Search Results (40)

Global High-Resolution Land Cover Maps (GHRLCs), characterized by spatial resolutions higher than 30 m per pixel, have become essential tools for environmental monitoring, urban planning, and climate modeling. Over the past two decades, new GHRLCs have emerged, offering increasingly detailed and timely representations of Earth’s surface. This review provides an in-depth analysis of recent developments by examining the data sources, methodologies, and validation techniques utilized in 19 global binary and multi-class land cover products. The evolution of GHRLC production techniques is analyzed, starting from the use of singular source input data, such as multi-temporal remotely sensed optical imagery, to the integration of satellite radar and other geospatial data. The article highlights significant advances in data pre-processing and processing, showcasing a shift from classical methods to modern approaches, including machine learning (ML) and deep learning techniques (e.g., neural networks and transformers), and their direct application on powerful cloud-computing platforms. A comprehensive analysis of the temporal dimension of land cover products, where available, is conducted, highlighting a shift from decadal intervals to production intervals of less than a month. This review also addresses the ongoing challenge of land cover legend harmonization, a topic that remains crucial for ensuring consistency and comparability across datasets. Validation remains another critical aspect of GHRLC production. The methods used to assess map accuracy and reliability, including statistical techniques and visual inspections, are briefly discussed. The validation approaches adopted in recent studies are summarized, with an emphasis on their importance in maintaining data integrity and addressing emerging needs, such as the development of common validation datasets. Ultimately, this review aims to provide a comprehensive overview of the current state and future directions of GHRLC production and validation, highlighting the advancements that have shaped this rapidly evolving field. Full article

Understanding microclimatic changes driven by urbanization is critical in the context of global warming and climate change. This study investigates the land surface temperature (LST), the normalized difference vegetation index (NDVI), and changes in land use types for 10 major cities across seven continents between 2001 and 2021. Utilizing MODIS satellite data processed on the Google Earth Engine (GEE) platform, the analysis focused on yearly median values to examine variations in LST during the day and night, as well as temperature dynamics across different land types, including vegetation and bare land. The global mean LST trend from 2001 to 2021, derived from Terra MODIS MOD11A2 data, was found to be 0.025 °C/year. The analysis of daytime and nighttime (nocturnal) land surface temperature (LST) trends across the ten cities examined in this study reveals notable variations, with most cities exhibiting an increasing trend in LST within urban mosaics. Airports exhibited a mean daytime land surface temperature (LST) that was 2.5 °C higher than surrounding areas, while industrial zones demonstrated an even greater temperature disparity, with an average increase of 2.81 °C. In contrast, cold spots characterized by dense vegetation showed a notable cooling effect, with LST differences reaching −3.7 °C. Similarly, proximity to water bodies contributed to temperature mitigation, as areas near significant water sources recorded lower daytime LST differences, averaging −4.09 °C. A strong negative correlation was found between NDVI and LST, underscoring the cooling effect of vegetation through evapotranspiration and shading. This study provides a comprehensive global perspective on the commonalities of urban temperature dynamics in cities across diverse geographical regions and climates, contributing to a deeper understanding of how urbanization and land use changes influence surface temperatures and climate change. Full article

At least 10 global digital elevation models (DEMs) at one-arc-second resolution now cover Earth. Comparing derived grids, like slope or curvature, preserves surface spatial relationships, and can be more important than just elevation values. Such comparisons provide more nuanced DEM rankings than just elevation root mean square error (RMSE) for a small number of points. We present three new comparison categories: fraction of unexplained variance (FUV) for grids with continuous floating point values; accuracy metrics for integer code raster classifications; and comparison of stream channel vector networks. We compare six global DEMs that are digital surface models (DSMs), and four edited versions that use machine learning/artificial intelligence techniques to create a bare-earth digital terrain model (DTM) for different elevation ranges: full Earth elevations, under 120 m, under 80 m, and under 10 m. We find edited DTMs improve on elevation values, but because they do not incorporate other metrics in their training they do not improve overall on the source Copernicus DSM. We also rank 17 common geomorphic-derived grids for sensitivity to DEM quality, and document how landscape characteristics, especially slope, affect the results. None of the DEMs perform well in areas with low average slope compared to reference DTMs aggregated from 1 m airborne lidar data. This indicates that accurate work in low-relief areas grappling with global climate change should use airborne lidar or very high resolution image-derived DTMs. Full article

