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The concept of resilience can be helpful in describing the relationship between vegetation and climate, especially when considering the likelihood of more extreme climate events due to global warming. However, the quantification and characterization of resilience is a challenge, due to the inherent complexity of the concept, as well as difficulty in comparing different ecosystems across the globe. In order to explore ecosystem resilience to drought, we estimated the resilience and related metrics from a series of land surface model (LSM) simulations with altered climate forcing data, focusing on the responses to changing precipitation. These simulations were performed in the semi-arid region of Caatinga biome, northeastern Brazil. Results showed that the quantification of resilience can be represented as a function between precipitation variation and gross primary productivity (GPP) variation. We compared the resilience components estimated for different vegetation types, which showed differences in the response of vegetation to precipitation variability. The study shows the potential of using LSMs to improve our understanding of the vegetation response to climate change, allowing us to explore possible scenarios that are usually not available in field experiments.

Deforestation in the Brazilian Amazon is related to the use of fire to remove natural vegetation and install crop cultures or pastures. In this study, we evaluated the relation between deforestation, land-use and land-cover (LULC) drivers and fire emissions in the Apyterewa Indigenous Land, Eastern Brazilian Amazon. In addition to the official Brazilian deforestation data, we used a geographic object-based image analysis (GEOBIA) approach to perform the LULC mapping in the Apyterewa Indigenous Land, and the Brazilian biomass burning emission model with fire radiative power (3BEM_FRP) to estimate emitted particulate matter with a diameter less than 2.5 &micro, m (PM2.5), a primary human health risk. The GEOBIA approach showed a remarkable advancement of deforestation, agreeing with the official deforestation data, and, consequently, the conversion of primary forests to agriculture within the Apyterewa Indigenous Land in the past three years (200 km2), which is clearly associated with an increase in the PM2.5 emissions from fire. Between 2004 and 2016 the annual average emission of PM2.5 was estimated to be 3594 ton year&minus, 1, while the most recent interval of 2017&ndash, 2019 had an average of 6258 ton year&minus, 1. This represented an increase of 58% in the annual average of PM2.5 associated with fires for the study period, contributing to respiratory health risks and the air quality crisis in Brazil in late 2019. These results expose an ongoing critical situation of intensifying forest degradation and potential forest collapse, including those due to a savannization forest-climate feedback, within &ldquo, protected areas&rdquo, in the Brazilian Amazon. To reverse this scenario, the implementation of sustainable agricultural practices and development of conservation policies to promote forest regrowth in degraded preserves are essential.

EnglishVis-NIR-SWIR reflectance spectra of leaf samples, collected in the laboratory, allow the calibration of predictive models to quantify their physicochemical attributes in a practical manner and without producing chemical residues. This technique should enable the development of management strategies for intensification of pasture use. However, spectral analysis performed in the laboratory may be affected by the deterioration of plant material during transport from the field to the lab, so storage methods are necessary. This research aimed to evaluate the effects of different storage methods on the spectral response of Mombasa grass leaves. Three methods were evaluated: (i) artificially refrigerated environment, (ii) humid environment, and (iii) without microenvironment control. These methods were tested in five different storage times: 2 hours, 4 hours, 8 hours, 24 hours and 48 hours. The spectral behavior of the leaves still inserted in the plant was used as a quality reference. Results showed notable changes at the earliest storage time for the treatment without microenvironment control. Both methods with microenvironment control stabilized the occurrence of spectral changes over 48 hours of the samples storage, thus both were suggested for this species. portuguesEspectros de reflectância vis-NIR-SWIR de amostras foliares, coletados em laboratorio, permitem a calibracao de modelos preditivos para quantificacao de seus atributos fisico-quimicos de maneira pratica e sem producao de residuos quimicos. Esta tecnica permite o desenvolvimento de estrategias de manejo para a intensificacao do uso de pastagens. Contudo, analises espectrais realizadas em laboratorio podem ser afetadas pela deterioracao do material vegetal durante o transporte do campo ao laboratorio, fazendo-se necessario a utilizacao de metodos de armazenamento. O presente trabalho objetivou avaliar o efeito de diferentes metodos de armazenamento na resposta espectral de folhas de capim Mombaca. Avaliou-se tres metodos: (i) ambiente refrigerado artificialmente; (ii) ambiente umido; e (iii) ao ar livre, sem controle do microambiente; assim como, cinco diferentes tempos de armazenamento: 2 horas, 4 horas, 8 horas, 24 horas e 48 horas. O comportamento espectral das folhas ainda inseridas na planta foi utilizado como referencia de qualidade. Os resultados mostraram alteracoes pronunciadas para o armazenamento ao ar livre ja nos primeiros intervalos de tempo. Ambos metodos com controle de microambiente permitiram estabilizar a ocorrencia de alteracoes espectrais ao longo das 48h de armazenamento das amostras, sendo ambos sugeridos para esta especie

With deforestation and associated fires ongoing at high rates, and amidst urgent need to preserve Amazonia, improving the understanding of biomass burning emissions drivers is essential. The use of orbital remote sensing data enables the estimate of both biomass burning emissions and deforestation. In this study, we have estimated emissions of particulate matter with diameter less than 2.5 µm (PM2.5) associated with biomass burning, a primary human health risk, using the Brazilian Biomass Burning emission model with Fire Radiative Power (3BEM_FRP), and estimated deforestation based on the MapBiomas dataset. Using these estimates, we have assessed for the first time how deforestation drove biomass burning emissions in Amazonia over the last two decades at three scales of analysis: Amazonia-wide, country/state and pixel. Amazonia accounted for 48% of PM2.5 emitted from biomass burning in South America and current deforestation rates have reached values on par with those of the early 21st Century. Emissions and deforestation were concentrated in the Eastern and Central-Southern portions of Amazonia. Amazonia-wide deforestation and emissions were linked through time (R = 0.65). Countries/states with the widest spread agriculture were less likely to be correlated at this scale, likely because of the importance of biomass burning in agricultural practices. Concentrated in regions of ongoing deforestation, in 18% of Amazonia grid cells PM2.5 emissions associated with biomass burning and deforestation were significantly positively correlated. Deforestation is an important driver of emissions in Amazonia but does not explain biomass burning alone. Therefore, future work must link climate and other non-deforestation drivers to completely understand biomass burning emissions in Amazonia. The advance of anthropogenic activities over forested areas, which ultimately leads to more fires and deforestation, is expected to continue, worsening a crisis of dangerous emissions.