Spatiotemporal Trends of Bora Bora’s Shoreline Classification and Movement Using High Resolution Imagery From 1955 to 2019

Coastal urbanisation is a widespread phenomenon throughout the world and is often linked to increased erosion. Small Pacific islands are not spared from this issue, which is of great importance in the context of climate change. The French Polynesian island of Bora Bora was used as a case study to investigate the historical evolution of its coastline classification and position from 1955 to 2019. A time series of very-high-resolution aerial imagery was processed to highlight the changes of the island’s coastline. The overall length of natural shores, including beaches, decreased by 46% from 1955 to 2019 while man-made shores such as seawalls increased by 476%, and as of 2019 represented 61% of the coastline. This evolution alters sedimentary processes: the time series of aerial images highlights increased erosion in the vicinity of seawalls and embankments, leading to the incremental need to construct additional walls. In addition, the gradual removal of natural shoreline types modifies landscapes and may negatively impact marine biodiversity. Through documenting coastal changes on Bora Bora through time, this study highlights the impacts of man-made structures on erosional processes and underscores the need for sustainable coastal management plans in French Polynesia.

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Spatiotemporal Trends of Bora Bora’s Shoreline Classification and Movement Using High-Resolution Imagery from 1955 to 2019

Remote Sensing ◽

10.3390/rs13224692 ◽

2021 ◽

Vol 13 (22) ◽

pp. 4692

Author(s):

Emma Gairin ◽

Antoine Collin ◽

Dorothée James ◽

Tehani Maueau ◽

Yoann Roncin ◽

...

Keyword(s):

Time Series ◽

High Resolution ◽

Pacific Islands ◽

French Polynesia ◽

Aerial Images ◽

Marine Biodiversity ◽

Spatiotemporal Trends ◽

High Resolution Imagery ◽

Management Plans ◽

Widespread Phenomenon

Coastal urbanisation is a widespread phenomenon throughout the world and is often linked to increased erosion. Small Pacific islands are not spared from this issue, which is of great importance in the context of climate change. The French Polynesian island of Bora Bora was used as a case study to investigate the historical evolution of its coastline classification and position from 1955 to 2019. A time series of very high-resolution aerial imagery was processed to highlight the changes of the island’s coastline. The overall length of natural shores, including beaches, decreased by 46% from 1955 to 2019 while human-made shores such as seawalls increased by 476%, and as of 2019 represented 61% of the coastline. This evolution alters sedimentary processes: the time series of aerial images highlights increased erosion in the vicinity of seawalls and embankments, leading to the incremental need to construct additional walls. In addition, the gradual removal of natural shoreline types modifies landscapes and may negatively impact marine biodiversity. Through documenting coastal changes to Bora Bora over time, this study highlights the impacts of human-made structures on erosional processes and underscores the need for sustainable coastal management plans in French Polynesia.

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Whale counting in satellite and aerial images with deep learning

Scientific Reports ◽

10.1038/s41598-019-50795-9 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 9

Author(s):

Emilio Guirado ◽

Siham Tabik ◽

Marga L. Rivas ◽

Domingo Alcaraz-Segura ◽

Francisco Herrera

Keyword(s):

High Resolution ◽

Open Data ◽

Cost Effective ◽

Google Earth ◽

Aerial Images ◽

Deep Convolutional Neural Networks ◽

Detection Model ◽

Counting Approach ◽

Threat Status ◽

High Resolution Imagery

Abstract Despite their interest and threat status, the number of whales in world’s oceans remains highly uncertain. Whales detection is normally carried out from costly sighting surveys, acoustic surveys or through high-resolution images. Since deep convolutional neural networks (CNNs) are achieving great performance in several computer vision tasks, here we propose a robust and generalizable CNN-based system for automatically detecting and counting whales in satellite and aerial images based on open data and tools. In particular, we designed a two-step whale counting approach, where the first CNN finds the input images with whale presence, and the second CNN locates and counts each whale in those images. A test of the system on Google Earth images in ten global whale-watching hotspots achieved a performance (F1-measure) of 81% in detecting and 94% in counting whales. Combining these two steps increased accuracy by 36% compared to a baseline detection model alone. Applying this cost-effective method worldwide could contribute to the assessment of whale populations to guide conservation actions. Free and global access to high-resolution imagery for conservation purposes would boost this process.

