Integrating Multi-Source Spatial Data for Monitoring and Managing Land Cover Impacts of Open Rubbish Fires: case study of United States

Integrating Multi-Source Spatial Data for Monitoring and Managing Land Cover Impacts of Open Rubbish Fires: case study of United States | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Integrating Multi-Source Spatial Data for Monitoring and Managing Land Cover Impacts of Open Rubbish Fires: case study of United States Jan Stefan Bihałowicz, Wioletta Rogula-Kozłowska, Adam Krasuski This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7496057/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The aim of this study is to analyse land cover change (LCC) using the integration of data from three databases: FIRMS (Fire Information for Resource Management System), NLCD (National Land Cover Database) and NFIRS (National Fire Incident Reporting System). The main idea is to utilise data snapshots from these databases to identify and assess changes in land cover, without undertaking a comprehensive analysis of individual incidents. The research used data about open rubbish fires from 2015, the year just before NLCD 2016. Locations of fires from NFIRS were confirmed by FIRMS satellite observations and the impact, the LCC and tree density at these sites, was assessed using NLCD. In fire-affected areas, the most common LCC were shifts from shrubland to forest (29.9%), forest to herbaceous vegetation (29.7%), and herbaceous vegetation to shrubland (16.5%). While in the whole study area: i) herbaceous vegetation to shrubland (22.9%); ii) forest into herbaceous vegetation (19.8%); iii) shrubland into forest (17.0%); iv) shrubland into herbaceous vegetation (9.0%); v) forest into shrubland (6.1%); vi) herbaceous vegetation into cultivated areas (5.5%). The results demonstrate that processes of silviculture, as well as natural growing, can be distinguished, and that areas near open rubbish fires undergo different changes than those typical of the continental US. The proposed methodology is versatile and innovative, rendering it readily applicable in diverse geographical and temporal contexts. This approach allows for the efficient utilisation and enhancement of spatial data, which is vital for the monitoring and management of land cover change. Biological sciences/Ecology Earth and environmental sciences/Ecology Earth and environmental sciences/Environmental sciences remote sensing fire statistics land cover change waste fires FIRMS NLCD Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction Fire has been shaping the diversity of life on Earth for millions of years. Some of the variation in fire regimes continues to be a source of biodiversity across the globe. Many plants, animals, and ecosystems depend on particular temporal and spatial patterns of fire (He et al. 2019 ). Although people have been using fire to modify environments for millennia, the combined effects of human activities are now changing patterns of fire at a global scale – to the detriment of human society, biodiversity, and ecosystems (Kelly et al. 2020 ). Anthropogenic drivers such as climate change, land use, and invasive species are changing the nature of fire in terrestrial ecosystems. Although the large and long-lasting fires in forest or rural areas are critical for the conservation of Earth’s biological diversity, in this study we are mostly concentrated on the negative effects of fires induced by inappropriate management of wastes by humans. Landfilling is the most common means of disposal of waste in the USA as well as in many other countries (Manheim et al. 2021 ). The most common type of waste landfilled is municipal solid waste. They are responsible for the contribution to the environment of three primary products: gas, leachate, and heat (Yeşiller et al. 2016 ). Landfill sites contribute to global warming as they generate and release biogas into the atmosphere. According to (Hanson et al. 2023 ) landfills are able to enter biogases in amounts up to 1320 Mg CO 2 -eq./ha-yr. Biogas is a mixture formed primarily of methane gas (CH₄) and carbon dioxide (CO₂). There is also a challenge to control the release of harmful chemicals into the environment, which can be infiltrated by leachates (El-Saadony et al. 2023 ). The surrounding flora and fauna are affected by airborne substances from landfills or various types of leachates. Landfill sites have a particularly negative impact on birds, which have access to landfill sites as a source of food. They ingest plastics, metals or other materials that can ultimately prove to be fatal to them (Bialas et al. 2021 ). Alternation of migration activity or even ceasing the migration is another danger posed to birds by landfill sites. Finally, the methane produced by the waste, as well as biological processes that generate heat, can cause fires or even explosions. Particulate matters, metals, PAH and dioxin emissions are also very harmful to the environment from these spontaneous, uncontrolled fires (J. S. Bihałowicz et al. 2021 ). These fires are not regular fires with visible flames. They often occur inside the landfill, which makes is difficult to detect. Fires that occur deep within a landfill can damage insulating waterproof layers and cause further environmental contamination. Due to the complex and diverse mechanisms by which landfill fires can affect the environment, this phenomenon is not fully understood. The aim of this study is to evaluate of the effect of landfill fires on neighbouring landcover. The interaction between a fire and its surroundings or environment proceeds via direct gaseous and particulate emissions to the atmosphere. Then localised deposition to soil and water or remaining in the atmosphere in the gas phase. Other pathways of interaction between fire and environment are through extinguishing water running out from fire or directly by debris and semi-burnout effluents carried by convection forces or spread over the surrounding area during firefighting operations. Essentially, the substances produced in the hot phase of a damaging fire are discharged in the form of smoke; these include carbon monoxide, carbon dioxide, hydrogen chloride, hydrogen cyanide and other substances. The main distribution path for these substances is initially the air path; the combustion products can be discharged from the fire in gaseous, liquid or solid form. As a result, gaseous substances can condense on cold surfaces of leaf or trees. As the thermals decrease, soot particles and ash will rain down. A significant amount of work has been done to recognize and quantify the emissions from specific burning species or more complex fires. A very good survey of these works can be found in (McNamee et al. 2020 ). The vast majority of studies found through in this literature are related to material emissions or forest fire emissions, which is not the part of this study. Only few are available for products that are relevant for the municipal wastes. The total emission from fires is generally assessed by relating to the yield of the mass or the object or it content. Then using the emission factor, the total emission from the fire of the structure is estimated. An approach to assess the amount of the emission from the waste fires was proposed by Bihalowicz et. al. (J. S. Bihałowicz et al. 2021 ). The approach was used to evaluated emission for main air pollutants: CO, NOx, PM10, SO2, CO2 and CH4. More in-depth research was conducted by Astrom et. al. (Åström et al. 2023 ). They collected data regarding emission factors from various sources as well as conducted their own experiments. Small and large-scale experiments on various type of apparatus allowed to recognize emissions factors for compounds, particles, but also Polycyclic Aromatic Hydrocarbons (PAH), Volatile Organic Compounds (VOC), Polychlorinated dibenzo-p-dioxins and furans (PCDD/DF). The experiments also addressed the problem of emission factors depending on fire phase (steady, flaming and smouldering). In most cases, the fire will eventually damage the liner, causing leachate and runoff of water collected in the landfill, which is also contaminated with combustion products. In addition, at various stages of fire development, firefighters arrive on the scene and provide water or other extinguishing agents to the fire. In most applications, they use water. Only part of the water evaporates and absorbs heat from the fire. The majority of it (depending on the burning material, type of water stream and its intensity) runs off the fire without developing its most important property, the cooling effect. The water is absorbed by materials or drains away. If this so-called fire water runoff is not retained, it encounters water and soil. The composition of fire-water runoff is easy to predict when it comes to fire of materials of known chemical compounds. In this case it is assumed that these compounds are mixing or dissolving in extinguishing water and then when an-retained enter the environment. However, more difficult is prediction of compounds in fire-water runoff as the effect of landfill fire. The analyses of fire-water runoff from fire of solid materials which release chemical compounds as the result of combustion, showed that also not only industrial or biocidal chemicals storing or processing facilities fire may pose high risk to environment. During the fire of solid PVC in Canada in fire-water runoff high concentration of metal, VOCs, and PAHs was registered (Fowles et al. 2001 ). Steer et. al. found that the fire-water contained PCDD/Fs however below the safety water criteria value (Steer et al. 1995 ). More municipal solid wastes fires related fire-water runoff were investigated by (Noiton and Fowles 2001 ). They examined samples from house, fruit shop, vehicular and industrial/warehouse fires. The experiment was directed towards contamination of fire-water runoff with metals, cyanide, PAHs and VOCs. The runoff from the auto shop fire posed the greatest ecotoxicological hazard from PAHs, copper, and zinc (Noiton and Fowles 2001 ). Metal and organic contaminants were also high in concentration and volume in fruit shop fire, as a result of burnout of the structure. In the runoff from all five fires, metal contaminants exceeding water standards. Cyanide was found in a fairly narrow range of concentrations. Åström, J., et al. (Åström et al. 2023 ) compared PAH distribution in smoke from burning of plastics mix during free burning and those cooling due to water impingement. The results show that fire-water runoff contains large spectrum of PAH. Moreover, they stated that water pollution depends on both the proportion of the individual in the smoke together with its solubility. Rogula et. al. conducted small scale experiments measuring ecotoxicity mutagenicity of fire-water runoff from extinguishing pine and oak wood, chipboard, PMMA and polyurethane foam. The ecotoxicity was evaluated using commercial biotests, i.e., Daphtoxkit F magna (crustaceans), Rotoxkit F (rotifers), Spirodela duckweed toxkit (plants: Spirodela polyrhiza) and Microtox test (bacteria Alivibrio fischeri), while mutagenicity was assessed with Ames test (Salmonella typhi-murium strains TA98 and TAmix). The results of the conducted experiment showed that water runoff deriving from the burning of polyurethane foam had the highest toxicity compared with other tested materials. Moreover, also the results of Ames test confirmed that this material is characterized with the highest mutagenicity values, and in consequence may pose the hazard on environment. When fire involves the solid waste, debris can be generated. They can be dispersed into the environment as a result of buoyancy and flow in the convection column, or because of firefighting activities where burning material is scattered to be doused with water. The affection for soil and water depends on the heat release rate of the fire, weather condition such as wind and falls, and fire service activities. The contamination of water and soil by fire debris in the case of landfill fire in poorly recognized. Stec et. al. (Stec et al. 2019 ) investigated soil contamination by semi-burnt fire debris and char samples resulted from Grenfell Tower fire. The building is much higher that landfills but, samples are collected in the surrounding of 160 