Wetlands are integral components of agricultural landscapes, providing a wide range of ecological, economic, and social benefits essential for sustainable development and rural livelihoods. Globally, they are vulnerable ecological assets facing several significant threats including water extraction and regulation, land clearing and reclamation, and climate change. Classification and mapping of wetlands in agricultural landscapes is crucial for conserving these ecosystems to maintain their ecological integrity amidst ongoing land-use changes and environmental pressures. This study aims to establish a robust framework for wetland classification and mapping in intensive agricultural landscapes using time series of Sentinel-2 imagery, with a focus on the Gwydir Wetland Complex situated in the northern Murray–Darling Basin—Australia’s largest river system. Using the Google Earth Engine (GEE) platform, we extracted two groups of predictors based on six vegetation indices time series calculated from multi-temporal Sentinel-2 surface reflectance (SR) imagery: the first is statistical features summarizing the time series and the second is phenological features based on harmonic analysis of time series data (HANTS). We developed and evaluated random forest (RF) models for each level of classification with combination of different groups of predictors. Our results show that RF models involving both HANTS and statistical features perform strongly with significantly high overall accuracy and class-weighted F1 scores (p < 0.05) when comparing with models with either statistical or HANTS variables. While the models have excellent performance (F-score greater than 0.9) in distinguishing wetlands from other landcovers (croplands, terrestrial uplands, and open waters), the inter-class discriminating power among wetlands is class-specific: wetlands that are frequently inundated (including river red gum forests and wetlands dominated by common reed, water couch, and marsh club-rush) are generally better identified than the ones that are flooded less frequently, such as sedgelands and woodlands dominated by black box and coolabah. This study demonstrates that HANTS features extracted from time series Sentinel data can significantly improve the accuracy of wetland mapping in highly fragmentated agricultural landscapes. Thus, this framework enables wetland classification and mapping to be updated on a regular basis to better understand the dynamic nature of these complex ecosystems and improve long-term wetland monitoring. Full article

The destiny of mankind is closely intertwined, and exploring new paths for the governance of the global commons of the earth’s surface (GCES) has become a worldwide topic. Thus, this paper attempts to carry out the following work by taking the Antarctic as an example: (1) Identify the characteristics of the governance subjects by using a literature review and the inductive analysis method. (2) Construct a network of responsive relationships among the governance subjects based on the evolutionary game approach. This study found the following: (1) A multi-subject participation in governance, i.e., with the Antarctic Treaty parties as the core, the seven major organizations as the main force, the United Nations as the support, enterprises as the emerging force, and other countries as the potential subjects. (2) Response-relational networks have high clustering coefficients and characteristic path lengths close to those of random networks, with significant small-world features of relational networks. (3) As important components of the benefit function, long-term benefits and benefit intensities together influence cooperative strategies for governance in the Antarctic. In the future, we should actively focus on long-term interests based on the concept of a community with a shared future for mankind, improve the governance structure at the right time, and strive to build a robust globalization. Full article

