scholarly journals Application of Supervised Machine Learning Technique on LiDAR Data for Monitoring Coastal Land Evolution

2021 ◽  
Vol 13 (23) ◽  
pp. 4782
Author(s):  
Maurizio Barbarella ◽  
Alessandro Di Benedetto ◽  
Margherita Fiani
Keyword(s):  
Machine Learning ◽  
Digital Terrain Model ◽  
Roc Curves ◽  
Supervised Machine Learning ◽  
Lidar Data ◽  
Accuracy Score ◽  
Feature Maps ◽  
Terrain Model ◽  
Gravity Driven ◽  
Coastal Land

Machine Learning (ML) techniques are now being used very successfully in predicting and supporting decisions in multiple areas such as environmental issues and land management. These techniques have also provided promising results in the field of natural hazard assessment and risk mapping. The aim of this work is to apply the Supervised ML technique to train a model able to classify a particular gravity-driven coastal hillslope geomorphic model (slope-over-wall) involving most of the soft rocks of Cilento (southern Italy). To train the model, only geometric data have been used, namely morphometric feature maps computed on a Digital Terrain Model (DTM) derived from Light Detection and Ranging (LiDAR) data. Morphometric maps were computed using third-order polynomials, so as to obtain products that best describe landforms. Not all morphometric parameters from literature were used to train the model, the most significant ones were chosen by applying the Neighborhood Component Analysis (NCA) method. Different models were trained and the main indicators derived from the confusion matrices were compared. The best results were obtained using the Weighted k-NN model (accuracy score = 75%). Analysis of the Receiver Operating Characteristic (ROC) curves also shows that the discriminating capacity of the test reached percentages higher than 95%. The model, resulting more accurate in the training area, will be extended to similar areas along the Tyrrhenian coastal land.

scholarly journals Stand types discrimination comparing machine-learning algorithms in Monteverde, Canary Islands.

2018 ◽  
Vol 27 (3) ◽  
pp. eSC03 ◽  
Author(s):  
Miguel Garcia-Hidalgo ◽  
Ángela Blázquez-Casado ◽  
Beatriz Águeda ◽  
Francisco Rodriguez
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Digital Terrain Model ◽  
Image Data ◽  
Tree Height ◽  
Support Vector ◽  
Lidar Data ◽  
Support Vector Machine Algorithm ◽  
Terrain Model ◽  
Sentinel 2

Aim of study: The main objective is to determine the best machine-learning algorithm to classify the stand types of Monteverde forests combining LiDAR, orthophotography, and Sentinel-2 data, thus providing an easy and cheap method to classify Monteverde stand types.Area of study: 1500 ha forest in Monteverde, North Tenerife, Canary Islands.Material and methods: RF, SVML, SVMR and ANN algorithms are used to classify the three Monteverde stand types.  Before training the model, feature selection of LiDAR, orthophotography, and Sentinel-2 data through VSURF was carried out.  Comparison of its accuracy was performed.Main results: Five LiDAR variables were found to be the most efficient for classifying each object, while only one Sentinel-2 index and one Sentinel-2 band was valuable.  Additionally, standard deviation and mean of the Red orthophotography colour band, and ratio between Red and Green bands were also found to be suitable.  SVML is confirmed as the most accurate algorithm (0.904, 0.041 SD) while ANN showed the lowest value of 0.891 (0.073 SD).  SVMR and RF obtain 0.902 (0.060 SD) and 0.904 (0.056 SD) respectively.  SVML was found to be the best method given its low standard deviation.Research highlights: The similar high accuracy values among models confirm the importance of taking into account diverse machine-learning methods for stand types classification purposes and different explanatory variables.  Although differences between errors may not seem relevant at a first glance, due to the limited size of the study area with only three plus two categories, such differences could be highly important when working at large scales with more stand types.ADDITIONAL KEY WORDSRF algorithm, SVML algorithm, SVMR algorithm, ANN algorithm, LiDAR, orthophotography, Sentinel-2ABBREVIATIONS USEDANN, artificial neural networks algorithm; Band04, Sentinel-2 band 04 image data; BR, brezal; DTHM, digital tree height model; DTHM-2016, digital tree height model based on 2016 LiDAR data; DTM, digital terrain model; DTM-2016, digital terrain model based on 2016 LiDAR data; FBA, fayal-brezal-acebiñal; FCC, canopy cover; HEIGHT-2009, maximum height based on 2009 LiDAR data; HGR, height growth based on 2009 and 2016 LiDAR data; LA, laurisilva; NDVI705, Sentinel-2 index image data; NMF, non-Monteverde forest; NMG, non-Monteverde ground; P95-2016, height percentile 95 based on 2016 LiDAR data; RATIO R/G, ratio between Red and Green bands orthophotograph data; RED, Red band orthophotograph data; Red-SD, standard deviation of the Red band orthophotograph data; RF, random forest algorithm; SVM, support vector machine algorithm; SVML, linear support vector machine algorithm; SVMR, radial support vector machine algorithm; VSURF, variable selection using random forest. 

