Dual-season and full-polarimetric C band SAR assessment for vegetation mapping in the Amazon várzea wetlands

Furtado, Luiz Felipe Almeida de Almeida; Silva, Thiago Sanna Freire; Novo, Evlyn Márcia Leão de Moraes

doi:10.1016/j.rse.2015.12.013

Cited by 78 publications

(42 citation statements)

References 52 publications

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“…This is because cross-polarized observations are produced by volume scattering within the vegetation canopy and have a higher sensitivity to vegetation structures [55]. σ 0 HH is an ideal SAR observation for wetland mapping due to its sensitivity to double-bounce scattering over flooded vegetation [41,56]. Furthermore, σ 0 HH is less sensitive to the surface roughness compared to σ 0 VV , making the former advantageous for discriminating water and non-water classes.…”

Section: Sar Imagerymentioning

confidence: 99%

“…RF can be tuned by adjusting two input parameters [70], namely the number of trees (Ntree), which is generated by randomly selecting samples from the training data, and the number of variables (Mtry), which is used for tree node splitting [71]. In this study, these parameters were selected based on (a) direction from previous studies (e.g., [56,69,72]) and (b) a trial-and-error approach. Specifically, Mtry was assessed for the following values (when Ntree was adjusted to 500): (a) One third of the total number of input features; (b) the square root of the total number of input features; (c) half of the total number of input features; (d) two thirds of the total number of input features; and (e) the total number of input features.…”

Section: Random Forestmentioning

confidence: 99%

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The First Wetland Inventory Map of Newfoundland at a Spatial Resolution of 10 m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform

et al. 2018

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Wetlands are one of the most important ecosystems that provide a desirable habitat for a great variety of flora and fauna. Wetland mapping and modeling using Earth Observation (EO) data are essential for natural resource management at both regional and national levels. However, accurate wetland mapping is challenging, especially on a large scale, given their heterogeneous and fragmented landscape, as well as the spectral similarity of differing wetland classes. Currently, precise, consistent, and comprehensive wetland inventories on a national- or provincial-scale are lacking globally, with most studies focused on the generation of local-scale maps from limited remote sensing data. Leveraging the Google Earth Engine (GEE) computational power and the availability of high spatial resolution remote sensing data collected by Copernicus Sentinels, this study introduces the first detailed, provincial-scale wetland inventory map of one of the richest Canadian provinces in terms of wetland extent. In particular, multi-year summer Synthetic Aperture Radar (SAR) Sentinel-1 and optical Sentinel-2 data composites were used to identify the spatial distribution of five wetland and three non-wetland classes on the Island of Newfoundland, covering an approximate area of 106,000 km2. The classification results were evaluated using both pixel-based and object-based random forest (RF) classifications implemented on the GEE platform. The results revealed the superiority of the object-based approach relative to the pixel-based classification for wetland mapping. Although the classification using multi-year optical data was more accurate compared to that of SAR, the inclusion of both types of data significantly improved the classification accuracies of wetland classes. In particular, an overall accuracy of 88.37% and a Kappa coefficient of 0.85 were achieved with the multi-year summer SAR/optical composite using an object-based RF classification, wherein all wetland and non-wetland classes were correctly identified with accuracies beyond 70% and 90%, respectively. The results suggest a paradigm-shift from standard static products and approaches toward generating more dynamic, on-demand, large-scale wetland coverage maps through advanced cloud computing resources that simplify access to and processing of the “Geo Big Data.” In addition, the resulting ever-demanding inventory map of Newfoundland is of great interest to and can be used by many stakeholders, including federal and provincial governments, municipalities, NGOs, and environmental consultants to name a few.

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Section: Sar Imagerymentioning

confidence: 99%

Section: Random Forestmentioning

confidence: 99%

The First Wetland Inventory Map of Newfoundland at a Spatial Resolution of 10 m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform

et al. 2018

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“…Isso é representado pelos valores de σ° de -7,75 db para terra com cobertura vegetal e -10,96 db para água. Nesta frequência estudada, existem estudos similares para mapear zonas úmidas vegetadas como aqueles de Furtado et al (2016) A acurácia temática do mapeamento das mudanças costeiras pode ser qualifi cada como razoável, com exatidão global de 48,54% e coefi ciente Kappa de 0,32, adotando-se como referência imagens TM/Landsat-5 e OLI/Landsat-8. O melhor desempenho ocorreu para feições de acresção, com acurácia do produtor igual a 79,49% (erro de omissão de 20,51%) e o pior desempenho foi para erosão, com acurácia do usuário de 41,89% (erro de comissão de 58,47%).…”

Section: Separabilidade E Acurácia Temáticaunclassified

Aplicação De Dados Ers, Envisat E Sentinel Para Detecção De Mudanças Nos Ambientes Costeiros Amazônicos

Guimarães

Narvaes

Galo

2017

Rev. Bras. Geomorfol.

