2022. The LHGI dataset effectively captured spatial variations in grazing intensity, with validation at 73 sites showing a correlation coefficient (R2) of 0.78. The county-level validation showed an averaged R2 values of 0.73 ± 0.03 from 1980 to 2022. This dataset serves as a vital resource for estimating grassland carbon cycling and livestock system CH4 emissions, as well as contributing to grassland management.

The role of the electricity grid in operation-induced greenhouse gas emissions by a residential building: A multi-year retrospective simulation study

In view of the growing shares of renewables in the electricity grid in combination with the electrification of hvac (heating, ventilation, air conditioning) systems in residential buildings, the grid intensity (in terms of carbon dioxide emissions per unit of electric energy) becomes increasingly sensitive to weather conditions, and synchronicity between weather and the grid becomes a more critical aspect in building performance assessment. Using building performance simulation techniques to seek robust building designs requires awareness about the uncertainties in circumstantial factors that affect performance. This 2016 – 2022 retrospective study highlights the effects of using low or high temporal resolution grid emissions intensity data on projected operation-induced carbon dioxide emissions for a terraced dwelling in the Netherlands. Building fabric quality, the occupant profile, and systems configurations (i.e., hvac and photovoltaics) are varied to investigate the effects of the applied grid model resolution. This study shows that ignoring high-resolution grid intensity data is getting increasingly problematic; applying low resolution (annual) instead of high-resolution (15-minute) grid intensity data leads to an increasingly unjustified optimistic assessment both for net and gross emissions (either or not allowing for carbon displacement by feeding locally generated electricity into the grid).

Revealing Cropping Intensity Dynamics Using High-Resolution Imagery: A Case Study in Shaanxi Province, China

Reliable and continuous information on cropping intensity is crucial for assessing cropland utilization and formulating policies regarding cropland protection and management. However, there is still a lack of high-resolution cropping intensity maps for recent years, particularly in fragmented agricultural regions. In this study, we combined Landsat-8 and Sentinel-2 imagery to generate cropping intensity maps from 2019 to 2023 at a 10 m resolution for Shaanxi Province, China. First, the satellite imagery was harmonized to construct 10-day composite enhanced vegetation index (EVI) time series. Then, the cropping intensity was determined by counting the number of valid EVI peaks within a year. Assessment based on 578 sample points showed a high level of accuracy, with overall accuracy and Kappa coefficient values exceeding 0.96 and 0.93, respectively. We further analyzed the spatiotemporal patterns of cropping intensity and generated a map of abandoned cropland in Shaanxi. The results indicated that cropland in Shaanxi Province was mainly utilized for single-cropping (52.9% of area), followed by double-cropping (35.2%), with non-cropping accounting for 11.9%. Cropping intensity tended to be lower in the north and higher in the south. Temporally, the average cropping intensity of Shaanxi increased from 1.1 to over 1.3 from 2019 to 2023. Despite this upward trend, large areas of cropland were abandoned in northern Shaanxi. These results demonstrate the potential of utilizing Landsat-8 and Sentinel-2 imagery to identify cropping intensity dynamics in fragmented agricultural regions and to guide more efficient cropland management.

Asian winter monsoon controls marine primary productivity in north Arabian Sea during the Holocene

The Arabian Sea (AS), which is strongly impacted by monsoon winds, is one of the most biologically productive regions. The forcing of Asian monsoons on Holocene productivity remains debated in north AS, an area influenced by summer and winter monsoon. A sediment core from Makran margin was utilized to analyze organic biomarkers and high-resolution bromine intensities, unveiling the forces of biological productivity through the past 8000 years. Alkenone sea surface temperature (SST) records show significant cooling in north AS and augmented SST difference between north and west AS toward the late Holocene, suggesting Asian winter monsoon (AWM) strengthening during the period. The Holocene productivity signals in north AS, indicated by the bromine to rubidium ratio and multiple biomarkers, are responsive to SST changes and thus mainly controlled by the AWM variation, not modulated by Asian summer monsoon (ASM). Millennial-to-centennial-scale variability of marine productivity during the Holocene is closely related to North Atlantic climate oscillations, mainly through atmospheric circulations in winter.

