Correction: Anderson, H.B. et al. Using Ordinary Digital Cameras in Place of Near-Infrared Sensors to Derive Vegetation Indices for Phenology Studies of High Arctic Vegetation. Remote Sens. 2016, 8, 847

Commercially available digital cameras can be mounted on an unmanned aerial vehicle (UAV) for crop growth monitoring in open-air fields as a low-cost, highly effective observation system. However, few studies have investigated their potential for nitrogen (N) status monitoring, and the performance of camera-derived vegetation indices (VIs) under different conditions remains poorly understood. In this study, five commonly used VIs derived from normal color (RGB) images and two typical VIs derived from color near-infrared (CIR) images were used to estimate leaf N concentration (LNC). To explore the potential of digital cameras for monitoring LNC at all crop growth stages, two new VIs were proposed, namely, the true color vegetation index (TCVI) from RGB images and the false color vegetation index (FCVI) from CIR images. The relationships between LNC and the different VIs varied at different stages. The commonly used VIs performed well at some stages, but the newly proposed TCVI and FCVI had the best performance at all stages. The performances of the VIs with red (or near-infrared) and green bands as the numerator were limited by saturation at intermediate to high LNCs (LNC > 3.0%), but the TCVI and FCVI had the ability to mitigate the saturation. The results of model validations further supported the superiority of the TCVI and FCVI for LNC estimation. Compared to the other VIs derived using RGB cameras, the relative root mean square errors (RRMSEs) of the TCVI were improved by 8.6% on average. For the CIR images, the best-performing VI for LNC was the FCVI (R2 = 0.756, RRMSE = 14.18%). The LNC–TCVI and LNC–FCVI were stable under different cultivars, N application rates, and planting densities. The results confirmed the applicability of UAV-based RGB and CIR cameras for crop N status monitoring under different conditions, which should assist the precision management of N fertilizers in agronomic practices.

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The AIDSS Module for Data Acquisition in Crisis Situations and Environmental Protection

Sensors ◽

10.3390/s20051267 ◽

2020 ◽

Vol 20 (5) ◽

pp. 1267

Author(s):

Andrija Krtalić ◽

Milan Bajić ◽

Tamara Ivelja ◽

Ivan Racetin

Keyword(s):

Data Acquisition ◽

Near Infrared ◽

Measurement Unit ◽

Digital Cameras ◽

Infrared Sensors ◽

Multiple Sensors ◽

Hazardous Area ◽

Remotely Piloted Aircraft ◽

Antipersonnel Mines ◽

Line Scanner

The Toolbox implementation for removal of antipersonnel mines, submunitions and unexploded ordnance (TIRAMISU) Advanced Intelligence Decision Support System is an operational system proposed to Mine Action Centres worldwide for conducting non-technical surveys in humanitarian demining. The system consists of three modules, one of which is the module for data acquisition introduced and described in this study. The module has been designed, produced, improved, used and operationally tested and validated on several platforms (helicopters, remotely piloted aircraft systems (RPAS) and a blimp), with various sensors and acquisition units (Global Positioning System (GPS) and inertial measurement unit) in a variety of combinations for additional data acquisition from deep inside a suspected hazardous area. For the purposes of aerial data acquisition over a suspected hazardous area, the use of multiple sensors such as visible digital cameras and multi-spectral visible, near infrared (VNIR), hyperspectral VNIR and thermal infrared sensors are of benefit, because they display the scene in different ways. Off-the-shelf equipment and software were mostly used, but some specific equipment, such as sensor pods, was developed and also some software solutions for data acquisition and pre-processing (transforming hyperspectral line scanner data into hyperspectral images, and producing hyperspectral cubes). The technical stability and robustness of the module were confirmed by operationally testing and evaluating the systems on the aforementioned platforms and missions in several actual suspected hazardous areas in Croatia and Bosnia and Herzegovina, between 2001 and 2015.

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

Remote Sensing ◽

10.3390/rs13030536 ◽

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.

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Assessing Leaf Biomass of Agave sisalana Using Sentinel-2 Vegetation Indices

Remote Sensing ◽

10.3390/rs13020233 ◽

2021 ◽

Vol 13 (2) ◽

pp. 233

Author(s):

Ilja Vuorinne ◽

Janne Heiskanen ◽

Petri K. E. Pellikka

Keyword(s):

