Special Issue "Application of Remote Sensing on Fire Ecology"

A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Inventory, Modeling and Remote Sensing".

Deadline for manuscript submissions: closed (30 April 2019) | Viewed by 12288

Special Issue Editor

Prof. Dr. José M.C. Pereira
E-Mail Website
Guest Editor
Forest Research Centre, School of Agriculture, University of Lisbon, Tapada da Ajuda, 1349-017 Lisbon, Portugal
Interests: pyrogeography; remote sensing; landscape ecology of fire
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Many fire ecology research and applications require georeferenced, dynamic data, and disciplines such as pyrogeography and landscape ecology are intrinsically spatial. Current rates of land use change and climate change lead to fast and widespread dynamics on terrestrial environments that affect spatial and temporal patterns of fire, at scales ranging from local to global. Remote sensing is essential to analyze those spatial dynamics and can provide data at a wide range of spatial, temporal and spectral resolutions.

For this Special Issue we invite submissions that address: 1) processes preceding fire occurrence, such as fuel structure and fuel moisture dynamics; 2) the incorporation of remote sensing information in dynamic vegetation models and in landuse/land cover change models; 3) post-fire environments, namely the characterization of burned areas in terms of fire patch attributes, fire severity assessment, and vegetation recovery rates; 4) methodological and technological issues in remote sensing of fire, such as the use of state-of-the-art artificial intelligence techniques for image analysis, or the use of Google Earth Engine for large scale processing of spatial time series of satellite imagery.

Prof. Dr. José M.C. Pereira
Guest Editor

Manuscript Submission Information

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Keywords

  • fuels mapping
  • burned area mapping
  • fire severity assessment
  • vegetation recovery monitoring
  • AI for image classification
  • time series analysis

Published Papers (5 papers)

