Remote Sensing Leaf Chlorophyll Content Using a Visible Band Index

Hunt, E. Raymond; Daughtry, Craig S. T.; Eitel, Jan U. H.; Long, Dan S.

doi:10.2134/agronj2010.0395

Cited by 328 publications

(164 citation statements)

References 62 publications

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“…Red-edge bands are deployed on many satellite sensors (Eitel et al, 2007;Herrmann et al, 2011;Ramoelo et al, 2012) and increase sensitivity to chlorophyll content (Gitelson et al, 2005;Gitelson, 2012). However, red-edge bands are generally not available on low-cost multispectral sensors, which have broad bands at visible wavelengths; therefore, a visible-band index called the triangular greenness index (TGI) was developed (Hunt et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

A visible band index for remote sensing leaf chlorophyll content at the canopy scale

Hunt

Doraiswamy

McMurtrey

et al. 2013

International Journal of Applied Earth Observation and Geoinfor

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428

214

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Doraiswamy, Paul C.; McMurtrey, James E.; Daughtry, Craig S.T.; Perry, Eileen M.; and Akhmedov, Bakhyt, "A visible band index for remote sensing leaf chlorophyll content at the canopy scale" (2013 and soil type at low crop LAI, whereas at high LAI and canopy closure, TGI was only affected by leaf chlorophyll content. Therefore, TGI may be the best spectral index to detect crop nitrogen requirements with low-cost digital cameras mounted on low-altitude airborne platforms.

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Section: Introductionmentioning

confidence: 99%

A visible band index for remote sensing leaf chlorophyll content at the canopy scale

Hunt

Doraiswamy

McMurtrey

et al. 2013

International Journal of Applied Earth Observation and Geoinfor

Self Cite

428

214

View full text Add to dashboard Cite

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“…sis rely on the identification of key spectral wavebands -related to specific physiological and structural characteristics of plantscombined into algebraic indices, which are implemented using empirical or semi-empirical methods to estimate vegetation dynamics and parameters (e.g., vigour or greenness, leaf area index, fractional cover, density, biomass, and the fraction of absorbed photosynthetically active radiation) (asrar et al, 1989;goward and huemmrich, 1992;Penuelas et al, 1993;gitelson and Merzlyak, 1996;haboudane et al, 2004). Most of the studies on this topic, and especially on the use of spectral information and sis to estimate and map vegetation morphological traits, are still based on terrestrial vegetation and agricultural crops (e.g., rouse et al, 1974;Tucker et al, 1979;huete, 1988;gobron et al, 2000;Broge and leblanc, 2001;dash and curran, 2004;haboudane et al, 2004;Tian et al, 2005;gitelson et al, 2006;Wu et al, 2009;hunt et al, 2011;Maccioni et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Remote sensing of macrophyte morphological traits: Implications for the management of shallow lakes

et al. 2017

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Macrophytes are important elements of freshwater ecosystems, fulfilling a pivotal role in biogeochemical cycles. The synoptic capabilities provided by remote sensing make it a powerful tool for monitoring aquatic vegetation characteristics and the functional status of shallow lake systems in which they occur. The latest generation of airborne and spaceborne imaging sensors can be effectively exploited for mapping morphologically -and physiologically -relevant vegetation features based on their canopy spectral response. The objectives of this study were to calibrate semi-empirical models for mapping macrophyte morphological traits (i.e., fractional cover, leaf area index and above-water biomass) from hyperspectral data, and to investigate the capabilities of remote sensing in supporting macrophyte monitoring and management. We calibrated spectral models using in situ reflectance and morphological trait measures and applied them to airborne hyperspectral imaging data, acquired over two shallow european water bodies (lake hídvégi, in hungary, and Mantua lakes system, in italy) in two key phenological phases. Maps of morphological traits were produced covering a broad range of aquatic plant types (submerged, floating, and emergent), common to temperate and continental regions, with an error level of 5.4% for fractional cover, 0.10 m 2 m -2 for leaf area index, and 0.06 kg m -2 for abovewater biomass. Based on these maps, we discuss how remote sensing could support monitoring strategies and shallow lake management with reference to our two case studies: i.e., by providing insight into spatial and species-wise variability, by assessing nutrient uptake by aquatic plants, and by identifying hotspot areas where invasive species could become a threat to ecosystem functioning and service provision.

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“…We have utilized the images to estimate the relative green index (RGI) of each plot, to evaluate the efficacy of the treatment, in conjunction with the field observations. The RGI is computed by first computing the Triangular Greenness Index (TGI) (Raymond Hunt et al, 2011) of each pixel: T GI = 1.0Green − 0.39Red − 0.61Blue. We then segment the images using the average Otsu threshold value for all the images (Otsu, 1979).…”

Section: Methodsmentioning

confidence: 99%

Robotic in-row weed control in vegetables

Utstumo

Urdal

Brevik

et al. 2018

Computers and Electronics in Agriculture

124

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Vegetables and other row-crops represent a large share of the agricultural production. There is a large variation in crop species, and a limited availability in specialized herbicides. The robot presented here utilizes the systematic cultivation techniques of row crops to navigate and operate in the eld. By the use of machine vision it separates seeded vegetable crops from weed, and treat each weed within the row with individual herbicide droplets, without aecting the crop. This Dropon-Demand (DoD) method allow the use of non-selective herbicides and signicant reductions in herbicide use.This thesis presents six research papers concerning the development of the DoD system and the mobile robot. The robot is tailored to its purpose with cost, maintainability, ecient operation and robustness in mind. The three-wheeled design is unconventional, and the design maintains maneuverability and stability with the benet of reduced weight, complexity and cost. The DoD system for herbicide application has been developed within and in connection with this project. The inuence of liquid properties viscosity and surface tension on the formation and stability of droplets has been tested in lab trials. A control circuit for synchronized control of solenoid valves was developed and tested.Indoor pot trials with four weed species demonstrated that the Drop-on-Demand system (DoD) could control the weeds with as little as 7.6 µg glyphosate or 0.15 µg iodosulfuron per plant. The results also highlight the importance of liquid characteristics for leaf retention, as the common herbicide glyphosate had no eect unless mixed with suitable additives. The trials document the DoD eect on weed species not previously described in literature, and with an alternative herbicide to glyphosate, iodosulfuron. A eld trial with the robot was performed in a carrot eld, and all the weeds were eectively controlled with the DoD system.iii Abstract The robot and DoD system represent an important contribution to the range of systems presented witin Precision Agriculture for in-row weed control -a movement which as a whole represent a paradigm shift to the environmental impact and health risks of weed control, while providing valuable new tools to the producers. My advisor, Prof. Jan Tommy Gravdahl has the ability to delve deeply into a wide array of technical elds. He has been a solid and consistent support, which has been vital for me to bridge the divide between the industrial and academic worlds. I will strive for the opportunities to work more together.Friendships have grown both at NTNU, UMN and Adigo which will outlast this thesis. I thank you, my family, colleagues and friends for the support to complete this journey. And not the least, I thank my wife Camilla. This introduction will describe the background for the project and its motivation, and the context and relationship between each paper is described in Section 1.4. The conclusion in Chapter 3 will tie together our ndings and describe our perspective on the path forwards for Drop on De...

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Remote Sensing Leaf Chlorophyll Content Using a Visible Band Index

Cited by 328 publications

References 62 publications

A visible band index for remote sensing leaf chlorophyll content at the canopy scale

A visible band index for remote sensing leaf chlorophyll content at the canopy scale

Remote sensing of macrophyte morphological traits: Implications for the management of shallow lakes

Robotic in-row weed control in vegetables

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