Towards scalable estimation of plant functional diversity from Sentinel-2: In-situ validation in a heterogeneous (semi-)natural landscape

Intertidal areas provide key ecosystem services but are declining worldwide. Digital elevation models (DEMs) are important tools to monitor the evolution of such areas. In this study, we aim at (i) estimating the intertidal topography based on an established pixel-wise algorithm, from Sentinel-2 MultiSpectral Instrument scenes, (ii) implementing a set of procedures to improve the quality of such estimation, and (iii) estimating the exposure period of the intertidal area of the Bijagós Archipelago, Guinea-Bissau. We first propose a four-parameter logistic regression to estimate intertidal topography. Afterwards, we develop a novel method to estimate tide-stage lags in the area covered by a Sentinel-2 scene to correct for geographical bias in topographic estimation resulting from differences in water height within each image. Our method searches for the minimum differences in height estimates obtained from rising and ebbing tides separately, enabling the estimation of cotidal lines. Tidal-stage differences estimated closely matched those published by official authorities. We re-estimated pixel heights from which we produced a model of intertidal exposure period. We obtained a high correlation between predicted and in-situ measurements of exposure period. We highlight the importance of remote sensing to deliver large-scale intertidal DEM and tide-stage data, with relevance for coastal safety, ecology and biodiversity conservation.

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Industrial Plume Properties Retrieved by Optimal Estimation Using Combined Hyperspectral and Sentinel-2 Data

Remote Sensing ◽

10.3390/rs13101865 ◽

2021 ◽

Vol 13 (10) ◽

pp. 1865

Author(s):

Gabriel Calassou ◽

Pierre-Yves Foucher ◽

Jean-François Léon

Keyword(s):

Matched Filter ◽

Estimation Method ◽

Optimal Estimation ◽

Model Parameters ◽

Surface Reflectance ◽

Sequential Method ◽

Modal Radius ◽

Sentinel 2

Stack emissions from the industrial sector are a subject of concern for air quality. However, the characterization of the stack emission plume properties from in situ observations remains a challenging task. This paper focuses on the characterization of the aerosol properties of a steel plant stack plume through the use of hyperspectral (HS) airborne remote sensing imagery. We propose a new method, based on the combination of HS airborne acquisition and surface reflectance imagery derived from the Sentinel-2 Multi-Spectral Instrument (MSI). The proposed method detects the plume footprint and estimates the surface reflectance under the plume, the aerosol optical thickness (AOT), and the modal radius of the plume. Hyperspectral surface reflectances are estimated using the coupled non-negative matrix factorization (CNMF) method combining HS and MSI data. The CNMF reduces the error associated with estimating the surface reflectance below the plume, particularly for heterogeneous classes. The AOT and modal radius are retrieved using an optimal estimation method (OEM), based on the forward model and allowing for uncertainties in the observations and in the model parameters. The a priori state vector is provided by a sequential method using the root mean square error (RMSE) metric, which outperforms the previously used cluster tuned matched filter (CTMF). The OEM degrees of freedom are then analysed, in order to refine the mask plume and to enhance the quality of the retrieval. The retrieved mean radii of aerosol particles in the plume is 0.125 μμm, with an uncertainty of 0.05 μμm. These results are close to the ultra-fine mode (modal radius around 0.1 μμm) observed from in situ measurements within metallurgical plant plumes from previous studies. The retrieved AOT values vary between 0.07 (near the source point) and 0.01, with uncertainties of 0.005 for the darkest surfaces and above 0.010 for the brightest surfaces.

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Plant functional diversity improves short-term yields in a low-input intercropping system

Agriculture Ecosystems & Environment ◽

10.1016/j.agee.2015.01.018 ◽

2015 ◽

Vol 203 ◽

pp. 1-10 ◽

Cited By ~ 15

Author(s):

Jose G. Franco ◽

Stephen R. King ◽

Joseph G. Masabni ◽

Astrid Volder

Keyword(s):

Functional Diversity ◽

Short Term ◽

Low Input ◽

Plant Functional Diversity

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Microbial Functional Gene Diversity with a Shift of Subsurface Redox Conditions duringIn SituUranium Reduction

Applied and Environmental Microbiology ◽

10.1128/aem.06528-11 ◽

2012 ◽

Vol 78 (8) ◽

pp. 2966-2972 ◽

Cited By ~ 35

Author(s):

Yuting Liang ◽

Joy D. Van Nostrand ◽

Lucie A. N′Guessan ◽

Aaron D. Peacock ◽

Ye Deng ◽

...