A Joint Intensity-Neuromorphic Event Imaging System With Bandwidth-Limited Communication Channel.

We present a novel adaptive multimodal intensity-event algorithm to optimize an overall objective of object tracking under bit rate constraints for a host-chip architecture. The chip is a computationally resource-constrained device acquiring high-resolution intensity frames and events, while the host is capable of performing computationally expensive tasks. We develop a joint intensity-neuromorphic event rate-distortion compression framework with a quadtree (QT)-based compression of intensity and events scheme. The goal of this compression framework is to optimally allocate bits to the intensity frames and neuromorphic events based on the minimum distortion at a given communication channel capacity. The data acquisition on the chip is driven by the presence of objects of interest in the scene as detected by an object detector. The most informative intensity and event data are communicated to the host under rate constraints so that the best possible tracking performance is obtained. The detection and tracking of objects in the scene are done on the distorted data at the host. Intensity and events are jointly used in a fusion framework to enhance the quality of the distorted images, in order to improve the object detection and tracking performance. The performance assessment of the overall system is done in terms of the multiple object tracking accuracy (MOTA) score. Compared with using intensity modality only, there is an improvement in MOTA using both these modalities in different scenarios.

Range-Intensity-Profile-Guided Gated Light Ranging and Imaging Based on a Convolutional Neural Network.

Three-dimensional (3D) range-gated imaging can obtain high spatial resolution intensity images as well as pixel-wise depth information. Several algorithms have been developed to recover depth from gated images such as the range-intensity correlation algorithm and deep-learning-based algorithm. The traditional range-intensity correlation algorithm requires specific range-intensity profiles, which are hard to generate, while the existing deep-learning-based algorithm requires large number of real-scene training data. In this work, we propose a method of range-intensity-profile-guided gated light ranging and imaging to recover depth from gated images based on a convolutional neural network. In this method, the range-intensity profile (RIP) of a given gated light ranging and imaging system is obtained to generate synthetic training data from Grand Theft Auto V for our range-intensity ratio and semantic network (RIRS-net). The RIRS-net is mainly trained on synthetic data and fine-tuned with RIP data. The network learns both semantic depth cues and range-intensity depth cues in the synthetic data, and learns accurate range-intensity depth cues in the RIP data. In the evaluation experiments on both a real-scene and synthetic test dataset, our method shows a better result compared to other algorithms.

Large-scale and high-resolution paddy rice intensity mapping using downscaling and phenology-based algorithms on Google Earth Engine

Accurate mapping of paddy rice cropping intensity (PRCI) affects precision agriculture, water use management, and informed decision-making. Many PRCI mapping approaches have been developed and achieved remarkable performance. However, large-scale and high-resolution PRCI mapping in southern China, especially in the Hunan Province, remains challenging. In this region, optical data suffer from serious data loss owing to frequent cloud coverage, whereas radar data are usually affected by mountainous terrain. Multisource data fusion may mitigate data loss; however, many data fusion approaches cannot be used in large-scale applications because of their significant computational complexity. To achieve accurate PRCI mapping in large-scale areas of southern China, we propose a novel large-scale downscaling method for multi-source data fusion. A new framework for PRCI mapping was developed using the Google Earth Engine. To test its performance, we mapped the 10 m PRCI of Hunan Province in 2020. The overall accuracy of the mapping was 90.70 % with a kappa coefficient of 0.81. Furthermore, our estimated sown area was highly correlated with the statistical yearbook (R2 = 0.84, RMSE = 11.47 kha). Our experimental results demonstrated the remarkable performance of the proposed framework for large-scale and high-resolution PRCI mapping in southern China.

