Long historical records and free accessibility have made Landsat data valuable for time-series analysis. However, Landsat time-series analysis is restricted for coastal areas due to the lack of suf...

This paper presents new methods to analyze a category’s change through a time series of maps, even when the time intervals have inconsistent durations. The methods include an option to facilitate c...

Most of our knowledge about the Amazon River plume is limited from the field experiments or modeling due to various challenges for satellite remote sensing, i.e., constant cloud coverage, atmospher...

A landslide inventory is essential for numerous applications, including disaster prevention and mitigation. The emergence of advanced satellite technologies and the proliferation of extensive satel...

Offshore wind power is a crucial clean energy source for coastal countries and advances blue economies. Accurate spatial mapping of offshore wind turbines supports energy assessment and the sustain...

Urban functional zones (UFZs) serve as the spatial carriers embodying urban economic and social activities, thus making the accurate mapping of UFZs imperative for urban planning, management, and s...

Developing a precise and interpretable spatiotemporal model is need for establishing evidence-based adaptation strategies on climate change-driven disasters. This study introduced a diagnostic pred...

Invasive plants are threats to biodiversity, ecosystem functioning, and services. Previous studies have reported that the impacts of biological invasions on ecosystem characteristics can be scale- ...

Submersed aquatic plant (SAP) communities are important determinants of estuarine and lacustrine food web structure, nutrient cycling, and water quality. Variation in water quality, SAP community c...

Mining latent information from human trajectories for understanding our cities has been persistent endeavors in urban studies and spatial information science. Many previous studies relied on manual...

Accurate crop mapping provides important information for government decision-making and agricultural management. Recent advancements in deep learning have significantly enhanced the capabilities of...

Forest canopy height (FCH) is one of the most important variables for carbon stock estimation. While many studies have focused on extracting FCH from spaceborne LiDAR in regions with spatially cont...

Managing total suspended solids (TSS) in inland water is crucial for maintaining water quality and aquatic ecosystems. To obtain spatial data, the conventional method of point sampling TSS is a tim...

Woody plant encroachment (WPE), a global phenomenon documented across various biomes and continents, has the potential to significantly impact ecosystem carbon and water cycling. However, the impac...

Negative externalities stemming from overtourism have been widely reported in the literature. However, a consequence less explored in scientific research is the increase in crime rates derived from...

Exposure to ambient fine particulate matter (PM2.5) poses a significant global health challenge. However, a major obstacle for epidemiological studies and risk assessment lies in the absence of hig...

The Global Precipitation Measurement (GPM) Microwave Imager (GMI) is a microwave (MW) radiometer that has near-global coverage and frequent revisit time. To date, operational total column water vap...

Drought ranks among the costliest of all climate-related phenomena and manifests in various forms, posing significant challenges in understanding its influence on agriculture and natural ecosystems...

Reliable classification results are crucial for guiding agricultural production, forecasting crop yield, and ensuring food security. Generating reliable classification results is relatively simple ...

The dynamic changes in grassland types are crucial for conserving grassland biodiversity, conducting comprehensive health assessments, and gaining insights into ecosystem evolution. However, accura...

North China is one of China’s most severely warming and drying regions, and understanding the dynamics of forest carbon sinks in North China is critical for forest management in response to climate...

Effectively identifying urban polycentric spatial structure (UPSS) is essential for data-driven evaluation of urban performance, and it serves as a scientific basis for urban spatial planning. Howe...

Anthropogenic activities have significantly altered land cover on a global scale. These changes often have a negative effect on biodiversity limiting the distribution of species. The extent of the ...

Deep-learning-based change detection methods have received wide attention, thanks to their strong capability in obtaining rich features from images. However, existing AI-based change detection (CD)...

Effective planning for natural resource management and wildlife conservation requires detailed information on vegetation structure at landscape scales and how structure is influenced by land-use pr...

Owing to the vast development of Synthetic Aperture Radar (SAR), especially the improvement of spatio-temporal resolution, observing and quantifying the complex and dynamic flood process becomes in...

Detailed and accurate statistics on crop productivity are key to inform decision-making related to sustainable food production and supply ensuring global food security. However, annual and high-res...

Desertification is a form of land degradation observed in arid, semiarid, and dry subhumid ecosystems. Assessing the global trends and drivers of desertification in arid land is crucial for develop...

With temperatures in Central Asia (CA) increasing more than the global average, this region is one of the global hotspots affected by climate change. CA is mostly characterized by arid climate, whi...

Timely and accurate mapping of paddy rice cultivation based on remote sensing technology is crucial and valuable for ensuring food security and sustainable environmental management. In most relevan...

Time series analysis of medium-resolution multispectral satellite imagery is critical to investigate forest disturbance dynamics at the landscape scale. In particular, the spatial, temporal, and ra...

