Orbital spectral variables, growth analysis and sugarcane yield

Temporal analysis of crop development in commercial fields requires tools for large area monitoring, such as remote sensing. This paper describes the temporal evolution of sugar cane biophysical parameters such as total biomass (BMT), yield (TSS), leaf area index (LAI), and number of plants per linear meter (NPM) correlated to Landsat data. During the 2000 and 2001 cropping seasons, a commercial sugarcane field in Araras, São Paulo state, Brazil, planted with the SP80-1842 sugarcane variety in the 4th and 5th cuts, was monitored using nine Landsat images. Spectral data were correlated with agronomic data, obtained simultaneously to the imagery acquisition. Two methodologies were used to collect spectral data from the images: four pixels (2 × 2) window and average of total pixels in the field. Linear and multiple regression analysis was used to study the spectral behavior of the plants and to correlate with agronomic variables (days after harvest-DAC, LAI, NPM, BMT and TSS). No difference was observed between the methodologies to collect spectral data. The best models to describe the spectral crop development in relation to DAC were the quadratic and cubic models. Ratio vegetation index and normalized difference vegetation index demonstrated correlation with DAC, band 3 (B3) was correlated with LAI, and NDVI was well correlated with TSS and BMT. The best fit curves to estimate TSS and BMT presented r² between 0.68 and 0.97, suggesting good potential in using orbital spectral data to monitor sugarcane fields.

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Spectral variables, growth analysis and yield of sugarcane

Scientia Agricola ◽

10.1590/s0103-90162005000300001 ◽

2005 ◽

Vol 62 (3) ◽

pp. 199-207 ◽

Cited By ~ 21

Author(s):

Maurício dos Santos Simões ◽

Jansle Vieira Rocha ◽

Rubens Augusto Camargo Lamparelli

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Vegetation Indices ◽

Lower Amount ◽

Total Biomass ◽

Area Index ◽

Spectral Vegetation Indices ◽

Green Vegetation ◽

Highly Correlated ◽

Simple Ratio

Spectral information is well related with agronomic variables and can be used in crop monitoring and yield forecasting. This paper describes a multitemporal research with the sugarcane variety SP80-1842, studying its spectral behavior using field spectroscopy and its relationship with agronomic parameters such as leaf area index (LAI), number of stalks per meter (NPM), yield (TSS) and total biomass (BMT). A commercial sugarcane field in Araras/SP/Brazil was monitored for two seasons. Radiometric data and agronomic characterization were gathered in 9 field campaigns. Spectral vegetation indices had similar patterns in both seasons and adjusted to agronomic parameters. Band 4 (B4), Simple Ratio (SR), Normalized Difference Vegetation Index (NDVI), and Soil Adjusted Vegetation Index (SAVI) increased their values until the end of the vegetative stage, around 240 days after harvest (DAC). After that stage, B4 reflectance and NDVI values began to stabilize and decrease because the crop reached ripening and senescence stages. Band 3 (B3) and RVI presented decreased values since the beginning of the cycle, followed by a stabilization stage. Later these values had a slight increase caused by the lower amount of green vegetation. Spectral variables B3, RVI, NDVI, and SAVI were highly correlated (above 0.79) with LAI, TSS, and BMT, and about 0.50 with NPM. The best regression models were verified for RVI, LAI, and NPM, which explained 0.97 of TSS variation and 0.99 of BMT variation.

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The Use of Remote Sensing to Assess the Effects of Water Stress on Wheat

Experimental Agriculture ◽

10.1017/s0014479700019402 ◽

1991 ◽

Vol 27 (4) ◽

pp. 423-429 ◽

Cited By ~ 20

Author(s):

R. K. Mahey ◽

Rajwant Singh ◽

S. S. Sidhu ◽

R. S. Narang

Keyword(s):

Remote Sensing ◽

Water Stress ◽

Grain Yield ◽

Spectral Data ◽

Growth And Development ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Spectral Response ◽

