High-resolution Vegetation Maps Research Articles

Mapping the wildland-urban interface at municipal level for wildfire exposure analysis in mainland Portugal

The Wildland-Urban Interface (WUI), where vegetation and built-up structures intermingle, encompasses a variety of territorial elements that interact spatially, being variable both in space and time. Mapping the WUI at finer scales is paramount to assess wildfire exposure and define tailored mitigation strategies. Our aim was to develop a semi-automated method to map the WUI at municipal level, leveraging recent advances in data and technology. We tested the procedure in four municipalities of mainland Portugal with different fire history, biophysical conditions, and sociodemographic contexts. We considered WUI as either intermix or interface. Our approach integrates both building location data and high-resolution vegetation maps, to calculate the density of buildings and forest cover proportion within different circular moving window sizes. Within each radius, we evaluated the total area and spatial distribution of the WUI types, as well as the number of buildings within WUI and within the fire perimeters recorded between the years 2000 and 2022 and analysed the differences between municipalities. We then compared the mapped WUI with previous WUI mappings for mainland Portugal, to identify common spots and potential spatial divergences. We found that the area mapped as WUI within all four municipalities ranged from about 400 km2 to 1135 km2 depending on the radius size. A distinct distribution for each type of WUI was observed as the radius size increased: the intermix WUI showed a tendency to increase, and the interface WUI increased only between the radius of 100 and 200 m, decreasing gradually in subsequent radii. Between 39.4% and 45.5% of the nearly 200,000 buildings in the study areas were within WUI, depending on radius size and a total of 5436 buildings were within the historic fire perimeter. Although the comparison with other maps showed fair agreement, due to differences in data and methodology, common areas mapped as WUI were found, which suggests that these areas should receive greater attention from decision-makers regarding fire management strategies, since their classification as WUI remains consistent across different methodologies.

Assessment of the mixed coniferousbroadleaved forest canopy disturbance induced by typhoon Maysak (2020) using drone-borne images near Vladivostok, Russia

We assess the impact of typhoon Maysak (2020) on the canopy structure of mixed coniferous-broadleaved forests with Abies holophylla, Pinus koraiensis and a complex of deciduous broad-leaved species and secondary oak forests of Quercus mongolica. The study was conducted in the experimental forest of the Botanical Garden-Institute FEB RAS. We classified the natural forest vegetation of the study area, created a high-resolution vegetation map, and assessed the damage from typhoon Maysak remotely using the ultra-high resolution images, obtained with an unmanned aerial vehicle. We found that the largest number and area of canopy gaps are associated with the conditionally primary coniferous-broadleaved forests, where Abies holophylla and Pinus koraiensis form the main canopy layer. The smallest number and area of gaps relate to the secondary oak forests.

Estimating Ground Elevation and Vegetation Characteristics in Coastal Salt Marshes Using UAV-Based LiDAR and Digital Aerial Photogrammetry

This study evaluates the skills of two types of drone-based point clouds, derived from LiDAR and photogrammetric techniques, in estimating ground elevation, vegetation height, and vegetation density on a highly vegetated salt marsh. The proposed formulation is calibrated and tested using data measured on a Spartina alterniflora-dominated salt marsh in Little Sapelo Island, USA. The method produces high-resolution (ground sampling distance = 0.40 m) maps of ground elevation and vegetation characteristics and captures the large gradients in the proximity of tidal creeks. Our results show that LiDAR-based techniques provide more accurate reconstructions of marsh vegetation (height: MAEVH = 12.6 cm and RMSEVH = 17.5 cm; density: MAEVD = 6.9 stems m−2 and RMSEVD = 9.4 stems m−2) and morphology (MAEM = 4.2 cm; RMSEM = 5.9 cm) than Digital Aerial Photogrammetry (DAP) (MAEVH = 31.1 cm; RMSEVH = 38.1 cm; MAEVD = 12.7 stems m−2; RMSEVD = 16.6 stems m−2; MAEM = 11.3 cm; RMSEM = 17.2 cm). The accuracy of the classification procedure for vegetation calculation negligibly improves when RGB images are used as input parameters together with the LiDAR-UAV point cloud (MAEVH = 6.9 cm; RMSEVH = 9.4 cm; MAEVD = 10.0 stems m−2; RMSEVD = 14.0 stems m−2). However, it improves when used together with the DAP-UAV point cloud (MAEVH = 21.7 cm; RMSEVH = 25.8 cm; MAEVD = 15.2 stems m−2; RMSEVD = 18.7 stems m−2). Thus, we discourage using DAP-UAV-derived point clouds for high-resolution vegetation mapping of coastal areas, if not coupled with other data sources.