Exploring geomagnetic variations in ancient mesopotamia: Archaeomagnetic study of inscribed bricks from the 3rd-1st millennia BCE.

This study presents 32 high-resolution geomagnetic intensity data points from Mesopotamia, spanning the 3rd to the 1st millennia BCE. These data contribute to rectifying geographic disparities in the resolution of the global archaeointensity curve that have hampered our understanding of geomagnetic field dynamics and the viability of applying archaeomagnetism as a method of absolute dating of archaeological objects. A lack of precise and well-dated intensity data in the region has also limited our ability to identify short-term fluctuations in the geomagnetic field, such as the Levantine Iron Age geomagnetic Anomaly (LIAA), a period of high field intensity from ca. 1050 to 550 BCE. This phenomenon has hitherto not been well-demonstrated in Mesopotamia, contrary to predictions from regional geomagnetic models. To address these issues, this study presents precise archaeomagnetic results from 32 inscribed baked bricks, tightly dated to the reigns of 12 Mesopotamian kings through interpretation of their inscriptions. Results confirm the presence of the high field values of the LIAA in Mesopotamia during the first millennium BCE and drastically increase the resolution of the archaeointensity curve for the 3rd-1st millennia BCE. This research establishes a baseline for the use of archaeomagnetic analysis as an absolute dating technique for archaeological materials from Mesopotamia.

Exploring the Spatial Patterning of Sociodemographic Disparities in Extreme Heat Exposure at Multiple Scales Across the Conterminous United States.

Climate change has led to an increase in heat-related morbidity and mortality. The impact of heat on health is unequally distributed amongst different socioeconomic and demographic groups. We use high-resolution daily air temperature-based heat wave intensity (HWI) and neighborhood-scale sociodemographic information from the conterminous United States to evaluate the spatial patterning of extreme heat exposure disparities. Assuming differences in spatial patterns at national, regional, and local scales; we assess disparities in heat exposure across race, housing characteristics, and poverty level. Our findings indicate small differences in HWI based on these factors at the national level, with the magnitude and direction of the differences varying by region. The starkest differences are present over the Northeast and Midwest, where primarily Black neighborhoods are exposed to higher HWI than predominantly White areas. At the local level, we find the largest difference by socioeconomic status. We also find that residents of nontraditional housing are more vulnerable to heat exposure. Previous studies have either evaluated such disparities for specific cities and/or used a satellite-based land surface temperature, which, although correlated with air temperature, does not provide the true measure of heat exposure. This study is the first of its kind to incorporate high-resolution gridded air temperature-based heat exposure in the evaluation of sociodemographic disparities at a national scale. The analysis suggests the unequal distribution of heat wave intensities across communities-with higher heat exposures characterizing areas with high proportions of minorities, low socioeconomic status, and homes in need of retrofitting to combat climate change.

GaAs hetero-epitaxial layers grown by MOVPE on exactly-oriented and off-cut (1 1 1)Si: Lattice tilt, mosaicity and defects content

Integration of III-V devices with Si-photonics and fabrication of monolithic III-V/Si tandem solar cells require the heteroepitaxy of III-V compounds on Si. We report on the lattice tilt, mosaicity and defects content of relaxed GaAs grown by MOVPE on exactly-oriented and 4°-offcut (111)Si. Thin GaAs single-layers grown at 400 °C and annealed at 700 °C show ∼ 3 × 108 cm−2 density of surface pinholes. Double-layer samples were obtained by GaAs overgrowth at 700 °C. GaAs epilayers are tilted by (0.05–0.14)° with respect to Si. Rotational twins were observed in X-ray diffraction (XRD) pole figures: the most abundant ones originate from 60°-rotation of GaAs around the 1¯1¯1¯ growth direction and are identified as micro-twins along the GaAs/Si hetero-interface. Twins obtained by rotations around the 1¯1¯1, 11¯1¯, and 1¯11¯ directions or by combined rotations around the growth direction and one of the former, were also observed. The GaAs mosaicity and block size were studied through high-resolution XRD intensity mapping: for single-layer samples crystal blocks are ascribed to 3–5 nm thin micro-twins, whose size does not change upon annealing. In double-layer samples thicker (32–35 nm) micro-twins occur. GaAs samples grown on offcut (111)Si show less rotational twins but a reduced mosaic block size with respect to exactly-oriented Si.

