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Article

Including Small Fires in Global Historical Burned Area Products: Promising Results from a Landsat-Based Product

by
Davide Fornacca
1,2,
Yuhan Ye
1,
Xiaokang Li
1 and
Wen Xiao
1,2,3,4,*
1
Institute of Eastern-Himalaya Biodiversity Research, Dali University, Dali 671003, China
2
International Centre for Biodiversity and Primate Conservation, Dali University, Dali 671003, China
3
Yunling Black-and-White Snub-Nosed Monkey Observation and Research Station of Yunnan Province, Dali 671003, China
4
Collaborative Innovation Centre for Biodiversity and Conservation in the Three Parallel Rivers Region of China, Dali 671003, China
*
Author to whom correspondence should be addressed.
Fire 2025, 8(11), 422; https://doi.org/10.3390/fire8110422
Submission received: 18 September 2025 / Revised: 27 October 2025 / Accepted: 28 October 2025 / Published: 30 October 2025
(This article belongs to the Special Issue Integrating AI and Remote Sensing for Monitoring and Mapping Fire Impacts on Agroforestry and Wildlife Systems)
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Abstract

State-of-the-art historical global burned area (BA) products largely rely on MODIS data, offering long temporal coverage but limited spatial resolution. As a result, small fires and complex landscapes remain underrepresented in global fire history reconstructions. By contrast, Landsat provides the only continuous satellite record extending back to the 1980s, with substantially finer resolution. However, its use at a global scale has long been hindered by infrequent revisit times, cloud contamination, massive data volumes, and processing demands. We compared MODIS FireCCI51 with the only existing Landsat-based global product, GABAM, in a mountainous region characterized by frequent, small-scale fires. GABAM detected a higher number of burn scars, including small events, with higher Producer’s Accuracy (0.68 vs. 0.08) and similar User’s Accuracy (0.85 vs. 0.83). These results emphasize the value of Landsat for reconstructing past fire regimes in complex landscapes. Crucially, recent advances in cloud computing, data cubes, and processing pipelines now remove many of the former barriers to exploiting the Landsat archive globally. A more systematic integration of Landsat data into MODIS-based routines may help produce more complete and accurate databases of historical fire activity, ultimately enabling improved understanding of long-term global fire dynamics.

