Estimating chlorophyll content of spartina alterniflora at leaf level using hyper-spectral data

<p>Biofilms have received great attention in the last few decades including their potential contribution to carbon fluxes and ecosystem engineering in aquatic ecosystems. Quantifying the spatial distribution of biofilms and their dynamics through time is a critical challenge. Satellite imagery is one solution, and can provide multi- and hyper-spectral data but not necessarily the spatial resolution that such studies need. Multi- and hyper-spectral data sets may be of particular value for not simply detecting the presense/absence of biofilms but also indicators of primary productivity such as chlorophyll-a concentrations. Spatial resolution is sensor quality dependent, but also controlled by sensor elevation above the ground. Hence, higher resolutions can be achieved either by using a very expensive sensor or by decreasing the distance between the target area and the sensor itself. To date, sensor technology has advanced to a point where multi- or even hyper-spectral cameras can be easily transported by UAVs, potentially yielding wide-range spectral information at unprecedented spatial resolutions. That said, such set ups have often exorbitant costs (several 1000s of US$) that few research institutions can afford or, due to the high probability of sensor lost, are risky to use. This is particularly true for glacier forefields where low air temperatures, dust and sudden wind gusts can easily damage both UAV and sensor components.</p><p>In this paper we test the performance of visible band ratios for mapping both biofilms and chlorophyll-a concentrations in an alpine glacier forefield characterized by a well-developed and heterogeneous (kryal, krenal and rhithral) stream system. The paper shows that low-cost and consumer grade UAVs can be easily deployed in such extreme environments, delivering high temporal resolution datasets and with sufficient quality RGB images for photogrammetric (SfM-MVS) processing and post-processing image analysis (i.e., band ratios). This paper shows also that visible band ratios correlates with chlorophyll-a concentrations yielding reliable chlorophyll-a information of the forefield and at the centimetric scale. This in turn allows for precise identification of the environmental conditions that lead to both biofilm development and removal through perturbation.</p>

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Hyper-spectral data based investigations for snow wetness mapping

Geocarto International ◽

10.1080/10106049.2018.1438528 ◽

2018 ◽

Vol 34 (6) ◽

pp. 664-687 ◽

Cited By ~ 4

Author(s):

Chander Shekhar ◽

Sunita Srivastava ◽

Harendra Singh Negi ◽

Manish Dwivedi

Keyword(s):

Spectral Data ◽

Hyper Spectral

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Non-Destructive Estimation of Total Chlorophyll Content of Apple Fruit Based on Color Feature, Spectral Data and the Most Effective Wavelengths Using Hybrid Artificial Neural Network—Imperialist Competitive Algorithm

Plants ◽

10.3390/plants9111547 ◽

2020 ◽

Vol 9 (11) ◽

pp. 1547

Author(s):

Razieh Pourdarbani ◽

Sajad Sabzi ◽

Mario Hernández-Hernández ◽

José Luis Hernández-Hernández ◽

Iván Gallardo-Bernal ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Chlorophyll Content ◽

Spectral Data ◽

Total Chlorophyll ◽

Total Chlorophyll Content ◽

Color Data ◽

Artificial Neural ◽

Non Destructive ◽

Hybrid Artificial Neural Network

Non-destructive assessment of the physicochemical properties of food products, especially fruits, makes it possible to examine the internal quality without any damage. This is applicable at different stages of fruit growth, harvesting stage, and storage as well as at the market stage. In this regard, the present study aimed to estimate the total chlorophyll content using three types of data: color data, spectral data, and spectral data related to the most effective wavelengths. The most important steps of the proposed algorithms include extracting spectral and color data from each sample of Fuji cultivar apple, selecting the most effective wavelengths at the range of 660–720 nm using hybrid artificial neural network–particle swarm optimization (ANN-PSO), non-destructive assessment of the chemical property of total chlorophyll content based on color data, and spectral data using hybrid artificial neural network-Imperialist competitive algorithm (ANN-ICA). In order to assess the reliability of the hybrid ANN-ICA, 1000 iterations were performed after selecting the optimal structure of the artificial neural network. According to the results, in the best training mode and using spectral data and the most effective wavelength, total chlorophyll content was predicted with the R2 and RMSE of 0.991 and 0.0035, 0.997 and 0.001, 0.997 and 0.0006, respectively.

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Study on the Intelligent Extraction Based on Hyper-Spectral Characteristics of Typical Vegetation

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1073-1076.1960 ◽

2014 ◽

Vol 1073-1076 ◽

pp. 1960-1964

Author(s):

Jie Zhang ◽

Hao Yan Zhao ◽

Min Xia Zhang

Keyword(s):

Spectral Data ◽

Spectral Characteristics ◽

Remote Sensing Data ◽

Vegetation Coverage ◽

Desert Vegetation ◽

Hyper Spectral ◽

Differential Transform ◽

Green Vegetation ◽

Low Coverage ◽

Grassland Types

By using hyper-spectral remote sensing data of desert vegetation, the original spectral data was simply pretreated firstly, then first order differential transform and smoothing was the hyper-spectral data. The spectral characteristics of different grassland types were extracted. The results showed that: desert vegetation has some unique spectral features of common green vegetation. However, affected by the underlying surface of spared leaves, low coverage, the spectrum of desert vegetation does not have obvious green peak, and the red edge characteristics decreased with the decline of vegetation coverage.

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