Evaluating Heavy-Metal Stress Levels in Rice Using a Theoretical Model of Canopy-Air Temperature and Leaf Area Index Based on Remote Sensing

Crops under various types of stresses, such as stress caused by heavy metals, drought and pest/disease exhibit similar changes in physiological-biochemical parameters (e.g., leaf area index [LAI] and chlorophyll). Thus, differentiating between heavy metal stress and nonheavy metal stress presents a great challenge. However, different stressors in crops do cause variations in spatiotemporal characteristics. This study aims to develop a spatiotemporal index based on LAI time series to identify heavy metal stress under complex stressors on a regional scale. The experimental area is located in Zhuzhou City, Hunan Province. The situ measured data and Sentinel-2A images from 2017 and 2018 were collected. First, a series of LAI in rice growth stages was simulated based on the WOrld FOod STudies (WOFOST) model incorporated with Sentinel 2 images. Second, the local Moran’s I and dynamic time warping (DTW) of LAI were calculated. Third, a stress index based on spatial and temporal features (SIST) was established to assess heavy metal stress levels according to the spatial autocorrelation and temporal dissimilarity of LAI. Results revealed the following: (1) The DTW of LAI is a good indicator for distinguishing stress levels. Specifically, rice subjected to high stress levels exhibits high DTW values. (2) Rice under heavy metal stress is well correlated with high-high SIST clusters. (3) Rice plants subjected to high pollution are observed in the northwest of the study regions and rice under low heavy metal stress is found in the south. The results suggest that SIST based on a sensitive indicator of rice biochemical impairment can be used to accurately detect regional heavy metal stress in rice. Combining spatial-temporal features and spectral information appears to be a highly promising method for discriminating heavy metal stress from complex stressors.

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Thermal infrared imaging of the variability of canopy-air temperature difference distribution for heavy metal stress levels discrimination in rice

Journal of Applied Remote Sensing ◽

10.1117/1.jrs.11.026036 ◽

2017 ◽

Vol 11 (2) ◽

pp. 026036 ◽

Cited By ~ 3

Author(s):

Biyao Zhang ◽

Xiangnan Liu ◽

Meiling Liu ◽

Dongmin Wang

Keyword(s):

Heavy Metal ◽

Air Temperature ◽

Temperature Difference ◽

Infrared Imaging ◽

Heavy Metal Stress ◽

Thermal Infrared ◽

Metal Stress ◽

Thermal Infrared Imaging ◽

Stress Levels

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Evaluating Heavy Metal Stress Levels in Rice Based on Remote Sensing Phenology

Sensors ◽

10.3390/s18030860 ◽

2018 ◽

Vol 18 (3) ◽

pp. 860 ◽

Cited By ~ 7

Author(s):

Tianjiao Liu ◽

Xiangnan Liu ◽

Meiling Liu ◽

Ling Wu

Keyword(s):

Remote Sensing ◽

Heavy Metal ◽

Heavy Metal Stress ◽

Metal Stress ◽

Stress Levels

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Classification of Rice Heavy Metal Stress Levels Based on Phenological Characteristics Using Remote Sensing Time-Series Images and Data Mining Algorithms

Sensors ◽

10.3390/s18124425 ◽

2018 ◽

Vol 18 (12) ◽

pp. 4425 ◽

Cited By ~ 1

Author(s):

Tianjiao Liu ◽

Xiangnan Liu ◽

Meiling Liu ◽

Ling Wu

Keyword(s):

Remote Sensing ◽

Heavy Metal ◽

Time Series ◽

Heavy Metal Stress ◽

Metal Stress ◽

Series Data ◽

Stress Levels ◽

Fusion Images ◽

Phenological Metrics

Heavy metal pollution in crops leads to phenological changes, which can be monitored by remote sensing technology. The present study aims to develop a method for accurately evaluating heavy metal stress in rice based on remote sensing phenology. First, the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) was applied to blend Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat to generate a time series of fusion images at 30 m resolution, and then the vegetation indices (VIs) related to greenness and moisture content of the rice canopy were calculated to create the time-series of VIs. Second, phenological metrics were extracted from the time-series data of VIs, and a feature selection scheme was designed to acquire an optimal phenological metric subset. Finally, an ensemble model with optimal phenological metrics as classification features was built using random forest (RF) and gradient boosting (GB) classifiers, and the classification of stress levels was implemented. The results demonstrated that the overall accuracy of discrimination for different stress levels is greater than 98%. This study suggests that fusion images can be utilized to detect heavy metal stress in rice, and the proposed method may be applicable to classify stress levels.