1. Introduction

The rapid development of Earth Observation technology has enabled an era of continuous, global-scale monitoring of both sea and land surface dynamics, as well as atmospheric processes [1]. However, reconstructing long-term trends and predicting future changes requires consistent and comparable time series data. While some long-term satellite datasets exist, their applicability can be limited by the sensors’ original design. A prime example is burned area mapping [2], where several global inventories leveraging various satellite data sources have been released, such as the ESA’s Climate Change Initiative Fire Disturbance (FireCCI) suite (thirteen datasets available on https://climate.esa.int/en/projects/fire/, last accessed 2 September 2025) and the MODIS Standard Fire series (Active Fire and Burned Area products retrievable on https://modis-fire.umd.edu, last accessed 2 September 2025), which are among the most widely used. These products serve as base inputs for integrated, higher-level models like the Global Fire Emissions Database (currently at version 5, see https://www.globalfiredata.org, last accessed 2 September 2025).
Despite continuous improvements of burned area inventories, two key requirements for meeting the fire monitoring objectives defined by GCOS (Global Climate Observing System) Essential Climate Variables (https://gcos.wmo.int/en/essential-climate-variables/fire, last accessed 2 September 2025) persist. These are achieving higher spatial resolution and extending time series data [3,4,5]. Several researchers have emphasized the importance of accounting for small fires because their environmental impacts often extend far beyond their immediate location. This is particularly relevant in mountainous regions and in dense agricultural areas, where small fires play a significant role in defining recent fire regimes, identifying burning pattern shifts, and contributing to regional and global atmospheric emissions and forest loss [6,7,8,9,10,11]. While specific user communities may prioritize one improvement over the other, achieving both goals with the data accumulated in the past satellite history remains challenging. For example, addressing the issue of dataset length, FireCCILT11 by Oton et al. [12] combines data from different sensors spanning in total 36 years, but at a relatively coarse spatial resolution (0.05 degrees). Efforts to include smaller fires often integrate information from thermal sensors (e.g., MODIS, VIIRS, ATSR), allowing the detection of fires below the nominal resolution of final products [8,13]. Additionally, images from the Landsat program, offering the longest-running consistent multispectral data at 30 m resolution since the mid-1980s, have been employed to build historical burned area datasets. However, despite the potential applicability to the global scale, Landsat-based initiatives have focused on restricted regions. Prominent examples among numerous ones are the MapBiomas Fire product from Brazil [14], the Burned Area Essential Climate Variable (BAECV) developed by Hawbaker et al. [15], and the Monitoring Trends in Burn Severity (MTBS) program for the conterminous United States [16] (see https://www.mtbs.gov, last accessed 2 September 2025). To date, the only freely available, consistent global burned area product developed using the historical Landsat archive is the Global Annual Burned Area Maps (GABAM) by Long et al. [17]. The main limitation of using the Landsat archive for reconstructing historical fire inventories is its relatively low temporal resolution, with a nominal revisit cycle of 16 days. Because fire effects on the land surface are often short-lived, the delay between the fire event and the first cloud-free acquisition may prevent timely detection of burn scars [18,19]. Moreover, the change detection process is further complicated by the potential confusion of burned areas with other surface disturbances, including clear-cutting, flooding, agricultural cycles, shadows, and vegetation senescence [5,20,21,22].
Validating burned area inventories derived from satellite data is crucial for assessing their reliability, usability, and guiding future enhancements. While robust validation protocols for global products that specifically consider the localized and temporary spatiotemporal nature of burned areas have been established and continually evolve [23,24,25], sampling designs often focus on major flammable biomes while neglecting other important burning environments such as agricultural areas [26,27,28], circumpolar boreal forests and tundra [29,30] or fragile mountainous regions [31,32]. A prominent example of this limitation is illustrated by BARD—an updatable standard database of burned areas distributed globally to serve as reference sites [33]. The selection of these sites has been performed using a random sampling approach stratified across the main terrestrial ecoregions defined by Olson’s classification [34]. This statistical sampling method ensures that a fair number of fires from each considered ecoregion (theoretically representing the diversity of burning conditions on Earth) is included in the final dataset, while preserving independence (random selection). However, in some cases, systematic or convenience sampling was used to account for rarer land cover classes and particular fire season conditions which have little probability to be selected using the primary stratification approach. Following the example of BARD, we argue that this type of adaptation should be encouraged. By considering assessments that focus on specific regions with peculiar fire systems and challenging mapping conditions, we can provide valuable insights into global burned area products’ strengths and limitations.
This study aims to evaluate two recently released global burned area products, GABAM [17] and FireCCI51 [13], in a biodiversity hotspot and fire-prone mountainous area characterized by frequent, small fires. These products were selected based on three key attributes: they offer global coverage, span at least two decades prior to the Sentinel-2 era, and are delivered at a relatively high spatial resolution. A central motivation of this work is that, to date, the Landsat archive has remained largely underutilized in the development of global burned area products for the pre-Sentinel period. Here, we investigate its performance and assess its potential for the improvement of global applications. Given the difficulties associated with burned area detection in this region [19] and remote sensing application in heterogeneous landscapes more generally [35,36], this study contributes to a more comprehensive understanding of these new inventories’ capabilities and limitations, providing valuable insights for future advancements in global burned area extraction approaches using satellite imagery.

2. Materials and Methods

2.1. Study Region

Northwest Yunnan is a mountainous region in southwest China located between approximately 24.5° N–29.5° N latitude and 98° E–101.5° E longitude (Figure 1a), covering an area of over 67,000 square km, almost as big as a country such as Sierra Leone. The region is renowned for its rich biological and cultural diversity dwelling within a highly heterogeneous landscape [37,38]. The climate is under the influence of the East Asian monsoon providing distinct wet and dry seasons. The dry season, from November to May, coupled with strong wind, elevates the region’s susceptibility to fire [39]. The prevalence of fire-adapted Pinus yunnanensis [40] further exacerbates this. Annually, unauthorized human-caused fires (negligence, accidents) burn relatively small patches of wildland, making northwest Yunnan a hotspot for fire activity in Yunnan province and China as a whole [41].

