Object-Based Ensemble Learning for Pan-European Riverscape Units Mapping Based on Copernicus VHR and EU-DEM Data Fusion

Demarchi, Luca; Bund, Wouter van de; Pistocchi, Alberto

doi:10.3390/rs12071222

Cited by 23 publications

(25 citation statements)

References 67 publications

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“…Continental-scale high-spatial resolution fluvial accurately document the changes (e.g., [12,13]). Continental-scale high-spatial resolution fluvial mapping initiatives such as [14] are not yet available for South America. The data readily available to Brazilian agencies and others studying these regions are usually generated from moderate resolution images with pixel sizes of 30 m or more, and can lead to substantial uncertainties in calculations of impact area, greenhouse gas emissions, habitat loss, etc.…”

Section: Discussionmentioning

confidence: 99%

“…In order to quantify the extent of the flooded area due to the main and artificial reservoirs of the dam, we developed a high spatial resolution surface water classification for 2011 (pre Belo Monte) and 2019 (after operationalization of the dam). These datasets were produced from RapidEye (5 m) and PlanetScope (3 m) imagery classified with a Geographic Object Based Image Analysis (GEOBIA) approach [14,16].…”

Section: Discussionmentioning

confidence: 99%

“…The images from Tables 1 and 2 were classified through a GEOBIA approach in eCognition Developer 9.4 (Trimble Geospatial, Sunnyvale, CA, USA). The objective of GEOBIA is to improve upon and replicate human interpretation of imagery in an automated manner [21], allowing for large areas to be analyzed efficiently [14], and is effective for land cover classifications of high spatial resolution imagery with a low spectral resolution [16,22]. Images were first segmented with the multiresolution segmentation algorithm using the following parameters: scale = 75, shape = 0.1 and compactness = 0.5.…”

Section: Methodsmentioning

confidence: 99%

“…It has one of the highest annual variations in flow of all Amazon tributaries. The imagery from 2011 (4 July) is earlier in the receding period than that of 2019 (11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24) due to the availability of imagery with minimal cloud cover for the entire area. There is an approximate 1000 m 3 /s difference in discharge between July and August [8].…”

Section: User Notesmentioning

confidence: 99%

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High-Resolution Surface Water Classifications of the Xingu River, Brazil, Pre and Post Operationalization of the Belo Monte Hydropower Complex

Kalácska

Lucanus

Sousa

et al. 2020

Data

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We describe a new high spatial resolution surface water classification dataset generated for the Xingu river, Brazil, from its confluence with the Iriri river to the Pimental dam prior to construction of the Belo Monte hydropower complex, and after its operationalization. This river is well-known for its exceptionally high diversity and endemism in ichthyofauna. Pre-existing datasets generated from moderate resolution satellite imagery (e.g., 30 m) do not adequately capture the extent of the river. Accurate measurements of water extent are important for a range of applications utilizing surface water data, including greenhouse gas emission estimation, land cover change mapping, and habitat loss/change estimates, among others. We generated the new classifications from RapidEye imagery (5 m pixel size) for 2011 and PlanteScope imagery (3 m pixel size) for 2019 using a Geographic Object Based Image Analysis (GEOBIA) approach.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: User Notesmentioning

confidence: 99%

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High-Resolution Surface Water Classifications of the Xingu River, Brazil, Pre and Post Operationalization of the Belo Monte Hydropower Complex

Kalácska

Lucanus

Sousa

et al. 2020

Data

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“…computer vision), river remote sensing studies do not achieve classification accuracies above 90%, (e.g. Boruah et al, 2008;Casado et al, 2015;Demarchi et al, 2020;Gilvear et al, 2008;Legleiter and Goodchild, 2005;Rusnák et al, 2018;Smikrud et al, 2008;Wang et al, 2016). This is largely because at meter-scale and centimeter-scale resolution, the assumption that a semantic class can be described by a set of unimodal distributions of brightness values is not necessarily valid.…”

Section: Introductionmentioning

confidence: 99%

Adopting deep learning methods for airborne RGB fluvial scene classification.