Climate exposure shows high risk and few climate refugia for Chilean native vegetation

The many Gondwanic vegetation types found across the extensive latitudes and elevation gradients of South America's southern cone contribute to Chile's global biodiversity hotspot ranking. Species loss in global biodiversity hotspots is an ongoing climate change concern and land managers need spatially explicit climate risk maps to adapt conservation strategies to climate change in these areas. We modeled future climate risk for Chile's terrestrial vegetation using a high-resolution vegetation map and tested the relationship to climate risk for each type's latitudinal and elevation range. We found that 43.6% of all vegetation has high climate risk in Global Circulation Models (GCMs) under a high emissions scenario (RCP8.5). All forest types in the country, including Southern Beech (Nothofagus sp.), Alerce (Fitzroya cupressoides), Araucaria (Araucaria araucana), and Sclerophyllous, as well as the Valdivian rainforest, Altiplanic Steppes, and Salares, face high levels of climate risk. Tests for trends in risk across elevation and latitude showed that exposure for all types increased with elevation based on the MIROC5 GCM, and decreased with latitude based on the Had2GEM-ES GCM. Our results suggest that vegetation types with smaller latitudinal ranges typically have higher levels of climate risk, but a type's elevation range is not significantly correlated with risk of exposure. We identified climatically stable areas which could act as vegetation refugia in Patagonia, the central Andes mountains between latitudes 27.5°S and 32.5°S, and some coastal areas. Conservation strategies in Chile should include the protection of climatically stable areas to safeguard current Gondwanic biodiversity and active habitat restoration in climatically exposed areas to facilitate vegetation shifts.

Applying a high-precision tracking system to distinguish the spatiotemporal patterns of animal movement in grassland ecology

Spatial heterogeneity in vegetation may derive from variation in animal movement patterns, but these patterns have been difficult to study at the fine spatial and temporal resolutions necessary to relate them to small-scale vegetation patterns. Here, we demonstrated the utility of Ultra-WideBand (UWB) technology to examine animal movement patterns. We evaluated UWB performance in a field setting and illustrated how these data could distinguish movement patterns of different types of livestock during day and night. The data were of high spatial resolution (within 9.7 cm ± 1.7 cm of actual locations) and high temporal resolution (every second). The positional data clearly demonstrated differential movement patterns between a cow and ewe and between daytime and nighttime. Furthermore, patterns were spatially heterogeneous: the cow spent up to 5% of the day in a single 1 m2 square and the ewe up to 7.6% of the day in a single square. Over the course of one day, the cow visited about two-thirds of the plot whereas the ewe visited about half of the plot. Our study suggests that UWB technology is a practical tool to describe animal movement at fine spatial and temporal scales. We note some limitations of this technology (battery life, receiver range, and cost) and end by identifying some opportunities for it to improve ecology and conservation, particularly when combined with other data or technologies such as high-resolution vegetation mapping.

