Rapid-response tools and datasets for post-fire remediation: linking remote sensing and process-based hydrological models

2016 ◽  
Vol 25 (10) ◽  
pp. 1061 ◽  
Author(s):  
M. E. Miller ◽  
W. J. Elliot ◽  
M. Billmire ◽  
P. R. Robichaud ◽  
K. A. Endsley
Keyword(s):  
Remote Sensing ◽  
Soil Properties ◽  
Remote Sensing Data ◽  
Burn Severity ◽  
Spatially Explicit ◽  
Vegetative Cover ◽  
Sediment Delivery ◽  
Soil Burn Severity ◽  
Set Up ◽  
Induced Changes

Post-wildfire flooding and erosion can threaten lives, property and natural resources. Increased peak flows and sediment delivery due to the loss of surface vegetation cover and fire-induced changes in soil properties are of great concern to public safety. Burn severity maps derived from remote sensing data reflect fire-induced changes in vegetative cover and soil properties. Slope, soils, land cover and climate are also important factors that require consideration. Many modelling tools and datasets have been developed to assist remediation teams, but process-based and spatially explicit models are currently underutilised compared with simpler, lumped models because they are difficult to set up and require properly formatted spatial inputs. To facilitate the use of models in conjunction with remote sensing observations, we developed an online spatial database that rapidly generates properly formatted modelling datasets modified by user-supplied soil burn severity maps. Although assembling spatial model inputs can be both challenging and time-consuming, the methods we developed to rapidly update these inputs in response to a natural disaster are both simple and repeatable. Automating the creation of model inputs facilitates the wider use of more accurate, process-based models for spatially explicit predictions of post-fire erosion and runoff.

scholarly journals Rapid response tools and datasets for post-fire modeling: linking Earth Observations and process-based hydrological models to support post-fire remediation

2015 ◽  
Vol XL-7/W3 ◽  
pp. 469-476 ◽  
Author(s):  
M. E. Miller ◽  
M. Billmire ◽  
W. J. Elliot ◽  
K. A. Endsley ◽  
P. R. Robichaud
Keyword(s):  
Land Cover ◽  
Soil Properties ◽  
Burn Severity ◽  
Spatially Explicit ◽  
Surface Erosion ◽  
Hydrological Models ◽  
Earth Observations ◽  
Soil Burn Severity ◽  
Induced Changes ◽  
Process Based Models

Preparation is key to utilizing Earth Observations and process-based models to support post-wildfire mitigation. Post-fire flooding and erosion can pose a serious threat to life, property and municipal water supplies. Increased runoff and sediment delivery due to the loss of surface cover and fire-induced changes in soil properties are of great concern. Remediation plans and treatments must be developed and implemented before the first major storms in order to be effective. One of the primary sources of information for making remediation decisions is a soil burn severity map derived from Earth Observation data (typically Landsat) that reflects fire induced changes in vegetation and soil properties. Slope, soils, land cover and climate are also important parameters that need to be considered. Spatially-explicit process-based models can account for these parameters, but they are currently under-utilized relative to simpler, lumped models because they are difficult to set up and require spatially-explicit inputs (digital elevation models, soils, and land cover). Our goal is to make process-based models more accessible by preparing spatial inputs before a fire, so that datasets can be rapidly combined with soil burn severity maps and formatted for model use. We are building an online database (http://geodjango.mtri.org/geowepp /) for the continental United States that will allow users to upload soil burn severity maps. The soil burn severity map is combined with land cover and soil datasets to generate the spatial model inputs needed for hydrological modeling of burn scars. Datasets will be created to support hydrological models, post-fire debris flow models and a dry ravel model. Our overall vision for this project is that advanced GIS surface erosion and mass failure prediction tools will be readily available for post-fire analysis using spatial information from a single online site.

scholarly journals Rapid Response Tools and Datasets for Post-fire Erosion Modeling: Linking Remote Sensing and Process-based Hydrological Models to support Post-fire Remediation

