scholarly journals Using Color, Texture and Object-Based Image Analysis of Multi-Temporal Camera Data to Monitor Soil Aggregate Breakdown

Sensors ◽  
2017 ◽  
Vol 17 (6) ◽  
pp. 1241 ◽  
Author(s):  
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◽  
◽  
◽  
◽  
...  
Keyword(s):  
Image Analysis ◽  
Soil Aggregate ◽  
Object Based Image Analysis ◽  
Aggregate Breakdown ◽  
Object Based ◽  
Multi Temporal ◽  
Color Texture

Multi-temporal mapping of seagrass cover, species and biomass: A semi-automated object based image analysis approach

2014 ◽  
Vol 150 ◽  
pp. 172-187 ◽  
Author(s):  
Chris M. Roelfsema ◽  
Mitchell Lyons ◽  
Eva M. Kovacs ◽  
Paul Maxwell ◽  
Megan I. Saunders ◽  
...  
Keyword(s):  
Image Analysis ◽  
Analysis Approach ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Multi Temporal ◽  
Seagrass Cover

scholarly journals Multi-Temporal UAV Data and Object-Based Image Analysis (OBIA) for Estimation of Substrate Changes in a Post-Bleaching Scenario on a Maldivian Reef

2020 ◽  
Vol 12 (13) ◽  
pp. 2093 ◽  
Author(s):  
Luca Fallati ◽  
Luca Saponari ◽  
Alessandra Savini ◽  
Fabio Marchese ◽  
Cesare Corselli ◽  
...  
Keyword(s):  
Image Analysis ◽  
Coral Reefs ◽  
Shallow Water ◽  
Regional Scale ◽  
Spatial And Temporal Patterns ◽  
Coral Rubble ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Reef Environments ◽  
Multi Temporal

Coral reefs are declining worldwide as a result of the effects of multiple natural and anthropogenic stressors, including regional-scale temperature-induced coral bleaching. Such events have caused significant coral mortality, leading to an evident structural collapse of reefs and shifts in associated benthic communities. In this scenario, reasonable mapping techniques and best practices are critical to improving data collection to describe spatial and temporal patterns of coral reefs after a significant bleaching impact. Our study employed the potential of a consumer-grade drone, coupled with structure from motion and object-based image analysis to investigate for the first time a tool to monitor changes in substrate composition and the associated deterioration in reef environments in a Maldivian shallow-water coral reef. Three key substrate types (hard coral, coral rubble and sand) were detected with high accuracy on high-resolution orthomosaics collected from four sub-areas. Multi-temporal acquisition of UAV data allowed us to compare the classified maps over time (February 2017, November 2018) and obtain evidence of the relevant deterioration in structural complexity of flat reef environments that occurred after the 2016 mass bleaching event. We believe that our proposed methodology offers a cost-effective procedure that is well suited to generate maps for the long-term monitoring of changes in substrate type and reef complexity in shallow water.

scholarly journals New tools for old problems — comparing drone- and field-based assessments of a problematic plant species

2021 ◽  
Vol 193 (2) ◽  
Author(s):  
Jens Oldeland ◽  
Rasmus Revermann ◽  
Jona Luther-Mosebach ◽  
Tillmann Buttschardt ◽  
Jan R. K. Lehmann
Keyword(s):  
Image Analysis ◽  
Plant Species ◽  
Just In Time ◽  
Post Processing ◽  
Tall Grass ◽  
Field Surveys ◽  
Distribution Maps ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Before And After

AbstractPlant species that negatively affect their environment by encroachment require constant management and monitoring through field surveys. Drones have been suggested to support field surveyors allowing more accurate mapping with just-in-time aerial imagery. Furthermore, object-based image analysis tools could increase the accuracy of species maps. However, only few studies compare species distribution maps resulting from traditional field surveys and object-based image analysis using drone imagery. We acquired drone imagery for a saltmarsh area (18 ha) on the Hallig Nordstrandischmoor (Germany) with patches of Elymus athericus, a tall grass which encroaches higher parts of saltmarshes. A field survey was conducted afterwards using the drone orthoimagery as a baseline. We used object-based image analysis (OBIA) to segment CIR imagery into polygons which were classified into eight land cover classes. Finally, we compared polygons of the field-based and OBIA-based maps visually and for location, area, and overlap before and after post-processing. OBIA-based classification yielded good results (kappa = 0.937) and agreed in general with the field-based maps (field = 6.29 ha, drone = 6.22 ha with E. athericus dominance). Post-processing revealed 0.31 ha of misclassified polygons, which were often related to water runnels or shadows, leaving 5.91 ha of E. athericus cover. Overlap of both polygon maps was only 70% resulting from many small patches identified where E. athericus was absent. In sum, drones can greatly support field surveys in monitoring of plant species by allowing for accurate species maps and just-in-time captured very-high-resolution imagery.

scholarly journals Monitoring the Efficacy of Crested Floatingheart (Nymphoides cristata) Management with Object-Based Image Analysis of UAS Imagery

2021 ◽  
Vol 13 (4) ◽  
pp. 830
Author(s):  
Adam R. Benjamin ◽  
Amr Abd-Elrahman ◽  
Lyn A. Gettys ◽  
Hartwig H. Hochmair ◽  
Kyle Thayer
Keyword(s):  
Image Analysis ◽  
Aquatic Vegetation ◽  
Accuracy Assessment ◽  
Unmanned Aerial Systems ◽  
Temporal Area ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Significant Difference ◽  
Additional Processing ◽  
Multispectral Sensors

This study investigates the use of unmanned aerial systems (UAS) mapping for monitoring the efficacy of invasive aquatic vegetation (AV) management on a floating-leaved AV species, Nymphoides cristata (CFH). The study site consists of 48 treatment plots (TPs). Based on six unique flights over two days at three different flight altitudes while using both a multispectral and RGB sensor, accuracy assessment of the final object-based image analysis (OBIA)-derived classified images yielded overall accuracies ranging from 89.6% to 95.4%. The multispectral sensor was significantly more accurate than the RGB sensor at measuring CFH areal coverage within each TP only with the highest multispectral, spatial resolution (2.7 cm/pix at 40 m altitude). When measuring response in the AV community area between the day of treatment and two weeks after treatment, there was no significant difference between the temporal area change from the reference datasets and the area changes derived from either the RGB or multispectral sensor. Thus, water resource managers need to weigh small gains in accuracy from using multispectral sensors against other operational considerations such as the additional processing time due to increased file sizes, higher financial costs for equipment procurements, and longer flight durations in the field when operating multispectral sensors.

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