Quantifying the immediate carbon emissions from El Niño-mediated wildfires in humid tropical forests

Wildfires produce substantial CO2 emissions in the humid tropics during El Niñomediated extreme droughts, and these emissions are expected to increase in coming decades. Immediate carbon emissions from uncontrolled wildfires in human-modified tropical forests can be considerable owing to high necromass fuel loads. Yet, data on necromass combustion during wildfires are severely lacking. The present study evaluated necromass carbon stocks before and after the 2015– 2016 El Niño in Amazonian forests distributed along a gradient of prior human disturbance. Landsat-derived burn scars were used to extrapolate regional immediate wildfire CO2 emissions during the 2015–2016 El Niño. Before the El Niño, necromass stocks varied significantly with respect to prior disturbance and were largest in undisturbed primary forests (30.2 ± 2.1 Mg ha-1, mean ± s.e.) and smallest in secondary forests (15.6 ± 3.0 Mg ha-1). However, neither prior disturbance nor a proxy of fire intensity (median char height) explained necromass losses due to wildfires. In the 6.5 million hectare (6.5 Mha) study region, almost 1 Mha of primary (disturbed and undisturbed) and 20,000 ha of secondary forest burned during the 2015–2016 El Niño. Covering less than 0.2% of Brazilian Amazonia, these wildfires resulted in expected immediate CO2 emissions of approximately 30 Tg, three to four times greater than comparable estimates from global fire emissions databases. Uncontrolled understorey wildfires in humid tropical forests during extreme droughts are a large and poorly quantified source of CO2 emissions

State-of-the-art imaging in oesophago-gastric cancer

Radiological investigations are essential in the management of oesophageal and gastro-oesophageal junction cancers. The current multimodal combination of CT, 18F-fluorodeoxyglucose positron emission tomography combined with CT (PET/CT) and endoscopic ultrasound (EUS) has limitations, which hinders the prognostic and predictive information that can be used to guide optimum treatment decisions. Therefore, the development of improved imaging techniques is vital to improve patient management. This review describes the current evidence for state-of-the-art imaging techniques in oesophago-gastric cancer including high resolution MRI, diffusion-weighted MRI, dynamic contrast-enhanced MRI, whole-body MRI, perfusion CT, novel PET tracers, and integrated PET/MRI. These novel imaging techniques may help clinicians improve the diagnosis, staging, treatment planning, and response assessment of oesophago-gastric cancer

Caracterizacão da heterogeneidade espacial da paisagem utilizando parâmetros do semivariograma derivados de imagens NDVI

[EN] Assuming a relationship between landscape heterogeneity and measures of spatial dependence by using remotely sensed data, the aim of this work was to evaluate the potential of semivariogram parameters, derived from satellite images with different spatial resolutions, to characterize landscape spatial heterogeneity of forested and human modified areas. The NDVI (Normalized Difference Vegetation Index) was generated in an area of Brazilian amazon tropical forest (1,000 km²). We selected samples (1 x 1 km) from forested and human modified areas distributed throughout the study area, to generate the semivariogram and extract the sill (¿²-overall spatial variability of the surface property) and range (¿-the length scale of the spatial structures of objects) parameters. The analysis revealed that image spatial resolution influenced the sill and range parameters. The average sill and range values increase from forested to human modified areas and the greatest between-class variation was found for LANDSAT 8 imagery, indicating that this image spatial resolution is the most appropriate for deriving sill and range parameters with the intention of describing landscape spatial heterogeneity. By combining remote sensing and geostatistical techniques, we have shown that the sill and range parameters of semivariograms derived from NDVI images are a simple indicator of landscape heterogeneity and can be used to provide landscape heterogeneity maps to enable researchers to design appropriate sampling regimes. In the future, more applications combining remote sensing and geostatistical features should be further investigated and developed, such as change detection and image classification using object-based image analysis (OBIA) approaches.[PT] Assumindo a existência de uma relação entre a heterogeneidade da paisagem e medidas de dependência espacial obtidas de dados de sensoriamento remoto, o objetivo deste estudo foi avaliar o potencial dos parâmetros do semivariograma derivados de imagens de satélite com diferentes resoluções espaciais, para caracterizar áreas cobertas por floresta e áreas sob ação antrópica. Para isso, o NDVI (Índice de Vegetação da Diferença Normalizada) de cada umas das imagens (SPOT 6, Landsat 8 e MODIS Terra) foi gerado em uma área de floresta tropical Amazônica (1.000 km²), onde foram selecionadas amostras (1 x 1 km) de áreas florestadas e áreas antrópicas. A partir destes dados, foram gerados os semivariogramas e extraídos os parâmetros patamar (¿²-variabilidade espacial total) e alcance (¿-distância dentro da qual as amostras apresentam-se estruturadas espacialmente). A análise revelou que a resolução espacial das imagens influencia os parâmetros ¿² e ¿, apresentando significativo aumento das áreas de florestas para as áreas sob ação antrópica. A maior variação entre estas classes foi obtida com as imagens Landsat 8, indicando estas imagens, com resolução espacial de 30 metros, a mais apropriada para a obtenção dos parâmetros do semivariograma objetivando a caracterização da heterogeneidade espacial da paisagem. Combinando o sensoriamento remoto e técnicas geostatisticas, demonstrou-se que os parâmetros do semivariograma derivados de imagens NDVI podem ser utilizados como um simples indicador de heterogeneidade da paisagem, gerando mapas que permitem aos pesquisadores delinearem com maior eficácia o regime de amostragem. Outras aplicações combinando estas duas técnicas devem ser investigadas, como por exemplo a detecção de mudanças na cobertura do solo e a classificação de imagens utilizando análises orientada a objetos (OBIA).The authors are grateful to the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES), Department of Forest Science of the Federal University of Lavras (UFLA) and the ONF Brazil group for supporting this work.De Oliveira Silveira, EM.; De Mello, JM.; Acerbi Junior, FW.; Dos Reis, AA.; Withey, KD.; Ruiz Fernández, LÁ. (2017). Characterizing Landscape Spatial Heterogeneity Using Semivariogram Parameters Derived from NDVI Images. Cerne. 23(4):413-422. https://doi.org/10.1590/01047760201723042370S41342223