Near Infrared ◽

Vegetation Indices ◽

Fibre Production ◽

Additive Models ◽

Leaf Biomass ◽

Ground Vegetation ◽

Perennial Crop ◽

Red Edge ◽

Agave Sisalana ◽

Sentinel 2

Biomass is a principal variable in crop monitoring and management and in assessing carbon cycling. Remote sensing combined with field measurements can be used to estimate biomass over large areas. This study assessed leaf biomass of Agave sisalana (sisal), a perennial crop whose leaves are grown for fibre production in tropical and subtropical regions. Furthermore, the residue from fibre production can be used to produce bioenergy through anaerobic digestion. First, biomass was estimated for 58 field plots using an allometric approach. Then, Sentinel-2 multispectral satellite imagery was used to model biomass in an 8851-ha plantation in semi-arid south-eastern Kenya. Generalised Additive Models were employed to explore how well biomass was explained by various spectral vegetation indices (VIs). The highest performance (explained deviance = 76%, RMSE = 5.15 Mg ha−1) was achieved with ratio and normalised difference VIs based on the green (R560), red-edge (R740 and R783), and near-infrared (R865) spectral bands. Heterogeneity of ground vegetation and resulting background effects seemed to limit model performance. The best performing VI (R740/R783) was used to predict plantation biomass that ranged from 0 to 46.7 Mg ha−1 (mean biomass 10.6 Mg ha−1). The modelling showed that multispectral data are suitable for assessing sisal leaf biomass at the plantation level and in individual blocks. Although these results demonstrate the value of Sentinel-2 red-edge bands at 20-m resolution, the difference from the best model based on green and near-infrared bands at 10-m resolution was rather small.

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Rapid Quantification of Microalgae Growth with Hyperspectral Camera and Vegetation Indices

Plants ◽

10.3390/plants10020341 ◽

2021 ◽

Vol 10 (2) ◽

pp. 341

Author(s):

Pauliina Salmi ◽

Matti A. Eskelinen ◽

Matti T. Leppänen ◽

Ilkka Pölönen

Keyword(s):

Chlorophyll Fluorescence ◽

Chlorophyll A ◽

Near Infrared ◽

Vegetation Indices ◽

Hyperspectral Data ◽

Strong Correlations ◽

Algae Biomass ◽

Chlorophyll A Concentration ◽

Cost Efficient ◽

Hyperspectral Camera

Spectral cameras are traditionally used in remote sensing of microalgae, but increasingly also in laboratory-scale applications, to study and monitor algae biomass in cultures. Practical and cost-efficient protocols for collecting and analyzing hyperspectral data are currently needed. The purpose of this study was to test a commercial, easy-to-use hyperspectral camera to monitor the growth of different algae strains in liquid samples. Indices calculated from wavebands from transmission imaging were compared against algae abundance and wet biomass obtained from an electronic cell counter, chlorophyll a concentration, and chlorophyll fluorescence. A ratio of selected wavebands containing near-infrared and red turned out to be a powerful index because it was simple to calculate and interpret, yet it yielded strong correlations to abundances strain-specifically (0.85 < r < 0.96, p < 0.001). When all the indices formulated as A/B, A/(A + B) or (A − B)/(A + B), where A and B were wavebands of the spectral camera, were scrutinized, good correlations were found amongst them for biomass of each strain (0.66 < r < 0.98, p < 0.001). Comparison of near-infrared/red index to chlorophyll a concentration demonstrated that small-celled strains had higher chlorophyll absorbance compared to strains with larger cells. The comparison of spectral imaging to chlorophyll fluorescence was done for one strain of green algae and yielded strong correlations (near-infrared/red, r = 0.97, p < 0.001). Consequently, we described a simple imaging setup and information extraction based on vegetation indices that could be used to monitor algae cultures.

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Mapping leaf metal content over industrial brownfields using airborne hyperspectral imaging and optimized vegetation indices

Scientific Reports ◽

10.1038/s41598-020-79439-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Guillaume Lassalle ◽

Sophie Fabre ◽

Anthony Credoz ◽

Rémy Hédacq ◽

Dominique Dubucq ◽

...

Keyword(s):

Metal Uptake ◽

Near Infrared ◽

Vegetation Indices ◽

Good Alternative ◽

Entire Study ◽

Metal Contents ◽

Very High Spatial Resolution ◽

Airborne Imaging ◽

Very High ◽

Plant Metal Uptake

AbstractMonitoring plant metal uptake is essential for assessing the ecological risks of contaminated sites. While traditional techniques used to achieve this are destructive, Visible Near-Infrared (VNIR) reflectance spectroscopy represents a good alternative to monitor pollution remotely. Based on previous work, this study proposes a methodology for mapping the content of several metals in leaves (Cr, Cu, Ni and Zn) under realistic field conditions and from airborne imaging. For this purpose, the reflectance of Rubus fruticosus L., a pioneer species of industrial brownfields, was linked to leaf metal contents using optimized normalized vegetation indices. High correlations were found between the vegetation indices exploiting pigment-related wavelengths and leaf metal contents (r ≤ − 0.76 for Cr, Cu and Ni, and r ≥ 0.87 for Zn). This allowed predicting the metal contents with good accuracy in the field and on the image, especially Cu and Zn (r ≥ 0.84 and RPD ≥ 2.06). The same indices were applied over the entire study site to map the metal contents at very high spatial resolution. This study demonstrates the potential of remote sensing for assessing metal uptake by plants, opening perspectives of application in risk assessment and phytoextraction monitoring in the context of trace metal pollution.

