scholarly journals Copernicus Sentinel imagery for more risk-resilient historic cities in coastal zones: contribution to the monitoring of Albenga archaeological site and delta of river Centa

2020 ◽  
Author(s):  
Branka Cuca
Keyword(s):  
Risk Assessment ◽  
Change Detection ◽  
Ground Truth ◽  
Archaeological Site ◽  
Coastal Zones ◽  
Ground Truth Data ◽  
Heritage Sites ◽  
Flooding Hazard ◽  
Medieval Church ◽  
Extreme Flooding

Abstract Earth observation (EO) technologies and products are traditionally providing an extremely important contribution to a more efficient management of built areas and people during emergencies. Their employment could be extremely useful in monitoring and risk assessment of protected monument and sites situated in potentially vulnerable areas such as costal zones. Change detection has a crucial role in such activities, especially in case of hazardous events—on one side, this method can provide inputs in the phases of risk assessment and rapid mapping for immediate response; on the other side, over prolonged periods of time after the event, change detection can be used for purposes of option analysis of technical solutions and for overall recovery planning of the site. Workflow proposed is based on the use of Copernicus Sentinel-2 data to provide the comparison of changes occurring during extreme flooding events of river Centa, Liguria. Firstly, an investigation of NDVI and NDWI of the extreme flooding event occurring in November 2019 was conducted. The event was then put in correlation with another previously studied flooding hazard occurring in 2016 in the same area that has caused severe damages to the archaeological remains of the medieval church of San Clemente, situated within the Centa riverbed. In conclusion, the results from both years have been compared with ground truth data of topographic database in order to observe the extension of the flooded area and to provide contribution to the monitoring activities. The aim of the research was to offer some novel insight for a more informed decision-making during the risk assessment, risk management and resilience phases regarding the vulnerable built heritage sites found in coastal areas.

scholarly journals ASSESSMENT OF LAND USE AND LAND COVER CHANGE DETECTION BY USING REMOTE SENSING AND GIS TECHNIQUES IN THE COASTAL DESERTS, SOUTH OF IRAN

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 489-492
Author(s):  
S. A. R. Hosseini ◽  
H. Gholami ◽  
Y. Esmaeilpoor
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Urban Areas ◽  
Ground Truth ◽  
Landsat Images ◽  
Ground Truth Data ◽  
Sensing Technology ◽  
Time Period

Abstract. Land use/land cover (LULC) changes have become a central issue in current global change and sustainability research. Due to the large expanse of land change detection by the traditional methods is not sufficient and efficient; therefore, using of new methods such as remote sensing technology is necessary and vital This study evaluates LULC change in chabahar and konarak Coastal deserts, located in south of sistan and baluchestan province from 1988 to 2018 using Landsat images. Maximum likelihood classification were used to develop LULC maps. The change detection was executed using post-classification comparison and GIS. Then, taking ground truth data, the classified maps accuracy were assessed by calculating the Kappa coefficient and overall accuracy. The results for the time period of 1988–2018 are presented. Based on the results of the 30-year time period, vegetation has been decreased in area while urban areas have been developed. The area of saline and sandy lands has also increased.

scholarly journals Integrated tsunami vulnerability and risk assessment: application to the coastal area of El Salvador

2014 ◽  
Vol 14 (5) ◽  
pp. 1223-1244 ◽  
Author(s):  
P. González-Riancho ◽  
I. Aguirre-Ayerbe ◽  
O. García-Aguilar ◽  
R. Medina ◽  
M. González ◽  
...  
Keyword(s):  
Risk Assessment ◽  
El Salvador ◽  
Risk Reduction ◽  
Integrated Approach ◽  
Spatiotemporal Variability ◽  
Integral Approach ◽  
Coastal Zones ◽  
Natural Systems ◽  
Flooding Hazard ◽  
Risk Reduction Measures