Roles of horizontal and vertical tree canopy structure in mitigating daytime and nighttime urban heat island effects

The urban heat island (UHI) is increasingly recognized as a serious, worldwide problem because of urbanization and climate change. Urban vegetation is capable of alleviating UHI and improving urban environment by shading together with evapotranspiration. While the impacts of abundance and spatial configuration of vegetation on land surface temperature (LST) have been widely examined, very little attention has been paid to the role of vertical structure of vegetation in regulating LST. In this study, we investigated the relationships between horizontal/vertical structure characteristics of urban tree canopy and LST as well as diurnal divergence in Nanjing City, China, with the help of high resolution vegetation map, Light Detection and Ranging (LiDAR) data and various statistical analysis methods. The results indicated that composition, configuration and vertical structure of tree canopy were all significantly related to both daytime LST and nighttime LST. Tree canopy showed stronger influence on LST during the day than at night. Note that the contribution of composition of tree canopy to explaining spatial heterogeneity of LST, regardless of day and night, was the highest, followed by vertical structure and configuration. Combining composition, configuration and vertical structure of tree canopy can take advantage of their respective advantages, and best explain variation in both daytime LST and nighttime LST. As for the independent importance of factors affecting spatial variation of LST, percent cover of tree canopy (PLAND), mean tree canopy height (TH_Mean), amplitude of tree canopy height (TA) and patch cohesion index (COHESION) were the most influential during the day, while the most important variables were PLAND, maximum height of tree canopy (TH_Max), variance of tree canopy height (TH_SD) and COHESION at night. This research extends our understanding of the impacts of urban trees on the UHI effect from the horizontal to three-dimensional space. In addition, it may offer sustainable and effective strategies for urban designers and planners to cope with increasing temperature.

Coastal Dune Vegetation Mapping Using a Multispectral Sensor Mounted on an UAS

Vegetation mapping, identifying the type and distribution of plant species, is important for analysing vegetation dynamics, quantifying spatial patterns of vegetation evolution, analysing the effects of environmental changes and predicting spatial patterns of species diversity. Such analysis can contribute to the development of targeted land management actions that maintain biodiversity and ecological functions. This paper presents a methodology for 3D vegetation mapping of a coastal dune complex using a multispectral camera mounted on an unmanned aerial system with particular reference to the Buckroney dune complex in Co. Wicklow, Ireland. Unmanned aerial systems (UAS), also known as unmanned aerial vehicles (UAV) or drones, have enabled high-resolution and high-accuracy ground-based data to be gathered quickly and easily on-site. The Sequoia multispectral sensor used in this study has green, red, red edge and near-infrared wavebands, and a regular camer with red, green and blue wavebands (RGB camera), to capture both visible and near-infrared (NIR) imagery of the land surface. The workflow of 3D vegetation mapping of the study site included establishing coordinated ground control points, planning the flight mission and camera parameters, acquiring the imagery, processing the image data and performing features classification. The data processing outcomes included an orthomosaic model, a 3D surface model and multispectral imagery of the study site, in the Irish Transverse Mercator (ITM) coordinate system. The planimetric resolution of the RGB sensor-based outcomes was 0.024 m while multispectral sensor-based outcomes had a planimetric resolution of 0.096 m. High-resolution vegetation mapping was successfully generated from these data processing outcomes. There were 235 sample areas (1 m × 1 m) used for the accuracy assessment of the classification of the vegetation mapping. Feature classification was conducted using nine different classification strategies to examine the efficiency of multispectral sensor data for vegetation and contiguous land cover mapping. The nine classification strategies included combinations of spectral bands and vegetation indices. Results show classification accuracies, based on the nine different classification strategies, ranging from 52% to 75%.

High-Resolution Vegetation Mapping Using eXtreme Gradient Boosting Based on Extensive Features

