ABOVEGROUND BIOMASS ESTIMATION IN A TROPICAL FOREST WITH SELECTIVE LOGGING USING RANDOM FOREST AND LIDAR DATA

The tropical forest is characterized by expressive biomass and stores high amounts of carbon, which is an important variable for climate monitoring. Thus, studies aiming to analyze suitable methods to predict biomass are crucial, especially in the tropics, where dense vegetation makes modeling difficult. Thus, the objective of the present study was to estimate aboveground biomass (AGB) in a tropical forest area with selective logging in the Amazon forest using the Random Forest (RF) machine learning algorithm and LiDAR data. For this, 85 sample units were used at Fazenda Cauaxi, in the municipality of Paragominas, Pará State. LiDAR data were collected in 2014 and made available by the Sustainable Landscapes Project. The software R was used for data analysis. Among the LiDAR metrics, the average height was used as it had the greatest significance to compose the model. The model presented a pseudo R² of 0.69 (value obtained by the RF), Spearman's Correlation Coefficient of 0.80, RMSE of 47.05 Mg.ha-1 (19.84%), and Bias of 2.06 Mg.ha-1 (0.87%). With the results, it was possible to infer that the average height metric was enough to estimate AGB in a tropical forest with selective logging, in addition, the RF algorithm the biomass to be estimated, which can be used to assist in monitoring and action management in areas of selective logging and serve as a basis for climate change mitigation policies

COMPARAÇÃO ENTRE A DELIMITAÇÃO MANUAL E AUTOMÁTICA DA BACIA DO ARROIO CORUPÁ, RS, BRASIL

http://dx.doi.org/10.5902/223611707277Several environmental impacts such as deforestation, soil erosion, water pollution and chaoticurban occupation are related to lack of planning in land use in watersheds. For proper planningand management of water resources is critical to proper delineation of watersheds. Thus, thisstudy aimed to analyze a methodology for automatic delineation of watersheds in the basin of thecreek Corupá, Rio Grande do Sul/RS, Brazil, using design data Shuttle Radar Topographic Mission(SRTM) and compare with the values obtained in the area of manual scanning with based ontopographic maps with scale of 1:50 000. For this, the data were integrated and processed usingthe software ArcGIS 9.2 (ESRI, 2006). The methodology used in this process was divided into six stages, as follows: filling depressions ("fill sinks"), flow direction ("flow direction"), accumulatedflow ("flow accumulation"), extraction of drainage ("conditional - > con "), linking crossing stream("stream link ") and delineation of watersheds ("watershed"). The basin area delimited from thecontours of the topographical was 29.162,4 ha and for the automatic delineation met total area of28.472,2 ha, representing a difference of 2,4% of total area 690,2 ha. The results show very closevalues confirming be advantageous to automate the delineation of watersheds by several factorssuch as gratuity, the SRTM data accuracy and the great coverage of available data, since not allregions are data availability and the cartographic results may vary according to the humanperception.http://dx.doi.org/10.5902/223611707277Diversos impactos ambientais como desmatamento, erosão, poluição da água e a ocupação urbana desordenada estão relacionados à falta de planejamento no uso do solo em bacias hidrográficas. Para um bom planejamento e gerenciamento dos recursos hídricos é fundamental a delimitação adequada de bacias hidrográficas. Nesse sentido, este trabalho teve como objetivo analisar uma metodologia de delimitação automática de bacias hidrográficas na bacia do Arroio Corupá, RS, Brasil, utilizando dados do projeto Shuttle Radar Topographic Mission(SRTM) e comparar com os valores de área obtidos na digitalização manual com base em cartas topográficas, com escala de 1:50 000. Para isso, os dados foram integrados e processados no software ArcGis 9.3. A metodologia utilizada nesse processo subdividiu-se em seis etapas, sendo: preenchimento de depressões (“fill sinks”), direção de fluxo (“flow direction”), fluxo acumulado (“flow accumulation”), extração de drenagens (“Conditional -> Con”), ligação de cruzamentos de fluxo (“stream Link”) e delimitação de bacias (“Watershed”). A área da bacia delimitada a partir das curvas de nível da carta topográfica foi de 29.162 ha e para a delimitação automática encontrou-se área total de 28.472 ha, o que representa uma diferença de 2% da área total, 690 ha. Os resultados apresentam valores muito próximos confirmando ser vantajosa a automatização da delimitação de bacias hidrográficas por diversos fatores como gratuidade, precisão dos dados do SRTM e pela grande cobertura disponível dos dados, uma vez que nem todas as regiões apresentam disponibilidade de dados cartográficos e que os resultados podem variar de acordo com a percepção humana

