Monitoring land surface and cover in urban and peri-urban environments using digital aerial photography

Caccetta, Peter; Collings, Simon; Devereux, Andrew; Hingee, Kassel; McFarlane, Don; Traylen, Anthony; Wu, Xiaoliang; Zhou, Zhenxing

doi:10.1080/17538947.2015.1046510

Cited by 26 publications

(12 citation statements)

References 33 publications

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“…For reference and bench-marking purposes, aerial photography was available over the Kingston Reef site. The aerial photography was captured as part of the Urban Monitor Program to acquire annual imagery over Perth and its surrounding environs [30]. The imagery is 20 cm resolution.…”

Section: Aerial Photographymentioning

confidence: 99%

Findings from a Combined Subsea LiDAR and Multibeam Survey at Kingston Reef, Western Australia

et al. 2020

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Light Detection and Ranging (LiDAR), a comparatively new technology in the field of underwater surveying, has principally been used for taking precise measurement of undersea structures in the oil and gas industry. Typically, the LiDAR is deployed on a remotely operated vehicle (ROV), which will “land” on the seafloor in order to generate a 3D point cloud of its environment from a stationary position. To explore the potential of subsea LiDAR on a moving platform in an environmental context, we deployed an underwater LiDAR system simultaneously with a multibeam echosounder (MBES), surveying Kingston Reef off the coast of Rottnest Island, Western Australia. This paper compares and summarises the relative accuracy and characteristics of underwater LiDAR and multibeam sonar and investigates synergies between sonar and LiDAR technology for the purpose of benthic habitat mapping and underwater simultaneous localisation and mapping (SLAM) for Autonomous Underwater Vehicles (AUVs). We found that LiDAR reflectivity and multibeam backscatter are complementary technologies for habitat mapping, which can combine to discriminate between habitats that could not be mapped with either one alone. For robot navigation, SLAM can be effectively applied with either technology, however, when a Global Navigation Satellite System (GNSS) is available, SLAM does not significantly improve the self-consistency of multibeam data, but it does for LiDAR.

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Section: Aerial Photographymentioning

confidence: 99%

Findings from a Combined Subsea LiDAR and Multibeam Survey at Kingston Reef, Western Australia

et al. 2020

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“…The labels were made possible through cover trend information derived from Landsat satellite data (Wallace et al 2006). Tree canopy presence-absence maps with 20 cm spatial resolution were generated for each parcel from aerial photography captured in February 2016 following the methods described by Caccetta et al (2015).…”

Section: Estimators Applied To Forest Disturbance At Local Scalementioning

confidence: 99%

Computation of Lacunarity from Covariance of Spatial Binary Maps

Hingee

Baddeley

Caccetta

et al. 2019

JABES

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We consider a spatial binary coverage map (binary pixel image) which might represent the spatial pattern of the presence and absence of vegetation in a landscape. 'Lacunarity' is a generic term for the nature of gaps in the pattern: a popular choice of summary statistic is the 'gliding-box lacunarity' (GBL) curve. GBL is potentially useful for quantifying changes in vegetation patterns, but its application is hampered by a lack of interpretability and practical difficulties with missing data. In this paper we find a mathematical relationship between GBL and spatial covariance. This leads to new estimators of GBL that tolerate irregular spatial domains and missing data, thus overcoming major weaknesses of the traditional estimator. The relationship gives an explicit formula for GBL of models with known spatial covariance and enables us to predict the effect of changes in the pattern on GBL. Using variance reduction methods for spatial data, we obtain statistically efficient estimators of GBL. The techniques are demonstrated on simulated binary coverage maps and remotely sensed maps of local-scale disturbance and meso-scale fragmentation in Australian forests. Results show in some cases a fourfold reduction in mean integrated squared error and a twentyfold reduction in sensitivity to missing data.Supplementary materials accompanying the paper appear online and include a software implementation in the R language.

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“…The DSMs were generated as part of a wide-region, fine-scale urban monitoring study (Caccetta et al, 2015). See Figure 2 for an example DSM.…”

Section: Datamentioning

confidence: 99%

“…They also have applications in wireless communication (Sawada et al, 2006) and land cover map generation (Caccetta et al, 2015). It is popular to generate DEMs using remote sensors including light detection and ranging sensors (LiDAR), digital stereo (multiple view)-photography, and interferometric synthetic aperture radar (InSAR).…”

Section: Introductionmentioning

confidence: 99%

Digital Terrain From a Two-Step Segmentation and Outlier-Based Algorithm

Hingee¹,

Caccetta²,

Caccetta³

et al. 2016

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

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ABSTRACT:We present a novel ground filter for remotely sensed height data. Our filter has two phases: the first phase segments the DSM with a slope threshold and uses gradient direction to identify candidate ground segments; the second phase fits surfaces to the candidate ground points and removes outliers. Digital terrain is obtained by a surface fit to the final set of ground points. We tested the new algorithm on digital surface models (DSMs) for a 9600km 2 region around Perth, Australia. This region contains a large mix of land uses (urban, grassland, native forest and plantation forest) and includes both a sandy coastal plain and a hillier region (elevations up to 0.5km). The DSMs are captured annually at 0.2m resolution using aerial stereo photography, resulting in 1.2TB of input data per annum. Overall accuracy of the filter was estimated to be 89.6% and on a small semi-rural subset our algorithm was found to have 40% fewer errors compared to Inpho's Match-T algorithm.

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Monitoring land surface and cover in urban and peri-urban environments using digital aerial photography

Cited by 26 publications

References 33 publications

Findings from a Combined Subsea LiDAR and Multibeam Survey at Kingston Reef, Western Australia

Findings from a Combined Subsea LiDAR and Multibeam Survey at Kingston Reef, Western Australia

Computation of Lacunarity from Covariance of Spatial Binary Maps

Digital Terrain From a Two-Step Segmentation and Outlier-Based Algorithm

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