The Earth’s revolution is modified by changes in inclination of its rotation axis. Its trajectory is not closed and the equinoxes drift. Changes in polar motion and revolution are coupled through the Liouville–Euler equations. Milanković (1920) argued that the shortest precession period of solstices is 20,700 years: the summer solstice in one hemisphere takes place alternately every 11,000 year at perihelion and at aphelion. Milanković assumed that the planetary distances to the Sun and the solar ephemerids are constant. There are now observations that allow one to drop these assumptions. We have submitted the time series for the Earth’s pole of rotation, global mean surface temperature and ephemeris to iterative Singular Spectrum Analysis. iSSA extracts from each a trend a 1 year and a 60 year component. Both the apparent drift of solstices of Earth around the Sun and the global mean temperature exhibit a strong 60 year oscillation. We monitor the precession of the Earth’s elliptical orbit using the positions of the solstices as a function of Sun–Earth distance. The “fixed dates” of solstices actually drift. Comparing the time evolution of the winter and summer solstices positions of the rotation pole and the first iSSA component (trend) of the temperature allows one to recognize some common features. A basic equation from Milankovic links the derivative of heat received at a given location on Earth to solar insolation, known functions of the location coordinates, solar declination and hour angle, with an inverse square dependence on the Sun–Earth distance. We have translated the drift of solstices as a function of distance to the Sun into the geometrical insolation theory of Milanković. Shifting the inverse square of the 60 year iSSA drift of solstices by 15 years with respect to the first derivative of the 60 year iSSA trend of temperature, that is exactly a quadrature in time, puts the two curves in quasi-exact superimposition. The probability of a chance coincidence appears very low. Correlation does not imply causality when there is no accompanying model. Here, Milankovic’s equation can be considered as a model that is widely accepted. This paper identifies a case of agreement between observations and a mathematical formulation, a case in which an element of global surface temperature could be caused by changes in the Earth’s rotation axis. It extends the range of Milankovic cycles and resulting global temperature variations to shorter periods (1–100 year range), with a major role for the 60-year oscillation). Full article

Aridity is a condition in which there is a moisture deficit in the air and soil that affects large areas of the earth’s surface worldwide. It is a global problem caused mainly by factors related to climatic events and human actions. In the arid regions of Mexico, prolonged periods of drought are very common and water scarcity is the predominant feature. The main objective of this study is to develop a prospective geospatial simulation model for arid zones in the short and medium term (2030 and 2050) for the northwestern region of Mexico. A retrospective analysis of the variables that cause aridity was conducted based on historical data from satellite information obtained from various sources between 1985 and 2020, taking 2020 as the reference year; from this information the rate of change per year was obtained, followed by the simulated rates of change for the years 2030 and 2050. A methodology used to obtain arid zones using multicriteria evaluation techniques, weighted linear combination, and Geographic Information Systems. In order to generate the prospective model for arid zones, the variables were modeled to adjust the rate of change for each of them, with the same methodology subsequently applied to obtain the base year (2020), and aridity suitability maps were obtained for the years 2030 and 2050. The main results indicate that the prospective scenarios point to an increase in arid regions of 0.38% and 0.70%, respectively, which is equivalent to an area of approximately 240,164.63 km² and 241,760.75 km², respectively. This will cause a decrease in the subhumid–dry and humid regions of 0.10% and 0.19%, respectively, for the projected years. Statistical and geospatial aridity indicators were also generated at different levels, which helps to better understand the problem of aridity in vulnerable regions. Full article

