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California forests have recently experienced record breaking wildfires and tree mortality from droughts, However, there is inadequate monitoring, and limited data to inform policies and management strategies across the state. Although forest surveys and satellite observations of forest cover changes exist at medium to coarse resolutions (30-500 m) annually, they remain less effective in mapping small disturbances of forest patches (<5m) occurring multiple times a year. We introduce a novel method of tracking California forest cover using a supervised U-Net deep learning architecture and PlanetScope's Visual dataset which provides 3-band RGB (Red, Green, and Blue) mosaicked imagery. We created labels of forest and non-forest to train the U-Net model to map tree cover based on a semi-unsupervised classification method. We then detected changes of tree cover and disturbance with the U-Net model, achieving an overall accuracy of 98.97% over training data set, and 95.5% over an independent validation dataset, obtaining a precision of 82%, and a recall of 74%. With the predicted tree cover mask, we created wall to wall monthly tree cover maps over California at 4.77 m resolution for 2020, 2021, and 2022. These maps were then aggregated in a postprocessing step to develop annual maps of disturbance, while accounting for the time of disturbance and other confounding factors such as topography, phenological and snow cover variability. We compared our high-resolution disturbance maps with wildfire GIS survey data from CALFIRE, and satellitebased forest cover changes and achieved an F-1 score of 54% and 88% respectively. The results suggest that high-resolution maps capture variability of forest disturbance and fire that wildfire surveys and medium resolution satellite products cannot. From 2020 to 2021, California maintained 30,923.5 sq km of forest while 5,994.9 sq km were disturbed. The highest observed forest loss rate was located at the Sierra Nevada mountains at 21.4% of the forested area being disturbed between 2020 and 2021. Our findings highlight the strong potential of deep learning and high-resolution RGB optical imagery for mapping complex forest ecosystems and their changes across California, as well as the application of these techniques on a national to global scale. [ABSTRACT FROM AUTHOR]

Land management and deforestation in tropical regions cause wildfires and forest degradation, leading to a loss of ecosystem services and global climate regulation. The objective of the study was to provide a comprehensive assessment of the spatial extent and patterns of burned areas in a new deforestation frontier in the Amazonas state. The methodology applied cross-referenced burned area data from 2003 to 2019 with climate, land cover, private properties and Protected Areas information and performed a series of statistical tests. The influence of the Multivariate ENSO Index (MEI) contributed to a decreasing rainfall anomalies trend and increasing temperature anomalies trend. This process intensified the dry season and increased the extent of annual natural vegetation affected by fires, reaching a peak of 681 km2 in 2019. The results showed that the increased deforestation trend occurred mostly in public lands, mainly after the new forest code, leading to an increase in fires from 66 to 84% in 2019. The methods developed here could identify fire extent, trends, and relationship with land cover change and climate, thus pointing to priority areas for preservation. The conclusion presented that policy decisions affecting the Amazon Forest must include estimates of fire risk and impact under current and projected future climates. [ABSTRACT FROM AUTHOR]

Aim: Our aim was to quantify the influence of climate and land use on major fires that occurred during the 2020 drought over the Brazilian Pantanal region. Location: Alto Paraguay Basin, central‐western flank of Brazil. Time period: 2003–2020. Methods: We calculated climatic and burned area anomalies and Spearman's correlation between precipitation and sea surface temperature (SST). We assessed water coverage to identify the impact of the 2020 drought. We produced fire recurrence maps, identified the areas that burned for the first time in 2020, and estimated the carbon loss. Lastly, we assessed the burned areas within land‐use classes and land tenure. Results: The anomalies showed that climatic variables in 2020 had an atypical behaviour, being hotter and drier than the historical average. The water surface area in the Pantanal has been decreasing annually from 2003, and in 2020 the reduction was 34% greater than the annual average. Burned areas in 2020 were 200% greater than the long‐term average, and 35% of the burned areas occurred for the first time in this year. We showed that 84% of these new fires occurred within natural vegetation, with 39% of the burned areas occurring in forests, an increase of 514%. Forest fires were responsible for 47% of the carbon loss in 2020. Finally, 70% of the fires in 2020 occurred within rural properties, 5% in indigenous lands and 10% in protected areas. Main conclusions: The unprecedented fires in 2020 were not only a result of atypical climate, but also a consequence of the intensification of fire‐related human activities. Rural properties play a major role in Pantanal fires, requiring law enforcement and a consistent plan for increasing the protection of the conservation areas. We argue that ineffective fire prevention and the lack of suppression strategies were also key in enhancing the magnitude of these fires. [ABSTRACT FROM AUTHOR]

While the climate and human-induced forest degradation is increasing in the Amazon, fire impacts on forest dynamics remain understudied in the wetter regions of the basin, which are susceptible to large wildfires only during extreme droughts. To address this gap, we installed burned and unburned plots immediately after a wildfire in the northern Purus-Madeira (Central Amazon) during the 2015 El-Niño. We measured all individuals with diameter of 10 cm or more at breast height and conducted recensuses to track the demographic drivers of biomass change over 3 years. We also assessed how stem-level growth and mortality were influenced by fire intensity (proxied by char height) and tree morphological traits (size and wood density). Overall, the burned forest lost 27.3% of stem density and 12.8% of biomass, concentrated in small and medium trees. Mortality drove these losses in the first 2 years and recruitment decreased in the third year. The fire increased growth in lower wood density and larger sized trees, while char height had transitory strong effects increasing tree mortality. Our findings suggest that fire impacts are weaker in the wetter Amazon. Here, trees of greater sizes and higher wood densities may confer a margin of fire resistance; however, this may not extend to higher intensity fires arising from climate change. [ABSTRACT FROM AUTHOR]

