Search Results (429)

Due to climate effects and human influences, wildfire regimes in boreal forests are changing, leading to profound ecological consequences, including shortened fire return intervals and elevated tree mortality. However, a critical knowledge gap exists concerning the spatiotemporal dynamics of fire-induced tree mortality specifically within the vast North American boreal forest, as previous studies have predominantly focused on Mediterranean and tropical forests. Therefore, in this study, we used satellite observation data obtained by the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua and Terra MCD64A1 and related database data to study the spatial and temporal variability in burned area and forest mortality due to wildfires in North America (Alaska and Canada) over an 18-year period (2003 to 2020). By calculating the satellite reflectance data before and after the fire, fire-driven forest mortality is defined as the ratio of the area of forest loss in a given period relative to the total forest area in that period, i.e., the area of forest loss divided by the total forest area. Our findings have shown average values of burned area and forest mortality close to 8000 km²/yr and 40%, respectively. Burning and tree loss are mainly concentrated between May and September, with a corresponding temporal trend in the occurrence of forest fires and high mortality. In addition, large-scale forest fires were primarily concentrated in Central Canada, which, however, did not show the highest forest mortality (in contrast to the results recorded in Northern Canada). Critically, based on generalized linear models (GLMs), the results showed that fire size and duration, but not the burned area, had significant effects on post-fire forest mortality. Overall, this study shed light on the most sensitive forest areas and time periods to the detrimental effects of forest wildfire in boreal forests of North America, highlighting distinct spatial and temporal vulnerabilities within the boreal forest and demonstrating that fire regimes (size and duration) are primary drivers of ecological impact. These insights are crucial for refining models of boreal forest carbon dynamics, assessing ecosystem resilience under changing fire regimes, and informing targeted forest management and conservation strategies to mitigate wildfire impacts in this globally significant biome. Full article

Arbuscular mycorrhizal fungi (AMF) play a vital role in the restoration of tropical forests by enhancing soil fertility, facilitating plant establishment, and improving ecosystem resilience. This study presents a comprehensive bibliometric analysis of global scientific output on AMF in the context of ecological restoration, based on 3835 publications indexed in the Web of Science and Scopus databases from 2001 to 2024. An average annual growth rate of approximately 9.45% was observed, with contributions from 10,868 authors across 880 journals. The most prominent journals included Mycorrhiza (3.34%), New Phytologist (3.00%), and Applied Soil Ecology (2.79%). Thematically, dominant research areas encompassed soil–plant interactions, phytoremediation, biodiversity, and microbial ecology. Keyword co-occurrence analysis identified “arbuscular mycorrhizal fungi,” “diversity,” “soil,” and “plant growth” as core topics, while emerging topics such as rhizosphere interactions and responses to abiotic stress showed increasing prominence. Despite the expanding body of literature, key knowledge gaps remain, particularly concerning AMF–plant specificity, long-term restoration outcomes, and integration of microbial community dynamics. These findings offer critical insights into the development of AMF research and underscore its strategic importance in tropical forest restoration, providing a foundation for future studies and informing ecosystem management policies. Full article

Root phenology significantly influences ecosystem processes yet remains poorly characterized across biomes. This study synthesized data from 59 studies spanning Arctic to tropical ecosystems to identify woody plants root phenological patterns and their environmental drivers. The analysis revealed distinct climate-specific patterns. Arctic regions had a short growing season with remarkably low temperature threshold for initiation of root growth (0.5–1 °C). Temperate forests displayed pronounced spring-summer growth patterns with root growth initiation occurring at 1–9 °C. Mediterranean ecosystems showed bimodal patterns optimized around moisture availability, and tropical regions demonstrate seasonality primarily driven by precipitation. Root-shoot coordination varies predictably across biomes, with humid continental ecosystems showing the highest synchronous above- and belowground activity (57%), temperate regions exhibiting leaf-before-root emergence (55%), and Mediterranean regions consistently showing root-before-leaf patterns (100%). Winter root growth is more widespread than previously recognized (35% of studies), primarily in tropical and Mediterranean regions. Temperature thresholds for phenological transitions vary with climate region, suggesting adaptations to environmental conditions. These findings provide a critical, region-specific framework for improving models of terrestrial ecosystem responses to climate change. While our synthesis clarifies distinct phenological strategies, its conclusions are drawn from data focused primarily on Northern Hemisphere woody plants, highlighting significant geographic gaps in our current understanding. Bridging these knowledge gaps is essential for accurately forecasting how belowground dynamics will influence global carbon sequestration, nutrient cycling, and ecosystem resilience under changing climatic regimes. Full article