Spatially continuous canopy height is a vital input for modeling forest structures and functioning. The global ecosystem dynamics investigation (GEDI) waveform can penetrate a canopy to precisely find the ground and measure canopy height, but it is spatially discontinuous over the earth’s surface. A common method to achieve wall-to-wall canopy height mapping is to integrate a set of field-measured canopy heights and spectral bands from optical and/or microwave remote sensing data as ancillary information. However, due partly to the saturation of spectral reflectance to canopy height, the product of this method may misrepresent canopy height. As a result, neither GEDI footprints nor interpolated maps using the common method can accurately produce spatially continuous canopy height maps alone. To address this issue, this study proposes a framework of point-surface fusion for canopy height mapping (FPSF-CH) that uses GEDI data to calibrate the initial wall-to-wall canopy height map derived from a sub-model of FPSF-CH. The effectiveness of the proposed FPSF-CH was validated by comparison to canopy heights derived from (1) a high-resolution canopy height model derived from airborne discrete point cloud lidar across three test sites, (2) a global canopy height product (GDAL RH95), and (3) the results of the FPSF-CH sub-model without fusing with the GEDI canopy height. The results showed that the RMSE and rRMSE of FPSF-CH were 3.82, 4.05, and 3.48 m, and 18.77, 16.24, and 13.81% across the three test sites, respectively. The FPSF-CH achieved improvement over GDAL RH95, with reductions in RMSE values of 1.28, 2.25, and 2.23 m, and reductions in rRMSE values of 6.29, 9.01, and 8.90% across the three test sites, respectively. Additionally, the better performance of the FPSF-CH compared with its sub-model further confirmed the effectiveness of integrating GEDI data for calibrating wall-to-wall canopy height mapping. The proposed FPSF-CH integrates GEDI LiDAR data to provide a new avenue for accurate wall-to-wall canopy height mapping critical to applications, such as estimations of biomass, biodiversity, and carbon stocks. Full article

Sulfide ores are a major source of noble (Au, Ag, and Pt) and base (Cu, Pb, Zn, Sn, Co, Ni, etc.) metals and will, therefore, be vital for the self-sustainment of future Mars colonies. Martian meteorites are rich in sulfides, which is reflected in recent findings for surface Martian rocks analyzed by the Spirit and Curiosity rovers. However, the only high-resolution (18 m/pixel) infrared (IR) spectrometer orbiting Mars, the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM), onboard the Mars Reconnaissance Orbiter (MRO), is not well-suited for detecting sulfides on the Martian surface. Spectral interference with silicates impedes sulfide detection in the 0.4–3.9 μm CRISM range. In contrast, at least three common hydrothermal sulfides on Earth and Mars (pyrite, chalcopyrite, marcasite) have prominent absorption peaks in a narrow far-IR (FIR) wavelength range of 23–28 μm. Identifying the global distribution and chemical composition of sulfide ore deposits would help in choosing useful targets for future Mars exploration missions. Therefore, we have designed a new instrument suitable for measuring sulfides in the FIR range called the Martian far-IR Ore Spectrometer (MIRORES). MIRORES will measure radiation in six narrow bands (~0.3 µm in width), including three bands centered on the sulfide absorption bands (23.2, 24.3 and 27.6 µm), two reference bands (21.5 and 26.1) and one band for clinopyroxene interference (29.0 µm). Focusing on sulfides only will make it possible to adapt the instrument size (32 × 32 × 42 cm) and mass (<10 kg) to common microsatellite requirements. The biggest challenges related to this design are: (1) the small field of view conditioned by the high resolution required for such a study (<20 m/pixel), which, in limited space, can only be achieved by the use of the Cassegrain optical system; and (2) a relatively stable measurement temperature to maintain radiometric accuracy and enable precise calibration. Full article

The main source of the ionization of the Earth’s atmosphere is the cosmic radiation that depends on solar activity as well as geomagnetic activity. Galactic cosmic rays constitute a permanent radiation background and contribute significantly to the radiation exposure inside the atmosphere. In this work, the cosmic-ray-induced ionization of the Earth’s atmosphere, due to both solar and galactic cosmic radiation during the recent solar cycles 23 (1996–2008) and 24 (2008–2019), was studied globally. Estimations of the ionization were based on the CRAC:CRII model by the University of Oulu. The use of this model allowed for extensive calculations from the Earth’s surface (atmospheric depth 1033 g/cm²) to the upper limit of the atmosphere (atmospheric depth 0 g/cm²). Monte Carlo simulations were performed for the estimation quantities of radiobiological interest with the validated software DYASTIMA/DYASTIMA-R. This study was focused on specific altitudes of interest, such as the common flight levels used by commercial aviation. Full article

