Browsing by Autor "Douglas I. Kelley"

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Assessing MODIS Vegetation Continuous Fields tree cover product(collection 6): performance and applicability in tropical forests and savannas
(2021) Rahayu Adzhar; Douglas I. Kelley; Ning Dong; Mireia Torello Raventos; Elmar Veenendaal; Ted R. Feldpausch; Oliver L. Philips; Simon L. Lewis; Bonaventure Sonké; Herman Taedoumg
Abstract. The Moderate Resolution Imaging Spectroradiometer vegetation continuous fields (MODIS VCF) Earth observation product is widely used to estimate forest cover changes, parameterise vegetation and Earth System models, and as a reference for validation or calibration where field data is limited. However, although limited independent validations of MODIS VCF have shown that MODIS VCF's accuracy decreases when estimating tree cover in sparsely-vegetated areas, such as in tropical savannas, no study has yet assessed the impact this may have on the VCF based tree cover distributions used by many in their research. Using tropical forest and savanna inventory data collected by the TROpical Biomes In Transition (TROBIT) project, we produce a series of corrections that take into account (i) the spatial disparity between the in-situ plot size and the MODIS VCF pixel, and (ii) the trees' spatial distribution within in-situ plots. We then applied our corrections to areas identified as forest or savanna in the International Geosphere-Biosphere Programme (IGBP) land cover mapping product. All IGBP classes identified as savanna show substantial increases in cover after correction, indicating that the most recent version of MODIS VCF consistently underestimates woody cover in tropical savannas. We estimate that MODIS VCF could be underestimating tropical tree cover by between 9–15 %. Models that use VCF as their benchmark could be underestimating the carbon uptake in forest-savanna areas and misrepresenting forest-savanna dynamics. While more detailed in-situ field data is necessary to produce more accurate and reliable corrections, we recommend caution when using MODIS VCF in tropical savannas.
Comment on bg-2020-460
(2021) Rahayu Adzhar; Douglas I. Kelley; Ning Dong; Charles George; Mireia Torello Raventos; Elmar Veenendaal; Ted R. Feldpausch; Oliver L. Phillips; Simon L. Lewis; Bonaventure Sonké
<strong class="journal-contentHeaderColor">Abstract.</strong> The Moderate Resolution Imaging Spectroradiometer Vegetation Continuous Fields (MODIS VCF) Earth observation product is widely used to estimate forest cover changes and to parameterize vegetation and Earth system models and as a reference for validation or calibration where field data are limited. However, although limited independent validations of MODIS VCF have shown that MODIS VCF's accuracy decreases when estimating tree cover in sparsely vegetated areas such as tropical savannas, no study has yet assessed the impact this may have on the VCF-based tree cover data used by many in their research. Using tropical forest and savanna inventory data collected by the Tropical Biomes in Transition (TROBIT) project, we produce a series of calibration scenarios that take into account (i)Â the spatial disparity between the in situ plot size and the MODIS VCF pixel and (ii)Â the trees' spatial distribution within in situ plots. To identify if a disparity also exists in products trained using VCF, we used a similar approach to evaluate the finer-scale Landsat Tree Canopy Cover (TCC) product. For MODIS VCF, we then applied our calibrations to areas identified as forest or savanna in the International Geosphere-Biosphere Programme (IGBP) land cover mapping product. All IGBP classes identified as â savannaâ show substantial increases in cover after calibration, indicating that the most recent version of MODIS VCF consistently underestimates woody cover in tropical savannas. We also found that these biases are propagated in the finer-scale Landsat TCC. Our scenarios suggest that MODIS VCF accuracy can vary substantially, with tree cover underestimation ranging from 0â % to 29â %. Models that use MODIS VCF as their benchmark could therefore be underestimating the carbon uptake in forestâ savanna areas and misrepresenting forestâ savanna dynamics. Because of the limited in situ plot number, our results are designed to be used as an indicator of where the product is potentially more or less reliable. Until more in situ data are available to produce more accurate calibrations, we recommend caution when using uncalibrated MODIS VCF data in tropical savannas.
