Disponível on-line no endereço www.igc.usp.br/geologiausp -69 -Resumo Apresenta-se uma ferramenta computacional denominada de AzimuthFinder, para funcionamento dentro do conjunto de programas do ArcGis ® . O programa desenvolvido na linguagem Python tem por finalidade facilitar e otimizar a geração de tabelas de dados azimutais, a partir de lineamentos estruturais previamente traçados em imagens de satélite, fotografias aéreas ou mapas geológicos em meio digital. As características do AzimuthFinder permitem que, a critério do usuário, sejam geradas diferentes tabelas para o mesmo objeto de análise, dependendo do peso relativo atribuído aos lineamentos, por exemplo dando maior peso àqueles de traçado mais longo. Outra característica da ferramenta é a exportação dos dados no formato TXT, que é universal para vários tipos de programas, com formatação específica para softwares como o Stereo32, Win Tensor, OpenStereo e StereoNet 7, que por sua vez são softwares livres preparados para a confecção de diagramas de rosetas. Os testes com a ferramenta demonstraram que é bastante eficiente e rápida para a geração das tabelas azimutais, facilitando de maneira eficaz a confecção de diagramas de rosetas necessários à análise estrutural em áreas essencialmente submetidas à deformação frágil.Palavras-chave: Software; Análise da deformação; Lineamentos estruturais; Tabelas azimutais; Digrama de rosetas. AbstractHere is presented a computational tool named AzimuthFinder, for functioning inside the set of programs of ArcGis ® . The developed program is intended to facilitate and optimize the generation of azimuth data tables, using structural lineaments previously traced in maps that are being worked on that automatic information system. The characteristics of AzimuthFinder allow that, upon the user's choice, different tables get generated for the same analysis object, depending on the relative weight attributed to the lineaments, giving for example greater weight to those of greater extension. Another characteristic of the tool is the file exportation in TXT format, which is universal to several types of programs, with specific formatting to one the software between Stereo32, Win Tensor, OpenStereo and StereoNet7, which are all free software prepared to the confection of rose diagrams. Tests with the program showed that it is very efficient and fast for generating the azimuth data tables, effectively allowing easier confection of rose diagrams, which are necessary for the structural analysis in areas submitted to fragile deformation.
Coarse woody debris (CWD, parts of dead trees) is an important factor in forest management, given its roles in promoting local biodiversity and unique microhabitats, as well as providing carbon storage and fire fuel. However, parties interested in monitoring CWD abundance lack accurate methods to measure CWD accurately and extensively. Here, we demonstrate a novel strategy for mapping CWD volume (m 3 ) across a 4300-hectare study area in the boreal forest of Alberta, Canada using optical imagery and an infra-canopy vegetation-index layer derived from multispectral aerial LiDAR. Our models predicted CWD volume with a coefficient of determination (R2) value of 0.62 compared to field data, and a root-mean square error (RMSE) of 0.224 m 3 /100 m 2 . Models using multispectral LiDAR data in addition to image-analysis data performed with up to 12% lower RMSE than models using exclusively image-analysis layers. Site managers and researchers requiring reliable and comprehensive maps of CWD volume may benefit from the presented workflow, which aims to streamline the process of CWD measurement. As multispectral LiDAR radiometric calibration routines are developed and standardized, we expect future studies to benefit increasingly more from such products for CWD detection underneath canopy cover. 3 of 28 quantify CWD volume based on field data and we believed that ML data would be beneficial to predictions on occluded areas. Variables we predicted would have a relationship with occurrence and quantities of CWD include the height of trees, active and passive vegetation indices, canopy cover, presence of visible CWD, shadows, water, and wetlands. We tested our models both in a calibration area, where the models were developed, and in a verification area 4 km from the calibration area, and compared the best models using ML variables with the best models without ML variables to assess the impact of infra-canopy information on volume estimation accuracy. We then generated a series of CWD-volume maps designed to illustrate the utility of our approach to aid forest restoration efforts and fire-hazard assessments.
Coarse woody debris (CWD; large parts of dead trees) is a vital element of forest ecosystems, playing an important role in nutrient cycling, carbon storage, fire fuel, microhabitats, and overall forest structure. However, there is a lack of effective tools for identifying and mapping both standing (snags) and downed (logs) CWD in complex natural settings. We applied a random forest machine learning classifier to detect CWD in centimetric aerial imagery acquired over a 270-hectare study area in the boreal forest of Alberta, Canada. We used a geographic object-based image analysis (GEOBIA) approach in the classification with spectral, spatial, and LiDAR (light detection and ranging)-derived height predictor variables. We found CWD to be detected with great accuracy (93.4 ± 4.2% completeness and 94.5 ± 3.2% correctness) when training samples were located within the application area, and with very good accuracy (84.2 ± 5.2% completeness and 92.2 ± 3.2% correctness) when training samples were located outside the application area. The addition of LiDAR-derived variables did not increase the accuracy of CWD detection overall (<2%), but aided significantly (p < 0.001) in the distinction between logs and snags. Foresters and researchers interested in CWD can take advantage of these novel methods to produce accurate maps of logs and snags, which will contribute to the understanding and management of forest ecosystems.