SPECTRORADIOMETRY OF COMMERCIAL WOOD VENEERS IN THE VISIBLE AND NEAR INFRARED SPECTRA

AbstractThis study aimed to analyze the spectral response in ten commercial wood veneers from ten forest species, in the visible and near-infrared regions of the electromagnetic spectrum. Reflectance of samples was obtained using a Field Spec®3 spectroradiometer. Results were verified through the analysis of variance followed by a Tukey range test on multiple means, which showed significant differences in both the analyzed spectral regions for the studied species. The most expressive differences were observed in the visible region and occurred between the “tauari” and “nogueira” wood veneers. Those results may be due to the chemical properties of the analyzed material, to the cellulose content, to the proportion of the components in the cell wall and to the extractives in the lumen, to microfibril angle, density and to resistance and stiffness properties. Therefore, the applied technique was a tool capable to identify reflectance variations in regions of the electromagnetic spectrum which correspond to peculiar colorimetric characteristics of each species.Keywords: Spectroscopy; reflectance, spectral signature, wood veneers. ResumoEspectrorradiometria no visível e no infravermelho próximo de lâminas de madeiras comerciais. O presente estudo tem como objetivo analisar a resposta espectral na região do visível e no infravermelho próximo do espectro eletromagnético em laminas de madeiras de dez espécies florestais. As reflectâncias das amostras foram obtidas através do espectrorradiômetro Field Spec®3. Os resultados foram verificados por meio de análise de variância seguido do teste de múltiplas médias de Tukey, demonstrando existir diferenças significativas em ambas regiões do espectro eletromagnético nas espécies em estudo. As distinções de maior expressividade foram observadas na região do visível e ocorreram entre as laminas de madeira de tauari e nogueira. Essas respostas podem ser atribuídas às propriedades químicas do material investigado, bem como o teor de celulose, a proporção dos componentes presentes na parede celular e dos extrativos presentes no lume, à densidade, ângulo microfibrilar e às propriedades de resistência e rigidez. Dessa forma, a técnica empregada mostra-se como uma ferramenta capaz de registrar em regiões do espectro eletromagnético variações da reflectância que respondem à características colorimétricas  peculiares de cada espécie.Palavras-chave: Espectroscopia; reflectância; assinatura espectral; lâminas de madeira.AbstractThis study aimed to analyze the spectral response in ten commercial wood veneers from ten forest species, in the visible and near-infrared regions of the electromagnetic spectrum. Reflectance of samples was obtained using a Field Spec®3 spectroradiometer. Results were verified through the analysis of variance followed by a Tukey range test on multiple means, which showed significant differences in both the analyzed spectral regions for the studied species. The most expressive differences were observed in the visible region and occurred between the “tauari” and “nogueira” wood veneers. Those results may be due to the chemical properties of the analyzed material, to the cellulose content, to the proportion of the components in the cell wall and to the extractives in the lumen, to microfibril angle, density and to resistance and stiffness properties. Therefore, the applied technique was a tool capable to identify reflectance variations in regions of the electromagnetic spectrum which correspond to peculiar colorimetric characteristics of each species.Keywords: Spectroscopy; reflectance, spectral signature, wood veneers

INFLUENCE OF FOREST COVERAGE IN THE SURFACE ALBEDO

The surface albedo controls the energy balance between the surface and the atmosphere, being a primordial variable to identify climatic variations. The objective of this study was to evaluate the changes of the surface albedo in different Land Use and Land Cover in the Atlantic Forest biome from images TM/Landsat 5 and OLI/Landsat 8, verifying its variation in 30 years. The images used were path-row 221-080, which covered the Floresta Nacional de São Francisco de Paula on the dates of 1987 and 2017. The albedo was obtained by the method of the Surface Energy Balance Algorithm for Land, while the mapping of Land Use and Land Cover was performed by the Bhattacharyya algorithm, identifying four thematic classes. Finally, the albedo was crossed with the thematic classes, evidencing their variation in function of the changes in the land cover. The surface albedo ranged from 6 to 22%, but the year 1987 concentrated albedo values higher than in 2017. The native forest presented superior albedo to the Forest Plantations in both dates due to the structure of the canopy of this class. The spatial analysis of the albedo exposes the relation of this climatic variable to the cover of the terrestrial surface. Thus changes in the vegetation cover cause alterations in the albedo, influencing changes in the radiation and atmospheric fluxes

Use of Machine Learning Algorithms in the Classification of Forest Species

Optimization in the process of managing forest resources seeks alternatives that make data collection possible. One of them alternatives is spectroradiometry, which consists of measuring the spectral response, having as product the response of the target in relation to the incident radiation along the electromagnetic spectrum, and that, using machine learning, with pre-selected models, makes it possible to identify. Given the above, the study aimed to use machine learning algorithms to classify species by vegetation indices from reflectance data. The study was developed at the Federal University from Santa Maria, working with the species Ficus benjamina, Inga marginata, Handroanthus chrysotrichus, Psidium cattleianum, Salix humboldtiana, Corymbia citriodora and Myrcianthes pungens, and spectral readings of the leaves were taken using the FieldSpec®3 spectroradiometer connected to RTS-3ZC3 integrating  sphere. The reflectance values with wavelength ranged in amplitude from 350 ƞm to 2,500 ƞm and spectral resolution of 1 ƞm. Vegetation indices were calculated using the software R Studio, being: NDVI, SAVI, RVI, GNDVI, NDWI, NDWI2, GEMI, DVI, TVI, RVI, MSAVI, WDVI. The algorithms used to develop machine learning were: Random Forest (RF), k-Nearest Neighbors (K-NN), Naive Bayes (NB) and Support Vector Machine (SVM). RF proves to be the most appropriate for data validation, with 85% global accuracy, followed by SVM, with 71%, K-NN with 64% and NB with 35%. The indices with the best performance to point the species were NDWI and SAVI.