Canopy Cover ◽

Area Index

SUMMARYGround-based radiometric measurements in the red and infrared bands were used to monitor the growth and development of wheat under irrigated and stressed conditions throughout the 1987–88 and 1988–89 growth cycles. Spectral data were correlated with plant height, leaf area index, total fresh and total dry biomass, plant water content and grain yield. The radiance ratio (R) and normalized difference vegetation index (NDVI) were highly and linearly correlated with yield, establishing the potential which remote sensing has for predicting grain yield. The correlation for R and NDVI was at a maximum between 75 and 104 days after sowing, corresponding with maximum green crop canopy cover. The differences in spectral response over time between irrigated and unirrigated crops allowed detection of water stress effects on the crop, indicating that a hand-held radiometer can be used to collect spectral data which can supply information on wheat growth and development.Efectos de lafalta de agua en el trigo

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Mapping Biophysical Parameters for Land Surface Modeling over the Continental US Using MODIS and Landsat

Dataset Papers in Science ◽

10.1155/2015/564279 ◽

2015 ◽

Vol 2015 ◽

pp. 1-11 ◽

Cited By ~ 12

Author(s):

Lahouari Bounoua ◽

Ping Zhang ◽

Kurtis Thome ◽

Jeffrey Masek ◽

Abdelmounaime Safia ◽

...

Keyword(s):

Land Cover ◽

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Climate Modeling ◽

Surface Fluxes ◽

Biophysical Parameters ◽

Area Index ◽

Spatial And Temporal Scales ◽

The North

In terms of the space cities occupy, urbanization appears as a minor land transformation. However, it permanently modifies land’s ecological functions, altering its carbon, energy, and water fluxes. It is therefore necessary to develop a land cover characterization at fine spatial and temporal scales to capture urbanization’s effects on surface fluxes. We develop a series of biophysical vegetation parameters such as the fraction of photosynthetically active radiation, leaf area index, vegetation greenness fraction, and roughness length over the continental US using MODIS and Landsat products for 2001. A 13-class land cover map was developed at a climate modeling grid (CMG) merging the 500 m MODIS land cover and the 30 m impervious surface area from the National Land Cover Database. The landscape subgrid heterogeneity was preserved using fractions of each class from the 500 m and 30 m into the CMG. Biophysical parameters were computed using the 8-day composite Normalized Difference Vegetation Index produced by the North American Carbon Program. In addition to urban impact assessments, this dataset is useful for the computation of surface fluxes in land, vegetation, and urban models and is expected to be widely used in different land cover and land use change applications.

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Investigação do Uso de Parâmetros Biofísicos na Influência do Microclima Dentro de Áreas Urbanas

Ensaios e Ciência C Biológicas Agrárias e da Saúde ◽

10.17921/1415-6938.2021v25n1p16-20 ◽

2021 ◽

Vol 25 (1) ◽

pp. 16-20

Author(s):

Denise Pereira Canedo Meira Vieira ◽

Victor Hugo De Morais Danelichen ◽

Mariane Batista de Lima Moraes Brandão Campos

Keyword(s):

Remote Sensing ◽

Image Processing ◽

Digital Image Processing ◽

Digital Image ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Urban Spaces ◽

