Aim: Tropical cyclones are large‐scale disturbances that can shape the structure and dynamics of mangrove forests. Although tropical cyclone activity overlaps extensively with the latitudinal distribution of mangrove forests, the relationships between cyclone intensity and frequency and mangrove forest canopy damage and recovery are not understood at the global scale. Using remote sensing data, we examined how mangrove forest structure, climate and cyclone characteristics influence canopy cover loss and recovery dynamics.Location: Global tropics.Time period: 2000–2020.Major taxa studied: Mangrove trees.Methods: Using two satellite‐derived vegetation indices (the enhanced vegetation index and the normalized difference infrared index) from 86 cyclones affecting 56 mangrove sites across the globe, we quantified mangrove canopy loss in relationship to cyclones. Using linear regression and variance decomposition, we identified and ranked significant predictors of cyclone‐induced canopy loss and recovery.Results: Three‐quarters of the studied cyclone disturbances resulted in canopy damage. Stands exposed to high wind speeds and those close to the cyclone paths were more severely damaged, whereas lower damage magnitudes were found in sites with greater past cyclone frequency. Canopy damage was greater in tall mangrove stands but decreased with higher aboveground biomass. The distance from the cyclone path and maximum wind speed were the most important factors, representing > 50% of the explained variation in cyclone damage. There was considerable variation in canopy damage among cyclones, but rates of recovery were similar across all mangrove sites, with the main predictor of recovery time being the degree of canopy loss.Main conclusions: Our results suggest that the resistance of mangrove canopy cover to cyclone disturbance is variably tuned to the cyclone regime and vegetation characteristics, but resilience is inherent to the magnitude of canopy damage because the rate of forest canopy recovery appears to be consistent globally.

Optical remote sensing is a tool frequently used to assess cyclone-induced forest disturbances. However, the frequent cloud cover limits the availability of optical data in cyclone basins. On the other hand, radar remote sensing is not affected by cloud cover and has been used to detect windthrows. Yet, the potential of radar sensing in monitoring cyclone damages of varying magnitudes across forest landscapes remains unclear. Here, we compared radar remote sensing to optical remote sensing of four cyclone disturbances in the Fushan Experimental Forest of northern Taiwan and the El Yunque National Forest in Puerto Rico using Landsat 8 and C-band Sentinel-1 satellite data. We analyzed the change in two optical vegetation indices, EVI (Enhanced Vegetation Index) and NDII (Normalized Difference Infrared Index), and three radar-based metrics, co- and cross-polarized backscatters (VV, VH) and their ratio (Canopy Development Index, CDI) after cyclone disturbances and during approximately the same periods of non-cyclone years. We assessed the improved temporal resolution permitted by Sentinel-1 constellation on the detection of forest canopy disturbance. Bootstrapped comparisons indicated that both optical and radar indices detected canopy change, but their correlations were not significant. Improved temporal resolution of CDI allowed to distinguish cyclone-induced canopy change from the phenological variation and even change by nearby cyclones. Although this, VV and VH backscatters responded more closely to cyclone disturbances than their ratio. Our results demonstrate that the C-band backscatter intensities can track cyclone-induced change of forest canopies, and provide an assessment of C-band capabilities to monitor cyclone disturbances.

Remote sensing of crop diseases has traditionally focused on detecting visible symptoms, often limiting intervention to advanced stages of epidemic development. This study investigates whether high-resolution unmanned aerial vehicles (UAV)-based red–green–blue (RGB) imagery can reveal earlier physiological destabilization preceding visible symptoms of wheat stripe rust and wheat leaf rust. UAV imagery was acquired at four winter wheat-growing sites in Luxembourg during the 2018/2019 season. Temporal dynamics of green–red spectral slopes were analyzed and compared with ground-based disease severity observations to identify potential pre-symptomatic spectral signals. A consistent flattening of the green–red spectral slope was detected prior to a rapid increase in visually assessed severity for both diseases. However, the length of this pre-symptomatic window varied between the two diseases: it lasted 7 to 14 days for wheat stripe rust and 5 to 10 days for wheat leaf rust. Likewise, the reduction in spectral slope magnitude was slightly greater for wheat stripe rust (65–80%) than for wheat leaf rust (60–75%), indicating that the temporal lead time and intensity of the spectral response were disease-dependent. During the pre-symptomatic phase, the spectral dynamics reflected latent physiological changes rather than visible disease severity. Strong correlations emerged only after the epidemic transition. These findings demonstrate that UAV-based RGB imagery could capture a distinct pre-symptomatic phase of stripe rust and leaf rust epidemics in winter wheat. Interpreting RGB spectral dynamics as early-warning indicators rather than merely as static severity proxies can guide proactive disease monitoring and precision agriculture.

Biodiversity conservation is increasingly recognized as a product of coupled social–ecological systems shaped not only by ecological processes, but also by economic capacity, social inequality, and geopolitical stability. Here, we examined whether national conservation performance is systematically associated with economic prosperity, income inequality, peace, and the importance of natural resources in the national economy across 142 countries. Conservation performance was represented using the Nature Conservation Index (NCI), a composite metric integrating biodiversity threats, land management, governance capacity, and future environmental trends. We analysed relationships between NCI, Gross National Income per capita, the Gini coefficient, the National Peace Index, and the total nature resources rents using a generalized additive model that accounted for nonlinear relationships and income stratification. Together, these societal variables explained deviance of 52.2% for the NCI model. Economic prosperity was positively associated with NCI, although the relationship was nonlinear across income groups, suggesting that conservation capacity does not increase uniformly with development. Counter to commonly assumed relationships between inequality and biodiversity loss, income inequality showed a positive multivariate association with NCI scores. This result suggests that high national conservation performance can coexist with unequal social conditions, potentially reflecting centralized or exclusionary conservation systems that elevate institutional conservation metrics without necessarily improving equitable biodiversity outcomes. We also found that more peaceful countries consistently exhibited higher conservation performance, supporting the view that geopolitical stability provides enabling conditions for sustained conservation governance and institutional continuity. We further suggest that national conservation indices may partly capture governance capacity, reporting infrastructure, and political commitment rather than ecological condition alone, while telecoupled trade systems may allow wealthier nations to externalize environmental pressures beyond their borders. Although the cross-sectional design precludes causal inference, our findings highlight the need to interpret global conservation indicators cautiously and to incorporate social equity and externalized environmental impacts more explicitly into international biodiversity assessment frameworks.

UAV-based RGB imaging is increasingly used to track crop diseases and assess fungicide performance. However, standard disease severity measures may miss an important factor: fungicide-induced changes in canopy structure that modulate disease observability. Here we analyse a multi-site dataset of wheat yellow and leaf rust, integrating UAV-derived canopy cover and disease severity, and rater-based disease severity assessments. Results indicate that between visual and UAV imagery-based disease severity was high but fundamentally non-stationary, governed by canopy cover and epidemic intensity. Measurement error followed structured, canopy-dependent regimes. The canopy-normalized disease area, a metric that harmonizes severity with structural and spatial context, revealed ranking shifts in fungicide efficacy that are not captured through traditional metrics. This suggests that severity-based comparisons may combine true disease suppression with treatment-related changes in how visible symptoms are. Framing plant disease quantification as a measurement challenge, rather than only a detection, may support a more accurate assessment of treatment effects. Modelling the influence of canopy structure on symptom expression could provide a scalable and robust basis for disease monitoring and decision-making in precision agriculture.