Search Results (2,968)

Leaf phenology is key to ecosystem functioning by regulating carbon, water, and energy fluxes and influencing vegetation productivity. Yet, detecting land surface phenology (LSP) in drylands using remote sensing remains particularly challenging due to sparse and heterogeneous vegetation cover, high spatiotemporal variability, and complex spectral signals. Unlike the Northern Hemisphere, these challenges are further compounded in the Southern Hemisphere (SH), where several regions experience year-round moderate temperatures. When combined with irregular rainfall, this leads to highly variable vegetation activity throughout the year. However, LSP dynamics in the SH remain poorly understood. This study presents a review of remote sensing-based phenology research in drylands, integrating (i) a synthesis of global methodological advances and (ii) a systematic analysis of peer-reviewed studies published from 2015 through April 2025 focused on SH drylands. This review reveals a research landscape still dominated by conventional vegetation indices (e.g., NDVI) and moderate-spatial-resolution sensors (e.g., MODIS), though a gradual shift toward higher-resolution sensors such as PlanetScope and Sentinel-2 has emerged since 2020. Despite the widespread use of start- and end-of-season metrics, their accuracy varies greatly, especially in heterogeneous landscapes. Yet, advanced products such as solar-induced chlorophyll fluorescence or the fraction of absorbed photosynthetically active radiation were rarely employed. Gaps remain in the representation of hyperarid zones, grass- and shrub-dominated landscapes, and large regions of Africa and South America. Our findings highlight the need for multi-sensor approaches and expanded field validation to improve phenological assessments in dryland environments. The accurate differentiation of vegetation responses in LSP is essential not only for refining phenological metrics but also for enabling more realistic assessments of ecosystem functioning in the context of climate change and its impact on vegetation dynamics. Full article

Accurate land-use classification is critical for urban planning and environmental monitoring, yet effectively integrating heterogeneous data sources such as hyperspectral imagery and laser radar (LiDAR) remains challenging. To address this, we propose MixtureRS, a compact multimodal network that effectively integrates hyperspectral imagery and LiDAR data for land-use classification. Our approach employs a 3-D plus heterogeneous convolutional stack to extract rich spectral–spatial features, which are then tokenized and fused via a cross-modality transformer. To enhance model capacity without incurring significant computational overhead, we replace conventional dense feed-forward blocks with a sparse Mixture-of-Experts (MoE) layer that selectively activates the most relevant experts for each token. Evaluated on a 15-class urban benchmark, MixtureRS achieves an overall accuracy of 88.6%, an average accuracy of 90.2%, and a Kappa coefficient of 0.877, outperforming the best homogeneous transformer by over 12 percentage points. Notably, the largest improvements are observed in water, railway, and parking categories, highlighting the advantages of incorporating height information and conditional computation. These results demonstrate that conditional, expert-guided fusion is a promising and efficient strategy for advancing multimodal remote sensing models. Full article

We inspected a sector of the Apennines (central–southern Italy) in geographic and structural continuity with the Quaternary-active extensional belt but where clear geomorphic and seismological signatures of normal faulting are unexpectedly missing. The evidence of active tectonics in this area, between Abruzzo and Molise, does not align with geodetic deformation data and the seismotectonic setting of the central Apennines. To investigate the apparent disconnection between active deformation and the absence of surface faulting in a sector where high lithologic erodibility and landslide susceptibility may hide its structural evidence, we combined multi-scale and multi-source data analyses encompassing morphometric analysis and remote sensing techniques. We utilised high-resolution topographic data to analyse the topographic pattern and investigate potential imbalances between tectonics and erosion. Additionally, we employed aerial-photo interpretation to examine the spatial distribution of morphological features and slope instabilities which are often linked to active faulting. To discern potential biases arising from non-tectonic (slope-related) signals, we analysed InSAR data in key sectors across the study area, including carbonate ridges and foredeep-derived Molise Units for comparison. The topographic analysis highlighted topographic disequilibrium conditions across the study area, and aerial-image interpretation revealed morphologic features offset by structural lineaments. The interferometric analysis confirmed a significant role of gravitational movements in denudating some fault planes while highlighting a clustered spatial pattern of hillslope instabilities. In this context, these instabilities can be considered a proxy for the control exerted by tectonic structures. All findings converge on the identification of an ~20 km long corridor, the Castel di Sangro–Rionero Sannitico alignment (CaS-RS), which exhibits varied evidence of deformation attributable to active normal faulting. The latter manifests through subtle and diffuse deformation controlled by a thick tectonic nappe made up of poorly cohesive lithologies. Overall, our findings suggest that the CaS-RS bridges the structural gap between the Mt Porrara–Mt Pizzalto–Mt Rotella and North Matese fault systems, potentially accounting for some of the deformation recorded in the sector. Our approach contributes to bridging the information gap in this complex sector of the Apennines, offering original insights for future investigations and seismic hazard assessment in the region. Full article