Biophysical Parameters ◽

Electronic Library ◽

Area Index

Diante da necessidade de obtenção de informações relacionadas ao microclima e influência da vegetação dentro de um perímetro urbano na qualidade de vida dos seus habitantes se define como parâmetros biofísicos a serem estudados nesta pesquisa o Normalized Difference Vegetation Index (NDVI) e Leaf Area Index (LAI). Considerando que o Sensoriamento Remoto é uma tecnologia de baixo custo e fácil aquisição podendo ser obtidas gratuitamente, via banco de dados disponíveis na Internet, verifica-se que o sensoriamento pode ser utilizado como fonte confiável de levantamento desses parâmetros. Este estudo tem como objetivo analisar a produção científica sobre o uso do sensoriamento remoto como tecnologia alternativa para obtenção de informações de parâmetros biofísicos, como o NDVI e LAI, possibilitando a preservação e o planejamento da vegetação nos espaços urbanos. O artigo se trata de uma revisão narrativa realizada através de consultas aos bancos de dados Scientific Electronic Library Online (SciELO), Literatura Latino-Americana e, principalmente, do banco de dados Scopus (Elsevier) da CAPES. Como critérios de inclusão foram aplicados: artigos com disponibilidade completa de 2010 até 2020 e com relação direta com o estudo. É possível concluir que como o ambiente sofre alterações constantes pela ação antrópica e a interpretação de imagens de satélite é uma fonte direta de se determinar a dinâmica dos processos envolvidos em tais alterações, a fotointerpretação e o processamento digital de imagens assumem papel de grande importância. Tais ferramentas permitem fornecer subsídios para a compreensão dos fenômenos ambientais, além da possibilidade de planejamento estratégico no planejamento urbano. As revisões bibliográficas realizadas indicam a fotointerpretação e o processamento digital de imagens como ferramentas ainda pouco utilizadas para estimar os parâmetros biofísicos no contexto urbano. Palavras-chave: Sensoriamento Remoto. Espaços Urbanos. NDVI e LAI. AbstractGiven the need to obtain information related to the vegetation microclimate and influence within an urban perimeter on the quality of life of its inhabitants, it was defined as biophysical parameters to be studied in this research the Normalized Difference Vegetation Index (NDVI) and Leaf Area Index (LAI). Considering that Remote Sensing is a low cost and easy acquisition technology and can be obtained free of charge via the database available on the Internet, it will be verified that sensing can be used as a reliable source of survey of these parameters. This study aims to analyze the scientific production on the use of remote sensing as an alternative technology to obtain information from biophysical parameters, such as NDVI and LAI, enabling the vegetation preservation and planning in urban spaces. The article is a narrative review carried out through consultations with the Databases Scientific Electronic Library Online (SciELO), Latin American Literature and mainly the Scopus database (Elsevier) of CAPES. Inclusion criteria were applied: articles with complete availability from 2010 to 2020 and with direct relation to the study. It is possible to conclude that the environment undergoes constant changes by anthropic action and the satellite images interpretation is a direct source for determining the processes dynamics involved in such changes, photointerpretation and digital image processing play a major role. Such tools allow to provide subsidies for the understanding of environmental phenomena, in addition to the possibility of strategic planning in urban planning. The bibliographic reviews performed indicate photointerpretation and digital image processing as tools still little used to estimate biophysical parameters in the urban context. Keywords: Remote Sensing. Urban Spaces. NDVI and LAI

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Phytomass mapping of the "seridó caatinga" vegetation by the plant area and the normalized difference vegetation indeces

Scientia Agricola ◽

10.1590/s0103-90162002000400014 ◽

2002 ◽

Vol 59 (4) ◽

pp. 707-715 ◽

Cited By ~ 11

Author(s):

Thomaz Corrêa e Castro da Costa ◽

Luciano José de Oliveira Accioly ◽

Maria Ap. José de Oliveira ◽

Nivaldo Burgos ◽

Flávio Hugo Barreto Batista da Silva

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Linear Models ◽

Dry Weight ◽

Empirical Method ◽

Orbital Parameter ◽

Landsat Images ◽

Area Index ◽

Plant Area Index ◽

Plant Area

Phytomass is a critical information for economic and environmental activities like the establishment of policies for timber resources, forest management, studies of plant nutrient cycling, CO2 sink, among other. The phytomass of a Caatinga area was obtained by an empirical method using normalized difference vegetation index (NDVI) of Landsat images, the plant area index (PAI) and the phytomass inventory. At a first stage, linear, logarithmic and non-linear models were developed and tested. Bush and tree specimens were considered in the study, so that most of the individuals that contribute to the spectral answer detected by satellite images were included. At a second stage, the orbital parameter NDVI was used to map the PAI, which was used to map the phytomass, based on the relationship of this phytomass as a function of PAI. The residues between measurements and estimates based on NDVI varied from 0 to 84%, while the residues of total dry weight of phytomass per ha obtained by mapping and by dendrometrical equations varied from 5 to 104%, with a large trend of 166 and 448% in open Caatinga areas, due to the contribution of the herbaceous stratum to NDVI.

