High-Andean wetlands are crucial ecosystems for water regulation and carbon storage, whose long-term conservation requires a comprehensive understanding of their dynamics, especially in the context of climate change. In this framework, the present research aimed to evaluate vegetation behavior in response to climatic variables in eight high-Andean wetlands located in the upper, middle, and lower sections of the Estero Ortiga sub-basin, within the Santuario de la Naturaleza Los Nogales, Metropolitan Region, Chile, during the period 2017–2024. The time series of the NDVI and NDCI vegetation indexes derived from Sentinel-2 images (January 2017–September 2024) and climate data on temperature and precipitation obtained from the "ERA5-Land monthly averaged data" product (January 2016–September 2024) were used. The results revealed a progressive decrease in vegetation cover and chlorophyll content during the study period, with slopes of -2,04 x 10⁻⁵ for NDVI and -1,15 x 10⁻⁵ for NDCI. Correlation analysis showed a significant positive relationship between annual accumulated precipitation and average vegetation index values during the following summer. For NDVI, the correlation coefficient (R) ranged between 0,83 and 0,88, while for NDCI, R ranged between 0,84 and 0,9. To determine a healthy vegetation cover, NDVI values were reclassified for the dates corresponding to the maximum value recorded each year. This analysis revealed a reduction in healthy vegetation cover starting in 2020. These findings highlight the importance of annual accumulated precipitation for the maintenance of high-Andean wetlands and underscore the use of remote sensing tools for monitoring and designing management and conservation strategies for these ecosystems.

Ravine forests play a key role in environmental regulation and biodiversity in fragmented landscapes. Continuous monitoring is essential to understand their dynamics and promote sustainable management. In this context, with the use of remote sensing techniques, it is possible to analyze spatial and temporal patterns of vegetation, integrating environmental factors such as topography and surface temperature. The objective of this study is to analyze the phenology of the species present in the ravine forests of the Los Nogales Nature Sanctuary, located in Lo Barnechea, Metropolitan Region of Chile, in an area of 4,743.2 hectares. Using Sentinel-2 satellite images (2019-2024) and the Alos Palsar Digital Elevation Model (12.5 m), we calculated the Normalized Difference Vegetation Index (NDVI), which evaluates plant vigor and biomass; the Topographic Position Index (TPI), which characterizes the relief; and the Diurnal Anisotropic Heat (DAH), which measures the interaction between solar radiation and surface temperature. The Generalized Additive Model (GAM) was used to evaluate the phenological time series. The results showed phenological stability in trees affected by climatic variations and greater sensitivity in shrubs and herbaceous plants. The topographic analysis indicated that trees predominate in elevated areas and herbaceous plants predominate in low areas with higher water retention. These findings highlight how vegetation responds to interactions between topography and environmental conditions in dynamic ecosystems, highlighting the potential of remote sensing for monitoring, analyzing, and understanding the forest ecosystem.

Tropical forests are essential for maintaining carbon balances and preserving biodiversity, yet they continually experience human threats like illegal mining, agricultural expansion, and logging. This study explores the land use and land cover changes over two decades (2004–2024) in four forest reserves in Ghana: Atewa, Bosomtwe Range, Fure River, and Tano Suraw. Land covers (closed and open forests, water bodies, and bare land) and past changes were mapped using Landsat satellite imagery, Random Forest Classification, and Land Change modelling. CA Markov Chain modelling was adopted to predict the potential land cover of the forest reserves in 2034. The overall classification accuracies ranged between 91% and 97%. The Atewa forest had the most decline in closed-canopy forest (CCF) with 51.3 km² transitioning to open-canopy forest (OCF) between 2004 and 2024. Projections suggest further declines, with CCF expected to decline from 106.04 km² to 85.7 km² by 2034. Similarly, the Bosomtwe Range forest had bare land increasing steadily from 2.3 km² to 3.1 km² in 2034. The Fure River forest experienced severe degradation, losing 3.9 km² of CCF to bare land from 2015 to 2024, with bare land anticipated to expand from 7.79 km² to 14.4 km² by 2034. In the Tano Suraw forest, 3.9 km² of CCF transitioned to OCF, while bare land is projected to increase from 11.06 km² to 15.7 km² by 2034, reflecting intensifying deforestation. Overall, the emerging dominant land cover is OCF, signaling extensive habitat destruction. The forest degradation is largely driven by illegal mining and agricultural expansion, threatening biodiversity and ecosystem functionality. Predictions for 2034 indicate that, without intervention, these forests will continue to experience severe degradation, escalating carbon emissions, and biodiversity loss. Mapping the changes and future trends of the forests provides vital insights to inform policies to guide sustainable land management and conservation.

