Discriminating Wheat Aphid Damage Degree Using 2-Dimensional Feature Space Derived from Landsat 5 TM

Luo, Jiancheng; Zhao, Chunjiang; Huang, Wenjiang; Zhang, Jingcheng; Zhao, Jing; Dong, Yingying; Lin, Yuan; Du, Shizhou

doi:10.1166/sl.2012.1829

Cited by 6 publications

(6 citation statements)

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“…We conducted a ground survey on 9 May 2017, and investigated 27 sample plots in the region. The size of each sample point was 10 m × 10 m, and five sampling subplots with a 1 m × 1 m in each plot (five-point sampling) were used to record the average severity [ 45 ]. Each plot’s central coordinates were collected using a Trimble GeoXT DGPS with submeter accuracy.…”

Section: Methodsmentioning

confidence: 99%

New Spectral Index for Detecting Wheat Yellow Rust Using Sentinel-2 Multispectral Imagery

Zheng

Huang

Cui

et al. 2018

Sensors

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125

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Yellow rust is one of the most destructive diseases for winter wheat and has led to a significant decrease in winter wheat quality and yield. Identifying and monitoring yellow rust is of great importance for guiding agricultural production over large areas. Compared with traditional crop disease discrimination methods, remote sensing technology has proven to be a useful tool for accomplishing such a task at large scale. This study explores the potential of the Sentinel-2 Multispectral Instrument (MSI), a newly launched satellite with refined spatial resolution and three red-edge bands, for discriminating between yellow rust infection severities (i.e., healthy, slight, and severe) in winter wheat. The corresponding simulative multispectral bands for the Sentinel-2 sensor were calculated by the sensor’s relative spectral response (RSR) function based on the in situ hyperspectral data acquired at the canopy level. Three Sentinel-2 spectral bands, including B4 (Red), B5 (Re1), and B7 (Re3), were found to be sensitive bands using the random forest (RF) method. A new multispectral index, the Red Edge Disease Stress Index (REDSI), which consists of these sensitive bands, was proposed to detect yellow rust infection at different severity levels. The overall identification accuracy for REDSI was 84.1% and the kappa coefficient was 0.76. Moreover, REDSI performed better than other commonly used disease spectral indexes for yellow rust discrimination at the canopy scale. The optimal threshold method was adopted for mapping yellow rust infection at regional scales based on realistic Sentinel-2 multispectral image data to further assess REDSI’s ability for yellow rust detection. The overall accuracy was 85.2% and kappa coefficient was 0.67, which was found through validation against a set of field survey data. This study suggests that the Sentinel-2 MSI has the potential for yellow rust discrimination, and the newly proposed REDSI has great robustness and generalized ability for yellow rust detection at canopy and regional scales. Furthermore, our results suggest that the above remote sensing technology can be used to provide scientific guidance for monitoring and precise management of crop diseases and pests.

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Section: Methodsmentioning

confidence: 99%

New Spectral Index for Detecting Wheat Yellow Rust Using Sentinel-2 Multispectral Imagery

Zheng

Huang

Cui

et al. 2018

Sensors

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125

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“…Most of those existing models for predicting disease and pest infestation are exploiting meteorological data thorough statistical techniques. However, apart from the meteorological factors, lots of environmental factors could be derived from satellite remote sensing data have effect on aphid occurrence [11]. In addition, prediction models based on historical metrological data could only get the general and rough results of aphid occurrence in limited areas because of the uneven spatial distribution of meteorological stations.…”

Section: Discussionmentioning

confidence: 99%

“…Previous studies have reported that LST is one of the driving factors of aphid outbreak [11]. In this study, LST was derived from the thermal infrared band data of HJ-IB IRS using the revised single-channel algorithm developed in previous researches [18,19].…”