Abstract. Advances in the understanding and prediction of tsunami impacts allow for the development of risk reduction strategies for tsunami-prone areas. This paper presents a tsunami vulnerability and risk assessment for the case study of El Salvador, the applied methodology dealing with the complexity and variability of coastal zones by means of (i) an integral approach to cover the entire risk-related process from the hazard, vulnerability and risk assessments to the final risk management; (ii) an integrated approach to combine and aggregate the information stemming from the different dimensions of coupled human and natural systems; and (iii) a dynamic and scale-dependent approach to integrate the spatiotemporal variability considerations. This work also aims at establishing a clear connection to translate the vulnerability and risk assessment results into adequate target-oriented risk reduction measures, trying to bridge the gap between science and management for the tsunami hazard. The approach is applicable to other types of hazards, having been successfully applied to climate-change-related flooding hazard.

scholarly journals Monitoring the Recovery after 2016 Hurricane Matthew in Haiti via Markovian Multitemporal Region-Based Modeling

2021 ◽  
Vol 13 (17) ◽  
pp. 3509
Author(s):  
Andrea De Giorgi ◽  
David Solarna ◽  
Gabriele Moser ◽  
Deodato Tapete ◽  
Francesca Cigna ◽  
...  
Keyword(s):  
Change Detection ◽  
Optical Sensors ◽  
Spatial Information ◽  
Contextual Information ◽  
Temporal Correlation ◽  
Ground Truth ◽  
Recovery Phase ◽  
Geometrical Structures ◽  
Ground Truth Data ◽  
Optical Images

The aim of this paper is to address the monitoring of the recovery phase in the aftermath of Hurricane Matthew (28 September–10 October 2016) in the town of Jérémie, southwestern Haiti. This is accomplished via a novel change detection method that has been formulated, in a data fusion perspective, in terms of multitemporal supervised classification. The availability of very high resolution images provided by last-generation satellite synthetic aperture radar (SAR) and optical sensors makes this analysis promising from an application perspective and simultaneously challenging from a processing viewpoint. Indeed, pursuing such a goal requires the development of novel methodologies able to exploit the large amount of detailed information provided by this type of data. To take advantage of the temporal and spatial information associated with such images, the proposed method integrates multisensor, multisource, and contextual information. Markov random field modeling is adopted here to integrate the spatial context and the temporal correlation associated with images acquired at different dates. Moreover, the adoption of a region-based approach allows for the characterization of the geometrical structures in the images through multiple segmentation maps at different scales and times. The performances of the proposed approach are evaluated on multisensor pairs of COSMO-SkyMed SAR and Pléiades optical images acquired over Jérémie, in the aftermath of and during the three years after Hurricane Matthew. The effectiveness of the change detection results is analyzed both quantitatively, through the computation of accuracy measures on a test set, and qualitatively, by visual inspection of the classification maps. The robustness of the proposed method with respect to different algorithmic choices is also assessed, and the detected changes are discussed in relation to the recovery endeavors in the area and ground-truth data collected in the field in April 2019.

scholarly journals SENTINEL-1 BASED FLOOD MAPPING USING INTERFEROMETRIC COHERENCE AND INTENSITY CHANGE DETECTION APPROACH

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1697-1703
Author(s):  
I. Papila ◽  
U. Alganci ◽  
E. Sertel
Keyword(s):  
Change Detection ◽  
Urban Areas ◽  
Satellite Image ◽  
Region Growing ◽  
Ground Truth ◽  
Intensity Change ◽  
Flood Mapping ◽  
Satellite Mission ◽  
Ground Truth Data ◽  
Sar Data

Abstract. This study presents a semi-automatic algorithm for mapping floods. Both Optical and Synthetic Aperture Radar (SAR) data are used to observe the flood that hit the Cukurova region of Adana (Turkey) in 2019. The performance of the interferometric coherence in complementing intensity component of SAR data is investigated for mapping the floods occurred in agricultural and urban environments. There was no ground truth data available from the flooded area, thus classification result of optical satellite image is used as a seed for the region growing algorithm that defines the classes according to a threshold value. The advantage of using both intensity and coherence change detection is verified with the results. The results have been evaluated through very high-resolution SPOT-6 optical image which acquired simultaneously with Sentinel-1B SAR image. The comparison with the SPOT-6 data results shows that the proposed approach can map flooded areas with acceptable accuracy using the SAR data from Sentinel-1 satellite mission. Highly affected agricultural areas along with the river line could be mapped both by optical and SAR analysis. Comparison of results from VV and VH polarization provided that cross-polarization VH has a very little effect on flood mapping. The proposed algorithm successfully distinguishes the classes among the affected region, especially in urban areas.