scholarly journals On evaluation of the digital terrain model generated from the LiDAR data of Lithuanian territory

2014 ◽  
Author(s):  
Dominykas Šlikas ◽  
Aušra Kalantaitė ◽  
Boleslovas Krikštaponis ◽  
Eimuntas Kazimieras Paršeliūnas ◽  
Rosita Birvydienė
Keyword(s):  
Digital Terrain Model ◽  
Lidar Data ◽  
Terrain Model ◽  
Digital Terrain

scholarly journals ACCURACY ASSESSMENT OF LIDAR-DERIVED DIGITAL TERRAIN MODEL (DTM) WITH DIFFERENT SLOPE AND CANOPY COVER IN TROPICAL FOREST REGION

2015 ◽  
Vol II-2/W2 ◽  
pp. 183-189 ◽  
Author(s):  
M. R. M. Salleh ◽  
Z. Ismail ◽  
M. Z. A. Rahman
Keyword(s):  
Tropical Forest ◽  
Digital Terrain Model ◽  
Canopy Cover ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Terrain Model ◽  
Digital Terrain ◽  
Terrain Slope ◽  
Airborne Lidar Data

Airborne Light Detection and Ranging (LiDAR) technology has been widely used recent years especially in generating high accuracy of Digital Terrain Model (DTM). High density and good quality of airborne LiDAR data promises a high quality of DTM. This study focussing on the analysing the error associated with the density of vegetation cover (canopy cover) and terrain slope in a LiDAR derived-DTM value in a tropical forest environment in Bentong, State of Pahang, Malaysia. Airborne LiDAR data were collected can be consider as low density captured by Reigl system mounted on an aircraft. The ground filtering procedure use adaptive triangulation irregular network (ATIN) algorithm technique in producing ground points. Next, the ground control points (GCPs) used in generating the reference DTM and these DTM was used for slope classification and the point clouds belong to non-ground are then used in determining the relative percentage of canopy cover. The results show that terrain slope has high correlation for both study area (0.993 and 0.870) with the RMSE of the LiDAR-derived DTM. This is similar to canopy cover where high value of correlation (0.989 and 0.924) obtained. This indicates that the accuracy of airborne LiDAR-derived DTM is significantly affected by terrain slope and canopy caver of study area.

scholarly journals POSSIBILITIES FOR USING LIDAR AND PHOTOGRAMMETRIC DATA OBTAINED WITH AN UNMANNED AERIAL VEHICLE FOR LEVEE MONITORING

2016 ◽  
Vol XLI-B1 ◽  
pp. 773-780 ◽  
Author(s):  
K. Bakuła ◽  
W. Ostrowski ◽  
M. Szender ◽  
W. Plutecki ◽  
A. Salach ◽  
...  
Keyword(s):  
Laser Scanner ◽  
Digital Camera ◽  
Digital Terrain Model ◽  
Point Clouds ◽  
Lidar Data ◽  
Unmanned Aerial System ◽  
Navigation Systems ◽  
Control Points ◽  
Terrain Model ◽  
Increase In Accuracy

This paper presents the possibilities for using an unmanned aerial system for evaluation of the condition of levees. The unmanned aerial system is equipped with two types of sensor. One is an ultra-light laser scanner, integrated with a GNSS receiver and an INS system; the other sensor is a digital camera that acquires data with stereoscopic coverage. Sensors have been mounted on the multirotor, unmanned platform the Hawk Moth, constructed by MSP company. LiDAR data and images of levees the length of several hundred metres were acquired during testing of the platform. Flights were performed in several variants. Control points measured with the use of the GNSS technique were considered as reference data. The obtained results are presented in this paper; the methodology of processing the acquired LiDAR data, which increase in accuracy when low accuracy of the navigation systems occurs as a result of systematic errors, is also discussed. The Iterative Closest Point (ICP) algorithm, as well as measurements of control points, were used to georeference the LiDAR data. Final accuracy in the order of centimetres was obtained for generation of the digital terrain model. The final products of the proposed UAV data processing are digital elevation models, an orthophotomap and colour point clouds. The authors conclude that such a platform offers wide possibilities for low-budget flights to deliver the data, which may compete with typical direct surveying measurements performed during monitoring of such objects. However, the biggest advantage is the density and continuity of data, which allows for detection of changes in objects being monitored.

scholarly journals New ICESat-2 Satellite LiDAR Data Allow First Global Lowland DTM Suitable for Accurate Coastal Flood Risk Assessment

2020 ◽  
Vol 12 (17) ◽  
pp. 2827 ◽  
Author(s):  
Ronald Vernimmen ◽  
Aljosja Hooijer ◽  
Maarten Pronk
Keyword(s):  
Sea Level ◽  
Flood Risk ◽  
Digital Terrain Model ◽  
Mekong Delta ◽  
Local Area ◽  
Radar Data ◽  
Lidar Data ◽  
Terrain Model ◽  
Coastal Lowland ◽  
Satellite Radar

