In fluvial geomorphology as well as in freshwater ecology, rivers are commonly seen as nested hierarchical systems functioning over a range of spatial and temporal scales. Thus, for a comprehensive assessment, information on various scales is required. Over the past decade, remote sensing-based approaches have become increasingly popular in river science to increase the spatial scale of analysis. However, data-scarce areas have been widely ignored so far, even if most remaining free flowing rivers are located in such areas. In this study, we suggest an approach for river corridor mapping based on open access data only, in order to foster large-scale analysis of river systems in data-scarce areas. We take the more than 600 km long Naryn River in Kyrgyzstan as an example, and demonstrate the potential of the SRTM-1 elevation model and Landsat OLI imagery in the automated mapping of various riverscape parameters, like the riparian zone extent, distribution of riparian vegetation, active channel width and confinement, as well as stream power. For each parameter, a rigor validation is performed to evaluate the performance of the applied datasets. The results demonstrate that our approach to riverscape mapping is capable of providing sufficiently accurate results for reach-averaged parameters, and is thus well-suited to large-scale river corridor assessment in data-scarce regions. Rather than an ultimate solution, we see this remote sensing approach as part of a multi-scale analysis framework with more detailed investigation in selected study reaches.

River corridors, i.e. channel and adjacent floodplains, are hotspots of biodiversity and provide manifold ecosystem services. Their functioning and thus their ability to maintain biodiversity and to provide ecosystem services is controlled by a complex interplay of hydrologic, geomorphic and ecologic processes. These processes both affect and depend on hydrologic, geomorphic and ecologic connectivity within the river system. Today, process regimes of most (large) rivers are affected by human activities such as the construction of dams and reservoirs, flood protection measures or the withdrawal of water for agricultural irrigation. Dams modify longitudinal connectivity and thus the natural flow and sediment regime, while flood protection dikes disconnect channel and floodplain. There is a growing body of research on how hydrology-geomorphology-ecology-interactions shape river corridors and how these interactions are disturbed by humans. However, these insights tend to arise from studies at either the small river system or the reach scale. Truly understanding the impact of human interventions on rivers requires a dynamic, system scale perspective on process regimes. In our contribution, we take the river network in the Aral Sea Basin in Central Asia as an example and demonstrate the use of satellite time series to make a functional assessment of the process regimes controlling riparian ecosystem development. This river network has a total length of 75.000 km draining a catchment of 1.2 million km². We start the assessment with the delineation of the river network and the riparian zone from digital elevation models. Then, we use a novel unsupervised approach to create a map of landcover and general habitat types within the river corridors. In a second step, we create a dam and reservoir database in order to assess river fragmentation. In a third step, we use time series of Landsat and MODIS satellite imagery to assess hydrologic and geomorphic dynamics as well as vegetation development. These time series are the basis to analyze the relationship of e.g. floodplain inundation dynamics and vegetation trends or the impact of flood pulses on morphological change triggering vegetation change. The results show that the Aral Sea Basin is highly fragmented and that this fragmentation influences downstream process regimes and initiates modifications in the riparian ecosystems. Our satellite time series approach is able to capture relevant process dynamics and their impact on ecosystem development (i) in data-scarce regions, (ii) at large spatial scales (large river basins) and (iii) at high temporal frequency as enabled by short revisit times of current satellite constellations and cloud computing. Thus, it is a promising way to generate system-scale knowledge on the interaction of hydrologic, geomorphic and ecologic processes being the basis for biodiversity maintenance and ecosystem service provision in river corridors.

