Volumetric Change Detection with using Structure from Motion – The Impact of Repeat Station Imaging

With uninterrupted space-based data collection since 1972, Landsat plays a key role in systematic monitoring of the Earth’s surface, enabled by an extensive and free, radiometrically consistent, global archive of imagery. Governments and international organizations rely on Landsat time series for monitoring and deriving a systematic understanding of the dynamics of the Earth’s surface at a spatial scale relevant to management, scientific inquiry, and policy development. In this study, we identify trends in Landsat-informed change detection studies by surveying 50 years of published applications, processing, and change detection methods. Specifically, a representative database was created resulting in 490 relevant journal articles derived from the Web of Science and Scopus. From these articles, we provide a review of recent developments, opportunities, and trends in Landsat change detection studies. The impact of the Landsat free and open data policy in 2008 is evident in the literature as a turning point in the number and nature of change detection studies. Based upon the search terms used and articles included, average number of Landsat images used in studies increased from 10 images before 2008 to 100,000 images in 2020. The 2008 opening of the Landsat archive resulted in a marked increase in the number of images used per study, typically providing the basis for the other trends in evidence. These key trends include an increase in automated processing, use of analysis-ready data (especially those with atmospheric correction), and use of cloud computing platforms, all over increasing large areas. The nature of change methods has evolved from representative bi-temporal pairs to time series of images capturing dynamics and trends, capable of revealing both gradual and abrupt changes. The result also revealed a greater use of nonparametric classifiers for Landsat change detection analysis. Landsat-9, to be launched in September 2021, in combination with the continued operation of Landsat-8 and integration with Sentinel-2, enhances opportunities for improved monitoring of change over increasingly larger areas with greater intra- and interannual frequency.

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Reduction of ICESat systematic geolocation errors and the impact on ice sheet elevation change detection

Geophysical Research Letters ◽

10.1029/2005gl023689 ◽

2005 ◽

Vol 32 (21) ◽

Cited By ~ 51

Author(s):

S. B. Luthcke

Keyword(s):

Change Detection ◽

Ice Sheet ◽

Elevation Change ◽

The Impact

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Use of Automated Change Detection and VGI Sources for Identifying and Validating Urban Land Use Change

Remote Sensing ◽

10.3390/rs12071186 ◽

2020 ◽

Vol 12 (7) ◽

pp. 1186 ◽

Cited By ~ 2

Author(s):

A.-M. Olteanu-Raimond ◽

L. See ◽

M. Schultz ◽

G. Foody ◽

M. Riffler ◽

...

Keyword(s):

Land Use ◽

Change Detection ◽

Urban Areas ◽

Detection Algorithm ◽

Geographic Information ◽

Urban Land Use ◽

Basic Knowledge ◽

User Profiles ◽

Local Authorities ◽

The Impact

Land use and land cover (LULC) mapping is often undertaken by national mapping agencies, where these LULC products are used for different types of monitoring and reporting applications. Updating of LULC databases is often done on a multi-year cycle due to the high costs involved, so changes are only detected when mapping exercises are repeated. Consequently, the information on LULC can quickly become outdated and hence may be incorrect in some areas. In the current era of big data and Earth observation, change detection algorithms can be used to identify changes in urban areas, which can then be used to automatically update LULC databases on a more continuous basis. However, the change detection algorithm must be validated before the changes can be committed to authoritative databases such as those produced by national mapping agencies. This paper outlines a change detection algorithm for identifying construction sites, which represent ongoing changes in LU, developed in the framework of the LandSense project. We then use volunteered geographic information (VGI) captured through the use of mapathons from a range of different groups of contributors to validate these changes. In total, 105 contributors were involved in the mapathons, producing a total of 2778 observations. The 105 contributors were grouped according to six different user-profiles and were analyzed to understand the impact of the experience of the users on the accuracy assessment. Overall, the results show that the change detection algorithm is able to identify changes in residential land use to an adequate level of accuracy (85%) but changes in infrastructure and industrial sites had lower accuracies (57% and 75 %, respectively), requiring further improvements. In terms of user profiles, the experts in LULC from local authorities, researchers in LULC at the French national mapping agency (IGN), and first-year students with a basic knowledge of geographic information systems had the highest overall accuracies (86.2%, 93.2%, and 85.2%, respectively). Differences in how the users approach the task also emerged, e.g., local authorities used knowledge and context to try to identify types of change while those with no knowledge of LULC (i.e., normal citizens) were quicker to choose ‘Unknown’ when the visual interpretation of a class was more difficult.

