Multi-year MODIS Active Fire Type Classification Over the Brazilian Tropical Moist Forest Biome

The Brazilian Tropical Moist Forest Biome (BTMFB) spans almost 4 million km2 and is subject to extensive annual fires that have been categorized into deforestation, maintenance, and forest fire types. Information on fire types is important as they have different atmospheric emissions and ecological impacts. A supervised classification methodology is presented to classify the fire type of MODerate resolution Imaging Spectroradiometer (MODIS) active fire detections using training data defined by consideration of Brazilian government forest monitoring program annual land cover maps, and using predictor variables concerned with fuel flammability, fuel load, fire behavior, fire seasonality, fire annual frequency, proximity to surface transportation, and local temperature. The fire seasonality, local temperature, and fuel flammability were the most influential on the classification. Classified fire type results for all 1.6 million MODIS Terra and Aqua BTMFB active fire detections over eight years (2003–2010) are presented with an overall fire type classification accuracy of 90.9% (kappa 0.824). The fire type user’s and producer’s classification accuracies were respectively 92.4% and 94.4% (maintenance fires), 88.4% and 87.5% (forest fires), and, 88.7% and 75.0% (deforestation fires). The spatial and temporal distribution of the classified fire types are presente

Fire Type Classification in the Brazilian Tropical Moist Forest Biome

The Brazilian Tropical Moist Forest Biome (BTMFB) is “Earth’s greatest biological treasure and a major component of the earth system” and forest degradation and deforestation by fire is a serious issue in this region. Fires in the BTMFB can be broadly classified as maintenance, deforestation and forest fire types. Spatially and temporally explicit information on the incidences of fire types are important as they have widely varying atmospheric emissions and ecological impacts. Satellite based remote sensing is a practical means of monitoring the BTMFB that spans almost 4 million km2. However, there has been no way to reliably classify satellite active fire type to date. In this work, methods to characterize MODIS active fire detections are developed using physically based and geographic context/proximity approaches. The research methodology is developed by addressing four hypotheses concerning differences among active fire type characteristics including factors that drive and mediate fire in the BTMFB. Differences in the active fire characteristics among different fire types are presented and discussed. The spatio-temporal distribution of fire types over 8 year (2003-2010) period is documented, analyzed and presented. This dissertation has, to date, resulted in one published, one in press, and one submitted paper

Characterizing global fire regimes from satellite-derived products

We identified four global fire regimes based on a k-means algorithm using five variables covering the spatial, temporal and magnitude dimensions of fires, derived from 19-year long satellite burned area and active fire products. Additionally, we assessed the relation of fire regimes to forest fuels distribution. The most extensive fire regime (35% of cells having fire activity) was characterized by a long fire season, medium size fire events, small burned area, high intensity and medium variability. The next most extensive fire regime (25.6%) presented a long fire season, large fire events and the highest mean burned area, yet it showed the lowest intensity and the least variability. The third group (22.07%) presented a short fire season, the lowest burned area, with medium-low intensity, the smallest fire patches and large variability. The fourth group (17.3%) showed the largest burned area with large fire patches of moderate intensity and low variability. Fire regimes and fuel types showed a statistically significant relation (CC = 0.58 and CC? = 0.67, p < 0.001), with most fuel types sustaining all fire regimes, although a clear prevalence was observed in some fuel types. Further efforts should be directed towards the standardization of the variables in order to facilitate comparison, analysis and monitoring of fire regimes and evaluate whether fire regimes are effectively changing and the possible drivers.Agencia Estatal de Investigació

Madagaskarin suojelualueiden tehokkuuden arvioiminen kolmella satelliittipohjaisella aineistolla matching-menetelmää hyödyntäen