Caracterização espectral de mudas de Pinus taeda expostas a incidência de geadas

The frost occurence is a climatic factor that causes negatives consequences to a less or higher degree in all steps of forestry production, and can even be characterized as a limiting factor in commercial plantations in southern Brazil. Through remote sensing and espectroradiometry techniques, its possible to obtain information about the plants stress conditions, caused by the intense cold and the ice formation. The aim of this paper was to evaluate and analyze reflectance curves in the spectral range 400-2000ηm, from seedlings of Pinus taeda exposed to frost incidence in the Nursery of Universidade Federal de Santa Maria in four harsh winter day. The radiometric measurements were made with spectroradiometer FieldSpec\uae3connected to RTS-3ZC unit (integrating sphere). Statistical tests (ANOVA and Tukey), showed the significant differences existence in reflectance curves between measurement periods. Over the time there was a significant increase in reflectance factor in the visible range, which shows the loss of photosynthetic pigments. The disintegration of mesophyll is also evidenced by the substantial increase in reflectance in the near infrared region. In this way, this technique was capable of detecting changes in the physiology of leaves submitted to stress caused by frost. What enhances the use of this tool in planning forest activities subject to losses by this phenomenon.Pages: 3171-317

Remotely piloted aircraft systems and forests: a global state of the art and future challenges

Remotely piloted aircraft system (RPAS) platforms are able to optimize the process of acquiring aerial images and improve the quality of the products generated in terms of spatial and temporal resolution. The exponential advance of the use of RPAS platforms in forestry, especially from the year 2010, is noteworthy. In this review, we present the global state of the art of the development and applications of RPAS technology in forestry, structured from a systematic review. Our results reveal a trend towards the use of multirotor RPAS platforms compared with fixed-wing platforms and that sensors that register in the visible spectral range are still the most widely used. More recent research has shown applications geared especially for areas such as forest inventory, with many innovations based on the detection of individual trees. Special focus has also been given to new alternatives for pest and disease mapping and phenological phenomena that occur at short intervals, as well as the monitoring of fires and postharvest areas. Therefore, there is a great potential for the use of RPAS platforms in a wide range of forest applications, whether linked to the productive sector or to the conservation of biodiversity, with great advances for spatiotemporal forest monitoring and expectations of further progress for the coming years.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

ARTIFICIAL INTELLIGENCE AND ORBITAL IMAGES APPLICATION FOR ANALYSIS OF SPATIAL LAND USE AND COVERAGE PATTERNS

The study aimed to analyze the performance of different machine learning (ML) algorithms in predicting land use and land cover patterns from time series spectral data from Thematic Mapper (TM) and Operational Land Imager (OLI) sensors. The QGIS software was used, where the import of TM / Landsat 5 images began in 2004 and 2009 and OLI/Landsat 8 for 2015 and 2019, to obtain information to characterize and differentiate usage patterns and land cover. Subsequently, training and testing of the algorithms, Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (K-NN), and Naive Bayes (NB), were carried out in the proportions of 80%-20%, 70%-30%, 60%-40% in the KNIME software. The performance was analyzed based on global accuracy and the Kappa index. The RF and SVM for the years 2004 and 2009 showed the best performance (global accuracy), while for the years 2015 and 2019, they were the K-NN and the RF. The Kappa index values indicated that the classifications of the algorithms varied from 0.80 – 1.00. The proportion of 60% (training) and 40% (test) was the one that provided the best results for all the dates analyzed. The data from the pixels sampled from the land use and land cover patterns of the TM and OLI sensor images proved to be efficient for the ML process in the KNIME software

Estimativa de volume florestal com imagem landsat 5

The arboreal volume estimation studies can be made in faster and economic way by indirect methods, such as the techniques of remote sensing. In this context, this work objective to develop estimating models of arboreal volume for orbital images based on forest inventory data from Rio Grande do Sul state. In the Landsat 5 scene was converted the digital number to radiance and to reflectance, making possible associate a biophysics variable to a digital one. The sample units from the inventory went allocated on the image, whose areas had gotten the digital variable information used like independents on the statistical tests: B1 (0,45 - 0,52 μm), B2 (0,52 - 0,60 μm), B3 (0,63 - 0,69 μm), B4 (0,76 - 0,90 μm), NDVI (Normalized Difference Vegetation Index) and RAZÃO (Reason between bands). The dependent variable was the log volume (m³/ha). The stepwise variable selection method returned one linear model with the RAZÃO index as significant to explain the variation in volume. Finally, the selected model was implemented in the software SPRING, generating a numerical grid whose values of pixels represent the estimated volume in m³/ha and volume thematic maps for Eucalyptus sp. per production units.Pages: 1744-175