ABSTRACT Airborne LiDAR has become an essential data source for large-scale, high-resolution modeling of forest aboveground biomass and carbon stocks, enabling predictions with much higher resolution and accuracy than can be achieved using optical imagery alone. Ground noise filtering – that is, excluding returns from LiDAR point clouds based on simple height thresholds – is a common practice meant

ABSTRACT This study examines the use of the Multi-Spectral Instrument (MSI) in Sentinel-2 satellite in combination with regression-based random forest models to estimate bathymetry along the extended southwestern Florida nearshore region. In this study, we focused on the development of a framework leading to a generalized Satellite-Derived Bathymetry (SDB) model applicable to an extensive and diversified

ABSTRACT Remote sensing has evolved as an alternative to traditional techniques in the spatio-temporal monitoring of the Antarctic ecosystem, especially with the rapid expansion of the use of Unmanned Aerial Vehicles (UAVs), providing a centimeter-scale spatial resolution. In this study, the potential of a high-spatial resolution multispectral sensor embedded in a UAV is compared to medium resolution

ABSTRACT In the research of automatic interpretation of remote sensing images, semantic segmentation based on deep convolutional neural networks has been rapidly developed and applied, and the feature segmentation accuracy and network model generalization ability have been gradually improved. However, most of the network designs are mainly oriented to the three visible RGB bands of remote sensing images

ABSTRACT Building detection from very high resolution (VHR) optical remote sensing images, which is an essential but challenging task in remote sensing, has attracted increased attention in recent years. However, despite the many methods that have been developed, an in-depth review of the recent literature on building extraction from VHR optical images is still lacking. In this article, we present

ABSTRACT Abstract: Rangelands have a dynamic response to climate change, fire, and other anthropogenic disturbances. The Rangeland Condition, Monitoring, Assessment, and Projection (RCMAP) product aims to capture this response by quantifying the percent cover of eight rangeland components, associated error, and trends across the western United States using Landsat from 1985 to 2020. The current generation

ABSTRACT Landslides are major geological hazards and frequently occur in mountainous areas with steep slopes, often causing significant loss. Interferometric Synthetic Aperture Radar (InSAR) has been widely used in landslide measurement over the last three decades. However, InSAR only can measure one-dimensional displacements (i.e. those in the radar’s line of sight (LOS) direction), resulting in the

ABSTRACT Snow plays a crucial role in the climate. In particular, snow controls the surface temperature and stabilizes the energy budget due to its spectral characteristics. However, snow can be affected by climate change. If the snow cover extent (SCE) diminishes due to global warming, the rate of introducing radiance to the surface increases because non-snow areas can absorb more solar radiance.

ABSTRACT Cropland retirement is a widespread phenomenon across the world. The conversion of inefficient cropland to forest or grassland is a policy aimed at restoring ecology, improving the environment, and promoting economic development. However, in most developing countries, the results of cropland retirement and land restoration are characterized by spatial fragmentation, and there are significant

ABSTRACT Surface urban heat island (SUHI) can considerably influence the urban environment and the quality of life. It is vital to examine how underlying surface properties impact seasonal and diurnal SUHIs. However, the influence of three-dimensional (3D) urban morphological parameters (UMPs) on SUHIs has not been thoroughly studied under varying climatic settings. To fill this knowledge gap, the

ABSTRACT Cropland products are of great importance in water and food security assessments, especially in South Asia, which is home to nearly 2 billion people and 230 million hectares of net cropland area. In South Asia, croplands account for about 90% of all human water use. Cropland extent, cropping intensity, crop watering methods, and crop types are important factors that have a bearing on the quantity

ABSTRACT Global land cover has undergone extensive and rapid changes as a result of human activities and climate change. These changes have had a significant impact on biodiversity, the surface energy balance, and sustainable development. Global land cover data underpins research on the development of earth system models, resource management, and evaluation of the ecological environment. However, there

ABSTRACT The last decade has seen considerable progress in scientific research on vegetation ecosystem services. While much research has focused on forests and wetlands, grasslands also provide a variety of different provisioning, supporting, cultural, and regulating services. With recent advances in remote sensing technology, there is a possibility that Earth observation data could contribute extensively

ABSTRACT Forest covers about one-third of the land area of the conterminous United States (CONUS) and plays an important role in offsetting carbon emissions and supporting local economies. Growing interest in forests as relatively cost-effective nature-based climate solutions, particularly restoration and reforestation activities has increased the demand for information on forest regrowth and recovery

ABSTRACT The Global Ecosystem Dynamics Investigation (GEDI), a new spaceborne LiDAR system of the National Aeronautics and Space Administration (NASA), has the potential to revolutionize global measurements of vertical vegetation structure. However, GEDI performance among different forest types and factors influencing GEDI performance needs to be evaluated against similar measurements from existing

