Newly-developed three-band hyperspectral vegetation index for estimating leaf relative chlorophyll content of mangrove under different severities of pest and disease

Jiang, Xiapeng; Zhen, Jianing; Jing, Miao; Zhao, Demei; Shen, Zhen; Jiang, Jincheng; Gao, Changjun; Wu, Guofeng; Wang, Junjie

doi:10.1016/j.ecolind.2022.108978

Cited by 30 publications

(13 citation statements)

References 73 publications

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“…The high and low value of the vegetation index is influenced by the health condition of the plant or the chlorophyll content of the leaves. Pests / diseases invasion will damage the structure of the leaf tissue [10].…”

Section: Results Of Vegetation Index Processing In the Vegetative Phasementioning

confidence: 99%

The identification of pests and diseases of rice plants using sentinel-2 satellite imagery data at the end of the vegetative stage

Ainunnisa,

Haerani

2023

IOP Conf. Ser.: Earth Environ. Sci.

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One of the factors that affect production yield of cultivation crops, including rice plants, is pest/disease attacks. Various types of pests/diseases are often found in rice cultivation such as rats, stem borers and rice leaf folders. The damage caused by these pests/diseases can lead to production decreases and crop failure. To prevent an increase in pest infestation, monitoring can be carried out by employing remote sensing technologies such as Sentinel 2 satellite imagery. The identification of rice plant pests/diseases can be done using Vegetation Indices such as NDVI and NDRE, due to their sensitivity to plant chlorophyll. The purpose of this study was to identify infected pest and disease of rice plants in paddy fields by using Sentinel-2 satellite imagery. The methods of this study were spatial analysis and simple linear regression analysis, by looking at the relation between Vegetation Indices and rice productivity. The research results showed that based on the low value of NDVI and NDRE at the end of vegetative stage, some of rice fields were infected with pests and diseases. From field observation, it was found that the pests and diseases included rats, rice leaf folders, borers and blasts. Pests/diseases infestation in paddy fields lead to a low Vegetation Index value which results in low rice production. Comparing the NDVI and NDRE, NDRE was better in detecting pests and diseases due to its sensitivity to the plants’ chlorophyll.

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Section: Results Of Vegetation Index Processing In the Vegetative Phasementioning

confidence: 99%

The identification of pests and diseases of rice plants using sentinel-2 satellite imagery data at the end of the vegetative stage

Ainunnisa,

Haerani

2023

IOP Conf. Ser.: Earth Environ. Sci.

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“…Jiang et al. (2022) and Zhen et al. (2021) also used the equation to convert the SPAD value to the leaf chlorophyll content in mangrove.…”

Section: Methodsmentioning

confidence: 99%

“…Leaf chlorophyll content (C ab , unit: g cm −2 ) is an input parameter of PROSAIL model, and the relative chlorophyll content (SPAD value) was further converted to leaf chlorophyll content according to the equation provided by Markwell et al (1995). Jiang et al (2022) and Zhen et al (2021) also used the equation to convert the SPAD value to the leaf chlorophyll content in mangrove. Moreover, Binh et al (2022) andNiu et al (2022) showed that the methods for the determination of other parameters (e.g., EWT and C m ) are applicable to both crops and mangroves.…”

Section: Data Collectionmentioning

confidence: 99%

Mapping mangrove leaf area index (LAI) by combining remote sensing images with PROSAIL‐D and XGBoost methods

