Avaliação do potencial da interferometria sar para o mapeamento altimétrico de áreas reflorestadas por eucalyptus sp

Gama, Fábio Furlan; Mura, José Cláudio; Albuquerque, Paulo Cesar Gurgel; Santos, João Roberto dos

doi:10.1590/s1982-21702010000400003

Cited by 3 publications

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“…The process of feature selection was unanimous in selecting the interferometric height (Hint) as the most relevant feature for all areas of study, both in the case of qualitative and quantitative theme-features, in agreement with the results obtained by [23][24][56][57]. Likewise, there was an emphasis on features obtained by target decomposition techniques on the L band, from the ALOS PALSAR 2 sensor.…”

Section: Discussionsupporting

confidence: 76%

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Comparison between Quantitative and Qualitative Theme-Feature Forest Biomass Estimation Models built over SAR Data

Castro-Filho¹,

Bias²

2021

IJAERS

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International organizations are still in need for methodologies that accurately measures forests above ground biomass (AGB). Among the remote sensing technologies, those of Synthetic Aperture Radar (SAR) stands out in the modeling of forest biomass due to their ability to characterize the geometry of the imaged region. The semantic representation, through thematic maps, is one of the main means for the geospatial situational understanding. However, there is a gap of knowledge for models that are built by the analysis of quantitative and qualitative theme-feature in a complementary way. This article aims to develop and compare forest biomass estimation models, through an innovative methodology, over quantitative and qualitative theme-features. To this end, extracted SAR data and specific machine learning (ML) and feature selection techniques are applied for each case. The models developed are based into forest inventories with 128 plots located in two different Brazilian Amazon Forest areas and were built over 231 extracted independent variables. The methodology applied used techniques to categorize numeric data and, afterwards, comparatively evaluate numeric quantitative and categorized qualitative results. The constructions of the models were based on ML algorithms such as Multilayer Perceptron, Suport Vector Machine and Random Forest. The results showed that the different study areas had very different vegetation characteristics, significantly impacting the feature selection and ML algorithms. The different biomes of the Amazon Forest and their respective characteristics demanded specific models and techniques, not fitting into a single pattern. importance. I. INTRODUCTIONIn 2016 more than 190 countries participated in the 21 st United Nations Conference of the Parties on Climate Change (COP-21), held in Paris. This conference aimed to continue the Kyoto Protocol, expired in 2012, and, consequently, to define goals regarding the emission of polluting gases into the atmosphere. Despite the intense work, a legally binding treaty, capable of compelling the international community to cut greenhouse gas emissions, has not been signed. Among the reasons for this failure, one of the highlights was the lack of methodologies that accurately measures these cuts and establishes mechanisms for this reduction [1,2]. Castro-Filho et al.

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Section: Discussionsupporting

confidence: 76%

“…By 2022, a total area of 1,800,000 km 2 of the Amazon region will be covered with airborne sensors in the X and P bands [21]. In addition to the 1:50,000 scale mapping, the project also has the potential to generate data to support infrastructure projects and sustainable exploitation of natural resources in the region [22][23][24].…”

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confidence: 99%

Comparison between Quantitative and Qualitative Theme-Feature Forest Biomass Estimation Models built over SAR Data

Castro-Filho¹,

Bias²

2021

IJAERS

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“…The authors observed good prediction of tree diameter, commercial height and volume data, with determination indices ranging from 84 to 88% against forest inventory in field, indicating, therefore, the high potential of SAR interferometry to be used as support in inventories of large areas. Gama et al (2010a) using SAR interferometry in X and P bands, with different polarizations to generate elevation models (EM) and assess altimetric quality estimate in populations of Eucalyptus, observed that, in forest area, P band and particularly HH polarization suffer lower disturbance for interacting with the vegetation horizontal components instead of vertical components. Gama et al (2010b) by integrating polarimetry and interferometry modeled volume and biomass of Eucalyptus planted forests, observed a prediction error of around 10% for both variables using the models with combination of interferometric height and Canopy Scattering Index (CSI) which is obtained by the relation of backscatter coefficient (σ°) VV by the sum of σ°V V and σ°H H , using X and P band SAR and OrbiSAR-1 (airborne radar) sensor images.…”

Section: Radar Applications and Forest Biophysical Data In Brazilmentioning

confidence: 99%

Applications of Sar Data to Estimate Forest Biophysical Variables in Brazil

Jesus

Kuplich

2020

CERNE

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Increasing number of articles presenting the use SAR remote sensing for forestry studies in Brazil.Most of articles presenting the use SAR remote sensing for forestry studies have Brazilian Amazonia as study site or Eucaliptus plantations elsewhere.L and P bands data are the most indicated for biomass estimates, although still subject to the backscatter saturation effect.Polarimetric and interferometric studies are becoming a reality with adequate free SAR data and processing systems available.

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Avaliação de dados polarimétricos e de atributos de textura em imagens SAR para discriminar a floresta secundária em uma área de domínio de floresta amazônica

Kiyohara

Sano

2023

Ciênc. Florest.

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O objetivo do presente estudo foi avaliar a capacidade de atributos polarimétricos e de retroespalhamento do Sentinel-1 em relação às feições de textura e de retroespalhamento do COSMO-SkyMed (CSM), em discriminar diferentes estágios de floresta secundária em uma área de domínio de Floresta Amazônica, no estado do Mato Grosso. Neste estudo, utilizou-se uma imagem de Radar de Abertura Sintética (SAR) do Sentinel-1 nas polarizações VV e VH e uma imagem SAR do CSM na polarização HH, ambas no formato Single Look Complex. Na imagem Sentinel-1 foi gerada a matriz de covariância e aplicado o teorema de decomposição de alvos H-Alpha, para obtenção dos atributos Entropia e Ângulo alfa. Na imagem CSM, foram obtidos os atributos de textura a partir da matriz de co-ocorrência de níveis de cinza (GLCM): dissimilaridade, contraste, homogeneidade e segundo momento. Para a classificação, foi utilizado o algoritmo Máquina de Vetores de Suporte (SVM). A classificação derivada dos atributos polarimétricos do Sentinel-1, com índice Kappa de 0,70 e exatidão global de 79,58%, apresentou desempenho superior àquela derivada do CSM, com índice Kappa de 0,56 e exatidão global de 63,67%. Entretanto, tanto os atributos derivados do Sentinel-1 como do CSM não apresentaram resultados satisfatórios para discriminar os diferentes estágios de floresta secundária.

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Avaliação do potencial da interferometria sar para o mapeamento altimétrico de áreas reflorestadas por eucalyptus sp

Cited by 3 publications

References 3 publications

Comparison between Quantitative and Qualitative Theme-Feature Forest Biomass Estimation Models built over SAR Data

Comparison between Quantitative and Qualitative Theme-Feature Forest Biomass Estimation Models built over SAR Data

Applications of Sar Data to Estimate Forest Biophysical Variables in Brazil

Avaliação de dados polarimétricos e de atributos de textura em imagens SAR para discriminar a floresta secundária em uma área de domínio de floresta amazônica

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