3D modelling of urban areas is an attractive and active research topic, as 3D digital models of cities are becoming increasingly common for urban management as a consequence of the constantly growing number of people living in cities. Viewed as a digital representation of the Earth’s surface, an urban area modeled in 3D includes objects such as buildings, trees, vegetation and other anthropogenic structures, highlighting the buildings as the most prominent category. A city’s 3D model can be created based on different data sources, especially LiDAR or photogrammetric point clouds. This paper’s aim is to provide an end-to-end pipeline for 3D building modeling based on oblique UAS images only, the result being a parametrized 3D model with the Open Geospatial Consortium (OGC) CityGML standard, Level of Detail 2 (LOD2). For this purpose, a flight over an urban area of about 20.6 ha has been taken with a low-cost UAS, i.e., a DJI Phantom 4 Pro Professional (P4P), at 100 m height. The resulting UAS point cloud with the best scenario, i.e., 45 Ground Control Points (GCP), has been processed as follows: filtering to extract the ground points using two algorithms, CSF and terrain-mark; classification, using two methods, based on attributes only and a random forest machine learning algorithm; segmentation using local homogeneity implemented into Opals software; plane creation based on a region-growing algorithm; and plane editing and 3D model reconstruction based on piece-wise intersection of planar faces. The classification performed with ~35% training data and 31 attributes showed that the Visible-band difference vegetation index (VDVI) is a key attribute and 77% of the data was classified using only five attributes. The global accuracy for each modeled building through the workflow proposed in this study was around 0.15 m, so it can be concluded that the proposed pipeline is reliable. Full article

The global distribution of forest trees is essential for monitoring land-use sustainability. For this reason, FAO developed the Global Forest Survey (GFS), a systematic regional level survey with a homogeneous sampling design. Preliminary results revealed an interest in analyzing “Trees Outside Forests” (TOFs). We analyzed more than 11,150 sample GFS plots in temperate FAO-ecozones in Europe through a photo-interpretation approach using FAO’s Collect Earth platform. Our results showed that forest land was the most predominant use of land (41%) in temperate FAO-ecozones in Europe in 2015. Forest IPCC land use followed a moderate upward trend from 2000 to 2015 (0.2%). Trees Outside Forests in temperate FAO-ecozones in Europe accounted for 22% of the inventoried area. Trees Outside Forests represent 30% and 48% of cropland and grassland IPCC land uses, respectively, as well as 75% in settlement uses. Comparing our results with previous studies on the Mediterranean, temperate FAO-ecozone TOFs showed a downward trend in Europe and an upward trend in the Mediterranean area, despite its smaller surface area. The greening of the Common Agricultural Policy may increase the extension of TOFs, although our results may indicate that this effect is not yet evident in the field. Full article

Digital elevation models (DEMs) provide fundamental depictions of the three-dimensional shape of the Earth’s surface and are useful to a wide range of disciplines. Ideally, DEMs record the interface between the atmosphere and the lithosphere using a discrete two-dimensional grid, with complexities introduced by the intervening hydrosphere, cryosphere, biosphere, and anthroposphere. The treatment of DEM surfaces, affected by these intervening spheres, depends on their intended use, and the characteristics of the sensors that were used to create them. DEM is a general term, and more specific terms such as digital surface model (DSM) or digital terrain model (DTM) record the treatment of the intermediate surfaces. Several global DEMs generated with optical (visible and near-infrared) sensors and synthetic aperture radar (SAR), as well as single/multi-beam sonars and products of satellite altimetry, share the common characteristic of a georectified, gridded storage structure. Nevertheless, not all DEMs share the same vertical datum, not all use the same convention for the area on the ground represented by each pixel in the DEM, and some of them have variable data spacings depending on the latitude. This paper highlights the importance of knowing, understanding and reflecting on the sensor and DEM characteristics and consolidates terminology and definitions of key concepts to facilitate a common understanding among the growing community of DEM users, who do not necessarily share the same background. Full article