ABSTRACT Crop calendars provide valuable information on the timing of important stages of crop development such as the planting or Start of Season (SOS) and harvesting dates or End of Season (EOS). This information is critical for many crop monitoring applications such as crop-type mapping, crop condition monitoring, and crop yield estimation and forecasting. Spatially detailed information on the crop

ABSTRACT Collaborative geographic analysis can lead to better outcomes but requires complicated interactions among participants, support resources and analytic tools. A process expression with explicit structure and content can help coordinate and guide these interactions. For different geographic problems, the structure and content of collaborative geographic analysis are generally distinct. Since

ABSTRACT Unmanned aerial vehicle (UAV) and satellite data have considerable complementarity for platform inter-operability, data fusion studies, calibration and validation efforts, and various multiscale analyses. To optimize cross-platform synergies between field-deployable UAV and space-based satellite systems, an understanding of spectral characteristics and compatibility is required. Here, we present

ABSTRACT Urban building extraction from high-resolution remote sensing imagery is important for urban planning, population statistics, and disaster assessment. However, the high density and slight boundary differences of urban building regions pose a great challenge for accurate building extraction. Although existing building extraction methods have achieved better results in urban building extraction

ABSTRACT Measuring tree structure using three-dimensional (3D) mapping tools such as light detection and ranging (LiDAR) remote sensing is needed to provide well-managed and designed green spaces. The metrics used to estimate tree structure could be different depending on which LiDAR systems are used. This may lead to confusion and reduce confidence when evaluating tree structures and their derived

ABSTRACT One of the most important challenges of agriculture today is increasing its productivity gains, while controlling its environmental footprint. Because of that plastic covered greenhouses (PCG) mapping via remote sensing is receiving a great attention throughout this century. In this study, a fair comparison was carried out in four PCG study areas around the world to test 14 spectral indices

ABSTRACT The urban development boundary (UDB) is a core issue in territorial spatial planning and remains a research focus in urban development. However, in the past, land use simulations of coastal cities have had some limitations. For example, new urban land outside the research boundary could not be effectively simulated, resulting in a large gap between the simulation results and reality. In this

ABSTRACT Geostationary meteorological satellites provide precipitation estimates with a high spatio-temporal resolution, which is important for near real-time precipitation monitoring. This study systematically evaluated geostationary orbit (GEO) satellite-based quantitative precipitation estimates (QPEs) from Chinese Fengyun-2 G (FY-2 G), Fengyun-4A (FY-4A), and South Korean Geo-KOMPSAT-2A (GK-2A)

ABSTRACT Aerosol loadings and their spatial distribution are among the most important atmospheric information needed for a range of applications such as air quality monitoring, climate research, and public health. A key measure of aerosol quantity is aerosol optical depth (AOD) and it has been routinely observed from space by Earth observing satellites/instrument, especially the Moderate Resolution

ABSTRACT The PlanetScope satellite constellation has over 130 Dove satellites running 24/7, which collect weekly and even daily images globally at 3–5 m resolution. It has global data coverage and high temporal resolution, which constitute the most attractive features for medium-resolution 3D reconstruction and change detection in remote sensing applications. One shortfall of the PlanetScope images

ABSTRACT Remotely sensed data are increasingly used together with National Forest Inventory (NFI) data to improve the spatial precision of forest variable estimates. In this study, we combined data from the 4th Spanish National Forest Inventory (SNFI-4) and from the 2nd nationwide Airborne Laser Scanning (ALS) survey to develop predictive forest inventory variables (total over bark volume (V), basal

ABSTRACT Changes in ice shelf dynamics significantly impact the discharge rate of grounded ice, sea ice formation, and marine environments. In particular, changes of a sub-shelf pinning point induce complex dynamics of an ice shelf. In this study, we investigated decadal changes (2010–2020) in the area, ice velocity, and grounding line position of Campbell Glacier Tongue (CGT) in East Antarctica, which

ABSTRACT Cellular automata (CA) model is a useful tool for simulating spatiotemporal changes of land-use evolution. However, previous CA models have usually ignored the inhomogeneous and mixed land-use situations and simplified the land grid cell as one single land-use type. This study developed a multi-label (ML) convolutional neural network CA (ML-CNN-CA) model to simulate the complex evolution of

ABSTRACT Material exchange between oceans and currents is crucial for biogeochemical processes in marginal seas, such as the Kuroshio intrusion in the Luzon Strait, which contributes to nutrient and phytoplankton growth in the adjacent water. Therefore, it is necessary to understand changes in major current systems and their possible effects on marine ecology. In this study, we applied an edge-detection

ABSTRACT Stingless/meliponine bees are eusocial insects whose polylactic nature enables interaction with a wide variety of wild plants and crops that enhance pollination and, hence, support ecosystem services. However, their true potential regarding pollination services and honey production is yet to be fully recognized. Worldwide, there are over 800 species of meliponine bees, with over 20 species