2.2. Dataset Selection and Processing

FireCCI51 was developed by Lizundia-Loiola and colleagues [13] as part of the ESA Fire Disturbance Climate Change Initiative framework. The algorithm combines information from both MODIS thermal channels and near-infrared reflectances, as well as land cover maps from the Land Cover CCI project, and generates spatio-temporal clusters of potential fires that will be then filtered according to adaptive thresholds, then finally applies a contextual Region Growing algorithm to detect the perimeter of the burned patches. We obtained the monthly pixel products, which have a spatial resolution of 250 m and cover the period 2001–2020. These products provide information on detection time, confidence level, and the land cover for burned pixels.
GABAM [17] is a Landsat-based product generated using Google Earth Engine. Its burned area map generation routine includes the computation of spectral indices from the images’ reflectance bands, a Random Forest model [42] calculating per-pixel burn probabilities, a conditional filtering step based on bi-yearly comparisons of specific derived metrics, and a Region Growing process to obtain final burned areas. The delivered product consists of annual binary grids (burned/unburned) with a 30-m spatial resolution, spanning 1985 to 2021. Because seed pixels for the Region Growing step are first aggregated into connected components of at least 11 Landsat pixels, the effective minimum mapped fire size in GABAM is approximately 1 ha.
Following Fornacca et al. [32], we spatially and temporally aggregated neighboring burned pixels from the original raster products to form vector polygons representing single fire events. Although FireCCI51 provides detection date and time for each burned pixel, to allow for comparability with GABAM, the temporal window for the aggregation was set to one calendar year. Moreover, we established a minimum burned area of 1 pixel (62,500 m2) from the lower-resolution dataset (i.e., FireCCI51), after verifying the existence of such small detections. Consequently, smaller polygons in the GABAM dataset were excluded from the analyses. Finally, due to uncertainties in burned area detection within agricultural land, the difficulty of defining reliable reference burn areas, and for consistency with previous evaluations in this region [32], we excluded polygons detected over agricultural fields using the decadal GlobeLand30 [43] and the annual LC_cci v2.0.7 [44] land cover datasets. These preprocessing steps may introduce bias and increase uncertainty in the assessment of both products, particularly for the higher-resolution dataset (burned areas of 10,000–62,500 m2). However, we consider this harmonization necessary to enable a like-for-like comparison.

