A new vegetation index for detecting vegetation anomalies due to mineral deposits with application to a tropical forest area

Hede, Arie Naftali Hawu; Kashiwaya, Koki; Koike, Katsuaki; Sakurai, Shigeki

doi:10.1016/j.rse.2015.10.006

Cited by 46 publications

(22 citation statements)

References 46 publications

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“…In recent years, many vegetation indexes have been developed (Zarcotejada, Miller, Noland, Mohammed, & Sampson, ; Maire, François, & Dufrene, ; Haboudanem, Tremblay, Miller, & Vigneault, ; Vincini, Frazzi, & Alessio, ; Ziosi et al, ;). Using such indices, 22 varieties index of Longjing fresh tea have been classified (Gamon, Penuelas, & Field, ; Gitelson, Kaufman, Stark, & Rundquist, ; Hede, Kashiwaya, Koike, & Sakurai, ; Huete, ; Huete et al, ; Kaufman & Tanre, ; Penuelas, Filella, Biel, Serrano, & Save, ; Rondeaux, Steven, & Baret, ; Roujean & Breon, ; Shi, Wang, Liu, & Wu, ; Sims & Gamon, ). These indexes were divided into five categories according to combination form, which are listed in Table .…”

Section: Methodsmentioning

confidence: 99%

Recognition of different Longjing fresh tea varieties using hyperspectral imaging technology and chemometrics

Lei

Pang

Wang³

et al. 2020

J Food Process Engineering

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Hyperspectral imaging technology was applied to detect and recognize six different varieties of Longjing fresh tea. The data contained image and spectral information at 370–1042 nm; color and texture features were the foci of the image research. Spectral pre‐processing was performed by multiplicative scatter correction (MSC) and standard normal variate (SNV), and then, we selected the corresponding position variable and vegetation indexes as spectral features. Representative features including the most information were chose by principal component analysis (PCA). A novel back propagation (BP) neural network, with a self‐generated number of hidden layer neurons, was proposed. Using spectral features, image features, and spectral image fusion features as input, three fresh tea recognition models were established: the improved BP neural network, traditional BP neural network, and support vector machine (SVM). Results suggested that the improved BP neural network could promote performance of the model, especially for the spectral pre‐processed data. Mixed‐feature models did better than individual feature models, with 100% accuracy of the predictive set. This study shows that hyperspectral imaging technology can be a potential rapid and nondestructive approach to identify different varieties of Longjing fresh teas. Practical applications This article introduced application of hyperspectral imaging technology to identify Longjing fresh tea of different origins and varieties. Samples were analyzed using spectral and image characteristics. We provided a basis for full utilization of tea characteristics. At the same time, an improved BP neural network, with less calculation complexity and workload than the traditional BP neural network, was proposed. In summary, we outlined a convenient and reliable method for differentiation of Longjing fresh teas. Furthermore, we established a theoretical foundation for development of portable instruments to be used in similar studies.

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Section: Methodsmentioning

confidence: 99%

Recognition of different Longjing fresh tea varieties using hyperspectral imaging technology and chemometrics

Lei

Pang

Wang³

et al. 2020

J Food Process Engineering

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“…For a Landsat ETM+ image, G , R , N , S 1 , and S 2 correspond to the reflectance at bands 2, 3, 4, 5, and 7, respectively. The w 1 , w 2 , w 3 , and w 4 are weights for each term and a combination of w 1 = 1.0, w 2 = 0.5, w 3 = 1.5, and w 4 = 1.5 was employed in this study based on our previous result [ Hede et al ., ]. Equation of NDVI is:

NDVI = \frac{N - R}{N + R}

…”

Section: Methodsmentioning

confidence: 99%

“…To emphasize a reflectance change caused by the stress and clarify the spatial pattern of anomalies, a ratio of the mean ( m ) to the standard deviation ( σ ) calculated on a per‐pixel basis from the VIGS data set was used. Selection of this ratio was founded on our previous studies that showed that the VIGS values tended to be large at metal contamination zones common to the Landsat images in the growing season [ Hede et al ., ]. The present five images were within this season.…”

Section: Methodsmentioning

confidence: 99%

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How can satellite imagery be used for mineral exploration in thick vegetation areas?

Hede

Koike

Kashiwaya

et al. 2017

Geochem Geophys Geosyst

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The Hokuroku district, northern Japan, is globally recognized for rich ore deposits (kuroko and vein types), which have been thoroughly explored under thick vegetation cover. This situation is ideal to evaluate the effects of ore deposits on vegetation anomalies through geobotanical remote sensing. Here we present novel methods to detect vegetation anomalies caused by ore deposits and verify their usefulness by comparing the anomalies with a deposit potential map produced from multiple geological data. We use the reflectance spectra of Landsat ETM+ images acquired in summer and autumn to calculate a vegetation index for plant physiological activity. A key variable to detect the anomalies is a variation of vegetation index with time at each pixel. Difference in variation is enlarged by a sequence of image enhancement methods for the detection. We find that the vegetation anomalies, defined by the large ratios, correspond well to the high potential zones of ore deposits and known major deposits. Consequently, our methods can extend the applicability of remote sensing‐based mineral exploration to the areas covered by thick vegetation, in addition to traditional arid and semiarid areas.

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“…These methods are based on the optical properties of plants and can be used for identifying nutrient deficiencies, plants diseases, water stress and excess water, insect damage, weeds, etc. (Baret, 2016;Haboudane, Miller, Pattey, Zarco-Tejada, & Strachan, 2004;Hede, Kashiwaya, Koike, & Sakurai, 2015;Sankaran et al, 2010). Combination of plant knowledge and remote sensing data can provide information for plant management and even initial advices about plant stress to prevent the spread of disease or pest infestation by adopting appropriate reactions in the early stages of stress.…”

Section: Introductionmentioning

confidence: 99%

Aerial multispectral imagery for plant disease detection: radiometric calibration necessity assessment

Pourazar

Samadzadegan

Javan

2019

European Journal of Remote Sensing

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This paper focused on the necessity of radiometric calibration to distinguish diseased trees in orchards based on aerial multi-spectral images. For this purpose, two study sites were selected where multispectral images were collected using a multirotor UAV. The impact of radiometric correction on plant disease detection was assessed in two ways: 1) comparison of separability between the healthy and diseased classes using T-test and entropy distances; 2) radiometric calibration effect on the accuracy of classification. The experimental results showed the insignificant effect of radiometric calibration on separability criteria. In the second strategy, the experimental results showed that radiometric calibration had a negligible effect on the accuracy of classification. As a result, the overall accuracy and kappa values for un-calibrated and calibrated orthomosaic classifications of the citrus orchard were 96.49%, 0.941, 96.57% and 0.942, respectively, using five spectral bands as well as DVI, NDRE, NDVI and GNDVI vegetation indices using a random forest classifier. The experimental results were also similar at the other study site. Therefore, the overall accuracy and kappa values for the un-calibrated and calibrated orthomosaic classifications were 95.58%, 0.913, 95.56% and 0.913, respectively, using five spectral bands as well as NDRE, BNDVI, GNDVI, DVI, and NDVI vegetation indices.

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A new vegetation index for detecting vegetation anomalies due to mineral deposits with application to a tropical forest area

Cited by 46 publications

References 46 publications

Recognition of different Longjing fresh tea varieties using hyperspectral imaging technology and chemometrics

Recognition of different Longjing fresh tea varieties using hyperspectral imaging technology and chemometrics

How can satellite imagery be used for mineral exploration in thick vegetation areas?

Aerial multispectral imagery for plant disease detection: radiometric calibration necessity assessment

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