In the field of meteorological remote sensing data prediction, capturing both low-frequency trends and high-frequency oscillations poses significant challenges. Conventional models often excel at long-term trend prediction but struggle with short-term oscillatory components, leading to suboptimal performance in highly dynamic atmospheric systems. To address these issues, this study proposes a two-stage framework that combines wavelet transform for signal decomposition with adaptive high- and low-frequency fusion strategies.

First, wavelet transform is utilized to decompose the meteorological data into low-frequency (trend) and high-frequency (oscillation) components. An improved Transformer-based model is then utilized to independently train the two components, effectively capturing their distinct patterns. Subsequently, the following two fusion strategies are employed to integrate the predictions: (1) Residual Prediction Fusion, which treats the high-frequency model as a residual predictor to refine the low-frequency predictions, and (2) Dynamic Weight Fusion, where a neural network dynamically learns and adjusts the weights of low-frequency and high-frequency components based on the signal's features. These fusion methods aim to balance long-term trend stability with short-term variability sensitivity.

The proposed methodology is expected to enhance prediction accuracy for meteorological remote sensing datasets, such as those from the FY-4A and Himawari-8 satellites, as well as other datasets like ERA5 and Weather. Experimental results demonstrate that the proposed method achieves a 5% to 20% reduction in prediction error across different datasets, effectively capturing both chaotic and deterministic atmospheric properties. This improvement highlights the model's potential to provide more accurate short- to medium-term weather forecasting, thereby advancing the understanding of atmospheric dynamics.

With the rapid development of space information networks and global commercial spaceflight, the deployment of low Earth orbit (LEO) mega-constellation systems using a large number of low-cost satellites to provide internet and various other services to the ground has become a significant direction for future space and communication sectors. Among these, routing technology is a crucial element to ensure the interconnection between nodes within the satellite network. However, as constellations become larger and their orbital altitudes decrease, LEO mega-constellation networks encounter issues such as complex and highly dynamic topologies, frequent transitions between satellite and ground, and node failures. Existing LEO mega-constellation routing algorithms perform poorly under conditions of Inter-SatelliteLink (ISL) failures and node failures.

A Manhattan-like topology and low intersatellite link delay-based routing scheme is a near-optimal routing scheme, but it does not consider the increased delay caused by sudden node or ISL failures during transmission. To address this challenge, an improved mega-constellation routing algorithm is proposed that maintains near-optimal routing while considering node and ISL failures during transmission, with a lower routing complexity of O(V/4), where V is the number of satellite nodes in the constellation.

Firstly, based on the source satellite, the topology of the LEO mega-constellation is divided into four interference-free areas. Secondly, the algorithm is applied to obtain near-minimum delay paths for all targets within each area under the minimum hop (MH) path. Finally, through simulation comparisons on the LEO mega-constellation, it is verified that the proposed algorithm has near-optimal routing and achieves lower complexity. Current research and this study both explore constellation routing transmission strategies at the same orbital altitude, and future research is expected to explore new routing algorithms for heterogeneous orbital mega-constellation networks.

The accurate detection and quantification of regional vegetation trends are essential for understanding the dynamics of landscape ecology and vegetation distribution. We applied a comprehensive trend analysis to satellite data to describe geospatial changes in vegetation along the Pacific slope of Peru and northern Chile, from sea level to the continental divide, a region characterised by biologically unique and highly sensitive arid and semi-arid environments.
Our statistical analyses show broad regional patterns of positive trends in EVI, called “greening”, alongside patterns of “browning”, where trends are negative, between 2000 and 2020. The coastal plain and foothills, up to 1000m, contain notable greening of the coastal Lomas and newly irrigated agricultural lands occurring alongside browning trends related to changes in land use practices and urban development. Strikingly, the precordillera shows a distinct 'greening strip', which extends from approximately 6°S to 22°S, with an altitudinal trend, ascending from the tropical lowlands (170-780 m) in northern Peru to the subtropics (1000-2800 m) in central Peru, and a temperate zone (2600-4300 m) in southern Peru and northern Chile. We find that the geographical characteristics of the greening strip do not match climate zones previously established by Köppen and Geiger. Greening and browning trends in the coastal deserts and the high Andes lie within well-defined climatic and life zones, producing variable but identifiable trends. However, the distinct Pacific slope greening presents an unexpected distribution with respect to the regional Köppen–Geiger climate and life zones. This work provides insights into understanding the effects of climate change on Peru's diverse ecosystems in highly sensitive, biologically unique arid and semi-arid environments on the Pacific slope.