Suojelualueiden tehokkuuden (tehokkuus = suojelualueen arvioitu kyky estää ei-luonnollisista syistä johtuvia häiriöitä) arvioiminen on välttämätöntä, sillä ne ovat tärkeimpiä työkaluja monimuotoisuuskadon hillitsemisessä, ja niiden tehokkuudessa on huomattavaa vaihtelua. Metsäisillä suojelualueilla yleisin muuttuja tehokkuuden estimointiin on metsäkato, mutta myös tulipaloja voidaan käyttää kuvaamaan maankäytön muutosta. Vankkoja vertailuja erilaisten tietoaineistojen ja proxy-muuttujien välillä ei kuitenkaan ole tehty. Tässä tutkielmassa vastataan kyseiseen puutteeseen vertailemalla kolmea satelliittipohjaista aineistoa (metsäpeite/-kato, aktiiviset palot, palanut alue) Madagaskarin suojelualueiden tehokkuuden arvioinnissa. Tutkielmassa vastataan seuraaviin kysymyksiin: tuottavatko kyseiset aineistot ja niistä muodostetut vastemuuttujat yhtäläisiä suojelualueiden tehokkuusestimaatteja ja voisiko aineistoja käyttää ”ristiin” (ovatko ne keskenään vaihdettavissa) tutkimuksessa ja suojelualueiden käytännön hallinnoinnissa. Hypoteesit ovat seuraavat: H1: Aineistot ja niistä muodostetut vastemuuttujat yhtäläisiä tehokkuusestimaatteja, joista kahdella tulimuuttujalla lienee vahvin samankaltaisuus. H2: Aineistoja voi käyttää ristiin niin tutkimuksessa kuin suojelualueiden käytännön hallinnoinnissa. Vuonna 2005 tai aiemmin perustettujen Madagaskarin suojelualueiden (N=42) tehokkuutta tutkittiin ajanjaksolla 2005–2017. Kolmea binääristä vastemuuttujaa vertailtiin: metsäkato, aktiiviset tulipalot, palanut alue. Lisäksi tarkasteltiin jatkuvaa metsäkatomuuttujaa. Metsäalueita ja koko kaikkia alueita (metsäalueet + muut alueet) tarkasteltiin erikseen. Vastemuuttujien lisäksi analyyseissa käytettiin sopivia ympäristömuuttujia, joilla kontrolloitiin suojelualueisiin kohdistuviin paineisiin vaikuttavia tekijöitä: kaikilla alueilla korkeus, kaltevuus, etäisyys kaupunkeihin, etäisyys vesiväyliin ja vuotuinen sadanta, metsäalueilla näiden lisäksi etäisyys metsänreunaan. Kaikki muuttujat yhdistettiin solukooltaan 1 kilometrin hilaan. Matching-menetelmää (lähimmän naapurin Mahalanobiksen etäisyys) käytettiin kontrolliotoksen muodostamiseksi suojelluille soluille (”lohkoille”, parcels). Suhteellinen tehokkuus, yhdistetty (pooled) suhteellinen tehokkuus ja verkoston (network) suhteellinen tehokkuus laskettiin binäärisillä muuttujilla, keskiarvoinen tehokkuus jatkuvalla muuttujalla. Tehokkuusestimaatit tuotettiin koko maalle, biomeille (trooppiset ja subtrooppiset kosteat lehtipuumetsät, trooppiset ja subtrooppiset kuivat lehtipuumetsät, aavikot ja kuivat pensaikot) ja yksittäisille suojelualueille. Suojelualueet olivat yleisesti vähintään kohtalaisen tehokkaita, ja kaikki muuttujat tuottivat samansuuntaisia, yhtenäisiä tuloksia koko maan ja biomien tasolla, etenkin yhdistetyn suhteellisen tehokkuuden tapauksessa. Keskimäärin kosteiden metsien suojelualueet olivat tehokkaimpia maankäyttöpaineiden torjumisessa, kuivien metsien suojelualueet hieman vähemmän tehokkaita ja kuivien pensaikoiden suojelualueet olivat tehottomimpia. Yksittäisten suojelualueiden tehokkuuksien välillä oli huomattavaa vaihtelua, ja noin puolessa kaikista suojelualueista kaikki muuttujat osoittivat alueen olevan tilastollisesti merkitsevästi (α = 0,05) tehokas. Hieman yli puolessa suojelualueista osa muuttujista osoitti suojelualueen olevan tehokas, osa taas vaikutuksen olevan päinvastainen eli suojelualue lisäsi maankäytön muutosta. Metsäalueilla yksikään suojelualue ei kuitenkaan lisännyt muutosta kaikilla muuttujilla mitattuna; kaikilla alueilla tällaisia suojelualueita oli yksi. Tehokkuudessa oli pieniä eroja kaikilla tasoilla (koko maa, biomit, yksittäiset suojelualueet) metsäalueiden ja kaikkien alueiden välillä, mutta erot olivat tilastollisesti merkitseviä vain muutamassa tapauksessa. Tulokset viittaavat siihen, että analysoituja aineistoja voisi käyttää ristiin ainakin maa- ja biomitasolla suojelualueiden tehokkuuden arvioimisessa trooppisilla alueilla. Aineistoja voi käyttää myös yksittäisten suojelualueiden tasolla tietyin varauksin, aineistojen luonteeseen ja käyttäytymiseen perehtyneenä. Aineistot soveltuvat hyvin tutkimukseen: käytännön toimissa metsäpeite/-kato ja aktiiviset