2.3. Evaluation Method

To evaluate the products’ ability to detect burned areas, we conducted a year-by-year assessment from 2001 to 2019 using a 10-km grid overlaying the study area as in Fornacca et al. 2020 [19]. Based on GlobeLand30, sample squares with more than 50% non-vegetated land cover (bare, snow, urban, water) and agricultural land were excluded, resulting in 430 valid sample squares (Figure 1b). We categorized sample squares according to high and low fire frequency with adapted thresholds for each year based on MODIS MCD14ML thermal anomaly detections [45] within vegetated areas. Because of the extremely skewed distributions resulting from the localized nature of fire occurrences, the majority of sample squares did not include any thermal anomalies. Therefore, for each year, we excluded these squares from the threshold calculation and set the minimum number of detections necessary to qualify as “high fire frequency” to that of the 0.75 percentile. Afterwards, ten sample squares were randomly selected each year, evenly divided between high and low fire frequency squares.
For each selected sample square, three analysts visually interpreted all available Landsat and Sentinel-2 (post-2015) images within the corresponding year and manually digitized all detected burned areas to be used as reference. To support this process, we generated annual image time series with a custom script in Google Earth Engine (https://earthengine.google.com/), which loaded the Landsat and Sentinel-2 image collections, applied spatial filtering to each 10 km sample square (the sampling grid was uploaded to the platform), restricted images by date, and exported clipped stacks for local inspection. The analysts then examined these images in QGIS 3.34 (https://www.qgis.org/) using both true-color and shortwave/near-infrared composites to enhance burn scar visibility. Each 10 km square was inspected at fine scale, and the yearly sequence of images was browsed chronologically to identify newly burned surfaces and confirm burn timing. In addition, active fire detection from MCD14ML and the NWY_Fire_LS v2 invectory [19], a previously generated burned area dataset for northwest Yunnan based on a dedicated fire extraction routine, were used as ancillary evidence to guide interpretation. The latter model has an omission error of 20% and commission error of 22% in its v1, while v2 underwent an extensive visual revision of the dataset aiming at removing erroneous detections, significantly decreasing errors of commission (no specific metric provided). Due to the data gap between the decommissioning of Landsat 5 and the launch of Landsat 8, the years 2012 and 2013 were excluded from this evaluation. The mapped polygons produced independently by the three analysts were then compared and merged. Any significant discrepancies (missing or additional polygons from one analyst, as well as high area size mismatches) were re-examined and discussed until a consensus was reached on whether to include or exclude each polygon from the reference dataset. It is important to note that this visual interpretation approach is inherently susceptible to a degree of subjectivity. We mitigated this by using multiple interpreters and by consulting ancillary datasets. In addition, all analysts have several years of experience mapping burned areas in this region and have carried out multiple field campaigns, which improves their ability to distinguish true burn scars from other spectral changes in the landscape, particularly vegetation senescence, bare soil oxidation or color changes, wet soils, and naturally sparse/dry shrubland, all of which can be confused with burned area in optical imagery.
Giving the particular obstacles for burned area detection in this kind of landscape, in particular rapidly fading burn scars that significantly limit the delimitation of full perimeters (reference), we opted for a simplified approach to assess performances. We focused our assessment on fire counts without evaluating the precision of the perimeters mapped by the two models. Successful detection (True Positives or TP) occurred when modeled and reference polygons intersected, regardless of the overlapping extent. Non-detected fire events were tagged as False Negatives (FN), while erroneously mapped fires were counted as False Positives (FP). Based on these metrics, we calculated User’s Accuracy ( U A = T P T P + F P ), indicating the percentage of predicted positive cases that were correct, and Producer’s Accuracy ( P A = T P T P + F N ), reflecting how well the actual positive cases were identified by the model. To evaluate the impact of burned area size, we classified reference fire events into three categories, <25 ha (approximately four FireCCI51 pixels), 25–100 ha, and >100 ha, and calculated PA for each. Finally, we assessed the spatial distribution of fires predicted by the two models as compared with MCD14ML and NWY_Fire_LS v2 by the means of correlation of fire counts within each 10 km sample grid. This analysis was done for the period 2001–2018, not including 2011, 2012, and 2013, to match the temporal coverage of the benchmark dataset NWY_Fire_LS v2.
All operations were performed using Google Earth Engine (https://earthengine.google.com/), the Python 3.12 programming language, and QGIS 3.34 software (https://www.qgis.org/).

3. Results

Of the 170 samples distributed across 17 years, 101 squares included at least one fire event. This demonstrates the effectiveness of stratified sampling using third-party fire frequencies data in maximizing the statistical representativeness of burned patches [25]. In total, 198 fire events were recorded in these squares and used as reference. FireCCI51 detected only 15 (PA = 0.08), while GABAM identified 135 (PA = 0.68). Both products achieved a User’s Accuracy of over 0.83 (Table 1), proving good skills in distinguishing from other types of landcover change. When looking at different fire sizes, FireCCI51 exhibited a noticeable decline in detection probability (PA) as burned area decreased, while GABAM performed consistently with fires smaller than 100 ha and significantly improved detection of larger fires (Table 2).
The correlation of fire frequency distribution within the 10 km with the two reference datasets was highly significant (p-value < 0.001) for both models. However, the coefficients were much higher for GABAM. The maps drawn in Figure 2 clearly show that fires occur in most part of the study region (reference datasets MDC14ML and NWY_Fire_LS v2), but FireCCI51 omits large areas of lower to moderate fire frequency.

