scholarly journals Understanding Vine Hyperspectral Signature through Different Irrigation Plans: A First Step to Monitor Vineyard Water Status

2021 ◽  
Vol 13 (3) ◽  
pp. 536
Author(s):  
Eve Laroche-Pinel ◽  
Mohanad Albughdadi ◽  
Sylvie Duthoit ◽  
Véronique Chéret ◽  
Jacques Rousseau ◽  
...  
Keyword(s):  
Near Infrared ◽  
Water Status ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Short Wave ◽  
Spatial And Temporal Scales ◽  
Spectral Bands ◽  
Main Challenge ◽  
Spectral Scale ◽  
Grape Varieties

The main challenge encountered by Mediterranean winegrowers is water management. Indeed, with climate change, drought events are becoming more intense each year, dragging the yield down. Moreover, the quality of the vineyards is affected and the level of alcohol increases. Remote sensing data are a potential solution to measure water status in vineyards. However, important questions are still open such as which spectral, spatial, and temporal scales are adapted to achieve the latter. This study aims at using hyperspectral measurements to investigate the spectral scale adapted to measure their water status. The final objective is to find out whether it would be possible to monitor the vine water status with the spectral bands available in multispectral satellites such as Sentinel-2. Four Mediterranean vine plots with three grape varieties and different water status management systems are considered for the analysis. Results show the main significant domains related to vine water status (Short Wave Infrared, Near Infrared, and Red-Edge) and the best vegetation indices that combine these domains. These results give some promising perspectives to monitor vine water status.

Early stress detection in grapevine – can remotely-sensed sun-induced chlorophyll fluorescence do the job?

2021 ◽  
Author(s):  
Georg Wohlfahrt ◽  
Albin Hammerle ◽  
Barbara Rainer ◽  
Florian Haas
Keyword(s):  
Chlorophyll Fluorescence ◽  
Vegetation Index ◽  
Near Infrared ◽  
Census Data ◽  
Meteorological Data ◽  
Vegetation Indices ◽  
Wavelength Region ◽  
Remote Sensing Data ◽  
Warning Signs ◽  
Mitigation Measures

<p>Ongoing changes in climate (both in the means and the extremes) are increasingly challenging grapevine production in the province of South Tyrol (Italy). Here we ask the question whether sun-induced chlorophyll fluorescence (SIF) observed remotely from space can detect early warning signs of stress in grapevine and thus help guide mitigation measures.</p><p>Chlorophyll fluorescence refers to light absorbed by chlorophyll molecules that is re-emitted in the red to far-red wavelength region. Previous research at leaf and canopy scale indicated that SIF correlates with the plant photosynthetic uptake of carbon dioxide as it competes for the same energy pool.</p><p>To address this question, we use time series of two down-scaled SIF products (GOME-2 and OCO-2, 2007/14-2018) as well as the original OCO-2 data (2014-2019). As a benchmark, we use several vegetation indices related to canopy greenness, as well as a novel near-infrared radiation-based vegetation index (2000-2019). Meteorological data fields are used to explore possible weather-related causes for observed deviations in remote sensing data. Regional DOC grapevine census data (2000-2019) are used as a reference for the analyses.</p>

scholarly journals TOPOGRAPHIC EFFECT ON SPECTRAL VEGETATION INDICES FROM LANDSAT TM DATA: IS TOPOGRAPHIC CORRECTION NECESSARY?

2016 ◽  
Vol 22 (1) ◽  
pp. 95-107 ◽  
Author(s):  
Eder Paulo Moreira* ◽  
Márcio de Morisson Valeriano ◽  
Ieda Del Arco Sanches ◽  
Antonio Roberto Formaggio
Keyword(s):  
Near Infrared ◽  
Incidence Angle ◽  
Spectral Band ◽  
Vegetation Indices ◽  
Topographic Effect ◽  
Topographic Correction ◽  
Spectral Bands ◽  
Spectral Vegetation Indices ◽  
Current Availability ◽  
Elevation Model

The full potentiality of spectral vegetation indices (VIs) can only be evaluated after removing topographic, atmospheric and soil background effects from radiometric data. Concerning the former effect, the topographic effect was barely investigated in the context of VIs, despite the current availability correction methods and Digital elevation Model (DEM). In this study, we performed topographic correction on Landsat 5 TM spectral bands and evaluated the topographic effect on four VIs: NDVI, RVI, EVI and SAVI. The evaluation was based on analyses of mean and standard deviation of VIs and TM band 4 (near-infrared), and on linear regression analyses between these variables and the cosine of the solar incidence angle on terrain surface (cos i). The results indicated that VIs are less sensitive to topographic effect than the uncorrected spectral band. Among VIs, NDVI and RVI were less sensitive to topographic effect than EVI and SAVI. All VIs showed to be fully independent of topographic effect only after correction. It can be concluded that the topographic correction is required for a consistent reduction of the topographic effect on the VIs from rugged terrain.