ABSTRACT Satellite-based aerosol optical depth (AOD) retrieval products are essential in air pollution and climate change research. Unfortunately, cloud contaminations and unfavorable surface conditions result in a considerable proportion of missing AOD data. Numerous studies have been conducted to reconstruct large-scale AOD data gaps by utilizing adjacent spatiotemporal information or modeling AOD

ABSTRACT Satellite sensors usually compromise between their spatial and spectral resolutions due to the limitations of data volume and signal-to-noise ratio, such as the Very High spatial Resolution (VHR) PlanetScope (PS, four or five 3-m bands) and medium spatial resolution (med-resolution) Sentinel-2 (S2, ten 10-m or 20-m bands) constellations. Concomitant with the growing demand for satellite images

ABSTRACT Although remote sensing techniques have been used to monitor toxic cyanobacteria with hyperspectral data in inland water, it is difficult to optimize conventional bio-optical algorithms for individual water bodies because of the complex optical properties of various water components. Therefore, this study adopted a spatial attention convolutional neural network (spatial attention CNN) to estimate

ABSTRACT Lidar has been regarded as the most accurate data source for forest-growing stock volume (FGSV) estimation, but inconsistent acquisition dates of lidar data with field survey often result in poor FGSV estimation accuracy. Spaceborne stereo imagery is captured at regular intervals, providing new opportunities for mapping and updating FGSV spatial distributions. Digital Surface Model derived

ABSTRACT Land surface curvature (LSC) is a basic attribute of topography and influences local effects of gravitational energy and surface material transport. However, the calculation of LSCs based on triangulated irregular networks (TINs) has not been fully studied, which restricts further geoscience studies based on TIN digital elevation models (DEMs). The triangular facets and vertices of a TIN are

ABSTRACT The rapid increase in large-scale photovoltaic installations, or solar parks, causes a need to monitor their amount and allocation, and assess their impacts. While their spectral signature suggests that solar parks can be identified among other land covers, this detection is challenged by their low occurrence. Here, we develop an object-based random forest (RF) classification approach, using

ABSTRACT Management intensity deeply influences meadow structure and functioning, therefore affecting grassland ecosystem services. Conservation and management measures, including European Common Agricultural Policy subsidies, should therefore be based on updated and publicly available data about management intensity. The mowing frequency is a crucial trait to describe meadows management intensity

ABSTRACT Deep learning models have been extensively employed to simulate dynamic Land Use Changes (LUC) at the regional level. However, existing studies were found to be limited by the inadequate extraction of Spatiotemporal Neighborhood Features (SNF) and ignored obvious Long-Term Dependence (LTD) in time series data, which low simulation accuracies. Consequently, it remains to be explored whether

ABSTRACT In this investigation, optical (SPOT-7 NAOMI), airborne LiDAR, and PolInSAR L-band data, along with forest inventories, were employed to develop models for estimating total dry aboveground biomass (AGB) over the tropical forests in the Congo Basin (Gabon) of Central Africa. Remote sensing-based variables like texture (from SPOT), median canopy height (from LiDAR), and backscattering coefficient

ABSTRACT Reconstructing the missing data for cloud/shadow-covered optical satellite images has great significance for enhancing the data availability and multi-temporal analysis. In this study, we proposed a deep-learning-based method for cloud/shadow-covered missing data reconstruction for time-series Landsat images. Central to this method is the combined use of autoencoder, long-short-term memory

ABSTRACT The spatiotemporal inhomogeneity of the total column NO2 amounts (TCN) in the Seoul Metropolitan Area (SMA), Korea, was quantitatively assessed through year-round (October 2019–May 2021) TROPOMI and ground-based Pandora measurements. The average TCN over the SMA was comparable to that of major Chinese megacities, being consistently high (> 0.8 DU; Dobson Unit) during the daytime (10–17 local

ABSTRACT Displacement time series (TS) provides temporal and spatial information related to ground deformation. This study aims to investigate temporal behavior of ground deformation TS, including classification of displacement trends and periodicity evaluation, which ease the interpretation of movements. To this end, we propose several modifications to an existing automatic classification workflow

ABSTRACT Phenological changes caused by urbanization may provide evidence of how vegetation responds to global warming. However, the phenological characteristics of vegetation in metropolitan areas have been poorly studied, especially in terms of spatiotemporal variations. In this study, we explored the applicability of Local Climate Zones (LCZs) to investigate the spatiotemporal variations in urban

ABSTRACT Convolutional neural networks (CNN) provide state-of-the-art performance in many computer vision tasks, including those related to remote-sensing image analysis. Successfully training a CNN to generalize well to unseen data, however, requires training on samples that represent the full distribution of variation of both the target classes and their surrounding contexts. With remote sensing