4. Discussion

Northwest Yunnan, like many alpine regions, ranks among the world’s biodiversity hotspots. In these fragile mountain ecosystems, even small-scale environmental changes can disrupt local ecological balance, alter species distributions, and reduce habitat resilience. Moreover, disturbances in alpine regions often have cascading effects beyond their boundaries: changes in vegetation cover can modify hydrological regimes, increase erosion risk, and trigger landslides, with consequences for the densely populated lowlands downstream. Given the ecological and socio-economic significance, assessing disturbance dynamics in these areas is essential. A defining characteristic of mountainous regions is their high degree of landscape heterogeneity. This complexity poses significant challenges for remote sensing analyses, especially when detecting short-term land surface changes such as burn scars, where topography, variable illumination, frequent cloud cover, and fine-scale mosaics of vegetation can obscure or distort signals [19,36,46]. These challenges, combined with the predominance of small fires in the study region [19,47], have led to the relatively poor performance of global burned area products in a previous assessment [32] and in other regions with comparable landscape/fire characteristics [48,49]. Given these constraints, our evaluation focused on the detection of individual fire events rather than on the accuracy of burned area perimeters. This approach allows us to assess the capacity of each product to capture fire frequency, while not directly evaluating the precision of mapped burned extents.
The results highlighted contrasting capabilities between two leading global burned area products. FireCCI51 makes use of MODIS data at its highest resolution (250 m), providing greater precision than other established burned area products such as MCD64A1 [50]. Being an improved version of its predecessor FireCCI41, it is acknowledged as better including smaller fires and increasing burned area estimation globally [13]. However, comparisons against modern higher-resolution satellite products such as Sentinel-2 found very divergent accuracies among different biomes, with the lowest omission found in temperate savanna (33.8%), lowest commission in tropical savanna (29.5%), and the most problematic ecosystems being deserts and xeric shrublands, with omission and commission errors reaching 98% and 40.8%, respectively [51]. The errors of omissions found in our study were higher than the predecessor FireCCI41 in rugged landscapes [32]. Limited performance of the current FireCCI51 was also reported in other heterogeneous or particularly challenging landscapes of the Mediterranean [52], the Amazon [53], areas dominated by croplands [54], tropical peatlands [55], high northern latitudes [29], and alpine regions [31], especially with decreasing burned area size. Because the size of fires is the main factor determining the accuracy of burned area products [56], spatial resolution and the trade-off between omission and commission errors significantly impact product performance in specific environments. Automated algorithms designed for global burned area extraction must concurrently accommodate diverse vegetation and landscape conditions, making it difficult to achieve optimal and consistent results across all scenarios. This is certainly one of the main contributing factors to differences in mapped burned areas from different models [20,53].
GABAM, using the Landsat archive, offers significantly higher spatial resolution than FireCCI51. However, this comes at the cost of temporal revisiting, hindering certain applications such as the early detection and monitoring of ongoing fires [2,4]. Cloud cover further limits analyzable scenes, especially in regions with persistent cloudiness, seriously limiting the detection of rapidly recovering burn scars [57,58] and the analysis of seasonality trends, because day-of-burns cannot be accurately and systematically determined. Despite these challenges, GABAM demonstrated a relatively strong performance, comparable to the local benchmark dataset from Fornacca and colleagues [19]. The few validations performed up to date have reported a higher burned area mapped by GABAM with low commission error compared to other global products, including FireCCI51 [53,59]. However, an analysis by Zubkova et al. [60] of three protected areas of South Africa highlighted missing data issues in GABAM due to persistent cloud cover and inabilities in detecting scars in areas that burn frequently. During our evaluation, we observed heavy and frequent artifacts in GABAM, likely due to issues with Landsat scene alignment, WRS-2 edges overlaps, and the use of Landsat-7 images post-SLC instrument failure, leaving the notorious no-data stripes clearly visible in the final product (also reported by [61]). Moreover, the documentation of GABAM, beyond the prototype year 2015 [17] and a few selected years [39], lacks comprehensive information on time series generation, uncertainties assessment, and extensive independent validation. Considering the latter point, we recognize that our assessment process, primarily reliant on Landsat imagery—the same data source used by GABAM—introduces a potential bias favoring GABAM over FireCCI51. This constraint limits our ability to independently assess model accuracy, as the reference fire scars derived from Landsat visual interpretation may inherently reflect the same feature detection patterns as GABAM’s algorithm.
We initially expected FireCCI51 to perform better than its previous version [32] and GABAM to be less accurate; however, our results revealed the opposite. Anticipating that both products would face challenges in our study region, we focused our assessment on their ability to detect fire events rather than on mapping burned area perimeters, which inevitably limits the depth of the insights provided by this study. The significance of our findings should also be interpreted primarily within the regional context of our study area, despite its global ecological relevance. Nonetheless, similar performance patterns may occur in other regions with comparable landscape characteristics. Further research should evaluate mapping accuracy and expand the analysis to additional regions to better understand product performance across diverse and challenging mountain environments.
Despite significant advancements in contemporary Earth Observation, Landsat remains an invaluable resource for reconstructing past land surface dynamics. Newer sensors offer higher spatial resolution and, in some cases, cloud-penetrating capabilities, yet only Landsat provides a nearly four-decade-long, consistent record of moderate-resolution observations. This large archive makes it indispensable for historical analyses. By contrast, MODIS has been instrumental in numerous burned area applications, particularly thanks to its near-daily temporal coverage. However, its availability only since 2000 limits its usefulness for reconstructing longer fire histories. This limitation is particularly relevant because the importance of small fires, crucial for refining atmospheric emission estimates and assessing ecological impacts in heterogeneous landscapes, has been repeatedly emphasized by researchers and requested by the broader user community [5]. Several regionally focused, long-term burned area products based on Landsat have been developed [14,15,16], but GABAM remains the only effort to date that has attempted a global-scale solution. Substantial progress has also been made in developing change detection tools to reconstruct past disturbance and recovery patterns using Landsat time series, many of which are now implemented in Google Earth Engine [62,63,64,65,66,67,68]. However, unlike other applications such as grassland [69] or wetland [70] mapping, these tools have not yet delivered a finalized global and annual burned area product, other than GABAM.
Hybrid approaches that combine Landsat with other satellite data sources have also shown promise in historical burned area mapping [21,28,71,72,73]. MODIS is still the most suitable platform for producing consistent global burned area inventories, but fusing it with Landsat and complementary sensors offers a path to extending records back in time while improving the detection of smaller fires. For the pre-MODIS era, Landsat may again be combined with alternative missions, although the varying active periods of different satellites inevitably introduce inconsistencies in the final historical inventory. Nonetheless, the unifying thread across all approaches remains the Landsat archive, which provides an irreplaceable backbone for long-term reconstructions.
Addressing these challenges is essential for future burned area products that aspire to reach the quality and reliability standards required to serve as inputs for higher-level applications, including fire emission inventories and fire-enabled dynamic global vegetation models.