High-Resolution Analysis of Fe–Mn Oxyhydroxide in Ferromanganese Nodules from the Northwestern Pacific Ocean and Insights on Element Mobility

Due to the multiple and poorly-crystallized phases in ferromanganese (Fe–Mn) nodules, research on the variability of micro-layers in nodules is limited and the accumulation of various elements with the growth of micro-layers have not been well documented. To address this gap, we examined the spatial distributions of elements in cross-sections of nodules from the Northwestern Pacific Ocean using in-situ electron microprobe analyses coupled with backscattered electron imaging and high-resolution X-ray elemental intensity mapping. Results suggest their significant selective enrichment of metal elements is similar to that of typical hydrogenetic nodules and diagenetic nodules. Moreover, WMn+Fe of Fe–Mn oxyhydroxide is equal to 50% after normalization and Co and Ni show positive relationships with Mn/Fe ratio, suggesting Fe oxyhydroxide would serve as a diluter at exposed surface during nodule growth. In suboxic conditions, Mn, Ni, and Co start to release, and the remaining Fe oxyhydroxide may react with the surrounding sediment to form Si-rich layers. Our studies reveal the diverse growth processes and show a much larger chemical heterogeneity of individual layers, which extend the models about the mechanisms of chemical precipitation and environmental conditions that existed during nodule growth.

Fourier Ptychographic Reconstruction Method of Self-Training Physical Model

Fourier ptychographic microscopy is a new microscopic computational imaging technology. A series of low-resolution intensity images are collected by a Fourier ptychographic microscopy system, and high-resolution intensity and phase images are reconstructed from the collected low-resolution images by a reconstruction algorithm. It is a kind of microscopy that can achieve both a large field of view and high resolution. Here in this article, a Fourier ptychographic reconstruction method applied to a self-training physical model is proposed. The SwinIR network in the field of super-resolution is introduced into the reconstruction method for the first time. The input of the SwinIR physical model is modified to a two-channel input, and a data set is established to train the network. Finally, the results of high-quality Fourier stack microscopic reconstruction are realized. The SwinIR network is used as the physical model, and the network hyperparameters and processes such as the loss function and optimizer of the custom network are reconstructed. The experimental results show that by using multiple different types of data sets, the two evaluation index values of the proposed method perform best, and the image reconstruction quality is the best after model training. Two different evaluation indexes are used to quantitatively analyze the reconstruction results through numerical results. The reconstruction results of the fine-tuning data set with some real captured images are qualitatively analyzed from the visual effect. The results show that the proposed method is effective, the network model is stable and feasible, the image reconstruction is realized in a short time, and the reconstruction effect is good.

Event-Guided Image Super-Resolution Reconstruction.

The event camera efficiently detects scene radiance changes and produces an asynchronous event stream with low latency, high dynamic range (HDR), high temporal resolution, and low power consumption. However, the large output data caused by the asynchronous imaging mechanism makes the increase in spatial resolution of the event camera limited. In this paper, we propose a novel event camera super-resolution (SR) network (EFSR-Net) based on a deep learning approach to address the problems of low spatial resolution and poor visualization of event cameras. The network model is capable of reconstructing high-resolution (HR) intensity images using event streams and active sensor pixel (APS) frame information. We design the coupled response blocks (CRB) in the network that are able of fusing the feature information of both data to achieve the recovery of detailed textures in the shadows of real images. We demonstrate that our method is able to reconstruct high-resolution intensity images with more details and less blurring in synthetic and real datasets, respectively. The proposed EFSR-Net can improve the peak signal-to-noise ratio (PSNR) metric by 1-2 dB compared with state-of-the-art methods.