scholarly journals Vegetation cover change detection and assessment in arid environment using multi-temporal remote sensing images and ecosystem management approach

Solid Earth ◽  
2016 ◽  
Vol 7 (2) ◽  
pp. 713-725 ◽  
Author(s):  
Anwar Abdelrahman Aly ◽  
Abdulrasoul Mosa Al-Omran ◽  
Abdulazeam Shahwan Sallam ◽  
Mohammad Ibrahim Al-Wabel ◽  
Mohammad Shayaa Al-Shayaa
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Ecosystem Management ◽  
Vegetation Cover ◽  
Secondary Data ◽  
Ground Truth ◽  
Management Approach ◽  
Landsat Images ◽  
Ground Truth Data ◽  
Multi Temporal

Abstract. Vegetation cover (VC) change detection is essential for a better understanding of the interactions and interrelationships between humans and their ecosystem. Remote sensing (RS) technology is one of the most beneficial tools to study spatial and temporal changes of VC. A case study has been conducted in the agro-ecosystem (AE) of Al-Kharj, in the center of Saudi Arabia. Characteristics and dynamics of total VC changes during a period of 26 years (1987–2013) were investigated. A multi-temporal set of images was processed using Landsat images from Landsat4 TM 1987, Landsat7 ETM+2000, and Landsat8 to investigate the drivers responsible for the total VC pattern and changes, which are linked to both natural and social processes. The analyses of the three satellite images concluded that the surface area of the total VC increased by 107.4 % between 1987 and 2000 and decreased by 27.5 % between years 2000 and 2013. The field study, review of secondary data, and community problem diagnosis using the participatory rural appraisal (PRA) method suggested that the drivers for this change are the deterioration and salinization of both soil and water resources. Ground truth data indicated that the deteriorated soils in the eastern part of the Al-Kharj AE are frequently subjected to sand dune encroachment, while the southwestern part is frequently subjected to soil and groundwater salinization. The groundwater in the western part of the ecosystem is highly saline, with a salinity  ≥ 6 dS m−1. The ecosystem management approach applied in this study can be used to alike AE worldwide.

scholarly journals Coral Reef Change Detection in Remote Pacific Islands Using Support Vector Machine Classifiers

2019 ◽  
Vol 11 (13) ◽  
pp. 1525 ◽  
Author(s):  
Justin J. Gapper ◽  
Hesham El-Askary ◽  
Erik Linstead ◽  
Thomas Piechota
Keyword(s):  
Support Vector Machine ◽  
Coral Reef ◽  
Change Detection ◽  
Cross Validation ◽  
Pacific Islands ◽  
Coral Cover ◽  
Ground Truth ◽  
Support Vector ◽  
Live Coral ◽  
Ground Truth Data

Despite the abundance of research on coral reef change detection, few studies have been conducted to assess the spatial generalization principles of a live coral cover classifier trained using remote sensing data from multiple locations. The aim of this study is to develop a machine learning classifier for coral dominated benthic cover-type class (CDBCTC) based on ground truth observations and Landsat images, evaluate the performance of this classifier when tested against new data, then deploy the classifier to perform CDBCTC change analysis of multiple locations. The proposed framework includes image calibration, support vector machine (SVM) training and tuning, statistical assessment of model accuracy, and temporal pixel-based image differencing. Validation of the methodology was performed by cross-validation and train/test split using ground truth observations of benthic cover from four different reefs. These four locations (Palmyra Atoll, Kingman Reef, Baker Island Atoll, and Howland Island) as well as two additional locations (Kiritimati Island and Tabuaeran Island) were then evaluated for CDBCTC change detection. The in-situ training accuracy against ground truth observations for Palmyra Atoll, Kingman Reef, Baker Island Atoll, and Howland Island were 87.9%, 85.7%, 69.2%, and 82.1% respectively. The classifier attained generalized accuracy scores of 78.8%, 81.0%, 65.4%, and 67.9% for the respective locations when trained using ground truth observations from neighboring reefs and tested against the local ground truth observations of each reef. The classifier was trained using the consolidated ground truth data of all four sites and attained a cross-validated accuracy of 75.3%. The CDBCTC change detection analysis showed a decrease in CDBCTC of 32% at Palmyra Atoll, 25% at Kingman Reef, 40% at Baker Island Atoll, 25% at Howland Island, 35% at Tabuaeran Island, and 43% at Kiritimati Island. This research establishes a methodology for developing a robust classifier and the associated Controlled Parameter Cross-Validation (CPCV) process for evaluating how well the model will generalize to new data. It is an important step for improving the scientific understanding of temporal change within coral reefs around the globe.

