Research

21 pages, 2352 KiB

Open AccessArticle

The Spatiotemporal Changing Dynamics of Miombo Deforestation and Illegal Human Activities for Forest Fire in Kundelungu National Park, Democratic Republic of the Congo

by Yannick Useni Sikuzani, Médard Mpanda Mukenza, François Malaisse, Paul Kazaba Kaseya and Jan Bogaert

Fire 2023, 6(5), 174; https://0-doi-org.brum.beds.ac.uk/10.3390/fire6050174 - 23 Apr 2023

Cited by 6 | Viewed by 1405

Abstract

In the Kundelungu National Park (KNP), southeast of the Democratic Republic of Congo, illicit human activities including recurrent bushfires contribute to constant regression of forest cover. This study quantifies the landscape dynamics and analyses the spatio-temporal distribution of bushfire occurrence within KNP. Based [...] Read more.

In the Kundelungu National Park (KNP), southeast of the Democratic Republic of Congo, illicit human activities including recurrent bushfires contribute to constant regression of forest cover. This study quantifies the landscape dynamics and analyses the spatio-temporal distribution of bushfire occurrence within KNP. Based on classified Landsat images from 2001, 2008, 2015 and 2022, the evolutionary trend of land cover was mapped and quantified through landscape metrics. The spatial transformation processes underlying the observed landscape dynamics were identified based on a decision tree. Finally, the spatio-temporal fire risk assessment was carried out after defining the burnt areas for each year between 2001 and 2022. The obtained results, expressed by the process of dissection and attrition of patches, show that the forest cover has regressed from 2339 km² to 1596 km² within the PNK, with an annual deforestation rate varying from 0.8% to 3.4% between 2001 and 2022. Over the same period, the average distance between forest patches has increased significantly, indicating fragmentation and spatial isolation. On the other hand, savannahs as well as field and fallow mosaics have expanded within KNP through the creation of new patches. In addition, several active fires affected more savannahs between 2001 (70 km² in Integral Zone, 239 km² in Annex Zone and 309 km² in KNP) and 2022 (76 km² in Integral Zone, 744 km² in Annex Zone and 819 km² in KNP), limiting their capacity to evolve into forests. Overall, anthropogenic pressure is higher in the Annex Zone of the KNP. Illegal agricultural development and vegetation fires have thus doubled the level of landscape disturbance in 21 years. Our observations justify the need to strengthen protection measures for KNP by limiting repeated human intrusions. Full article

(This article belongs to the Special Issue Detecting, Mapping, and Characterizing Wildfires Using Remote Sensing Data)

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33 pages, 5627 KiB

Open AccessArticle

Wildfire Risk Zone Mapping in Contrasting Climatic Conditions: An Approach Employing AHP and F-AHP Models

by Aishwarya Sinha, Suresh Nikhil, Rajendran Shobha Ajin, Jean Homian Danumah, Sunil Saha, Romulus Costache, Ambujendran Rajaneesh, Kochappi Sathyan Sajinkumar, Kolangad Amrutha, Alfred Johny, Fahad Marzook, Pratheesh Chacko Mammen, Kamal Abdelrahman, Mohammed S. Fnais and Mohamed Abioui

Fire 2023, 6(2), 44; https://0-doi-org.brum.beds.ac.uk/10.3390/fire6020044 - 24 Jan 2023

Cited by 13 | Viewed by 3224

Abstract

Wildfires are one of the gravest and most momentous hazards affecting rich forest biomes worldwide; India is one of the hotspots due to its diverse forest types and human-induced reasons. This research aims to identify wildfire risk zones in two contrasting climate zones, [...] Read more.

Wildfires are one of the gravest and most momentous hazards affecting rich forest biomes worldwide; India is one of the hotspots due to its diverse forest types and human-induced reasons. This research aims to identify wildfire risk zones in two contrasting climate zones, the Wayanad Wildlife Sanctuary in the Western Ghats and the Kedarnath Wildlife Sanctuary in the Himalayas, using geospatial tools, analytical hierarchy process (AHP), and fuzzy-AHP models to assess the impacts of various conditioning factors and compare the efficacy of the two models. Both of the wildlife sanctuaries were severely battered by fires in the past, with more than 100 fire incidences considered for this modeling. This analysis found that both natural and anthropogenic factors are responsible for the fire occurrences in both of the two sanctuaries. The validation of the risk maps, utilizing the receiver operating characteristic (ROC) method, proved that both models have outstanding prediction accuracy for the training and validation datasets, with the F-AHP model having a slight edge over the other model. The results of other statistical validation matrices such as sensitivity, accuracy, and Kappa index also confirmed that F-AHP is better than the AHP model. According to the F-AHP model, about 22.49% of Kedarnath and 17.12% of Wayanad fall within the very-high risk zones. The created models will serve as a tool for implementing effective policies intended to reduce the impact of fires, even in other protected areas with similar forest types, terrain, and climatic conditions. Full article