palot lienevät käyttökelpoisempia kuin palanut alue, sillä viimeksi mainitussa on muutamia piirteitä, jotka tekevät sen käyttämisestä kahta muuta haastavampaa (se muun muassa vaatii enemmän prosessointia käyttötarkoituksesta riippuen ja sen lataaminen vaatii erillisen ohjelmiston).Measuring the effectiveness of protected areas (PAs) is essential as they are key tools in tackling the ongoing biodiversity loss and there is substantial variation in their effectiveness (the estimated ability of protected areas to prevent unnatural disturbances). In forested PAs, the most common variable in effectiveness estimation is forest loss, but fire can also be used as a proxy for conversion. There is, however, a lack of robust comparisons between different data sets and proxies. This thesis aims to provide more insight into the issue by comparing three satellite-based data sets in protected area effectiveness assessment using Madagascar as a case study. The questions to be answered here are whether the data sets and variables derived from them produce similar PA effectiveness estimates and whether they could be used interchangeably in research and for practical management purposes. The hypotheses are as follows: H1: The three proxies produce similar results with the two fire proxies most likely having a stronger relationship. H2: The data sets can be used interchangeably both for science purposes and in practical management of PAs. The effectiveness of Malagasy protected areas established in or before 2005 (N=42) was examined from 2005 to 2017. Three binary response variables were compared: forest loss, fire incidence, and burned area. In addition, a continuous forest loss variable was examined. Forested areas and the full landscape were studied separately i.e. estimates were produced for both forested areas only and full landscape (forested areas + other areas). 1-kilometre parcels in a uniform grid were sampled using nearest neighbour Mahalanobis distance matching, controlling for the factors affecting conversion pressures with appropriate covariates: altitude, slope, distance to cities, distance to roads, distance to waterways, and rainfall for forested areas and full landscape, and in addition, distance to forest edge for forested areas. Relative effect, pooled relative effect, and network relative effect were calculated for the binary variables, mean effect for the continuous variable. The effects were calculated on country level, biome level (tropical and subtropical moist broadleaved forests, tropical and subtropical dry broadleaved forests, and deserts and xeric shrublands), and individual PA level. Protected areas appeared to be at least moderately effective, and all variables produced parallel, consistent results on the country and biome level, especially when using pooled relative effect. On average, PAs in tropical and subtropical moist broadleaf forests were most effective in avoiding land-use pressures, the ones in tropical and subtropical dry broadleaf forests slightly less, and the ones in deserts and xeric shrublands most ineffective. There was substantial variation between and inside individual PAs, and in approximately half of the PAs all variables indicate that the given area is significantly effective (α = 0,05). In a little over half of the PAs the effects were mixed, and in forested areas, no PA was indicated to be ineffective by all variables. In full landscape, this was the case for one PA. There were small differences between forested areas and the full landscape in all levels, but they were statistically significant only in a few cases. This study thus suggests that the data sets could be used interchangeably, at least on country and biome level, when conducting matching to assess PA effectiveness in a tropical setting. They could be utilised on individual PA level, too, with certain precautions and understanding of the nature and behaviour of the data. They are well suited for research; however, in practical management forest loss and fire incidence might be more feasible than burned area, due to its certain characteristics (it for example demands quite a lot of processing depending on the use purpose) and accessibility issues