Accurate mapping of vegetation is a premise for conserving, managing, and sustainably using vegetation resources, especially in conditions of intensive human activities and accelerating global changes. However, it is still challenging to produce high-resolution multiclass vegetation map in high accuracy, due to the incapacity of traditional mapping techniques in distinguishing mosaic vegetation classes with subtle differences and the paucity of fieldwork data. This study created a workflow by adopting a promising classifier, extreme gradient boosting (XGBoost), to produce accurate vegetation maps of two strikingly different cases (the Dzungarian Basin in China and New Zealand) based on extensive features and abundant vegetation data. For the Dzungarian Basin, a vegetation map with seven vegetation types, 17 subtypes, and 43 associations was produced with an overall accuracy of 0.907, 0.801, and 0.748, respectively. For New Zealand, a map of 10 habitats and a map of 41 vegetation classes were produced with 0.946, and 0.703 overall accuracy, respectively. The workflow incorporating simplified field survey procedures outperformed conventional field survey and remote sensing based methods in terms of accuracy and efficiency. In addition, it opens a possibility of building large-scale, high-resolution, and timely vegetation monitoring platforms for most terrestrial ecosystems worldwide with the aid of Google Earth Engine and citizen science programs.

Three mires in the south-eastern Alps (northern Italy)

High-resolution vegetation maps, at 1:500 scale, were realized in three mires located in the Italian south-eastern Alps (Coltrondo West, 12,800 m 2 ; Coltrondo South, 22,300 m 2 and Palu di sotto, 50,600 m 2 ). Vegetation maps were based on topography surveyed by GPS, performed on ground surveys and produced using ArcGIS 9.1. A total of 36 plant communities, whose distribution was reported on the maps, were individuated and classified according to the Braun-Blanquet method. The mires strongly differed from each other with regard to vegetation cover, with Coltrondo West having a prevalent bog-like vegetation and Coltrondo South a prevalently fen-like vegetation. At Palu di sotto, bog-like vegetation covered a surface fed by ground water. This pattern could be explained by the lowering of water table determined by intensive water extraction for human activities. The maps will be profitably used as reference for future monitoring of vegetation dynamics under changing climatic and landuse conditions.

High-Resolution Vegetation Mapping in Japan by Combining Sentinel-2 and Landsat 8 Based Multi-Temporal Datasets through Machine Learning and Cross-Validation Approach

This paper presents an evaluation of the multi-source satellite datasets such as Sentinel-2, Landsat-8, and Moderate Resolution Imaging Spectroradiometer (MODIS) with different spatial and temporal resolutions for nationwide vegetation mapping. The random forests based machine learning and cross-validation approach was applied for evaluating the performance of different datasets. Cross-validation with the rich-feature datasets—with a sample size of 390—showed that the MODIS datasets provided highest classification accuracy (Overall accuracy = 0.80, Kappa coefficient = 0.77) compared with Landsat 8 (Overall accuracy = 0.77, Kappa coefficient = 0.74) and Sentinel-2 (Overall accuracy = 0.66, Kappa coefficient = 0.61) datasets. As a result, temporally rich datasets were found to be crucial for the vegetation physiognomic classification. However, in the case of Landsat 8 or Sentinel-2 datasets, sample size could be increased excessively as around 9800 ground truth points could be prepared within 390 MODIS pixel-sized polygons. The increase in the sample size significantly enhanced the classification using Landsat-8 datasets (Overall accuracy = 0.86, Kappa coefficient = 0.84). However, Sentinel-2 datasets (Overall accuracy = 0.77, Kappa coefficient = 0.74) could not perform as much as the Landsat-8 datasets, possibly because of temporally limited datasets covered by the Sentinel-2 satellites so far. A combination of the Landsat-8 and Sentinel-2 datasets slightly improved the classification (Overall accuracy = 0.89, Kappa coefficient = 0.87) than using the Landsat 8 datasets separately. Regardless of the fact that Landsat 8 and Sentinel-2 datasets have lower temporal resolutions than MODIS datasets, they could enhance the classification of otherwise challenging vegetation physiognomic types due to possibility of training a wider variation of physiognomic types at 30 m resolution. Based on these findings, an up-to-date 30 m resolution vegetation map was generated by using Landsat 8 and Sentinel-2 datasets, which showed better accuracy than the existing map in Japan.