m away from Tower. The results showed amongst other toxicants, polychlorinated dibenzo-p-dioxin, benzene and PAHs concentrations 60 to 20 times greater than UK urban reference soil levels. The issue of unsustainable waste management represents a significant obstacle to the advancement of sustainable development, resulting in the generation of excessive waste and the uncontrolled accumulation of waste materials. In consequence of inadequate waste management, including a lack of segregation or recycling, a situation emerges in which waste not only pollutes the environment but also becomes a source of hazards, such as spontaneous combustion. This situation has an adverse impact on the realisation of the Sustainable Development Goals (SDGs) (UN General Assembly 2015 ), in particular SDG 10, which aims to reduce social inequalities. Such inequalities can be further exacerbated by environmental pollution, which has a particularly detrimental impact on the most economically disadvantaged communities. Furthermore, failure to achieve SDG No. 10 has implications for SDG No. 12, which focuses on responsible consumption and production. Unsustainable waste management leads to the waste of resources and hinders the implementation of effective recycling practices. As a result, the environmental and social devastation that results from poor waste management can impede progress towards sustainable development, creating a vicious circle that requires urgent action and reform. The objective of this study is to ascertain whether waste fires exert an influence on environmental change and to determine the geographical areas where such changes occur. The analysis is based on data recorded in the National Fire Incident Reporting System (NFIRS) database, which is a repository of information on fire incidents in the United States. The project is specifically concerned with the identification of patterns of waste fires and their potential impacts on local ecosystems and public health. To corroborate the findings of the analysis, Moderate Resolution Imaging Spectroradiometer (MODIS) data will be employed to monitor alterations in land cover and detect extensive fires. By comparing the NFIRS data with the information obtained from MODIS, it will be feasible to identify regions where waste fires have occurred and assess their environmental impact. The objective is to furnish reliable information that can be utilized to develop waste management and environmental protection strategies in the affected regions. The role of land cover, particularly in the form of forests, in counteracting the effects of global warming is important when analysing the impact of waste fires on environmental change. An increase in forest cover is beneficial for the ecological balance, as trees absorb carbon dioxide, thereby reducing the concentration of this gas in the atmosphere. Furthermore, forests play a pivotal role in water retention, which serves to stabilise local ecosystems and mitigate the risk of flooding. The presence of forests facilitates the retention of rainwater in the soil, which in turn promotes the growth of vegetation and enhances soil quality. Furthermore, an increase in forest cover serves to mitigate fluctuations in temperature at both the local and global levels. Forests act as natural air conditioners, regulating air temperature through the process of transpiration and shading. In the context of studying the impact of waste fires, it is therefore crucial to understand the role of forests in climate and ecosystem stabilisation. Areas with abundant vegetation are less vulnerable to extreme temperature changes, which has a positive impact on biodiversity and ecosystem health. Consequently, the protection and enhancement of forest cover is becoming an important component of adaptation strategies to mitigate global warming and minimise the negative impacts of waste fires. In this paper we have attempted to indirectly assess the impact of landfill fires on the surrounding environment. In this paper we have attempted to indirectly assess the impact of landfill fires on the surrounding environment. The main objective was to integrate data from three major databases (FIRMS (Fire Information for Resource Management System), NLCD (National Land Cover Database) and NFIRS (National Fire Incident Reporting System)) to analyse land cover change. The measure used to assess the impact of landfill fires on the land cover is the change in soil and vegetation layers recorded in the NLCD database in the immediate vicinity of the landfill fire. We didn’t use controlled, randomized-sample studies, but we tried to understand the effects of fire in the natural environment. Methods and materials 2 Materials and methods 2.1 National Land Cover Database The National Land Cover Database (NLCD) serves as the definitive Landsat-based, 30-meter resolution land cover database for the Nation. NLCD provides spatial reference and descriptive data for land surface characteristics such as thematic class (e.g., urban, agricultural, and forest), percent impervious surface, and percent tree canopy cover. The NLCD allow to assess ecosystem condition and health, understand spatial patterns of biodiversity, predict the effects of climate change, and develop land management policy, and develop land management policies. The database is designed to provide five-year cyclical updates of United States land cover and associated changes. The landcover codes are organized in two-level hierarchy. Firstly, land cover is classified as one of the nine classes: water, developed, barren, forest, shrubland, herbaceous, planted/cultivated, and wetlands. The subdivision of these classes results in 20 second-level classes. The subdivision of these classes results in 20 second-level classes. The NLCD 2011 and 2016 include tree canopy percentage estimates (Yang et al. 2018 ) also in 30 m grid. In the work, both NLCD and Tree canopy data will be used. Data preparation includes Landsat image selection, cloud detection and cloud filling, and national-scale ancillary data set compilation and production (Homer et al. 2015 ; Yang et al. 2018 ). Land cover change detection includes single-date water and snow/ice detection algorithms and models, two-date multi-index integrated change detection models, and long-term multi-date change algorithms and models. Land cover classification includes seven-date training data generation and 14-run classifications. Prior to classification, pools of training data for change and no change areas were created based on integrated information from ancillary data, change detection results, Landsat spectral and temporal information, and knowledge-based trajectory analysis (Yang et al. 2018 ). In post-processing, comprehensive models for each land cover theme were developed in a hierarchical order to ensure spatial and temporal coherence of land cover and land cover change over 15 years. An accuracy assessment of four selected Landsat indicates overall accuracy of 82.0% (Homer et al. 2015 ). This is valid for Anderson Level II classification. After combining the primary and alternate reference labels Anderson Level I classification achieved 86.6%. This method was used for the operational production of the 2016 NLCD for the contiguous United States, which we used as the data source for this study. Three forest transition classes are defined: herbaceous-forest, shrub-forest, and young-forest (Jin et al. 2019 ). They are designed to represent different stages of forest growth. The young-forest class was created for those forests that have not yet regrown to mature trees after disturbance and provides additional information to reduce confusion between shrub and forest during classification. In the final published product, the young-forest class is cross walked to either shrub-forest or forest according to regional growth rates and successional characteristics (Jin et al. 2019 ). The transitional herbaceous-forest and shrub-forest classes are hierarchically subordinate to the NLCD legacy herbaceous and shrub classes. The NLCD legacy grass/herbaceous class included rangeland grassland and forest areas in very early successional stages following abrupt forest replacement disturbances such as clearcuts, fires, and hurricanes. Rangeland shrub and grassland ecosystems have very different spectral and temporal dynamic patterns than the forest transitional classes (Jin et al. 2019 ). 2.2 Fire Information for Resource Management System The Fire Information for Resource Management System (FIRMS) was developed to provide near real-time active fire locations to natural resource managers who have faced challenges in obtaining timely satellite-derived fire information (NASA 2021 ). FIRMS provides also archives data about the hotspots observed by two types of devices Moderate resolution Imaging Spectrometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) which are installed on four satellites. NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) active fire products were the first in a family of remotely sensed fire datasets produced by a new generation of moderate-resolution (~ 1 km) "fire-capable" sensors on board terrestrial satellites (Giglio et al. 2016 ). Since their inception in 2000, MODIS fire products have been used to answer a wide range of scientific questions about the role of biomass burning in the Earth system. Observation of a MODIS hotspot at a given coordinate means that one or more fires have occurred in the 1x1 km pixel. The Visible Infrared Imaging Radiometer Suite (VIIRS) active fire data products were intentionally designed to support data continuity between two major satellite programs (MOD14/MYD14) and their corresponding environmental data sets (Schroeder and Giglio 2017 ). Data processing is primarily divided into the following levels(Schroeder and Giglio 2017 ): Level 2) Swath data projection. No data resampling or other corrections are applied; Level 3) tiled datasets: spatial resampling performed using a predetermined projection type and fixed pixel size (e.g., 500 m) along with some temporal aggregation; Level 4) gridded datasets: spatial resampling performed to conform to Climate Modelling Grid (CMG) products. Gridded data are corrected for cloud cover and sampling frequency, which varies as a function of latitude. Finally, the resolution of the VIIRS grid is about 375 m. Satellite-observed hotspots are validated and available as annual summaries. 2.3 National Fire Incident Reporting System We started by acquiring fire data from the NFIRS databases. Data used in this work are raw data “as reported to the NFIRS”. NFIRS 5.0 is an information-based system that facilitates data entry, storage, and retrieval, whether for a single incident or in aggregate. The latter is achieved through a computer that interacts with the database. (U.S. Fire Administration National Fire Data Center 2015 ). It should be noted that not all fire departments utilise computers for the purposes of record-keeping. Consequently, paper forms are also available for this purpose. NFIRS Version 5.0 is comprised of 11 modules. It is required that the Basic Module be completed for each incident, with additional modules utilized as appropriate to provide a detailed account of the incident in question. The Basic Module (NFIRS–1) is designed to capture general information on every incident (or emergency call) to which the department responds. The Fire Module (NFIRS–2) is employed to delineate the specifics of each fire incident to which the department responds. In the event that the Wildland Module is available for use by state reporting authority, it may be utilized in lieu of the Fire Module for wildland fire incidents. The Wildland Fire Module (NFIRS–8) is completed to report incidents that involve wildland or vegetation fires. It is used in lieu of the Fire Module for wildland fire incidents. (U.S. Fire Administration National Fire Data Center 2015 ). Each incident, identified by a unique identifier, is recorded in this data set and includes information such as property loss, fire area, time, location, and many others. In this database, fire incidents are classified into different categories, namely, structural fires, outside fires (natural vegetation, cultivated vegetation, and outside rubbish), and other fires (vehicle-related and outside gas or vapor combustion explosion). 