Changing patterns of fire occurrence in proximity to forest edges, roads and rivers between NW Amazonian countries

Tropical forests in NW Amazonia are highly threatened by the expansion of the agricultural frontier and subsequent deforestation. Fire is used, both directly and indirectly, in Brazilian Amazonia to propagate deforestation and increase forest accessibility. Forest fragmentation, a measure of forest degradation, is also attributed to fire occurrence in the tropics. However, outside the Brazilian Legal Amazonia the role of fire in increasing accessibility and forest fragmentation is less explored. In this study, we compared fire regimes in five countries that share this tropical biome in the most north-westerly part of the Amazon Basin (Venezuela, Colombia, Ecuador, Peru and Brazil). We analysed spatial differences in the timing of peak fire activity and in relation to proximity to roads and rivers using 12 years of MODIS active fire detections. We also distinguished patterns of fire in relation to forest fragmentation by analysing fire distance to the forest edge as a measure of fragmentation for each country. We found significant hemispheric differences in peak fire occurrence with the highest number of fires in the south in 2005 vs. 2007 in the north. Despite this, both hemispheres are equally affected by fire. We also found difference in peak fire occurrence by country. Fire peaked in February in Colombia and Venezuela, whereas it peaked in September in Brazil and Peru, and finally Ecuador presented two fire peaks in January and October. We confirmed the relationship between fires and forest fragmentation for all countries and also found significant differences in the distance between the fire and the forest edge for each country. Fires were associated with roads and rivers in most countries. These results can inform land use planning at the regional, national and subnational scales to minimize the contribution of road expansion and subsequent access to the Amazonian natural resources to fire occurrence and the associated deforestation and carbon emissions

Long-term Landsat-based monthly burned area dataset for the Brazilian biomes using Deep Learning

Fire is a significant agent of landscape transformation on Earth, and a dynamic and ephemeral process that is challenging to map. Difficulties include the seasonality of native vegetation in areas affected by fire, the high levels of spectral heterogeneity due to the spatial and temporal variability of the burned areas, distinct persistence of the fire signal, increase in cloud and smoke cover surrounding burned areas, and difficulty in detecting understory fire signals. To produce a large-scale time-series of burned area, a robust number of observations and a more efficient sampling strategy is needed. In order to overcome these challenges, we used a novel strategy based on a machine-learning algorithm to map monthly burned areas from 1985 to 2020 using Landsat-based annual quality mosaics retrieved from minimum NBR values. The annual mosaics integrated year-round observations of burned and unburned spectral data (i.e., RED, NIR, SWIR-1, and SWIR-2), and used them to train a Deep Neural Network model, which resulted in annual maps of areas burned by land use type for all six Brazilian biomes. The annual dataset was used to retrieve the frequency of the burned area, while the date on which the minimum NBR was captured in a year, was used to reconstruct 36 years of monthly burned area. Results of this effort indicated that 19.6% (1.6 million km2) of the Brazilian territory was burned from 1985 to 2020, with 61% of this area burned at least once. Most of the burning (83%) occurred between July and October. The Amazon and Cerrado, together, accounted for 85% of the area burned at least once in Brazil. Native vegetation was the land cover most affected by fire, representing 65% of the burned area, while the remaining 35% burned in areas dominated by anthropogenic land uses, mainly pasture. This novel dataset is crucial for understanding the spatial and long-term temporal dynamics of fire regimes that are fundamental for designing appropriate public policies for reducing and controlling fires in Brazil

Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997-2009)

