Capability Assessment and Performance Metrics for the Titan Multispectral Mapping Lidar

Fernández-Diaz, Juan Carlos; Carter, William E.; Glennie, C. L.; Shrestha, Ramesh; Pan, Zhigang; Ekhtari, Nima; Singhania, Abhinav; Hauser, Darren; Sartori, Michael

doi:10.3390/rs8110936

Cited by 140 publications

(86 citation statements)

References 53 publications

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“…This sensor was released in the commercial market in 2015, and data acquired with this system was subject to scientific research shortly after the release. However, published studies are still few in number (e.g., [17][18][19][20][21]), and also for forestry and ecology applications [17,22]. In particular Budei et al [17], Yu et al [22], and Axelsson et al [20] focused on tree species classification.…”

Section: Introductionmentioning

confidence: 99%

Predicting Selected Forest Stand Characteristics with Multispectral ALS Data

et al. 2018

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Abstract:In this study, the potential of multispectral airborne laser scanner (ALS) data to model and predict some forest characteristics was explored. Four complementary characteristics were considered, namely, aboveground biomass per hectare, Gini coefficient of the diameters at breast height, Shannon diversity index of the tree species, and the number of trees per hectare. Multispectral ALS data were acquired with an Optech Titan sensor, which consists of three scanners, called channels, working in three wavelengths (532 nm, 1064 nm, and 1550 nm). Standard ALS data acquired with a Leica ALS70 system were used as a reference. The study area is located in Southern Norway, in a forest composed of Scots pine, Norway spruce, and broadleaf species. ALS metrics were extracted for each plot from both elevation and intensity values of the ALS points acquired with both sensors, and for all three channels of the ALS multispectral sensor. Regression models were constructed using different combinations of metrics. The results showed that all four characteristics can be accurately predicted with both sensors (the best R 2 being greater than 0.8), but the models based on the multispectral ALS data provide more accurate results. There were differences regarding the contribution of the three channels of the multispectral ALS. The models based on the data of the 532 nm channel seemed to be the least accurate.

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Section: Introductionmentioning

confidence: 99%

Predicting Selected Forest Stand Characteristics with Multispectral ALS Data

et al. 2018

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“…Fernandez-Diaz et al (2016) presented an overview on the use of the Titan data in land cover classification, measuring water depths in shallow water areas and forestry mapping. Most investigations have focused on extracting one or two objects, such as vegetation mapping (Nabucet et al, 2016), road mapping (Karila et al, 2017), discrimination of vegetation from built-up areas (Morsy et al, 2016a), or land/water classification (Morsy et al, 2017a).…”

Section: Introductionmentioning

confidence: 99%

Clustering of Multispectral Airborne Laser Scanning Data Using Gaussian Decomposition

Morsy

Shaker

El-Rabbany

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:With the evolution of the LiDAR technology, multispectral airborne laser scanning systems are currently available. The first operational multispectral airborne LiDAR sensor, the Optech Titan, acquires LiDAR point clouds at three different wavelengths (1.550, 1.064, 0.532 m), allowing the acquisition of different spectral information of land surface. Consequently, the recent studies are devoted to use the radiometric information (i.e., intensity) of the LiDAR data along with the geometric information (e.g., height) for classification purposes. In this study, a data clustering method, based on Gaussian decomposition, is presented. First, a ground filtering mechanism is applied to separate non-ground from ground points. Then, three normalized difference vegetation indices (NDVIs) are computed for both non-ground and ground points, followed by histograms construction from each NDVI. The Gaussian function model is used to decompose the histograms into a number of Gaussian components. The maximum likelihood estimate of the Gaussian components is then optimized using Expectation -Maximization algorithm. The intersection points of the adjacent Gaussian components are subsequently used as threshold values, whereas different classes can be clustered. This method is used to classify the terrain of an urban area in Oshawa, Ontario, Canada, into four main classes, namely roofs, trees, asphalt and grass. It is shown that the proposed method has achieved an overall accuracy up to 95.1% using different NDVIs.

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“…With Gemini, the energy per pulse weakens when it operates at a higher PRF, which makes the canopy penetration of each pulse more difficult. In other words, Titan can maintain high rates of canopy penetration per pulse when a higher PRF is used [30,31].…”

Section: Lidar Data Acquisitionmentioning

confidence: 99%

Archaeological Application of Airborne LiDAR with Object-Based Vegetation Classification and Visualization Techniques at the Lowland Maya Site of Ceibal, Guatemala

et al. 2017

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Abstract:The successful analysis of LiDAR data for archaeological research requires an evaluation of effects of different vegetation types and the use of adequate visualization techniques for the identification of archaeological features. The Ceibal-Petexbatun Archaeological Project conducted a LiDAR survey of an area of 20 × 20 km around the Maya site of Ceibal, Guatemala, which comprises diverse vegetation classes, including rainforest, secondary vegetation, agricultural fields, and pastures. We developed a classification of vegetation through object-based image analysis (OBIA), primarily using LiDAR-derived datasets, and evaluated various visualization techniques of LiDAR data. We then compared probable archaeological features identified in the LiDAR data with the archaeological map produced by Harvard University in the 1960s and conducted ground-truthing in sample areas. This study demonstrates the effectiveness of the OBIA approach to vegetation classification in archaeological applications, and suggests that the Red Relief Image Map (RRIM) aids the efficient identification of subtle archaeological features. LiDAR functioned reasonably well for the thick rainforest in this high precipitation region, but the densest parts of foliage appear to create patches with no or few ground points, which make the identification of small structures problematic.

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Capability Assessment and Performance Metrics for the Titan Multispectral Mapping Lidar

Cited by 140 publications

References 53 publications

Predicting Selected Forest Stand Characteristics with Multispectral ALS Data

Predicting Selected Forest Stand Characteristics with Multispectral ALS Data

Clustering of Multispectral Airborne Laser Scanning Data Using Gaussian Decomposition

Archaeological Application of Airborne LiDAR with Object-Based Vegetation Classification and Visualization Techniques at the Lowland Maya Site of Ceibal, Guatemala

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