2.4 Procedure The Fig. 1 depict the main idea of the experiment. From the NFIRS database only fires with codes 15x Outside rubbish fires were selected (x denotes digit from 0 to 5 used for more precise description). Data about these fires included, essentially for the further work, address of fires (since NFIRS do not provide GPS coordinates). The data then was manually checked for its correctness and appropriateness to eliminate uncertainty related to the reporting and quality of address and data. We query then the validated coordinates and timestamps against the Fire Information for Resource Management System databases for seeking the fire hotspots. Having the location and the area of the fire detected, we then check the information about land cover before and after the fire. Then we evaluate changes in land cover. In the following subsections, we described each of the steps in more detail. We limited the area of the study to the Contiguous United States. Each address provided in NFIRS was geocoded into geographic coordinates, creating a set of points. Each point was then rounded with an individual 10 km buffer. For each point representing an NFIRS fire, the age of the MODIS/VIIRS points in the buffer was determined. This was calculated as the difference between the date in the NFIRS report and the date in the MODIS/VIIRS observation. For further analysis, only hotspots within ± 1 day were included. The hotspots provide more detailed information about the location of the fire in suburban areas, while the NFIRS addresses are more accurate in urban areas. In the event that the fire was detected by spectrometers, the geocoded NFIRS addresses were adjusted according to the MODIS/VIIRS location. Otherwise, the NFIRS address was treated as the most accurate. This process yielded a set of points of varying types: The data set comprised three types of points: MODIS hotspots, VIIRS hotspots, and NFIRS points. Furthermore, numerous MODIS/VIIRS points could be attributed to the same fire. To delineate regions of interest based on the aforementioned data points, circular buffers were constructed around the MODIS/VIIRS hotspots and around the NFIRS addresses. Given the disparate resolutions of the MODIS and VIIRS spectrometers, the buffers around MODIS had a radius of 1000 m, while those around VIIRS hotspots had a radius of 375 m. It is possible that multiple hotspots may be identified from different runs and instruments on the same fire. The hotspots, which were identified as being associated with the same fire, were then enveloped with a convex hull. The NFIRS points were surrounded by circular buffers with a 500 m radius, as the evaluation was not focused on the geocoding service, but rather on the landscape change. Consequently, for wildfires identified by MODIS/VIIRS, the buffer around the hotspots was analysed, which was not influenced by potential inaccuracies in geocoding or database address values. In contrast, for NFIRS, the buffer was analysed around the geocoded address. The study analyses the impact of fires in 2015 on NLCD land cover in 2016, using the NLCD from 2011 as a reference point. The years for this analysis were determined by the 5-year release cycle of the NLCD land cover maps. A secondary rationale pertains to the objective of capturing the immediate impact of fires. In instances where fires occurred prior to 2015, the processes of land recultivation have the potential to obscure the direct effects of these fires. This approach to the selection of the study area, which involves the use of buffers around fires from 2015 only, is more precise than taking all fires from the 2011–2015 period. The results obtained in this approach are more accurate, as the buffers provide precision in the assessment of land cover changes. Consequently, a primary focus of the research was the accurate geocoding of addresses from the NFIRS databases, followed by validation through remote sensing to ensure precise identification of the study area. A histogram of raster values representing land cover change between 2011 and 2016 was prepared for each buffer, irrespective of whether a convex hull or circular buffer was used. This was done to facilitate the preparation of a 2D histogram representing the land cover change for each buffer. The columns of the histogram represented the NLCD codes in 2011, while the rows represented the NLCD codes in 2016. A second land cover change analysis was conducted using the 2011 tree canopy raster and the 2016 raster. For each pixel within the fire envelope, the canopy density in 2011 was correlated with the density in 2016. In this manner, the 2D histogram was populated with data, wherein the x-axis represents tree cover in 2011, and the y-axis represents tree cover in 2016, while the z-value represents the area. The interpretation of such a histogram, in which the axes are parametric, is more straightforward. The diagonal of the histogram, which represents the constant tree cover in both years, divides the histogram into two regions. The first region encompasses the deforestation of the envelopes around landfill fires, while the second region represents forestation. The third land cover analysis is based on the canopy change provided by MRCL. This analysis employed a zonal histogram of raster values in the buffers. 3 Results and discussion 3.1 Fire statistics and location summary In 2015 there were 24 819 640 incident reports. Among them 599 422 were fire incidents. Among fires 5.2% were reported as Outside rubbish fire according to (U.S. Fire Administration National Fire Data Center 2015 ). Among the parameters reported in the NFIRS there is the estimate of acres burned during fire \(\:{B}_{3}\) . The area of burned land was reported only for 455 fires in 2015. The size distribution of fires is provided in the Table 1 below, expressed in metric units. Table 1 The dristribution of outside rubbish fires sizes. Area in hectares \(\:B\le\:0.5\) \(\:0.5<B\le\:1\) \(\:1<B\le\:2\) \(\:2<B\le\:4\) \(\:4<B\le\:8\) \(\:8<B\le\:16\) \(\:1632\) Number of outside rubbish fires 306 52 41 23 19 6 3 5 The subsequent parameter documented in the database pertains to the geographical location of the fire. It is regrettable that the coordinates have not been provided; instead, an address has been supplied. Three geocoding services were utilised: Nominatim API (“Nominatim” 2021), US Census Bureau (US Census Bureau 2024 ), and Google Geocoding API (Google 2024a ) through MMQGIS plugin (Minn 2021 ). All of the aforementioned services encountered difficulties, either in identifying addresses (the US Census Bureau service was able to identify 233 out of 455 locations) or in assigning precise locations to addresses (the US Census Bureau service assigned 61% of its matches as non-exact, while the validation of Google API geocoding demonstrated discrepancies in addresses up to 14 km). Consequently, all fires were manually geocoded using the Google Maps (Google 2024b ) webpage and validated using Apple Maps (Apple 2024 ). Furthermore, in instances where satellite images were accessible on Google Maps, discernible indications of the incinerated region at the specified address were observed. Consequently, the location was adjusted to align with the affected area. It was determined that 9% of the addresses documented in NFIRS could not be matched with any existing address, while 7% exhibited minor inconsistencies, such as typographical errors, that were nevertheless identifiable. The analysis revealed inconsistencies between data reported by various fire departments. Over 60% of fires with \(\:{B}_{3}\le\:0.5\) ha were documented in Cincinnati, Ohio, predominantly in residential areas. This indicates a potential for systematic inaccuracies in data reporting. Given the inability to ascertain the accuracy of reported areas for these fires, the analysis was limited to 136 geocoded fires with \(\:{B}_{3}>0.5\) ha. 3.2 Land cover type transitions Figure 2 presents a matrix of changes in land cover (J. S. Bihałowicz and Rogula-Kozłowska 2022 ). The matrix presents a comparison of land cover between the years 2011 and 2016, with one axis representing the former and the other the latter. As the matrix only covers changes, the diagonal is empty. For the sake of simplicity, the matrix has been normalised to 100%. The upper and lower triangles of the matrix present reciprocal processes, thus facilitating the identification of the dominant direction. For instance, in Fig. 2 (a), 2.2% of the area changed from water to wetlands, while 1.2% changed from wetlands to water. Consequently, the net outcome is an increase of 1% in wetlands, which is offset by a decrease in water area. Figure 2 (a) allows us to identify several principal alterations to the land cover in the United States. Of the total number of changes, six processes account for over 75% of all transitions, with the area of transition representing at least 2% of all changes. The most frequent processes are as follows: 1. herbaceous vegetation into shrubland (22.9%), 2. forest into herbaceous vegetation (19.8%), 3. shrubland into forest (17.0%), 4. shrubland into herbaceous vegetation (9.0%), 5. forest into shrubland (6.1%), 6. herbaceous vegetation into cultivated areas (5.5%). The aforementioned alterations in the remote sensed land cover can be readily discerned as processes of deforestation (2, 5), the cultivation of plants (1, 3), and land reclamation (4, 6). However, the circumstances appear somewhat distinct in regions that were impacted by waste fires, as there are only three processes accountable for over 75% of the observed changes: 1. shrubland into forest (29.9%), 2. forest into herbaceous vegetation (29.7%), 3. herbaceous into shrubland (16.5%). The three processes are identical to those observed in the CONUS case but are presented in a different sequence and with a higher magnitude (higher shares). The direct impact of the fire can be deforestation, representing almost one-third of the total changes, while in the CONUS case this figure is only 19.8%. Two of the remaining processes (1, 3) are typical of abandoned land. This suggests that waste fires are present in locations that are not intensively exploited. In order to evaluate the observed differences, a comparison was conducted between the two matrixes using the Wilcoxon Signed-Rank test (Virtanen et al. 2020 ; Wilcoxon 1945 ), based on the value of the matrix elements. The results demonstrated that the two matrices are significantly different (p < 0.001). We are aware that in changes in land cover between these years are not only affected by fires in 2015 but also in 2011–2014, what seemingly restricts our study. Nevertheless, it is the utilisation of data from one year prior to the development of the subsequent NLCD 2016 land cover map, in conjunction with the identification of specific areas (i.e. buffers surrounding the address of fire in NFIRS), that results in the precision of the analysis and the identification of only the impact of fire on land cover. 