Comment on bg-2020-460
(2021) Rahayu Adzhar; Douglas I. Kelley; Ning Dong; Mireia Torello Raventos; Elmar Veenendaal; Ted R. Feldpausch; Oliver L. Philips; Simon Lewis; Bonaventure Sonké; Herman Taedoumg
The Moderate Resolution Imaging Spectroradiometer vegetation continuous fields (MODIS VCF) Earth observation product is widely used to estimate forest cover changes, parameterise vegetation and Earth System models, and as a reference for validation or calibration where field data is limited. However, although limited independent validations of MODIS VCF have shown that MODIS VCF's accuracy decreases when estimating tree cover in sparsely-vegetated areas, such as in tropical savannas, no study has yet assessed the impact this may have on the VCF based tree cover distributions used by many in their research. Using tropical forest and savanna inventory data collected by the TROpical Biomes In Transition (TROBIT) project, we produce a series of corrections that take into account (i) the spatial disparity between the in-situ plot size and the MODIS VCF pixel, and (ii) the trees' spatial distribution within in-situ plots. We then applied our corrections to areas identified as forest or savanna in the International Geosphere-Biosphere Programme (IGBP) land cover mapping product. All IGBP classes identified as savanna show substantial increases in cover after correction, indicating that the most recent version of MODIS VCF consistently underestimates woody cover in tropical savannas. We estimate that MODIS VCF could be underestimating tropical tree cover by between 9–15 %. Models that use VCF as their benchmark could be underestimating the carbon uptake in forest-savanna areas and misrepresenting forest-savanna dynamics. While more detailed in-situ field data is necessary to produce more accurate and reliable corrections, we recommend caution when using MODIS VCF in tropical savannas.
MODIS Vegetation Continuous Fields tree cover needs calibrating in tropical savannas
(Copernicus Publications, 2022) Rahayu Adzhar; Douglas I. Kelley; Ning Dong; Charles George; Mireia Torello Raventos; Elmar Veenendaal; Ted R. Feldpausch; Oliver L. Phillips; Simon L. Lewis; Bonaventure Sonké
Abstract. The Moderate Resolution Imaging Spectroradiometer Vegetation Continuous Fields (MODIS VCF) Earth observation product is widely used to estimate forest cover changes and to parameterize vegetation and Earth system models and as a reference for validation or calibration where field data are limited. However, although limited independent validations of MODIS VCF have shown that MODIS VCF's accuracy decreases when estimating tree cover in sparsely vegetated areas such as tropical savannas, no study has yet assessed the impact this may have on the VCF-based tree cover data used by many in their research. Using tropical forest and savanna inventory data collected by the Tropical Biomes in Transition (TROBIT) project, we produce a series of calibration scenarios that take into account (i) the spatial disparity between the in situ plot size and the MODIS VCF pixel and (ii) the trees' spatial distribution within in situ plots. To identify if a disparity also exists in products trained using VCF, we used a similar approach to evaluate the finer-scale Landsat Tree Canopy Cover (TCC) product. For MODIS VCF, we then applied our calibrations to areas identified as forest or savanna in the International Geosphere-Biosphere Programme (IGBP) land cover mapping product. All IGBP classes identified as “savanna” show substantial increases in cover after calibration, indicating that the most recent version of MODIS VCF consistently underestimates woody cover in tropical savannas. We also found that these biases are propagated in the finer-scale Landsat TCC. Our scenarios suggest that MODIS VCF accuracy can vary substantially, with tree cover underestimation ranging from 0 % to 29 %. Models that use MODIS VCF as their benchmark could therefore be underestimating the carbon uptake in forest–savanna areas and misrepresenting forest–savanna dynamics. Because of the limited in situ plot number, our results are designed to be used as an indicator of where the product is potentially more or less reliable. Until more in situ data are available to produce more accurate calibrations, we recommend caution when using uncalibrated MODIS VCF data in tropical savannas.