Urban electricity power consumption (EPC) prediction plays a crucial role in urban management and sustainable development. Nighttime light (NTL) remote sensing imagery has demonstrated significant potential in estimating urban EPC due to its strong correlation with human activities and energy use. However, most existing models focus on annual-scale estimations, limiting their ability to capture month-scale EPC. To address this limitation, a novel monthly EPC prediction model that incorporates monthly average temperature, and the interaction between NTL data and temperature was proposed in this study. The proposed method was applied to cities within the Yangtze River Delta (YRD) urban agglomeration, and was validated using datasets constructed from NPP/VIIRS and SDGSAT-1 satellite imageries, respectively. For the NPP/VIIRS dataset, the proposed method achieved a Mean Absolute Relative Error (MARE) of 7.96% during the training phase (2017–2022) and of 10.38% during the prediction phase (2023), outperforming the comparative methods. Monthly EPC spatial distribution maps from VPP/VIIRS data were generated, which not only reflect the spatial patterns of EPC but also clearly illustrate the temporal evolution of EPC at the spatial level. Annual EPC estimates also showed superior accuracy compared to three comparative methods, achieving a MARE of 7.13%. For the SDGSAT-1 dataset, leave-one-out cross-validation confirmed the robustness of the model, and high-resolution (40 m) monthly EPC maps were generated, enabling the identification of power consumption zones and their spatial characteristics. The proposed method provides a timely and accurate means for capturing monthly EPC dynamics, effectively supporting the dynamic monitoring of urban EPC at the monthly scale in the YRD urban agglomeration. Full article

This study examines the complex interactions among soil moisture, evaporation, extreme weather events, and lightning, and their influence on fire activity across the extratropical and Pan-Arctic regions. Leveraging reanalysis and remote-sensing datasets from 2000 to 2020, we applied cross-correlation analysis, a modified Mann–Kendall trend test, and assessments of interannual variability to key variables including soil moisture, fire frequency and risk, evaporation, and lightning. Results indicate a significant increase in dry days (up to 40%) and heatwave events across Central Eurasia and Siberia (up to 50%) and Alaska (25%), when compared to the 1980–2000 baseline. Upward trends have been detected in evaporation across most of North America, consistent with soil moisture trends, while much of Eurasia exhibits declining soil moisture. Fire danger shows a strong positive correlation with evaporation north of 60° N (r ≈ 0.7, p ≤ 0.005), but a negative correlation in regions south of this latitude. These findings suggest that in mid-latitude ecosystems, fire activity is not solely driven by water stress or atmospheric dryness, highlighting the importance of region-specific surface–atmosphere interactions in shaping fire regimes. In North America, most fires occur in temperate grasslands, savannas, and shrublands (47%), whereas in Eurasia, approximately 55% of fires are concentrated in forests/taiga and temperate open biomes. The analysis also highlights that lightning-related fires are more prevalent in Eastern Europe and Southeastern Asia. In contrast, Western North America exhibits high fire incidence in temperate conifer forests despite relatively low lightning activity, indicating a dominant role of anthropogenic ignition. These findings underscore the importance of understanding land–atmosphere interactions in assessing fire risk. Integrating surface conditions, climate extremes, and ignition sources into fire prediction models is crucial for developing more effective wildfire prevention and management strategies. Full article

Most existing cloud base height (CBH) retrieval algorithms are only applicable for daytime satellite observations due to their dependence on visible observations. This study presents a novel algorithm to retrieve day and night CBH using infrared observations of the geostationary Advanced Himawari Imager (AHI). The algorithm is featured by integrating deep learning techniques with a physical model. The algorithm first utilizes a convolutional neural network-based model to extract cloud top height (CTH) and cloud water path (CWP) from the AHI infrared observations. Then, a physical model is introduced to relate cloud geometric thickness (CGT) to CWP by constructing a look-up table of effective cloud water content (ECWC). Thus, the CBH can be obtained by subtracting CGT from CTH. The results demonstrate good agreement between our AHI CBH retrievals and the spaceborne active remote sensing measurements, with a mean bias of −0.14 ± 1.26 km for CloudSat-CALIPSO observations at daytime and −0.35 ± 1.84 km for EarthCARE measurements at nighttime. Additional validation against ground-based millimeter wave cloud radar (MMCR) measurements further confirms the effectiveness and reliability of the proposed algorithm across varying atmospheric conditions and temporal scales. Full article