New burned area datasets and top-down constraints from atmospheric concentration measurements of pyrogenic gases have decreased the large uncertainty in fire emissions estimates. However, significant gaps remain in our understanding of the contribution of deforestation, savanna, forest, agricultural waste, and peat fires to total global fire emissions. Here we used a revised version of the Carnegie-Ames-Stanford-Approach (CASA) biogeochemical model and improved satellite-derived estimates of area burned, fire activity, and plant productivity to calculate fire emissions for the 1997–2009 period on a 0.5° spatial resolution with a monthly time step. For November 2000 onwards, estimates were based on burned area, active fire detections, and plant productivity from the MODerate resolution Imaging Spectroradiometer (MODIS) sensor. For the partitioning we focused on the MODIS era. We used maps of burned area derived from the Tropical Rainfall Measuring Mission (TRMM) Visible and Infrared Scanner (VIRS) and Along-Track Scanning Radiometer (ATSR) active fire data prior to MODIS (1997–2000) and estimates of plant productivity derived from Advanced Very High Resolution Radiometer (AVHRR) observations during the same period. Average global fire carbon emissions according to this version 3 of the Global Fire Emissions Database (GFED3) were 2.0 Pg C year⁻¹ with significant interannual variability during 1997–2001 (2.8 Pg C year⁻¹ in 1998 and 1.6 Pg C year⁻¹ in 2001). Globally, emissions during 2002–2007 were relatively constant (around 2.1 Pg C year⁻¹) before declining in 2008 (1.7 Pg C year⁻¹) and 2009 (1.5 Pg C year⁻¹) partly due to lower deforestation fire emissions in South America and tropical Asia. On a regional basis, emissions were highly variable during 2002–2007 (e.g., boreal Asia, South America, and Indonesia), but these regional differences canceled out at a global level. During the MODIS era (2001–2009), most carbon emissions were from fires in grasslands and savannas (44%) with smaller contributions from tropical deforestation and degradation fires (20%), woodland fires (mostly confined to the tropics, 16%), forest fires (mostly in the extratropics, 15%), agricultural waste burning (3%), and tropical peat fires (3%). The contribution from agricultural waste fires was likely a lower bound because our approach for measuring burned area could not detect all of these relatively small fires. Total carbon emissions were on average 13% lower than in our previous (GFED2) work. For reduced trace gases such as CO and CH₄, deforestation, degradation, and peat fires were more important contributors because of higher emissions of reduced trace gases per unit carbon combusted compared to savanna fires. Carbon emissions from tropical deforestation, degradation, and peatland fires were on average 0.5 Pg C year⁻¹. The carbon emissions from these fires may not be balanced by regrowth following fire. Our results provide the first global assessment of the contribution of different sources to total global fire emissions for the past decade, and supply the community with an improved 13-year fire emissions time series

Vulnerability of Protected Areas to Human Encroachment, Climate Change and Fire in the Fragmented Tropical Forests of West Africa

The Upper Guinean region of West Africa is home to some of the most globally significant tropical biodiversity hotspots, providing ecosystem services that are crucial for the region’s socio-economic and environmental wellbeing. Nonetheless, following decades of human-caused destruction of natural habitats, protected areas currently remain the only significant refugia of original vegetation relics in landscapes that are highly fragmented. Aside from having strong geographic variation in land use, climate, vegetation, and human population, the region has also experienced remarkable biophysical and socio-economic changes in recent decades. All these factors influence the fire regime and the vulnerability of forests within protected areas to fire-mediated changes and forest loss, yet little is known about fire regimes and fire-vegetation interactions within the region. Therefore, the overarching goal of this dissertation was to improve our understanding of the interactions of climate, land use, and fire regimes, as well as effects of fire on forest resilience in the Upper Guinean region of West Africa. I conducted the first comprehensive regional analysis of the fire regime across the gradient from humid tropical forests to drier woodlands and woody savanna. This analysis revealed that different components of the fire regime were influenced by different environmental drivers. As a result, the various combinations of these environmental factors create distinctive fire regimes throughout the region. The results further showed increasing active fire trends in parts of the forested areas, and decreasing trend in fire activity across much of the savannas that were likely linked with land cover changes. An analysis of fire-vegetation interactions in the forest zone of Ghana provided evidence of alternative stable states involving tropical forest and a novel non-forest vegetation community maintained by fire-vegetation feedbacks. Furthermore, an analysis exploring recent drought-associated wildfires in the forest zone of Ghana revealed widespread fire encroachment into hitherto fire-resistant moist tropical forests, which were associated with forest degradation. These findings suggest that ongoing regional landscape and socio-economic changes along with climate change will lead to further changes in the fire regimes and forest vegetation of West Africa. Hence, efforts to project future fire regimes and develop regional strategies for adaptation will require an integrated approach, which encompasses multiple components of the fire regime and consider multiple drivers, including land use and climate. Furthermore, projections of future vegetation dynamics in the region will need to consider land use, vegetation, fires, and their dynamic landscape-scale interactions in the context of broader responses to climate change and human population growth. Overall, this dissertation produced novel results about the pathways and drivers of disturbance land cover change that are necessary for improving our understanding of ongoing changes in a lesser-known part of the tropics. These findings are also relevant for predicting and mitigating similar fire impacts in tropical forests worldwide