scholarly journals Image Segmentation Techniques Using K-Means Clustering to Identify the Land Use Change Detection

2021 ◽  
Vol 10 (2) ◽  
pp. 1200-1206
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Image Segmentation ◽  
Land Use Change ◽  
Change Detection ◽  
Ground Truth ◽  
Important Application ◽  
Major Objective ◽  
Ground Truth Data ◽  
Point Of Interest

The change detection of the agriculture land and other land useis one of the important application of remote sensing imagery.The major objective of this paper tomeasure the different boundary regionsof the land classes using an image segmentation techniques. The initial categorizing of different land use classes is experimented by using k-means clustering, which basically clusters the point of interest with the pixel similarity. The measurement of the different pixel region represent the different classes of agriculture area is a challenging task with the real and synthetic images. The important characteristics of the algorithm preserves the cluster pixel details at most of the iterations, however for the similar canopy values the cluster effeminacy varies and the identification of the land clusters also deviates as compared with the ground truth data.

scholarly journals Assessing Wildfire Burn Severity and Its Relationship with Environmental Factors: A Case Study in Interior Alaska Boreal Forest

2021 ◽  
Vol 13 (10) ◽  
pp. 1966
Author(s):  
Christopher W Smith ◽  
Santosh K Panda ◽  
Uma S Bhatt ◽  
Franz J Meyer ◽  
Anushree Badola ◽  
...  
Keyword(s):  
Boreal Forest ◽  
Ground Truth ◽  
Burn Severity ◽  
Classification Methods ◽  
Spectral Indices ◽  
Ground Truth Data ◽  
Burn Scar ◽  
Interior Alaska ◽  
Remote Sensing Methods ◽  
The Relationship

In recent years, there have been rapid improvements in both remote sensing methods and satellite image availability that have the potential to massively improve burn severity assessments of the Alaskan boreal forest. In this study, we utilized recent pre- and post-fire Sentinel-2 satellite imagery of the 2019 Nugget Creek and Shovel Creek burn scars located in Interior Alaska to both assess burn severity across the burn scars and test the effectiveness of several remote sensing methods for generating accurate map products: Normalized Difference Vegetation Index (NDVI), Normalized Burn Ratio (NBR), and Random Forest (RF) and Support Vector Machine (SVM) supervised classification. We used 52 Composite Burn Index (CBI) plots from the Shovel Creek burn scar and 28 from the Nugget Creek burn scar for training classifiers and product validation. For the Shovel Creek burn scar, the RF and SVM machine learning (ML) classification methods outperformed the traditional spectral indices that use linear regression to separate burn severity classes (RF and SVM accuracy, 83.33%, versus NBR accuracy, 73.08%). However, for the Nugget Creek burn scar, the NDVI product (accuracy: 96%) outperformed the other indices and ML classifiers. In this study, we demonstrated that when sufficient ground truth data is available, the ML classifiers can be very effective for reliable mapping of burn severity in the Alaskan boreal forest. Since the performance of ML classifiers are dependent on the quantity of ground truth data, when sufficient ground truth data is available, the ML classification methods would be better at assessing burn severity, whereas with limited ground truth data the traditional spectral indices would be better suited. We also looked at the relationship between burn severity, fuel type, and topography (aspect and slope) and found that the relationship is site-dependent.

scholarly journals Automatic Evaluation of Wheat Resistance to Fusarium Head Blight Using Dual Mask-RCNN Deep Learning Frameworks in Computer Vision