In the semiarid climate of Central Asia the rivers and their associated floodplain ecosystems have a high relevance as regional hotspots of biodiversity and for the provision of ecosystem services. One of these rivers is the Naryn River in Kyrgyzstan which is part of the headwaters of the Aral Sea basin. Upstream of the Toktogul Reservoir, which is the first barrier in the river course, the Naryn is still in a nearly natural state over a length of 600 km. The valuable floodplain habitats directly depend on the natural dynamics of this river. In particular rejuvenation and thus community structure of floodplain forests depends on the interaction of flow regime and hydromorphology controlled by longitudinal and lateral connectivity of water and sediment. Despite the ecological relevance, riparian ecosystems along the Naryn have not been investigated yet in detail. Especially the relative importance of the ecological and physical processes as well as anthropogenic effects on floodplain forest succession trajectories are not yet understood. This is a crucial issue for biodiversity conservation and environmental management as ongoing plans for additional dam construction are likely to heavily modify hydrologic and sediment connectivity. This is likely to have significant downstream impacts such as channel incision caused by sediment deficit or modification of the hydrological regime. The modifications therefore impact the current natural riparian ecosystem structure and functioning. In this study, we contribute to fill this knowledge gap and develop a conceptual succession model for this semiarid floodplain forest explaining the recent shape and distribution of habitat patterns. For this purpose, we use detailed ecological information derived from field data collected on 44 plots in the floodplain forest of the central Naryn basin together with river corridor scale remote sensing analysis using Sentinel-2 for detailed habitat derivation. These assessments are complemented by Landsat time series analysis with the LandTrendr segmentation algorithm to gain insights on large-scale spatio-temporal dynamics of vegetation and hydromorphology. Based on the results, we were able to determine the relevance of local morphology for longitudinal habitat patterns and to quantify the relationship between lateral connectivity and floodplain forest community structure at the scale of an entire river corridor. The conceptual model derived from the results explains the complex longitudinal and lateral pattern as well as the succession trajectories of floodplain forest communities along the Naryn River. It helps to better understand the natural processes and potential effects of anthropogenic activities to support forest and biodiversity conservation. In addition, it provides a basis for further research in other semiarid floodplain ecosystems.

Central Asia combines extensive agricultural land, high solar resources and increasing pressure on land, water and energy systems, creating a relevant context for agrivoltaic (Agri-PV) deployment. This study develops a spatially explicit GIS-based multi-criteria framework for regional-scale screening of Agri-PV potential across Kazakhstan, Uzbekistan, Turkmenistan, Tajikistan, Kyrgyzstan and Afghanistan. Five spatial criteria were considered: cropland occurrence, global horizontal irradiation, terrain slope, maximum air temperature and proximity to the electricity grid. Each criterion was transformed to a dimensionless 0–1 score using criterion-specific membership functions and combined through a multiplicative overlay. The resulting composite index represents technical and infrastructural screening potential on existing cropland rather than crop-specific agronomic suitability. Approximately 207,276 km² of cropland received a non-zero composite score under the baseline screening definition. The largest absolute areas occur in Qostanay, North Kazakhstan and Aqmola, whereas several regions in Uzbekistan and Turkmenistan exhibit higher median composite scores. A threshold-sensitivity analysis shows that administrative-region rankings remain highly stable for minimum composite scores of 0.1–0.3, with Spearman rank correlations of 0.998, 0.992 and 0.976 relative to the baseline ranking, respectively. PV resource quality within screened-in cropland is additionally characterized using specific electricity yield rather than an assumed installed-capacity potential. The framework is intended for regional strategic screening; crop-specific performance, irrigation requirements, hydrology and project-level feasibility are not explicitly modelled. Potential Water–Energy–Food nexus implications are therefore interpreted as hypotheses for subsequent assessment rather than as benefits directly quantified by the GIS model.

