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Amazon forests are becoming increasingly vulnerable to disturbances such as droughts, fires, windstorms, logging, and forest fragmentation, all of which lead to forest degradation. Nevertheless, quantifying the extent and severity of disturbances and their cumulative impact on forest degradation remains a significant challenge. In this study, we combined multispectral data from Landsat sensors with hyperspectral data from the Earth Observing-One (Hyperion/EO-1) sensor to evaluate the efficacy of multiple vegetation indices in detecting forest responses to disturbances in an experimentally burned forest in southeastern Amazonia. Our experimental area was adjacent to an agricultural field and consisted of three 50-ha treatments – an unburned Control, a plot burned every three years, and a plot burned annually from 2004 to 2010. All plots were monitored to assess vegetation recovery after fire disturbance. These areas were also affected by three drought events (2007, 2010, and 2016) over the study period. We evaluated a total of 18 Vegetation Indices (VI), one unique to Landsat, 12 unique to Hyperion/EO-1, and five commons to both satellites (i.e., 6 total from Landsat and 17 from Hyperion). We used linear models (LM) to evaluate how changes in ground observations of forest structure (biomass, leaf area index [LAI], and litter production) associated with fire were captured by the two VIs most sensitive to forest degradation. Our results indicate that the Plant Senescence Reflectance Index (PSRI) derived from Hyperion/EO-1 was the most sensitive to vegetation changes associated with forest fires, increasing by 94% in burned vs. unburned forests. Of the Landsat-derived VIs, we found that the Green-Red Normalized Difference (GRND) were the most sensitive to forest degradation by fire, showing a marked decline (87%) in the burned plots compared with the unburned Control. However, compared to PSRI, the GRND was a better predictor of changes associated with fire, both in the forest interior or forest edge, for the three ground variables: biomass stocks (r2 =0.5–0.8), LAI (r2=0.8–0.9), and litter production (r2=0.4–0.7). This study demonstrate that VIs can detect forest responses to fire and other disturbances over time, highlighting the relative strengths of each VI. In doing so, it shows how the integration of multispectral and hyperspectral data can be useful for monitoring tropical forest degradation and recovery. Moreover, it provides valuable insights into the limitations of existing approaches, which can inform the design of next-generation sensors for global forest monitoring. 

Abstract The contemporary fire regime of southern Amazonian forests has been dominated by interactions between droughts and sources of fire ignition associated with deforestation and slash-and-burn agriculture. Until recently, wildfires have been concentrated mostly on private properties, with protected areas functioning as large-scale firebreaks along the Amazon’s agricultural frontier. However, as the climate changes, protected forests have become increasingly flammable. Here, we have quantified forest degradation in the Território Indígena do Xingu (TIX), an iconic area of 2.8 million hectares where over 6000 people from 16 different ethnic Indigenous groups live across 100 villages. Our main hypothesis was that forest degradation, defined here as areas with lower canopy cover, inside the TIX is increasing due to pervasive sources of fire ignition, more frequent extreme drought events, and changing slash-and-burn agricultural practices. Between 2001 and 2020, nearly 189 000 hectares (∼7%) of the TIX became degraded by recurrent drought and fire events that were the main factors driving forest degradation, particularly in seasonally flooded forests. After three fire events, the probability of forest loss was higher in seasonally flooded areas (63%) compared to upland areas (41%). Given the same fire frequency, areas that have not suffered with extreme droughts showed a 24% lower probability of forest loss compared to areas that experienced three drought events. Distance from villages and human density also had a marked effect on forest cover loss, which was generally higher in areas close to the largest villages. In one of the most culturally diverse Indigenous lands of the Amazon, in a landscape highly threatened by deforestation, our findings demonstrate that climate change may have already exceeded the conditions to which the system has adapted.