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Quantifying coconut palm extent on Pacific islands using spectral and textural analysis of very high resolution imagery

International Journal of Remote Sensing ◽

10.1080/01431161.2019.1594440 ◽

2019 ◽

Vol 40 (19) ◽

pp. 7329-7355 ◽

Cited By ~ 2

Author(s):

Michael W. Burnett ◽

Timothy D. White ◽

Douglas J. McCauley ◽

Giulio A. De Leo ◽

Fiorenza Micheli

Keyword(s):

High Resolution ◽

Pacific Islands ◽

Textural Analysis ◽

Coconut Palm ◽

High Resolution Imagery ◽

Very High Resolution Imagery ◽

Very High

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Using Unmanned Aerial Vehicles (UAV) for mornitoring the construction progress of Ho Chi Minh City urban railway project

Science and Technology Development Journal ◽

10.32508/stdj.v18i2.1064 ◽

2015 ◽

Vol 18 (2) ◽

pp. 104-112

Author(s):

Nhat Huu Nguyen ◽

Tam Minh Dao ◽

Trung Van Le ◽

Chon Trung Le

Keyword(s):

High Resolution ◽

Unmanned Aerial Vehicles ◽

Aerial Images ◽

3D Objects ◽

Ground Control Points ◽

Aerial Vehicles ◽

High Resolution Imagery ◽

Gis Data ◽

High Flexibility ◽

Railway Construction Project

This paper describes a new approach for monitoring the construction progress of the Urban Railway Construction Project “Metro line1 - Ben Thanh - Suoi Tien” by using Unmanned Aerial Vehicles (UAV) to capture high resolution imagery at different stages of the project. The advantage of the AscTec Falcon 8 systems lies in their high flexibility and efficiency in capturing the surface of an area from a low flight altitude. In addition, further information such as orthoimages, elevation models and 3D objects can easily be processed by Pix4Dmapper software. The Ground Control Points (GCPs) and GIS data were used to compare the achieved accuracy of UAV method. This study shows the feasibility of using an UAV system for acquiring the high resolution aerial images and the new opportunities for managing construction progress over time.

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PHOTOGRAMMETRIC POINT CLOUDS GENERATION IN URBAN AREAS FROM INTEGRATED IMAGE MATCHING AND SEGMENTATION

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-2-w4-279-2017 ◽

2017 ◽

Vol IV-2/W4 ◽

pp. 279-285

Author(s):

L. Ye ◽

B. Wu

Keyword(s):

High Resolution ◽

Image Matching ◽

Urban Areas ◽

Laser Scanning ◽

Tall Buildings ◽

Point Clouds ◽

Aerial Images ◽

High Resolution Imagery ◽

Dense Matching ◽

High Resolution Satellite Images

High-resolution imagery is an attractive option for surveying and mapping applications due to the advantages of high quality imaging, short revisit time, and lower cost. Automated reliable and dense image matching is essential for photogrammetric 3D data derivation. Such matching, in urban areas, however, is extremely difficult, owing to the complexity of urban textures and severe occlusion problems on the images caused by tall buildings. Aimed at exploiting high-resolution imagery for 3D urban modelling applications, this paper presents an integrated image matching and segmentation approach for reliable dense matching of high-resolution imagery in urban areas. The approach is based on the framework of our existing self-adaptive triangulation constrained image matching (SATM), but incorporates three novel aspects to tackle the image matching difficulties in urban areas: 1) occlusion filtering based on image segmentation, 2) segment-adaptive similarity correlation to reduce the similarity ambiguity, 3) improved dense matching propagation to provide more reliable matches in urban areas. Experimental analyses were conducted using aerial images of Vaihingen, Germany and high-resolution satellite images in Hong Kong. The photogrammetric point clouds were generated, from which digital surface models (DSMs) were derived. They were compared with the corresponding airborne laser scanning data and the DSMs generated from the Semi-Global matching (SGM) method. The experimental results show that the proposed approach is able to produce dense and reliable matches comparable to SGM in flat areas, while for densely built-up areas, the proposed method performs better than SGM. The proposed method offers an alternative solution for 3D surface reconstruction in urban areas.

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