Articles | Volume 19, issue 13
https://doi.org/10.5194/bg-19-3317-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-19-3317-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
15 Jul 2022
Research article |
| 15 Jul 2022
| 15 Jul 2022
Monitoring post-fire recovery of various vegetation biomes using multi-wavelength satellite remote sensing
Emma Bousquet
CORRESPONDING AUTHOR
Centre d'Etudes Spatiales de la Biosphère (CESBIO), Université
de Toulouse (CNES/CNRS/INRAE/IRD/UPS), 18 av. Edouard Belin, bpi 2801, 31401
Toulouse CEDEX 9, France
Arnaud Mialon
Centre d'Etudes Spatiales de la Biosphère (CESBIO), Université
de Toulouse (CNES/CNRS/INRAE/IRD/UPS), 18 av. Edouard Belin, bpi 2801, 31401
Toulouse CEDEX 9, France
Nemesio Rodriguez-Fernandez
Centre d'Etudes Spatiales de la Biosphère (CESBIO), Université
de Toulouse (CNES/CNRS/INRAE/IRD/UPS), 18 av. Edouard Belin, bpi 2801, 31401
Toulouse CEDEX 9, France
Stéphane Mermoz
GlobEO, 31400 Toulouse, France
Yann Kerr
Centre d'Etudes Spatiales de la Biosphère (CESBIO), Université
de Toulouse (CNES/CNRS/INRAE/IRD/UPS), 18 av. Edouard Belin, bpi 2801, 31401
Toulouse CEDEX 9, France
Viewed
Total article views: 2,886 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 11 Nov 2021)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 2,034 | 785 | 67 | 2,886 | 68 | 50 | 62 |
- HTML: 2,034
- PDF: 785
- XML: 67
- Total: 2,886
- Supplement: 68
- BibTeX: 50
- EndNote: 62
Total article views: 1,997 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 15 Jul 2022)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 1,447 | 501 | 49 | 1,997 | 68 | 39 | 47 |
- HTML: 1,447
- PDF: 501
- XML: 49
- Total: 1,997
- Supplement: 68
- BibTeX: 39
- EndNote: 47
Total article views: 889 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 11 Nov 2021)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 587 | 284 | 18 | 889 | 11 | 15 |
- HTML: 587
- PDF: 284
- XML: 18
- Total: 889
- BibTeX: 11
- EndNote: 15
Viewed (geographical distribution)
Total article views: 2,886 (including HTML, PDF, and XML)
Thereof 2,787 with geography defined
and 99 with unknown origin.
Total article views: 1,997 (including HTML, PDF, and XML)
Thereof 1,932 with geography defined
and 65 with unknown origin.
Total article views: 889 (including HTML, PDF, and XML)
Thereof 855 with geography defined
and 34 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
17 citations as recorded by crossref.
- Global patterns and drivers of post-fire vegetation productivity recovery H. Xu et al. 10.1038/s41561-024-01520-3
- Widespread and systematic effects of fire on plant–soil water relations M. Baur et al. 10.1038/s41561-024-01563-6
- A novel deep Siamese framework for burned area mapping Leveraging mixture of experts S. Seydi et al. 10.1016/j.engappai.2024.108280
- An empirical assessment of the potential of post-fire recovery of tree-forest communities in Mediterranean environments M. Rodrigues et al. 10.1016/j.foreco.2023.121587
- A new global C-band vegetation optical depth product from ASCAT: Description, evaluation, and inter-comparison X. Liu et al. 10.1016/j.rse.2023.113850
- Restoration of Western Siberia meadow vegetation after fires according to Sentinel-2 data A. Karpachevskiy et al. 10.1051/e3sconf/202346202039
- Using ensemble machine learning algorithm to predict forest fire occurrence probability in Madhya Pradesh and Chhattisgarh, India S. Surbhi Singh & C. Jeganathan 10.1016/j.asr.2023.12.054
- Unmixing-based forest recovery indicators for predicting long-term recovery success L. Mandl et al. 10.1016/j.rse.2024.114194
- Vegetation change detection and recovery assessment based on post-fire satellite imagery using deep learning R. Priya & K. Vani 10.1038/s41598-024-63047-2
- Soil Moisture and Sea Surface Salinity Derived from Satellite-Borne Sensors J. Boutin et al. 10.1007/s10712-023-09798-5
- Quantifying forest resilience post forest fire disturbances using time-series satellite data S. Singh & C. Jeganathan 10.1007/s10661-023-12183-9
- Influence of Vegetation Height, Plant Area Index, and Forest Intactness on SMOS L-VOD, for Different Seasons and Latitude Ranges C. Vittucci et al. 10.1109/TGRS.2023.3299529
- Spatial heterogeneity in post-fire vegetation productivity recovery and its drivers 10.1038/s41561-024-01521-2
- Development of a new indicator for identifying vegetation destruction events using remote sensing data C. Zhao et al. 10.1016/j.ecolind.2024.112553
- Monitoring of deforestation events in the tropics using multidimensional features of Sentinel 1 radar data C. Zhao et al. 10.3389/ffgc.2023.1257806
- Critical slowing down of the Amazon forest after increased drought occurrence J. Van Passel et al. 10.1073/pnas.2316924121
- Model-driven estimation of closed and open shrublands live fuel moisture content G. Lai et al. 10.1080/15481603.2022.2139404
16 citations as recorded by crossref.