Tropical secondary forests sequester carbon up to 20 times faster than old-growth forests. This rate does not capture spatial regrowth patterns due to environmental and disturbance drivers. Here we quantify the influence of such drivers on the rate and spatial patterns of regrowth in the Brazilian Amazon using satellite data. Carbon sequestration rates of young secondary forests (<20 years) in the west are ~60% higher (3.0 ± 1.0 Mg C ha−1 yr−1) compared to those in the east (1.3 ± 0.3 Mg C ha−1 yr−1). Disturbances reduce regrowth rates by 8–55%. The 2017 secondary forest carbon stock, of 294 Tg C, could be 8% higher by avoiding fires and repeated deforestation. Maintaining the 2017 secondary forest area has the potential to accumulate ~19.0 Tg C yr−1 until 2030, contributing ~5.5% to Brazil's 2030 net emissions reduction target. Implementing legal mechanisms to protect and expand secondary forests whilst supporting old-growth conservation is, therefore, key to realising their potential as a nature-based climate solution. This study uses regional and global remote sensing data to assess the regrowth of secondary forests in the Brazilian Amazon biome. The authors find differences of regrowth rates due to climate, forest fires and deforestation actions and further quantify their carbon capture potential. [ABSTRACT FROM AUTHOR]

Tropical rainforests from the Brazilian Amazon are frequently degraded by logging, fire, edge effects and minor unpaved roads. However, mapping the extent of degradation remains challenging because of the lack of frequent high-spatial resolution satellite observations, occlusion of understory disturbances, quick recovery of leafy vegetation, and limitations of conventional reflectance-based remote sensing techniques. Here, we introduce a new approach to map forest degradation caused by logging, fire, and road construction based on deep learning (DL), henceforth called DL-DEGRAD, using very high spatial (4.77 m) and bi-annual to monthly temporal resolution of the Planet NICFI imagery. We applied DL-DEGRAD model over forests of the state of Mato Grosso in Brazil to map forest degradation with attributions from 2016 to 2021 at six-month intervals. A total of 73,744 images (256 × 256 pixels in size) were visually interpreted and manually labeled with three semantic classes (logging, fire, and roads) to train/validate a U-Net model. We predicted the three classes over the study area for all dates, producing accumulated degradation maps biannually. Estimates of accuracy and areas of degradation were performed using a probability design-based stratified random sampling approach (n = 2678 samples) and compared it with existing operational data products at the state level. DL-DEGRAD performed significantly better than all other data products in mapping logging activities (F 1 -score = 68.9) and forest fire (F 1 -score = 75.6) when compared with the Brazil's national maps (SIMEX, DETER, MapBiomas Fire) and global products (UMD-GFC, TMF, FireCCI, FireGFL, GABAM, MCD64). Pixel-based spatial comparison of degradation areas showed the highest agreement with DETER and SIMEX as Brazil official data products derived from visual interpretation of Landsat imagery. The U-Net model applied to NICFI data performed as closely to a trained human delineation of logged and burned forests, suggesting the methodology can readily scale up the mapping and monitoring of degraded forests at national to regional scales. Over the state of Mato Grosso, the combined effects of logging and fire are degrading the remaining intact forests at an average rate of 8443 km2 year−1 from 2017 to 2021. In 2020, a record degradation area of 13,294 km2 was estimated from DL-DEGRAD, which was two times the areas of deforestation. • Mapping forest degradation is a major gap for carbon emissions in tropical forests. • Here we train a deep learning model using Planet NICFI data to map degradation. • The U-Net model segments degraded forests due to logging, fire, and roads. • Our maps show the highest overlap with official Brazilian data from DETER and SIMEX. • This novel approach provides mapping and attribution of tropical forest degradation. [ABSTRACT FROM AUTHOR]

Bamboo-dominated forests comprise 1 % of the world’s forests. In southwest Amazon, a prior investigation mapped 16.15 million ha of bamboo-dominated forests by visual interpretation of Landsat and MODIS satellite images. They observed that the near infrared (NIR) wavelength was important to discriminate adult and dead bamboo areas, and estimated an average life cycle of 28 years for the Guadua spp. bamboo that dominate the region. In these bamboo areas, flowering and fruiting occur once in a lifetime before death, which produce massive quantities of necromass. The "bamboo-fire hypothesis" argues that increased dry fuel after die-off enhances fire probability, creating opportunities that favor bamboo. In this study, we developed and validated a method to map the bamboo die-off and its spatial distribution using satellite-derived time series of vegetation reflectance from MODIS (MAIAC) data and explored the fire hypothesis by evaluating the relationship between bamboo die-off and fires detected by MODIS thermal anomalies product in southwest Amazon. Our findings show that the NIR is the most sensitive spectral interval to characterize bamboo growth and cohort age. Automatic detection of historical bamboo die-off achieved an accuracy above 79 %. We mapped and estimated 15.5 million ha of bamboo-dominated forests in the region. The mean patch size during 2001–2017 die-off events was 85 km2, while the largest patch covered up to 6162 km2. The "bamboo-fire hypothesis" was not fully supported, because most bamboo cohorts did not burn after die-off. Nonetheless, fire occurrence was 45 % higher in dead than live bamboo in drought years, associated with ignition sources from land use, suggesting a bamboo-human-fire association. Since fire favors bamboo development, this may contribute to the maintenance of high bamboo density where it is already present, the expansion of bamboo into adjacent bamboo-free forests, or even bring deadly consequences to these adjacent forests under climate change effects. [ABSTRACT FROM AUTHOR]