The study area is located in the southwest Amazon between the longitudes 74 and 67∘ W and latitudes 13 and 6∘ S, covering parts of Brazil, Peru and Bolivia (Fig. ). The predominant forest type is non-flooded open-canopy rain forest on terra firme, often dominated by bamboos of Guadua genera and mostly (93 %) preserved from human disturbances .

The most important soil types are Chromic Alisol, Red-yellow Argisol, Haplic Cambisol, Ferrocarbic Podsol, Haplic Gleysol, Red-yellow Latosol, Chromic Luvisol and Haplic Plinthosol . In bamboo-dominated areas, the soils have a tendency to be more fertile, richer in exchangeable cations, more easily eroded, more poorly drained and more clay-rich than the soils where bamboo is excluded . Naturally high erosion leads to a gently rolling hilly landscape with muddy streams and rivers. Based on the 19-year time series of the Tropical Rainfall Measuring Mission (TRMM) satellite, annual rainfall ranges from 1800 to 3400 mm, with 0 to 5 dry months (i.e., less than 100 mm month-1). The average temperature is 27 ∘C. Minimum rainfall and temperature are recorded in July .

Active fire detections from MODIS/Aqua during 2002–2017 were filtered using yearly non-forest fraction masks. This ensured that active fires occurring over deforested and degraded forests, pastures or swidden areas were removed, and only pixels over forested areas remained in each year. To visualize where the fire occurred, the active fires were plotted over the bamboo spatial distribution map. The number of fires occurring over live bamboo and dead bamboo (died off during 2001–2017) was calculated.

To test whether there was a higher fire occurrence over recently dead bamboo than live bamboo, the active fire detections were analyzed as a function of dead (28, 0 and 1 years) and live bamboo (2 to 27 years) classes. For this purpose, each active fire detection was labeled accordingly to the bamboo age of the pixel where it occurred from the prediction map and then merged into the two classes. We investigated three factors that could affect fire probability: area of bamboo mortality, climate and proximity to ignition sources. Since the total area of a specific age class could interfere with fire frequency, that is, more area would mean higher probability of fire occurrence, we normalized the fire frequency by the area (ha) of its respective age class within the buffer with most fire occurrences, in the year of fire occurrence. Severe droughts affected Amazonia in 2005, 2010 and 2015/2016, and especially the southwest in 2005 . As drought years can enhance fire occurrence in Amazonia e.g.,, we analyzed the fire frequency in regular and drought years separately. To assess the influence of ignition sources on the fire occurrence, we filtered active fire detections using buffers of 1, 2 and 3 km around the non-forested areas using the yearly non-forest fraction mask and assessed the number of active fire detections considering each buffer.

The area-normalized fire frequency over dead and live bamboo was compared using a two-way Analysis of Variance (ANOVA) to test whether there were more fires in dead than live bamboo. We tested the effects of bamboo life stage (live or dead), year of fire occurrence and their interactions in active fire detections.

Bamboo-dominated forest, as mapped by , spanned a very narrow range of values in the Landsat-derived percent of tree cover product. The 1st and 99th percentiles of tree cover in the bamboo areas were 96.95 % and 99.88 %, respectively, while the median was 99.18 % (Fig. ). Forests identified as bamboo-free by had tree cover above the 99th percentile in the northeast of the study area, but below the 1st percentile in the southwest of the study area. In the northwest, bamboo-free forests presented tree cover similar to that of bamboo-dominated forests, i.e., between the 1st and 99th percentile.

The MODIS NIR-1 reflectance values over the 2000–2017 period in bamboo-free forests that had tree cover above the 99th percentile of bamboo-dominated areas (Fig. b) did not significantly differ from normal distribution (p=0.33). The bamboo-free forests showed the lowest standard deviation (mean = 27.3 % reflectance; SD = 0.9 %) when compared to the bamboo-dominated forests (Fig. c). Bamboo-free forests that had tree cover below the 1st percentile of bamboo-dominated areas (Fig. a) presented a left-skewed distribution with similar reflectance to the 99th percentile but with higher SD (mean = 27.2 %, SD = 2.6 %, and xi = 1.2 %). Bamboo-dominated forests (Fig. c, pixels inside the hatched polygon in Fig. ) presented a right-skewed distribution with higher NIR-1 reflectance (mean = 28.7 %, SD = 2.1 % and xi = 1.9 %) than the bamboo-free forests.