- Global patterns and drivers of post-fire vegetation productivity recovery H. Xu et al. 10.1038/s41561-024-01520-3
- Widespread and systematic effects of fire on plant–soil water relations M. Baur et al. 10.1038/s41561-024-01563-6
- A novel deep Siamese framework for burned area mapping Leveraging mixture of experts S. Seydi et al. 10.1016/j.engappai.2024.108280
- An empirical assessment of the potential of post-fire recovery of tree-forest communities in Mediterranean environments M. Rodrigues et al. 10.1016/j.foreco.2023.121587
- A new global C-band vegetation optical depth product from ASCAT: Description, evaluation, and inter-comparison X. Liu et al. 10.1016/j.rse.2023.113850
- Restoration of Western Siberia meadow vegetation after fires according to Sentinel-2 data A. Karpachevskiy et al. 10.1051/e3sconf/202346202039
- Using ensemble machine learning algorithm to predict forest fire occurrence probability in Madhya Pradesh and Chhattisgarh, India S. Surbhi Singh & C. Jeganathan 10.1016/j.asr.2023.12.054
- Unmixing-based forest recovery indicators for predicting long-term recovery success L. Mandl et al. 10.1016/j.rse.2024.114194
- Vegetation change detection and recovery assessment based on post-fire satellite imagery using deep learning R. Priya & K. Vani 10.1038/s41598-024-63047-2
- Soil Moisture and Sea Surface Salinity Derived from Satellite-Borne Sensors J. Boutin et al. 10.1007/s10712-023-09798-5
- Quantifying forest resilience post forest fire disturbances using time-series satellite data S. Singh & C. Jeganathan 10.1007/s10661-023-12183-9
- Influence of Vegetation Height, Plant Area Index, and Forest Intactness on SMOS L-VOD, for Different Seasons and Latitude Ranges C. Vittucci et al. 10.1109/TGRS.2023.3299529
- Spatial heterogeneity in post-fire vegetation productivity recovery and its drivers 10.1038/s41561-024-01521-2
- Development of a new indicator for identifying vegetation destruction events using remote sensing data C. Zhao et al. 10.1016/j.ecolind.2024.112553
- Monitoring of deforestation events in the tropics using multidimensional features of Sentinel 1 radar data C. Zhao et al. 10.3389/ffgc.2023.1257806
- Critical slowing down of the Amazon forest after increased drought occurrence J. Van Passel et al. 10.1073/pnas.2316924121
1 citations as recorded by crossref.
Latest update: 23 Nov 2024
Short summary
Pre- and post-fire values of four climate variables and four vegetation variables were analysed at the global scale, in order to observe (i) the general fire likelihood factors and (ii) the vegetation recovery trends over various biomes. The main result of this study is that L-band vegetation optical depth (L-VOD) is the most impacted vegetation variable and takes the longest to recover over dense forests. L-VOD could then be useful for post-fire vegetation recovery studies.
Pre- and post-fire values of four climate variables and four vegetation variables were analysed...
Altmetrics
Final-revised paper
Preprint