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Ambientes costeiros são naturalmente vulneráveis a processos morfodinâmicos como erosão e deposição de sedimentos e essa condição se intensifica em condições tropicais úmidas. A costa amazônica é marcada pela elevada descarga de sedimentos e água doce sob influência do rio Amazonas e caracterizada por uma ampla plataforma continental, extensas planícies inundáveis e planaltos mais elevados formados em rochas sedimentares mais antigas. O presente estudo apresenta uma abordagem para mapeamentos morfológicos e detecção de mudanças em zonas costeiras, baseado em dados de radar de abertura sintética (Synthetic Aperture Radar-SAR) em banda C, em áreas com intensa dinâmica costeira e de constante cobertura de nuvens. O método se baseia na cadeia de processamento SAR em amplitude aplicada a um perfil temporal de dados ERS-1/2, ENVISAT e SENTINEL-1A entre 1992 a 2015 e fornecidos pela European Space Agency (ESA). Os ambientes costeiros foram discriminados pela informação primária do coeficiente de retroespalhamento, e dados auxiliares derivados da textura de Haralick e índice de forma. As mudanças costeiras detectadas no período se estenderam por uma área de 646,15 km2, com balanço sedimentar erosivo em -2,32 km2 e sob a taxa de -0,10 km2.ano-1. O perfil temporal de mudanças costeiras está em equilíbrio e apresentou condição erosiva no período de 1992 a 1993 e 1996 a 1999 e, enquanto que no período de 1993 a 2006 e 2006 a 2015 ocorreu uma condição de acresção. A acurácia do mapeamento dos ambientes e das mudanças costeiras resultaram, respectivamente, em exatidão global de 78,35% e 48,54%, e coeficiente Kappa de 0,65 e 0,32. Os dados ERS, ENVISAT e SENTINEL permitiram mapear satisfatoriamente as mudanças morfológicas no relevo plano e dinâmico da costa amazônica, devido à disponibilidade de uma série histórica de imagens SAR em banda C, com polarização vertical paralela e incidência de íngreme a rasante. Tal aplicação foi essencial ao monitoramento sistemático de linha de costa em escala espacial regional (1:250.000) e escala temporal eventual (anual a decadal)

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“…With four polarimetric channels, the fully polarimetric SAR can obtain more abundant scattering information than the ordinary single polarimetric SAR data. Some researchers have applied this information in wetlands monitoring and proved the superiority of this kind of data [17][18][19]. Most current research uses only one polarimetric decomposition algorithm to extract the scattering information contained in PolSAR images [20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Scattering Feature Set Optimization and Polarimetric SAR Classification Using Object-Oriented RF-SFS Algorithm in Coastal Wetlands

Chen

et al. 2020

Remote Sensing

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The utilization of advanced remote sensing methods to monitor the coastal wetlands is essential for conservation and sustainable development. With multiple polarimetric channels, the polarimetric synthetic aperture radar (PolSAR) is increasingly employed in land cover classification and information extraction, as it has more scattering information than regular SAR images. Polarimetric decomposition is often used to extract scattering information from polarimetric SAR. However, distinguishing all land cover types using only one polarimetric decomposition in complex ecological environments such as coastal wetlands is not easy, and thus integration of multiple decomposition algorithms is an effective means of land cover classification. More than 20 decompositions were used in this research to extract polarimetric scattering features. Furthermore, a new algorithm combining random forest (RF) with sequential forward selection (SFS) was applied, in which the importance values of all polarimetric features can be evaluated quantitatively, and the polarimetric feature set can be optimized. The experiments were conducted in the Jiangsu coastal wetlands, which are located in eastern China. This research demonstrated that the classification accuracies were improved relative to regular decision tree methods, and the process of polarimetric scattering feature set optimization was intuitive. Furthermore, the scattering matrix elements and scattering features derived from H / α , Yamaguchi3, VanZyl3, and Krogager decompositions were determined to be very supportive of land cover identification in the Jiangsu coastal wetlands.

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Dual-season and full-polarimetric C band SAR assessment for vegetation mapping in the Amazon várzea wetlands

Cited by 78 publications

References 52 publications

The First Wetland Inventory Map of Newfoundland at a Spatial Resolution of 10 m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform

The First Wetland Inventory Map of Newfoundland at a Spatial Resolution of 10 m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform

Aplicação De Dados Ers, Envisat E Sentinel Para Detecção De Mudanças Nos Ambientes Costeiros Amazônicos

Scattering Feature Set Optimization and Polarimetric SAR Classification Using Object-Oriented RF-SFS Algorithm in Coastal Wetlands

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