Spatial, seasonal, and topographical patterns of surface albedo in Norwegian forests and cropland

ABSTRACTLand surface albedo is a key parameter of the Earth’s climate system. It has high variability in space, time, and land cover and it is among the most important variables in climate models. Extensive large-scale estimates can help model calibration and improvement to reduce uncertainties in quantifying the influence of surface albedo changes on the planetary radiation balance. Here, we use satellite retrievals of Moderate Resolution Imaging Spectroradiometer (MODIS) surface albedo (MCD43A3), high-resolution land-cover maps, and meteorological records to characterize climatological albedo variations in Norway across latitude, seasons, land-cover type (deciduous forests, coniferous forests, and cropland), and topography. We also investigate the net changes in surface albedo and surface air temperature through site pair analysis to mimic the effects of land-use transitions between forests and cropland and among different tree species. We find that surface albedo increases at increasing latitude in the snow season, and cropland and deciduous forests generally have higher albedo values than coniferous forests, but for few days in spring. Topography has a large influence on MODIS albedo retrievals, with values that can change up to 100% for the same land-cover class (e.g. spruce in winter) under varying slopes and aspect of the terrain. Cropland sites have surface air temperature higher than adjacent forested sites, and deciduous forests are slightly colder than adjacent coniferous forests. By integrating satellite measurements and high-resolution vegetation maps, our results provide a large semi-empirical basis that can assist future studies to better predict changes in a fundamental climate-regulating service such as surface albedo.

Mapping Arctic Tundra Vegetation Communities Using Field Spectroscopy and Multispectral Satellite Data in North Alaska, USA

The Arctic is currently undergoing intense changes in climate; vegetation composition and productivity are expected to respond to such changes. To understand the impacts of climate change on the function of Arctic tundra ecosystems within the global carbon cycle, it is crucial to improve the understanding of vegetation distribution and heterogeneity at multiple scales. Information detailing the fine-scale spatial distribution of tundra communities provided by high resolution vegetation mapping, is needed to understand the relative contributions of and relationships between single vegetation community measurements of greenhouse gas fluxes (e.g., ~1 m chamber flux) and those encompassing multiple vegetation communities (e.g., ~300 m eddy covariance measurements). The objectives of this study were: (1) to determine whether dominant Arctic tundra vegetation communities found in different locations are spectrally distinct and distinguishable using field spectroscopy methods; and (2) to test which combination of raw reflectance and vegetation indices retrieved from field and satellite data resulted in accurate vegetation maps and whether these were transferable across locations to develop a systematic method to map dominant vegetation communities within larger eddy covariance tower footprints distributed along a 300 km transect in northern Alaska. We showed vegetation community separability primarily in the 450–510 nm, 630–690 nm and 705–745 nm regions of the spectrum with the field spectroscopy data. This is line with the different traits of these arctic tundra communities, with the drier, often non-vascular plant dominated communities having much higher reflectance in the 450–510 nm and 630–690 nm regions due to the lack of photosynthetic material, whereas the low reflectance values of the vascular plant dominated communities highlight the strong light absorption found here. High classification accuracies of 92% to 96% were achieved using linear discriminant analysis with raw and rescaled spectroscopy reflectance data and derived vegetation indices. However, lower classification accuracies (~70%) resulted when using the coarser 2.0 m WorldView-2 data inputs. The results from this study suggest that tundra vegetation communities are separable using plot-level spectroscopy with hand-held sensors. These results also show that tundra vegetation mapping can be scaled from the plot level (<1 m) to patch level (<500 m) using spectroscopy data rescaled to match the wavebands of the multispectral satellite remote sensing. We find that developing a consistent method for classification of vegetation communities across the flux tower sites is a challenging process, given the spatial variability in vegetation communities and the need for detailed vegetation survey data for training and validating classification algorithms. This study highlights the benefits of using fine-scale field spectroscopy measurements to obtain tundra vegetation classifications for landscape analyses and use in carbon flux scaling studies. Improved understanding of tundra vegetation distributions will also provide necessary insight into the ecological processes driving plant community assemblages in Arctic environments.