Section: Derivation Of Lst and Pdimentioning

confidence: 99%

“…Moreover, recent studies have reported that the occurrence and development of crop disease and insect pest are related to some variables that can be derived from satellite remote sensing, such as LST, NDVI, modified normalized difference water index (MNDWI) [10]. Luo et al (2012) found that the incidence of wheat yellow rust had relationship with LST, while another study showed that aphid occurrence and the damage degree were related to LST, NDVI and MNDWI [11]. This suggests that the occurrence and development of crop disease and pest may be related with habitat and crop growth factors, derived from both in situ and remote sensing data.…”

Section: Introductionmentioning

confidence: 99%

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Predicting the probability of wheat aphid occurrence using satellite remote sensing and meteorological data

Luo

Huang

Zhao

et al. 2014

Optik

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“…Although these hyperspectral based studies gave more detailed information and demonstrated the effectiveness of hyperspectral sensors in detecting and discriminating crop diseases and pests, its high hardware and computational costs restrict its application over large areas [12,13]. Based on the acceptable spatial and temporal resolutions, multispectral satellite technique becomes a feasible method for crop diseases and pests monitoring [8,14,15]. For instance, based on Landsat-5 Thematic Mapper (TM) data, Mirik et al [16] successfully assessed the infection and progression of wheat streak mosaic.…”

Section: Introductionmentioning

confidence: 99%

Integrating Growth and Environmental Parameters to Discriminate Powdery Mildew and Aphid of Winter Wheat Using Bi-Temporal Landsat-8 Imagery

Huang

Jing

et al. 2019

Remote Sensing

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Monitoring and discriminating co-epidemic diseases and pests at regional scales are of practical importance in guiding differential treatment. A combination of vegetation and environmental parameters could improve the accuracy for discriminating crop diseases and pests. Different diseases and pests could cause similar stresses and symptoms during the same crop growth period, so combining growth period information can be useful for discerning different changes in crop diseases and pests. Additionally, problems associated with imbalanced data often have detrimental effects on the performance of image classification. In this study, we developed an approach for discriminating crop diseases and pests based on bi-temporal Landsat-8 satellite imagery integrating both crop growth and environmental parameters. As a case study, the approach was applied to data during a period of typical co-epidemic outbreak of winter wheat powdery mildew and aphids in the Shijiazhuang area of Hebei Province, China. Firstly, bi-temporal remotely sensed features characterizing growth indices and environmental factors were calculated based on two Landsat-8 images. The synthetic minority oversampling technique (SMOTE) algorithm was used to resample the imbalanced training data set before model construction. Then, a back propagation neural network (BPNN) based on a new training data set balanced by the SMOTE approach (SMOTE-BPNN) was developed to generate the regional wheat disease and pest distribution maps. The original training data set-based BPNN and support vector machine (SVM) methods were used for comparison and testing of the initial results. Our findings suggest that the proposed approach incorporating both growth and environmental parameters of different crop periods could distinguish wheat powdery mildew and aphids at the regional scale. The bi-temporal growth indices and environmental factors-based SMOTE-BPNN, BPNN, and SVM models all had an overall accuracy high than 80%. Meanwhile, the SMOTE-BPNN method had the highest G-means among the three methods. These results revealed that the combination of bi-temporal crop growth and environmental parameters is essential for improving the accuracy of the crop disease and pest discriminating models. The combination of SMOTE and BPNN could effectively improve the discrimination accuracy of the minor disease or pest.

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Discriminating Wheat Aphid Damage Degree Using 2-Dimensional Feature Space Derived from Landsat 5 TM

Cited by 6 publications

References 0 publications

New Spectral Index for Detecting Wheat Yellow Rust Using Sentinel-2 Multispectral Imagery

New Spectral Index for Detecting Wheat Yellow Rust Using Sentinel-2 Multispectral Imagery

Predicting the probability of wheat aphid occurrence using satellite remote sensing and meteorological data

Integrating Growth and Environmental Parameters to Discriminate Powdery Mildew and Aphid of Winter Wheat Using Bi-Temporal Landsat-8 Imagery

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