5. Conclusions

The datacube structure and computing power of Google Earth Engine have transformed large-scale data processing, making advanced analyses accessible to a broader research community. Machine learning algorithms and time series tools that were once constrained by prohibitive computational demands are now widely applicable within this platform. Given this technological landscape, it is, to say the least, surprising that the full potential of the Landsat archive for reconstructing global-scale historical fire records remains largely unrealized. Our analysis highlights a critical conclusion: the experience of GABAM serves as a proof of concept for enhancing the quantification of fire activity during the three decades preceding the Sentinel-2 era, particularly in regions underrepresented in current global products. At the same time, Landsat’s relatively infrequent temporal coverage suggests that its strengths are best realized when used in combination with other satellite data sources, each tailored to its respective period of availability. Such integration offers the most promising pathway to reconstruct fire history consistently back to the mid-1980s. We therefore urge the research community to make fuller use of the Landsat archive, not only in fire science but across Earth system disciplines, by integrating it with complementary datasets to advance our understanding of past global land surface dynamics and their implications for ecological and climate processes.

Author Contributions

Conceptualization, D.F. and W.X.; methodology, D.F.; validation, W.X.; formal analysis, D.F., Y.Y. and X.L.; investigation, D.F.; resources, W.X.; data curation, D.F., Y.Y. and X.L.; writing—original draft preparation, D.F.; writing—review and editing, D.F., Y.Y., X.L. and W.X.; visualization, D.F. and Y.Y.; supervision, W.X.; project administration, W.X.; funding acquisition, D.F. and W.X. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Swiss National Science Foundation (P500PB_214369), the National Natural Science Foundation of China (3241101652), and the Project for Talent and Platform of Science and Technology in Yunnan Province Science and Technology Department (202105AM070008; 202205AM070007).