3.3 Tree canopy density change The analysis of land cover type transitions indicated that the highest changes were observed for the land covers of herbaceous vegetation, shrubland, and forest. Therefore, we proceeded to evaluate the changes in tree canopy. The three-canopy change matrix is a valuable analytical tool that enables the identification of underlying processes. The matrices that are illustrated in Fig. 3 , while the majority of mechanisms observed in forest ecosystems are visible in Fig. 3 (a). The matrix is characterised by an axis of stability, whereby the diagonal from the lower left to the upper right represents a zone of no change in tree canopy. As forests are living ecosystems, it is evident that almost no area retained its tree cover over the five-year period. However, of greater significance is the fact that this axis divides the matrix into two distinct sections: the upper triangle, which represents an increase in tree cover density, and the lower triangle, which represents deforestation. The frames around the matrix in in Fig. 3 (a) represent forest management processes. The bottom row of the matrix depicts logging practices, specifically clearcutting. The right column of the matrix presents alternative silvicultural techniques, including selective logging, shelterwood cutting, and similar approaches. The right-hand column represents forest planting, which results in a significant increase in tree canopy density in regions where it was previously absent. Upon omitting the frames (first/last row/column), it becomes evident that these triangles exhibit a relatively symmetrical shape. However, the observed intensity of deforestation exceeds that of tree growth. The right-hand panel of in Fig. 3 illustrates that the area surrounding waste fires exhibits a distinct pattern of change. It is evident that neither clear-cutting nor the planting of forests is taking place. Secondly, the deforestation process has resulted in a net loss of tree canopy density, with a significant proportion of the total area affected in 2011 (the most right-hand column of the matrix). This column represents the selective loss of tree canopy, which is caused by fires. The process that was not observed in CONUS is the achievement of 100% tree canopy density. This can have multiple causes, but one of them is the increase in short-term nutrient availability after burning (Chungu et al. 2020 ; Rai et al. 2023 ). The tree canopy percentage change in CONUS and in buffers around waste fires was evaluated using the layer prepared by the Multi-Resolution Land Characteristics Consortium with tree canopy change data from 2011 to 2016. A histogram of tree cover densities is provided in Fig. 4 . The mean tree canopy density change in CONUS (exclusive of clearance) is + 21%, and the distribution of these changes is presented in Fig. 4 (top). In contrast, the distribution of changes in the buffers around the waste fires is markedly different (Fig. 4 bottom). It is notable that there are no instances of "little losses" in tree canopy density; if a loss occurs, it is above 55%. The mean tree canopy density change in these buffers is -30%. This confirms the previous studies of initiating forest fires from waste fires in Sweden (Ibrahim et al. 2022 ). 4 Conclusions The remote sensing data with raw fire data evidence can lead to obtain the better, adjusted waste fires’ locations databases. The changes of the land cover can be evaluated with the help of land cover change matrix. Such matrixes can be compared using non-parametric Wilcoxon signed-rank test. Such comparison for the continental United States and buffers around the waste fires show significant difference – waste fires change the landscape in a different way than the typical processes. The highest transitions are related to the forests. The tree canopy change matrix presents in a clear way processes of natural forest growing as well as processes of silviculture. The tree canopy density at buffers around waste fires decrease hence waste fires The above-presented methodology can be applied to the US area in the context of different types of fires or used in any region where landcover data are digitalized, like CORINE in Europe and data about landfill fires (J. S. Bihałowicz et al. 2021 ). Moreover, the recent studies on the CORINE and other land cover types allow to improve it not only in case of resolution but also in case of confidence about results (J. Bihałowicz et al. 2024 ). Declarations Acknowledgements The first author would like to express gratitude to Kathleen Carter of the United States Fire Administration for help with the NFIRS data for analysis. The co-authors are also appreciative of this contribution. Funding The research was supported by the National Science Centre (Poland) within the PRELUDIUM 19 funding scheme, Grant The impact of landfill fires on the atmospheric air quality—methodology and estimation of emission No. 2020/37/N/ST10/02997 awarded to Jan Stefan Bihałowicz. The work was made as a part of HORIZON 2020 Integrated Technological and Information Platform for Wildfire Management, SILVANUS, Grant agreement ID: 101037247. Data availability statement All the data used in the work are publicly available from U.S. Fire Administration https://www.usfa.fema.gov/nfirs/ and Multi-Resolution Land Characteristics (MRLC) Consortium https://www.mrlc.gov/. All the data created in this work are presented in manuscript. Author contribution Conceptualization: JSB; Data curation JSB; Formal analysis JSB, WRK, AK; Funding acquisition JSB, WKR, AK; Investigation JSB; Methodology JSB; Software JSB; Supervision WRK, AK; Visualization JSB; Writing – original draft JSB, AK; Writing – review & editing WRK Competing interests The authors have no relevant financial or non-financial interests to disclose. References Apple. (2024). Maps. Apple . https://www.apple.com/maps/. Accessed 19 September 2024 Åström, J., McNamee, M., Truchot, B., Marlair, G., & Van Hees, P. (2023). Experimental Assessment of Emission Factors from Fires in the Built Environment Including Scaling Effects. Fire Technology . https://doi.org/10.1007/s10694-023-01440-5 Bialas, J. T., Dylewski, Ł., Dylik, A., Janiszewski, T., Kaługa, I., Królak, T., et al. (2021). Impact of land cover and landfills on the breeding effect and nest occupancy of the white stork in Poland. Scientific Reports , 11 (1), 7279. https://doi.org/10.1038/s41598-021-86529-z Bihałowicz, J., Rogula-Kozłowska, W., Gromek, P., & Bihałowicz, J. S. (2024). What is the actual composition of specific land cover? An evaluation of the accuracy at a national scale – Remote sensing in comparison to topographic land cover. Remote Sensing Applications: Society and Environment , 36 , 101319. https://doi.org/10.1016/j.rsase.2024.101319 Bihałowicz, J. S., & Rogula-Kozłowska, W. (2022). Do the waste fires change our neighborhood? Analysis of combined data to asses landscape impact of waste fires. Presented at the URBAN TRANSITIONS 2022, Sitges, Barcelona. Bihałowicz, J. S., Rogula-Kozłowska, W., & Krasuski, A. (2021). Contribution of landfill fires to air pollution – An assessment methodology. Waste Management , 125 , 182–191. https://doi.org/10.1016/j.wasman.2021.02.046 Chungu, D., Ng’andwe, P., Mubanga, H., & Chileshe, F. (2020). Fire alters the availability of soil nutrients and accelerates growth of Eucalyptus grandis in Zambia. Journal of Forestry Research , 31 (5), 1637–1645. https://doi.org/10.1007/s11676-019-00977-y El-Saadony, M. T., Saad, A. M., El-Wafai, N. A., Abou-Aly, H. E., Salem, H. M., Soliman, S. M., et al. (2023). Hazardous wastes and management strategies of landfill leachates: A comprehensive review. Environmental Technology & Innovation , 31 , 103150. https://doi.org/10.1016/j.eti.2023.103150 Fowles, J., Person, M., & Noiton, D. (2001). The Ecotoxicity of Fire-Water Runoff. Part One: Review of the Literature . New Zealand Fire Service Commission. Giglio, L., Schroeder, W., & Justice, C. O. (2016). The collection 6 MODIS active fire detection algorithm and fire products. 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Photogrammetric Engineering & Remote Sensing , 81 (5), 345–354. https://doi.org/10.1016/S0099-1112(15)30100-2 Ibrahim, M. A., Lönnermark, A., & Hogland, W. (2022). Safety at waste and recycling industry: Detection and mitigation of waste fire accidents. Waste Management , 141 , 271–281. https://doi.org/10.1016/j.wasman.2022.02.004 Jin, S., Homer, C., Yang, L., Danielson, P., Dewitz, J., Li, C., et al. (2019). Overall Methodology Design for the United States National Land Cover Database 2016 Products. Remote Sensing , 11 (24), 2971. https://doi.org/10.3390/rs11242971 Kelly, L. T., Giljohann, K. M., Duane, A., Aquilué, N., Archibald, S., Batllori, E., et al. (2020). Fire and biodiversity in the Anthropocene. Science , 370 (6519), eabb0355. https://doi.org/10.1126/science.abb0355 Manheim, D. C., Yeşiller, N., & Hanson, J. L. (2021). Gas Emissions from Municipal Solid Waste Landfills: A Comprehensive Review and Analysis of Global Data. Journal of the Indian Institute of Science , 101 (4), 625–657. https://doi.org/10.1007/s41745-021-00234-4 McNamee, M., Marlair, G., Truchot, B., & Meacham, B. J. (2020). Research Roadmap: Environmental Impact of Fires in the Built Environment . Fire Protection Research Foundation. Minn, M. (2021). MMQGIS. https://www.michaelminn.com/linux/mmqgis/. Accessed 19 September 2024 NASA. (2021). Fire Information for Resource Management System (FIRMS). Earth Data. Open access for open science . https://www.earthdata.nasa.gov/learn/find-data/near-real-time/firms. Accessed 26 October 2021 Noiton, D., & Fowles, J. (2001). The Ecotoxicity of Fire-Water Runoff. Part Two: Analytical Results . New Zealand Fire Service Commission. Nominatim. (2021). https://nominatim.org/. Accessed 19 September 2024 Rai, D., Silveira, M. L., Strauss, S. L., Meyer, J. L., Castellano-Hinojosa, A., Kohmann, M. M., et al. (2023). Short-term prescribed fire-induced changes in soil microbial communities and nutrients in native rangelands of Florida. Applied Soil Ecology , 189 , 104914. https://doi.org/10.1016/j.apsoil.2023.104914 Schroeder, W., & Giglio, L. (2017). Visible Infrared Imaging Radiometer Suite (VIIRS) 375 m & 750 m Active Fire Detection Data Sets Based on Nasa VIIRS Land Science Investigator Processing System (SIPS) Reprocessed Data-Version 1. National Aeronautics and Space Administration (NASA) . Stec, A. A., Dickens, K., Barnes, J. L. J., & Bedford, C. (2019). Environmental contamination following the Grenfell Tower fire. Chemosphere , 226 , 576–586. https://doi.org/10.1016/j.chemosphere.2019.03.153 Steer, P. J., Tashiro, C. H. M., Mcillveen, W. D., & Clement, R. E. (1995). PCDD and PCDF in air, soil, vegetation and oily runoff from a tire fire. Water, Air, & Soil Pollution , 82 (3–4), 659–674. https://doi.org/10.1007/BF00479418 UN General Assembly. (2015). Transforming our world : the 2030 Agenda for Sustainable Development :: resolution /: adopted by the General Assembly. https://digitallibrary.un.org/record/3923923. Accessed 19 September 2024 US Census Bureau. (2024). Census Geocoder Documentation. Census.gov . https://www.census.gov/programs-surveys/geography/technical-documentation/complete-technical-documentation/census-geocoder.html. Accessed 19 September 2024 U.S. Fire Administration National Fire Data Center. (2015). National Fire Incident Reporting System 5.0 Complete Reference Guide January 2015 . Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., et al. (2020). SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods , 17 (3), 261–272. https://doi.org/10.1038/s41592-019-0686-2 Wilcoxon, F. (1945). Individual Comparisons by Ranking Methods. Biometrics Bulletin , 1 (6), 80–80. https://doi.org/10.2307/3001968 Yang, L., Jin, S., Danielson, P., Homer, C., Gass, L., Bender, S. M., et al. (2018). A new generation of the United States National Land Cover Database: Requirements, research priorities, design, and implementation strategies. ISPRS Journal of Photogrammetry and Remote Sensing , 146 , 108–123. https://doi.org/10.1016/j.isprsjprs.2018.09.006 Yeşiller, N., Hanson, J. L., Kopp, K. B., & Yee, E. H. (2016). Heat management strategies for MSW landfills. Waste Management , 56 , 246–254. https://doi.org/10.1016/j.wasman.2016.07.011 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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1","display":"","copyAsset":false,"role":"figure","size":19425,"visible":true,"origin":"","legend":"\u003cp\u003eThe workflow used in the experiment.