scholarly journals A Deep Learning Approach for Burned Area Segmentation with Sentinel-2 Data

2020 ◽  
Vol 12 (15) ◽  
pp. 2422
Author(s):  
Lisa Knopp ◽  
Marc Wieland ◽  
Michaela Rättich ◽  
Sandro Martinis
Keyword(s):  
Deep Learning ◽  
Network Performance ◽  
Near Infrared ◽  
Remote Sensing Data ◽  
Economic Consequences ◽  
Validation Dataset ◽  
Burned Area ◽  
Burned Areas ◽  
Spectral Bands ◽  
Sentinel 2

Wildfires have major ecological, social and economic consequences. Information about the extent of burned areas is essential to assess these consequences and can be derived from remote sensing data. Over the last years, several methods have been developed to segment burned areas with satellite imagery. However, these methods mostly require extensive preprocessing, while deep learning techniques—which have successfully been applied to other segmentation tasks—have yet to be fully explored. In this work, we combine sensor-specific and methodological developments from the past few years and suggest an automatic processing chain, based on deep learning, for burned area segmentation using mono-temporal Sentinel-2 imagery. In particular, we created a new training and validation dataset, which is used to train a convolutional neural network based on a U-Net architecture. We performed several tests on the input data and reached optimal network performance using the spectral bands of the visual, near infrared and shortwave infrared domains. The final segmentation model achieved an overall accuracy of 0.98 and a kappa coefficient of 0.94.

Vegetation indices and NIR-SWIR spectral bands as a phenotyping tool for water status determination in soybean

2020 ◽  
Vol 22 (1) ◽  
pp. 249-266
Author(s):  
P. Braga ◽  
L. G. T. Crusiol ◽  
M. R. Nanni ◽  
A. L. H. Caranhato ◽  
M. B. Fuhrmann ◽  
...  
Keyword(s):  
Water Status ◽  
Vegetation Indices ◽  
Spectral Bands

scholarly journals USING LEGACY SOIL DATA FOR STANDARDIZING PREDICTIONS OF TOPSOIL CLAY CONTENT OBTAINED FROM VNIR/SWIR HYPERSPECTRAL AIRBORNE IMAGES

2015 ◽  
Vol XL-3/W3 ◽  
pp. 439-444
Author(s):  
C. Gomez ◽  
A. Gholizadeh ◽  
L. Borůvka ◽  
P. Lagacherie
Keyword(s):  
Soil Properties ◽  
Spectral Index ◽  
Near Infrared ◽  
Soil Property ◽  
Clay Content ◽  
Short Wave ◽  
Soil Samples ◽  
Spectral Bands ◽  
Large Sets ◽  
The One

Mapping of topsoil properties using Visible, Near-Infrared and Short Wave Infrared (VNIR/SWIR) hyperspectral imagery requires large sets of ground measurements for calibrating the models that estimate soil properties. To avoid collecting such expensive data, we proposed a procedure including two steps that involves only legacy soil data that were collected over and?or around the study site: <i>1)</i> estimation of a soil property using a spectral index of the literature and <i>2)</i> standardisation of the estimated soil property using legacy soil data. This approach was tested for mapping clay contents in a Mediterranean region in which VNIR/SWIR AISA-DUAL hyperspectral airborne data were acquired. The spectral index was the one proposed by Levin et al (2007) using the spectral bands at 2209, 2133 and 2225 nm. Two legacy soil databases were tested as inputs of the procedure: the <i>Focused-Legacy</i> database composed of 67 soil samples collected in 2000 over the study area, and the No-Focused-Legacy database composed of 64 soil samples collected between 1973 and 1979 around but outside of the study area. The results were compared with those obtained from 120 soil samples collected over the study area during the hyperspectral airborne data acquisition, which were considered as a reference. <br><br> Our results showed that: <i>1)</i> the spectral index with no further standardisation offered predictions with high accuracy in term of coefficient of correlation <i>r</i> (0.71), but also high <i>bias</i> (&minus;414 g/kg) and <i>SEP</i> (439 g/kg), <i>2)</i> the standardisation using both legacy soil databases allowed an increase of accuracy (<i>r</i> = 0.76) and a reduction of <i>bias</i> and <i>SEP</i> and <i>3)</i> a better standardisation was obtained by using the <i>Focused-Legacy</i> database rather than the <i>No-Focused-Legacy</i> database. Finally, the clay predicted map obtained with standardisation using the <i>Focused-Legacy</i> database showed pedologically-significant soil spatial structures with clear short-scale variations of topsoil clay contents in specific areas. <br><br> This study, associated with the coming availability of a next generation of hyperspectral VNIR/SWIR satellite data for the entire globe, paves the way for inexpensive methods for delivering high resolution soil properties maps.