Changes in coastal morphology have significant implications for the sustainability of communities, infrastructure, and ecosystems. The lack of high-spatial and -temporal resolution data makes coastal monitoring challenging. Optical remote sensing has proven to be an effective tool for studying large areas, providing consistent and comparable data. This study analyzes morphological changes in the Pichilemu Bay, O'Higgins region, Chile, using satellite images from Landsat (5, 7, 8, 9) and Sentinel-2 between 1985 and 2024. The objective is to understand the relationship between natural drivers and shoreline fluctuations, with an emphasis on the effects of the 2010 earthquake, which established a new baseline for the coastline. The bay was divided into three sectors based on wave influence to analyze coastal dynamics and morphosedimentary patterns. The methodology included the following: a) massive extraction of shorelines; b) wave analysis using ERA 5 simulations; c) erosion rate calculations and spatiotemporal analysis of beach width; and d) sediment analysis across three beach profiles. The results reveal significant erosion rates of up to -1.17 m/year, exacerbated by extreme events such as storms and ENSO cycles, which alter wave patterns and sediment distribution. Coastal storms are key drivers of the bay's evolution, as they activate transport processes absent under normal conditions, such as increased longshore transport and sediment redistribution toward deeper areas. This impact is particularly evident during multi-year erosive cycles, such as the one recorded between 2012 and 2020, associated with recurring storms and ENSO-driven wave patterns. This spatiotemporal model reconstructs the beach’s evolution and shoreline position, highlighting the influence of disruptive events on its development.

Although agriculture contributes to GHG emissions, the role of crops as carbon sinks has attracted great environmental interest in the scientific community. Furthermore, there is also growing economic interest as crops may become part of the carbon market. Therefore, the estimation of above-ground biomass and the subsequently stored carbon of crops through new technologies is crucial for adopting different strategies to promote sustainable management. In recent years, the LiDAR technique (Laser Imaging Detection and Ranging) has emerged as an important tool to estimate vegetation biomass accurately in forest ecosystems. Therefore, this technique could be useful also for estimating the biomass of crops, especially woody crops. The main objective of this project is to estimate stored carbon by LIDAR in two important woody crops (olive groves and almond groves) in the south of Spain. Preliminary results showed that LiDAR sensors could be used for the detailed extraction of structural metrics and the modelling of the three-dimensional structure of individual trees, enabling the analysis of tree growth, biomass, and carbon-storage capacity to understand the behavior and capacity of these crops as carbon sinks.

In the last few decades, the role of forests as carbon sinks has become a fundamental scientific issue due to their potential effect on climate change. Tropical forests represent one half of Earth’s carbon stored in terrestrial vegetation. Therefore, estimating the above-ground biomass and carbon of these forests through new technologies is crucial for adopting different strategies that promote sustainable management. In recent years, the LiDAR technique (Laser Imaging Detection and Ranging) has emerged as an important tool to estimate forest biomass accurately, especially in tropical forests where vegetation is dense and the acquisition of field data is a difficult task. The main objective of this work is to compare two technologies of LiDAR, the full-wave LiDAR (LiDARfw) and discrete LiDAR (LiDARd), for estimating biomass in a tropical forest of Costa Rica. The results showed that LiDARfw provided a higher point density (+14.5%) and captured greater vertical structure variability than LiDARd, particularly in lower forest strata. This demonstrates its effectiveness in modeling complex forest environments. In conclusion, LiDARfw excels in capturing detailed vertical profiles and identifying structural heterogeneity, making it ideal for biomass estimation and precise ecological studies.

The Hunga-Tonga volcanic eruption occurred on January 15, 2022, triggering a complex interplay of thermochemical explosions, air–ocean interactions, and atmospheric physics on a planetary scale. This catastrophic event generated transoceanic tsunamis, seismic activity, and acoustic-gravity waves. The extensive scientific data collected during this event present exciting opportunities for interdisciplinary collaborative research. In addition to traditional fields such as seismology, volcanology, and tsunami simulation, the utilization of multiple satellites has provided innovative observations and unresolved insights into the mechanisms involved in the eruption's evolution. However, the presence of clouds and haze particles in the atmosphere makes it challenging for satellite sensors to accurately determine the exact start and end time of the eruption through observation alone.

To gain a profound understanding of the atmospheric evolution mechanism through optical observations, we introduced the non-stationary permutation entropy decision-tree algorithm. This algorithm reveals the inherent chaotic dynamics of space inhomogeneity and full-spectrum distributions. The chaotic nature of the atmospheric infrared radiation field implies that its long-term evolutionary process is unpredictable, thereby challenging classical physics theories in accurately depicting such intricate phenomena. In this study, we employ widely-used reservoir computing algorithms in chaotic processes to predict short-term variations in thermal infrared fields prior to volcanic eruptions (referred to as background fields). By utilizing the structural similarity index (SSIM), we can compare predicted data with real-time region measurements to determine the critical timing of abnormal events and forecast the termination of thermal anomalies.

Early warnings of thermal anomalies prior to volcanic eruptions has often been overlooked in previous research. However, early prediction not only captures timely and subtle indicators before a disaster occurs but also allows for more time in making decisions regarding disaster warnings, thereby minimizing economic and human losses.