The behavior of cyanobacteria and its potential use for biofuel production in scale-up conditions is a topic of growing importance. The aim of our work is to study the effects of illumination, stirring, and different growth phases on the cultivation of the cyanobacteria Spirulina platensis in 10 L raceways. The cultivations were carried out in a greenhouse under measured, but not controlled, illumination and in agitated raceways with stirring speeds varying from 0.1 to 0.4 m s⁻¹, using culture media with nutrient depletion. At the end of the stationary phase (SP) and decline of culture, the biomass was harvested and used to determine the chemical composition. The stirring rate and the growing phase influenced the carbohydrate concentration. In both phases of cultivation, compared to high-speed stirring, stirring at lower speeds produced fewer carbohydrates in the culture. Biomass grown until the end of the SP with a stirring speed of 0.35 m s⁻¹ had a carbohydrate content of 72%, which is very high compared to that reported in the literature. GRAPHICAL ABSTRACT

In the last decades, several methodologies for estimating crop phenology based on remote sensing data have been developed and used to create different algorithms. Although many studies have been conducted to evaluate the different methodologies, a comprehensive understanding of the potential of the different current algorithms to detect changes in the growing season is still lacking, especially in large regions and with more than one crop per season. Therefore, this work aimed to evaluate different phenological metrics extraction methodologies. Using data from over 1500 fields distributed across Brazil’s central area, six algorithms, including CropPhenology, Digital Earth Australia tools package (DEA), greenbrown, phenex, phenofit, and TIMESAT, to extract soybean crop phenology were applied. To understand how robust the algorithms are to different input sources, the NDVI and EVI2 time series derived from MODIS products (MOD13Q1 and MOD09Q1) and from Sentinel-2 satellites were used to estimate the sowing date (SD) and harvest date (HD) in each field. The algorithms produced significantly different phenological date estimates, with Spearman’s R ranging between 0.26 and 0.82 when comparing sowing and harvesting dates. The best estimates were obtained using TIMESAT and phenex for SD and HD, respectively, with R greater than 0.7 and RMSE of 16–17 days. The DEA tools and greenbrown packages showed higher sensitivity when using different data sources. Double cropping is an added challenge, with no method adequately identifying it.

In protected areas of the Brazilian savannas (Cerrado), Land Surface Phenology (LSP) is influenced by both precipitation and fire, but the nature of these relationships remains unexplored. Here, we assessed the impacts of precipitation and fire on LSP metrics derived from the Normalized Difference Vegetation Index (NDVI) at Emas National Park (ENP). Using TIMESAT, along with the 250-m Moderate Resolution Imaging Spectroradiometer (MODIS) MOD13Q1 and 30-m Harmonized Landsat Sentinel (HLS) products, we investigated these effects in both grassland and woodland areas. To evaluate the effects of precipitation, we identified the driest and wettest seasonal cycles between 2002 and 2023 and analyzed the relationships between accumulated rainfall during the rainy season and each of the 13 TIMESAT metrics. To assess the effects of fire, three major events were examined: 1 September 2005 (affecting 45% of the park’s area), 12 August 2010 (90%), and 10 July 2021 (21%). The burned grassland area and the subsequent vegetation recovery following the 2021 event were analyzed in detail using a non-burned control site and LSP metrics extracted from the HLS product, covering both pre- and post-disturbance cycles. The results indicated that the metrics most positively correlated to precipitation were Amplitude (AMP), End of Season (EOS), Large and Small Seasonal Integrals (LSI and SSI), and Rate of Increase at the Beginning of the Season (RIBS). The highest correlation coefficients were found in woodland areas, which were less affected by fire disturbance than grassland areas. Similar trends were observed in the behavior of AMP, EOS, and SSI in response to both precipitation and fire, with fire exerting a stronger influence. By decoupling the fire effects from rainfall influence using the control site, we identified Base Level (BL), SSI, EOS, AMP, and Values at the End and Start of the Season (VES and VSS), as the metrics most sensitive to fire and subsequent vegetation recovery in burned areas. The effects of fire were evident for most metrics, both during the disturbance cycle and in the post-fire cycle. Our study underscores the importance of combining MODIS and HLS time series to understand vegetation phenology in the Cerrado.

A bacia hidrográfica do Rio dos Sinos concentra uma intensa atividade agrícola e industrial em seu território, o que ao longo dos anos tem provocado um desequilíbrio ambiental e um comprometimento dos seus recursos naturais. Entendendo a bacia como uma unidade de gestão territorial, se faz necessário o aprimoramento e a criação de ferramentas que auxiliem na gestão deste território. Neste sentido, o presente artigo tem como objetivo a aplicação de uma metodologia de zoneamento ambiental, denominada Potencial de Uso Conservacionista (PUC), com fins de estratificar o território da bacia em níveis de potencial natural de uso. Para isso, foram utilizadas bases geoespaciais de solo, litologia e declividade da região, como dados de entrada deste modelo, determinando pesos para essas variáveis, através de uma Análise Hierárquica de Processos (AHP). A partir disto, foram obtidos os níveis de PUC da bacia do Rio dos Sinos, que foram combinados com um mapa de uso e cobertura do solo da região para um diagnóstico dos níveis de conservação da área. Os resultados mostraram que na bacia predominam os níveis de potencial de uso entre “Médio” e “Alto”. A bacia apresenta certo nível de conservação em seu uso do solo no alto curso, o que combina com os níveis “Baixo” e “Muito Baixo” de PUC ali presentes. O desafio principal reside na necessidade de adequação das atividades agrícolas e indústrias no médio e baixo curso da referida bacia, onde se encontram os níveis de potencial de uso “Alto” e “Muito Alto”, a fim de promover o uso sustentável dos recursos ambientais ali presentes e a mitigação dos impactos ambientais destas atividades.