High-resolution mapping of aboveground shrub biomass in Arctic tundra using airborne lidar and imagery

Abstract Accurate monitoring of climate-driven expansion of low-stature shrubs in Arctic tundra requires high-resolution maps of shrub biomass that can accurately quantify the current baseline over relevant spatial and temporal extents. In this study, our goal was to use airborne lidar and imagery to build accurate high-resolution shrub biomass maps for an important research landscape in the American Arctic. In a leave-one-out cross-validation analysis, optimized lidar-derived canopy volume was a good single predictor of harvested shrub biomass (R2 = 0.62; RMSD = 219 g m− 2; slope = 1.08). However, model accuracy was improved by incorporating additional lidar-derived canopy metrics and airborne spectral metrics in a Random Forest regression approach (pseudo R2 = 0.71; RMSD = 197 g m− 2; slope = 1.02). The best Random Forest model was used to map shrub biomass at 0.80 m resolution across three lidar collection footprints (~ 12.5 km2 total) near Toolik Field Station on Alaska's North Slope. We characterized model uncertainty by creating corresponding maps of the coefficient of variation in Random Forest shrub biomass estimates. We also explore potential benefits of incorporating lidar-derived topographic metrics, and consider tradeoffs inherent in employing different data sources for high-resolution vegetation mapping efforts. This study yielded maps that provide valuable, high-resolution spatial estimates of aboveground shrub biomass and canopy volume in a rapidly changing tundra ecosystem.

Vegetation mapping from high-resolution satellite images in the heterogeneous arid environments of Socotra Island (Yemen)

Socotra Island (Yemen), a global biodiversity hotspot, is characterized by high geomorphological and biological diversity. In this study, we present a high-resolution vegetation map of the island based on combining vegetation analysis and classification with remote sensing. Two different image classification approaches were tested to assess the most accurate one in mapping the vegetation mosaic of Socotra. Spectral signatures of the vegetation classes were obtained through a Gaussian mixture distribution model, and a sequential maximum a posteriori (SMAP) classification was applied to account for the heterogeneity and the complex spatial pattern of the arid vegetation. This approach was compared to the traditional maximum likelihood (ML) classification. Satellite data were represented by a RapidEye image with 5 m pixel resolution and five spectral bands. Classified vegetation relevés were used to obtain the training and evaluation sets for the main plant communities. Postclassification sorting was performed to adjust the classification through various rule-based operations. Twenty-eight classes were mapped, and SMAP, with an accuracy of 87%, proved to be more effective than ML (accuracy: 66%). The resulting map will represent an important instrument for the elaboration of conservation strategies and the sustainable use of natural resources in the island.

Differential effects of goat browsing on herbaceous plant community in a two-phase mosaic

The impact of herbivores on herbaceous plant communities is usually attributed to direct consumption of plants. We hypothesized that goats affect herbaceous plants both directly (consumption by foraging) and indirectly, by changing environmental conditions through modification of woody plant structure. We assessed the effects of goats browsing on environmental conditions, landscape structure, and herbaceous plants to link the direct and indirect effects of goats on herbaceous communities. Our model system was the Mediterranean woodland in Mt. Carmel, Israel. This is a two-phase mosaic landscape, composed of herbaceous (open) and woody patches. We delineated 10 plots of 1000 m2, goats were introduced to five plots and five plots remained without goats. We monitored plant species richness and composition in two adjacent patch types (woody and open) in each plot. For each patch type, in all plots, we collected data on environmental conditions. We analyzed landscape structure using landscape metrics derived from a high-resolution vegetation map. We found that goats modified the structure of woody plants and hence the landscape mosaic. This alteration was associated with changes in environmental conditions, with more light penetration and higher temperatures. The impact of goats on the herbaceous plant community depended on patch type. In open patches, goats affected the herbaceous community mostly by direct consumption, whereas in woody patches they affected the herbaceous community mainly by modification of abiotic conditions. Our results stress the importance of considering landscape and patch structure in analyzing the effect of herbivory on plant communities.

