A MACHINE LEARNING APPROACH TO MULTISPECTRAL SATELLITE DERIVED BATHYMETRY

Abstract. Bathymetry in coastal environment plays a key role in understanding erosion dynamics and evolution along coasts. In the presented investigation depth along the shore-line was estimated using different multispectral satellite data. Training and validation data derived from a traditional bathymetric survey developed along transects in Cesenatico; measured data were collected with a single-beam sonar returning centimetric precision. To limit spatial auto-correlation training and validation dataset were built choosing alternatively one transect as training and another as validation. Each set was composed by a total of ~6000 points. To estimate water depth two methods were tested, Support Vector Machine (SVM) and Random Forest (RF). The RF method provided the higher accuracy with a root mean square error value of 0.228 m and mean absolute error of 0.158 m, against values of 0.409 and 0.226 respectively for SVM. Results show that application of machine learning methods to predict depth near shore can provide interesting results that can have practical applications

SENTINEL-5P NO2 DATA: CROSS-VALIDATION AND COMPARISON WITH GROUND MEASUREMENTS

Abstract. Sentinel-5P (S5P) data provide information on atmospheric pollutants daily, and, for higher latitudes, consequent orbits partially overlap the same day. Provided clear atmospheric conditions, these data can provide insights on emission hotspots and on spatial distribution of critical air quality issues. The purpose of this work is to analyse several aspects of NO2 data from S5P over the years 2019, 2020 and 2021, in particular: (i) yearly average values between S5P data and 624 ground measurement stations were tested for correlation; (ii) 387 pairs of images from overlapping orbits on the same day were used to test for correlation on consecutive images with four different methods – simple linear regression over all valid cell values across the two images, over a subset with a low cloud fraction, and linear and tree-based methods using multiple predictors; (iii) local maxima values extracted from yearly NO2 emission maps were analysed to check potential hotspots of NO2 emissions.Results show that ground measurements correlate with S5P values, with r-squared values of 0.37 and 0.43 and RMSE of 7.4 and 8.6 µmol/m2 respectively for 2019 and 2020. Simple linear regression of overlapping consequent images returned average and standard deviation (sd) on r-squared respectively of 0.50(sd=0.21) and for RMSE of 11.3(sd=4.2) µmol/m2. Points from local maxima clearly detected 19 specific positions in large cities or nearby industrial areas, mostly in the north of Italy, with average NO2 values above 90 µmol/m2 in some cases consistently over the three years, proving that S5P imagery is a valid index for spatial distribution of NO2 concentration and air quality

SEAWEED PRESENCE DETECTION USING MACHINE LEARNING AND REMOTE SENSING

Abstract. The human pressure over coastal areas is becoming increasingly relevant, due to the combinations of resource depletion, climate change effects and ocean eutrophication. Coastal ecosystems are so exposed to a huge number of stress factors that endanger their ecosystem services, like carbon uptake and biodiversity maintenance, that can be crucial in facing the effects of climate changes. With a particular focus on seaweeds, these ecosystems are becoming rapidly relevant both for carbon sinks and as a source of high value products, for example thanks to cosmetic and food industries that produce high added values products.In this contest the capability of conducting efficient monitoring is crucial to monitor environmental dynamics and resources trends. Traditionally seaweed monitoring was carried out with on field surveys that could be based on botanic analysis combined with genetic study, depending on the aims. Recently Remote Sensing techniques, combined with Artificial Intelligence ones, gave a new perspective to seaweed monitoring, introducing tools that are always more efficient.In this contest the present work aims to test the potentiality of remote sensing and artificial intelligence techniques for seaweed monitoring along the Irish west coast, building the basis for a fully automated tool for monitoring. The results showed that, with a supervised classification approach, it is possible to train Random Forest (RF) to perform very precise classification over the entire West Coast of Ireland. In particular, with all the RF configurations tested the Overall Accuracy (OA) was greater than 98.61, with the best performance obtained with the configuration Ntree = 600 and mtry = 2 that produced an OA = 98.87

Classification of aerial laser scanning point clouds using machine learning: a comparison between Random Forest and Tensorflow