(This article belongs to the Special Issue Detecting, Mapping, and Characterizing Wildfires Using Remote Sensing Data)

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18 pages, 2146 KiB

Open AccessArticle

Burn Severity Drivers in Italian Large Wildfires

by Francesco Malandra, Alessandro Vitali, Donato Morresi, Matteo Garbarino, Daniel E. Foster, Scott L. Stephens and Carlo Urbinati

Fire 2022, 5(6), 180; https://0-doi-org.brum.beds.ac.uk/10.3390/fire5060180 - 31 Oct 2022

Cited by 4 | Viewed by 2214

Abstract

The increase of wildfire incidence in highly populated areas significantly enhances the risk for ecosystems and human lives, activities and infrastructures. In central and southern Italy, recent decades’ fire records indicate that 2007 and 2017 were extreme years in terms of the number [...] Read more.

The increase of wildfire incidence in highly populated areas significantly enhances the risk for ecosystems and human lives, activities and infrastructures. In central and southern Italy, recent decades’ fire records indicate that 2007 and 2017 were extreme years in terms of the number of fires and total burned area. Among them, we selected large fire events and explored their features and drivers of burn severity. We used a standardized extraction procedure to identify large wildfires (>100 ha) from the MODIS burned areas database and Landsat multi-spectral images. We mapped burn severity with the Relative Difference Normalized Burn Ratio index and explored the main drivers of severity using topographic, land-cover and anthropogenic predictors. We selected 113 wildfires for a collective total burned area of over 100,000 ha. Large fires were more frequent in the southern than in the central and northern regions, especially in July and August. The average fire size was about 900 ha and occurred mainly in shrublands (30.4%) and broadleaf forests (19.5%). With a random forest model, we observed that the highest severity occurred in conifer plantations and shrublands, in highly populated areas and at lower elevations. Burn severity models, at the landscape or regional scales, can be very useful tools for pre- and post-fire forest management planning. Full article

(This article belongs to the Special Issue Detecting, Mapping, and Characterizing Wildfires Using Remote Sensing Data)

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20 pages, 3566 KiB

Open AccessEditor’s ChoiceArticle

Evaluating Satellite Fire Detection Products and an Ensemble Approach for Estimating Burned Area in the United States

by Amy L. Marsha and Narasimhan K. Larkin

Fire 2022, 5(5), 147; https://0-doi-org.brum.beds.ac.uk/10.3390/fire5050147 - 22 Sep 2022

Cited by 4 | Viewed by 2154

Abstract

Fire location and burning area are essential parameters for estimating fire emissions. However, ground-based fire data (such as fire perimeters from incident reports) are often not available with the timeliness required for real-time forecasting. Fire detection products derived from satellite instruments such as [...] Read more.

Fire location and burning area are essential parameters for estimating fire emissions. However, ground-based fire data (such as fire perimeters from incident reports) are often not available with the timeliness required for real-time forecasting. Fire detection products derived from satellite instruments such as the GOES-16 Advanced Baseline Imager or MODIS, on the other hand, are available in near real-time. Using a ground fire dataset of 2699 fires during 2017–2019, we fit a series of linear models that use multiple satellite fire detection products (HMS aggregate fire product, GOES-16, MODIS, and VIIRS) to assess the ability of satellite data to detect and estimate total burned area. It was found that on average models fit with fire detections from GOES-16 products performed better than those developed from other satellites in the study (modelled R² = 0.84 and predictive R² = 0.88). However, no single satellite product was found to best estimate incident burned area, highlighting the need for an ensemble approach. With our proposed modelling ensemble, we demonstrate its ability to estimate burned area and suggest its further use in daily fire tracking and emissions-modeling frameworks. Full article

(This article belongs to the Special Issue Detecting, Mapping, and Characterizing Wildfires Using Remote Sensing Data)

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20 pages, 2152 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Modern Pyromes: Biogeographical Patterns of Fire Characteristics across the Contiguous United States

by Megan E. Cattau, Adam L. Mahood, Jennifer K. Balch and Carol A. Wessman

Fire 2022, 5(4), 95; https://0-doi-org.brum.beds.ac.uk/10.3390/fire5040095 - 10 Jul 2022

Cited by 3 | Viewed by 4686

Abstract

In recent decades, wildfires in many areas of the United States (U.S.) have become larger and more frequent with increasing anthropogenic pressure, including interactions between climate, land-use change, and human ignitions. We aimed to characterize the spatiotemporal patterns of contemporary fire characteristics across [...] Read more.

