Past and future assessment of vegetation activity for the state of Amazonas-Brazil

Five challenges to humanity: Learning from pattern/repeat failures in past disasters?

“…The 1997–1998 Indonesian forest fires which burnt 8 million hectares, killed 240 people and caused widespread health-damaging pollution affecting surrounding countries originated from a combination of an El-Nino event, illegal logging and fires deliberately lit for land-clearing purposes, principally to grow palm oil. This was mirrored in subsequent fires in Indonesia, Brazil, Peru and elsewhere, where powerful economic interests undermined regulatory responses, suppressed native peoples, disempowered small landholders and encouraged land-use practices that increased the risk of uncontained fires and long-term environmental degradation/species diversity loss (Harrison et al, 2009; Purnomo et al, 2019; Vilanova et al, 2020). In the 2008 Sichuan earthquake, large numbers of school buildings collapsed (official death toll almost 5000 students and injuring 3 times as many) with subsequent investigation revealing widespread corruption and shoddy construction (design, engineering and materials) were responsible for many failures (Miyamoto et al, 2008; Yamamura, 2013).…”

Future spatial modeling of vegetation in the Central Atlantic Forest Corridor, Brazil

“…The ARIMA model used in this study showed satisfactory results in the NMDI spatiotemporal modeling, enabling identification of the CAFC areas that will become drier and, therefore, may be more prone to the occurrence of fires. The reliability of using the ARIMA model for temporal modeling of environmental variables in tropical forests has been shown in other studies on the Atlantic Forest (Silva Junior et al, 2019;Santana et al, 2020;Vilanova et al, 2020;Santos et al, 2021).…”

“…The ARIMA model (Equation 2) uses past data to perform the future forecast based on two main characteristics of autocorrelation and moving averages. The future modeling methodology used in this study was adapted from (Silva Junior et al, 2019;Santana et al, 2020;Vilanova et al, 2020;Santos et al, 2021).…”

Assessing precipitation extremes (1981–2018) and deep convective activity (2002–2018) in the Amazon region with CHIRPS and AMSU data

“…The increase in CDD along southern and eastern edges of the Amazon region in the dry and dry-to-wet transition seasons, qualitatively coincide with areas where deforestation were or still are rampant; nevertheless, however tempting it is to suggest a causality between them, an initial statistical analysis between land cover changes and changes in PEs did not yield any significant signal (not shown). Even though major droughts or intense precipitation/flood events in the region are primarily controlled by oceanic-atmospheric forcing (Towner et al, 2020), drought conditions may be particularly worsened by deforestation leading to a feedback that increases the vulnerability of remaining vegetation especially at the end of dry season (September-November; Bagley et al, 2013;Zemp et al, 2017b; RR wj , where RR wj ≥ P95w days and Spracklen, 2019; Staal et al, 2020;Vilanova et al, 2020;Caioni et al, 2020). At the same time that technological progress is heavily relied upon to counter the effects of rising temperatures and precipitation pattern changes, there arises a paradox in which the very deforestation for crop and pasture expansion may be amplifying global climate change effects on local scale (Staal et al, 2020).…”