Principal component analysis (PCA) is a widely applied multivariate statistical technique across scientific disciplines, with forestry being one of its most dynamic areas of use. Its primary strength lies in reducing data dimensionality and classifying parameters within complex ecological datasets. This study provides the first comprehensive bibliometric and literature review focused exclusively on PCA applications in forestry. A total of 96 articles published between 1993 and 2024 were analyzed using the Web of Science database and visualized using VOSviewer software, version 1.6.20. The bibliometric analysis revealed that the most active scientific fields were environmental sciences, forestry, and engineering, and the most frequently published journals were Forests and Sustainability. Contributions came from 198 authors across 44 countries, with China, Spain, and Brazil identified as leading contributors. PCA has been employed in a wide range of forestry applications, including species classification, biomass modeling, environmental impact assessment, and forest structure analysis. It is increasingly used to support decision-making in forest management, biodiversity conservation, and habitat evaluation. In recent years, emerging research has demonstrated innovative integrations of PCA with advanced technologies such as hyperspectral imaging, LiDAR, unmanned aerial vehicles (UAVs), and remote sensing platforms. These integrations have led to substantial improvements in forest fire detection, disease monitoring, and species discrimination. Furthermore, PCA has been combined with other analytical methods and machine learning models—including Lasso regression, support vector machines, and deep learning algorithms—resulting in enhanced data classification, feature extraction, and ecological modeling accuracy. These hybrid approaches underscore PCA’s adaptability and relevance in addressing contemporary challenges in forestry research. By systematically mapping the evolution, distribution, and methodological innovations associated with PCA, this study fills a critical gap in the literature. It offers a foundational reference for researchers and practitioners, highlighting both current trends and future directions for leveraging PCA in forest science and environmental monitoring. Full article

The possibility of collecting new archaeological elements useful in reconstructing the dynamics of population, production and commercial activities in the Bronze Age at the edge of the central-southern Venice Lagoon was provided between 2023 and 2024 thanks to an intervention of rescue archaeology planned during some water restoration works in the Giare–Mira area. Three small excavations revealed, approximately one meter below the current surface and covered by alluvial sediments, a rather complex palimpsest dated to the late Recent and the early Final Bronze Age. Three large circular pits containing exclusively purified grey/blue clay and very rare inclusions of vegetable fibres, and many large, fired clay vessels’ bases, walls and rims clustered in concentrated assemblages and random deposits point to potential on-site production. Two pyro-technological structures, one characterised by a sub-circular combustion chamber and a long inlet channel/praefurnium, and the second one with a sub-rectangular shape with arched niches along its southern side, complete the exceptional context here discovered. To analyse the relationship between the site and the natural sedimentary succession and to evaluate the possible extension of this site, three electrical resistivity tomography (ERT) and low-frequency electromagnetic (FDEM) measurements were collected. Several manual core drillings associated with remote sensing integrated the geophysical data in the analysis of the geomorphological evolution of this area, clearly related to different phases of fluvial activity, in a framework of continuous relative sea level rise. The typology and chronology of the archaeological structures and materials, currently undergoing further analyses, support the interpretation of the site as a late Recent/early Final Bronze Age productive site. Geophysical and geomorphological data provide information on the palaeoenvironmental setting, suggesting that the site was located on a fine-grained, stable alluvial plain at a distance of a few kilometres from the lagoon shore to the south-east and the course of the Brenta River to the north. The archaeological site was buried by fine-grained floodplain deposits attributed to the Brenta River. The good preservation of the archaeological structures buried by fluvial sediments suggests that the site was abandoned soon before sedimentation started. Full article