In recent decades, wildfires in many areas of the United States (U.S.) have become larger and more frequent with increasing anthropogenic pressure, including interactions between climate, land-use change, and human ignitions. We aimed to characterize the spatiotemporal patterns of contemporary fire characteristics across the contiguous United States (CONUS). We derived fire variables based on frequency, fire radiative power (FRP), event size, burned area, and season length from satellite-derived fire products and a government records database on a 50 km grid (1984–2020). We used k-means clustering to create a hierarchical classification scheme of areas with relatively homogeneous fire characteristics, or modern ‘pyromes,’ and report on the model with eight major pyromes. Human ignition pressure provides a key explanation for the East-West patterns of fire characteristics. Human-dominated pyromes (85% mean anthropogenic ignitions), with moderate fire size, area burned, and intensity, covered 59% of CONUS, primarily in the East and East Central. Physically dominated pyromes (47% mean anthropogenic ignitions) characterized by relatively large (average 439 mean annual ha per 50 km pixel) and intense (average 75 mean annual megawatts/pixel) fires occurred in 14% of CONUS, primarily in the West and West Central. The percent of anthropogenic ignitions increased over time in all pyromes (0.5–1.7% annually). Higher fire frequency was related to smaller events and lower FRP, and these relationships were moderated by vegetation, climate, and ignition type. Notably, a spatial mismatch between our derived modern pyromes and both ecoregions and historical fire regimes suggests other major drivers for modern U.S. fire patterns than vegetation-based classification systems. This effort to delineate modern U.S. pyromes based on fire observations provides a national-scale framework of contemporary fire regions and may help elucidate patterns of change in an uncertain future. Full article

(This article belongs to the Special Issue Detecting, Mapping, and Characterizing Wildfires Using Remote Sensing Data)

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28 pages, 5454 KiB

Open AccessArticle

Subpixel Analysis of Primary and Secondary Infrared Emitters with Nighttime VIIRS Data

by Christopher D. Elvidge, Mikhail Zhizhin, Feng Chi Hsu, Tamara Sparks and Tilottama Ghosh

Fire 2021, 4(4), 83; https://0-doi-org.brum.beds.ac.uk/10.3390/fire4040083 - 07 Nov 2021

Cited by 7 | Viewed by 4136

Abstract

Biomass burning is a coupled exothermic/endothermic system that transfers carbon in several forms to the atmosphere, ultimately leaving mineral ash. The exothermic phases include flaming and smoldering, which produce the heat that drives the endothermic processes. The endothermic components include pre-heating and pyrolysis, [...] Read more.

Biomass burning is a coupled exothermic/endothermic system that transfers carbon in several forms to the atmosphere, ultimately leaving mineral ash. The exothermic phases include flaming and smoldering, which produce the heat that drives the endothermic processes. The endothermic components include pre-heating and pyrolysis, which produce the fuel consumed by flaming and smoldering. These components can be broadly distinguished from each other based on temperature. For several years, we have researched the subpixel analysis of two temperature phases present in fire pixels detected in nighttime VIIRS data. Here, we present the flaming subtractive method, with which we have successfully derived temperatures and source areas for two infrared (IR) emitters and a cooler background. This is developed as an add-on to the existing VIIRS nightfire algorithm version 3 (VNF v.3) which uses Planck curve fitting to calculate temperatures and source areas for a single IR emitter and background. The flaming subtractive method works with data collected in four spectral ranges: near-infrared (NIR), short-wave infrared (SWIR), mid-wave infrared (MWIR) and long-wave infrared (LWIR). With sunlight eliminated, the NIR and SWIR radiances can be fully attributed to the primary IR emitter. The analysis begins with Planck curve modeling for the primary emitter based on the NIR and SWIR radiances, yielding temperature, source area and primary emitter radiances in all spectral bands. The primary emitter radiances are subtracted from each spectral band and then the residual radiance is analyzed for a secondary IR emitter and the background. Spurious results are obtained in pixels lacking a discernable secondary emitter. These misfit pixels revert back to the single IR emitter analysis of VNF v.3. In tests run for two California megafires, we found that the primary emitters straddle the temperature ranges for flaming and smoldering, the exothermic portions of biomass burning, which are apparently commingled on the ground. The secondary emitter temperatures span 350–750 K, corresponding to pre-heating and slow pyrolysis. The natural gas flare test case had few numbers of successful secondary emitter retrievals and a wide range of secondary emitter temperatures. The flaming subtractive analysis is the key addition to VNF version 4, which will commence production later in 2021. In 2022, we will seek validation of the VNF v.4 from nighttime Landsat and other data sources. Full article