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ESTIMATIVA DE PARÂMETROS BIOFÍSICOS E EVAPOTRANSPIRAÇÃO REAL NO SEMIÁRIDO PERNAMBUCANO UTILIZANDO SENSORIAMENTO REMOTO

Irriga ◽

10.15809/irriga.2017v1n1p64-75 ◽

2017 ◽

Vol 1 (1) ◽

pp. 64-75 ◽

Cited By ~ 1

Author(s):

Frederico Abraão Costa Lins ◽

Diego Cezar Dos Santos Araújo ◽

Jhon Lennon Bezerra Da Silva ◽

Pabricio Marcos Oliveira Lopes ◽

José Diorgenes Alves Oliveira ◽

...

Keyword(s):

Remote Sensing ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Temporal Distribution ◽

Semiarid Region ◽

Landsat 8 ◽

Biophysical Parameters ◽

Area Index ◽

Rainfall Index ◽

E Mail

ESTIMATIVA DE PARÂMETROS BIOFÍSICOS E EVAPOTRANSPIRAÇÃO REAL NO SEMIÁRIDO PERNAMBUCANO UTILIZANDO SENSORIAMENTO REMOTO FREDERICO ABRAÃO COSTA LINS1; DIEGO CEZAR DOS SANTOS ARAÚJO2; JHON LENNON BEZERRA DA SILVA2; PABRÍCIO MARCOS OLIVEIRA LOPES3; JOSÉ DIORGENES ALVES OLIVEIRA2 E ANDREY THYAGO CARDOSO SANTOS GOMES DA SILVA1 1 Mestrandos em Engenharia Agrícola – Departamento de Engenharia Agrícola, Universidade Federal Rural de Pernambuco (UFRPE). Av. D. Manoel de Medeiros, SN; Dois Irmãos, Recife, Pernambuco, Brasil; CEP: 52171-900. E-mail: [email protected] (Autor para correspondência); [email protected]; 2 Mestres em Engenharia Agrícola e Doutorandos – Departamento de Engenharia Agrícola da UFRPE. E-mail: [email protected]; [email protected]; [email protected]; 3 Doutor em Sensoriamento Remoto; Professor adjunto da Universidade Federal Rural de Pernambuco (UFRPE), Recife, Pernambuco, Brasil. E-mail: [email protected]. 1 RESUMO Objetivou-se estimar e avaliar a distribuição espaço-temporal de parâmetros biofísicos e a evapotranspiração real diária para o município de Arcoverde, Pernambuco, durante estações seca e chuvosa, utilizando imagens orbitais do satélite Landsat-8 de sensores OLI/TIRS, para as datas de passagem: 14/01/2015 e 02/12/2016, processadas com o algoritmo SEBAL. Foram gerados os seguintes mapas temáticos: Índice de Vegetação da Diferença Normalizada (NDVI), Índice de Área Foliar (IAF), albedo e temperatura de superfície (Ts), saldo de radiação instantâneo (Rn) e evapotranspiração real diária (ETr). O NDVI foi maior em janeiro de 2015 e o albedo e Ts foram maiores em 2016 (0,23 e 37,44 °C), ao passo que em 2015, os valores foram de 0,20 e 34,11 °C, relacionando-se às condições meteorológicas e uso do solo. O Rn variou de 520,06 a 540,22 W m-2 nos dois anos e, para a ETr, verificou-se a maior média em janeiro de 2015 (3,41 mm dia-1), devido ao maior NDVI e precipitações, evidenciando maior disponibilidade de água na vegetação e no solo. As técnicas de sensoriamento remoto possibilitaram o monitoramento do município de Arcoverde-PE, determinando os parâmetros biofísicos nos diferentes usos do solo, predizendo os processos futuros de degradação e consequente desertificação na localidade. Palavras-chave: caatinga, vegetação, monitoramento ambiental, uso do solo, agrometeorologia. LINS, F. A. C.; ARAÚJO, D. C. dos S.; SILVA, J. L. B. da; LOPES, P. M. O.; OLIVEIRA, J. D. A.; SILVA, A. T. C. S. G. daBIOPHYSICAL PARAMETERS ESTIMATE AND ACTUAL EVAPOTRANSPIRATION IN THE SEMIARID REGION OF THE STATE OF PERNAMBUCO, BRAZIL, USING REMOTE SENSING 2 ABSTRACT The purpose of this paper is to estimate and evaluate the spatial-temporal distribution of biophysical parameters and the actual daily evapotranspiration index for the municipality of Arcoverde, Pernambuco, during the dry and rainy season, using orbital images from the Landsat-8 satellite and OLI/TIRS sensors for the following dates in which the satellite passed over the region: 01/14/2015 and 02/12/2016, processed using the SEBAL algorithm. The following thematic maps were generated: Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), albedo and surface temperature (Ts), Net radiation (Rn) and daily reference evapotranspiration (ETr). The NDVI was higher on January 2015 and the albedo and Ts were higher in 2016 (0.23 and 37.44 °C), whereas in 2015, the values were 0.20 and 34.11 °C, related to the meteorological conditions and the land use. The Rn ranged from 520.06 to 540.22 W m-² in two years of study and, for the ETr, the highest average was recorded on January 2015 (3.41 mm day-1), due to the higher NDVI and rainfall index, evidencing greater availability of water in the vegetation and soil. The remote sensing techniques allowed the monitoring of the municipality of Arcoverde, determining the biophysical parameters in the different uses of soil, anticipating the future degradation processes and consequent desertification in the place. Keywords: caatinga, vegetation, environmental monitoring, use of the soil, agrometeorology.