scholarly journals Thermal remote sensing from Airborne Hyperspectral Scanner data in the framework of the SPARC and SEN2FLEX projects: an overview

2009 ◽  
Vol 6 (3) ◽  
pp. 4107-4124
Author(s):  
J. A. Sobrino ◽  
J. C. Jiménez-Muñoz ◽  
P. J. Zarco-Tejada ◽  
G. Sepulcre-Cantó ◽  
E. de Miguel ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Near Infrared ◽  
Short Wave ◽  
Scanner Data ◽  
Data Errors ◽  
Spectral Bands ◽  
Field Campaigns ◽  
First Time

Abstract. The AHS (Airborne Hyperspectral Scanner) instrument has 80 spectral bands covering the visible and near infrared (VNIR), short wave infrared (SWIR), mid infrared (MIR) and thermal infrared (TIR) spectral range. The instrument is operated by Instituto Nacional de Técnica Aerospacial (INTA), and it has been involved in several field campaigns since 2004. This paper presents an overview of the work performed with the AHS thermal imagery provided in the framework of the SPARC and SEN2FLEX campaigns, carried out respectively in 2004 and 2005 over an agricultural area in Spain. The data collected in both campaigns allowed for the first time the development and testing of algorithms for land surface temperature and emissivity retrieval as well as the estimation of evapotranspiration from AHS data. Errors were found to be around 1.5 K for land surface temperature and 1 mm/day for evapotranspiration.

scholarly journals Índice de vegetação ajustado ao solo (IVAS): estado da arte e suas potencialidades (Adjusted Vegetation Index to Solo (SAVI): state of the art and its potential)

2016 ◽  
Vol 9 (6) ◽  
pp. 2054
Author(s):  
Gabrielle de Araújo Ribeiro ◽  
João Nailson De Castro Silva ◽  
Janaína Barbosa Da Silva
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Near Infrared ◽  
State Of The Art ◽  
Vegetation Indices ◽  
Quantitative Information ◽  
Qualitative And Quantitative ◽  
Spectral Bands ◽  
Satellite Sensors ◽  
Best Parameters

A utilização do Sensoriamento Remoto para a avaliação do meio ambiente é cada vez mais aplicado em pesquisas. As imagens adquiridas pelos sensores acoplados aos satélites fornecem dados qualitativos e quantitativos do estado da vegetação através da aplicação dos índices de vegetação. Os índices são obtidos pela combinação matemáticas das reflectâncias dos alvos nas faixas espectrais, principalmente do vermelho e infravermelho próximo e podem ser afetados por diferentes fatores tais como reflectância, irradiancia e o brilho do solo. Um dos índices comumente utilizados, principalmente em áreas semiáridas, onde se tem influencia do brilho do solo, é o índice de vegetação ajustado ao solo (IVAS). Este índice introduz um fator de ajuste (L) ao índice de vegetação normalizada (IVDN) para minimizar os efeitos da presença do solo. Porém para cada região deve-se estudar e determinar os melhores parâmetros para o mesmo. Portanto este trabalho tem como objetivo apresentar uma revisão de literatura em relação ao índice de vegetação ajustado ao solo em diferentes biomas brasileiro e outras aplicações.   A B S T R A C T The use of remote sensing for environmental assessment is increasingly applied in research. The images acquired by the satellite sensors coupled to provide qualitative and quantitative information on the state of the vegetation by the application of vegetation indices. The indices are obtained by mathematical combination of the reflectance of the targets in the spectral bands, especially the red and near infrared and can be affected by different factors such as reflectance, irradiance and the brightness of the soil. One of the commonly used indices, especially in semi-arid areas where it has influence of soil brightness, is the vegetation index adjusted to the ground (UAI). This index introduces an adjustment factor (L) normalized vegetation index (NDVI) to minimize the effects of soil present. However, for each region should study and determine the best parameters for the same. Therefore this work aims to present a literature review regarding the vegetation index adjusted to the soil in different Brazilian biomes and other applications. Keywords : Remote Sensing; vegetation index; spectral analysis, biome.   

