High-resolution TIR data are essential for a wide range of studies, e.g., in environmental monitoring, urban effects, water stress, agricultural productivity, and land/water resources. Landsat series provides high-resolution TIR data, but data accuracy must be ensured to contribute to these fields. After the calibration problems of Landsat 8 (L8) TIRS due to a stray light effect, efforts were made

Interannual variations in the end of the growing season (EOS) play a crucial role in assessing carbon and energy cycling within grassland ecosystems. Previous studies have often fixed the trend breakpoint in autumn phenology around the year 2000 to examine the response of the vegetation EOS to long-term climate change. However, the asymmetry of climate change and the diversity of grass species may

One of the primary applications of satellite Land Surface Temperature (LST) observations lies in their utilization for modeling of actual evapotranspiration (ET) in agricultural crops, with the primary goals of monitoring and enhancing irrigation practices and improving crop water use productivity, as stipulated by Sustainable Development Goal (SDG) indicator 6.4.1. Evapotranspiration is a complex

Collaboration between Light Detection and Ranging (LiDAR) point clouds and deep learning has been proven to be an effective approach for vegetation mapping. Current studies have predominantly focused on 2D vegetation mapping, whereas 3D mapping, which directly classifies point clouds at point level, offers a more comprehensive understanding of the stratified structural information of vegetation. However

Climate-driven permafrost degradation and an intensification of the hydrological cycle are rapidly altering the intricate ecohydrological processes of Arctic wetlands, threatening their long-term carbon sequestration capabilities. Addressing this concern through effective management holds immense potential for climate regulation, mitigation, and adaptation efforts. As such, there is growing need for

To address the difficulty of individual vine-plant segmentation in large orchards, this study proposes a segmentation model for individual canopies that combines the characteristics of tree deciduousness and natural growth patterns. This enables the landscape mapping of orchards under intensive planting conditions. In this study, winter and summer unmanned aerial vehicle (UAV) remote-sensing images

Over the last two decades, Semarang, located in the central region of Java island, Indonesia, has been undergoing significant land subsidence. Mainly attributed to reservoir compaction and fluid extraction, this subsidence has pronounced effects in coastal regions. However, the existing study on the details of subsidence and its application to accurately depict present circumstances has been insufficient

Multi-object tracking (MOT) systems typically rely on object detection results for tracking, so the accuracy of the MOT system is significantly affected by the error of the detector. Changes in error usually lead to unstable tracking. Regarding this problem, we proposed an adaptive MOT method based on detection confidence. At first, we use a simple data fusion method to combine the detection results

Ensemble-based change detection can improve map accuracies by combining information from multiple datasets. There is a growing literature investigating ensemble inputs and applications for forest disturbance detection and mapping. However, few studies have evaluated ensemble methods other than Random Forest classifiers, which rely on uninterpretable “black box” algorithms with hundreds of parameters

With the fast development of 3D data acquisition techniques, topographic point clouds have become easier to acquire and have promoted many geospatial applications. Ground filtering (GF), as one of the most fundamental and challenging tasks for the post-processing of large-scale topographic point clouds, has been extensively studied but has yet to be well solved. To reveal future superior solutions

Sentinel−1 (S1) data enables effective monitoring of displacements using persistent scatterer interferometry (PSI). S1 includes VV and VH polarization channels, allowing us to apply polarimetric techniques to PSI. In short, polarimetric PSI (PolPSI) exploits the available polarization channels to enhance the identification and processing of measurement points including persistent scatterers (PS) and

Monitoring vertical land motion (VLM) along coastlines, which influences the dynamics of sea level changes in relation to the land, is a challenging task due to its inherent high spatiotemporal variability and limited availability of observations. This study aimed to investigate the rates, patterns, and drivers of land subsidence near the coastal town of San Leon, TX, United States, since the 1990s

High-resolution soil moisture (SM) products are crucial for managing water in agricultural regions, irrigation scheduling, and land–atmosphere model simulations. Most satellite-based SM products have spatial resolutions in the tens of kilometers, which cannot satisfy the aforementioned applications, and there is an urgent need to disaggregate them to fine spatial resolutions. Currently, disaggregation