Inland open water bodies often pose a systematic error source in the passive remote sensing retrievals of soil moisture. Water temperature is a necessary variable used to compute water emissions that is required to be subtracted from satellite observation to yield actual emissions from the land portion, which in turn generates accurate soil moisture retrievals. Therefore, overestimation of soil moisture can often be corrected using concurrent water temperature data in the overall mitigation procedure. In recent years, several data sets of lake water temperature have become available, but their specifications and accuracy have rarely been investigated in the context of passive soil moisture remote sensing on a global scale. For this reason, three lake temperature products were evaluated against in-situ measurements from 2007 to 2011. The data sets include the lake surface water temperature (LSWT) from Global Observatory of Lake Responses to Environmental Change (GloboLakes), the Copernicus Global Land Operations Cryosphere and Water (C-GLOPS), as well as the lake mix-layer temperature (LMLT) from the European Centers for Medium-Range Weather Forecast (ECMWF) ERA5 Land Reanalysis. GloboLakes, C-GLOPS, and ERA5 Land have overall comparable performance with Pearson correlations (R) of 0.87, 0.92 and 0.88 in comparison with in-situ measurements. LSWT products exhibit negative median biases of −0.27 K (GloboLakes) and −0.31 K (C-GLOPS), whereas the median bias of LMLT is 1.56 K. When mapped from their respective native resolutions to a common 9 km Equal-Area Scalable Earth (EASE) Grid 2.0 projection, similar relative performance was observed. LMLT and LSWT data are closer in performance over the 9 km grid cells that exhibit a small range of lake cover fractions (0.05–0.5). Despite comparable relative performance, ERA5 Land shows great advantages in spatial coverage and temporal resolution. In summary, an integrated evaluation on data accuracy, long-term availability, global coverage, temporal resolution, and regular forward processing with modest data latency led us to conclude that LMLT from the ERA5 Land Reanalysis product represents the most optimal path for use in the development of a long-term soil moisture product. Full article

Climate changes are due to anthropogenic factors, volcano eruptions and the natural variability of the Earth’s system. Herein the natural variability of the global surface temperature is modeled using a set of harmonics spanning from the inter-annual to the millennial scales. The model is supported by the following considerations: (1) power spectrum evaluations show 11 spectral peaks (from the sub-decadal to the multi-decadal scales) above the 99% confidence level of the known temperature uncertainty; (2) spectral coherence analysis between the independent global surface temperature periods 1861–1937 and 1937–2013 highlights at least eight common frequencies between 2- and 20-year periods; (3) paleoclimatic temperature reconstructions during the Holocene present secular to millennial oscillations. The millennial oscillation was responsible for the cooling observed from the Medieval Warm Period (900–1400) to the Little Ice Age (1400–1800) and, on average, could have caused about 50% of the warming observed since 1850. The finding implies an equilibrium climate sensitivity of 1.0–2.3 °C for CO${}_2$ doubling likely centered around 1.5 °C. This low sensitivity to radiative forcing agrees with the conclusions of recent studies. Semi-empirical models since 1000 A.D. are developed using 13 identified harmonics (representing the natural variability of the climate system) and a climatic function derived from the Coupled Model Intercomparison Project 5 (CMIP5) model ensemble mean simulation (representing the mean greenhouse gas—GHG, aerosol, and volcano temperature contributions) scaled under the assumption of an equilibrium climate sensitivity of 1.5 °C. The harmonic model is evaluated using temperature data from 1850 to 2013 to test its ability to predict the major temperature patterns observed in the record from 2014 to 2020. In the short, medium, and long time scales the semi-empirical models predict: (1) temperature maxima in 2015–2016 and 2020, which is confirmed by the 2014–2020 global temperature record; (2) a relatively steady global temperature from 2000 to 2030–2040; (3) a 2000–2100 mean projected global warming of about 1 °C. The semi-empirical model reconstructs accurately the historical surface temperature record since 1850 and hindcasts mean surface temperature proxy reconstructions since the medieval period better than the model simulation that is unable to simulate the Medieval Warm Period. Full article