State of Wildfires 2024–2025
(Copernicus Publications, 2025) Douglas I. Kelley; Chantelle Burton; Francesca Di Giuseppe; Matthew W. Jones; Maria Lucia Ferreira Barbosa; Esther Brambleby; Joe McNorton; Zhongwei Liu; Alexander S. Bradley; Katie Blackford
Abstract. Climate change is increasing the frequency and intensity of extreme wildfires globally, yet our understanding of these high-impact events remains uneven and shaped by media attention and regional research biases. The State of Wildfires project systematically tracks global and regional fire activity of each annual fire season, analyses the causes of prominent extreme wildfire events, and projects the likelihood of similar events occurring in future climate scenarios. This, its second annual report, covers the March 2024 to February 2025 fire season. During the 2024–2025 fire season, fire-related carbon (C) emissions totalled 2.2 Pg C, 9 % above average and the sixth highest on record since 2003, despite below-average global burned area (BA). Extreme fire seasons in South America's rainforests, dry forests, and wetlands and in Canada's boreal forests pushed up the global C emissions total. Fire C emissions were over 4 times above average in Bolivia, 3 times above average in Canada, and ∼ 50 % above average in Brazil and Venezuela. Wildfires in 2024–2025 caused 100 fatalities in Nepal, 34 in South Africa, and 31 in Los Angeles, with additional fatalities reported in Canada, Côte d'Ivoire, Portugal, and Türkiye. The Eaton and Palisades fires in Southern California caused 150 000 evacuations and USD 140 billion in damages. Communities in Brazil, Bolivia, Southern California, and northern India were exposed to fine particulate matter at concentrations 13–60 times WHO's daily air quality standards. We evaluated the causes and predictability of four extreme wildfire episodes from the 2024–2025 fire season, including in Northeast Amazonia (January–March 2024), the Pantanal–Chiquitano border regions of Brazil and Bolivia (August–September 2024), Southern California (January 2025), and the Congo Basin (July–August 2024). Anomalous weather created conditions for these regional extremes, while fuel availability and human ignitions shaped spatial patterns and temporal fire dynamics. In the three tropical regions, prolonged drought was the dominant fire enabler, whereas in California, extreme heat, wind, and antecedent fuel build-up were compounding enablers. Our attribution analyses show that climate change made extreme fire weather in Northeast Amazonia 30–70 times more likely, increasing BA roughly 4-fold compared to a scenario without climate change. In the Pantanal–Chiquitano, fire weather was 4–5 times more likely, with 35-fold increases in BA. Meanwhile, our analyses suggest that BA was 25 times higher in Southern California due to climate change. The Congo Basin's fire weather was 3–8 times more likely with climate change, with a 2.7-fold increase in BA. Socioeconomic changes since the pre-industrial period, including land-use change, also likely increased BA in Northeast Amazonia. Our models project that events on the scale of 2024–2025 will become up to 57 %, 34 %, and 50 % more frequent than in the modern era in Northeast Amazonia, the Pantanal–Chiquitano, and the Congo Basin, respectively, under a medium–high scenario (SSP370) by 2100. Climate action can limit the added risk, with frequency increases held to below 15 % in all three regions under a strong mitigation scenario (SSP126). In Southern California, the future trajectory of extreme fire likelihood remains highly uncertain due to poorly constrained climate–vegetation–fire interactions influencing fuel moisture, though our models suggest that risk may decline in future. This annual report from the State of Wildfires project integrates and advances cutting-edge fire observations and modelling with regional expertise to track changing global wildfire hazard, guiding policy and practice towards improved preparedness, mitigation, adaptation, and societal benefit. Thirteen new datasets and model codebases presented in this work are available from the State of Wildfires Project's Zenodo community, including updated annual statistics on wildfire extent (Jones et al., 2025; https://doi.org/10.5281/zenodo.15525674), outputs from modelling of fire causality using PoF model (Di Giuseppe, 2025; https://doi.org/10.24433/CO.8570224.v1) and codebase for the extreme event attribution/projections model, ConFLAME (Barbosa et al., 2025a, https://doi.org/10.5281/zenodo.16790787).