Active fire detections were not found in all bamboo patches that died (Fig. ). We found a total of 2371 MODIS active fire detections inside bamboo-dominated forests between 2002 and 2017, from which 1424 detections (60 %) occurred in bamboo patches that died off and 947 detections (40 %) occurred in live bamboo patches. Active fires were detected mostly near non-forested areas (Fig.  in gray). When we excluded the detections up to 1, 2 and 3 km around these areas, the total detections decreased to 1330 (56 %), 18 (0.76 %) and 3 (0.12 %), respectively.

Overall, there was a similar number of active fire detections per hectare in dead and live bamboo (0.18 fires ha-1) (Fig. ). The ANOVA did not show statistically significant differences in the area-normalized mean active fire detections for the interaction between bamboo stage (dead or live) and year of fire occurrence factors (p=0.67). Individually, bamboo stage also did not show statistical significance in area-normalized mean active fire detections (p=0.986). On the other hand, year of fire did show statistical significance on area-normalized mean active fire detections (p<0.01). The years 2017 and 2016 presented significant higher area-normalized mean active fire detections (0.46 and 0.35 fires ha-1, respectively) than the other years (p<0.01).

For severe drought years, the area-normalized active fire detections in 2005 (0.32 and 0.18 fires ha-1), 2010 (0.22 and 0.12 fires ha-1), 2015 (0.35 and 0.20 fires ha-1) and 2016 (0.57 and 0.33 fires ha-1) in dead and live bamboo, respectively, were not statistically different between the two bamboo life stages (p=0.127). However, drought years presented on average 45 % higher area-normalized mean active fire detections in dead (0.342 fires ha-1) than live (0.236 fires ha-1) bamboo.

We found that the bamboo-dominated forests had a narrow range of tree cover values (96.95 % to 99.89 %), and this was below the tree cover values of the closed forests nearby (above 99.89 %). This suggests that these forests have a largely closed canopy but are slightly more open than closed forests without bamboo. Evergreen trees are the dominant life form over most of the southwest Amazon forests, including the ones where bamboo is very abundant. The trees generally comprise 50 % or more of the canopy area in a Landsat or MODIS pixel, even when the bamboo cohorts are at adult stage and show high density . They also fully dominate the canopy during 30 % of the bamboo life cycle, while juvenile bamboo is confined to the forest understory . However, the tree cover percent of bamboo-dominated forests was slightly smaller than the bamboo-free areas. We believe this might be related to (i) an increased gap opening associated with faster forest dynamics and tree mortality of these areas influenced by bamboo , or (ii) artifacts of the tree cover computation method that uses the pixels' reflectance from .

The MODIS NIR-1 reflectance was normally distributed over bamboo-free forests (Fig. b), while it showed a right skewed distribution over the bamboo-dominated forests (Fig. c). This result was expected, considering that undisturbed old-growth bamboo-free forests are more or less stable over time, while bamboo-dominated forests' canopies undergo structural changes when the bamboo reaches the height of tree crowns after 12 years of age . This is supported by our results that show a continuous increase of NIR reflectance with bamboo age and an abrupt increase of NIR around the age of 12 years.

The automatic detection of bamboo die-off performed very well, with an accuracy above 79 % when estimating the exact year of bamboo death and a mean error of 0.5 years. When comparing the NIR-1 and NIR-2 bands, the leaf–canopy water sensitivity from NIR-2 might have contributed to a slightly better performance in bamboo die-off detection and the detection of different areas between the bands, which contributed to a larger coverage of the bamboo-dominated forests (Fig. S3). This different sensitivity to vegetation structure is especially highlighted in Fig. , which shows that the NIR-2 remains at its lowest during 0–2 years, explaining why the NIR-2 band maps different areas than NIR-1.

Our die-off map is an improvement of the current available maps from the literature because the die-off detection conducted in previous works was solely based on the visual inspection of Landsat and MODIS color composites during 2000–2008, thus leading toward the identification of big clusters of pixels that went through die-off (Table ) e.g.,. Our method is automatic, is easy to implement and can detect relatively small patches because it runs on a per-pixel basis. However, we do not advise attempting detection of very small patches (e.g., <10 km2) when using MODIS data due to limitations of the spatial resolution of the sensor (1 km). It is important to note that the detected bamboo die-off areas were not confounded with recently deforested areas, as the tree cover product did not point out forest losses in bamboo die-off areas. Since the method can detect bamboo die-off without a priori knowledge of the bamboo spatial distribution (Fig. ), it could be used to better describe and understand the spatial organization of the bamboo stands that show synchronized die-off in forests around the world.