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Effect of the Illumination Angle on NDVI Data Composed of Mixed Surface Values Obtained over Vertical-Shoot-Positioned Vineyards

Remote Sensing ◽

10.3390/rs13050855 ◽

2021 ◽

Vol 13 (5) ◽

pp. 855

Author(s):

Pedro C. Towers ◽

Carlos Poblete-Echeverría

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Vegetation Indices ◽

Biophysical Parameters ◽

Area Index ◽

Factors Affecting ◽

Soil Fractions ◽

Illumination Direction ◽

Moderate Resolution ◽

Canopy Size

Accurate quantification of the spatial variation of canopy size is crucial for vineyard management in the context of Precision Viticulture. Biophysical parameters associated with canopy size, such as Leaf Area Index (LAI), can be estimated from Vegetation Indices (VI) such as the Normalized Difference Vegetation Index (NDVI), but in Vertical-Shoot-Positioned (VSP) vineyards, common satellite, or aerial imagery with moderate-resolution capture information at nadir of pixels whose values are a mix of canopy, sunlit soil, and shaded soil fractions and their respective spectral signatures. VI values for each fraction are considerably different. On a VSP vineyard, the illumination direction for each specific row orientation depends on the relative position of sun and earth. Respective proportions of shaded and sunlit soil fractions change as a function of solar elevation and azimuth, but canopy fraction is independent of these variations. The focus of this study is the interaction of illumination direction with canopy orientation, and the corresponding effect on integrated NDVI. The results confirm that factors that intervene in determining the direction of illumination on a VSP will alter the integrated NDVI value. Shading induced considerable changes in the NDVI proportions affecting the final integrated NDVI value. However, the effect of shading decreases as the row orientation approaches the solar path. Therefore, models of biophysical parameters using moderate-resolution imagery should consider corrections for variations caused by factors affecting the angle of illumination to provide more general solutions that may enable canopy data to be obtained from mixed, integrated vine NDVI.

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Detecting Forest Degradation in the Three-North Forest Shelterbelt in China from Multi-Scale Satellite Images

Remote Sensing ◽

10.3390/rs13061131 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1131

Author(s):

Tao Yu ◽

Pengju Liu ◽

Qiang Zhang ◽

Yi Ren ◽

Jingning Yao

Keyword(s):

Remote Sensing ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Net Primary Production ◽