Damage Detection and Virtual Reconstruction of Built Heritage: An Approach Using High-Resolution Range and Intensity Data

Damage Detection and Virtual Reconstruction of Built Heritage: An Approach Using High-Resolution Range and Intensity Data

High-resolution depth imaging with a small-scale SPAD array based on the temporal-spatial filter and intensity image guidance.

Currently single-photon avalanche diode (SPAD) arrays suffer from a small-scale pixel count, which makes it difficult to achieve high-resolution 3D imaging directly through themselves. We established a CCD camera-assisted SPAD array depth imaging system. Based on illumination laser lattice generated by a diffractive optical element (DOE), the registration of the low-resolution depth image gathered by SPAD and the high-resolution intensity image gathered by CCD is realized. The intensity information is used to guide the reconstruction of a resolution-enhanced depth image through a proposed method consisting of total generalized variation (TGV) regularization and temporal-spatial (T-S) filtering algorithm. Experimental results show that an increasement of 4 × 4 times for native depth image resolution is achieved and the depth imaging quality is also improved by applying the proposed method.

Uncommon Electromagnetic Radiations Related to Extra-High Voltage/Ultra-High Voltage Power Projects in China

Based on the high-resolution electromagnetic field intensity observed by the DEMETER satellite, the harmonic electromagnetic radiations in the ionosphere closely above the extra-high voltage and ultra-high voltage power projects in the Chinese power grid have been detected. In the time-frequency spectrograms, these radiations are characterized by parallel spectral lines with spacings of 50, 100, 150, or 600 Hz. The highest frequency can reach approximately 8,850 Hz. The radiations are probably attributed to the nonlinear devices such as converters or geomagnetic disturbance. The harmonic radiations over alternating current-to-direct current (AC-to-DC) substations of power system are generally more diverse and stronger than those over AC-to-AC substations. The measurements on the ground demonstrate that the weak magnetic radiations at several kilohertz with an intensity in the order of nT do exist around substations. A simple physical model is established to understand the characteristics of harmonic radiations propagating from the Earth’s surface to the ionosphere.

Depth Map Super-Resolution via Cascaded Transformers Guidance

Depth information captured by affordable depth sensors is characterized by low spatial resolution, which limits potential applications. Several methods have recently been proposed for guided super-resolution of depth maps using convolutional neural networks to overcome this limitation. In a guided super-resolution scheme, high-resolution depth maps are inferred from low-resolution ones with the additional guidance of a corresponding high-resolution intensity image. However, these methods are still prone to texture copying issues due to improper guidance by the intensity image. We propose a multi-scale residual deep network for depth map super-resolution. A cascaded transformer module incorporates high-resolution structural information from the intensity image into the depth upsampling process. The proposed cascaded transformer module achieves linear complexity in image resolution, making it applicable to high-resolution images. Extensive experiments demonstrate that the proposed method outperforms state-of-the-art techniques for guided depth super-resolution.

An ultra-high-resolution autonomous uncrewed helicopter aeromagnetic survey in Izu-Oshima Island, Japan

We conducted a high-resolution aeromagnetic survey using an autonomously driven uncrewed helicopter that flew as low as several tens of meters above the ground along precise flight tracks with 1 m accuracy. The geomagnetic total intensity was measured by a total intensity magnetometer suspended beneath the helicopter at a ~ 50 m or less flight spacing over the entire caldera of Mt. Mihara, located on Izu-Oshima Island, Japan. From the observed geomagnetic data, we estimated high-resolution subsurface magnetization intensity. A high average magnetization intensity of 13.5 A/m was obtained for the entire caldera. The distribution of the magnetization intensity was not only consistent with the results of conventional airborne surveys, but it also had a high spatial resolution of less than 100 m. Highly magnetized areas were observed along the NW–SE lines that intersected the summit pit crater, Crater A, which is consistent with the principal stress direction of Izu-Oshima Island. These highly magnetized areas might be solidified magma that did not reach the surface during past eruptions. A large and deep-rooted weakly magnetized area was found just outside of the NE side of the central cone, which corresponds to the location of Fissure B, and the conduit must have been demagnetized at the previous event. Other weakly magnetized areas were also observed at the N, E, and SW sides around the pit crater. These regions correspond to the location of fumaroles in the crater. The high-resolution subsurface magnetization imaged by the autonomous uncrewed helicopter will be helpful for the mitigation of future eruption damage by enabling the assessment of potential fissure eruption areas.