Farmers face increasing pressure to maintain vital populations of the critically endangered field hamster (Cricetus cricetus) while managing crop damage caused by field mice. This challenge is linked to the UN Sustainable Development Goals (SDGs) 2 and 15, addressing food security and biodiversity. Consequently, the reliable detection of hamster activity in agricultural fields is essential. While remote sensing offers potential for wildlife monitoring, commonly used RGB imagery has limitations in detecting small burrow entrances in vegetated areas. This study investigates the potential of drone-based Light Detection and Ranging (LiDAR) data for identifying field hamster burrow entrances in agricultural landscapes. A geostatistical method was developed to detect local elevation minima as indicators of burrow openings. The analysis used four datasets captured at varying flight altitudes and spatial resolutions. The method successfully detected up to 20 out of 23 known burrow entrances and achieved an F1-score of 0.83 for the best-performing dataset. Detection was most accurate at flight altitudes of 30 m or lower, with performance decreasing at higher altitudes due to reduced point density. These findings demonstrate the potential of UAV-based LiDAR to support non-invasive species monitoring and habitat management in agricultural systems, contributing to sustainable conservation practices in line with the SDGs. Full article

Port green spaces are essential protective barriers, enhancing safety and environmental resilience in high-activity port regions. Given the intensity of human activities in these areas, understanding the factors influencing the carbon sequestration capacity and ecological benefits of port green spaces is crucial for developing sustainable green ports. This study integrated field investigations and remote sensing data to estimate carbon density and carbon sequestration capacity in the Dongjiakou Port area, examining their relationship with port green space planning. The results indicated that carbon density in green spaces showed a significant negative correlation with the number of lanes in adjacent roads, where an increase in lane numbers corresponded to lower carbon density. Additionally, carbon density decreased significantly with increasing distance from the shipping center. In contrast, a significant positive correlation was observed between carbon density and distance from large water bodies, indicating that green spaces closer to large water bodies exhibited smaller carbon density. Infrastructure development in Dongjiakou substantially negatively impacted vegetation carbon sequestration capacity, with effects not reversible in the short term. However, green space enhancement efforts provided additional ecological benefits, leading to a 50.9 ha increase in green space area. When assessing carbon density in urbanizing areas, geographical influences should be prioritized. Furthermore, the long-term environmental impacts of urban expansion must be considered at the early planning stages, ensuring the implementation of proactive protective measures to mitigate potential ecological disruptions. Full article

Urbanization is one of the most important challenges contributing to the trend of replacing agricultural land with high-value land uses, such as housing, as well as industrial and commercial activities, as a result of significant population growth. To face these challenges and improve urban sustainability, integrating an embedded concept of spatial planning, taking into account urban and peri-urban agriculture, will contribute to mitigating food security issues and the negative impact of climate change, while improving social and economic development. This project aims to analyze land use/cover changes in the Casablanca metropolitan area and its surrounding cities, which are undergoing rapid urban growth. To achieve this, time series of remote sensing data were analyzed in order to investigate the spatio-temporal changes in LU/LC and to evaluate the dynamics and spatial pattern of the city’s expansion over the past three decades, which has come at the expense of agricultural land. The study will also examine the relationship between urbanization and agricultural land use change over time. The results of this study show that Casablanca and its outskirts experience significant urban expansion and a decline in arable lands, with rates of 45% and 42%, respectively. The analysis of SDG indicator 11.3.1 has also shown that land consumption in the provinces of Mediouna, Mohammadia, and Nouaceur has exceeded population growth, due to rapid, uncontrolled urbanization at the expense of agricultural land, which highlights the need to develop a new conceptual framework for regenerating land systems based on the implementation of urban and peri-urban agriculture in vacant sites within urban and peri-urban areas. This will offer valuable insights for policymakers to investigate measures that can ensure sustainable land use planning strategies that effectively integrate agriculture into urban development. Full article

Climate change has intensified drought stress, threatening global food security by affecting sensitive crops like maize (Zea mays). This study evaluated the potential of Antarctic fungal endophytes (Penicillium chrysogenum and P. brevicompactum) to enhance maize drought tolerance under field conditions with different irrigation regimes. Drought stress reduced soil moisture to 59% of field capacity. UAV-based multispectral imagery monitored plant physiological status using vegetation indices (NDVI, NDRE, SIPI, GNDVI). Inoculated plants showed up to two-fold higher index values under drought, indicating improved stress resilience. Physiological analysis revealed increased photochemical efficiency (0.775), higher chlorophyll and carotenoid contents (45.54 mg/mL), and nearly 80% lower lipid peroxidation in inoculated plants. Lower proline accumulation suggested better water status and reduced osmotic stress. Secondary metabolites such as phenolics, flavonoids, and anthocyanins were elevated, particularly under well-watered conditions. Antioxidant enzyme activity shifted: SOD, CAT, and APX were suppressed, while POD activity increased, indicating reprogrammed oxidative stress responses. Yield components, including cob weight and length, improved significantly with inoculation under drought. These findings demonstrate the potential of Antarctic endophytes to enhance drought resilience in maize and underscore the value of integrating microbial biotechnology with UAV-based remote sensing for sustainable crop management under climate-induced water scarcity. Full article

