scholarly journals Utilizing the Available Open-Source Remotely Sensed Data in Assessing the Wildfire Ignition and Spread Capacities of Vegetated Surfaces in Romania

2021 ◽  
Vol 13 (14) ◽  
pp. 2737
Author(s):  
Artan Hysa ◽  
Velibor Spalevic ◽  
Branislav Dudic ◽  
Sanda Roșca ◽  
Alban Kuriqi ◽  
...  
Keyword(s):  
Roc Analysis ◽  
Area Under The Curve ◽  
Remotely Sensed ◽  
Remotely Sensed Data ◽  
Building Stock ◽  
Ignition Probability ◽  
Country Level ◽  
Mining Procedure ◽  
Level Scale ◽  
Social Environmental

We bring a practical and comprehensive GIS-based framework to utilize freely available remotely sensed datasets to assess wildfire ignition probability and spreading capacities of vegetated landscapes. The study area consists of the country-level scale of the Romanian territory, characterized by a diversity of vegetated landscapes threatened by climate change. We utilize the Wildfire Ignition Probability/Wildfire Spreading Capacity Index (WIPI/WSCI). WIPI/WSCI models rely on a multi-criteria data mining procedure assessing the study area’s social, environmental, geophysical, and fuel properties based on open access remotely sensed data. We utilized the Receiver Operating Characteristic (ROC) analysis to weigh each indexing criterion’s impact factor and assess the model’s overall sensitivity. Introducing ROC analysis at an earlier stage of the workflow elevated the final Area Under the Curve (AUC) of WIPI from 0.705 to 0.778 and WSCI from 0.586 to 0.802. The modeling results enable discussion on the vulnerability of protected areas and the exposure of man-made structures to wildfire risk. Our study shows that within the wildland–urban interface of Bucharest’s metropolitan area, there is a remarkable building stock of healthcare, residential and educational functions, which are significantly exposed and vulnerable to wildfire spreading risk.

A Practical Approach to Assess the Wildfire Ignition and Spreading Capacities of Vegetated Areas at Landscape-scale

2021 ◽  
Author(s):  
Artan Hysa ◽  
Velibor Spalevic ◽  
Branislav Dudic ◽  
Sanda Roșca ◽  
Alban Kuriqi ◽  
...  
Keyword(s):  
Roc Analysis ◽  
Operating Characteristic ◽  
Area Under The Curve ◽  
Building Stock ◽  
Ignition Probability ◽  
Country Level ◽  
The Social ◽  
Mining Procedure ◽  
Level Scale ◽  
Social Environmental

Abstract We bring a practical and comprehensive GIS-based framework to utilize freely available remote sensed datasets to assess wildfire ignition probability and spreading capacities of vegetated landscapes. The study area consists of the country-level scale of the Romanian territory, characterized by a diversity of vegetated landscapes threatened by the consequences of climate change. We utilize the Wildfire Ignition Probability/ Wildfire Spreading Capacity Index (WIPI/ WSCI). WIPI/ WSCI models rely on a multi-criteria data mining procedure assessing the social, environmental, geophysical, and fuel properties of the study area based on open access remote sensed data. We utilized the Receiver Operating Characteristic (ROC) analysis to weigh each indexing criterion's impact factor and assess the model's overall sensitivity. Introducing ROC analysis at an earlier stage of the workflow elevated the final Area Under the Curve (AUC) of WIPI from 0.705 to 0.778 and WSCI from 0.586 to 0.802. The modeling results enable discussion on the vulnerability of protected areas and the exposure of man-made structures to wildfire risk. Our study shows that within the wildland-urban interface of Bucharest's metropolitan area, there is a remarkable building stock like healthcare, residential and educational that are significantly exposed to wildfire spreading the risk.

THE ROLE OF REMOTE SENSING DATA FOR COASTAL ZONE MONITIORING AND MANAGEMENT (CASE STUDY FOR THE EAST PART OF GULF OF FINLAND)

2017 ◽  
Author(s):  
Nikifor Ostanin ◽  
Nikifor Ostanin
Keyword(s):  
Remote Sensing ◽  
Coastal Zone ◽  
Environmental Changes ◽  
Rapid Development ◽  
Water Environment ◽  
Remote Sensing Data ◽  
Remotely Sensed ◽  
Gulf Of Finland ◽  
Coastal Protection ◽  
Remotely Sensed Data

Coastal zone of the Eastern Gulf of Finland is subjected to essential natural and anthropogenic impact. The processes of abrasion and accumulation are predominant. While some coastal protection structures are old and ruined the problem of monitoring and coastal management is actual. Remotely sensed data is important component of geospatial information for coastal environment research. Rapid development of modern satellite remote sensing techniques and data processing algorithms made this data essential for monitoring and management. Multispectral imagers of modern high resolution satellites make it possible to produce advanced image processing, such as relative water depths estimation, sea-bottom classification and detection of changes in shallow water environment. In the framework of the project of development of new coast protection plan for the Kurortny District of St.-Petersburg a series of archival and modern satellite images were collected and analyzed. As a result several schemes of underwater parts of coastal zone and schemes of relative bathymetry for the key areas were produced. The comparative analysis of multi-temporal images allow us to reveal trends of environmental changes in the study areas. This information, compared with field observations, shows that remotely sensed data is useful and efficient for geospatial planning and development of new coast protection scheme.

scholarly journals Multilayer Soil Moisture Mapping at a Regional Scale from Multisource Data via a Machine Learning Method

2019 ◽  
Vol 11 (3) ◽  
pp. 284 ◽  
Author(s):  
Linglin Zeng ◽  
Shun Hu ◽  
Daxiang Xiang ◽  
Xiang Zhang ◽  
Deren Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Regional Scale ◽  
Remotely Sensed ◽  
Temporal Variations ◽  
Training Data ◽  
Estimation Accuracy ◽  
Learning Approaches ◽  
Remotely Sensed Data ◽  
Deep Soil

Soil moisture mapping at a regional scale is commonplace since these data are required in many applications, such as hydrological and agricultural analyses. The use of remotely sensed data for the estimation of deep soil moisture at a regional scale has received far less emphasis. The objective of this study was to map the 500-m, 8-day average and daily soil moisture at different soil depths in Oklahoma from remotely sensed and ground-measured data using the random forest (RF) method, which is one of the machine-learning approaches. In order to investigate the estimation accuracy of the RF method at both a spatial and a temporal scale, two independent soil moisture estimation experiments were conducted using data from 2010 to 2014: a year-to-year experiment (with a root mean square error (RMSE) ranging from 0.038 to 0.050 m3/m3) and a station-to-station experiment (with an RMSE ranging from 0.044 to 0.057 m3/m3). Then, the data requirements, importance factors, and spatial and temporal variations in estimation accuracy were discussed based on the results using the training data selected by iterated random sampling. The highly accurate estimations of both the surface and the deep soil moisture for the study area reveal the potential of RF methods when mapping soil moisture at a regional scale, especially when considering the high heterogeneity of land-cover types and topography in the study area.

Sample size for ground and remotely sensed data

1986 ◽  
Vol 20 (1) ◽  
pp. 31-41 ◽  
Author(s):  
P.J. Curran ◽  
H.D. Williamson
Keyword(s):  
Sample Size ◽  
Remotely Sensed ◽  
Remotely Sensed Data
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