Vegetation Index to estimate chlorophyll content from multispectral remote sensing data

Carmona, Facundo; Rivas, Raúl Eduardo; Fonnegra, Diana C.

doi:10.5721/eujrs20154818

Cited by 33 publications

(12 citation statements)

References 16 publications

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“…There are other approaches that capture images at different wavelengths. In particular, multispectral and hyperspectral images taken in a range of 300–1200 nm have been used to analyze the health of food crops [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ]. In general, multispectral/hyperspectral-based approaches can obtain relatively high accuracy and real-time processing; unfortunately, they have a high cost and large size.…”

Section: Introductionmentioning

confidence: 99%

Optical Method for Estimating the Chlorophyll Contents in Plant Leaves

Pérez-Patricio

Camas-Anzueto

Sanchez-Alegría

et al. 2018

Sensors

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This work introduces a new vision-based approach for estimating chlorophyll contents in a plant leaf using reflectance and transmittance as base parameters. Images of the top and underside of the leaf are captured. To estimate the base parameters (reflectance/transmittance), a novel optical arrangement is proposed. The chlorophyll content is then estimated by using linear regression where the inputs are the reflectance and transmittance of the leaf. Performance of the proposed method for chlorophyll content estimation was compared with a spectrophotometer and a Soil Plant Analysis Development (SPAD) meter. Chlorophyll content estimation was realized for Lactuca sativa L., Azadirachta indica, Canavalia ensiforme, and Lycopersicon esculentum. Experimental results showed that—in terms of accuracy and processing speed—the proposed algorithm outperformed many of the previous vision-based approach methods that have used SPAD as a reference device. On the other hand, the accuracy reached is 91% for crops such as Azadirachta indica, where the chlorophyll value was obtained using the spectrophotometer. Additionally, it was possible to achieve an estimation of the chlorophyll content in the leaf every 200 ms with a low-cost camera and a simple optical arrangement. This non-destructive method increased accuracy in the chlorophyll content estimation by using an optical arrangement that yielded both the reflectance and transmittance information, while the required hardware is cheap.

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Section: Introductionmentioning

confidence: 99%

Optical Method for Estimating the Chlorophyll Contents in Plant Leaves

Pérez-Patricio

Camas-Anzueto

Sanchez-Alegría

et al. 2018

Sensors

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“…Chlorophyll absorbs light more strongly in the blue (~430 nm) and red (~660 nm) range, being the least efficient pigment to absorb light in the middle range, reflecting green light (~550 nm) (Taiz & Zeiger, 2008). The relationship between reflectance, represented by the VI, and leaf chlorophyll content was demonstrated by Carmona et al (2015) using spectra for several cultivated areas and by Kooistra & Clevers (2016) for potato (Solanum tuberosum L.) fields. However, Amaral et al (2015) observed a higher correlation between sugarcane canopy reflectance, measured by proximal sensing, and biomass and no correlation with the relative chlorophyll concentrations.…”

Section: Resultsmentioning

confidence: 99%

Delimitation of Homogeneous Zones in Vineyards Using Geostatistics and Multivariate Analysis of Different Vegetation Indices

et al. 2019

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Vegetation indices (VIs) are quantitative measures used to describe the distribution and spatial variability of the vegetation cover of natural or cultivated areas. The aim of this study was to delimit homogeneous zones (HZs) of different VIs using geostatistics and multivariate analysis in order to identify vegetation patterns in Cabernet Franc and Cabernet Sauvignon vineyards. The evaluation was performed in two vineyards in the municipality of Espírito Santo do Pinhal in the state of São Paulo, Brazil. Reflectance (ρ) was measured at three wavelengths of the electromagnetic spectrum (670, 730, and 780 nm) at canopy height in georeferenced points along planting rows using the Crop Circle ACS-430 active sensor. Nine VIs were calculated based on the ratios between the ρ values. Geostatistical data analysis allowed the spatial prediction of VIs by ordinary kriging interpolation. Principal component analysis and fuzzy k-means clustering were applied for HZs delimitation and the optimal number of zones was defined according to cluster validity functions. Despite the variations of the VIs spatial distribution patterns, the multivariate analysis resulted in a representative categorization of the grapevine vegetative vigor and delimitation of HZs for this characteristic. This was validated according to the observed significant differences between VIs.

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“…The normalized area over reflectance curve (NAOC) index was derived using the red-edge wavelengths (i.e., 643 and 795 nm) (Carmona et al 2015). In the Sentinel-2A, the nearest bands, namely, 665 and 783 nm are available which was employed to derive the leaf Chl content.…”

Section: Methodsmentioning

confidence: 99%

Mapping tea plantations dynamics during 2000–2020 and monitoring biophysical attributes using multi-temporal satellite data in North Bengal (India)

Parida

Kumari

2021

Arab J Geosci

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Tea is an important cash crop, and it becomes necessary to map the spatial distribution of tea plantations. The tea industry has been expanding rapidly in the Northeast region of India, and consequently, the area under tea plantations is changing rapidly which needs periodic monitoring. In this study, satellite data such as Landsat-5 Thematic Mapper (TM) and Sentinel-2A were deployed for tea plantation identification and to analyze the dynamics of tea extent in the North Bengal district during 2000 and 2020. The result showed that the tea plantation area has increased from 1097 km 2 (2000) to 1377 km 2 (2020), depicting an increase in tea plantation area by 280 km 2 (or 25%) during the last 20 years. The highest increase in tea plantations was in Darjeeling (+137 km 2 ) and Jalpaiguri (+ 83 km 2 ) districts due to the expansion of tea estates along with the increasing population. The accuracy of tea plantation was evaluated using recall and precision with F1 score of 0.90 in 2000 and 0.92 in 2020. The tea plantation health condition was also assessed using both biophysical (LAI, EVI, FPAR) and biochemical parameters (leaf chlorophyll content). The results revealed that tea plants leaf area index (LAI) varied from 1 to 3, with healthy tea plants showed LAI > 2. Similarly, healthy tea plants depicted leaf chlorophyll content of 40 to 70 μg/cm 2 . Assessment of dynamics of tea plantation is required for promoting sustainable tea practices and safeguarding economic and social importance of people living in North Bengal.

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Vegetation Index to estimate chlorophyll content from multispectral remote sensing data

Cited by 33 publications

References 16 publications

Optical Method for Estimating the Chlorophyll Contents in Plant Leaves

Optical Method for Estimating the Chlorophyll Contents in Plant Leaves

Delimitation of Homogeneous Zones in Vineyards Using Geostatistics and Multivariate Analysis of Different Vegetation Indices

Mapping tea plantations dynamics during 2000–2020 and monitoring biophysical attributes using multi-temporal satellite data in North Bengal (India)

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