In the semiarid climate of Central Asia the rivers and their associated floodplain ecosystems have a high relevance as regional hotspots of biodiversity and for the provision of ecosystem services. One of these rivers is the Naryn River in Kyrgyzstan. Upstream of the Toktogul Reservoir, which is the first barrier in the river course, the Naryn is still in a nearly natural state. The floodplain forests in its corridor depend directly on the disturbance regime of this river. Despite their ecological relevance they have not been investigated yet in detail. In particular the role of natural disturbance and anthropogenic effects for succession trajectories are not yet understood. This is a crucial issue for biodiversity conservation as ongoing plans for dam construction will lead to heavy modification of the natural disturbance regime. In this study, we contribute to fill this knowledge gap and use remote sensing to derive detailed ecological information for the entire central Naryn basin. We use multispectral satellite data of Sentinel-2 and digital elevation data from TanDEM-X to derive the floodplain forests in a supervised classification approach. The floodplain forests include among others pioneer vegetation, several classes of herbaceous vegetation and different forest types. 500 ground control points were collected in the field in 2019 and were complemented with additional points created based on high resolution rgb imagery. These points have been split into a training and validation data set to create a random forest classification model. As predictors, different multispectral indices like the NDVI and temporal metrics of them were used along with different terrain attributes like the distance to the river channel.The results show that the random forest model with the combination of Sentinel-2 and TanDEM-X data can represent the complex structure of the floodplain forests along the Naryn river with high accuracies ranging from 62.4% for pioneer vegetation and 99.8% for open broad-leaved shrub. The forest structure shows a very heterogenous distribution along the longitudinal and lateral profile. The ecosystem response on the potential modification of the disturbance regime due to dam constructions is expected to be spatially heterogenous as well. Detailed forest habitat maps derived by remote sensing help to better understand natural processes and the potential effects of anthropogenic activities. Sentinel-2 data have high potentials for a efficient monitoring of forest habitats and their disturbance. Thus they are a very interesting data source for supporting forest conservation. Our forest habitat mapping for the Naryn floodplain provides a basis for further research, conservation planning and efficient monitoring.

Satellite remote sensing has gained significant relevance in the monitoring of riverine processes over the past years. Time series of satellite imagery, e.g. from the Landsat or Sentinel-1 and -2 constellations, are of particular interest as they allow us to assess not only distinct landcover classes but also dynamic processes such as inundation duration. Over the recent years, the rise of AI supports the analysis of these big earth observation archives. The increasing volume of earth observation data and computational burden of large AI models, however, lead to challenges in data processing. In the remote sensing community, the use of foundation models, large neural networks trained on massive amounts of data, promise to reduce the computational burden for end users. In addition, raw data encoded by these models, so-called earth embeddings, provide a ready-to-use dataset which transforms complex, multitemporal data of hundreds of satellite scenes from different sensors into annual images with abstract numerical representations of the original data. While in the remote sensing community, there is an increasing number of studies evaluating the application of geospatial foundation models and embeddings for various applications, there is to the best of our knowledge no comprehensive evaluation of the use of these recent developments for assessing dynamic hydro-geomorphic patterns and processes of river corridors.In our study, we assess the potential of two openly available embedding databases for their potential to represent typical riverine habitat types and river dynamics such as the inundation duration or erosion/formation features related to channel shifting. Specifically, we evaluate the recently released AlphaEarth embeddings (available on Google Earth Engine) and University of Cambridges TESSERA embeddings database (retrievable from their online database through a dedicated python package). Primary case study is the Naryn River in Kyrgyzstan. This is still a near-natural river on a length of more than 600 km. Along with an average active channel width of 400 m and average river corridor width of 1200 m, this makes the Naryn an ideal example for remote sensing applications in river science. We use a total number of 1873 ground truth points representing different geomorphic features and typical riparian habitat types as reference to evaluate how well embeddings are capable to distinguish these classes. To test the predictive capability of embeddings, we train supervised classification models based on the embeddings. In addition, we use satellite derived time series of inundation duration and inter-annual change derived from Planet Scope images to evaluate the potential of embeddings to represent dynamic characteristics and enable change detection. To analyze how this approach generalizes to other river systems, we apply the trained models to selected European rivers and validate the outcomes. Our initial results show a high potential of embeddings for analyzing riverscapes and their dynamics. We discuss how geospatial foundation models and embeddings as novel, AI driven tools in earth observation can contribute to generalizing remote sensing models across different river systems and how this can path the way towards global monitoring of riverscapes and their dynamics.