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7496057/v1/e3c0889f5a99cd09c20f2cf1.png"},{"id":91488953,"identity":"9117b7de-bb77-43e4-9cb6-0d16aeb44f43","added_by":"auto","created_at":"2025-09-17 05:10:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":74912,"visible":true,"origin":"","legend":"\u003cp\u003eFraction of change in land cover for CONUS (a) and buffers around waste fires (b)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7496057/v1/e00ff436016490dd3724134d.png"},{"id":91487388,"identity":"3c62f3c0-6913-4467-8413-f8c3c1208b4a","added_by":"auto","created_at":"2025-09-17 05:02:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":51968,"visible":true,"origin":"","legend":"\u003cp\u003eTree canopy density matrix for CONUS (a) and buffers around waste fires (b)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7496057/v1/3592df1824e0be37d964027e.png"},{"id":91488956,"identity":"63a5ea13-dc06-4a90-9347-597d16eecae3","added_by":"auto","created_at":"2025-09-17 05:10:13","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":49481,"visible":true,"origin":"","legend":"\u003cp\u003eThe tree canopy density percentage change in CONUS (top) and buffers around waste fires (bottom)\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7496057/v1/596dbccdc917c138bf4803c1.png"},{"id":94102127,"identity":"c2ef9071-5b9d-4b81-a275-448f20b9cad2","added_by":"auto","created_at":"2025-10-22 11:23:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":715152,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7496057/v1/faab04d8-008b-4111-91dc-f643e73b289e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrating Multi-Source Spatial Data for Monitoring and Managing Land Cover Impacts of Open Rubbish Fires: case study of United States","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eFire has been shaping the diversity of life on Earth for millions of years. Some of the variation in fire regimes continues to be a source of biodiversity across the globe. Many plants, animals, and ecosystems depend on particular temporal and spatial patterns of fire (He et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Although people have been using fire to modify environments for millennia, the combined effects of human activities are now changing patterns of fire at a global scale \u0026ndash; to the detriment of human society, biodiversity, and ecosystems (Kelly et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Anthropogenic drivers such as climate change, land use, and invasive species are changing the nature of fire in terrestrial ecosystems. Although the large and long-lasting fires in forest or rural areas are critical for the conservation of Earth\u0026rsquo;s biological diversity, in this study we are mostly concentrated on the negative effects of fires induced by inappropriate management of wastes by humans.\u003c/p\u003e\u003cp\u003eLandfilling is the most common means of disposal of waste in the USA as well as in many other countries (Manheim et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The most common type of waste landfilled is municipal solid waste. They are responsible for the contribution to the environment of three primary products: gas, leachate, and heat (Yeşiller et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eLandfill sites contribute to global warming as they generate and release biogas into the atmosphere. According to (Hanson et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) landfills are able to enter biogases in amounts up to 1320 Mg CO\u003csub\u003e2\u003c/sub\u003e-eq./ha-yr. Biogas is a mixture formed primarily of methane gas (CH₄) and carbon dioxide (CO₂). There is also a challenge to control the release of harmful chemicals into the environment, which can be infiltrated by leachates (El-Saadony et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe surrounding flora and fauna are affected by airborne substances from landfills or various types of leachates. Landfill sites have a particularly negative impact on birds, which have access to landfill sites as a source of food. They ingest plastics, metals or other materials that can ultimately prove to be fatal to them (Bialas et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Alternation of migration activity or even ceasing the migration is another danger posed to birds by landfill sites.\u003c/p\u003e\u003cp\u003eFinally, the methane produced by the waste, as well as biological processes that generate heat, can cause fires or even explosions. Particulate matters, metals, PAH and dioxin emissions are also very harmful to the environment from these spontaneous, uncontrolled fires (J. S. Bihałowicz et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These fires are not regular fires with visible flames. They often occur inside the landfill, which makes is difficult to detect. Fires that occur deep within a landfill can damage insulating waterproof layers and cause further environmental contamination.\u003c/p\u003e\u003cp\u003eDue to the complex and diverse mechanisms by which landfill fires can affect the environment, this phenomenon is not fully understood. The aim of this study is to evaluate of the effect of landfill fires on neighbouring landcover.\u003c/p\u003e\u003cp\u003eThe interaction between a fire and its surroundings or environment proceeds via direct gaseous and particulate emissions to the atmosphere. Then localised deposition to soil and water or remaining in the atmosphere in the gas phase. Other pathways of interaction between fire and environment are through extinguishing water running out from fire or directly by debris and semi-burnout effluents carried by convection forces or spread over the surrounding area during firefighting operations.\u003c/p\u003e\u003cp\u003eEssentially, the substances produced in the hot phase of a damaging fire are discharged in the form of smoke; these include carbon monoxide, carbon dioxide, hydrogen chloride, hydrogen cyanide and other substances. The main distribution path for these substances is initially the air path; the combustion products can be discharged from the fire in gaseous, liquid or solid form. As a result, gaseous substances can condense on cold surfaces of leaf or trees. As the thermals decrease, soot particles and ash will rain down.\u003c/p\u003e\u003cp\u003eA significant amount of work has been done to recognize and quantify the emissions from specific burning species or more complex fires. A very good survey of these works can be found in (McNamee et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The vast majority of studies found through in this literature are related to material emissions or forest fire emissions, which is not the part of this study. Only few are available for products that are relevant for the municipal wastes.\u003c/p\u003e\u003cp\u003eThe total emission from fires is generally assessed by relating to the yield of the mass or the object or it content. Then using the emission factor, the total emission from the fire of the structure is estimated. An approach to assess the amount of the emission from the waste fires was proposed by Bihalowicz et. al. (J. S. Bihałowicz et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The approach was used to evaluated emission for main air pollutants: CO, NOx, PM10, SO2, CO2 and CH4.\u003c/p\u003e\u003cp\u003eMore in-depth research was conducted by Astrom et. al. (\u0026Aring;str\u0026ouml;m et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). They collected data regarding emission factors from various sources as well as conducted their own experiments. Small and large-scale experiments on various type of apparatus allowed to recognize emissions factors for compounds, particles, but also Polycyclic Aromatic Hydrocarbons (PAH), Volatile Organic Compounds (VOC), Polychlorinated dibenzo-p-dioxins and furans (PCDD/DF). The experiments also addressed the problem of emission factors depending on fire phase (steady, flaming and smouldering).\u003c/p\u003e\u003cp\u003eIn most cases, the fire will eventually damage the liner, causing leachate and runoff of water collected in the landfill, which is also contaminated with combustion products. In addition, at various stages of fire development, firefighters arrive on the scene and provide water or other extinguishing agents to the fire. In most applications, they use water. Only part of the water evaporates and absorbs heat from the fire. The majority of it (depending on the burning material, type of water stream and its intensity) runs off the fire without developing its most important property, the cooling effect. The water is absorbed by materials or drains away. If this so-called fire water runoff is not retained, it encounters water and soil.\u003c/p\u003e\u003cp\u003eThe composition of fire-water runoff is easy to predict when it comes to fire of materials of known chemical compounds. In this case it is assumed that these compounds are mixing or dissolving in extinguishing water and then when an-retained enter the environment. However, more difficult is prediction of compounds in fire-water runoff as the effect of landfill fire. The analyses of fire-water runoff from fire of solid materials which release chemical compounds as the result of combustion, showed that also not only industrial or biocidal chemicals storing or processing facilities fire may pose high risk to environment. During the fire of solid PVC in Canada in fire-water runoff high concentration of metal, VOCs, and PAHs was registered (Fowles et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Steer et. al. found that the fire-water contained PCDD/Fs however below the safety water criteria value (Steer et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1995\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMore municipal solid wastes fires related fire-water runoff were investigated by (Noiton and Fowles \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). They examined samples from house, fruit shop, vehicular and industrial/warehouse fires. The experiment was directed towards contamination of fire-water runoff with metals, cyanide, PAHs and VOCs. The runoff from the auto shop fire posed the greatest ecotoxicological hazard from PAHs, copper, and zinc (Noiton and Fowles \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Metal and organic contaminants were also high in concentration and volume in fruit shop fire, as a result of burnout of the structure. In the runoff from all five fires, metal contaminants exceeding water standards. Cyanide was found in a fairly narrow range of concentrations.\u003c/p\u003e\u003cp\u003e\u0026Aring;str\u0026ouml;m, J., et al. (\u0026Aring;str\u0026ouml;m et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) compared PAH distribution in smoke from burning of plastics mix during free burning and those cooling due to water impingement. The results show that fire-water runoff contains large spectrum of PAH. Moreover, they stated that water pollution depends on both the proportion of the individual in the smoke together with its solubility.\u003c/p\u003e\u003cp\u003eRogula et. al. conducted small scale experiments measuring ecotoxicity mutagenicity of fire-water runoff from extinguishing pine and oak wood, chipboard, PMMA and polyurethane foam. The ecotoxicity was evaluated using commercial biotests, i.e., Daphtoxkit F magna (crustaceans), Rotoxkit F (rotifers), Spirodela duckweed toxkit (plants: Spirodela polyrhiza) and Microtox test (bacteria Alivibrio fischeri), while mutagenicity was assessed with Ames test (Salmonella typhi-murium strains TA98 and TAmix). The results of the conducted experiment showed that water runoff deriving from the burning of polyurethane foam had the highest toxicity compared with other tested materials. Moreover, also the results of Ames test confirmed that this material is characterized with the highest mutagenicity values, and in consequence may pose the hazard on environment.\u003c/p\u003e\u003cp\u003eWhen fire involves the solid waste, debris can be generated. They can be dispersed into the environment as a result of buoyancy and flow in the convection column, or because of firefighting activities where burning material is scattered to be doused with water. The affection for soil and water depends on the heat release rate of the fire, weather condition such as wind and falls, and fire service activities. The contamination of water and soil by fire debris in the case of landfill fire in poorly recognized. Stec et. al. (Stec et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) investigated soil contamination by semi-burnt fire debris and char samples resulted from Grenfell Tower fire. The building is much higher that landfills but, samples are collected in the surrounding of 160 m away from Tower. The results showed amongst other toxicants, polychlorinated dibenzo-p-dioxin, benzene and PAHs concentrations 60 to 20 times greater than UK urban reference soil levels.