Zhao

Zhen

Zhang

et al. 2022

Remote Sens Ecol Conserv

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Leaf area index (LAI) is a vital parameter reflecting vegetation structure, physio‐ecological process and growth development. Accurate estimation of mangrove LAI is fundamental for assessing the ecological restoration and sustainable development of mangrove ecosystems. To date, very few studies have explored the hybrid method of radiative transfer model (RTM) and machine‐learning model in retrieving mangrove LAI with different satellite sensors. This study investigated the capabilities of combining the PROSAIL‐D model, XGBoost (extreme gradient boosting) and remote sensing images in estimating mangrove LAI, considering the spatial resolutions and spectral vegetation indices (VIs) of Landsat‐8, Sentinel‐2, Worldview‐2 and Zhuhai‐1 images, and further explored the role of eco‐environmental factors in the spatial distribution of LAI in Gaoqiao Mangrove Reserve, China. The results showed that the Zhuhai‐1 acquires the best estimation accuracy (RVal2 (the determination coefficient of validation) = 0.86, RPD (residual prediction deviation) = 3.36 and RMSE (root mean square error) = 0.31), followed by Worldview‐2 (RVal2 = 0.84, RPD = 2.64 and RMSE = 0.33), Sentinel‐2 (RVal2 = 0.34, RPD = 1.33 and RMSE = 0.62) and Landsat‐8 (RVal2 = 0.29, RPD = 1.03 and RMSE = 0.71). The newly developed three‐band VIs (B443−B864/B443+B864−2×B561 with Landsat‐8, B490−B842/B490+B842−2×B705 with Sentinel‐2, B427−B832/B908−B832 with Worldview‐2 and B896−B700/B776−B700 with Zhuhai‐1) were efficient estimators of mangrove LAI. Moreover, elevation and species composition could greatly affect the spatial distribution of mangrove LAI. We concluded that the hybrid method of PROSAIL‐D and XGBoost model using VIs derived from Zhuhai‐1 hyperspectral image could be deemed as basic method and input variables of mapping mangrove LAI, and could be effectively and widely applied in generating mangrove LAI products at the regional and national scales.

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“…Different species exhibit IOP Publishing doi:10.1088/1755-1315/1276/1/012055 2 varying levels of biomass and different growth rates, directly impacting their carbon storage capacity [9,10]. Hence, identifying and quantifying mangrove coverage, accounting for species-level variations, become essential for precise carbon stock assessments [11,12,13].…”

Section: Introductionmentioning

confidence: 99%

Satellite-based Mangrove species abundance estimate using Machine Learning ensemble

Harsa,

Winarso,

Setiawan

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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The mangrove ecosystem is a vital feature in a coastal area, playing a critical role in carbon sequestration beneath the soil. Carbon preservation capacity varies among different species of mangrove. Thus, by quantifying the number of mangrove species in a given area, the volume of carbon sequestered can be estimated. Satellite imagery is highly effective for gathering such data across vast territories. In this study, we present an evaluation of mangrove species abundance across a large coastal area using Landsat satellite imagery. We employed machine learning algorithms to classify species based on spectral field observation data to achieve this. These algorithms were trained individually and ensembled to enhance prediction performance. There are 466 models generated in a two-hour training phase. After assessing these models, we identified that a stacked ensemble consisting of Deep Learning, two Distributed Random Forests, a Generalized Boosting Model, a Generalized Linear Model, and Extreme Gradient Boosting algorithms has the most superior predictive accuracy. The model achieved a mean accuracy value of 95% when tested on observation data. After applying the best model to the satellite data, our results indicate that Rhizophora Apiculata and Excoecaria Agallocha are the two most abundant mangrove species in the study area, covering 17.71% (19502.37 Ha) and 10.49% (11549.79 Ha), respectively.

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Newly-developed three-band hyperspectral vegetation index for estimating leaf relative chlorophyll content of mangrove under different severities of pest and disease

Cited by 30 publications

References 73 publications

The identification of pests and diseases of rice plants using sentinel-2 satellite imagery data at the end of the vegetative stage

The identification of pests and diseases of rice plants using sentinel-2 satellite imagery data at the end of the vegetative stage

Mapping mangrove leaf area index (LAI) by combining remote sensing images with PROSAIL‐D and XGBoost methods

Satellite-based Mangrove species abundance estimate using Machine Learning ensemble

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