Our validation dataset was composed of 390 pixels visually detected in 78 bamboo patches during 2001–2017. Therefore, we are confident that the sampling was representative to our study area given that we found 802 patches in the same time period, that is, the sample consisted in around 10 % patches. It is noted, however, that our visual analysis mostly sampled big patches that died off because those were the ones that we could be sure that were bamboo die-off. The high detection accuracy of bamboo die-off events also highlights the quality of the MODIS (MAIAC) data, which are suitable for bamboo-dominated forests' mapping. The MAIAC algorithm improves the accuracy of cloud detection, aerosol retrieval and atmospheric correction compared to the standard MODIS product processing . Combined with the appropriate normalization for sun-sensor-target geometry using BRDF modeling, the MAIAC contributed to minimize interannual artifacts in the time series for an accurate detection.

A total of 155 159 km2 (15.5 million ha) of bamboo-dominated forests was mapped in the southwestern Amazon by combining the automatic detection of dead and live bamboo (Fig. ). Most of the detected areas (72.5 %) were located inside the 16.5 million ha of the bamboo-dominated forests mapped by , although covering only 68.8 % of the previous detected areas. This difference was partially due to the increased land cover change in the region post-2010 – the period when performed their analysis, and areas for which our method did not detect bamboo-dominated forests. Despite the differences, we detected clusters of pixels that were very likely bamboo-dominated patches outside of the previously mapped areas (e.g., 11.5∘ S, 70∘ W, and 13∘ S, 71∘ W). These areas should be further investigated in the field to verify if they are indeed bamboo-dominated forests.

Compared to our results with 1 km spatial resolution, the map from (30 m spatial resolution) underestimated the bamboo-dominated forests in the order of 30 %. A possible explanation is that the authors considered live adult bamboo and used only two Landsat mosaic images 10 years apart from each other (1990 and 2000) for mapping, thus not observing part of the bamboos that were at a juvenile stage and hidden in the understory at that time. Another possibility is the limitation of visual interpretation and manual delineation of small bamboo patches. Our map was obtained on a per-pixel basis by assessing each pixel's spectral trajectory, thus reducing errors of omission by considering both live and dead bamboo for mapping, and by using a longer time series (18 years) for the detection.

The potential limitations of our map include the coarser spatial resolution (1 km) when compared to the previous map (30 m). In addition, we likely underestimated the true bamboo distribution because of the previously discussed uncertainties in detecting juvenile bamboos, given the limited temporal coverage of the MODIS (MAIAC) time series. A more accurate mapping of bamboo spatial distribution would require that all bamboo died off during the time series (i.e., requiring at least 28 years of data). Currently, the only dataset that has such temporal coverage comes from the Landsat satellites with 47 years of data of variable spatial resolution (1972–2018) or 34 years of data of 30 m spatial resolution (1985–2018). The challenge in applying such detection with Landsat imagery is that it relies on the dataset acquisition of yearly time series of cloud- and aerosol-free images for the whole area and signal normalization between images.

When reconstructing the spectral response of the bamboo-dominated forest as a function of cohort age (Fig. ), we found that two spectral bands, the NIR-1 and NIR-2, followed our initial assumption of overall reflectance increase with bamboo cohort age and of sharp decrease at the time of die-off.

Between 1 and 12 years of cohort age, the NIR-1 reflectance did not show a continuous increase (Fig. ), while it presented strong correlation (r=0.41) with bamboo-free forest (Fig. ). The NIR-2 reflectance, however, showed a slight monotonical increase (Fig. ) with weak correlation (r=0.19) to bamboo-free forest (Fig. ). However, the accuracy on detecting juvenile bamboos was poor (Fig. ). Thus, it is very difficult to identify the bamboo-dominated patches in this hidden juvenile age without identifying the prior die-off event, as reported in a previous study .

The NIR signal suddenly increased at 12–14 years of age, which we believe had two possible explanations. First, there was a change in the density of leafy bamboo branches in the upper forest canopy, where they are visible to the satellite. This is supported by the field observations of , in which juvenile bamboo cohorts reach the upper forest canopy by 10 years of age and accelerate in growth due to increased access to solar radiance. They observed that bamboo density doubled (from 1000 to 2000 culms ha-1) and basal area almost tripled (from 2.1 to 5 m2 ha-1) between 10 and 12 years of age. The second explanation could be an artifact of our unbalanced sampling for this set of cohort ages. The reflectance values collected for 12–15 years of cohort age were only available from the extremes of our time series (2000 and 2017) due to the 28-year life cycle of bamboo and the 18 years of MAIAC data availability.