\u003c/p\u003e\u003cp\u003eThe issue of unsustainable waste management represents a significant obstacle to the advancement of sustainable development, resulting in the generation of excessive waste and the uncontrolled accumulation of waste materials. In consequence of inadequate waste management, including a lack of segregation or recycling, a situation emerges in which waste not only pollutes the environment but also becomes a source of hazards, such as spontaneous combustion. This situation has an adverse impact on the realisation of the Sustainable Development Goals (SDGs) (UN General Assembly \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), in particular SDG 10, which aims to reduce social inequalities. Such inequalities can be further exacerbated by environmental pollution, which has a particularly detrimental impact on the most economically disadvantaged communities.\u003c/p\u003e\u003cp\u003eFurthermore, failure to achieve SDG No. 10 has implications for SDG No. 12, which focuses on responsible consumption and production. Unsustainable waste management leads to the waste of resources and hinders the implementation of effective recycling practices. As a result, the environmental and social devastation that results from poor waste management can impede progress towards sustainable development, creating a vicious circle that requires urgent action and reform.\u003c/p\u003e\u003cp\u003eThe objective of this study is to ascertain whether waste fires exert an influence on environmental change and to determine the geographical areas where such changes occur. The analysis is based on data recorded in the National Fire Incident Reporting System (NFIRS) database, which is a repository of information on fire incidents in the United States. The project is specifically concerned with the identification of patterns of waste fires and their potential impacts on local ecosystems and public health.\u003c/p\u003e\u003cp\u003eTo corroborate the findings of the analysis, Moderate Resolution Imaging Spectroradiometer (MODIS) data will be employed to monitor alterations in land cover and detect extensive fires. By comparing the NFIRS data with the information obtained from MODIS, it will be feasible to identify regions where waste fires have occurred and assess their environmental impact. The objective is to furnish reliable information that can be utilized to develop waste management and environmental protection strategies in the affected regions.\u003c/p\u003e\u003cp\u003eThe role of land cover, particularly in the form of forests, in counteracting the effects of global warming is important when analysing the impact of waste fires on environmental change. An increase in forest cover is beneficial for the ecological balance, as trees absorb carbon dioxide, thereby reducing the concentration of this gas in the atmosphere. Furthermore, forests play a pivotal role in water retention, which serves to stabilise local ecosystems and mitigate the risk of flooding. The presence of forests facilitates the retention of rainwater in the soil, which in turn promotes the growth of vegetation and enhances soil quality.\u003c/p\u003e\u003cp\u003eFurthermore, an increase in forest cover serves to mitigate fluctuations in temperature at both the local and global levels. Forests act as natural air conditioners, regulating air temperature through the process of transpiration and shading. In the context of studying the impact of waste fires, it is therefore crucial to understand the role of forests in climate and ecosystem stabilisation. Areas with abundant vegetation are less vulnerable to extreme temperature changes, which has a positive impact on biodiversity and ecosystem health. Consequently, the protection and enhancement of forest cover is becoming an important component of adaptation strategies to mitigate global warming and minimise the negative impacts of waste fires.\u003c/p\u003e\u003cp\u003eIn this paper we have attempted to indirectly assess the impact of landfill fires on the surrounding environment. In this paper we have attempted to indirectly assess the impact of landfill fires on the surrounding environment. The main objective was to integrate data from three major databases (FIRMS (Fire Information for Resource Management System), NLCD (National Land Cover Database) and NFIRS (National Fire Incident Reporting System)) to analyse land cover change. The measure used to assess the impact of landfill fires on the land cover is the change in soil and vegetation layers recorded in the NLCD database in the immediate vicinity of the landfill fire. We didn\u0026rsquo;t use controlled, randomized-sample studies, but we tried to understand the effects of fire in the natural environment. Methods and materials\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 National Land Cover Database\u003c/h2\u003e\u003cp\u003eThe National Land Cover Database (NLCD) serves as the definitive Landsat-based, 30-meter resolution land cover database for the Nation. NLCD provides spatial reference and descriptive data for land surface characteristics such as thematic class (e.g., urban, agricultural, and forest), percent impervious surface, and percent tree canopy cover. The NLCD allow to assess ecosystem condition and health, understand spatial patterns of biodiversity, predict the effects of climate change, and develop land management policy, and develop land management policies. The database is designed to provide five-year cyclical updates of United States land cover and associated changes. The landcover codes are organized in two-level hierarchy. Firstly, land cover is classified as one of the nine classes: water, developed, barren, forest, shrubland, herbaceous, planted/cultivated, and wetlands. The subdivision of these classes results in 20 second-level classes. The subdivision of these classes results in 20 second-level classes. The NLCD 2011 and 2016 include tree canopy percentage estimates (Yang et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) also in 30 m grid. In the work, both NLCD and Tree canopy data will be used.\u003c/p\u003e\u003cp\u003eData preparation includes Landsat image selection, cloud detection and cloud filling, and national-scale ancillary data set compilation and production (Homer et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Yang et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Land cover change detection includes single-date water and snow/ice detection algorithms and models, two-date multi-index integrated change detection models, and long-term multi-date change algorithms and models. Land cover classification includes seven-date training data generation and 14-run classifications. Prior to classification, pools of training data for change and no change areas were created based on integrated information from ancillary data, change detection results, Landsat spectral and temporal information, and knowledge-based trajectory analysis (Yang et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In post-processing, comprehensive models for each land cover theme were developed in a hierarchical order to ensure spatial and temporal coherence of land cover and land cover change over 15 years. An accuracy assessment of four selected Landsat indicates overall accuracy of 82.0% (Homer et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This is valid for Anderson Level II classification. After combining the primary and alternate reference labels Anderson Level I classification achieved 86.6%. This method was used for the operational production of the 2016 NLCD for the contiguous United States, which we used as the data source for this study.\u003c/p\u003e\u003cp\u003eThree forest transition classes are defined: herbaceous-forest, shrub-forest, and young-forest (Jin et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). They are designed to represent different stages of forest growth. The young-forest class was created for those forests that have not yet regrown to mature trees after disturbance and provides additional information to reduce confusion between shrub and forest during classification. In the final published product, the young-forest class is cross walked to either shrub-forest or forest according to regional growth rates and successional characteristics (Jin et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The transitional herbaceous-forest and shrub-forest classes are hierarchically subordinate to the NLCD legacy herbaceous and shrub classes. The NLCD legacy grass/herbaceous class included rangeland grassland and forest areas in very early successional stages following abrupt forest replacement disturbances such as clearcuts, fires, and hurricanes. Rangeland shrub and grassland ecosystems have very different spectral and temporal dynamic patterns than the forest transitional classes (Jin et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Fire Information for Resource Management System\u003c/h2\u003e\u003cp\u003eThe Fire Information for Resource Management System (FIRMS) was developed to provide near real-time active fire locations to natural resource managers who have faced challenges in obtaining timely satellite-derived fire information (NASA \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). FIRMS provides also archives data about the hotspots observed by two types of devices Moderate resolution Imaging Spectrometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) which are installed on four satellites.\u003c/p\u003e\u003cp\u003eNASA's Moderate Resolution Imaging Spectroradiometer (MODIS) active fire products were the first in a family of remotely sensed fire datasets produced by a new generation of moderate-resolution (~\u0026thinsp;1 km) \"fire-capable\" sensors on board terrestrial satellites (Giglio et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Since their inception in 2000, MODIS fire products have been used to answer a wide range of scientific questions about the role of biomass burning in the Earth system. Observation of a MODIS hotspot at a given coordinate means that one or more fires have occurred in the 1x1 km pixel.\u003c/p\u003e\u003cp\u003eThe Visible Infrared Imaging Radiometer Suite (VIIRS) active fire data products were intentionally designed to support data continuity between two major satellite programs (MOD14/MYD14) and their corresponding environmental data sets (Schroeder and Giglio \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Data processing is primarily divided into the following levels(Schroeder and Giglio \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e): Level 2) Swath data projection. No data resampling or other corrections are applied; Level 3) tiled datasets: spatial resampling performed using a predetermined projection type and fixed pixel size (e.g., 500 m) along with some temporal aggregation; Level 4) gridded datasets: spatial resampling performed to conform to Climate Modelling Grid (CMG) products. Gridded data are corrected for cloud cover and sampling frequency, which varies as a function of latitude. Finally, the resolution of the VIIRS grid is about 375 m. Satellite-observed hotspots are validated and available as annual summaries.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 National Fire Incident Reporting System\u003c/h2\u003e\u003cp\u003eWe started by acquiring fire data from the NFIRS databases. Data used in this work are raw data \u0026ldquo;as reported to the NFIRS\u0026rdquo;. NFIRS 5.0 is an information-based system that facilitates data entry, storage, and retrieval, whether for a single incident or in aggregate. The latter is achieved through a computer that interacts with the database. (U.S. Fire Administration National Fire Data Center \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). It should be noted that not all fire departments utilise computers for the purposes of record-keeping. Consequently, paper forms are also available for this purpose. NFIRS Version 5.0 is comprised of 11 modules. It is required that the Basic Module be completed for each incident, with additional modules utilized as appropriate to provide a detailed account of the incident in question. The Basic Module (NFIRS\u0026ndash;1) is designed to capture general information on every incident (or emergency call) to which the department responds. The Fire Module (NFIRS\u0026ndash;2) is employed to delineate the specifics of each fire incident to which the department responds. In the event that the Wildland Module is available for use by state reporting authority, it may be utilized in lieu of the Fire Module for wildland fire incidents. The Wildland Fire Module (NFIRS\u0026ndash;8) is completed to report incidents that involve wildland or vegetation fires. It is used in lieu of the Fire Module for wildland fire incidents. (U.S. Fire Administration National Fire Data Center \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eEach incident, identified by a unique identifier, is recorded in this data set and includes information such as property loss, fire area, time, location, and many others. In this database, fire incidents are classified into different categories, namely, structural fires, outside fires (natural vegetation, cultivated vegetation, and outside rubbish), and other fires (vehicle-related and outside gas or vapor combustion explosion).