No abstract

The functions of river floodplains often conflict spatially, for example, water conveyance during peak discharge and diverse riparian ecology. Such functions are often associated with floodplain vegetation. Frequent monitoring of floodplain land cover is necessary to capture the dynamics of this vegetation. However, low classification accuracies are found with existing methods, especially for relatively similar vegetation types, such as grassland and herbaceous vegetation. Unmanned aerial vehicle (UAV) imagery has great potential to improve the classification of these vegetation types owing to its high spatial resolution and flexibility in image acquisition timing. This study aimed to evaluate the increase in classification accuracy obtained using multitemporal UAV images versus single time step data on floodplain land cover classification and to assess the effect of varying the number and timing of imagery acquisition moments. We obtained a dataset of multitemporal UAV imagery and field reference observations and applied object-based Random Forest classification (RF) to data of different time step combinations. High overall accuracies (OA) exceeding 90% were found for the RF of floodplain land cover, with six vegetation classes and four non-vegetation classes. Using two or more time steps compared with a single time step increased the OA from 96.9% to 99.3%. The user’s accuracies of the classes with large similarity, such as natural grassland and herbaceous vegetation, also exceeded 90%. The combination of imagery from June and September resulted in the highest OA (98%) for two time steps. Our method is a practical and highly accurate solution for monitoring areas of a few square kilometres. For large-scale monitoring of floodplains, the same method can be used, but with data from airborne platforms covering larger extents.

Rising surface water temperatures in fluvial systems increasingly affect biodiversity negatively in riverine ecosystems, and a more frequent exceedance of thermal tolerance levels of species is expected to impoverish local species assemblages. Reliable prediction of the effect of increasing water temperature on habitat suitability requires detailed temperature measurements over time. We assessed (1) the accuracy of high-resolution images of water temperature of a side channel in a river floodplain acquired using a consumer-grade thermal camera mounted on an unmanned airborne vehicle (UAV), and (2) the associated habitat suitability for native and alien fish assemblages. Water surface temperatures were mapped four times throughout a hot summer day and calibrated with 24 in-situ temperature loggers in the water at 0.1 m below the surface using linear regression. The calibrated thermal imagery was used to calculate the potentially occurring fraction (POF) of freshwater fish using species sensitivity distributions. We found high temperatures (25–30 °C) in the side channel during mid-day resulting in reduced habitat suitability. The accuracy of water temperature estimates based on the RMSE was 0.53 °C over all flights (R2 = 0.94). Average daily POF was 0.51 and 0.64 for native and alien fish species in the side channel. The error of the POF estimates is 76% lower when water temperature is estimated with thermal UAV imagery compared to temperatures measured at an upstream gauging station. Accurately quantifying water temperature and the heterogeneity thereof is a critical step in adaptation of riverine ecosystems to climate change. Our results show that measurements of surface water temperature can be made accurately and easily using thermal imagery from UAVs allowing for an improved habitat management, but coincident collection of long wave radiation is needed for a more physically-based prediction of water temperature. Because of climate change, management of riverine ecosystems should consider thermal pollution control and facilitate cold water refugia and connectivity between waterbodies in floodplains and the cooler main channel for fish migration during extremely hot summer periods.

ABSTRACT:River restoration projects, which aim at improved flood safety and increased ecological value, have resulted in more heterogeneous vegetation. However, they also resulted in increasing hydraulic roughness, which leads to higher flood water levels during peak discharges. Due to allowance of vegetation development and succession, both ecological and hydraulic characteristics of the floodplain change more rapidly over time. Monitoring of floodplain vegetation has become essential to document and evaluate the changing floodplain characteristics and associated functioning. Extraction of characteristics of low vegetation using single-epoch remote sensing data, however, remains challenging. The aim of this study was to (1) evaluate the performance of multi-temporal, high-spatial-resolution UAV imagery for extracting temporal vegetation height profiles of grassland and herbaceous vegetation in floodplains and (2) to assess the relation between height development and NDVI changes. Vegetation height was measured six times during one year in 28 field plots within a single floodplain. UAV true-colour and false-colour imagery of the floodplain were recorded coincidently with each field survey. We found that: (1) the vertical accuracy of UAV normalized digital surface models (nDSMs) is sufficiently high to obtain temporal height profiles of low vegetation over a growing season, (2) vegetation height can be estimated from the time series of nDSMs, with the highest accuracy found for combined imagery from February and November (RMSE = 29-42 cm ), (3) temporal relations between NDVI and observed vegetation height show different hysteresis behaviour for grassland and herbaceous vegetation. These results show the high potential of using UAV imagery for increasing grassland and herbaceous vegetation classification accuracy.

ABSTRACT:River restoration projects, which aim at improved flood safety and increased ecological value, have resulted in more heterogeneous vegetation. However, they also resulted in increasing hydraulic roughness, which leads to higher flood water levels during peak discharges. Due to allowance of vegetation development and succession, both ecological and hydraulic characteristics of the floodplain change more rapidly over time. Monitoring of floodplain vegetation has become essential to document and evaluate the changing floodplain characteristics and associated functioning. Extraction of characteristics of low vegetation using single-epoch remote sensing data, however, remains challenging. The aim of this study was to (1) evaluate the performance of multi-temporal, high-spatial-resolution UAV imagery for extracting temporal vegetation height profiles of grassland and herbaceous vegetation in floodplains and (2) to assess the relation between height development and NDVI changes. Vegetation height was measured six times during one year in 28 field plots within a single floodplain. UAV true-colour and false-colour imagery of the floodplain were recorded coincidently with each field survey. We found that: (1) the vertical accuracy of UAV normalized digital surface models (nDSMs) is sufficiently high to obtain temporal height profiles of low vegetation over a growing season, (2) vegetation height can be estimated from the time series of nDSMs, with the highest accuracy found for combined imagery from February and November (RMSE = 29-42 cm ), (3) temporal relations between NDVI and observed vegetation height show different hysteresis behaviour for grassland and herbaceous vegetation. These results show the high potential of using UAV imagery for increasing grassland and herbaceous vegetation classification accuracy.