No accurate global lowland digital terrain model (DTM) exists to date that allows reliable quantification of coastal lowland flood risk, currently and with sea-level rise. We created the first global coastal lowland DTM that is derived from satellite LiDAR data. The global LiDAR lowland DTM (GLL_DTM_v1) at 0.05-degree resolution (~5 × 5 km) is created from ICESat-2 data collected between 14 October 2018 and 13 May 2020. It is accurate within 0.5 m for 83.4% of land area below 10 m above mean sea level (+MSL), with a root-mean-square error (RMSE) value of 0.54 m, compared to three local area DTMs for three major lowland areas: the Everglades, the Netherlands, and the Mekong Delta. This accuracy is far higher than that of four existing global digital elevation models (GDEMs), which are derived from satellite radar data, namely, SRTM90, MERIT, CoastalDEM, and TanDEM-X, that we find to be accurate within 0.5 m for 21.1%, 12.9%, 18.3%, and 37.9% of land below 10 m +MSL, respectively, with corresponding RMSE values of 2.49 m, 1.88 m, 1.54 m, and 1.59 m. Globally, we find 3.23, 2.12, and 1.05 million km2 of land below 10, 5, and 2 m +MSL. The 0.93 million km2 of land below 2 m +MSL identified between 60N and 56S is three times the area indicated by SRTM90 that is currently the GDEM most used in flood risk assessments, confirming that studies to date are likely to have underestimated areas at risk of flooding. Moreover, the new dataset reveals extensive forested land areas below 2 m +MSL in Papua and the Amazon Delta that are largely undetected by existing GDEMs. We conclude that the recent availability of satellite LiDAR data presents a major and much-needed step forward for studies and policies requiring accurate elevation models. GLL_DTM_v1 is available in the public domain, and the resolution will be increased in later versions as more satellite LiDAR data become available.

Topographic Map Update of Esan Central LGA, Edo State, Nigeria, Using SPOT-5 and ASTER LiDAR Data

2014 ◽  
Vol 1 (1) ◽  
pp. 52-69
Author(s):  
S.O. Ogedegbe
Keyword(s):  
Spatial Data ◽  
Digital Terrain Model ◽  
Topographic Maps ◽  
Lidar Data ◽  
Topographic Map ◽  
Terrain Model ◽  
Digital Elevation ◽  
Système D’Information ◽  
Elevation Model ◽  
Spot 5

This study examines the effectiveness and accuracy of SPOT-5 and ASTER LiDAR data satellite images, Global Pos1t1on1ng System (GPS), Digital Terrain Model (DTM), and Geographic Information System (GIS) in carrying out a revision of Nigerian topographic maps at the scale of 1:50,000. The data for the study were collected by extraction of relevant spatial data from the 1964 topographic map, delineation and interpretation of 2009 SPOT-5 data, and field surveys. The landscape changes extracted from SPOT- 5 were used to update the topographic base map and to determine the nature and direction of changes that have taken place in the study area. The findings revealed that changes have occurred in both cultural and relief features over time. The coefficient of correlation and t-test was calculated to show that changes in point, linear and areal features are significant. Also significant were the planh11etric and height accuracies of the revised map. The study shows that satellite data especially SPOT-5 is useful for the revision of topographic maps at scales of 1:50,000 and even larger. And, high-resolution remote sensing at Sm and ASTER data (30m) with GPS (±1.9m) can be used to c.reate a digital elevation model (DEM) on the map which is an essential dataset for complete revision. Cette étude examine l'efficacité et la précision des images satellites de données SPOT-5 et ASTER LiDAR, du système de positionnement global (GPS), du modèle numérique de terrain (MNT) et du système d'information géographique (SIG) pour effectuer une révision des cartes topographiques nigérianes au échelle de 1:50 000. Les données de l'étude ont été recueillies par extraction de données spatiales pertinentes à partir de la carte topographique de 1964, délimitation et interprétation des données SPOT-5 de 2009 et relevés de terrain. Les changements de paysage extraits de SPOT-5 ont été utilisés pour mettre à jour le fond de carte topographique et pour déterminer la nature et la direction des changements qui ont eu lieu dans la zone d'étude. Les résultats ont révélé que des changements se sont produits dans les caractéristiques culturelles et du relief au fil du temps. Le coefficient de corrélation et le test t ont été calculés pour montrer que les changements dans les caractéristiques ponctuelles, linéaires et aréales sont significatifs. Les précisions planimétriques et altimétriques de la carte révisée étaient également importantes. L'étude montre que les données satellitaires, en particulier SPOT-5, sont utiles pour la révision des cartes topographiques à des échelles de 1:50 000 et même plus. De plus, la télédétection haute résolution aux données Sm et ASTER (30 m) avec GPS (± 1,9 m) peut être utilisée pour créer un modèle d'élévation numérique (DEM) sur la carte qui est un ensemble de données essentiel pour une révision complète.

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