From 14 to 27 years, a smooth steady increase only occurs in the NIR-1 signal until the synchronous cohort death, while the NIR-2 signal seems to have saturated at about 15 years of age, maintaining a constant signal of 0.3 reflectance until it drops steeply at cohort death. Thus, NIR-1 should present better results for predicting the bamboo age of live adult stands. Finally, the sharp decrease of NIR-1 and NIR-2 at 28–29 years explains why our bilinear model performed well in detecting the time of death. At the time of death, there is a high abundance of dead/dry bamboo branches in the canopy, which reflects a lower amount of NIR energy than leafy and photosynthetically active bamboo.

The increases in red reflectance at the die-off, as well as at 1 year of age (Fig. a), can also be related to the high abundance of dry bamboo with decreased leaf chlorophyll content and increased non-photosynthetic content. Dry, or dead, vegetation is non-photosynthetically active, and, thus, the incoming red energy near 672 nm is not absorbed by the plant's chlorophyll, that is, causing an increase in the red reflectance . The dry culms can take up a few years to decompose , which may explain the reason for still observing an increased red signal at 1 year of age.

The curves also showed a large spectral variability in bamboo-dominated forests with age, which very likely occurs due to different bamboo abundance and/or forest structure in the area, as well as the interannual variability in the signal. However, we were able to extract the annual changes in reflectance and predict bamboo ages with 2.92 and 4.25 years RMSE (Fig. ) using NIR-1 and NIR-2, respectively. The data of each age class were merged from different year composites of the whole time series, thus incorporating the noise in interannual variability. Three factors contributing to such noise could be (i) the temporary formation of green leafy secondary forest, spectrally similar to adult bamboo, in large forest gaps left by the dead bamboo; (ii) the semideciduous nature of the trees that are mixed in with bamboo, in the seasonally drier parts of the bamboo range and (iii) the fact that the death of bamboo revealed suppressed trees below the bamboo canopy. Nevertheless, because our detection and prediction methods were not based on absolute reflectance values, but on the correlation between the time series and a reference, such as the bilinear model or the empirical curve, we do not believe that the large spectral variability should have a major impact on the detection/prediction.

By applying the empirical bamboo-age reflectance curves, we estimated the bamboo die-off year for all bamboo patches of the region, providing a detailed map of the age structure of bamboo-dominated forest (Fig. ) and bamboo patch size description (Table ). The estimated die-off events between 2000 and 2017 were similar to the ones detected using the bilinear model because of the abrupt spectral changes with die-off. Regarding predictions between 2018 and 2028, the estimate of the exact die-off year was not so accurate (at best 20 % accuracy) because those bamboo patches were mainly at the juvenile stage during the MODIS (MAIAC) time series period and did not die. However, we believe that the predictions using the NIR-1 were at acceptable levels (RMSE = 2.92 years) when considering that (i) the Landsat validation points based on visual interpretation can have a deviation of 1 year; and (ii) we assumed that every bamboo cohort had the same life cycle length of 28 years, while we know that it can vary between 27 and 32 years . The validation dataset for the predictions (2017–2028) corresponded to 175 pixels in 35 bamboo patches and represented 4.5 % of the 778 bamboo patches predicted for the 2018–2028 time period.

We believe that knowing where and when the bamboo dies is important information for future studies of bamboo-dominated forest ecosystems, and the potential applications of the bamboo die-off year or age map are various. Since areas with dead bamboo are difficult to maintain trails and hinder the work of rubber trappers , it can be used in forest management planning in order to avoid areas where the die-off year occurred in the last 3 years and dry culms are still not decomposed, or to avoid areas with likely future die-off. It can also be used for public policy planning regarding food and human health security, for example, in bamboo forests in Southeast Asia, where bamboo reproductive events cause huge rodent invasion and proliferation that then damage nearby crop plantations . It could also be used to explore broader scientific questions on the ecology of bamboo-dominated forests such as studies on maintenance/expansion of bamboo patches, flowering waves, cross-pollination between patches, fauna habitat dynamics and impacts on short- and long-term carbon dynamics.

