scholarly journals Advances in Remote Sensing of Post-Fire Vegetation Recovery Monitoring - A Review

10.5772/20571 ◽  
2012 ◽  
Author(s):  
Ioannis Gitas ◽  
George Mitri ◽  
Sander Veraverbeke ◽  
Anastasia Polychronaki
Keyword(s):  
Remote Sensing ◽  
Vegetation Recovery ◽  
Recovery Monitoring

scholarly journals Wildfires Vegetation Recovery through Satellite Remote Sensing and Functional Data Analysis

Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1305
Author(s):  
Feliu Serra-Burriel ◽  
Pedro Delicado ◽  
Fernando M. Cucchietti
Keyword(s):  
Remote Sensing ◽  
Data Analysis ◽  
Functional Data Analysis ◽  
Functional Data ◽  
Vegetation Index ◽  
Vegetation Recovery ◽  
Functional Responses ◽  
Elapsed Time ◽  
Synthetic Controls ◽  
Available Information

In recent years, wildfires have caused havoc across the world, which are especially aggravated in certain regions due to climate change. Remote sensing has become a powerful tool for monitoring fires, as well as for measuring their effects on vegetation over the following years. We aim to explain the dynamics of wildfires’ effects on a vegetation index (previously estimated by causal inference through synthetic controls) from pre-wildfire available information (mainly proceeding from satellites). For this purpose, we use regression models from Functional Data Analysis, where wildfire effects are considered functional responses, depending on elapsed time after each wildfire, while pre-wildfire information acts as scalar covariates. Our main findings show that vegetation recovery after wildfires is a slow process, affected by many pre-wildfire conditions, among which the richness and diversity of vegetation is one of the best predictors for the recovery.

scholarly journals Multitemporal Mapping of Post-Fire Land Cover Using Multiplatform PRISMA Hyperspectral and Sentinel-UAV Multispectral Data: Insights from Case Studies in Portugal and Italy

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3982
Author(s):  
Giacomo Lazzeri ◽  
William Frodella ◽  
Guglielmo Rossi ◽  
Sandro Moretti
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Fire Severity ◽  
Experimental Methodology ◽  
Vegetation Recovery ◽  
Burned Area ◽  
Remotely Sensed Data ◽  
The Impact ◽  
Sentinel 2

Wildfires have affected global forests and the Mediterranean area with increasing recurrency and intensity in the last years, with climate change resulting in reduced precipitations and higher temperatures. To assess the impact of wildfires on the environment, burned area mapping has become progressively more relevant. Initially carried out via field sketches, the advent of satellite remote sensing opened new possibilities, reducing the cost uncertainty and safety of the previous techniques. In the present study an experimental methodology was adopted to test the potential of advanced remote sensing techniques such as multispectral Sentinel-2, PRISMA hyperspectral satellite, and UAV (unmanned aerial vehicle) remotely-sensed data for the multitemporal mapping of burned areas by soil–vegetation recovery analysis in two test sites in Portugal and Italy. In case study one, innovative multiplatform data classification was performed with the correlation between Sentinel-2 RBR (relativized burn ratio) fire severity classes and the scene hyperspectral signature, performed with a pixel-by-pixel comparison leading to a converging classification. In the adopted methodology, RBR burned area analysis and vegetation recovery was tested for accordance with biophysical vegetation parameters (LAI, fCover, and fAPAR). In case study two, a UAV-sensed NDVI index was adopted for high-resolution mapping data collection. At a large scale, the Sentinel-2 RBR index proved to be efficient for burned area analysis, from both fire severity and vegetation recovery phenomena perspectives. Despite the elapsed time between the event and the acquisition, PRISMA hyperspectral converging classification based on Sentinel-2 was able to detect and discriminate different spectral signatures corresponding to different fire severity classes. At a slope scale, the UAV platform proved to be an effective tool for mapping and characterizing the burned area, giving clear advantage with respect to filed GPS mapping. Results highlighted that UAV platforms, if equipped with a hyperspectral sensor and used in a synergistic approach with PRISMA, would create a useful tool for satellite acquired data scene classification, allowing for the acquisition of a ground truth.

scholarly journals Remote Sensing of Vegetation Recovery in Grasslands after the 1988 Yellowstone Fires in Yellowstone National Park

1990 ◽  
Vol 14 ◽  
pp. 151-153
Author(s):  
Evelyn Merrill ◽  
Cathy Wilson ◽  
Ronald Marrs
Keyword(s):  
Remote Sensing ◽  
Spectral Data ◽  
Satellite Imagery ◽  
Yellowstone National Park ◽  
National Park ◽  
Large Scale ◽  
Vegetation Recovery ◽  
Electromagnetic Spectrum ◽  
Traditional Methods ◽  
Vegetative Biomass

Traditional methods for measurement of vegetative biomass can be time-consuming and labor­intensive, especially across large areas. Yet such estimates are necessary to investigate the effects of large scale disturbances on ecosystem components and processes. One alternative to traditional methods for monitoring rangeland vegetation is to use satellite imagery. Because foliage of plants differentially absorbs and reflects energy within the electromagnetic spectrum, remote sensing of spectral data can be used to quantify the amount of green vegetative biomass present in an area (Tucker and Sellers 1986).

scholarly journals Remote Sensing Analysis of Vegetation Recovery following Short-Interval Fires in Southern California Shrublands

PLoS ONE ◽  
2014 ◽  
Vol 9 (10) ◽  
pp. e110637 ◽  
Author(s):  
Ran Meng ◽  
Philip E. Dennison ◽  
Carla M. D’Antonio ◽  
Max A. Moritz
Keyword(s):  
Remote Sensing ◽  
Southern California ◽  
Short Interval ◽  
Vegetation Recovery

Vegetation recovery monitoring over the waste dump in Haizhou opencast coalmine area, China, during 1975–2000 using NDVI and VF index

2009 ◽  
Vol 37 (4) ◽  
pp. 631-645 ◽  
Author(s):  
Wei Su ◽  
Zhong-Ping Sun ◽  
Dao-Liang Li ◽  
Raaj Ramsankaran ◽  
Xiang Zhu ◽  
...  
Keyword(s):  
Vegetation Recovery ◽  
Waste Dump ◽  
Recovery Monitoring

scholarly journals Remote Sensing of Vegetation Recovery in Grasslands after the 1988 Yellowstone Fires

1989 ◽  
Vol 13 ◽  
pp. 198-199
Author(s):  
Evelyn Merrill ◽  
Ronald Marrs
Keyword(s):  
Remote Sensing ◽  
Predictive Model ◽  
Vegetation Recovery ◽  
Herbaceous Biomass ◽  
Summer Range ◽  
Grassland Communities

In 1988, we developed an algorithm to predict green herbaceous biomass in sagebrush grassland communities of elk summer range using Landsat multi-spectral scanner (MSS) data (Merrill et al. 1988). Our objectives during this contract period were (1) to improve this predictive model and (2) to use the model for predicting vegetation recovery on grasslands burned in 1988.

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