Evaluation of geostatistical techniques to estimate the spatial distribution of aboveground biomass in the Amazon rainforest using high-resolution remote sensing data

ABSTRACT The spatial distribution of forest biomass in the Amazon is heterogeneous with a temporal and spatial variation, especially in relation to the different vegetation types of this biome. Biomass estimated in this region varies significantly depending on the applied approach and the data set used for modeling it. In this context, this study aimed to evaluate three different geostatistical techniques to estimate the spatial distribution of aboveground biomass (AGB). The selected techniques were: 1) ordinary least-squares regression (OLS), 2) geographically weighted regression (GWR) and, 3) geographically weighted regression - kriging (GWR-K). These techniques were applied to the same field dataset, using the same environmental variables derived from cartographic information and high-resolution remote sensing data (RapidEye). This study was developed in the Amazon rainforest from Sucumbíos - Ecuador. The results of this study showed that the GWR-K, a hybrid technique, provided statistically satisfactory estimates with the lowest prediction error compared to the other two techniques. Furthermore, we observed that 75% of the AGB was explained by the combination of remote sensing data and environmental variables, where the forest types are the most important variable for estimating AGB. It should be noted that while the use of high-resolution images significantly improves the estimation of the spatial distribution of AGB, the processing of this information requires high computational demand.

Sensitivity of the Amazon biome to high resolution climate change projections

ABSTRACT: Despite the reduction in deforestation rate in recent years, the impact of global warming by itself can cause changes in vegetation cover. The objective of this work was to investigate the possible changes on the major Brazilian biome, the Amazon Rainforest, under different climate change scenarios. The dynamic vegetation models may simulate changes in vegetation distribution and the biogeochemical processes due to climate change. Initially, the Inland dynamic vegetation model was forced with initial and boundary conditions provided by CFSR and the Eta regional climate model driven by the historical simulation of HadGEM2-ES. These simulations were validated using the Santarém tower data. In the second part, we assess the impact of a future climate change on the Amazon biome by applying the Inland model forced with regional climate change projections. The projections show that some areas of rainforest in the Amazon region are replaced by deciduous forest type and grassland in RCP4.5 scenario and only by grassland in RCP8.5 scenario at the end of this century. The model indicates a reduction of approximately 9% in the area of tropical forest in RCP4.5 scenario and a further reduction in the RCP8.5 scenario of about 50% in the eastern region of Amazon. Although the increase of CO2 atmospheric concentration may favour the growth of trees, the projections of Eta-HadGEM2-ES show increase of temperature and reduction of rainfall in the Amazon region, which caused the forest degradation in these simulations.

Prodromal Questionnaire: translation, adaptation to Portuguese and preliminary results in ultra-high risk individuals and first episode psychosis

OBJECTIVE: The Prodromal Questionnaire (PQ) is a 92-item self-report screening tool for individuals at ultra-high risk (UHR) to develop psychosis. This study aims to present the translation to Portuguese and preliminary results in UHR and first episode (FE) psychosis in a Portuguese sample. METHODS: The PQ was translated from English to Portuguese by two bilingual researchers from the research program on early psychosis of the Instituto de Psiquiatria HCFMUSP, São Paulo, Brazil (ASAS - "Evaluation and Follow up of Adolescents and Young Adults in São Paulo") and back translated by two other researchers. The study participants (n = 11-) were evaluated through the Portuguese version of the Prodromal Questionnaire (PQ) and SIPS. RESULTS: The individuals at UHR (n = 7) presented a lower score than first episode patients (n = 4). The UHR mean scores and standard deviation on Portuguese version of the PQ were: 13.0 ± 10.0 points on positive symptoms subscale, and FE patients: 33.0 ± 10.0. CONCLUSION: The UHR and FE patients' of this study presented PQ scores similar to the ones found in the literature; what suggests that it is possible to use the PQ in Brazilian help-seeking individuals as a screening tool.

Is psychodynamic psychotherapy an effective intervention for individuals at ultra-high risk (UHR) of psychosis?: a case report

OBJECTIVE: To report a case and to discuss the use of psychodynamic psychotherapy (PD-P) to treat individuals at ultra-high risk (UHR) of psychosis. METHODS: An individual at UHR was followed up for 24 months. The baseline evaluation included a psychiatric interview, the Structured Interview for Prodromal Symptoms (SIPS), the Scale of Prodromal Symptoms (SOPS), and neuropsychological assessment. He underwent weekly sessions of PD-P for 12 months and was followed up for 12 months after the end of PD-P. The evaluations were at baseline, after 6-, 12-, and 24-month follow-up. No medication was prescribed during the 24-month follow-up. RESULTS: The prodromal symptoms remitted. The initial total score on the SIPS/SOPS was 37 points. After the first 12 months of PD-P, there was a reduction to 12 points on the SIPS/SOPS score, which stabilized in the 24-month follow-up. There was also a slight improvement in his performance on the neuropsychological evaluations. CONCLUSION: This case report suggests that PD-P can reduce prodromal symptoms; nevertheless, a better understanding of the specificity and efficacy of PD-P as an option of treatment for UHR individuals is needed.