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Estuarine landscapes form through interactions between fluvio-coastal processes and ecological processes within the boundaries imposed by hard substrate layers and man-made dikes and dams. As estuaries are ecologically valuable areas, monitoring and quantification of trends in habitats is needed for objective comparison and management. However, datasets of tidal flat and saltmarsh habitats along entire estuaries are scarce. The objective was to compare trends of biogeomorphological areas and habitat transitions along three estuaries in the Netherlands and assess whether these are generally comparable or mainly determined by system-specific histories. We present data for these estuaries obtained by automated classification of false-color aerial imagery. The automated method allows objective mapping of entire estuaries at unprecedented resolution. The estuaries are dominated by subtidal areas and tidal flats. The tidal flats have similar area along the estuaries while saltmarsh area decreases. Collective lengths of ecologically important transitions between saltmarsh, low-energy tidal flats and water differ more between the estuaries. These variations are due to presence of mid-channel bars and shore-connected embayments. Saltmarsh area is mainly determined by the different formation and embankment histories of the estuaries, embayments and side-branches. Much of the past saltmarsh flanking the estuaries was lost due to past land reclamation. In one system, ecologically important low-energy tidal flats are reduced by a sudden decrease of tidal amplitude, causing increase of subtidal area at the cost of intertidal area. Large areas of high-energy tidal flats in one estuary remain unexplained. The automated method can be applied in other estuaries, provided that high-quality areal imagery is available. Extensions of the data to other estuaries would allow for system-scale trend comparison between estuaries of ecologically relevant biogeomorphological characteristics.

Fluvial biomorphodynamics in actively meandering rivers entail interactions between hydromorphodynamics and pioneering tree species that have eco-engineering effects. Here we study spatiotemporal patterns of vegetation patches smaller than 150 m2 in a 4 km reach of the river Allier in France in order to unravel causes for tree persistence and mortality and identify spatial trends across the river valley. To this end we analysed aerial photographs by object-based image analysis over a period of 56 years and tracked individual patches through time. Furthermore the cover and surface age of the study reach were classified. The large-scale shifts of channels, bars and vegetation are consistent with the meandering process and chute cutoffs. However, the spatiotemporal patterns of the vegetation patches are surprising in that they are ubiquitous and have ages up to decades on the highly dynamic meander belt, but hardly expand into larger vegetation patches. Patches disappear exponentially as a function of their age, and faster so in the last decades. Causes are amalgamation into the riparian forest flanking the meander belt and mortality likely due to desiccation or erosion. Patches have a higher probability of survival when further away from the active channel and closer to high vegetation patches and valley boundary. The window of opportunity of vegetation settlement widens towards the valley boundaries and in floodplain lows of former channels and chutes. These results imply a gradual cross-valley gradient of riparian vegetation settling, survival and succession.

Automatic extraction of channel networks from topography in systems with multiple interconnected channels, like braided rivers and estuaries, remains a major challenge in hydrology and geomorphology. Representing channelized systems as networks provides a mathematical framework for analyzing transport and geomorphology. In this paper, we introduce a mathematically rigorous methodology and software for extracting channel network topology and geometry from digital elevation models (DEMs) and analyze such channel networks in estuaries and braided rivers. Channels are represented as network links, while channel confluences and bifurcations are represented as network nodes. We analyze and compare DEMs from the field and those generated by numerical modeling. We use a metric called the volume parameter that characterizes the volume of deposited material separating channels to quantify the volume of reworkable sediment deposited between links, which is a measure for the spatial scale associated with each network link. Scale asymmetry is observed in most links downstream of bifurcations, indicating geometric asymmetry and bifurcation stability. The length of links relative to system size scales with volume parameter value to the power of 0.24–0.35, while the number of links decreases and does not exhibit power law behavior. Link depth distributions indicate that the estuaries studied tend to organize around a deep main channel that exists at the largest scale while braided rivers have channel depths that are more evenly distributed across scales. The methods and results presented establish a benchmark for quantifying the topology and geometry of multichannel networks from DEMs with a new automatic extraction tool., Key Points A new volume‐based method enables extraction of multithread channel networks from bathymetry across bed level jumpsBoth network topology and geomorphic information can be extracted at a range of spatial scalesEstuaries typically form a single dominant channel at the largest spatial scale, while braided rivers show no scaling break