Keyword(s):

Functional Diversity ◽

Field Experiments ◽

Gene Diversity ◽

Functional Gene ◽

Redox Conditions ◽

Functional Genes ◽

Microbial Functional Diversity ◽

Content Type ◽

Microbial Functional Gene

ABSTRACTTo better understand the microbial functional diversity changes with subsurface redox conditions duringin situuranium bioremediation, key functional genes were studied with GeoChip, a comprehensive functional gene microarray, in field experiments at a uranium mill tailings remedial action (UMTRA) site (Rifle, CO). The results indicated that functional microbial communities altered with a shift in the dominant metabolic process, as documented by hierarchical cluster and ordination analyses of all detected functional genes. The abundance ofdsrABgenes (dissimilatory sulfite reductase genes) and methane generation-relatedmcrgenes (methyl coenzyme M reductase coding genes) increased when redox conditions shifted from Fe-reducing to sulfate-reducing conditions. The cytochrome genes detected were primarily fromGeobactersp. and decreased with lower subsurface redox conditions. Statistical analysis of environmental parameters and functional genes indicated that acetate, U(VI), and redox potential (Eh) were the most significant geochemical variables linked to microbial functional gene structures, and changes in microbial functional diversity were strongly related to the dominant terminal electron-accepting process following acetate addition. The study indicates that the microbial functional genes clearly reflect thein situredox conditions and the dominant microbial processes, which in turn influence uranium bioreduction. Microbial functional genes thus could be very useful for tracking microbial community structure and dynamics during bioremediation.

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ESTIMATION OF SURFACE SNOW WETNESS USING SENTINEL-2 MULTISPECTRAL DATA

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-5-223-2018 ◽

2018 ◽

Vol IV-5 ◽

pp. 223-228 ◽

Cited By ~ 7

Author(s):

D. Varade ◽

O. Dikshit

Keyword(s):

Remote Sensing ◽

Water Resource Management ◽

Remote Sensing Data ◽

Microwave Remote Sensing ◽

In Situ Measurements ◽

Physical Relationship ◽

Sensing Data ◽

Surface Snow ◽

Sentinel 2

<p><strong>Abstract.</strong> Snow cover characterization and estimation of snow geophysical parameters is a significant area of research in water resource management and surface hydrological processes. With advances in spaceborne remote sensing, much progress has been achieved in the qualitative and quantitative characterization of snow geophysical parameters. However, most of the methods available in the literature are based on the microwave backscatter response of snow. These methods are mostly based on the remote sensing data available from active microwave sensors. Moreover, in alpine terrains, such as in the Himalayas, due to the geometrical distortions, the missing data is significant in the active microwave remote sensing data. In this paper, we present a methodology utilizing the multispectral observations of Sentinel-2 satellite for the estimation of surface snow wetness. The proposed approach is based on the popular triangle method which is significantly utilized for the assessment of soil moisture. In this case, we develop a triangular feature space using the near infrared (NIR) reflectance and the normalized differenced snow index (NDSI). Based on the assumption that the NIR reflectance is linearly related to the liquid water content in the snow, we derive a physical relationship for the estimation of snow wetness. The modeled estimates of snow wetness from the proposed approach were compared with in-situ measurements of surface snow wetness. A high correlation determined by the coefficient of determination of 0.94 and an error of 0.535 was observed between the proposed estimates of snow wetness and in-situ measurements.</p>

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Representative benthic habitat mapping on Lovina coral reefs in Northern Bali, Indonesia

Biodiversitas Journal of Biological Diversity ◽

10.13057/biodiv/d221108 ◽

2021 ◽

Vol 22 (11) ◽

Author(s):

Anggita Kartikasari ◽

TODHI PRISTIANTO ◽

RIZKI HANINTYO ◽

EGHBERT ELVAN AMPOU ◽

TEJA ARIEF WIBAWA ◽

...