Understanding spatial variations in land surface temperature (LST) is critical for analyzing urban climate dynamics, especially within the framework of two-dimensional (2D) turbulence theory. This study assesses the spatial sampling behavior of urban thermal fields across eight metropolitan areas, encompassing diverse morphologies, surface materials, and Köppen–Geiger climate zones. We analyzed thermal infrared (TIR) imagery from two remote sensing platforms—MODIS (1 km) and Landsat (30 m)—to evaluate resolution-dependent turbulence indicators such as spectral slopes and breakpoints. Power spectral analysis revealed systematic divergences across spatial scales. Landsat exhibited more negative breakpoint values, indicating a greater ability to capture fine-scale thermal heterogeneity tied to vegetation, buildings, and surface cover. MODIS, in contrast, emphasized broader thermal gradients, suitable for regional-scale assessments. Seasonal differences reinforced the turbulence framework: summer spectra displayed steeper, more variable slopes, reflecting increased thermal activity and surface–atmosphere decoupling. Despite occasional agreement between sensors, spectral metrics remain inherently resolution-dependent. MODIS is better suited for macro-scale thermal structures, while Landsat provides detailed insights into intra-urban processes. Our findings confirm that 2D turbulence indicators are not fully scale-invariant and vary with sensor resolution, season, and urban form. This multi-sensor comparison offers a framework for interpreting LST data in support of climate adaptation, urban design, and remote sensing integration. Full article

Reliable remote sensing-based Land Use and Land Cover (LULC) information is crucial for assessing Earth’s surface activities. Brazil’s agricultural dynamics, including year-round cropping, multiple cropping, and regional climate variability, make LULC monitoring a highly challenging task. The country has thirteen remote sensing-based LULC products specifically tailored for this purpose. However, the differences and the results of these products have not yet been synthesized to provide coherent guidance in assessing their spatio-temporal agricultural dynamics and identifying promising approaches and issues that affect LULC analysis. This review represents the first comprehensive assessment of the advantages, challenges, and limitations, highlighting the main issues when dealing with contrasting LULC maps. These challenges include incompatibility, a lack of updates, non-systematic classification ontologies, and insufficient data to monitor Brazilian LULC information. The consequences include impacts on intercropping estimation, diminished representation or misrepresentation of croplands; temporal discontinuity; an insufficient number of classes for subannual cropping evaluation; and reduced compatibility, comparability, and spectral separability. The study provides insights into the use of these products as primary input data for remote sensing-based applications. Moreover, it provides prospects for enhancing existing mapping efforts or developing new national-level initiatives to represent the spatio-temporal variation of Brazilian agriculture. Full article

The ecological environment of Inner Mongolia constitutes a critical component of China’s ecological civilization construction. To comprehensively assess and monitor ecological quality dynamics in this region, this study employed MODIS remote sensing data products (2000–2020) and derived four key indicators, —vegetation index (NDVI), wetness index (WET), build-up and soil index (NDBSI), and land surface temperature (LST)—via the Google Earth Engine (GEE) platform. A Remote Sensing-based Ecological Index (RSEI) was constructed using principal component analysis (PCA) to establish an annual long-term time series, thereby eliminating subjective bias from artificial weight assignment. Integrated methodologies—including Theil–Sen Median and Mann–Kendall trend analysis, Hurst exponent, and geographical detector—were applied to investigate the spatiotemporal evolution of ecological quality in Inner Mongolia and its responses to climatic and anthropogenic drivers. This study proposes a novel framework for large-scale ecological quality assessment using remote sensing. Key findings include the following: The mean RSEI value of 0.41 (2000–2020) indicates an overall improving trend in ecological quality. Areas with ecological improvement and degradation accounted for 76.06% and 23.84% of the region, respectively, exhibiting a spatial pattern of “northwestern improvement versus southeastern degradation.” Pronounced regional disparities were observed: optimal ecological conditions prevailed in the Greater Khingan Range (northeast), while the Alxa League (southwest) exhibited the poorest conditions. Northwestern improvement was primarily driven by increased precipitation, rising temperatures, and conservation policies, whereas southeastern degradation correlated with rapid urbanization and intensified socioeconomic activities. Our results demonstrate that MODIS-derived RSEI effectively enables large-scale ecological monitoring, providing a scientific basis for regional green development strategies. Full article