s ERT techniques are especially promising in (semi-arid) areas with shallow and rocky soils where other methods fail to produce soil moisture maps and to obtain soil profile information. Electrical Resistivity Tomography (ERT) was performed in the Peyne catchment in southern France at four sites located on four different geological substrates. The main objective was to test the usefulness of such geoelectrical method for the assessment of spatial and temporal variability of soil water content and to demarcate subsurface horizontal zones: topsoil, active zone and bedrock. We used time lapse ERT to separate lithological variability from soil moisture content and to evaluate its potential to demarcate subsurface soil horizons. Three measurement campaigns were carried out and field data were collected using the Sting R1/IP advanced resistivity meter with one-channel receiver. We used an array of 28 electrodes with 1 m spacing, which provided reliable resistivity information to a depth of approximately 5.5 m. ERT were followed by soil sampling carried out with a hand auger, directly below the respective geoelectrical profiles. Empirical linear regression was used to determine the specific relationships between volumetric soil moisture of the field samples and electrical resistivity data obtained in situ. Our conclusions are that ERT is a useful technique for providing information on the spatial–temporal variability of water content in semi-arid areas and for reaching depths otherwise inaccessible. In this study we proposed a method to delineate three important different soil zones: topsoil, active zone, bedrock, based on resistivity data. These three layers are essential information in understanding and modelling the interaction between subsurface and vegetation water uptake, particularly in semi-arid regions.

Irrigated agriculture practiced by smallholders is essential for food security in East Africa. Insight in the spatio-temporal distribution of irrigated agriculture is required to optimize irrigation water use. Irrigation-mapping efforts in the complex smallholder-dominated agricultural landscape in the Horn of Africa so far are generally too coarse and often the extent of smallholder irrigated agriculture is underestimated. The arrival of Sentinel-2 (10-m resolution) considerably enhanced the prospect of analyzing agriculture at field level. The objective of this study is to demonstrate the feasibility to map spatio-temporal patterns of smallholder irrigated agriculture in the Horn of Africa using a novel method based on object-based image analysis and Sentinel-2 imagery. The method includes segmentation at field level and smart process-based rules on neighbouring objects and NDVI time series to distinguish irrigated agriculture from rainfed agriculture. The assumption is that irrigation is applied at field level, while a rainfall event is not restricted to field borders and that this information on the local context of irrigated agriculture can be exploited in an object-based approach. Monthly land-use maps on irrigated agriculture were produced for September 2016 to August 2017 at 10-m resolution field level (objects). Three different spatial-heterogeneity thresholds were used to describe the vegetation development of neighbouring objects and to assign crop growth to either rainfall or irrigation. This method is unique as it can discriminate irrigation- and rainfall-induced crop growth, even in the rainy season. The estimates of irrigated agriculture in the Horn of Africa range from 27.96 Mha to 37.13 Mha. This is 2.8 to 3.7 times higher than the current highest estimate, the Global Irrigated Area Map at 1000 m resolution, and 1.2 to 1.7 times higher than the Irrigated Area Map Asia (2000-2010) and Africa (2010) when including water-managed non-irrigated croplands. For the dry season (October-March), the estimates of irrigated agriculture range from 17.67 Mha to 23.72 Mha. The irrigation frequency, the number of time steps (months) with irrigation events in the studied year, varies strongly. Irrigated area with an irrigation frequency of 1 to 2 events has a mapped surface area of 22.57 Mha to 23.13 Mha. Irrigated area with an irrigation frequency of 3 or more events has a mapped surface area of 4.83 Mha to 14.56 Mha. The produced maps will provide valuable information for the development of irrigated agriculture and optimization of irrigation water use in the Horn of Africa. In addition, the portability of this method to other (semi-)arid regions seems feasible as the local context of irrigated agriculture, used in this study for irrigation classification, describes universal characteristics regarding irrigated agriculture. This is especially valuable in the context of food security and water availability for other large data-poor regions in low- and middle-income countries.

In developing countries, irrigated agriculture is key to achieve domestic food security and mitigate poverty. The often complex landscapes dominated by traditional smallholder farming (fields ~< 1 ha) pose a challenge with regard to mapping irrigated agriculture. Irrigation mapping efforts are complicated by the spectral overlap between irrigated crops, rainfed crops and other land cover. Efforts to identify smallholder irrigation are often too coarse, and, generally the area under smallholder irrigation is underestimated. This study presents an object-based approach for mapping irrigated agriculture in complex landscapes for both smallholder farming as well as modern large-scale agriculture. The aim here is twofold: 1) a proof-of-concept of irrigation mapping in a case study in the Central Rift Valley, Ethiopia, using SPOT6 imagery and 2) upscaling of the mapping to the Horn of Africa using Sentinel-2 imagery. Our assumption is that the application of irrigation has a positive effect on the crop throughout the field, following the field's borders. Next, we hypothesise that the use of shape, texture, neighbour and location features in addition to spectral information is beneficial for the classification of irrigated agriculture. The case study area is located in the Awash River basin and contains two irrigation schemes serving both traditional smallholder farming as well as modern large-scale agriculture. Three SPOT6 images spanning the dry season were acquired. Objects were generated at the field level on the basis of similarities in vegetation behavior (NDVI change) assuming that this matched the response of the crop on receiving irrigation water. The resulting map shows whether and where fields received irrigation water in between the three satellite acquisition moments and whether it belongs to traditional smallholder farming or modern large-scale agriculture. The object-based classification had an overall accuracy of 94% and a kappa coefficient of 0.85 and showed an improvement for mapping irrigated agriculture compared to a pixel-based classification (overall accuracy of 88% and a kappa coefficient of 0.69). Producer's and user's accuracies of individual classes were generally higher for the object-based approach compared to the pixel-based classification. Type of agriculture (i.e. smallholder or modern large-scale) was mapped with an accuracy of 95% and a kappa coefficient of 0.88. The addition of shape, texture, neighbour and location information in the object-based approach proved essential for the identification of cropland plots, irrigation period, and type of agricultural system. For the upscaling we use Sentinel-2 imagery as they are available with the highest spatio-temporal resolution among publicly-available imagery. The 10-meter resolution and 5-day revisit time has tremendous potential for mapping smallholder irrigation in complex landscapes. At the moment we are processing the data. A mosaic of best-cloud-free images acquired in the dry season (October to March) is segmented at field level. Monthly NDVI composites, combined with shape, texture and neighbour information are used to map irrigated agriculture. The output will be a map showing the spatio-temporal distribution of irrigated agriculture, for both smallholder farming and modern large-scale agriculture, for the Horn of Africa.