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Research

Article
Assessing Landscape Fire Hazard by Multitemporal Automatic Classification of Landsat Time Series Using the Google Earth Engine in West-Central Spain
Forests 2019, 10(6), 518; https://doi.org/10.3390/f10060518 - 20 Jun 2019
Cited by 10 | Viewed by 2312
Abstract
Annual Land Use and Land Cover (LULC) maps are needed to identify the interaction between landscape changes and wildland fires. Objectives: In this work, we determined fire hazard changes in a representative Mediterranean landscape through the classification of annual LULC types and fire [...] Read more.
Annual Land Use and Land Cover (LULC) maps are needed to identify the interaction between landscape changes and wildland fires. Objectives: In this work, we determined fire hazard changes in a representative Mediterranean landscape through the classification of annual LULC types and fire perimeters, using a dense Landsat Time Series (LTS) during the 1984–2017 period, and MODIS images. Methods: We implemented a semiautomatic process in the Google Earth Engine (GEE) platform to generate annual imagery free of clouds, cloud shadows, and gaps. We compared LandTrendr (LT) and FormaTrend (FT) algorithms that are widely used in LTS analysis to extract the pixel tendencies and, consequently, assess LULC changes and disturbances such as forest fires. These algorithms allowed us to generate the following change metrics: type, magnitude, direction, and duration of change, as well as the prechange spectral values. Results and conclusions: Our results showed that the FT algorithm was better than the LT algorithm at detecting low-severity changes caused by fires. Likewise, the use of the change metrics’ type, magnitude, and direction of change increased the accuracy of the LULC maps by 4% relative to the ones obtained using only spectral and topographic variables. The most significant hazardous LULC change processes observed were: deforestation and degradation (mainly by fires), encroachment (i.e., invasion by shrublands) due to agriculture abandonment and forest fires, and hazardous densification (from open forests and agroforestry areas). Although the total burned area has decreased significantly since 1985, the landscape fire hazard has increased since the second half of the twentieth century. Therefore, it is necessary to implement fire management plans focused on the sustainable use of shrublands and conifer forests; this is because the stability in these hazardous vegetation types is translated into increasing fuel loads, and thus an elevated landscape fire hazard. Full article
(This article belongs to the Special Issue Application of Remote Sensing on Fire Ecology)
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Article
Geographically Weighted Negative Binomial Regression Model Predicts Wildfire Occurrence in the Great Xing’an Mountains Better Than Negative Binomial Model
Forests 2019, 10(5), 377; https://doi.org/10.3390/f10050377 - 30 Apr 2019
Cited by 8 | Viewed by 1724
Abstract
Wildfire is a major disturbance that affects large area globally every year. Thus, a better prediction of the likelihood of wildfire occurrence is essential to develop appropriate fire prevention measures. We applied a global negative Binomial (NB) and a geographically weighted negative Binomial [...] Read more.
Wildfire is a major disturbance that affects large area globally every year. Thus, a better prediction of the likelihood of wildfire occurrence is essential to develop appropriate fire prevention measures. We applied a global negative Binomial (NB) and a geographically weighted negative Binomial regression (GWNBR) models to determine the relationship between wildfire occurrence and its drivers factors in the boreal forests of the Great Xing’an Mountains, northeast China. Using geo-weighted techniques to consider the geospatial information of meteorological, topographic, vegetation type and human factors, we aimed to verify whether the performance of the NB model can be improved. Our results confirmed that the model fitting and predictions of GWNBR model were better than the global NB model, produced more precise and stable model parameter estimation, yielded a more realistic spatial distribution of model predictions, and provided the detection of the impact hotpots of these predictor variables. We found slope, vegetation cover, average precipitation, average temperature, and average relative humidity as important predictors of wildfire occurrence in the Great Xing’an Mountains. Thus, spatially differing relations improves the explanatory power of the global NB model, which does not explain sufficiently the relationship between wildfire occurrence and its drivers. Thus, the GWNBR model can complement the global NB model in overcoming the issue of nonstationary variables, thereby enabling a better prediction of the occurrence of wildfires in large geographical areas and improving management practices of wildfire. Full article
(This article belongs to the Special Issue Application of Remote Sensing on Fire Ecology)
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Article
A Comparison of Burned Area Time Series in the Alaskan Boreal Forests from Different Remote Sensing Products
Forests 2019, 10(5), 363; https://doi.org/10.3390/f10050363 - 26 Apr 2019
Cited by 6 | Viewed by 1984
Abstract
Alaska’s boreal region stores large amounts of carbon both in its woodlands and in the grounds that sustain them. Any alteration to the fire system that has naturally regulated the region’s ecology for centuries poses a concern regarding global climate change. Satellite-based remote [...] Read more.