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Procedure\u003c/h2\u003e\u003cp\u003eThe Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e depict the main idea of the experiment. From the NFIRS database only fires with codes 15x Outside rubbish fires were selected (x denotes digit from 0 to 5 used for more precise description). Data about these fires included, essentially for the further work, address of fires (since NFIRS do not provide GPS coordinates). The data then was manually checked for its correctness and appropriateness to eliminate uncertainty related to the reporting and quality of address and data. We query then the validated coordinates and timestamps against the Fire Information for Resource Management System databases for seeking the fire hotspots. Having the location and the area of the fire detected, we then check the information about land cover before and after the fire. Then we evaluate changes in land cover. In the following subsections, we described each of the steps in more detail. We limited the area of the study to the Contiguous United States.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eEach address provided in NFIRS was geocoded into geographic coordinates, creating a set of points. Each point was then rounded with an individual 10 km buffer. For each point representing an NFIRS fire, the age of the MODIS/VIIRS points in the buffer was determined. This was calculated as the difference between the date in the NFIRS report and the date in the MODIS/VIIRS observation. For further analysis, only hotspots within \u0026plusmn;\u0026thinsp;1 day were included.\u003c/p\u003e\u003cp\u003eThe hotspots provide more detailed information about the location of the fire in suburban areas, while the NFIRS addresses are more accurate in urban areas. In the event that the fire was detected by spectrometers, the geocoded NFIRS addresses were adjusted according to the MODIS/VIIRS location. Otherwise, the NFIRS address was treated as the most accurate.\u003c/p\u003e\u003cp\u003eThis process yielded a set of points of varying types: The data set comprised three types of points: MODIS hotspots, VIIRS hotspots, and NFIRS points. Furthermore, numerous MODIS/VIIRS points could be attributed to the same fire. To delineate regions of interest based on the aforementioned data points, circular buffers were constructed around the MODIS/VIIRS hotspots and around the NFIRS addresses. Given the disparate resolutions of the MODIS and VIIRS spectrometers, the buffers around MODIS had a radius of 1000 m, while those around VIIRS hotspots had a radius of 375 m.\u003c/p\u003e\u003cp\u003eIt is possible that multiple hotspots may be identified from different runs and instruments on the same fire. The hotspots, which were identified as being associated with the same fire, were then enveloped with a convex hull. The NFIRS points were surrounded by circular buffers with a 500 m radius, as the evaluation was not focused on the geocoding service, but rather on the landscape change. Consequently, for wildfires identified by MODIS/VIIRS, the buffer around the hotspots was analysed, which was not influenced by potential inaccuracies in geocoding or database address values. In contrast, for NFIRS, the buffer was analysed around the geocoded address.\u003c/p\u003e\u003cp\u003eThe study analyses the impact of fires in 2015 on NLCD land cover in 2016, using the NLCD from 2011 as a reference point. The years for this analysis were determined by the 5-year release cycle of the NLCD land cover maps. A secondary rationale pertains to the objective of capturing the immediate impact of fires. In instances where fires occurred prior to 2015, the processes of land recultivation have the potential to obscure the direct effects of these fires. This approach to the selection of the study area, which involves the use of buffers around fires from 2015 only, is more precise than taking all fires from the 2011\u0026ndash;2015 period. The results obtained in this approach are more accurate, as the buffers provide precision in the assessment of land cover changes. Consequently, a primary focus of the research was the accurate geocoding of addresses from the NFIRS databases, followed by validation through remote sensing to ensure precise identification of the study area.\u003c/p\u003e\u003cp\u003eA histogram of raster values representing land cover change between 2011 and 2016 was prepared for each buffer, irrespective of whether a convex hull or circular buffer was used. This was done to facilitate the preparation of a 2D histogram representing the land cover change for each buffer. The columns of the histogram represented the NLCD codes in 2011, while the rows represented the NLCD codes in 2016.\u003c/p\u003e\u003cp\u003eA second land cover change analysis was conducted using the 2011 tree canopy raster and the 2016 raster. For each pixel within the fire envelope, the canopy density in 2011 was correlated with the density in 2016. In this manner, the 2D histogram was populated with data, wherein the x-axis represents tree cover in 2011, and the y-axis represents tree cover in 2016, while the z-value represents the area. The interpretation of such a histogram, in which the axes are parametric, is more straightforward. The diagonal of the histogram, which represents the constant tree cover in both years, divides the histogram into two regions. The first region encompasses the deforestation of the envelopes around landfill fires, while the second region represents forestation.\u003c/p\u003e\u003cp\u003eThe third land cover analysis is based on the canopy change provided by MRCL. This analysis employed a zonal histogram of raster values in the buffers.\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Results and discussion","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Fire statistics and location summary\u003c/h2\u003e\n \u003cp\u003eIn 2015 there were 24 819 640 incident reports. Among them 599 422 were fire incidents. Among fires 5.2% were reported as \u003cem\u003eOutside rubbish fire\u003c/em\u003e according to (U.S. Fire Administration National Fire Data Center \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). Among the parameters reported in the NFIRS there is the estimate of acres burned during fire \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{B}_{3}\\)\u003c/span\u003e\u003c/span\u003e. The area of burned land was reported only for 455 fires in 2015. The size distribution of fires is provided in the Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e below, expressed in metric units.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe dristribution of outside rubbish fires sizes.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eArea in hectares\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:B\\le\\:0.5\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:0.5\u0026lt;B\\le\\:1\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:1\u0026lt;B\\le\\:2\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:2\u0026lt;B\\le\\:4\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:4\u0026lt;B\\le\\:8\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:8\u0026lt;B\\le\\:16\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:16\u0026lt;B\\le\\:32\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:B\u0026gt;32\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of outside rubbish fires\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe subsequent parameter documented in the database pertains to the geographical location of the fire. It is regrettable that the coordinates have not been provided; instead, an address has been supplied. Three geocoding services were utilised: Nominatim API (\u0026ldquo;Nominatim\u0026rdquo; 2021), US Census Bureau (US Census Bureau \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), and Google Geocoding API (Google \u003cspan class=\"CitationRef\"\u003e2024a\u003c/span\u003e) through MMQGIS plugin (Minn \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). All of the aforementioned services encountered difficulties, either in identifying addresses (the US Census Bureau service was able to identify 233 out of 455 locations) or in assigning precise locations to addresses (the US Census Bureau service assigned 61% of its matches as non-exact, while the validation of Google API geocoding demonstrated discrepancies in addresses up to 14 km). Consequently, all fires were manually geocoded using the Google Maps (Google \u003cspan class=\"CitationRef\"\u003e2024b\u003c/span\u003e) webpage and validated using Apple Maps (Apple \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). Furthermore, in instances where satellite images were accessible on Google Maps, discernible indications of the incinerated region at the specified address were observed. Consequently, the location was adjusted to align with the affected area. It was determined that 9% of the addresses documented in NFIRS could not be matched with any existing address, while 7% exhibited minor inconsistencies, such as typographical errors, that were nevertheless identifiable.\u003c/p\u003e\n \u003cp\u003eThe analysis revealed inconsistencies between data reported by various fire departments. Over 60% of fires with \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{B}_{3}\\le\\:0.5\\)\u003c/span\u003e\u003c/span\u003e ha were documented in Cincinnati, Ohio, predominantly in residential areas. This indicates a potential for systematic inaccuracies in data reporting. Given the inability to ascertain the accuracy of reported areas for these fires, the analysis was limited to 136 geocoded fires with \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{B}_{3}\u0026gt;0.5\\)\u003c/span\u003e\u003c/span\u003e ha.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Land cover type transitions\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e presents a matrix of changes in land cover (J. S. Bihałowicz and Rogula-Kozłowska \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). The matrix presents a comparison of land cover between the years 2011 and 2016, with one axis representing the former and the other the latter. As the matrix only covers changes, the diagonal is empty. For the sake of simplicity, the matrix has been normalised to 100%. The upper and lower triangles of the matrix present reciprocal processes, thus facilitating the identification of the dominant direction. For instance, in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e(a), 2.2% of the area changed from water to wetlands, while 1.2% changed from wetlands to water. Consequently, the net outcome is an increase of 1% in wetlands, which is offset by a decrease in water area. Figure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e(a) allows us to identify several principal alterations to the land cover in the United States. Of the total number of changes, six processes account for over 75% of all transitions, with the area of transition representing at least 2% of all changes. The most frequent processes are as follows:\u003c/p\u003e\u003cspan\u003e\n \u003cp\u003e1. herbaceous vegetation into shrubland (22.9%),\u003c/p\u003e\n \u003c/span\u003e\u003cspan\u003e\n \u003cp\u003e2. forest into herbaceous vegetation (19.8%),\u003c/p\u003e\n \u003c/span\u003e\u003cspan\u003e\n \u003cp\u003e3. shrubland into forest (17.0%),\u003c/p\u003e\n \u003c/span\u003e\u003cspan\u003e\n \u003cp\u003e4. shrubland into herbaceous vegetation (9.0%),\u003c/p\u003e\n \u003c/span\u003e\u003cspan\u003e\n \u003cp\u003e5. forest into shrubland (6.1%),\u003c/p\u003e\n \u003c/span\u003e\u003cspan\u003e\n \u003cp\u003e6. herbaceous vegetation into cultivated areas (5.5%).