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Estimation of Forest Leaf Area Index from Remote Sensing Data Using the Algorithm Based on Geometric-optical Model

Geo-information Science ◽

10.3724/sp.j.1047.2012.00358 ◽

2012 ◽

Vol 14 (3) ◽

pp. 358-365

Author(s):

Hanyue CHEN ◽

Wenjiang HUANG ◽

Zheng NIU ◽

Shuai GAO

Keyword(s):

Remote Sensing ◽

Leaf Area Index ◽

Leaf Area ◽

Optical Model ◽

Remote Sensing Data ◽

Area Index ◽

Sensing Data

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Remote Sensing to Study Mangrove Fragmentation and Its Impacts on Leaf Area Index and Gross Primary Productivity in the South of Peninsular Malaysia

Remote Sensing ◽

10.3390/rs13081427 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1427

Author(s):

Kasturi Devi Kanniah ◽

Chuen Siang Kang ◽

Sahadev Sharma ◽

A. Aldrie Amir

Keyword(s):

Remote Sensing ◽

Leaf Area Index ◽

Leaf Area ◽

Primary Productivity ◽

Peninsular Malaysia ◽

Area Index ◽

Mangrove Area ◽

The Mean ◽

Shape Complexity ◽

The Impact

Mangrove is classified as an important ecosystem along the shorelines of tropical and subtropical landmasses, which are being degraded at an alarming rate despite numerous international treaties having been agreed. Iskandar Malaysia (IM) is a fast-growing economic region in southern Peninsular Malaysia, where three Ramsar Sites are located. Since the beginning of the 21st century (2000–2019), a total loss of 2907.29 ha of mangrove area has been estimated based on medium-high resolution remote sensing data. This corresponds to an annual loss rate of 1.12%, which is higher than the world mangrove depletion rate. The causes of mangrove loss were identified as land conversion to urban, plantations, and aquaculture activities, where large mangrove areas were shattered into many smaller patches. Fragmentation analysis over the mangrove area shows a reduction in the mean patch size (from 105 ha to 27 ha) and an increase in the number of mangrove patches (130 to 402), edge, and shape complexity, where smaller and isolated mangrove patches were found to be related to the rapid development of IM region. The Moderate Resolution Imaging Spectro-radiometer (MODIS) Leaf Area Index (LAI) and Gross Primary Productivity (GPP) products were used to inspect the impact of fragmentation on the mangrove ecosystem process. The mean LAI and GPP of mangrove areas that had not undergone any land cover changes over the years showed an increase from 3.03 to 3.55 (LAI) and 5.81 g C m−2 to 6.73 g C m−2 (GPP), highlighting the ability of the mangrove forest to assimilate CO2 when it is not disturbed. Similarly, GPP also increased over the gained areas (from 1.88 g C m−2 to 2.78 g C m−2). Meanwhile, areas that lost mangroves, but replaced them with oil palm, had decreased mean LAI from 2.99 to 2.62. In fragmented mangrove patches an increase in GPP was recorded, and this could be due to the smaller patches (<9 ha) and their edge effects where abundance of solar radiation along the edges of the patches may increase productivity. The impact on GPP due to fragmentation is found to rely on the type of land transformation and patch characteristics (size, edge, and shape complexity). The preservation of mangrove forests in a rapidly developing region such as IM is vital to ensure ecosystem, ecology, environment, and biodiversity conservation, in addition to providing economical revenue and supporting human activities.

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