Spatial and temporal variability in the ratio of trace gases emitted from biomass burning

Fires are a major source of trace gases and aerosols to the atmosphere. The amount of biomass burned is becoming better known, most importantly due to improved burned area datasets and a better representation of fuel consumption. The spatial and temporal variability in the partitioning of biomass burned into emitted trace gases and aerosols, however, has received relatively little attention. To convert estimates of biomass burned to trace gas and aerosol emissions, most studies have used emission ratios (or emission factors (EFs)) based on the arithmetic mean of field measurement outcomes, stratified by biome. However, EFs vary substantially in time and space, even within a single biome. In addition, it is unknown whether the available field measurement locations provide a representative sample for the various biomes. Here we used the available body of EF literature in combination with satellite-derived information on vegetation characteristics and climatic conditions to better understand the spatio-temporal variability in EFs. While focusing on CO, CH&lt;sub&gt;4&lt;/sub&gt;, and CO&lt;sub&gt;2&lt;/sub&gt;, our findings are also applicable to other trace gases and aerosols. We explored relations between EFs and different measurements of environmental variables that may correlate with part of the variability in EFs (tree cover density, vegetation greenness, temperature, precipitation, and the length of the dry season). Although reasonable correlations were found for specific case studies, correlations based on the full suite of available measurements were lower and explained about 33%, 38%, 19%, and 34% of the variability for respectively CO, CH&lt;sub&gt;4&lt;/sub&gt;, CO&lt;sub&gt;2&lt;/sub&gt;, and the Modified Combustion Efficiency (MCE). This may be partly due to uncertainties in the environmental variables, differences in measurement techniques for EFs, assumptions on the ratio between flaming and smoldering combustion, and incomplete information on the location and timing of EF measurements. We derived new mean EFs, using the relative importance of each measurement location with regard to fire emissions. These weighted averages were relatively similar to the arithmetic mean. When using relations between the environmental variables and EFs to extrapolate to regional and global scales, we found substantial differences, with for savannas 13% and 22% higher CO and CH&lt;sub&gt;4&lt;/sub&gt; EFs than the arithmetic mean of the field studies, possibly linked to an underrepresentation of woodland fires in EF measurement locations. We argue that from a global modeling perspective, future measurement campaigns could be more beneficial if measurements are made over the full fire season, and if relations between ambient conditions and EFs receive more attention

The global wildland–urban interface

The wildland–urban interface (WUI) is where buildings and wildland vegetation meet or intermingle1,2. It is where human–environmental conflicts and risks can be concentrated, including the loss of houses and lives to wildfire, habitat loss and fragmentation and the spread of zoonotic diseases3. However, a global analysis of the WUI has been lacking. Here, we present a global map of the 2020 WUI at 10 m resolution using a globally consistent and validated approach based on remote sensing-derived datasets of building area4 and wildland vegetation5. We show that the WUI is a global phenomenon, identify many previously undocumented WUI hotspots and highlight the wide range of population density, land cover types and biomass levels in different parts of the global WUI. The WUI covers only 4.7% of the land surface but is home to nearly half its population (3.5 billion). The WUI is especially widespread in Europe (15% of the land area) and the temperate broadleaf and mixed forests biome (18%). Of all people living near 2003–2020 wildfires (0.4 billion), two thirds have their home in the WUI, most of them in Africa (150 million). Given that wildfire activity is predicted to increase because of climate change in many regions6, there is a need to understand housing growth and vegetation patterns as drivers of WUI change