The registration of multimodal satellite images is essential for a prerequisite for accruing complementary observational data. Nevertheless, the differential imaging nuances amongst non-linear radiometric multimodal images precipitate a complexity in keypoint detection, rendering it a great challenge. This complexity exacerbates the difficulty encountered in matching multimodal satellite images. In

In dynamic coastal areas where rapid changes in the seabed can have a significant impact on day–to–day operations, as is the case of Figueira da Foz harbor inlet, it is important to have updated knowledge of the bathymetry. As in energetic wave conditions it is not possible to carry out traditional bathymetric surveys, this work proposes a methodology that allows, using UAVs, to estimate the bathymetry

The 3D real scene model of high-steep rock slope established based on UAV image provides convenience for non-contact identification and interpretation of rock mass structures. The quality of 3D models directly influences the interpretability of rock mass structures. However, quantitative studies on the relationship between these two aspects are scarce. Therefore, this study investigates the influence

Researchers and engineers have increasingly used Deep Learning (DL) for a variety of Remote Sensing (RS) tasks. However, data from local observations or via ground truth is often quite limited for training DL models, especially when these models represent key socio-environmental problems, such as the monitoring of extreme, destructive climate events, biodiversity, and sudden changes in ecosystem states

The invasion of Spartina alterniflora has posed significant threats to the ecosystem health and biodiversity in coastal wetlands in China. In recent years, China has enacted large-scale S. alterniflora removal projects. Accurate monitoring of the S. alterniflora removal dynamics is crucial for evaluating the effectiveness of the removal projects and coastal wetland management. In this study, we presented

Hyperspectral and multispectral remote sensing image fusion (HMIF) can effectively enhance image spatial-spectral information representation. However, existing deep learning algorithms usually use neighboring pixels for interpolation in the sampling process, which ignores the correlation of different receptive field pixels. To solve the above problem, this study proposes an adaptive multi-perceptual

The ongoing full-scale Russian invasion of Ukraine has led to widespread damage of agricultural lands, jeopardizing global food security. Timely detection of impacted fields enables quantification of production losses, guiding recovery policies and monitoring military actions. This study presents a robust methodology to automatically identify agricultural areas damaged by wartime ground activities

The outbreak and spread of COVID-19 remind us again of the devastating attack that human-to-human transmitted respiratory infectious diseases (H-HRIDs) bring to global economics and public health. Predicting the trend of H-HRIDs yields important suggestions for making response strategies. Existing methods predict the future trend of H-HRIDs based on the prophase epidemiological data. However, it is

Most urban applications necessitate building footprints in the form of concise vector graphics with sharp boundaries rather than pixel-wise raster images. This need contrasts with the majority of existing methods, which typically generate over-smoothed footprint polygons. Editing these automatically produced polygons can be inefficient, if not more time-consuming than manual digitization. This paper

Urban trees mitigate urban heat by altering evapotranspiration processes and providing shade to their surrounding environment. Nevertheless, the impact through which three-dimensional tree characteristics alleviate Land Surface Temperature (LST) remain uncertain, especially for climatic zone differences. In this study, we investigated the potential of trees to mitigate LST in 35 Chinese major cities

Having a complete and high-quality geospatial catalogue of existing large-scale photovoltaic (PV) panels is very important nowadays, due to the rapid increase in the use of this type of installations. This catalogue could be used to estimate, with a higher level of granularity, the energy that can be produced from solar radiation forecasts, to operate the electricity system efficiently by adjusting

Accurately identifying and systematically mapping winter-time cover crops and their phenological characteristics offer significant benefits to agricultural producers and policymakers, as cover crops are one of several potential solutions to climate change mitigation. We present a methodological framework for identifying and mapping the presence of winter-time cover crops at the field level and aggregated

Marginal agricultural lands are considered unsuitable for conventional crop production but could be utilized for biofuel production, groundwater recharge, afforestation, and other land use purposes. Effective management of these lands requires accurate identification and understanding of their spatial distribution. We used random forest regression (RFR) with remotely sensed vegetation indices, soil

Wildfires have long been a danger to the atmosphere and ecological environment. With the advancement of deep learning technology and sensor equipment, wildfire identification methods based on optical (RGB) or thermal infrared (TIR) images have made significant progress. However, single-modal identification technologies often experience serious performance degradation in challenging scenarios. Therefore