ResumoO trabalho objetiva mostrar a aplicação no geoturismo de uma nova ferramenta computacional de análise morfométrica da rede de drenagem baseada nos métodos de Hack (1957Hack ( , 1973 e Etchebehere (2004), para aplicação em estudos de enfoque neotectônico. A rotina criada trabalha a partir de um modelo digital de elevação (MDE) de modo a gerar um mapa de pontos de ruptura de declive de drenagem e/ou quebra de relevo (knickpoints) e foi programada em Python para uso acoplado ao software de sistemas de informação geográfica ArcGIS®, denominada de Knickpoint Finder.Uma área de estudo foi selecionada de maneira a testar e avaliar a capacidade do software na análise e identificação de knickpoints a partir do estudo da morfologia de um recorte geográfico na Serra do Mar no estado do Paraná, com o objetivo de determinar possíveis geossítios com interesse geoturístico. Após a aplicação da ferramenta na área de estudo constatou-se que os dados de knickpoints obtidos podem caracterizar com rapidez e eficácia pontos de interesse relevante à pesquisa geoturística inicial, principalmente no que tange ao inventário de pontos de beleza cênica relevante em se tratando de corredeiras, cachoeiras ou cascatas. Para que a análise regional possa ser realizada a contento é necessário o emprego de técnicas de representação espacial de dados que podem ser realizadas pelo próprio ArcGIS®, imediatamente após o processamento do Knickpoint Finder. Os resultados da técnica mostraram-se satisfatórios na correlação da maior ocorrência de knickpoints com a probabilidade do encontro de geossítios em áreas de grande amplitude, constatando-se ganho de velocidade de delimitação dos mesmos. Desta forma pode-se considerar a ferramenta virtual obtida como satisfatório recurso de auxílio na análise morfométrica voltada ao geoturismo, podendo ser aplicada em qualquer área onde haja cobertura de modelos digitais de elevação.Palavras-chave: Geoturismo; geossítios; MDE; knickpoint; software. AbstractThe study aims to illustrate the geotourism application of a new computational tool for morphometric analysis of the drainage network that is based on Hack (1957Hack ( , 1973 and Etchebehere (2004), for application in studies of neotectonic approach. The computational routine works starting from a digital elevation model (DEM) to generate a map of drainage-slope breaking points and/or relief break (knickpoints) and was programmed in Python for use coupled to the geographical information systems software ArcGIS®, and has been named Knickpoint Finder. An area of study was selected in order to test and evaluate the software's ability to analyze and identify knickpoints from the study of the morphology of a geographical cutout in the Serra do Mar in the state of Paraná, in order to determine possible geosites with geotouristic interest. After application of the tool in the area of study, it was verified that the knickpoint data obtained can characterize quickly and effectively points of interest relevant to the initial geotouristic research, especially in rega...
ResumoO condicionamento morfotectônico na evolução do relevo da Serra do Mar paranaense foi investigado a partir da análise de parâmetros geomorfométricos, geológicos e geográfi cos. Em seis áreas com escarpamento associado a grandes lineamentos estruturais, foram verifi cadas morfoestruturas morfotectonicamente condicionadas. As escarpas estruturais, knickpoints alinhados segundo a direção de importantes estruturas, depósitos aluvionares segmentados por falhas, anomalias de drenagem, remanescentes de paleosuperfícies deformados e bacias suspensas apontaram para evidências que permitem concluir por deformações recentes superimpostas a morfoestruturas mais antigas na formação da paisagem, em especial associadas a estruturas com direções NNE-SSW e E-W. Essas evidências apontam para a atividade neotectônica na conformação do relevo regional.
Forest land-use planning and restoration requires effective tools for mapping and attributing linear disturbances such as roads, trails, and asset corridors over large areas. Most existing linear-feature databases are generated by heads-up digitizing. While suitable for cartographic purposes, these datasets often lack the fine spatial details and multiple attributes required for more demanding analytical applications. To address this need, we developed the Forest Line Mapper (FLM), a semi-automated software tool for mapping and attributing linear features using LiDAR-derived canopy height models. Accuracy assessments conducted in the boreal forest of Alberta, Canada showed that the FLM reliably predicts both the center line (polyline) and footprint (extent polygons) of a variety of linear-feature types including roads, pipelines, seismic lines, and power lines. Our analysis showed that FLM outputs were consistently more accurate than publicly available datasets produced by human photo-interpreters, and that the tool can be reliably deployed across large application areas. In addition to accurately delineating linear features, the FLM generates a variety of spatial attributes associated with line geometry and vegetation characteristics from input canopy height data. Our statistical evaluation indicates that spatial attributes generated by the FLM may be useful for studying and classifying linear features based on disturbance type and ground conditions. The FLM is open-source and freely available and is aimed to assist researchers and land managers working in forested environments everywhere.
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