Analysis of Trends in the FireCCI Global Long Term Burned Area Product (1982–2018)

Otón, Gonzalo; Pereira, José M. C.; Silva, João M. N.; Chuvieco, Emilio

doi:10.3390/fire4040074

Cited by 15 publications

(22 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With a growing human population, a concomitant increase in frequency of bush fires would be expected. Surprisingly then, in northern sub-Saharan Africa the trend was downwards, at least between 1981 and 2018 (Otón et al 2021). The decline in the frequency of fires occurred against a varied context:…”

Section: Discussionmentioning

confidence: 99%

Birds and Bush Fires in African Savannahs

Zwarts¹,

Bijlsma²,

Kamp³

2023

Ardea

View full text Add to dashboard Cite

Bush fires are widespread in African savannahs. Their impact on birds varies. Many insectivores temporarily profit from the insects escaping fire and smoke, whereas the burnt-through grass and herb layer facilitates feeding for some ground-foraging bird species. Nevertheless, bush fires have a direct, negative impact on many other ground-foraging birds. The average density of seed-eating birds in humid, African savannahs (annual rainfall >800 mm) was 15.9 birds/ha in unburned savannahs, compared to 3.3 birds/ ha (–72%) in recently burned areas. No such difference was found for insectivorous bird species. Eleven of the 13 common ground-foraging migratory bird species were not affected by bush fires in Africa because they spend the northern winter in the arid and semiarid zone, beyond the main bush-fire zone. In the long run, savannah-inhabitant birds profit from bush fires, simply because fires prevent open landscape from becoming overgrown with trees. However, the short-term implications of bush fires might be severe for seed-eating birds that rely on humid savannah, because of the more than 3 million km2 in Africa burned annually, most comprises humid savannah.

show abstract

Section: Discussionmentioning

confidence: 99%

Birds and Bush Fires in African Savannahs

Zwarts¹,

Bijlsma²,

Kamp³

2023

Ardea

View full text Add to dashboard Cite

show abstract

“…E Q -T A R G E T ; t e m p : i n t r a l i n k -; e 0 1 4 ; 1 1 4 ; 9 3 Oe ¼ 1 − recall; (14) where t p represents true-positive samples, which belong to the same classes but in reference and classified data; t n represents true-negative samples, which belong to the same classes but in reference and classified data; f p represents false-positive samples, which wrongly indicate that the related class is present; and f n represents false-negative values, which wrongly indicate that the related class is not present. The variable p 0 describes the observed OA and p e is the expected probability.…”

Section: Comparison Of Accuracy Assessmentmentioning

confidence: 99%

“…12 The basic idea is to build a relationship model between forest fire and different sensitive bands by utilizing the disparity in radiation emitted by a burning forest and a forest in its normal state for extracting burned areas. 13,14 Due to the limited spectral resolution and spatial resolution of AVHRR and sensor orbit drift, 15 there are specific potential errors in extracting burned area information using AVHRR data, including radiation instability and radiation transmission problems. 16 Because of its enhanced radiometric stability when compared to AVHRR data, surface reflectance data from the SPOT-VEGETATION (VGT) sensor have been utilized extensively to produce burned area products on regional and global scales.…”

Section: Introductionmentioning

confidence: 99%

“…Burned area detection and monitoring were carried out using the advanced very-high-resolution radiometer (AVHRR) sensor on board National Oceanic and Atmospheric Administration satellites 12 . The basic idea is to build a relationship model between forest fire and different sensitive bands by utilizing the disparity in radiation emitted by a burning forest and a forest in its normal state for extracting burned areas 13 , 14 . Due to the limited spectral resolution and spatial resolution of AVHRR and sensor orbit drift, 15 there are specific potential errors in extracting burned area information using AVHRR data, including radiation instability and radiation transmission problems 16 .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Comparison of convolutional neural network and support vector machine for identification of forest types and burned areas

Li,

Ren,

Dong

et al. 2024

J. Appl. Rem. Sens.

View full text Add to dashboard Cite

The extraction of burned areas and the monitoring of forest type distribution are often affected by image classification methods. We aim to compare two image classification methods, convolutional neural network (CNN) and support vector machine (SVM), for identification of forest types and burned areas. A single post-fire Landsat 8 OLI image, forest management inventory data, and forest fire data were used to determine the optimal sample dataset. The CNN utilized PSPNet for training, while the ResNet34 served as the skeleton network to identify burned areas and forest types simultaneously. To compare and evaluate the effectiveness of the CNN model, the SVM was also used to classify the Landsat 8 OLI image with the same amount of sample data. The results indicate that the CNN model for per-pixel classification of seven classes (burned area, coniferous forest, broadleaved forest, mixed forest, residential area, water, and the other class) achieved an overall accuracy of 92.25% with a kappa coefficient of 0.8823. In contrast, the overall accuracy of the SVM algorithm was 86.72%, with a kappa coefficient of 0.8219. The results suggest that the CNN can achieve a higher classification accuracy than the SVM, and that the CNN is more reliable to support forest resources monitoring and management after a fire.

show abstract

“…We honestly consider that the authors of the comment are building a case over a relevant, but secondary aspect of our paper. In the Otón et al [2] paper, the correlation analysis between the two FireCCI datasets was included to assess the extent to which the trends detected in the full study period, which includes several years of the pre-MODIS era, may be credible. We proceeded fully aware of the limitations inherent in the comparison between burned area data time series obtained from products of different quality, different temporal consistency and different technical specifications, namely in terms of spatial, spectral, radiometric and temporal resolution of the underlying data.…”

Section: Main Commentsmentioning

confidence: 99%