2020 ◽  
Vol 13 (1) ◽  
pp. 26
Author(s):  
Wen-Hao Su ◽  
Jiajing Zhang ◽  
Ce Yang ◽  
Rae Page ◽  
Tamas Szinyei ◽  
...  
Keyword(s):  
Fusarium Head Blight ◽  
Ground Truth ◽  
Wheat Breeding ◽  
Head Blight ◽  
Detection Rates ◽  
Ground Truth Data ◽  
Resistant Cultivars ◽  
Feature Pyramid ◽  
Rater Error ◽  
Wheat Lines

In many regions of the world, wheat is vulnerable to severe yield and quality losses from the fungus disease of Fusarium head blight (FHB). The development of resistant cultivars is one means of ameliorating the devastating effects of this disease, but the breeding process requires the evaluation of hundreds of lines each year for reaction to the disease. These field evaluations are laborious, expensive, time-consuming, and are prone to rater error. A phenotyping cart that can quickly capture images of the spikes of wheat lines and their level of FHB infection would greatly benefit wheat breeding programs. In this study, mask region convolutional neural network (Mask-RCNN) allowed for reliable identification of the symptom location and the disease severity of wheat spikes. Within a wheat line planted in the field, color images of individual wheat spikes and their corresponding diseased areas were labeled and segmented into sub-images. Images with annotated spikes and sub-images of individual spikes with labeled diseased areas were used as ground truth data to train Mask-RCNN models for automatic image segmentation of wheat spikes and FHB diseased areas, respectively. The feature pyramid network (FPN) based on ResNet-101 network was used as the backbone of Mask-RCNN for constructing the feature pyramid and extracting features. After generating mask images of wheat spikes from full-size images, Mask-RCNN was performed to predict diseased areas on each individual spike. This protocol enabled the rapid recognition of wheat spikes and diseased areas with the detection rates of 77.76% and 98.81%, respectively. The prediction accuracy of 77.19% was achieved by calculating the ratio of the wheat FHB severity value of prediction over ground truth. This study demonstrates the feasibility of rapidly determining levels of FHB in wheat spikes, which will greatly facilitate the breeding of resistant cultivars.

scholarly journals Classification of Cattle Behaviours Using Neck-Mounted Accelerometer-Equipped Collars and Convolutional Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4050
Author(s):  
Dejan Pavlovic ◽  
Christopher Davison ◽  
Andrew Hamilton ◽  
Oskar Marko ◽  
Robert Atkinson ◽  
...  
Keyword(s):  
Neural Network ◽  
Model Performance ◽  
Ground Truth ◽  
Practical Implementation ◽  
Ground Truth Data ◽  
Battery Lifetime ◽  
Implementation Challenges ◽  
Memory Footprint ◽  
Commercial Farms ◽  
Using Data

Monitoring cattle behaviour is core to the early detection of health and welfare issues and to optimise the fertility of large herds. Accelerometer-based sensor systems that provide activity profiles are now used extensively on commercial farms and have evolved to identify behaviours such as the time spent ruminating and eating at an individual animal level. Acquiring this information at scale is central to informing on-farm management decisions. The paper presents the development of a Convolutional Neural Network (CNN) that classifies cattle behavioural states (`rumination’, `eating’ and `other’) using data generated from neck-mounted accelerometer collars. During three farm trials in the United Kingdom (Easter Howgate Farm, Edinburgh, UK), 18 steers were monitored to provide raw acceleration measurements, with ground truth data provided by muzzle-mounted pressure sensor halters. A range of neural network architectures are explored and rigorous hyper-parameter searches are performed to optimise the network. The computational complexity and memory footprint of CNN models are not readily compatible with deployment on low-power processors which are both memory and energy constrained. Thus, progressive reductions of the CNN were executed with minimal loss of performance in order to address the practical implementation challenges, defining the trade-off between model performance versus computation complexity and memory footprint to permit deployment on micro-controller architectures. The proposed methodology achieves a compression of 14.30 compared to the unpruned architecture but is nevertheless able to accurately classify cattle behaviours with an overall F1 score of 0.82 for both FP32 and FP16 precision while achieving a reasonable battery lifetime in excess of 5.7 years.

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