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.

scholarly journals Image Segmentation and Object-Based Image Analysis for Environmental Monitoring: Recent Areas of Interest, Researchers’ Views on the Future Priorities

2020 ◽  
Vol 12 (11) ◽  
pp. 1772
Author(s):  
Brian Alan Johnson ◽  
Lei Ma
Keyword(s):  
Remote Sensing ◽  
Image Analysis ◽  
Image Segmentation ◽  
Online Survey ◽  
Accuracy Assessment ◽  
Learning Approaches ◽  
Special Issue ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Wide Range

Image segmentation and geographic object-based image analysis (GEOBIA) were proposed around the turn of the century as a means to analyze high-spatial-resolution remote sensing images. Since then, object-based approaches have been used to analyze a wide range of images for numerous applications. In this Editorial, we present some highlights of image segmentation and GEOBIA research from the last two years (2018–2019), including a Special Issue published in the journal Remote Sensing. As a final contribution of this special issue, we have shared the views of 45 other researchers (corresponding authors of published papers on GEOBIA in 2018–2019) on the current state and future priorities of this field, gathered through an online survey. Most researchers surveyed acknowledged that image segmentation/GEOBIA approaches have achieved a high level of maturity, although the need for more free user-friendly software and tools, further automation, better integration with new machine-learning approaches (including deep learning), and more suitable accuracy assessment methods was frequently pointed out.

scholarly journals Using Unmanned Aerial Systems (UAS) and Object-Based Image Analysis (OBIA) for Measuring Plant-Soil Feedback Effects on Crop Productivity

Drones ◽  
2019 ◽  
Vol 3 (3) ◽  
pp. 54 ◽  
Author(s):  
Rik J. G. Nuijten ◽  
Lammert Kooistra ◽  
Gerlinde B. De Deyn
Keyword(s):  
Image Analysis ◽  
Template Matching ◽  
Large Scale ◽  
Crop Productivity ◽  
Unmanned Aerial Systems ◽  
Object Based Image Analysis ◽  
Plant Soil ◽  
Object Based ◽  
Soil Feedback

Unmanned aerial system (UAS) acquired high-resolution optical imagery and object-based image analysis (OBIA) techniques have the potential to provide spatial crop productivity information. In general, plant-soil feedback (PSF) field studies are time-consuming and laborious which constrain the scale at which these studies can be performed. Development of non-destructive methodologies is needed to enable research under actual field conditions and at realistic spatial and temporal scales. In this study, the influence of six winter cover crop (WCC) treatments (monocultures Raphanus sativus, Lolium perenne, Trifolium repens, Vicia sativa and two species mixtures) on the productivity of succeeding endive (Cichorium endivia) summer crop was investigated by estimating crop volume. A three-dimensional surface and terrain model were photogrammetrically reconstructed from UAS imagery, acquired on 1 July 2015 in Wageningen, the Netherlands. Multi-resolution image segmentation (MIRS) and template matching algorithms were used in an integrated workflow to detect individual crops (accuracy = 99.8%) and delineate C. endivia crop covered area (accuracy = 85.4%). Mean crop area (R = 0.61) and crop volume (R = 0.71) estimates had strong positive correlations with in situ measured dry biomass. Productivity differences resulting from the WCC treatments were greater for estimated crop volume in comparison to in situ biomass, the legacy of Raphanus was most beneficial for estimated crop volume. The perennial ryegrass L. perenne treatment resulted in a significantly lower production of C. endivia. The developed workflow has potential for PSF studies as well as precision farming due to its flexibility and scalability. Our findings provide insight into the potential of UAS for determining crop productivity on a large scale.

scholarly journals Damage Assessment and Spatio-Temporal Mapping Using Object Based Image Analysis Technique on Floods-2015 in Southern Punjab, Pakistan

2021 ◽  
Vol 3 (1) ◽  
pp. 9-18
Author(s):  
Malik Abid Hussain Khokhar ◽  
Sadaf Javed ◽  
Hisham Bin Hafeez Awan ◽  
Ishtiaq Yousaf ◽  
Amir Iqbal ◽  
...  
Keyword(s):  
Image Analysis ◽  
Damage Assessment ◽  
Accuracy Assessment ◽  
Large Area ◽  
Image Analysis Technique ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Analysis Technique ◽  
Flood Period ◽  
Spatio Temporal

Floods are the natural disasters which not only harm human lives but also damage entire structure in the inundated area. Pakistan has been witnessing extensive floods since very long because of long and severe spells of monsoon rainfalls with abrupt diurnal and seasonal disparity in the temperature level every year. Floods of 2010 and 2014 were so severe that they had broken the disaster record of earthquake-2005 in Pakistan and floods of 2015 had devastated the southern Punjab badly. These floods have damaged socioeconomic activities and people were displaced from the flood prone areas. Southern Punjab has been facing this calamity frequently from 2010 onward which resulting to huge destruction and gigantic loss of human lives every year. An immaculate large area of the Southern Punjab was damaged with crop land, built-up areas and road networks. To assess the damages and spatio-temporal mapping during flood period, remote sensing and GIS techniques have been incorporated by using spatial statistical and object-based image classification techniques. These techniques have been applied for estimating damages, mapping and extracting flood extents by embedding NDVI, NDBI and NDWI methods into object-based image analysis. This paper concludes assessment of damages, various extents of flood and accuracy assessment (79.3% and Khat0.72). Outcomes of the paper would be very beneficial for disaster management authorities for mitigating future floods and helpful for flood monitoring departments to guide competent management authorities at all tiers for quick response and rehabilitation programmes.

scholarly journals An Object-Based Image Analysis Workflow for Monitoring Shallow-Water Aquatic Vegetation in Multispectral Drone Imagery

2018 ◽  
Vol 7 (8) ◽  
pp. 294 ◽  
Author(s):  
Dominique Chabot ◽  
Christopher Dillon ◽  
Adam Shemrock ◽  
Nicholas Weissflog ◽  
Eric Sager
Keyword(s):  
Image Analysis ◽  
Near Infrared ◽  
Aquatic Vegetation ◽  
Supervised Machine Learning ◽  
Automated Classification ◽  
Infrared Band ◽  
Vegetation Monitoring ◽  
Object Based Image Analysis ◽  
Initial Separation ◽  
Object Based

High-resolution drone aerial surveys combined with object-based image analysis are transforming our capacity to monitor and manage aquatic vegetation in an era of invasive species. To better exploit the potential of these technologies, there is a need to develop more efficient and accessible analysis workflows and focus more efforts on the distinct challenge of mapping submerged vegetation. We present a straightforward workflow developed to monitor emergent and submerged invasive water soldier (Stratiotes aloides) in shallow waters of the Trent-Severn Waterway in Ontario, Canada. The main elements of the workflow are: (1) collection of radiometrically calibrated multispectral imagery including a near-infrared band; (2) multistage segmentation of the imagery involving an initial separation of above-water from submerged features; and (3) automated classification of features with a supervised machine-learning classifier. The approach yielded excellent classification accuracy for emergent features (overall accuracy = 92%; kappa = 88%; water soldier producer’s accuracy = 92%; user’s accuracy = 91%) and good accuracy for submerged features (overall accuracy = 84%; kappa = 75%; water soldier producer’s accuracy = 71%; user’s accuracy = 84%). The workflow employs off-the-shelf graphical software tools requiring no programming or coding, and could therefore be used by anyone with basic GIS and image analysis skills for a potentially wide variety of aquatic vegetation monitoring operations.

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