Forest fire damage assessment using Object Based Image Analysis

2014 ◽  
Author(s):  
Emin Kugu ◽  
Akin Kisa
Keyword(s):  
Image Analysis ◽  
Forest Fire ◽  
Damage Assessment ◽  
Fire Damage ◽  
Object Based Image Analysis ◽  
Object Based

scholarly journals UAV-based urban structural damage assessment using object-based image analysis and semantic reasoning

2015 ◽  
Vol 15 (6) ◽  
pp. 1087-1101 ◽  
Author(s):  
J. Fernandez Galarreta ◽  
N. Kerle ◽  
M. Gerke
Keyword(s):  
Image Analysis ◽  
Damage Assessment ◽  
Structural Damage ◽  
Point Cloud ◽  
Auxiliary Information ◽  
Remote Sensing Data ◽  
Cloud Data ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Oblique Images

Abstract. Structural damage assessment is critical after disasters but remains a challenge. Many studies have explored the potential of remote sensing data, but limitations of vertical data persist. Oblique imagery has been identified as more useful, though the multi-angle imagery also adds a new dimension of complexity. This paper addresses damage assessment based on multi-perspective, overlapping, very high resolution oblique images obtained with unmanned aerial vehicles (UAVs). 3-D point-cloud assessment for the entire building is combined with detailed object-based image analysis (OBIA) of façades and roofs. This research focuses not on automatic damage assessment, but on creating a methodology that supports the often ambiguous classification of intermediate damage levels, aiming at producing comprehensive per-building damage scores. We identify completely damaged structures in the 3-D point cloud, and for all other cases provide the OBIA-based damage indicators to be used as auxiliary information by damage analysts. The results demonstrate the usability of the 3-D point-cloud data to identify major damage features. Also the UAV-derived and OBIA-processed oblique images are shown to be a suitable basis for the identification of detailed damage features on façades and roofs. Finally, we also demonstrate the possibility of aggregating the multi-perspective damage information at building level.

scholarly journals UAV-based urban structural damage assessment using object-based image analysis and semantic reasoning

2014 ◽  
Vol 2 (9) ◽  
pp. 5603-5645 ◽  
Author(s):  
J. Fernandez Galarreta ◽  
N. Kerle ◽  
M. Gerke
Keyword(s):  
Image Analysis ◽  
Damage Assessment ◽  
Structural Damage ◽  
Point Cloud ◽  
Auxiliary Information ◽  
Remote Sensing Data ◽  
Cloud Data ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Oblique Images

Abstract. Structural damage assessment is critical after disasters but remains a challenge. Many studies have explored the potential of remote sensing data, but limitations of vertical data persist. Oblique imagery has been identified as more useful, though the multi-angle imagery also adds a new dimension of complexity. This paper addresses damage assessment based on multi-perspective, overlapping, very high resolution oblique images obtained with unmanned aerial vehicles (UAVs). 3-D point-cloud assessment for the entire building is combined with detailed object-based image analysis (OBIA) of façades and roofs. This research focuses not on automatic damage assessment, but on creating a methodology that supports the often ambiguous classification of intermediate damage levels, aiming at producing comprehensive per-building damage scores. We identify completely damaged structures in the 3-D point cloud, and for all other cases provide the OBIA-based damage indicators to be used as auxiliary information by damage analysts. The results demonstrate the usability of the 3-D point-cloud data to identify major damage features. Also the UAV-derived and OBIA-processed oblique images are shown to be a suitable basis for the identification of detailed damage features on façades and roofs. Finally, we also demonstrate the possibility of aggregating the multi-perspective damage information at building level.

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 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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