(This article belongs to the Special Issue Detecting, Mapping, and Characterizing Wildfires Using Remote Sensing Data)

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25 pages, 9501 KiB

Open AccessArticle

Mapping Wetland Burned Area from Sentinel-2 across the Southeastern United States and Its Contributions Relative to Landsat-8 (2016–2019)

by Melanie K. Vanderhoof, Todd J. Hawbaker, Casey Teske, Andrea Ku, Joe Noble and Josh Picotte

Fire 2021, 4(3), 52; https://0-doi-org.brum.beds.ac.uk/10.3390/fire4030052 - 25 Aug 2021

Cited by 18 | Viewed by 3604

Abstract

Prescribed fires and wildfires are common in wetland ecosystems across the Southeastern United States. However, the wetland burned area has been chronically underestimated across the region due to (1) spectral confusion between open water and burned area, (2) rapid post-fire vegetation regrowth, and [...] Read more.

Prescribed fires and wildfires are common in wetland ecosystems across the Southeastern United States. However, the wetland burned area has been chronically underestimated across the region due to (1) spectral confusion between open water and burned area, (2) rapid post-fire vegetation regrowth, and (3) high annual precipitation limiting clear-sky satellite observations. We developed a machine learning algorithm specifically for burned area in wetlands, and applied the algorithm to the Sentinel-2 archive (2016–2019) across the Southeastern US (>290,000 km²). Combining Landsat-8 imagery with Sentinel-2 increased the annual clear-sky observation count from 17 to 46 in 2016 and from 16 to 78 in 2019. When validated with WorldView imagery, the Sentinel-2 burned area had a 29% and 30% omission and commission rates of error for burned area, respectively, compared to the US Geological Survey Landsat-8 Burned Area Product (L8 BA), which had a 47% and 8% omission and commission rate of error, respectively. The Sentinel-2 algorithm and the L8 BA mapped burned area within 78% and 60% of wetland fire perimeters (n = 555) compiled from state and federal agencies, respectively. This analysis demonstrated the potential of Sentinel-2 to support efforts to track the burned area, especially across challenging ecosystem types, such as wetlands. Full article

(This article belongs to the Special Issue Detecting, Mapping, and Characterizing Wildfires Using Remote Sensing Data)

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19 pages, 5466 KiB

Open AccessArticle

Assessing Wildfire Regimes in Indigenous Lands of the Brazilian Savannah-Like Cerrado

by Pedro Melo, Javier Sparacino, Daihana Argibay, Vicente Sousa Júnior, Roseli Barros and Giovana Espindola

Fire 2021, 4(3), 34; https://0-doi-org.brum.beds.ac.uk/10.3390/fire4030034 - 05 Jul 2021

Cited by 4 | Viewed by 2823

Abstract

The Brazilian savannah-like Cerrado is classified as a fire-dependent biome. Human activities have altered the fire regimes in the region, and as a result, not all fires have ecological benefits. The indigenous lands (ILs) of the Brazilian Cerrado have registered the recurrence of [...] Read more.

The Brazilian savannah-like Cerrado is classified as a fire-dependent biome. Human activities have altered the fire regimes in the region, and as a result, not all fires have ecological benefits. The indigenous lands (ILs) of the Brazilian Cerrado have registered the recurrence of forest fires. Thus, the diagnosis of these events is fundamental to understanding the burning regimes and their consequences. The main objective of this paper is to evaluate the fire regimes in Cerrado’s indigenous lands from 2008 to 2017. We used the Landsat time series, at 30 m spatial resolution, available in the Google Earth Engine platform to delineate the burned areas. We used precipitation data from a meteorological station to define the rainy season (RS), early dry season (EDS), middle dry season (MDS), and late dry season (LDS) periods. During 2008–2017, our results show that the total burned area in the indigenous lands and surrounding area was 2,289,562 hectares, distributed in 14,653 scars. Most fires took place between June and November, and the annual burned area was quite different in the years studied. It was also possible to identify areas with high fire recurrence. The fire regime patterns described here are the first step towards understanding the fire regimes in the region and establishing directions to improve management strategies and guide public policies. Full article

(This article belongs to the Special Issue Detecting, Mapping, and Characterizing Wildfires Using Remote Sensing Data)

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