Piñon-juniper (PJ) woodlands are an expansive and dynamic dryland ecosystem in the US that encompass a wide range of spatial, temporal, and ecological diversity. The dynamism of PJ woodland extent ...

Artificial intelligence (AI) began to make its way into geoinformation science several decades ago and since then has constantly brought forth new cutting-edge approaches for diverse geographic use...

This study aims to reveal the relationships between understory coverage (principally, weed growth) beneath the canopies of Sumatran eucalyptus plantations and L-band backscatter data gathered by th...

Recent advances in deep learning have brought new opportunities for analyzing land dynamics, and Recurrent Neural Networks (RNNs) presented great potential in predicting land-use and land-cover (LU...

Deep convolutional neural networks (DCNNs) have been successfully used in semantic segmentation of high-resolution remote sensing images (HRSIs). However, this task still suffers from intra-class i...

Annual crop monitoring is a key parameter for managing agricultural strategies. Several studies have relied on remote sensing products such as the normalized difference vegetation index (NDVI) as a...

Satellite ocean transparency data have low spatial coverage due to cloud shading, sun glint, swath width, and temporal revisit. Merging multiple satellite ocean transparency data can improve spatia...

Published in GIScience & Remote Sensing (Vol. 61, No. 1, 2024)

With hourly measurements available during daytime between local times of 09:00–16:00, ocean color data derived from the Geostationary Ocean Color Imager (GOCI) onboard the Korean Communication, Oce...

Collecting reliable training samples plays a crucial role in improving the accuracy of land cover (LC) mapping products, which are essential foundational data for global environmental and climate c...

Climate change has contributed to the recent increase in wildfire occurrences, vegetation failures, human health risks, physical damage, and economic losses. Wildfire susceptibility mapping is an e...

Flower-visiting insects that are pollinators play a critical role in promoting biodiversity in agroecosystems and agricultural food production through their pollination ecosystem service. However, ...

The application of machine learning in crop yield prediction has gained considerable traction, yet uncertainties persist regarding the impact of the yield trends on these predictions and the differ...

Accurately and timely quantifying the dynamics of urban sprawl is essential for improving land use efficiency and land use planning. However, existing research mainly focused on the sprawl of a sin...

Global warming could affect vegetation growth, while land surface temperature has exhibited an asymmetric warming pattern over the past 50 years. The Tibetan Plateau, known as “the roof of the worl...

Wildlife surveys are key to assessing the health of global biodiversity. Traditional field and aerial methods however have significant limitations, including high costs, substantial time investment...

The difficult, time-consuming, and imbalanced acquisition of landslide inventories in complex and heterogeneous large areas often results in limited predictive performance for most statistical land...

The cellular automata (CA) model is the predominant method for predicting land use and land cover (LULC) changes. The accuracy of this model critically depends on well-defined transition rules, whi...

The foundational step in constructing a digital twin involves transforming the physical world into an immersive 3D virtual environment. Unmanned aerial vehicle (UAV)-based photogrammetry, mobile ma...

Accurate assessments of forest biomass carbon are invaluable for managing forest resources, evaluating effects on ecological protection, and achieving goals related to climate change and sustainabl...

The Blue Nile River is an indispensable source of livelihood for over 90 million people in Ethiopia, Sudan, and Egypt. Recently, Ethiopia has constructed the Grand Ethiopian Renaissance Dam (GERD),...

Industrial land status, encompassing quantity, distribution and efficiency, is pivotal in urban planning and sustainable development. However, obtaining detailed and up-to-date industrial land maps...

Monitoring rapidly changing inland water bodies using remote sensing requires a temporal resolution of at least 3 days and a spatial resolution of at least 30 meters. However, the current satellite...

In China, a nation facing farmland scarcity, accurate mapping of winter fallow arable lands is crucial for enhancing crop rotation and land use efficiency. The Dynamic Threshold (DT) method commonl...

With recent technological advances, the efficient extraction and utilization of valuable information from large-scale data sources have become increasingly important. The development of knowledge g...

To tackle the challenge of denoising spaceborne photon-counting laser altimeter point clouds with uneven noise density, this study proposes a denoising method based on adaptive parameter density cl...

The term “invasive noxious weed species” (INWS), which refers to noxious weed plants that invade native alpine grasslands, has increasingly become an ecological and economic threat in the alpine gr...

Optical remotely sensed time series data have various key applications in Earth surface dynamics. However, cloud cover significantly hampers data analysis and interpretation. Despite synthetic aper...

Geostationary satellites are valuable tools for monitoring the entire lifetime of tropical cyclones (TCs). Although the most widely used method for TC intensity estimation is manual, several automa...