2014 ◽  
Vol XL-1 ◽  
pp. 257-263
Author(s):  
M. E. Miller ◽  
W. J. Elliot ◽  
K. A. Endsley ◽  
P. R. Robichaud ◽  
M. Billmire
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Soil Properties ◽  
Burn Severity ◽  
Burned Area ◽  
Spatially Explicit ◽  
Observation Data ◽  
Sources Of Information ◽  
Modeling Tools ◽  
Induced Changes

Post-fire flooding and erosion can pose a serious threat to life, property, and municipal water supplies. Increased peak flows and sediment delivery due to the loss of surface cover and fire-induced changes in soil properties are of great concern to both resource managers and the public. To respond to this threat, interdisciplinary Burned Area Emergency Response (BAER) Teams are formed to assess potential erosion and flood risks. These teams are under tight deadlines as remediation plans and treatments must be developed and implemented before the first major storms in order to be effective. One of the primary sources of information for making these decisions is a burn severity map derived from remote sensing data (typically Landsat) that reflects fire induced changes in vegetative cover and soil properties. Slope, soils, land cover, and climate are also important parameters that need to be considered when accessing risk. Many modeling tools and datasets have been developed to assist BAER teams, but process-based and spatially explicit empirical models are currently under-utilized compared to simpler, lumped models because they are both more difficult to set up and require spatially explicit inputs such as digital elevation models, soils, and land cover. We are working to facilitate the use of models by preparing spatial datasets within a web-based tool that rapidly modifies model inputs using burn severity maps derived from earth observation data. Automating the creation of model inputs facilitates the wider use of more accurate, process-based models for spatially explicit predictions of post-fire erosion and runoff.

scholarly journals Evaluation of Composite Burn Index and Land Surface Temperature for Assessing Soil Burn Severity in Mediterranean Fire-Prone Pine Ecosystems

Forests ◽  
2018 ◽  
Vol 9 (8) ◽  
pp. 494 ◽  
Author(s):  
Elena Marcos ◽  
Víctor Fernández-García ◽  
Alfonso Fernández-Manso ◽  
Carmen Quintano ◽  
Luz Valbuena ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Surface Temperature ◽  
Soil Properties ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Burn Severity ◽  
Soil Burn Severity ◽  
Changes In Soil Properties

We analysed the relationship between burn severity indicators, from remote sensing and field observations, and soil properties after a wildfire in a fire-prone Mediterranean ecosystem. Our study area was a large wildfire in a Pinus pinaster forest. Burn severity from remote sensing was identified by studying immediate post-fire Land Surface Temperature (LST). We also evaluated burn severity in the field applying the Composite Burn Index (CBI) in a total of 84 plots (30 m diameter). In each plot we evaluated litter consumption, ash colour and char depth as visual indicators. We collected soil samples and pH, soil organic carbon, dry aggregate size distribution (MWD), aggregate stability and water repellency were analysed. A controlled heating of soil was also carried out in the laboratory, with soil from the control plots, to compare with the changes produced in soils affected by different severity levels in the field. Our results shown that changes in soil properties affected by wildfire were only observed in soil aggregation in the high severity situation. The laboratory-controlled heating showed that temperatures of about 300 °C result in a significant reduction in soil organic carbon and MWD. Furthermore, soil organic carbon showed a significant decrease when LST values increased. Char depth was the best visual indicator to show changes in soil properties (mainly physical properties) in large fires that occur in Mediterranean pine forests. We conclude that CBI and post-fire LST can be considered good indicators of soil burn severity since both indicate the impact of fire on soil properties.