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The Impact of Adaptive Speckle Filtering on Multi-Channel SAR Change Detection

IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2008.4779783 ◽

2008 ◽

Author(s):

Jason Fritz ◽

V. Chandrasekar

Keyword(s):

Change Detection ◽

Speckle Filtering ◽

The Impact

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Structure‐from‐motion photogrammetry analysis of historical aerial photography: Determining beach volumetric change over decadal scales

Earth Surface Processes and Landforms ◽

10.1002/esp.4911 ◽

2020 ◽

Vol 45 (11) ◽

pp. 2540-2555 ◽

Cited By ~ 2

Author(s):

Rafael C. Carvalho ◽

David M. Kennedy ◽

Yakufu Niyazi ◽

Chloe Leach ◽

Teresa M. Konlechner ◽

...

Keyword(s):

Structure From Motion ◽

Aerial Photography ◽

Volumetric Change ◽

Historical Aerial Photography

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Evaluating the potential of post-processing kinematic (PPK) georeferencing for UAV-based structure- from-motion (SfM) photogrammetry and surface change detection

Earth Surface Dynamics ◽

10.5194/esurf-7-807-2019 ◽

2019 ◽

Vol 7 (3) ◽

pp. 807-827 ◽

Cited By ~ 19

Author(s):

He Zhang ◽

Emilien Aldana-Jague ◽

François Clapuyt ◽

Florian Wilken ◽

Veerle Vanacker ◽

...

Keyword(s):

Change Detection ◽

Structure From Motion ◽

Low Cost ◽

Post Processing ◽

Positional Accuracy ◽

Surface Change ◽

Ground Control Points ◽

Single Lens ◽

Surface Representations ◽

Uav Images

Abstract. Images captured by unmanned aerial vehicles (UAVs) and processed by structure-from-motion (SfM) photogrammetry are increasingly used in geomorphology to obtain high-resolution topography data. Conventional georeferencing using ground control points (GCPs) provides reliable positioning, but the geometrical accuracy critically depends on the number and spatial layout of the GCPs. This limits the time and cost effectiveness. Direct georeferencing of the UAV images with differential GNSS, such as PPK (post-processing kinematic), may overcome these limitations by providing accurate and directly georeferenced surveys. To investigate the positional accuracy, repeatability and reproducibility of digital surface models (DSMs) generated by a UAV–PPK–SfM workflow, we carried out multiple flight missions with two different camera–UAV systems: a small-form low-cost micro-UAV equipped with a high field of view (FOV) action camera and a professional UAV equipped with a digital single lens reflex (DSLR) camera. Our analysis showed that the PPK solution provides the same accuracy (MAE: ca. 0.02 m, RMSE: ca. 0.03 m) as the GCP method for both UAV systems. Our study demonstrated that a UAV–PPK–SfM workflow can provide consistent, repeatable 4-D data with an accuracy of a few centimeters. However, a few flights showed vertical bias and this could be corrected using one single GCP. We further evaluated different methods to estimate DSM uncertainty and show that this has a large impact on centimeter-level topographical change detection. The DSM reconstruction and surface change detection based on a DSLR and action camera were reproducible: the main difference lies in the level of detail of the surface representations. The PPK–SfM workflow in the context of 4-D Earth surface monitoring should be considered an efficient tool to monitor geomorphic processes accurately and quickly at a very high spatial and temporal resolution.

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