\u003c/p\u003e\n \u003c/span\u003e\n \u003cp\u003eThe aforementioned alterations in the remote sensed land cover can be readily discerned as processes of deforestation (2, 5), the cultivation of plants (1, 3), and land reclamation (4, 6). However, the circumstances appear somewhat distinct in regions that were impacted by waste fires, as there are only three processes accountable for over 75% of the observed changes:\u003c/p\u003e\u003cspan\u003e\n \u003cp\u003e1. shrubland into forest (29.9%),\u003c/p\u003e\n \u003c/span\u003e\u003cspan\u003e\n \u003cp\u003e2. forest into herbaceous vegetation (29.7%),\u003c/p\u003e\n \u003c/span\u003e\u003cspan\u003e\n \u003cp\u003e3. herbaceous into shrubland (16.5%).\u003c/p\u003e\n \u003c/span\u003e\n \u003cp\u003eThe three processes are identical to those observed in the CONUS case but are presented in a different sequence and with a higher magnitude (higher shares). The direct impact of the fire can be deforestation, representing almost one-third of the total changes, while in the CONUS case this figure is only 19.8%. Two of the remaining processes (1, 3) are typical of abandoned land. This suggests that waste fires are present in locations that are not intensively exploited.\u003c/p\u003e\n \u003cp\u003eIn order to evaluate the observed differences, a comparison was conducted between the two matrixes using the Wilcoxon Signed-Rank test (Virtanen et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wilcoxon \u003cspan class=\"CitationRef\"\u003e1945\u003c/span\u003e), based on the value of the matrix elements. The results demonstrated that the two matrices are significantly different (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n \u003cp\u003eWe are aware that in changes in land cover between these years are not only affected by fires in 2015 but also in 2011\u0026ndash;2014, what seemingly restricts our study. Nevertheless, it is the utilisation of data from one year prior to the development of the subsequent NLCD 2016 land cover map, in conjunction with the identification of specific areas (i.e. buffers surrounding the address of fire in NFIRS), that results in the precision of the analysis and the identification of only the impact of fire on land cover.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Tree canopy density change\u003c/h2\u003e\n \u003cp\u003eThe analysis of land cover type transitions indicated that the highest changes were observed for the land covers of herbaceous vegetation, shrubland, and forest. Therefore, we proceeded to evaluate the changes in tree canopy. The three-canopy change matrix is a valuable analytical tool that enables the identification of underlying processes. The matrices that are illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, while the majority of mechanisms observed in forest ecosystems are visible in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e (a). The matrix is characterised by an axis of stability, whereby the diagonal from the lower left to the upper right represents a zone of no change in tree canopy. As forests are living ecosystems, it is evident that almost no area retained its tree cover over the five-year period. However, of greater significance is the fact that this axis divides the matrix into two distinct sections: the upper triangle, which represents an increase in tree cover density, and the lower triangle, which represents deforestation. The frames around the matrix in in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e (a) represent forest management processes. The bottom row of the matrix depicts logging practices, specifically clearcutting. The right column of the matrix presents alternative silvicultural techniques, including selective logging, shelterwood cutting, and similar approaches. The right-hand column represents forest planting, which results in a significant increase in tree canopy density in regions where it was previously absent. Upon omitting the frames (first/last row/column), it becomes evident that these triangles exhibit a relatively symmetrical shape. However, the observed intensity of deforestation exceeds that of tree growth. The right-hand panel of in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates that the area surrounding waste fires exhibits a distinct pattern of change. It is evident that neither clear-cutting nor the planting of forests is taking place. Secondly, the deforestation process has resulted in a net loss of tree canopy density, with a significant proportion of the total area affected in 2011 (the most right-hand column of the matrix). This column represents the selective loss of tree canopy, which is caused by fires. The process that was not observed in CONUS is the achievement of 100% tree canopy density. This can have multiple causes, but one of them is the increase in short-term nutrient availability after burning (Chungu et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Rai et al. \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe tree canopy percentage change in CONUS and in buffers around waste fires was evaluated using the layer prepared by the Multi-Resolution Land Characteristics Consortium with tree canopy change data from 2011 to 2016. A histogram of tree cover densities is provided in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. The mean tree canopy density change in CONUS (exclusive of clearance) is +\u0026thinsp;21%, and the distribution of these changes is presented in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e (top). In contrast, the distribution of changes in the buffers around the waste fires is markedly different (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e bottom). It is notable that there are no instances of \u0026quot;little losses\u0026quot; in tree canopy density; if a loss occurs, it is above 55%. The mean tree canopy density change in these buffers is -30%. This confirms the previous studies of initiating forest fires from waste fires in Sweden (Ibrahim et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4 Conclusions","content":"\u003cp\u003eThe remote sensing data with raw fire data evidence can lead to obtain the better, adjusted waste fires\u0026rsquo; locations databases. The changes of the land cover can be evaluated with the help of land cover change matrix. Such matrixes can be compared using non-parametric Wilcoxon signed-rank test. Such comparison for the continental United States and buffers around the waste fires show significant difference \u0026ndash; waste fires change the landscape in a different way than the typical processes. The highest transitions are related to the forests. The tree canopy change matrix presents in a clear way processes of natural forest growing as well as processes of silviculture. The tree canopy density at buffers around waste fires decrease hence waste fires\u003c/p\u003e\u003cp\u003eThe above-presented methodology can be applied to the US area in the context of different types of fires or used in any region where landcover data are digitalized, like CORINE in Europe and data about landfill fires (J. S. Bihałowicz et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Moreover, the recent studies on the CORINE and other land cover types allow to improve it not only in case of resolution but also in case of confidence about results (J. Bihałowicz et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eThe first author would like to express gratitude to Kathleen Carter of the United States Fire Administration for help with the NFIRS data for analysis. The co-authors are also appreciative of this contribution.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThe research was supported by the National Science Centre (Poland) within the PRELUDIUM 19 funding scheme, Grant The impact of landfill fires on the atmospheric air quality\u0026mdash;methodology and estimation of emission No. 2020/37/N/ST10/02997 awarded to Jan Stefan Bihałowicz.\u003c/p\u003e\n\u003cp\u003eThe work was made as a part of HORIZON 2020 Integrated Technological and Information Platform for Wildfire Management, SILVANUS, Grant agreement ID: 101037247.\u003c/p\u003e\n\u003cp\u003eData availability statement\u003c/p\u003e\n\u003cp\u003eAll the data used in the work are publicly available from U.S. Fire Administration https://www.usfa.fema.gov/nfirs/ and Multi-Resolution Land Characteristics (MRLC) Consortium https://www.mrlc.gov/. All the data created in this work are presented in manuscript.\u003c/p\u003e\n\u003cp\u003eAuthor contribution\u003c/p\u003e\n\u003cp\u003eConceptualization: JSB; Data curation JSB; Formal analysis JSB, WRK, AK; Funding acquisition JSB, WKR, AK; Investigation JSB; Methodology JSB; Software JSB; Supervision WRK, AK; Visualization JSB; Writing \u0026ndash; original draft JSB, AK; Writing \u0026ndash; review \u0026amp; editing WRK\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eApple. (2024). Maps. \u003cem\u003eApple\u003c/em\u003e. https://www.apple.com/maps/. Accessed 19 September 2024\u003c/li\u003e\n \u003cli\u003e\u0026Aring;str\u0026ouml;m, J., McNamee, M., Truchot, B., Marlair, G., \u0026amp; Van Hees, P. (2023). 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Heat management strategies for MSW landfills. \u003cem\u003eWaste Management\u003c/em\u003e, \u003cem\u003e56\u003c/em\u003e, 246\u0026ndash;254. https://doi.org/10.1016/j.wasman.2016.07.011\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"remote sensing, fire statistics, land cover change, waste fires, FIRMS, NLCD","lastPublishedDoi":"10.21203/rs.3.rs-7496057/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7496057/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe aim of this study is to analyse land cover change (LCC) using the integration of data from three databases: FIRMS (Fire Information for Resource Management System), NLCD (National Land Cover Database) and NFIRS (National Fire Incident Reporting System). The main idea is to utilise data snapshots from these databases to identify and assess changes in land cover, without undertaking a comprehensive analysis of individual incidents. The research used data about open rubbish fires from 2015, the year just before NLCD 2016. Locations of fires from NFIRS were confirmed by FIRMS satellite observations and the impact, the LCC and tree density at these sites, was assessed using NLCD. In fire-affected areas, the most common LCC were shifts from shrubland to forest (29.9%), forest to herbaceous vegetation (29.7%), and herbaceous vegetation to shrubland (16.5%). While in the whole study area: i) herbaceous vegetation to shrubland (22.9%); ii) forest into herbaceous vegetation (19.8%); iii) shrubland into forest (17.0%); iv) shrubland into herbaceous vegetation (9.0%); v) forest into shrubland (6.1%); vi) herbaceous vegetation into cultivated areas (5.5%). The results demonstrate that processes of silviculture, as well as natural growing, can be distinguished, and that areas near open rubbish fires undergo different changes than those typical of the continental US. The proposed methodology is versatile and innovative, rendering it readily applicable in diverse geographical and temporal contexts. This approach allows for the efficient utilisation and enhancement of spatial data, which is vital for the monitoring and management of land cover change.\u003c/p\u003e","manuscriptTitle":"Integrating Multi-Source Spatial Data for Monitoring and Managing Land Cover Impacts of Open Rubbish Fires: case study of United States","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-17 05:02:08","doi":"10.21203/rs.3.rs-7496057/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c606f8d9-02be-4f52-8d94-fa4016715eaa","owner":[],"postedDate":"September 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":54553861,"name":"Biological sciences/Ecology"},{"id":54553862,"name":"Earth and environmental sciences/Ecology"},{"id":54553863,"name":"Earth and environmental sciences/Environmental sciences"}],"tags":[],"updatedAt":"2025-10-22T11:23:26+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-17 05:02:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7496057","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7496057","identity":"rs-7496057","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}