Land cover-based models that rely on purpose-specific thematic details are common in environmental fields such as hydrology, water quality, and ecology. Global remotely sensed land cover from the Dynamic World dataset on Google Earth Engine has trees and built area classes, but enables modelers to create more thematically detailed classifications based on pixel class probabilities from their convolutional neural network (CNN) classifier. However, more information is needed about how these probabilities relate to actual heterogeneity within a land cover class. We used Dynamic World CNN class probabilities to subclassify temperate and tropical forest types from the trees class in the Eastern United States and Costa Rica, as well as developed area intensities from the built class. Subclassifications were evaluated against reference data and in a watershed they were not trained for. The results on dominant temperate forest type user’s accuracy (i.e., of all the pixels classified as a specific land cover type, how many are actually that type) ranged from 43% to 76%, while producer’s accuracy (i.e., of all the actual pixels of a specific land cover type, how many were correctly classified) ranged from 50% to 70%. In the untrained watershed, the overall accuracy was 85% for temperate forest types and 52% for developed areas, demonstrating reliability in classifying forest and developed land cover types. This approach creates opportunities to access up-to-date land cover information with greater thematic detail. Full article

Tropical rainforests are rapidly disappearing due to human activities, particularly land-use changes, resulting in a heterogeneous mosaic of landscapes that substantially contribute to global terrestrial biodiversity loss. We investigated how changes in land-use affect species richness, composition, and functional guilds of ground-dwelling ants within various land-use systems at a local scale in the Amazonian rainforest. Our focus was to respond to the following: (i) How do local species richness and community composition reflect differences among land-use systems? (ii) Are ground-dwelling ants, especially specialists, negatively impacted by intensified land-use changes? We surveyed 55 sites representing five land-use systems: primary forest, secondary forest, forest corridor, selective logging, and Eucalyptus plantation. We registered 150 ant species, and species richness ranged from 43 to 94. Richness varies according to the land-use systems, likely influenced by differences in habitat structural complexity both vertically and horizontally. Ant species composition and guilds distribution also varied among land-use systems studied. Environments characterized by reduced structural complexity or higher disturbed levels, such as Eucalyptus plantations, tend to support lower resource availability, which may lead to decreased species richness. However, the surrounding matrix appears to play a key role in maintaining regional biodiversity, as evidenced by the absence of differences in ground-dwelling ants diversity across all land-use systems studied. Full article

Aphis gossypii is a significant global pest that impacts numerous agricultural crops and vegetables, causing direct damage to food plants and indirect damage through the transmission of phytopathogenic viruses, primarily begomoviruses. In Panama, particularly in the Azuero region, viral infections transmitted by this aphid can affect a substantial share of tomato crops cultivated for industrial use. A traditional alternative to synthetic pesticides involves exploring plant extracts with insecticidal properties derived from wild plants found in our tropical forests, which can be easily prepared and applied by farmers. In this context, the present research aimed to evaluate the insecticidal activity of ethanolic extracts from the stems and leaves of Sphagneticola trilobata on both nymphs and adults of A. gossypii. Mortality was assessed at 24, 48, and 72 h after applying three doses of each extract (25, 50, and 100 µg/L). A standard phytochemical analysis to determine insecticidal activity revealed that both extracts exhibited significant efficacy at the highest concentration tested; however, the leaf extract demonstrated greater effectiveness at lower concentrations. A comprehensive metabolomic study indicated that the active compounds are diterpenes derived from the pimarenyl cation. These compounds have been extensively documented for their insecticidal potential against various insect species, suggesting that ethanolic extracts from this plant could serve as viable candidates for agricultural insecticides to combat aphid infestations. Full article

Global climate change has increased the frequency of compound drought–heatwave events (CDHEs), seriously threatening tropical forest ecosystems. However, due to the complex structure of natural tropical forests, related research remains limited. To address this, we focused on rubber plantations on Hainan Island, which have simpler structures, to explore the impacts of CDHEs on their primary productivity. We used Pearson and Spearman correlation analyses to select the optimal combination of drought and heatwave indices. Then, we constructed a Compound Drought–Heatwave Index (CDHI) using Copula functions to describe the temporal patterns of CDHEs. Finally, we applied a Bayes–Copula conditional probability model to estimate the probability of GPP loss under CDHE conditions. The main findings are as follows: (1) The Standardized Precipitation Evapotranspiration Index (SPEI-3) and Standardized Temperature Index (STI-1) formed the best index combination. (2) The CDHI successfully identified typical CDHEs in 2001, 2003–2005, 2010, 2015–2016, and 2020. (3) Temporally, CDHEs significantly increased the probability of GPP loss in April and May (0.58 and 0.64, respectively), while the rainy season showed a reverse trend due to water buffering (lowest in October, at 0.19). (4) Spatially, the northwest region showed higher GPP loss probabilities, likely due to topographic uplift. This study reveals how tropical plantations respond to compound climate extremes and provides theoretical support for the monitoring and management of tropical ecosystems. Full article