Data Availability Statement

All datasets are freely available on the Internet. References and links are mentioned in the manuscript.

Acknowledgments

The authors would like to express their gratitude and admiration to all individuals contributing to the advancement of Earth Observation science and deepening our understanding of nature. During the preparation of this manuscript/study, the author(s) used the free service Gemini (https://gemini.google.com/) to improve the language in some parts of the manuscript. The authors have reviewed and edited the output and take full responsibility for the content of this publication.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

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Fire 08 00422 g001
Figure 1. Map of northwest Yunnan (a); and sampling grid used for the validation (b).
Figure 1. Map of northwest Yunnan (a); and sampling grid used for the validation (b).
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Figure 2. Fire count distribution within 10 km square units and Spearman Rho correlation coefficients between assessed model and reference benchmarks. Fire counts were Min-Max re-scaled to allow comparison, while correlations were all significant at the 0.001 threshold.
Figure 2. Fire count distribution within 10 km square units and Spearman Rho correlation coefficients between assessed model and reference benchmarks. Fire counts were Min-Max re-scaled to allow comparison, while correlations were all significant at the 0.001 threshold.
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Table 1. Burned area detection metrics for two global burned area products. TP: True Positives, FN: False Negatives, FP: False Positives, PA: Producer’s Accuracy, UA: User’s Accuracy, NaN: Not a Number (caused by “divide-by-zero” error).
Table 1. Burned area detection metrics for two global burned area products. TP: True Positives, FN: False Negatives, FP: False Positives, PA: Producer’s Accuracy, UA: User’s Accuracy, NaN: Not a Number (caused by “divide-by-zero” error).
FireCCI51GABAM
YearLow–High
Threshold		High Frequency
Sample Squares		Reference
Fires		TPFNFPPAUATPFNFPPAUA
200122380800NaN6250.750.55
20022770700NaN6110.860.86
20033201311200.0817640.540.64
20044261001000NaN8230.80.73
20054171801800NaN21600.111
20067391831510.170.7514410.780.93
20076341501500NaN14110.930.93
20083161101100NaN9240.820.69
200964182600.2517100.881
201010431811700.06114400.781
20114111001000NaN9100.91
20143086146810.430.8611310.790.92
201542482610.250.677110.880.88
20163770700NaN5200.711
20175121201200NaN6620.50.75
20182890900NaN2700.221
20193141201200NaN8400.671
Overall study period1981518330.080.8313563230.680.85
Table 2. Burned area detection capabilities for different fire sizes. BA: Burned Area, TP: True Positives, PA: Producer’s Accuracy.
Table 2. Burned area detection capabilities for different fire sizes. BA: Burned Area, TP: True Positives, PA: Producer’s Accuracy.
BA < 25 ha
(n = 41)		BA 25–100 ha
(n = 71)		BA > 100 ha
(n = 86)
TPPATPPATPPA
GABAM260.63420.59670.78
FireCCI510040.06110.13
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Fornacca, D.; Ye, Y.; Li, X.; Xiao, W. Including Small Fires in Global Historical Burned Area Products: Promising Results from a Landsat-Based Product. Fire 2025, 8, 422. https://doi.org/10.3390/fire8110422

AMA Style

Fornacca D, Ye Y, Li X, Xiao W. Including Small Fires in Global Historical Burned Area Products: Promising Results from a Landsat-Based Product. Fire. 2025; 8(11):422. https://doi.org/10.3390/fire8110422

Chicago/Turabian Style

Fornacca, Davide, Yuhan Ye, Xiaokang Li, and Wen Xiao. 2025. "Including Small Fires in Global Historical Burned Area Products: Promising Results from a Landsat-Based Product" Fire 8, no. 11: 422. https://doi.org/10.3390/fire8110422

APA Style

Fornacca, D., Ye, Y., Li, X., & Xiao, W. (2025). Including Small Fires in Global Historical Burned Area Products: Promising Results from a Landsat-Based Product. Fire, 8(11), 422. https://doi.org/10.3390/fire8110422

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