We could not support the bamboo–fire hypothesis from because fire occurred only in a small fraction of bamboo-dominated areas during the 16 years of fire analysis (Fig. ), equivalent to 2371 km2 of burnt area or 0.0955 % of the total bamboo area (155 159 km2) burning each year. In addition, the statistical tests comparing dead and live bamboo fire frequency showed that dead bamboo did not burn more than live bamboo (Fig. ). Hence, we believe in other explanations for bamboo maintenance in the forest, such as bamboo itself being responsible for its maintenance in the forest due to the damage it causes in the trees while increasing tree mortality .

We also did not observe an overall increased fire probability over dead than live bamboo in non-drought years. However, our findings suggest that forests with recently dead bamboo exposed to severe drought are more susceptible to fire occurrence, as there were 45 % higher area-normalized mean active fire detections in dead than live bamboo during severe drought years, such as 2005, 2010, 2015 and 2016. When considering the total fire occurrence, we did not observe an overall significant increase in fire occurrence during the 2005 and 2010 major droughts when compared to the other regular years . We believe that this is because we filtered the active fire occurring inside the bamboo-dominated areas and pixels with, theoretically, zero non-forested areas using the tree cover products , thus excluding the areas of increased fire occurrence in 2005 and 2010 that were reported in the literature .

The fire occurrence beyond 2 km inside the forest was probably underestimated because the forest canopy can obscure fires that only occur on the understorey, and, thus, are not detected by the MODIS/Aqua satellite . In addition, the MODIS active fire detections should be treated as a lower bound of fire occurrence, as it underestimates fire occurrences in the order of 5 % for small fires with less than 0.09 km2, or 10 % of MODIS spatial resolution, due to the coarse spatial resolution, high cloud cover and high viewing angles (>15∘) . Nevertheless, we do not believe this might have an impact on rejecting the bamboo–fire hypothesis due to the minimal fraction of fire occurrences occurring over the large bamboo-dominated forests.

Large areas of bamboo die-off that burned occurred close to agricultural lands near the city of Sena Madureira in the state of Acre, Brazil, during 2015, 2016 and 2017. The combination of increased dry fuel material from bamboos and nearby ignition sources from land use might have contributed to this increased fire occurrence. This result supports the notion that bamboo die-off enhances fire probability by increasing the dry fuel material in the forest. As we observed in the red wavelength, the reflectance increase was probably associated with greater amounts of dry biomass or non-photosynthetic vegetation in the die-off year and up to 1 year of age (Fig. a).

The fire occurrences over bamboo-dominated forests were therefore associated with the proximity to ignition sources, as less than 1 % of forest fire events occurred more than 2 km away from non-forested areas. This was expected because fire depends on both fuel and ignition to occur. Thus, areas closer to deforested areas, roads and rivers would have higher probability to burn, as probably occurred in 2015, 2016 and 2017. The study of found that 50 % of MODIS active fire detections were found within 1 km of roads and rivers, and 95 % of the active fires were found within 10 km of roads and rivers in the Brazilian Amazon. Fire is known to be associated with deforestation and land use practices in Amazonia such as slash-and-burn farming and land preparation, where people remove trees of economic interest and then set the areas on fire in order to clear the land and implement crop plantations or pasture e.g.,. Thus, this reinforces a bamboo–human–fire association through the increased land use and cover change. This association is slightly different than it was in pre-Columbian times , where geoglyph builders could have used the bamboo die-off patches and fire as an easier way to clear the forest cover to build their monuments, but it should also favor increases in fire occurrence on the vicinities of bamboo-dominated areas, thus leading to potential bamboo expansion.

The higher fire probability in dead bamboo patches during drought events, along with the increasing human influence, can favor increases in bamboo abundance and expansion over time by assisting them in their competition with trees. A previous study showed that fire favored the Guadua bamboo expansion in the region because the bamboo individuals have faster responses to catastrophic disturbance such as fires than tree species . Thus, when a fire occurs inside or close to a bamboo forest patch, it may favor the growth of bamboo seedlings – derived from the massive number of seeds that have been dispersed during the reproductive phase and prior to death – and the vegetative expansion of the adult bamboo.

Our findings regarding bamboo die-off year being associated with fire occurrence, mainly in drought years, might have implications for fire control policies, such as in the state of Acre in Brazil, where many bamboo-dominated areas occur near human settlements, and these extreme climate events occur within a 5-year interval in Amazonia. By knowing where and when the die-offs are occurring, public policies can be made to avoid fire ignition in such areas or prepare the fire brigades to attend to potential fires.