A global product of 150-m urban building height based on spaceborne lidar.

Urban building height, as a fundamental 3D urban structural feature, has far-reaching applications. However, creating readily available datasets of recent urban building heights with fine spatial resolutions and global coverage remains a challenging task. Here, we provide a 150-m global urban building heights dataset around 2020 by combining the spaceborne lidar (Global Ecosystem Dynamics Investigation, GEDI), multi-sourced data (Landsat-8, Sentinel-2, and Sentinel-1), and topographic data.

Experimental Study on Drilling Signal Characteristics of PDC Drill Bit in Media of Different Strengths and Identification of Weak Media.

This study aimed to investigate the drilling signal characteristics when a PDC drill bit penetrates media of different strengths and to assess the potential of these signals for identifying weak layers within rock formations. Laboratory-scale experiments were conducted, and the response characteristics of the PDC drill bit in different-strength media were analyzed across the time domain, frequency domain, and time-frequency domain using statistical analysis, Fourier transform, and empirical mode decomposition (EMD). The results indicate that in the lowest-strength concrete (C10), the drilling speed was the fastest, while the mean, median, and primary distribution ranges of the thrust and torque were the smallest.

Array Radar Three-Dimensional Forward-Looking Imaging Algorithm Based on Two-Dimensional Super-Resolution.

Radar imaging is a technology that uses radar systems to generate target images. It transmits radio waves, receives the signal reflected back by the target, and realizes imaging by analyzing the target's position, shape, and motion information. The three-dimensional (3D) forward-looking imaging of missile-borne radar is a branch of radar imaging.

Vegetation disturbance and regrowth dynamics in shifting cultivation landscapes.

Shifting cultivation, an age-old agricultural practice, is a major factor in forest cover change across Southeast Asia, where repeated cycles of vegetation disturbance and regrowth lead to far-reaching environmental and socio-economic impacts. The present study aims to assess the spatio-temporal patterns of vegetation disturbance and regrowth caused by shifting cultivation in Tripura state of India, over the past three decades, utilizing temporal segmentation of time-series Landsat data. The study analyzed vegetation disturbance and regrowth patterns in a shifting cultivation landscape from 1991 to 2020 using normalized burn ratio trends through LandTrendr, validated by the TimeSync tool.

A method for evaluating ecological quality levels and trends using natural evolution time series.

Ecological quality assessment serves as a valuable tool for impartially measuring the effects of development and utilization activities on ecosystems, forming the bedrock of ecological governance. The ecological quality evaluation method, relying on a reference frame, takes into consideration the comparability of ecological quality, thus guaranteeing the presence of external benchmarks for the assessment results. However, present research on absolute assessment of ecological quality is limited, as the majority of existing studies focus on large-scale regions, and overlooking regional differences in smaller-scale regions to a certain extent.

Examining the e-cigarette scenario based on distribution, availability, marketing, and banning: A GIS-Based qualitative study in Bangladesh.

Background: Globally, over 81 million people use e-cigarettes, and the majority of them are young adults. Using e-cigarettes causes different types of adverse health effects both in adults and elderly people. Over time, using e-cigarettes has detrimental consequences on lung function, brain development and numerous other illnesses.

Improving global gross primary productivity estimation using two-leaf light use efficiency model by considering various environmental factors via machine learning.

Distinguishing gross primary productivity (GPP) into sunlit (GPP) and shaded (GPP) components is critical for understanding the carbon exchange between the atmosphere and terrestrial ecosystems under climate change. Recently, the two-leaf light use efficiency (TL-LUE) model has proven effective for simulating global GPP and GPP. However, no known physical method has focused on integrating the overall constraint of intricate environmental factors on photosynthetic capability, and seasonal differences in the foliage clumping index (CI), which most likely influences GPP estimation in LUE models.

Rice yield prediction through integration of biophysical parameters with SAR and optical remote sensing data using machine learning models.

In an era marked by growing global population and climate variability, ensuring food security has become a paramount concern. Rice, being a staple crop for billions of people, requires accurate and timely yield prediction to ensure global food security. This study was undertaken across two rice crop seasons in the Udham Singh Nagar district of Uttarakhand state to predict rice yield at 45, 60 and 90 days after transplanting (DAT) through machine learning (ML) models, utilizing a combination of optical and Synthetic Aperture Radar (SAR) data in conjunction with crop biophysical parameters.

Artificial intelligence for geoscience: Progress, challenges, and perspectives.

