The effect of seasonal variation on automated land cover mapping from multispectral airborne laser scanning data

Karila, Kirsi; Matikainen, Leena; Litkey, Paula; Hyyppä, Juha; Puttonen, Eetu

doi:10.1080/01431161.2018.1528023

Cited by 8 publications

(10 citation statements)

References 33 publications

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“…The second is a newer one corresponding to the situation in June 2016. 57 The main data sources used in the manual checking and updating of the land cover information of these point sets were multispectral ALS data acquisitions from the corresponding points of time. To select unchanged points for the satellite image analysis, the land cover was generalized into three classes: "built-up area," "tree," and "low vegetation."…”

Section: Study Area Reference Data and Map Datamentioning

confidence: 99%

“…According to visual evaluation, the intensity levels in the two datasets matched each other relatively well after the adjustment. 57 In addition to the preprocessed height and intensity data, land cover classifications carried out for the data 10,57 were used in the study. The land cover classifications were carried out using object-based random forest 59 classification.…”

Section: Multispectral Als Datamentioning

confidence: 99%

“…The overall accuracy of the original ALS classifications was as high as 96%. 57 However, there were some false positive changes in the reference change map due to classification errors (e.g., noise in forested areas, sports fields with artificial cover, nonhomogeneous gravel areas). In Sentinel-2 images, small image shifts (1 to 2 pixels) and some special vegetation types (e.g., waterfront vegetation) caused false-positive changes.…”

Section: Potential Of Sentinel-2 Satellite Images For Continuous Chanmentioning

confidence: 99%

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Toward utilizing multitemporal multispectral airborne laser scanning, Sentinel-2, and mobile laser scanning in map updating

Matikainen

Pandžić

et al. 2019

J. Appl. Rem. Sens.

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The rapid development of remote sensing technologies provides interesting possibilities for the further development of nationwide mapping procedures that are currently based mainly on passive aerial images. In particular, we assume that there is a large undiscovered potential in multitemporal airborne laser scanning (ALS) for topographic mapping. In this study, automated change detection from multitemporal multispectral ALS data was tested for the first time. The results showed that direct comparisons between height and intensity data from different dates reveal even small changes related to the development of a suburban area. A major challenge in future work is to link the changes with objects that are interesting in map production. In order to effectively utilize multisource remotely sensed data in mapping in the future, we also investigated the potential of satellite images and ground-based data to complement multispectral ALS. A method for continuous change monitoring from a time series of Sentinel-2 satellite images was developed and tested. Finally, a high-density point cloud was acquired with terrestrial mobile laser scanning and automatically classified into four classes. The results were compared with the ALS data, and the possible roles of the different data sources in a future map updating process were discussed. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Section: Study Area Reference Data and Map Datamentioning

confidence: 99%

Section: Multispectral Als Datamentioning

confidence: 99%

Section: Potential Of Sentinel-2 Satellite Images For Continuous Chanmentioning

confidence: 99%

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Toward utilizing multitemporal multispectral airborne laser scanning, Sentinel-2, and mobile laser scanning in map updating

Matikainen

Pandžić

et al. 2019

J. Appl. Rem. Sens.

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“…Electromagnetic waves are mostly absorbed at the water surface in the NIR and SWIR spectrum. Thus, the three scanning frequencies provided by the Teledyne Optech Titan make it possible to acquire various spectral responses of different materials and to obtain diverse information about the surface of the Earth (Karila et al, 2018). Detailed specifications of the Teledyne Optech Titan are listed in Table 1.…”

Section: Optech Titan Multispectral Als Systemmentioning

confidence: 99%

“…To solve the problems of data fusions, multispectral LiDAR techniques, which can acquire LiDAR data with multiple channels simultaneously, have been recently developed. The Teledyne Optech Titan, which contains three active imaging channels at different wavelengths, is the first commercial multispectral airborne active imaging LIDAR sensor in the world (Karila et al, 2018). Even though only a few related studies have been conducted (e.g.…”

Section: Introductionmentioning

confidence: 99%

Multispectral Airborne Laser Scanning Point-Clouds for Land Cover Classification Using Convolutional Neural Networks

Ma¹,

Chen²,

Li³

et al. 2019

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

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This paper presents an automated workflow for pixel-wise land cover (LC) classification from multispectral airborne laser scanning (ALS) data using deep learning methods. It mainly contains three procedures: data pre-processing, land cover classification, and accuracy assessment. First, a total of nine raster images with different information were generated from the pre-processed point clouds. These images were assembled into six input data combinations. Meanwhile, the labelled dataset was created using the orthophotos as the ground truth. Also, three deep learning networks were established. Then, each input data combination was used to train and validate each network, which developed eighteen LC classification models with different parameters to predict LC types for pixels. Finally, accuracy assessments and comparisons were done for the eighteen classification results to determine an optimal scheme. The proposed method was tested on six input datasets with three deep learning classification networks (i.e., 1D CNN, 2D CNN, and 3D CNN). The highest overall classification accuracy of 97.2% has been achieved using the proposed 3D CNN. The overall accuracy (OA) of the 2D and 3D CNNs was, on average, 8.4% higher than that of the 1D CNN. Although the OA of the 2D CNN was at most 0.3% lower than that of the 3D CNN, the runtime of the 3D CNN was five times longer than the 2D CNN. Thus, the 2D CNN was the best choice for the multispectral ALS LC classification when considering efficiency. The results demonstrated the proposed methods can successfully classify land covers from multispectral ALS data.

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Remote Sensing Tools for Monitoring Forests and Tracking Their Dynamics

Massey

Berner

Foster

et al. 2023

Advances in Global Change Research

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Remote sensing augments field data and facilitates foresight required for forest management by providing spatial and temporal observations of forest characteristics at landscape and regional scales. Statistical and machine-learning models derived from plot-level field observations can be extrapolated to larger areas using remote sensing data. For example, instruments such as light detection and ranging (LiDAR) and hyperspectral sensors are frequently used to quantify forest characteristics at the stand to landscape level. Moreover, multispectral imagery and synthetic aperture radar (SAR) data sets derived from satellite platforms can be used to extrapolate forest resource models to large regions. The combination of novel remote sensing technologies, expanding computing capabilities, and emerging geospatial methods ensures a data-rich environment for effective strategic, tactical, and operational planning and monitoring in forest resource management.

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The effect of seasonal variation on automated land cover mapping from multispectral airborne laser scanning data

Cited by 8 publications

References 33 publications

Toward utilizing multitemporal multispectral airborne laser scanning, Sentinel-2, and mobile laser scanning in map updating

Toward utilizing multitemporal multispectral airborne laser scanning, Sentinel-2, and mobile laser scanning in map updating

Multispectral Airborne Laser Scanning Point-Clouds for Land Cover Classification Using Convolutional Neural Networks

Remote Sensing Tools for Monitoring Forests and Tracking Their Dynamics

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