Prediction of National Vegetation Classification communities in the British uplands using environmental data at multiple spatial scales, aerial images and the classifier random forest

Summary 1. High-resolution vegetation maps are a valuable resource for conservation, land management and research. In Great Britain, the National Vegetation Classification (NVC) is widely used to describe vegetation communities. NVC maps are typically produced from ground surveys which are prohibitively expensive for large areas. An approach to produce NVC maps more cost-effectively for large areas would be valuable. 2. Creation of vegetation community maps from aerial or satellite images has often had limited success as the clusters separable by spectral reflectance frequently do not correspond well to vegetation community classes. Such maps have also been produced by exploring correlations between community occurrence and environmental variables. The latter approach can have limitations where anthropogenic activities have altered the distribution of vegetation communities. We combined these two approaches and classified 24 common NVC classes of the Yorkshire Dales and an additional class ‘wood’ consisting of trees and bushes at a resolution of 5 m from mostly remotely sensed variables with the algorithm random forest. 3. Classification accuracy was highest when environmental variables at low and high resolution (50 and 5–10 m, respectively) were added to aerial image information aggregated to a resolution of 5 m. Low-resolution environmental variables are likely to be correlated with the dominant vegetation surrounding a location and thus could represent critical area requirements or local species pool effects, while high-resolution environmental variables represent the environmental conditions at the location. 4. Overall classification accuracy was 87–92%. The median producer’s and user’s class accuracies were 95% (58–100%) and 92% (67–100%), respectively. 5. Synthesis and applications. The classification accuracies achieved in this study, the number of classes differentiated, their level of detail and the resolution were high compared with those of other studies. This approach could allow the production of good-quality NVC maps for large areas. In contrast to existing maps of broad land cover types, such maps would provide more detailed vegetation community data for applications like the monitoring of vegetation in a changing climate, the study of animal–habitat relationships, conservation management or land use planning.

Habitat loss of floodplain meadows in north Germany since the 1950s

Floodplain meadows are severely threatened by land use change and intensification in Central Europe. This study investigates quantitative and qualitative changes in the vegetation of wet and species-rich mesic meadows in the floodplains of north Germany since the 1950s, considering their spatial extent, fragmentation, and replacement by other land use types. Historical high-resolution vegetation maps were compared with recent vegetation surveys in seven study regions (six unprotected areas, one protected reference area) in former West and East Germany. The unprotected sites showed alarming losses in wet and species-rich mesic meadows in the past 50 years (>80%). Wet meadows were substituted by species-poor, intensively managed grasslands (26–60% of the former area), arable fields (0–47%) or set-asides (2–33%). Species-rich mesic meadows were transformed to arable fields (42–72%) or species-poor, intensively managed meadows (14–72%). Decreases in effective mesh size and patch size indicated increasing fragmentation of wet meadows, whilst changes in landscape structure were less consistent in mesic meadows. Only slight changes in the protected floodplain study area indicate that landscape change is mostly caused by local effects such as fertilisation and drainage, but not by general trends such as atmospheric N deposition or climate warming. Despite the contrasting political systems in West and East Germany with different agroeconomic frames, all unprotected study areas showed similar losses and increasing fragmentation of floodplain meadows, which may negatively influence the natural dynamics of, and the gene flow between, meadow plant populations. We conclude that floodplain meadows in north Germany urgently call for high-priority conservation measures.

A Fixed-Threshold Approach to Generate High-Resolution Vegetation Maps for IKONOS Imagery.

Vegetation distribution maps from remote sensors play an important role in urban planning, environmental protecting and related policy making. The normalized difference vegetation index (NDVI) is the most popular approach to generate vegetation maps for remote sensing imagery. However, NDVI is usually used to generate lower resolution vegetation maps, and particularly the threshold needs to be chosen manually for extracting required vegetation information. To tackle this threshold selection problem for IKONOS imagery, a fixed-threshold approach is developed in this work, which integrates with an extended Tasseled Cap transformation and a designed image fusion method to generate high-resolution (1-meter) vegetation maps. Our experimental results are promising and show it can generate more accurate and useful vegetation maps for IKONOS imagery.