This paper explores the evolution of geoscientific inquiry, tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence (AI) and data collection techniques. Traditional models, which are grounded in physical and numerical frameworks, provide robust explanations by explicitly reconstructing underlying physical processes. However, their limitations in comprehensively capturing Earth's complexities and uncertainties pose challenges in optimization and real-world applicability.

Estimation of the crop evapotranspiration for Udham Singh Nagar district using modified Priestley-Taylor model and Landsat imagery.

The main challenges for utilizing daily evapotranspiration (ET) estimation in the study area revolve around the need for accurate and reliable data inputs, as well as the interpretation of ET dynamics within the context of local agricultural practices and environmental conditions. Factors such as cloud cover, atmospheric aerosols, and variations in land cover pose challenges to the precise estimation of ET from remote sensing data. This research aimed to utilize Landsat 8 and 9 datasets from the 2022-23 period in the Udham Singh Nagar district to apply the modified Priestley-Taylor (MPT) model for estimating ET.

Monthly electricity consumption data at 1 km × 1 km grid for 280 cities in China from 2012 to 2019.

High spatio-temporal resolution estimates of electricity consumption are essential for formulating effective energy transition strategies. However, the data availability is limited by complex spatio-temporal heterogeneity and insufficient multi-source feature fusion. To address these issues, this study introduces an innovative downscaling method that combines multi-source data with machine learning and spatial interpolation techniques.

The comparison of gut microbiota between wild and captive Asian badgers (Meles leucurus) under different seasons.

The gut microbiota plays an important role in the immunology, physiology and growth and development of animals. However, currently, there is a lack of available sequencing data on the gut microbiota of Asian badgers. Studying the gut microbiota of Asian badgers could provide fundamental data for enhancing productivity and immunity of badgers' breeding, as well as for the protection of wild animals.

Contrasting nature of aerosols over South Asian cities and its surrounding environment.

Cross-country assessment of aerosol loading was made over several South Asian megacities using multiple high-resolution remote-sensing database to assess how aerosols vary within the city and its suburbs. Parameters sensitive to aerosol optical and microphysical properties were processed over city-core and its surrounding, separated by a buffer. Cities across the Indo-Gangetic Plain (IGP; AOD:0.

SCAG: A Stratified, Clustered, and Growing-Based Algorithm for Soybean Branch Angle Extraction and Ideal Plant Architecture Evaluation.

Three-dimensional (3D) phenotyping is important for studying plant structure and function. Light detection and ranging (LiDAR) has gained prominence in 3D plant phenotyping due to its ability to collect 3D point clouds. However, organ-level branch detection remains challenging due to small targets, sparse points, and low signal-to-noise ratios.

An Aeromagnetic Compensation Strategy for Large UAVs.

Aeromagnetic surveys are widely used in geological exploration, mineral resource assessment, environmental monitoring, military reconnaissance, and other areas. It is necessary to perform magnetic compensation for interference in these fields. In recent years, large unmanned aerial vehicles (UAVs) have been more suitable for magnetic detection missions because of the greater loads they can carry.

Improving rapeseed carbon footprint evaluation via the integration of remote sensing technology into an LCA approach.

Agricultural carbon footprint (CF) evaluation plays an important role in climate change mitigation and national food security. Many studies have been conducted worldwide to evaluate the CF of rapeseed and its byproducts; however, only a few of these studies have considered finer-scale spatial-temporal heterogeneity. Considering the advantages of using detailed crop information extracted by remote sensing (RS) techniques, we attempted to integrate RS into life cycle assessments to improve rapeseed CF evaluation.

Validation and refinement of cropland map in southwestern China by harnessing ten contemporary datasets.

Accurate cropland map serves as the cornerstone of effective agricultural monitoring. Despite the continuous enrichment of remotely sensed cropland maps, pervasive inconsistencies have impeded their further application. This issue is particularly evident in areas with limited valid observations, such as southwestern China, which is characterized by its complex topography and fragmented parcels.

Functional near-infrared spectroscopy-based neurofeedback training regulates time-on-task effects and enhances sustained cognitive performance.

Time-on-task effect is a common consequence of long-term cognitive demand work, which reflects reduced behavioral performance and increases the risk of accidents. Neurofeedback is a neuromodulation method that can guide individuals to regulate their brain activity and manifest as changes in related symptoms and cognitive behaviors. This study aimed to examine the effects of functional near-infrared spectroscopy-based neurofeedback training on time-on-task effects and sustained cognitive performance.