Mangrove forests serve as critical ecological barriers in coastal zones and play a vital role in global blue carbon sequestration strategies. In recent decades, China’s mangrove ecosystems have experienced complex interactions between degradation and restoration under intense coastal urbanization and systematic conservation efforts. However, the long-term spatiotemporal patterns and driving mechanisms of mangrove ecosystem health changes remain insufficiently quantified. This study developed a multi-temporal analytical framework using Landsat imagery (1986–2021) to derive kernel normalized difference vegetation index (kNDVI) time series—an advanced phenological indicator with enhanced sensitivity to vegetation dynamics. We systematically characterized mangrove growth patterns along China’s southeastern coast through integrated Theil–Sen slope estimation, Mann–Kendall trend analysis, and Hurst exponent forecasting. A Deep Forest regression model was subsequently applied to quantify the relative contributions of environmental drivers (mean annual sea surface temperature, precipitation, air temperature, tropical cyclone frequency, and relative sea-level rise rate) and anthropogenic pressures (nighttime light index). The results showed the following: (1) a nationally significant improvement in mangrove vitality (p < 0.05), with mean annual kNDVI increasing by 0.0072/yr during 1986–2021; (2) spatially divergent trajectories, with 58.68% of mangroves exhibiting significant improvement (p < 0.05), which was 2.89 times higher than the proportion of degraded areas (15.10%); (3) Hurst persistence analysis (H = 0.896) indicating that 74.97% of the mangrove regions were likely to maintain their growth trends, while 15.07% of the coastal zones faced potential degradation risks; and (4) Deep Forest regression id the relative rate of sea-level rise (importance = 0.91) and anthropogenic (nighttime light index, importance = 0.81) as dominant drivers, surpassing climatic factors. This study provides the first national-scale, 30 m resolution assessment of mangrove growth dynamics using kNDVI, offering a scientific basis for adaptive management and blue carbon strategies in subtropical coastal ecosystems. Full article

(1) Background: Changes in the spatial, temporal, and magnitude-related patterns of fires caused by humans are expected to exacerbate with climate change, significantly impacting ecosystems and societies worldwide. However, our understanding of fire regimes in many regions remains limited, largely due to the inherent complexity of fire as an ecological process. Pyrogeography, combined with unsupervised learning methods and the availability of long-term satellite data, offers a robust framework for approaching this problem. The purpose of the study is to identify the pyroregions of the Argentine Gran Chaco, the world’s largest continuous tropical dry forest region. (2) Methods: Using globally available fire occurrence datasets, we computed five fire metrics, related to the extent, frequency, intensity, size, and seasonality of fires at three spatial scales (5, 10, and 25 km). In addition, we tested two widely used cluster algorithms, the K-means algorithm and the Gaussian Mixture Model (GMM). (3) Results and Discussion: The identification of pyroregions was dependent on the clustering algorithm and scale of analysis. The GMM algorithm at a 25 km scale ultimately demonstrated more coherent ecological and spatial distributions. GMM identified six pyroregions, which were labeled based on three metrics in the following order: annual burned area (categorized in low, regular or high), interannual variability of fire (rare, occasional, frequent), and fire intensity (low, moderate, intense). The values were as follows: LRM (22% of study area), ROI (19%), ROM (14%), LOM (10%), ROL (9%), and HFL (4%). (4) Conclusions: Our study provides the most comprehensive delineation of the Argentine Gran Chaco’s Dry Forest pyroregions to date, and highlights both the importance of determining the optimal scale of analysis and the critical role of clustering algorithms in efforts to accurately characterize the diverse attributes of fire regimes. Furthermore, it emphasizes the importance of integrating fire ecology principles and fire management perspectives into pyrogeographic studies to ensure a more comprehensive and meaningful characterization of fire regimes. Full article