Spatial distribution of wood volume in brazilian savannas

Here we model and describe the wood volume of Cerrado Sensu Stricto, a highly heterogeneous vegetation type in the Savanna biome, in the state of Minas Gerais, Brazil, integrating forest inventory data with spatial-environmental variables, multivariate regression, and regression kriging. Our study contributes to a better understanding of the factors that affect the spatial distribution of the wood volume of this vegetation type as well as allowing better representation of the spatial heterogeneity of this biome. Wood volume estimates were obtained through regression models using different environmental variables as independent variables. Using the best fitted model, spatial analysis of the residuals was carried out by selecting a semivariogram model for generating an ordinary kriging map, which in turn was used with the fitted regression model in the regression kriging technique. Seasonality of both temperature and precipitation, along with the density of deforestation, explained the variations of wood volume throughout Minas Gerais. The spatial distribution of predicted wood volume of Cerrado Sensu Stricto in Minas Gerais revealed the high variability of this variable (15.32 to 98.38 m3 ha-1) and the decreasing gradient in the southeast-northwest direction914COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESSem informaçã

Quantifying immediate carbon emissions from El Nino-mediated wildfires in humid tropical forests

Wildfires produce substantial CO2 emissions in the humid tropics during El Niño-mediated extreme droughts, and these emissions are expected to increase in coming decades. Immediate carbon emissions from uncontrolled wildfires in human-modified tropical forests can be considerable owing to high necromass fuel loads. Yet, data on necromass combustion during wildfires are severely lacking. Here, we evaluated necromass carbon stocks before and after the 2015–2016 El Niño in Amazonian forests distributed along a gradient of prior human disturbance. We then used Landsat-derived burn scars to extrapolate regional immediate wildfire CO2 emissions during the 2015–2016 El Niño. Before the El Niño, necromass stocks varied significantly with respect to prior disturbance and were largest in undisturbed primary forests (30.2 ± 2.1 Mg ha−1, mean ± s.e.) and smallest in secondary forests (15.6 ± 3.0 Mg ha−1). However, neither prior disturbance nor our proxy of fire intensity (median char height) explained necromass losses due to wildfires. In our 6.5 million hectare (6.5 Mha) study region, almost 1 Mha of primary (disturbed and undisturbed) and 20 000 ha of secondary forest burned during the 2015–2016 El Niño. Covering less than 0.2% of Brazilian Amazonia, these wildfires resulted in expected immediate CO2 emissions of approximately 30 Tg, three to four times greater than comparable estimates from global fire emissions databases. Uncontrolled understorey wildfires in humid tropical forests during extreme droughts are a large and poorly quantified source of CO2 emissions. This article is part of a discussion meeting issue ‘The impact of the 2015/2016 El Niño on the terrestrial tropical carbon cycle: patterns, mechanisms and implications’

