Lidar-based change detection and change-type determination in urban areas

Teo, Tee-Ann; Shih, Tian‐Yuan

doi:10.1080/01431161.2012.714504

Cited by 66 publications

(47 citation statements)

References 11 publications

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“…The increased use of LiDAR systems to acquire data over forested areas can be attributed to their ability to cover extents of local or regional scales and accurately quantify the three-dimensional structure of the forest. Previous studies have demonstrated the usefulness of LiDAR for: (1) Forest measurements [2][3][4][5][6][7][8][9][10][11]; (2) habitat analysis [12][13][14]; (3) estimation of forest biophysical parameters [15][16][17][18][19][20][21][22][23][24][25][26][27]; (4) change detection [23,28,29]; and (5) estimation of wild land fire parameters [30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Modeling Forest Aboveground Biomass and Volume Using Airborne LiDAR Metrics and Forest Inventory and Analysis Data in the Pacific Northwest

et al. 2014

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Abstract:The United States Forest Service Forest Inventory and Analysis (FIA) Program provides a diverse selection of data used to assess the status of the nation's forests using sample locations dispersed throughout the country. Airborne laser scanning (ALS) systems are capable of producing accurate measurements of individual tree dimensions and also possess the ability to characterize forest structure in three dimensions. This study investigates the potential of discrete return ALS data for modeling forest aboveground biomass (AGBM) and gross volume (gV) at FIA plot locations in the Malheur National Forest, eastern Oregon utilizing three analysis levels: (1) individual subplot (r = 7.32 m); (2) plot, comprising four clustered subplots; and (3) hectare plot (r = 56.42 m). A methodology for the creation of three point cloud-based airborne LiDAR metric sets is presented. Models for estimating AGBM and gV based on LiDAR-derived height metrics were built and validated utilizing FIA estimates of AGBM and gV derived using regional allometric equations. Simple linear regression models based on the plot-level analysis out Results suggest that the current FIA plot design can be used with dense airborne LiDAR data to produce area-based estimates of AGBM and gV, and that the increased spatial scale of hectare plots may be inappropriate for modeling AGBM of gV unless exhaustive tree tallies are available. Overall, this study demonstrates that ALS data can be used to create models that describe the AGBM and gV of Pacific Northwest FIA plots and highlights the potential of estimates derived from ALS data to augment current FIA data collection procedures by providing a temporary intermediate estimation of AGBM and gV for plots with outdated field measurements.

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Section: Introductionmentioning

confidence: 99%

Modeling Forest Aboveground Biomass and Volume Using Airborne LiDAR Metrics and Forest Inventory and Analysis Data in the Pacific Northwest

et al. 2014

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“…Similarly, Vu et al [22] developed an automatic building change detection method based on LiDAR data from dense urban areas. Teo et al [23] proposed a change detection method that uses multi-temporal interpolated LiDAR data, in which the changes in building types were determined via geometric analysis. Pang et al [24] developed an object-oriented analysis for multi-temporal point cloud data to detect building changes.…”

Section: Introductionmentioning

confidence: 99%

Using Octrees to Detect Changes to Buildings and Trees in the Urban Environment from Airborne LiDAR Data

Cheng

et al. 2015

Remote Sensing

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Change detection is a major issue for urban area monitoring. In this paper, a new three-step point-based method for detecting changes to buildings and trees using airborne light detection and ranging (LiDAR) data is proposed. First, the airborne LiDAR data from two dates are accurately registered using the iterative closest point algorithm, and a progressive triangulated irregular network densification filtering algorithm is used to separate ground points from non-ground points. Second, an octree is generated from the non-ground points to store and index the irregularly-distributed LiDAR points. Finally, by comparing the LiDAR points from two dates and using the AutoClust algorithm, those areas of buildings and trees in the urban environment that have changed are determined effectively and efficiently. The key contributions of this approach are the development of a point-based method to effectively solve the problem of objects at different scales, and the establishment of rules to detect changes in buildings and trees to urban areas, enabling the use of the pointbased method over large areas. To evaluate the proposed method, a series of experiments using aerial images are conducted. The results demonstrate that satisfactory performance can be obtained using the proposed approach. OPEN ACCESSRemote Sens. 2015, 7 9683

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“…The first group uses new ALS data, possibly combined with new image data, and aims to detect changes compared to an existing building map (e.g., Matikainen et al, 2003Matikainen et al, , 2010Vosselman et al, 2005;Malpica et al, 2013). The second approach assumes that both old and new ALS datasets are available and the change detection method can rely on the comparison of these (e.g., Murakami et al, 1999;Richter et al, 2013;Teo and Shih, 2013). In the first group of methods, the methods are directly linked to the existing building objects on a map, and change information is normally obtained for these objects.…”

Section: Introductionmentioning

confidence: 99%

Multispectral Airborne Laser Scanning for Automated Map Updating

Matikainen¹,

Hyyppä²,

Litkey³

2016

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

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ABSTRACT:During the last 20 years, airborne laser scanning (ALS), often combined with multispectral information from aerial images, has shown its high feasibility for automated mapping processes. Recently, the first multispectral airborne laser scanners have been launched, and multispectral information is for the first time directly available for 3D ALS point clouds. This article discusses the potential of this new single-sensor technology in map updating, especially in automated object detection and change detection. For our study, Optech Titan multispectral ALS data over a suburban area in Finland were acquired. Results from a random forests analysis suggest that the multispectral intensity information is useful for land cover classification, also when considering ground surface objects and classes, such as roads. An out-of-bag estimate for classification error was about 3% for separating classes asphalt, gravel, rocky areas and low vegetation from each other. For buildings and trees, it was under 1%. According to feature importance analyses, multispectral features based on several channels were more useful that those based on one channel. Automatic change detection utilizing the new multispectral ALS data, an old digital surface model (DSM) and old building vectors was also demonstrated. Overall, our first analyses suggest that the new data are very promising for further increasing the automation level in mapping. The multispectral ALS technology is independent of external illumination conditions, and intensity images produced from the data do not include shadows. These are significant advantages when the development of automated classification and change detection procedures is considered.

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Lidar-based change detection and change-type determination in urban areas

Cited by 66 publications

References 11 publications

Modeling Forest Aboveground Biomass and Volume Using Airborne LiDAR Metrics and Forest Inventory and Analysis Data in the Pacific Northwest

Modeling Forest Aboveground Biomass and Volume Using Airborne LiDAR Metrics and Forest Inventory and Analysis Data in the Pacific Northwest

Using Octrees to Detect Changes to Buildings and Trees in the Urban Environment from Airborne LiDAR Data

Multispectral Airborne Laser Scanning for Automated Map Updating

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