Banana is an important cash and food crop worldwide. Recent outbreaks of banana diseases are threatening the global banana industry and smallholder livelihoods. Remote sensing data offer the potential to detect the presence of disease, but formal analysis is needed to compare inferred disease data with observed disease data. In this study, we present a novel remote-sensing-based framework that combines Landsat-8 imagery with meteorology-informed phenological models and machine learning to identify anomalies in banana crop health. Unlike prior studies, our approach integrates domain-specific crop phenology to enhance the specificity of anomaly detection. We used a pixel-level random forest (RF) model to predict 11 key vegetation indices (VIs) as a function of historical meteorological conditions, specifically daytime and nighttime temperature from MODIS and precipitation from NASA GES DISC. By training on periods of healthy crop growth, the RF model establishes expected VI values under disease-free conditions. Disease presence is then detected by quantifying the deviations between observed VIs from Landsat-8 imagery and these predicted healthy VI values. The model demonstrated robust predictive reliability in accounting for seasonal variations, with forecasting errors for all VIs remaining within 10% when applied to a disease-free control plantation. Applied to two documented outbreak cases, the results show strong spatial alignment between flagged anomalies and historical reports of banana bunchy top disease (BBTD) and Fusarium wilt Tropical Race 4 (TR4). Specifically, for BBTD in Australia, a strong correlation of 0.73 was observed between infection counts and the discrepancy between predicted and observed NDVI values at the pixel with the highest number of infections. Notably, VI declines preceded reported infection rises by approximately two months. For TR4 in Mozambique, the approach successfully tracked disease progression, revealing clear spatial spread patterns and correlations as high as 0.98 between VI anomalies and disease cases in some pixels. These findings support the potential of our method as a scalable early warning system for banana disease detection. Full article

Tropical forests harbor a significant portion of global biodiversity but are increasingly degraded by human activity. Assessing restoration efforts requires the systematic monitoring of tropical ecosystem status and recovery. Satellite-borne synthetic aperture radar (SAR) supports monitoring changes in vegetation structure and is of particular utility in tropical regions where clouds obscure optical satellite observations. To characterize tropical forest recovery in the Lowland Chocó Biodiversity Hotspot of Ecuador, we apply over a decade of dual-polarized (HH + HV) L-band SAR datasets from the Japanese Space Agency’s (JAXA) PALSAR and PALSAR-2 sensors. We assess the complementarity of the dual-polarized imagery with less frequently available fully-polarimetric imagery, particularly in the context of their respective temporal and informational trade-offs. We examine the radar image texture associated with the dual-pol radar vegetation index (DpRVI) to assess the associated determination of forest and nonforest areas in a topographically complex region, and we examine the equivalent performance of texture measures derived from the Freeman–Durden polarimetric radar decomposition classification scheme applied to the fully polarimetric data. The results demonstrate that employing a dual-polarimetric decomposition classification scheme and subsequently deriving the associated gray-level co-occurrence matrix mean from the DpRVI substantially improved the classification accuracy (from 88.2% to 97.2%). Through this workflow, we develop a new metric, the Radar Forest Regeneration Index (RFRI), and apply it to describe a chronosequence of a tropical forest recovering from naturally regenerating pasture and cacao plots. Our findings from the Lowland Chocó region are particularly relevant to the upcoming NASA-ISRO NISAR mission, which will enable the comprehensive characterization of vegetation structural parameters and significantly enhance the monitoring of biodiversity conservation efforts in tropical forest ecosystems. Full article

The Cerrado-Amazon Transition (CAT) in Brazil represents one of the most ecologically complex and dynamic tropical ecotones globally; however, it remains insufficiently characterized at high spatial resolution, primarily due to its intricate vegetation mosaics and the limited availability of reliable ground reference data. Accurate land cover maps are urgently needed to support conservation and sustainable land-use planning in this frontier region, especially for distinguishing critical vegetation types such as Amazon rainforest, Cerradão (dense woodland), and Savanna. In this study, we produce the first high-resolution land cover map of the CAT by integrating PlanetScope optical imagery, Sentinel-2 multispectral data, and Sentinel-1 SAR data within a U-net deep learning framework. This data fusion approach enables improved discrimination of ecologically similar vegetation types across heterogeneous landscapes. We systematically compare classification performance across single-sensor and fused datasets, demonstrating that multi-source fusion significantly outperforms single-source inputs. The highest overall accuracy was achieved using the fusion of PlanetScope, Sentinel-2, and Sentinel-1 (F1 = 0.85). Class-wise F1 scores for the best-performing model were 0.91 for Amazon Forest, 0.76 for Cerradão, and 0.76 for Savanna, indicating robust model performance in distinguishing ecologically important vegetation types. According to the best-performing model, 50.3% of the study area remains covered by natural vegetation. Cerradão, although ecologically important, covers only 8.4% of the landscape and appears highly fragmented, underscoring its vulnerability. These findings highlight the power of deep learning and multi-sensor integration for fine-scale land cover mapping in complex tropical ecotones and provide a critical spatial baseline for monitoring ecological changes in the CAT region. Full article