Seeking a low-cost, high-efficiency greenhouse mapping technology has immense significance. While greenhouse extraction methods using deep learning have been proposed, the challenge of extracting dense small objects remains an unresolved problem. The inherent downscaling strategy in general-purpose semantic segmentation (SS) models renders them unsuitable for such tasks. In contrast, the dramatically

Despite utilizing various remote sensing datasets, precise tree-cutting detection remains challenging due to spatial and spectral resolution constraints in satellite imagery, complex landscapes, data integration issues, and the need for accurate multi-temporal reference datasets. This study investigates the utilization of PlanetScope (PS) satellite images, along with pixel-based (PBIA) and object-based

Pine shoot beetle (PSB) is one of the most damaging forest insects of Yunnan pine plantations in southwest China. However, the subtle symptoms of heterogeneous tree crowns make it difficult to accurately detect at early stage of PSB attack. Here, we evaluated the potential of a combination of plant traits (PTs) and vegetation indices (VIs) to distinguish different levels of tree damage by integrating

This study focuses on the development of a new framework for evaluating bikeability in urban environments with the aim of enhancing sustainable urban transportation planning. To close the research gap that previous studies have disregarded the dynamic environmental factors and trajectory data, we propose a framework that comprises four sub-indices: safety, comfort, accessibility, and vitality. Utilizing

Accurate individual tree reconstruction based on laser point clouds is vital for precise biomass estimation, virtual geographic environment modeling, and simulation. However, mobile laser 3D scanning systems often capture tree point clouds obscured by leaves, resulting in missing or incomplete branch data, posing significant challenges to detailed tree reconstruction. In this paper, a novel method

Satellite radar imaging has been used as a remote sensing tool for studying Earth’s surface. High spatial resolution achieved by Synthetic Aperture Radar (SAR) allows to classify landcover and, using radar interferometry, to measure topography and surface deformation at millimeter scale. However, the handling of voluminous SAR data has presented a significant challenge over the past decade, leading

Geological remote sensing interpretation (GRSI), which aims to recognize multiple geological elements based on their characteristics on satellite remote sensing images, is vital in large-scale regional lithological mapping. However, due to the influence of long-term geological movements, the spatial distribution of geological elements (such as lithology, glaciers, and soils) on the image is often complex

Mapping large-scale coastal subsidence is significant in providing valuable support to decision-making stakeholders to recognize impacts of potential natural disasters. However, this task presents significant challenges due to its highly complex nature of the spatial–temporal variability. Recent advances in the observation capability of synthetic aperture radar (SAR) missions and the processing algorithms

The p-Center Problem (PCP) is a classical discrete facility location problem (FLP) with broad real-world application scenarios, such as public facilities and urban emergency services facilities. Most of the current methods for solving PCP are exact and heuristic. However, as the problem conditions are added or the problem scale is increased, the complexity of exact and heuristic methods for solving

Extreme wildfires are a major agent of disturbance in present-day forest ecosystems and, have serious impacts on society in terms of human casualties and economic losses. Prioritizing and implementing relief and recovery measures after fires require explicit spatial information on the disaster extent. Information for detecting and monitoring ecosystem disturbance due to wildfires is obtainable using

The relationship between the built environment and human perception of safety is well recognised in a growing literature of urban studies. However, there is a lack of attention to gender differences in perceptions of place, particularly in studies that assess perceived safety using street view images (SVIs). This limitation hinders the comprehensive assessment of safety perceptions. Traditional analyses

Segmentation is an essential step for remote sensing image processing. This study aims to advance the application of the Segment Anything Model (SAM), an innovative image segmentation model by Meta AI, in the field of remote sensing image analysis. SAM is known for its exceptional generalization capabilities and zero-shot learning, making it a promising approach to processing aerial and orbital images

Most multi-layer natural forest structures, usually containing obvious vertical structures including overstory, understory, and grass, show significant differences in structure, phenology variations, and photosynthetic capacity. However, it is still challenging to extract the waveforms of overstory and understory in forests with varied canopy covers and topographic conditions using the Global Ecosystem