In this investigation a comparison between two machine learning (ML) models for semantic classification of an aerial laser scanner point cloud is presented. One model is Random Forest (RF), the other is a multi-layer neural network, TensorFlow (TF). Accuracy results were compared over a growing set of training data, using a stratified independent sampling over classes from 5% to 50% of the total dataset. Results show RF to have average F1=0.823 for the 9 classes considered, whereas TF had average F1=0.450. F1 values where higher for RF than TF, due to complexity in the determination of a suitable composition of the hidden layers of the neural network in TF, and this can likely be improved to reach higher accuracy values. Further study in this sense is planned

ANALYSIS OF FILTERING TECHNIQUES FOR INVESTIGATING LANDSLIDE-INDUCED TOPOGRAPHIC CHANGES IN THE OETZ VALLEY (TYROL, AUSTRIA)

We thank the organizers of the ISPRS Innsbruck Summer School of Alpine Research 2019 held in Obergurgl (Tyrol, Austria) for giving us the opportunity to collect data and collaborate in a very inspiring environment. We further thank Jan Pfeiffer and Daniel Wujanz for providing the TLS data acquired in June 2017Peer reviewe

ANALYSIS OF FILTERING TECHNIQUES FOR INVESTIGATING LANDSLIDE-INDUCED TOPOGRAPHIC CHANGES IN THE OETZ VALLEY (TYROL, AUSTRIA)

Abstract. Landslides endanger settlements and infrastructure in mountain areas across the world. Monitoring of landslides is therefore essential in order to understand and possibly predict their behavior and potential danger. Terrestrial laser scanning has proven to be a successful tool in the assessment of changes on landslide surfaces due to its high resolution and accuracy. However, it is necessary to classify the 3D point clouds into vegetation and bare-earth points using filtering algorithms so that changes caused by landslide activity can be quantified. For this study, three classification algorithms are compared on an exemplary landslide study site in the Oetz valley in Tyrol, Austria. An optimal set of parameters is derived for each algorithm and their performances are evaluated using different metrics. The volume changes on the study site between the years 2017 and 2019 are compared after the application of each algorithm. The results show that (i) the tested filter techniques perform differently, (ii) their performance depends on their parameterization and (iii) the best-performing parameterization found over the vegetated test area will yield misclassifications on non-vegetated rough terrain. In particular, if only small changes have occurred the choice of the filtering technique and its parameterization play an important role in estimating volume changes

Artificial Intelligence for a Multi-temporal Classification of Fluvial Geomorphic Units of the River Isonzo: A Comparison of Different Techniques

The pressure of human activities is particularly relevant on fluvial ecosystems, where activity such hydroelectric energy production can change natural dynamics. For this reason it is important to monitor, with a systematic approach, river geomorphic units distribution and their evolution over time. In particular this work consists of an application of different AI techniques to process Sentinel-2 optical data to acquire a multitemporal classification of fluvial geomorphic units (Channels, Pools, Bars, Island, Vegetation) on a study area of the river Isonzo in Friuli Venezia Giulia (Italy). Results showed that all the AI methods tested allow to perform accurate classification, with best results obtained by Random Forest, that reach an overall accuracy of 0.986, and the most confusion between Bars and Island classes with F-measure of 0.931 and 0.961 respectively

Analysis of Filtering Techniques for Investigating Landslide-Induced Topographic Changes in the Oetz Valley (Tyrol, Austria)

Landslides endanger settlements and infrastructure in mountain areas across the world. Monitoring of landslides is therefore essential in order to understand and possibly predict their behavior and potential danger. Terrestrial laser scanning has proven to be a successful tool in the assessment of changes on landslide surfaces due to its high resolution and accuracy. However, it is necessary to classify the 3D point clouds into vegetation and bare-earth points using filtering algorithms so that changes caused by landslide activity can be quantified. For this study, three classification algorithms are compared on an exemplary landslide study site in the Oetz valley in Tyrol, Austria. An optimal set of parameters is derived for each algorithm and their performances are evaluated using different metrics. The volume changes on the study site between the years 2017 and 2019 are compared after the application of each algorithm. The results show that (i) the tested filter techniques perform differently, (ii) their performance depends on their parameterization and (iii) the best-performing parameterization found over the vegetated test area will yield misclassifications on non-vegetated rough terrain. In particular, if only small changes have occurred the choice of the filtering technique and its parameterization play an important role in estimating volume changes.</p