Carbonneau¹,

Dugdale²,

Breckon³

et al. 2020

Preprint

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River environments are among the world’s most threatened ecosystems. Enabled by the rapid development of drone technology, hyperspatial resolution (<10 cm) images of fluvial scenes are now a common data source used to better understand these sensitive habitats. However, the task of image classification remains challenging for this type of imagery and the application of traditional classification algorithms such as maximum likelihood, still in common use among the river remote sensing community, yields unsatisfactory results. We explore the possibility that a classifier of river imagery based on deep learning methods can provide a significant improvement in our ability to classify fluvial scenes. We assemble a dataset composed of existing RGB images from 11 rivers in Canada, Italy, Japan, the UK, and Costa Rica. The images were labelled into 5 classes. In total, >5 billion pixels were labelled and partitioned for the tasks of training (1 billion pixels) and validation (4 billion pixels). We develop a novel supervised learning workflow based on the NASNet convolutional neural network (CNN) called ‘CNN-Supervised Classification’ (CSC). First, we compare the classification success of maximum likelihood, a multilayer perceptron, a random forest, and CSC. Results show F1 scores (a best-practice quality meter in machine learning) of 71%, 78%, 72% and 95%, respectively. Second, we train our classifier using data for 5 of 11 rivers. We then predict the validation data for all 11 rivers. For the 5 rivers that were used in model training, F1 scores reach 98%. For the 6 rivers not used in model training, F1 scores are 90%. We reach two conclusions. First, in the traditional workflow where images are classified one at a time, CSC delivers an unprecedented mix of labour savings and classification F1 scores above 95%. Second, deep learning can determine land-cover classifications (F1 = 90%) for rivers not used in training. This demonstrates the potential to train a generalised open-source deep learning model for airborne river surveys suitable for most rivers ‘out of the box’. Research efforts should now focus on further development of a new generation of deep learning classification tools that will encode human image interpretation abilities and allow for fully automated, potentially real-time, interpretation of riverine landscape images.

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Basin‐wide hydromorphological analysis of ephemeral streams using machine learning algorithms^‡

Rabanaque

Martínez-Fernández

Calle

et al. 2021

Earth Surf Processes Landf

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Sustainable river management now encompasses a much wider concept that includes hydromorphological and fluvial habitat studies. In ephemeral streams, the geomorphological characterization of channels is complex due to episodic flows and riparian vegetation dynamics. Stream channel survey and classification at the watershed scale provide the basis for geomorphological conservation, process interpretation, assessing sensitivity to disturbance, and identifying reaches that supply and store sediment. Here, we present a stream classification based on a two‐step approach: (1) automatic river segmentation based on spatial variability in channel/valley morphology from topographic measurements (LiDAR, light, detection and ranging), and (2) fluvial landform and vegetation density mapping derived from multispectral open‐source satellite images (Sentinel‐2) using support vector machine (SVM) and Random Forest (RF) algorithms. These analyses provide continuous, quantitative spatial values of geometric (channel/valley width, slope gradient, and route distance), landform (active channel and gravel bars with five densities of vegetation cover), and hydraulic (specific stream power) variables. Four stream types were identified in the Rambla de la Viuda catchment (~1500 km2), an ephemeral gravel‐bed river in eastern Spain. The spatial distribution of channel types is explained by differences in geometry (active channel width, valley width, and slope gradient) and a hydraulic parameter (specific stream power). The landforms/vegetation patterns provided insight on causal relationships between erosion and deposition processes during high flow periods and the time since the most recent large disruptive flood event. Channel type distribution provided first‐order predictions about the location of reaches that supply and store sediment and thus information on sediment continuity along the river. Dam effects on downstream reaches resulted in geomorphological disequilibrium, producing narrowing of the active channel, slope reduction, and a decrease of gravel bar areal extension. The proposed catchment scale analysis provides a comprehensive and replicable methodology for environmental planning in Mediterranean ephemeral streams to guide further hydromorphological surveys at the reach scale.

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Object-Based Ensemble Learning for Pan-European Riverscape Units Mapping Based on Copernicus VHR and EU-DEM Data Fusion

Cited by 23 publications

References 67 publications

High-Resolution Surface Water Classifications of the Xingu River, Brazil, Pre and Post Operationalization of the Belo Monte Hydropower Complex

High-Resolution Surface Water Classifications of the Xingu River, Brazil, Pre and Post Operationalization of the Belo Monte Hydropower Complex

Adopting deep learning methods for airborne RGB fluvial scene classification.

Basin‐wide hydromorphological analysis of ephemeral streams using machine learning algorithms^‡

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Object-Based Ensemble Learning for Pan-European Riverscape Units Mapping Based on Copernicus VHR and EU-DEM Data Fusion

Cited by 23 publications

References 67 publications

High-Resolution Surface Water Classifications of the Xingu River, Brazil, Pre and Post Operationalization of the Belo Monte Hydropower Complex

High-Resolution Surface Water Classifications of the Xingu River, Brazil, Pre and Post Operationalization of the Belo Monte Hydropower Complex

Adopting deep learning methods for airborne RGB fluvial scene classification.

Basin‐wide hydromorphological analysis of ephemeral streams using machine learning algorithms‡

Contact Info

Product

Resources

About

Basin‐wide hydromorphological analysis of ephemeral streams using machine learning algorithms^‡