Do We Need a Higher Resolution? Case Study: Sentinel-1-Based Change Detection of the 2018 Hokkaido Landslides, Japan

Since 2014, Sentinel-1 (S1) Synthetic Aperture Radar (SAR) data have become an important source in the field of displacement detection thanks to regular acquisitions and 7.5 years of temporal coverage at global level. Despite the increasing number of publications on the role of S1 in landslide detection, there is still a need for research to further clarify the capabilities of the sensor and the applicable image analysis techniques. Previous studies have successfully exploited high-resolution ALOS-PALSAR image-based intensity and coherence analysis at the 2018 Hokkaido landslides. Nevertheless, they expressed a clear need to analyse the capabilities of other sensors (such as S1). This raises the question: Do we need SAR imagery with higher spatial resolution (such as ALOS-PALSAR) or are freely available S1 imagery also suitable for rapid landslide detection? The S1 images could provide suitable material for a comparative analysis and could answer the aforementioned question. Therefore, 17 ascending and 19 descending S1 images were analysed to test S1 accuracy on landslide detection. Multitemporal analyses of both intensity and coherence were performed along with coherence differences, multitemporal features (MTF) and MTF differences of coherence images. In addition, the spatial analysis of the classification results was also evaluated to highlight the potential of S1 coherence analysis. S1 was found to have limitations at the site, as single coherence differences provided low-quality results. However, the results were significantly improved by calculating the MTF on coherence and almost reached the success rate of the ALOS-PALSAR-based coherence analysis, even though the improvement of the results with intensity was not possible. Half of the false positives were identified in the 30–45-m buffer zone of the agreement, underlining that the spatial resolution of the S1 is not appropriate for accurate landslide detection. Only an approximation of the landslide-affected area can be given with considerable overestimation. Due to the inclusion of post-event images, the sensor is not perfectly applicable for rapid detection purposes here.

LiDAR Inertial Mapping of Non-Manhattan Topologies for UAV-Based Aircraft Inspection in GNSS-Denied Environments

In this paper we propose and evaluate a high resolution intensity yielding adaption of a 3D mapping approach for airplane inspection by autonomous unmanned aerial vehicles. We solely use a 16 column laserscanner and an industrial-grade inertial measurement unit, without a priori knowledge of the inspection object dimensions or its surroundings. Our approach includes the introduction of a novel cylinder feature detection and an according modification of the optimization-based state estimation. Robustness of the point cloud registration is proven based on measurements in a GNSS-denied hangar environment with a Boeing 737-500, comprising only sparse plane and edge features and for highly dynamic movement involving rapid roll or pitch rotation. The approach is suitable for the given application to datasets with strong non-Manhattan characteristics, as opposed to several state of the art algorithms. By means of including the intensity information, 2D projections of the 3D registration result depict a remarkable level of detail and sharpness, resembling that of camera images. Thus allowing for complete coverage of the environment with high spatial resolution using the discrete points and voxel grids with small leaf sizes, the obtained results perspectively can be used for visually matching multi-channel camera images. By fusion of such visual information with the LiDAR generated projections and using the LiDAR inertial odometry, 3D defect maps of the aircraft surface can be constructed, enabling the automated localization of structural damages that are detectable using convolutional neural networks, as shown in previous publications.