ABSTRACT The Aral Sea, once the fourth largest freshwater lake on Earth, has lost circa 90% of its original water surface in 1960. Maps of different land cover categories provide a suitable baseline to plan and implement effective measures to combat ongoing desertification, such as reforestation of dried out Aral Sea soils. In this study, we used satellite-based remote sensing data and applied a machine

ABSTRACT Drought is a natural disaster that occurs globally and is a main trigger of secondary environmental and socio-economic damages, such as food insecurity, land degradation, and sand-dust storms. As climate change is being accelerated by human activities and environmental changes, both the severity and uncertainties of drought are increasing. In this study, a diagnostic drought prediction model

ABSTRACT Population distribution is the most direct indicator used to describe human activities. Grid-based population distribution maps overcome the drawbacks of statistical data and are thus more suitable for integrated analysis with environmental data. However, current modeling methods seeking to improve accuracy ignore the role of many existing products, resulting in the ineffective use of advantageous

ABSTRACT As a powerful predictive technique based on machine learning, the maximum entropy (MaxEnt) model has been widely used in geographic modeling. However, its performance in calibrating cellular automata (CA) for urban growth simulation has not been investigated. This study compares the MaxEnt model with logistic regression (LR), artificial neural network (ANN), and support vector machine (SVM)

ABSTRACT Marine debris significantly influences the environment and economics of marine ecosystems. Measures to ban illegally discarded materials using monitoring techniques are expected to mitigate marine debris mismanagement. For example, derelict oyster farming rafts along the southwestern coast of Taiwan have been a source of marine debris pollution since the 1980s. An efficient inspection system

ABSTRACT It can be challenging to fuse remotely-sensed images with large differences in spatial resolutions. In this paper, we used additive wavelet transform (AWT) to fuse Landsat-8 (30 m) and unmanned aerial vehicle (UAV) images (7 cm and 3.7 cm for thermal and multispectral images, respectively) as one of the primary studies. AWT image fusion generated sharpened Landsat-8 (L-8) images which were

ABSTRACT Dense shale oil and gas extraction activities have a long history in the Marcellus Shale and the investigation of their evolution draws increasing interest. The spatiotemporal analysis of these shale oil and gas extraction activities is essential to understand their evolution in space-time, and important for the estimation of their historical impacts on local environments and communities.

ABSTRACT Investigating vegetation–climate interactions is critical for understanding behavioral patterns of terrestrial ecosystems and formulating food security strategies. However, the multi-spatiotemporal legacy effects of climate on terrestrial vegetation remain unclear. In this study, we examined the dynamic trends of vegetation distribution and climatic factors at multiple temporal and spatial

ABSTRACT Rapid economic development and interference by human activities in rapid urbanization regions have caused great land use/land cover change (LUCC), which significantly affects ecosystem functions and services. It is crucial to assess the spatiotemporal evolution of ecosystem service value (ESV) in such regions, especially for the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) in China. In

ABSTRACT Historical maps are almost the exclusive source to trace back the characteristics of earth before modern earth observation techniques came into being. Processing historical maps is challenging due to the factors such as diverse designs and scales, or inherent noise from painting, aging, and scanning. Our paper is the first to leverage uncertainty estimation under the framework of Bayesian

ABSTRACT Attentions over the health of evergreen vegetation are increasing owing to frequently occurrence of recent disturbance events (i.e. soil erosion, logging activities, and afforestation). However, vegetation indices that characterize canopy greenness have limitations in spectral saturation for representing the growth states of densely vegetated areas, and the continuous acquisition of satellite-derived

ABSTRACT Accurately quantifying the aboveground biomass (AGB) of forests is crucial for understanding global change-related issues such as the carbon cycle and climate change. Many studies have estimated AGB from multiple remotely sensed datasets using various algorithms, but substantial uncertainties remain in AGB predictions. In this study, we aim to explore whether diverse algorithms stacked together

ABSTRACT The increasing amount of geotagged social media data provides a possible resource for location prediction. However, existing location prediction methods rarely incorporate temporal changes in mobility patterns, which could lead to unreliable predictions. In particular, human mobility patterns have changed greatly in the COVID-19 era. We propose a novel model to predict individuals’ movements

ABSTRACT Knowledge about the optical features of benthic objects is essential for quantifying spectral signatures, remote sensing-based mapping, and ecological monitoring in coral reefs. However, the spectral identification of benthic species and the accurate measurement of the in situ reflectance spectra of relevant research objects remain underexplored. An underwater radiation measuring system suitable

ABSTRACT Surface deformation data can be used to provide early warnings of geohazards and are useful in a variety of research fields. The Small BAseline Subset InSAR (SBAS-InSAR) boosts the data sampling rate and improves the accuracy of deformation extraction by restricting the temporal and spatial baselines. However, various factors, such as the types of ground objects and seasons, affect the coherence