Accurate landslide detection is essential for disaster mitigation and relief. In this study, we develop a feature enhancement framework that integrates attention and multiscale mechanisms with U-Net (AMU-Net) for landslide detection. The framework has four steps. First, the attention module in the convolutional block enhances the feature response of landslides when extracting high-level feature representations

Forest height is of great significance for forest resource management and forest carbon sink estimation. Tomographic synthetic aperture radar (TomoSAR) technology provides an effective means for the accurate inversion of this parameter. Several multi-polarization synthetic aperture radar (SAR) images are generally required to obtain forest height. However, it is common that only a small number of single-polarization

Glasgow, Scotland, United Kingdom, has long-term issues with inequalities in health and food security, as well as large areas of vacant and derelict land. Urban agriculture projects can increase access to fresh food, improve mental health and nutrition, and empower and bring communities together. We investigated the distribution of urban agriculture in Glasgow and found that the current configuration

Knowledge distillation is one effective approach to compress deep learning models. However, the current distillation methods are relatively monotonous. There are still rare studies about the combination of distillation strategies using multiple types of knowledge and employing multiple teacher models. Besides, how to optimize the weights among different teacher models is still an open problem. To address

The wildland urban interface (WUI) is a transition zone between mostly undeveloped, vegetated lands and more densely populated areas with buildings and other infrastructure. This interface is of great interest and concern in wildfire prone areas like California, as significant increases in scale, frequency and intensity of fires have resulted in devastating impacts to life-safety, property and other

Detecting gas flaring activities during oil production on a regional scale is necessary, since it emits harmful gases and bring serious global environmental impacts. This study developed a new algorithm to detect gas flare sites (GFs) on daytime Sentinel-2 MSI images and Nighttime NOAA-20 VIIRS images. The algorithm includes three steps: Thermal Anomaly Index (TAI) on single-temporal MSI images was

Surface water mapping based on historical, neighbourhood, and other priori information has shown improved accuracy. However, the accuracy can be compromised due to the lack of consideration for water dynamics in the proximity period and the limited utilization of quantitative methods for integrating multiple types of priori information. In this study, an unsupervised surface water extraction method

The increasing warming and humidification have caused the dramatic degradation of Qinghai-Tibet Plateau permafrost. The active layer thickness (ALT) is particularly crucial to be monitored with a wide range as an indispensable variable to characterize permafrost status. Interferometric synthetic aperture radar (InSAR) technology has recently been widely used for ALT retrieval. However, these studies

Precision agriculture management with remote sensing big data provides a promising solution to monitor crops. Alfalfa is an important forage crop for various livestock around the world. Unlike corn and soybean, alfalfa growth is difficult to describe using a typical phenological curve since it is characterized by monthly harvest and rapid regrowth. Limited by the availability of high spatio-temporal

Timely and accurately predicting maize grain yields will contribute to making adaptive measures to improve management practice and to adjust consumption patterns for ensuring food security. Unmanned aerial vehicles (UAV) are widely used to obtain high-temporal and high-spatial resolution remote sensing images of crops, enabling a possible sensor performance comparison. To date, few studies have compared

Crack detection plays a pivotal role in civil engineering applications, where vision-based methods find extensive use. In practice, crack images are sourced from Unmanned Aerial Vehicles (UAV) and handheld photography, and the balance between the utilization of global and local information is the key to detecting cracks from images of different sources: the former tends to eliminate interferences with

Geo-simulation experiments (GSEs) are experiments allowing the simulation and exploration of Earth’s surface (such as hydrological, geomorphological, atmospheric, biological, and social processes and their interactions) with the usage of geo-analysis models (hereafter called ‘models’). Computational processes represent the steps in GSEs where researchers employ these models to analyze data by computer

In global navigation satellite system-denied environments, indoor point-cloud maps are valuable sources of detailed and inaccessible information. Dense visual point clouds can be produced quickly and inexpensively using stereo RGB cameras and simultaneous localization and mapping (SLAM) techniques. However, visual point-cloud mapping is affected by complex visual observation conditions, and a lack

Aquaculture, a crucial component of global food production, faces sustainability challenges due to increased competition for natural resources, environmental pollution, and subpar management practices. Understanding spatial–temporal changes in aquaculture distribution can offer insights into guiding its scientific and sustainable development. While Southeast Asia constitutes a key player in global