Tropical pasture degradation represents a major challenge for global food security and environmental conservation, particularly in Brazil, where up to 60% of pastures are degraded. This study evaluates the economic viability of recovery of degraded pastures using an integrated crop–livestock–forest (ICLF) system. A representative 2-hectare system in the Brazilian Cerrado was analyzed, featuring native Dipteryx alata trees interplanted with pasture for cattle grazing. A deterministic financial model was developed to simulate annual cash flows over a 20-year period under various financing scenarios, including self-financing and multiple subsidized rural credit lines (e.g., Pronaf and Pronamp programs, and ABC Ambiental). The analysis shows that subsidized credit lines with low interest rates and extended grace periods significantly improve project profitability, yielding positive NPVs and robust internal rates of return, while self-financing and high-cost credit options (such as Pronaf Mulher) result in negative NPVs. The dual cash flow strategy—where borrowed funds are immediately invested in secure fixed-income instruments—further enhances economic performance. The findings demonstrate that ICLF-based pasture recovery is economically viable when supported by appropriate financing, offering a scalable model for sustainable agriculture that delivers both economic and environmental benefits. Full article

Accurately estimating the forest canopy height is essential for quantifying forest biomass and carbon storage. Recently, the ICESat-2 and GEDI spaceborne LiDAR missions have significantly advanced global canopy height mapping. However, due to inherent sensor limitations, their footprint-level estimates often show systematic bias. Tall forests tend to be underestimated, while short forests are often overestimated. To address this issue, we used coincident G-LiHT airborne LiDAR measurements to correct footprint-level canopy heights from both ICESat-2 and GEDI, aiming to improve the canopy height retrieval accuracy across Puerto Rico’s tropical forests. The bias-corrected LiDAR dataset was then combined with multi-source predictors derived from Sentinel-1/2 and the 3DEP DEM. Using these inputs, we trained a canopy height inversion model based on the AutoGluon stacking ensemble method. Accuracy assessments show that, compared to models trained on uncorrected single-source LiDAR data, the new model built on the bias-corrected ICESat-2–GEDI fusion outperformed in both overall accuracy and consistency across canopy height gradients. The final model achieved a correlation coefficient (R) of 0.80, with a root mean square error (RMSE) of 3.72 m and a relative RMSE of 0.22. The proposed approach offers a robust and transferable approach for high-resolution canopy structure mapping and provides valuable support for carbon accounting and tropical forest management. Full article

Accurate and timely flood mapping is critical for informing emergency response and risk mitigation during extreme weather events. This study presents a synthetic aperture radar (SAR)-based approach for rapid flood extent mapping using Sentinel-1 imagery, demonstrated for Tropical Storm Imelda (17–21 September 2019) in southeastern Texas. Dual-polarization Sentinel-1 SAR data (VH and VV) were processed by computing the VH/VV backscatter ratio, and the resulting ratio image was classified using a supervised Random Forest classifier to delineate water and land. All Sentinel-1 images underwent radiometric calibration, speckle noise filtering, and terrain correction to ensure precision in flood delineation. The Random Forest classifier achieved an overall flood mapping accuracy exceeding 94%, with Cohen’s kappa coefficients of approximately 0.75–0.80, demonstrating the approach’s reliability in distinguishing transient floodwaters from permanent water bodies. The spatial distribution of flooding was strongly influenced by topography and land cover. Analysis of Shuttle Radar Topography Mission (SRTM) digital elevation data revealed that low-lying, flat terrain was most vulnerable to inundation; correspondingly, the land cover types most affected were hay/pasture, cultivated land, and emergent wetlands. Additionally, urban areas with low-intensity development experienced extensive flooding, attributed to impervious surfaces exacerbating runoff. A strong, statistically significant correlation (R² = 0.87, p < 0.01) was observed between precipitation and flood extent, indicating that heavier rainfall led to greater inundation; accordingly, the areas with the highest rainfall totals (e.g., Jefferson and Chambers counties) experienced the most extensive flooding, as confirmed by SAR-based change detection. The proposed approach eliminates the need for manual threshold selection, thereby reducing misclassification errors due to speckle noise and land cover heterogeneity. Harnessing globally available Sentinel-1 data with near-real-time processing and a robust classifier, this approach provides a scalable solution for rapid flood monitoring. These findings underscore the potential of SAR-based flood mapping under adverse weather conditions, thereby contributing to improved disaster preparedness and resilience in flood-prone regions. Full article