Measuring the response of pasture to superphosphate using aircraft and satellite remote sensing

1993 ◽  
Vol 33 (5) ◽  
pp. 597 ◽  
Author(s):  
RND Reid ◽  
PJ Vickery ◽  
DA Hedges ◽  
PM Williams
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Near Infrared ◽  
Processing System ◽  
Remote Sensing Data ◽  
Image Data ◽  
Short Wave ◽  
Stocking Rate ◽  
Remotely Sensed Data ◽  
Resultant Data

Remote sensing measurements in the visible, near infrared, and short-wave infrared were made on experimental areas of grass-legume pasture with different fertiliser and stocking rate treatments and on commercial pastures with added fertiliser.Divisive classification and ordination analyses of the remotely sensed data were used to allocate the image data to 11-15 classes from measurements in 2 successive years The resultant data were displayed on an image processing system which showed that the fertilised areas belonged to classes different from those without fertiliser. Soil and plant nutrient tests revealed differences between treated and untreated sites as mapped from the remote sensing data.

scholarly journals Identificación de perturbaciones en el bosque húmedo tropical colombiano usando series temporales de imágenes satelitales Landsat mediante el algoritmo Landtrendr

2019 ◽  
pp. 25
Author(s):  
L. Hurtado ◽  
I. Lizarazo
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Forest Disturbance ◽  
Spectral Response ◽  
Short Wave ◽  
Forest Vegetation ◽  
Landsat Images ◽  
Spectral Bands

<p>Time series analysis of satellite images for detection of deforestation and forest disturbances at specific dates has been a subject of research over the last few years. There are many limitations to identify the exact date of deforestation due mainly to the large volume of data and the criteria required for its correct characterization. A further limitation in the analysis of multispectral time series is the identification of true deforestation considering that forest vegetation may undergo different changes over time. This study analyzes deforestation in a zone within the Colombian Amazon using the Normalized Difference Vegetation Index (NDVI) based on semestral median mosaics generated from Landsat images collected from 2000 to 2017. Several samples representing trends of change over the time series were extracted and classified according to their degree of change and persistence in the series, using four categories: (i) deforestation, (ii) degradation, (iii) forest plantation, and (iv) regeneration. Specific deforestation samples were analyzed in the same way using the soil-adjusted vegetation index (SAVI) to reduce the effect of spectral response variations due to soil reflectance changes. It is concluded that the two indices used, together with the near infrared (NIR) and short-wave infrared (SWIR 1) spectral bands, allow to extract values and intervals where the change produced by deforestation on forest vegetation is identified with acceptable accuracy. The analysis of time series using the Landtrendr algorithm confirmed a reliable change detection in each of the forest disturbance categories.</p>

scholarly journals Aboveground Biomass Estimates of Araucaria angustifolia (Bertol.) Kuntze, Using Vegetation Indexes in Wolrdview-2 Image

2019 ◽  
Vol 11 (11) ◽  
pp. 93
Author(s):  
Luiz Carlos Pietrowski Basso ◽  
Vagner Alex Pesck ◽  
Mailson Roik ◽  
Afonso Figueiredo Filho ◽  
Thiago Floriani Stepka ◽  
...  
Keyword(s):  
Relative Error ◽  
Near Infrared ◽  
Direct Method ◽  
Satellite Image ◽  
Vegetation Indices ◽  
Coefficient Of Determination ◽  
Araucaria Angustifolia ◽  
Spectral Bands ◽  
Vegetation Indexes ◽  
Satellite Image Processing

The present research aims to evaluate the biomass estimates of Araucaria angustifolia (Bertol.) Kuntze trees obtained by the direct method, then present results generated from a 2.0 m resolution spectral image Worldview-2 satellite. The quantification of the biomass in the field was first carried out of 29 trees of the specie of interest with DBH &ge; 40 cm and then with the image aid the crowns were delimited for analysis. From the spectral bands (B2-blue, B3-green, B4-yellow, B5-red, B6-near red, B7-near infrared 2 and B8-near infrared 2), it was possible to obtain vegetation indexes proposed by the literature (NDVI, NDVI_2, RS and SAVI_0,25) and later incorporated with dendrometric data a correlation matrix was formed. Additionally, mathematical equations were used to estimate biomass and carbon as a function of dendrometric variables and information obtained from the satellite image processing. From these equations, the ones that presented better results were those that contained independent dendrometric variables (DBH) and those that contained vegetation indices (NDVI_2 and NDVI). For the dendrometers, the relative error found was 14.42% and 14.32% for biomass and carbon respectively, while for the digital ones, NDVI_2 found a relative error of 37.82% and an adjusted coefficient of determination of 0.88 in the biomass equations. In the carbon equations, the NDVI variable presented the best results, being 38.56% the relative error and 0.87 the determination coefficient.

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