Uncontrolled, large fires are a major threat to the biodiversity of protected heath landscapes. The severity of the fire is an important factor influencing vegetation recovery. We used airborne imaging spectroscopy data from the Airborne Prism Experiment (APEX) sensor to: (1) investigate which spectral regions and spectral indices perform best in discriminating burned from unburned areas; and (2) assess the burn severity of a recent fire in the Kalmthoutse Heide, a heathland area in Belgium. A separability index was used to estimate the effectiveness of individual bands and spectral indices to discriminate between burned and unburned land. For the burn severity analysis, a modified version of the Geometrically structured Composite Burn Index (GeoCBI) was developed for the field data collection. The field data were collected in four different vegetation types: Calluna vulgaris-dominated heath (dry heath), Erica tetralix-dominated heath (wet heath), Molinia caerulea (grass-encroached heath), and coniferous woodland. Discrimination between burned and unburned areas differed among vegetation types. For the pooled dataset, bands in the near infrared (NIR) spectral region demonstrated the highest discriminatory power, followed by short wave infrared (SWIR) bands. Visible wavelengths performed considerably poorer. The Normalized Burn Ratio (NBR) outperformed the other spectral indices and the individual spectral bands in discriminating between burned and unburned areas. For the burn severity assessment, all spectral bands and indices showed low correlations with the field data GeoCBI, when data of all pre-fire vegetation types were pooled (R² maximum 0.41). Analysis per vegetation type, however, revealed considerably higher correlations (R² up to 0.78). The Mid Infrared Burn Index (MIRBI) had the highest correlations for Molinia and Erica (R² = 0.78 and 0.42, respectively). In Calluna stands, the Char Soil Index (CSI) achieved the highest correlations, with R² = 0.65. In Pinus stands, the Normalized Difference Vegetation Index (NDVI) and the red wavelength both had correlations of R² = 0.64. The results of this study highlight the superior performance of the NBR to discriminate between burned and unburned areas, and the disparate performance of spectral indices to assess burn severity among vegetation types. Consequently, in heathlands, one must consider a stratification per vegetation type to produce more reliable burn severity maps.

Coastal marshes and their valuable ecosystem services are feared to be lost by sea level rise, yet the mechanisms of marsh degradation into ponds and potential recovery are poorly understood. We quantified and analyzed elevations of marsh surfaces and pond bottoms along a marsh loss gradient (Blackwater River, Maryland, USA). Our analyses show that ponds deepen with increasing tidal channel width connecting the ponds to the river, indicating a new feedback mechanism where channels lead to enhanced tidal export of pond bottom material. Pond elevations also decrease with increasing pond size, consistent with previous work identifying a positive feedback between wind wave erosion and pond size. These two positive feedbacks, combined with bimodal elevation distributions and sharp topographic boundaries between interior ponds and the marsh platform, indicate alternative elevation states and imply that marsh loss by pond formation is nearly irreversible once pond deepening exceeds a critical level.

Aim: To verify which vegetation and environmental factors are the most important in determining the spatial and temporal variability of average and maximum values of radiation use efficiency (RUE_ann and RUE_max, respectively) of cold and temperate forests.Location: Forty‐eight cold and temperate forests distributed across the Northern Hemisphere.Major taxa studied: Evergreen and deciduous trees.Time period: 2000–2011.Methods: We analysed the impact of 17 factors as potential determinants of mean RUE (at 8 days interval, annual and interannual level) and RUE_max (at annual and interannual level) in cold and temperate forests by using linear regression and random forests models.Results: Mean annual RUE (RUE_ann, c. 1.1 gC/MJ) and RUE_max (c. 0.8 gC/MJ) did not differ between cold and temperate forests. However, for cold forests, RUE_ann was affected by temperature‐related variables, while for temperate forests RUE_ann was affected by drought‐related variables. Leaf area index (LAI) was important for both forest types, while N deposition only for cold forests and cloud cover only for temperate forest. RUE_max of cold forests was mainly driven by N deposition and LAI, whereas for temperate forests only a weak relationship between RUE_max and CO₂ concentration was found. Short‐term variability of RUE was strongly related to the meteorological variables and varied during the season and was stronger in summer than spring or autumn. Interannual variability of RUE_ann and RUE_max was only weakly related to the interannual variability of the environmental drivers.Main conclusions: Cold and temperate forests show different relationships with the environment and vegetation properties. Among the RUE drivers observed, the least anticipated was N deposition. RUE is strongly related to short‐term and seasonal changes in meteorological variables among seasons and among sites. Our results should be considered in the formulation of climate zone‐specific tools for remote sensing and global models.