Alaska’s boreal region stores large amounts of carbon both in its woodlands and in the grounds that sustain them. Any alteration to the fire system that has naturally regulated the region’s ecology for centuries poses a concern regarding global climate change. Satellite-based remote sensors are key to analyzing those spatial and temporal patterns of fire occurrence. This paper compiles four burned area (BA) time series based on remote sensing imagery for the Alaska region between 1982–2015: Burned Areas Boundaries Dataset-Monitoring Trends in Burn Severity (BABD-MTBS) derived from Landsat sensors, Fire Climate Change Initiative (Fire_CCI) (2001–2015) and Moderate-Resolution Imaging Spectroradiometer (MODIS) Direct Broadcast Monthly Burned Area Product (MCD64A1) (2000–2015) with MODIS data, and Burned Area-Long-Term Data Record (BA-LTDR) using Advanced Very High Resolution Radiometer LTDR (AVHRR-LTDR) dataset. All products were analyzed and compared against one another, and their accuracy was assessed through reference data obtained by the Alaskan Fire Service (AFS). The BABD-MTBS product, with the highest spatial resolution (30 m), shows the best overall estimation of BA (81%), however, for the years before 2000 (pre-MODIS era), the BA sensed by this product was only 44.3%, against the 55.5% obtained by the BA-LTDR product with a lower spatial resolution (5 km). In contrast, for the MODIS era (after 2000), BABD-MTBS virtually matches the reference data (98.5%), while the other three time series showed similar results of around 60%. Based on the theoretical limits of their corresponding Pareto boundaries, the lower resolution BA products could be improved, although those based on MODIS data are currently limited by the algorithm’s reliance on the active fire MODIS product, with a 1 km nominal spatial resolution. The large inter-annual variation found in the commission and omission errors in this study suggests that for a fair assessment of the accuracy of any BA product, all available reference data for space and time should be considered and should not be carried out by selective sampling. Full article
(This article belongs to the Special Issue Application of Remote Sensing on Fire Ecology)
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Article
Illegal Selective Logging and Forest Fires in the Northern Brazilian Amazon
Forests 2019, 10(1), 61; https://doi.org/10.3390/f10010061 - 14 Jan 2019
Cited by 13 | Viewed by 4078
Abstract
Illegal selective logging and forest fires occur on a large scale in the northern Brazilian Amazon, contributing to an increase in tree mortality and a reduction in forest carbon stock. A total of 120 plots of 0.25 ha (30 ha) were installed in [...] Read more.
Illegal selective logging and forest fires occur on a large scale in the northern Brazilian Amazon, contributing to an increase in tree mortality and a reduction in forest carbon stock. A total of 120 plots of 0.25 ha (30 ha) were installed in transitional ecosystems or ecotones (LOt) between the forested shade-loving campinarana (Ld) and dense-canopy rainforest, submontane (Ds), in the National Forest (Flona) of Anauá, southern Roraima. Measuring the diameters at breast height (DBH ≥ 10 cm) and the heights of 171 dead trees (fallen naturally, illegally exploited, and affected by forest fires), enabled the estimation of carbon content from the application of a biomass equation developed at Manaus, and the calculation of a correction factor, using the average height of the largest trees. From 2015–2017, we mapped the real extent of illegal selective logging and forest fires across the region with CLASlite and INPE/Queimadas. From measurements of 14,730 live and dead trees across 30 hectares (491 ± 15 trees·ha−1), the illegal selective logging and associated forest fires, and aggravation by severe El Niño droughts resulted in an 8.2% mortality of trees (40 ± 9 dead trees·ha−1) and a 3.5% reduction in forest carbon stock (6 ± 3 Mg·ha−1) in the short-term. The surface area or influence of forest fires of very high density were estimated in the south-central region of Roraima (8374 km²) and the eastern region of the Flona Anauá (37 km²). Illegal selective logging and forest fires in forest areas totaled 357 km² in the mosaic area, and 6 km² within Flona Anaua. Illegal selective logging and forest fires in the years of severe El Niño droughts threatened the maintenance of environmental services provided by Amazonian forests. Full article
(This article belongs to the Special Issue Application of Remote Sensing on Fire Ecology)
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Article
Mapping Burn Severity of Forest Fires in Small Sample Size Scenarios
Forests 2018, 9(10), 608; https://doi.org/10.3390/f9100608 - 30 Sep 2018
Cited by 9 | Viewed by 1865
Abstract
Mapping burn severity of forest fires can contribute significantly to understanding, quantifying and monitoring of forest fire severity and its impacts on ecosystems. In recent years, several remote sensing-based methods for mapping burn severity have been reported in the literature, of which the [...] Read more.
Mapping burn severity of forest fires can contribute significantly to understanding, quantifying and monitoring of forest fire severity and its impacts on ecosystems. In recent years, several remote sensing-based methods for mapping burn severity have been reported in the literature, of which the implementations are mainly dependent on several field plots. Therefore, it is a challenge to develop alternative method of mapping burn severity using limited number of field plots. In this study, we proposed a support vector regression based method using multi-temporal satellite data to map the burn severity, evaluated its performance by calculating correlations between the predicted and the observed Composite Burn Index, and compared the performance with that of the regression analysis method (based on dNBR). The results show that the performance of support vector regression based mapping method is more accurate (RMSE = 0.46–0.57) than that of regression analysis method (RMSE = 0.53–0.68). Even with fewer training sets, it can map the detailed distribution of burn severity of forest fires and can achieve relatively better generalization, compared to regression analysis burn severity mapping methods. It could be concluded that the proposed support vector regression based mapping method is an alternative to the regression analysis method in small sample size scenarios. This method with excellent generalization performance should be recommended for future studies on burn severity of forest fires. Full article
(This article belongs to the Special Issue Application of Remote Sensing on Fire Ecology)
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