Floodplain vegetation has important implications for flood safety, food supply, biodiversity and recreation. Because of river restauration projects, floodplain vegetation is becoming more heterogeneous in space and time. To evaluate and document the development of floodplain vegetation, frequent monitoring is necessary. For monitoring, the detection of changes in vegetation type is a major objective, because of the potential consequences on hydraulic roughness or habitat availability. Manual photo interpretation is a common method to document these changes, but it is time-consuming and expensive. Moreover, interpretation errors can influence the accuracy. Hence, it is preferable to detect change without classification of the RS data. Change detection based on trend deviation or threshold exceedance (multitemporal spectral analysis) is complicated for floodplain vegetation and requires the use of a single type of sensor data, which limits the frequency of available data. Furthermore, the atmospheric corrections will influence the accuracy of the change detection. The aim of this study is to present a method that quickly detects changes (i.e. where did changes occur?) and allows focussed analysis of change identification (i.e. how did it change?). Rather than classification or multitemporal spectral analysis, we compare object attribute values within specific classes at a single moment and detect outlier objects. If objects are identified as outlier multiple times, we consider them as changed. The basic assumptions for this approach are that 1) objects belonging to the same class have similar attribute values; 2) classes differ from each other for (some) attribute values; 3) the majority of objects does not change class within the monitoring period. The method is tested in the floodplains along a 100-km river section along the River Waal in the Netherlands. Frequent and open remote-sensing data is available on the Google Earth Engine, such as Landsat, Sentinel, airborne imagery and surface models. A classified vegetation polygon map based on high resolution airborne imagery from 2008 is used to extract the zonal statistics from the multisensor and multitemporal data. These object statistics are used to detect the outliers at each time step and to monitor them. Changed objects are validated with yearly high resolution airborne imagery. In short, this study evaluates whether sequential deviation of a vegetation object from the average (e.g. spectral) value of its class can be used for change monitoring and whether our basic assumptions are correct. The advantages of this approach are 1) the high frequency of remote-sensing data, because any available spectral/LiDAR/RADAR dataset can be used, allows close monitoring of changes; 2) the independence of classification or multitemporal spectral analysis, which are problematic due to the small (e.g. spectral) differences in vegetation and (3) it allows for fast processing.

Water content of the vegetation canopy or individual leaves is an important variable in physiological plant processes. In Mediterranean regions where water availability is an important production limiting factor, it is a strong indicator of vegetation stress. Spectroscopic earth-observation techniques in the solar part of the electromagnetic spectrum provide opportunities to determine leaf and canopy-water content due to the presence of water-absorption bands around 970 and 1200 nm. We investigated the possibilities to predict leaf-water content of three dominant tree species in a study area in Mediterranean France using spectral indices. During a field campaign leaf-water content (EWT) was determined and high-resolution spectra were measured of the same leaves. The spectra were measured in two ways: using an optical cable with a field of view of 25° and using a leaf clip with its own artificial illumination source. The spectra were analyzed and related to leaf-water content as original reflectance spectra and as continuum-removed spectra using eight spectral leaf-water indices. Next, reflectance spectra were simulated to explore their sensitivity to environmental conditions like leaf area index and illumination angle using a radiative transfer model. Results show that a good correlation (0.70) exists between leaf-water content and spectral indices using the right slope of the 970 nm water-absorption band. Continuum-removal correction of the spectra improved the relations. The model sensitivity analysis illustrated that from a set of five environmental variables leaf area index has, as may be expected, an important impact on leaf-water estimates. This field and model study illustrates that it is feasible to determine foliar water content on the basis of spectral indices located around the minor water-absorption bands with a limited effect of environmental conditions.