Effect of Slope Mesorelief on the Spatial Variability of Soil Properties and Vegetation Index Based on Remote Sensing Data

2019 ◽  
Vol 55 (9) ◽  
pp. 1329-1337
Author(s):  
N. V. Gopp ◽  
T. V. Nechaeva ◽  
O. A. Savenkov ◽  
N. V. Smirnova ◽  
V. V. Smirnov
Keyword(s):  
Remote Sensing ◽  
Spatial Variability ◽  
Soil Properties ◽  
Vegetation Index ◽  
Remote Sensing Data ◽  
Sensing Data

Monitoring of levee breaching through remote sensing and artificial intelligence

2020 ◽  
Author(s):  
Rosa Di Maio ◽  
Eleonora Vitagliano ◽  
Rosanna Salone
Keyword(s):  
Artificial Intelligence ◽  
Remote Sensing ◽  
Time Series ◽  
Soil Properties ◽  
Remote Sensing Data ◽  
Ground Level ◽  
Model Parameters ◽  
Levee Failure ◽  
Geophysical Surveys ◽  
Derived Data

<p>The study of flooding events resulting from bank over-flooding and levee breaching is of large interest for both society and environment, because flood waves, resulting from levee failure, might cause loss of lives and destruction of properties and ecosystems. Understanding the subsoil mechanics and the fluid-solid interplay allows the stability condition estimate of dams, embankments and slopes and the development of early warning alarm systems. Changes in soil and hydraulic parameters are usually monitored by geotechnical and geophysical investigations that also provide the basic assumptions for developing hydraulic models. Nowadays, remote sensing approaches, including satellite techniques, are mainly used for flooding simulation studies. Indeed, remote sensing observations, such as discharge, flood area extent and water stage, have been used for retrieving flood hydrology information and modeling, calibrating and validating hydrodynamic models, improving model structures and developing data assimilation models. Although all these studies have contributed significantly to the recent advances, uncertainty in observations, as well as in model parameters and prediction, represents a critical aspect for using remote sensing data in the flooding defence. Compared to past and current methods for monitoring the fluvial levee failure, we propose a new procedure that provides a wide and fast alert system. The proposed methodological path is based on presumed relationships between ground level deformation and hydrological and surface soil properties, due to physical mechanisms and exhibited by geodetic and hydrological time series. The procedure is accomplished first through multi-methodological comparative analyses applied to geodetic, hydrological and soil-properties patterns, then through the mapping of the river zones prone to failure. Since the input consists of time series satellite-derived data, the geospatial Artificial Intelligence is applied for extracting knowledge from spatial big data and for increasing the performance of data computing. In particular, machine learning is initially developed for selecting the relevant geographical areas (i.e. rivers, levees and riverbanks) from large geo-referential datasets. Then, since the spatial-distributed points are also time-dependent, the trends of different datasets are compared point by point by selected analytical techniques. Finally, in accordance with the acquired knowledge from previous steps, the system extracts information on the correlation indexes in order to make sense of patterns in space and time and to identify hierarchic orders for the realization of hazard maps. The proposed method is “wide” because, unlike other direct surveys, it is able to monitor large spatial areas since it is based on satellite-derived data. It is also “fast” because it is based on the Earth’s surface observation and is not connected with Earth’s inland investigations (such as the geotechnical and geophysical ones) or with forecasting models (e.g. hydraulic and flooding simulations). Due to these peculiarities, the method can support flood protection studies and can be used for driving the localization of river portions prone to failure, where focusing detailed geotechnical and geophysical surveys.</p>

Removing Cloud of Grey Remote Sensing Image Based on Network Communication

2014 ◽  
Vol 513-517 ◽  
pp. 3165-3169
Author(s):  
Min Min Yue
Keyword(s):  
Remote Sensing ◽  
Rapid Development ◽  
Remote Sensing Data ◽  
Remote Sensing Image ◽  
Cloud Formation ◽  
Network Communication ◽  
Fusion Model ◽  
Modern Computer ◽  
Model Combining ◽  
Set Up

Remote sensing technology has rapid development in the past half one century, it is widely used in various fields and society. But the clouds have affected the quality of remote sensing data, how to effectively use the modern computer science and technology to remove the cloud is a hot issue in the field. From the theory of cloud formation in the remote sensing image, we analyze the formation mechanism, and based on this we do two layers decomposition and reconstruct the structure according to wavelet transform in network communication, and establish the image degradation model. Combining Fourier transformation, we set up the removing cloud fusion model of remote sensing image. Through the simulation experiment, the effect is significant. To a certain extent, it provides technical support for theory study and practice operation.

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