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Satellite derived maps of pasture growth status: association of classification with botanical composition

Australian Journal of Experimental Agriculture ◽

10.1071/ea97014 ◽

1997 ◽

Vol 37 (5) ◽

pp. 547 ◽

Cited By ~ 17

Author(s):

P. J. Vickery ◽

M. J. Hill ◽

G. E. Donald

Keyword(s):

Near Infrared ◽

Perennial Grass ◽

Vegetation Indices ◽

Fast Growth ◽

Perennial Grasses ◽

Bayesian Probability ◽

Cumulative Probability ◽

Botanical Composition ◽

South Wales ◽

Native Pastures

Summary. Spectral data from the green, red and near-infrared bands of Landsat MSS and Landsat TM satellite imagery acquired in mid-spring were classified into 3 and 6 pasture growth classes respectively. The classifications were compared with a site database of botanical composition for the Northern Tablelands of New South Wales to examine the association between spectral growth class and pasture composition. Pastures ranged in composition from unimproved native perennial grasses through semi-improved mixtures of native and naturalised grasses and legumes to highly improved temperate perennial grasses and legumes. For 3 years of MSS data, the fast growth class had a mean botanical composition of about 80% improved perennial grass and 0% native; medium growth class averaged 46% improved perennial grass and 14% native; while the slow growth class had about 60% native and 1% improved perennial grass when averaged over 3 years of MSS data. For the 6 class TM data from a single year, a predictive logistic regression of cumulative probability was developed for percentage of ‘very fast’ growth pixels and ordered 10 percentile categories of improved perennial grass or native grass. Differences in patch characteristics between classes with MSS disappeared with TM reclassified to the same 3 class level. Most probable pasture type was inferred from 3 class MSS and TM data using Bayesian probability analysis. The resulting maps were similar in general appearance but detail was better with the TM data. The pasture growth classification identified highly improved perennial grass pastures and native pastures but sensitivity to intermediate pasture types was poor. Future improvement will come from direct measurement of biophysical characteristics using vegetation indices or inversion of reflectance models.

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

10.5194/egusphere-egu21-5171 ◽

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

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.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.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.

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Using remote sensing for identification of late-season grass weed patches in wheat

Weed Science ◽

10.1614/ws-05-54.2.346 ◽

2006 ◽

Vol 54 (02) ◽

pp. 346-353 ◽

Cited By ~ 41

Author(s):

Francisca López-Granados ◽

Montse Jurado-Expósito ◽

Jose M. Peña-Barragán ◽

Luis García-Torres

Keyword(s):

Vegetation Index ◽

Near Infrared ◽

Normalized Difference Vegetation Index ◽

Vegetation Indices ◽

Field Research ◽

Aerial Images ◽

Wild Oat ◽

Late Season ◽

High Resolution Satellite Imagery ◽

Grass Weed

Field research was conducted to determine the potential of hyperspectral and multispectral imagery for late-season discrimination and mapping of grass weed infestations in wheat. Differences in reflectance between weed-free wheat and wild oat, canarygrass, and ryegrass were statistically significant in most 25-nm-wide wavebands in the 400- and 900-nm spectrum, mainly due to their differential maturation. Visible (blue, B; green, G; red, R) and near infrared (NIR) wavebands and five vegetation indices: Normalized Difference Vegetation Index (NDVI), Ratio Vegetation Index (RVI), R/B, NIR-R and (R − G)/(R + G), showed potential for discriminating grass weeds and wheat. The efficiency of these wavebands and indices were studied by using color and color-infrared aerial images taken over three naturally infested fields. In StaCruz, areas infested with wild oat and canarygrass patches were discriminated using the indices R, NIR, and NDVI with overall accuracies (OA) of 0.85 to 0.90. In Florida–West, areas infested with wild oat, canarygrass, and ryegrass were discriminated with OA from 0.85 to 0.89. In Florida–East, for the discrimination of the areas infested with wild oat patches, visible wavebands and several vegetation indices provided OA of 0.87 to 0.96. Estimated grass weed area ranged from 56 to 71%, 43 to 47%, and 69 to 80% of the field in the three locations, respectively, with per-class accuracies from 0.87 to 0.94. NDVI was the most efficient vegetation index, with a highly accurate performance in all locations. Our results suggest that mapping grass weed patches in wheat is feasible with high-resolution satellite imagery or aerial photography acquired 2 to 3 wk before crop senescence.

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