Keyword(s):

Coral Reef ◽

Coral Reefs ◽

Habitat Mapping ◽

Benthic Habitat ◽

Coral Rubble ◽

Spatial Coverage ◽

Coral Reef Ecosystems ◽

Optical Imagery ◽

Sentinel 2

Abstract. Kartikasari A, Pristianto T, Hanintyo R, Ampou EE, Wibawa TA, Borneo BB. 2021. Representative benthic habitat mapping on Lovina coral reefs in Northern Bali, Indonesia. Biodiversitas 22: 4766-4774. Satellite optical imagery datasets integrated with in situ measurements are widely used to derive the spatial distribution of various benthic habitats in coral reef ecosystems. In this study, an approach to estimate spatial coverage of those habitats based on observation derived from Sentinel-2 optical imagery and a field survey, is presented. This study focused on the Lovina coral reef ecosystem of Northern Bali, Indonesia to support deployment of artificial reefs within the Indonesian Coral Reef Garden (ICRG) programme. Three specific locations were explored: Temukus, Tukad Mungga, and Baktiseraga waters. Spatial benthic habitat coverages of these three waters was estimated based on supervised classification techniques using 10m bands of Sentinel-2 imagery and the medium scale approach (MSA) transect method of in situ measurement.The study indicates that total coverage of benthic habitat is 61.34 ha, 25.17 ha, and 27.88 ha for Temukus, Tukad Mungga, and Baktiseraga waters, respectively. The dominant benthic habitat of those three waters consists of sand, seagrass, coral, rubble, reef slope and intertidal zone. The coral reef coverage is 29.48 ha (48%) for Temukus covered by genus Acropora, Isopora, Porites, Montipora, Pocillopora. The coverage for Tukad Mungga is 8.69 ha (35%) covered by genus Acropora, Montipora, Favia, Psammocora, Porites, and the coverage for Baktiseraga is 11.37 ha (41%) covered by genus Montipora sp, Goniastrea, Pavona, Platygyra, Pocillopora, Porites, Acropora, Leptoseris, Acropora, Pocillopora, Fungia. The results are expected to be suitable as supporting data in restoring coral reef ecosystems in the northern part of Bali, especially in Buleleng District.

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SENSORIAMENTO REMOTO APLICADO AO MANEJO DA IRRIGAÇÃO EM ÁREAS COM ESCASSEZ DE DADOS: ESTUDO DE CASO EM PIVÔ CENTRAL EM ITATINGA-SP

Irriga ◽

10.15809/irriga.2021v1n3p585-598 ◽

2021 ◽

Vol 1 (3) ◽

pp. 585-598

Author(s):

Pedro Henrique Jandreice Magnoni ◽

Cesar De Oliveira Ferreira Silva ◽

Rodrigo Lilla Manzione

Keyword(s):