Remote Sensing Data ◽

Forest Degradation ◽

Observation Data ◽

Area Index ◽

Sensing Data ◽

Multi Scale

Detecting forest degradation from satellite observation data is of great significance in revealing the process of decreasing forest quality and giving a better understanding of regional or global carbon emissions and their feedbacks with climate changes. In this paper, a quick and applicable approach was developed for monitoring forest degradation in the Three-North Forest Shelterbelt in China from multi-scale remote sensing data. Firstly, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Ratio Vegetation Index (RVI), Leaf Area Index (LAI), Fraction of Photosynthetically Active Radiation (FPAR) and Net Primary Production (NPP) from remote sensing data were selected as the indicators to describe forest degradation. Then multi-scale forest degradation maps were obtained by adopting a new classification method using time series MODerate Resolution Imaging Spectroradiometer (MODIS) and Landsat Enhanced Thematic Mapper Plus (ETM+) images, and were validated with ground survey data. At last, the criteria and indicators for monitoring forest degradation from remote sensing data were discussed, and the uncertainly of the method was analyzed. Results of this paper indicated that multi-scale remote sensing data have great potential in detecting regional forest degradation.

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Outlier Detection at the Parcel-Level in Wheat and Rapeseed Crops Using Multispectral and SAR Time Series

Remote Sensing ◽

10.3390/rs13050956 ◽

2021 ◽

Vol 13 (5) ◽

pp. 956

Author(s):

Florian Mouret ◽

Mohanad Albughdadi ◽

Sylvie Duthoit ◽

Denis Kouamé ◽

Guillaume Rieu ◽

...

Keyword(s):

Outlier Detection ◽

Experimental Validation ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Multispectral Images ◽

Vegetation Indexes ◽

Crop Development ◽

Different Types ◽

Selection Of ◽

Sentinel 2

This paper studies the detection of anomalous crop development at the parcel-level based on an unsupervised outlier detection technique. The experimental validation is conducted on rapeseed and wheat parcels located in Beauce (France). The proposed methodology consists of four sequential steps: (1) preprocessing of synthetic aperture radar (SAR) and multispectral images acquired using Sentinel-1 and Sentinel-2 satellites, (2) extraction of SAR and multispectral pixel-level features, (3) computation of parcel-level features using zonal statistics and (4) outlier detection. The different types of anomalies that can affect the studied crops are analyzed and described. The different factors that can influence the outlier detection results are investigated with a particular attention devoted to the synergy between Sentinel-1 and Sentinel-2 data. Overall, the best performance is obtained when using jointly a selection of Sentinel-1 and Sentinel-2 features with the isolation forest algorithm. The selected features are co-polarized (VV) and cross-polarized (VH) backscattering coefficients for Sentinel-1 and five Vegetation Indexes for Sentinel-2 (among us, the Normalized Difference Vegetation Index and two variants of the Normalized Difference Water). When using these features with an outlier ratio of 10%, the percentage of detected true positives (i.e., crop anomalies) is equal to 94.1% for rapeseed parcels and 95.5% for wheat parcels.

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An Effective Method for Generating Spatiotemporally Continuous 30 m Vegetation Products

Remote Sensing ◽

10.3390/rs13040719 ◽

2021 ◽

Vol 13 (4) ◽

pp. 719

Author(s):

Xiuxia Li ◽

Shunlin Liang ◽

Huaan Jin

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Continuous Data ◽

Landsat Data ◽

Area Index ◽

Free Data ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer ◽

Resolution Data

Leaf area index (LAI) and normalized difference vegetation index (NDVI) are key parameters for various applications. However, due to sensor tradeoff and cloud contaminations, these data are often temporally intermittent and spatially discontinuous. To address the discontinuities, this study proposed a method based on spectral matching of 30 m discontinuous values from Landsat data and 500 m temporally continuous values from Moderate-resolution Imaging Spectroradiometer (MODIS) data. Experiments have proven that the proposed method can effectively yield spatiotemporally continuous vegetation products at 30 m spatial resolution. The results for three different study areas with NDVI and LAI showed that the method performs well in restoring the time series, fills in the missing data, and reasonably predicts the images. Remarkably, the proposed method could address the issue when no cloud-free data pairs are available close to the prediction date, because of the temporal information “borrowed” from coarser resolution data. Hence, the proposed method can make better use of partially obscured images. The reconstructed spatiotemporally continuous data have great potential for monitoring vegetation, agriculture, and environmental dynamics.

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