Vegetation indices are intended to emphasize the vegetation spectral behavior in relation to the soil and other terrestrial surface targets. The objective of this study was to evaluate the vegetation cover types present in the municipality of Campo Belo do Sul, Brazil, using data from five vegetation indices obtained through satellite images. In order to do so, calculations of the Normalized Difference Vegetation Index (NDVI), Soil-adjusted Vegetation Index (SAVI), Leaf Area Index (LAI), Enhanced Vegetation Index (EVI) and Normalized Difference Water Index (NDWI) were performed using Quantum Gis software. The generated maps allowed the detection of the different vegetation cover classes, thus the results indicated that there is no specific vegetation index that best represents all the evaluated classes in the study, however, NDVI, EVI, and SAVI had good adjustments in the majority of the thematic classes.

Light Detection and Ranging (LiDAR) remote sensing has been established as one of the most promising tools for large-scale forest monitoring and mapping. Continuous advances in computational techniques, such as machine learning algorithms, have been increasingly improving our capability to model forest attributes accurately and at high spatial and temporal resolution. While there have been previous studies exploring the use of LiDAR and machine learning algorithms for forest inventory modeling, as yet, no studies have demonstrated the combined impact of sample size and different modeling techniques for predicting and mapping stem total volume in industrial Eucalyptus spp. tree plantations. This study aimed to compare the combined effects of parametric and nonparametric modeling methods for estimating volume in Eucalyptus spp. tree plantation using airborne LiDAR data while varying the reference data (sample size). The modeling techniques were compared in terms of root mean square error (RMSE), bias, and R2 with 500 simulations. The best performance was verified for the ordinary least-squares (OLS) method, which was able to provide comparable results to the traditional forest inventory approaches using only 40% (n = 63; ~0.04 plots/ha) of the total field plots, followed by the random forest (RF) algorithm with identical sample size values. This study provides solutions for increasing the industry efficiency in monitoring and managing forest plantation stem volume for the paper and pulp supply chain.

Accurately quantifying forest aboveground biomass (AGB) is one of the most significant challenges in remote sensing, and is critical for understanding global carbon sequestration. Here, we evaluate the effectiveness of airborne LiDAR (Light Detection and Ranging) for monitoring AGB stocks and change (ΔAGB) in a selectively logged tropical forest in eastern Amazonia. Specifically, we compare results from a suite of different modelling methods with extensive field data. The calibration AGB values were derived from 85 square field plots sized 50 × 50 m field plots established in 2014 and which were estimated using airborne LiDAR data acquired in 2012, 2014, and 2017. LiDAR-derived metrics were selected based upon Principal Component Analysis (PCA) and used to estimate AGB stock and change. The statistical approaches were: ordinary least squares regression (OLS), and nine machine learning approaches: random forest (RF), several variations of k-nearest neighbour (k-NN), support vector machine (SVM), and artificial neural networks (ANN). Leave-one-out cross-validation (LOOCV) was used to compare performance based upon root mean square error (RMSE) and mean difference (MD). The results show that OLS had the best performance with an RMSE of 46.94 Mg/ha (19.7%) and R² = 0.70. RF, SVM, and ANN were adequate, and all approaches showed RMSE ≤54.48 Mg/ha (22.89%). Models derived from k-NN variations all showed RMSE ≥64.61 Mg/ha (27.09%). The OLS model was thus selected to map AGB across the time-series. The mean (±sd—standard deviation) predicted AGB stock at the landscape level was 229.10 (±232.13) Mg/ha in 2012, 258.18 (±106.53) in 2014, and 240.34 (sd ± 177.00) Mg/ha in 2017, showing the effect of forest growth in the first period and logging in the second period. In most cases, unlogged areas showed higher AGB stocks than logged areas. Our methods showed an increase in AGB in unlogged areas and detected small changes from reduced-impact logging (RIL) activities occurring after 2012. We also detected that the AGB increase in areas logged before 2012 was higher than in unlogged areas. Based on our findings, we expect our study could serve as a basis for programs such as REDD+ and assist in detecting and understanding AGB changes caused by selective logging activities in tropical forests.

Tropical forests, such as the Atlantic Forest, are among the most relevant forest formations regarding the provision of environmental ecosystem services, however, after centuries of human expansion, most of the Atlantic Forest is reduced to forest patches. This work aimed to map and analyse the landscape structure of the Atlantic Forest remnants in the city of Goiana, PE, Brazil. For that, metric indexes of landscape ecology were used, with the description of the spatial elements that determine the existing ecological processes and their importance on biological conservation in an Atlantic Forest patch, located in the municipality of Goiana, PE, Brazil. The native vegetation remnants map was obtained through supervised classification process, using images from LANDSAT 8 sensor, in the QGIS 2.18.9 computational application, and the SCP (Semi-Automatic Classification Plugin) with the Maximum Likelihood algorithm. The landscape ecology analysis was performed in the ArcGIS 10.1 software, aided by the Vector-based Landscape Analysis Tools Extension (V-LATE) 2.0 beta. The native vegetation fragmentation was related to the size class Original Research Articlewhich they belong, observing the landscape ecology indexes for each class to compare the vegetation patches conservation degree and size. That way, the map of the native vegetation occupied areas for the year of 2017 was obtained, with 241 patches being identified, of which approximately 45% were classified as very small patches. Therefore, it was noticed that there is a high degree of fragmentation in that region that can lead to the reduction of biodiversity. The smaller patches also presented higher edge density and greater edge effect. In relation to the proximity, when analysed together and with no size class distinction, the degree of isolation decreased dramatically, which indicates the importance of the smaller patches for the landscape and the ecological processes.

O diabetes mellitus é uma desordem metabólica de múltipla etiologia caracterizada pela hiperglicemia crônica com distúrbios no metabolismo que podem afetar de modo negativo a cicatrização e o reparo tecidual. Diversas vias de sinalização desempenham um papel essencial na manutenção da integridade do esqueleto pela regulação positiva ou negativa das células ósseas. As semaforinas constituem uma família de proteínas ligadas a superfície celular ou secretadas que são capazes de regular a interação, morfologia e função de diferentes tipos de células. A Semaforina 3A (Sem3A) tem sido envolvida em atividades de remodelamento ósseo, e também são encontrados relatos sobre a atividade da Semaforina 4D (Sem4D) durante a reabsorção óssea realizada pelos osteoclastos. O objetivo do presente estudo foi avaliar o nível de expressão das Semaforinas 3A e 4D do tecido ósseo alveolar neoformado de ratos normo e hiperglicêmicos. Os animais foram divididos nos seguintes grupos: Não Hiperglicemicos (NH, n=10) e hiperglicêmicos (H, n=10). A hiperglicemia foi induzida nos animais pela administração de água com 10% de frutose e estreptozotocina no 14º dia após. No 76º dia após indução da hiperglicemia, todos os animais foram anestesiados  e submetidos a uma exodontia para remoção dos molares inferiores. No 84º dia aconteceu à eutanásia, em seguida foi realizada uma curetagem do tecido ósseo neoformado que foi armazenado em RNA later® para extração do RNA total, tratamento com DNAse e preparo do cDNA e análise da expressão gênica de Sem3A e Sem4D. Os resultados foram submetidos a teste de normalidade e foram selecionados teste não paramétrico com nível de significância estabelecido em 5% (p <0,05). Os resultados do presente estudo sugerem que no modelo experimental utilizado, os níveis de expressão gênica de Sema 3A e 4D não estão associados ao impacto negativo da hiperglicemia sobre o reparo ósseo.