State of Wildfires 2024–25
(2025) Douglas I. Kelley; Chantelle Burton; Francesca Di Giuseppe; Matthew W. Jones; Maria Lucia Ferreira Barbosa; Esther Brambleby; Joe McNorton; Zhongwei Liu; Alexander S. Bradley; Katie Blackford
Abstract. Climate change is increasing the frequency and intensity of extreme wildfires globally, yet our understanding of these high-impact events remains uneven and shaped by media attention and regional research biases. The State of Wildfire Project systematically tracks and analyses global fire activity and this, its second annual report, covers the March 2024 to February 2025 fire season. During the 2024–25 fire season, fire-related carbon (C) emissions were totalled 2.2 Pg C, 9 % above average and the 6th highest on record since 2003, despite below-average global burned area (BA; 3.7 million km2). Extreme fire seasons in South America’s rainforests, dry forests and wetlands, and in Canada’s boreal forests pushed up the global C emissions total. Fire C emissions were over four times above average in Bolivia, three times above average in Canada, and ~50 % above average in Brazil and Venezuela. Wildfires in 2024–25 caused 100 fatalities in Nepal, 34 in South Africa, and 30 in Los Angeles, with additional fatalities reported in Canada, Côte d’Ivoire, Portugal, and Turkey. The Eaton and Palisades fires in Southern California caused 150,000 evacuations and US$140 billion in damages. Communities in Brazil, Bolivia, Southern California, and Northern India were exposed to fine particulate matter at concentrations 13–60 times WHO’s daily air quality standards. We evaluated the causes and predictability of four extreme wildfire episodes from the 2024–25 fire season, including in Northeast Amazonia (January–March 2024), the Pantanal-Chiquitano border regions of Brazil and Bolivia (July–September 2024), Southern California (January 2025), and the Congo Basin (July–August 2024). Anomalous weather created conditions for these regional extremes, while fuel availability and human ignitions shaped spatial patterns and temporal fire dynamics. In the three tropical regions, prolonged drought was the dominant fire enabler, whereas in California, extreme heat, wind, and antecedent fuel build-up were the dominant enablers. Our attribution analyses show that climate change made extreme fire weather in Northeast Amazonia 30–70 times more likely, increasing burned area roughly fourfold compared to a scenario without climate change. In the Pantanal–Chiquitano, fire weather was 4–5 times more likely, with up to 35-fold increases in burned area. In Southern California, climate change made larger burned area 89 % more likely, with burned area up to 25 times higher. The Congo Basin’s fire weather was 3–8 times more likely with climate change, with a 2.7-fold increase in burned area. Socioeconomic changes since the pre-industrial period, including land-use change, also likely increased burned area in Northeast Amazonia. Our models project that events on the scale of 2024–25 will become up to 57 %, 34 %, and 50 % more frequent than in the modern era in Northeast Amazonia, the Pantanal-Chiquitano, and the Congo Basin, respectively, under a middle-of-the-road scenario (SSP370). Climate action can limit the added risk, with frequency increases kept below 15 % in all three regions under a strong mitigation scenario (SSP126). In Southern California, the future trajectory of extreme fire likelihood remains highly uncertain due to poorly constrained climate-vegetation-fire interactions influencing fuel moisture, though our models suggest that risk may decline in future. This annual report from the State of Wildfires Project integrates and advances cutting-edge fire observations and modelling with regional expertise to track changing global wildfire hazard, guiding policy and practice towards improved preparedness, mitigation, adaptation, and societal benefit. Thirteen new datasets and model codebases presented in this work are available from the State of Wildfires Project’s Zenodo community (https://zenodo.org/communities/stateofwildfiresproject, last access: 11 August 2025).