Remote Sensing ◽

Water Budget ◽

Irrigation Management ◽

Irrigation Scheduling ◽

In Situ Data ◽

Joint Application ◽

E Mail ◽

Sentinel 2

SENSORIAMENTO REMOTO APLICADO AO MANEJO DA IRRIGAÇÃO EM ÁREAS COM ESCASSEZ DE DADOS: ESTUDO DE CASO EM PIVÔ CENTRAL EM ITATINGA-SP* PEDRO HENRIQUE JANDREICE MAGNONI1; CÉSAR DE OLIVEIRA FERREIRA SILVA1 E RODRIGO LILLA MANZIONE2 1 Departamento de Engenharia Rural, Faculdade de Ciências Agronômicas, Universidade Estadual Paulista", Avenida Universitária, n° 3780, Altos do Paraíso, 18610-034, Botucatu, São Paulo, Brasil, [email protected]; [email protected]. 2 Departamento de Engenharia de Biossistemas, Faculdade de Ciências e Engenharia, Universidade Estadual Paulista “Júlio de Mesquita Filho”, Rua Domingos da Costa Lopes, 780, CEP 17602496, Tupã – SP, Brasil. E-mail: [email protected]. *Este artigo é proveniente das dissertações de mestrado dos dois primeiros autores. 1 RESUMO Ferramentas baseadas em sensoriamento remoto possibilitam o monitoramento do balanço hídrico da água em diferentes resoluções espaciais e temporais. Ainda assim, modelos que exigem dados in-situ impossibilitam sua aplicação em áreas com escassez de dados. No sentido de lidar com esse desafio, o presente trabalho apresenta uma abordagem de escolha do momento de irrigar, pelo balanço hídrico da água no solo, baseada em estimativa da evapotranspiração real (ETA) obtida com o uso conjunto de imagens multiespectrais do sensor MSI/SENTINEL-2 e dados de uma estação meteorológica pública. A área de estudo foi um pivô central localizado no munícipio de Itatinga-SP. Para a tomada de decisão do momento de irrigar, com base em um manejo por lâmina de irrigação fixa, foi feita a interpolação da fração evapotranspirativa entre os dias com imagens disponíveis para obter a ETA nos dias sem imagens por meio do seu produto com a evapotranspiração de referência. Essa abordagem captou variações climáticas essenciais para a estimativa do balanço hídrico em dias sem imagem. Destaca-se nessa aplicação conjunta sua capacidade de ser realizada sem necessitar de parâmetros específicos da cultura, do microclima ou do relevo, tornando-se interessante para regiões com escassez de dados. Palavras-chave: evapotranspiração, momento de irrigar, agriwater. MAGNONI, P. H. J.; SILVA, C. O. F.; MANZIONE, R. L. REMOTE SENSING APPLIED TO IRRIGATION MANAGEMENT IN AREAS WITH LACK OF DATA: A CASE STUDY IN A CENTRAL PIVOT IN ITATINGA-SP 2 ABSTRACT Remote sensing-based tools allow the monitoring of water budgets over different spatial and temporal resolutions. Nevertheless, some models require in situ data, preventing their application in areas with a lack of data. To address this challenge, this work presents an approach for irrigation scheduling, based on soil water budget estimation using actual evapotranspiration (ETA) obtained using MSI/SENTINEL-2 multispectral images and data from a public meteorological station. The study area consisted of a central pivot located in the municipality of Itatinga-SP, Brazil. For decision-making of irrigation scheduling, considering a fixed irrigation rate, the evapotranspiration fraction was interpolated between the days with available images to obtain the ETA on the days without images using its product with the reference evapotranspiration. This approach captured essential climate variations for estimating the water budget on non-image days. Noteworthy in this joint application is its suitability to be performed not requiring crop-, microclimate- or relief-specific parameters, making it useful for regions with a lack of data. Keywords: evapotranspiration, irrigation scheduling, agriwater.

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SATELLITE-DERIVED BATHYMETRY USING CONVOLUTIONAL NEURAL NETWORKS AND MULTISPECTRAL SENTINEL-2 IMAGES

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliii-b3-2021-201-2021 ◽

2021 ◽

Vol XLIII-B3-2021 ◽

pp. 201-207

Author(s):

Y. A. Lumban-Gaol ◽

K. A. Ohori ◽

R. Y. Peters

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Window Size ◽

Short Wave ◽

Training Data ◽

Coefficient Of Determination ◽

Linear Transform ◽

Band Combinations ◽

Sentinel 2

Abstract. Satellite-Derived Bathymetry (SDB) has been used in many applications related to coastal management. SDB can efficiently fill data gaps obtained from traditional measurements with echo sounding. However, it still requires numerous training data, which is not available in many areas. Furthermore, the accuracy problem still arises considering the linear model could not address the non-relationship between reflectance and depth due to bottom variations and noise. Convolutional Neural Networks (CNN) offers the ability to capture the connection between neighbouring pixels and the non-linear relationship. These CNN characteristics make it compelling to be used for shallow water depth extraction. We investigate the accuracy of different architectures using different window sizes and band combinations. We use Sentinel-2 Level 2A images to provide reflectance values, and Lidar and Multi Beam Echo Sounder (MBES) datasets are used as depth references to train and test the model. A set of Sentinel-2 and in-situ depth subimage pairs are extracted to perform CNN training. The model is compared to the linear transform and applied to two other study areas. Resulting accuracy ranges from 1.3 m to 1.94 m, and the coefficient of determination reaches 0.94. The SDB model generated using a window size of 9x9 indicates compatibility with the reference depths, especially at areas deeper than 15 m. The addition of both short wave infrared bands to the four visible bands in training improves the overall accuracy of SDB. The implementation of the pre-trained model to other study areas provides similar results depending on the water conditions.

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