Reducing the effects of vegetation phenology on change detection in tropical seasonal biomes

Tropical seasonal biomes (TSBs), such as the savannas (Cerrado) and semi-arid woodlands (Caatinga) of Brazil, are vulnerable ecosystems to human-induced disturbances. Remote sensing can detect disturbances such as deforestation and fires, but the analysis of change detection in TSBs is affected by seasonal modifications in vegetation indices due to phenology. To reduce the effects of vegetation phenology on changes caused by deforestation and fires, we developed a novel object-based change detection method. The approach combines both the spatial and spectral domains of the normalized difference vegetation index (NDVI), using a pair of Operational Land Imager (OLI)/Landsat-8 images acquired in 2015 and 2016. We used semivariogram indices (SIs) as spatial features and descriptive statistics as spectral features (SFs). We tested the performance of the method using three machine-learning algorithms: support vector machine (SVM), artificial neural network (ANN) and random forest (RF). The results showed that the combination of spatial and spectral information improved change detection by correctly classifying areas with seasonal changes in NDVI caused by vegetation phenology and areas with NDVI changes caused by human-induced disturbances. The use of semivariogram indices reduced the effects of vegetation phenology on change detection. The performance of the classifiers was generally comparable, but the SVM presented the highest overall classification accuracy (92.27%) when using the hybrid set of NDVI-derived spectral-spatial features. From the vegetated areas, 18.71% of changes were caused by human-induced disturbances between 2015 and 2016. The method is particularly useful for TSBs where vegetation exhibits strong seasonality and regularly spaced time series of satellite images are difficult to obtain due to persistent cloud cover

The Merits of Playing It by the Book: Routine versus Deliberate Learning and the Development of Dynamic Capabilities

In this study, we investigate the nature of dynamic capabilities and use a fine-grained measurement to test how centralization, routinization, and formalization relate to the underlying learning components of dynamic capabilities. We find that the effects of our three dimensions of managerial practices are broadly similar for almost all components of dynamic capabilities, and that only a few show a different pattern. Centralization and routinization are negatively related to dynamic capabilities, formalization is shown to have a significantly positive effect. We provide insights into the role of three dimensions of managerial practice by explaining variation among the learning components of dynamic capabilities. This has implications for the nature and development of dynamic capabilities as well as for the routine versus deliberate learning debate

Modelling aboveground biomass in forest remnants of the Brazilian Atlantic Forest using remote sensing, environmental and terrain-related data

The Brazilian Atlantic Forest, one of the most threatened tropical regions in the world, exhibits high levels of terrestrial aboveground biomass (AGB). We propose a random forest approach to model, map and assess whether public lands provide protection for AGB in the Rio Doce watershed, one of the most important watercourses of the Atlantic Forest biome. We used 188 field plots and individual and hybrid features from remote sensing, environmental and terrain-related data. The hybrid model improved the AGB prediction by reducing the root mean square error to 33.43 Mg/ha and increasing the coefficient of determination (R2) to 0.57. The total estimated AGB was 178,967,656.73 Mg, ranging from 20.40 to 167.72 Mg/ha following the seasonal precipitation pattern and anthropogenic disturbance effects. Only 5.76% of the total AGB was located on public protected lands, totalling 10,305,501 Mg, while most of the remaining AGB were located on private properties

Object-Based Change Detection in the Cerrado Biome Using Landsat Time Series

Change detection methods are often incapable of accurately detecting changes within time series that are heavily influenced by seasonal variations. Techniques for de-seasoning time series or methods that apply the spatial context have been used to improve the results of change detection. However, few studies have explored Landsat’s shortwave infrared channel (SWIR 2) to discriminate between seasonal changes and land use/land cover changes (LULCC). Here, we explored the effectiveness of Operational Land Imager (OLI) spectral bands and vegetation indices for detecting deforestation in highly seasonal areas of Brazilian savannas. We adopted object-based image analysis (OBIA), applying a multidate segmentation to an OLI time series to generate input data for discrimination of deforestation from seasonal changes using the Random Forest (RF) algorithm. We found adequate separability between deforested objects and seasonal changes using SWIR 2. Using spectral indices computed from SWIR 2, the RF algorithm generated a change map with an overall accuracy of 88.3%. For deforestation, the producer’s accuracy was 88.0% and the user’s accuracy was 84.6%. The SWIR 2 channel as well as the mid-infrared burn index presented the highest importance among spectral variables computed by the RF average impurity decrease measure. Our results give support to further change detection studies regarding to suitable spectral channels and provided a useful foundation for savanna change detection using an object-based method applied to Landsat time series