Monitoring Locusta migratoria manilensis damage using ground level hyperspectral data

Zheng, Xiaomei; Song, Peilin; Li, Yingying; Zhang, Kangyu; Zhang, Huijuan; Liu, Li; Huang, Jingfeng

doi:10.1109/agro-geoinformatics.2019.8820212

Cited by 8 publications

(7 citation statements)

References 22 publications

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“…There are non-destructive sampling methods using remote sensing spectroscopy for measuring plant biomass, and these methods are mainly used in the macro or large-scale research [ 41 , 42 , 43 , 44 , 45 , 46 ]. In order to directly reflect the characteristics of biomass, combined with the sampling methods commonly used by previous researchers [ 47 , 48 , 49 , 50 , 51 , 52 ], we chose the harvesting method to measure the biomass.…”

Section: Methodsmentioning

confidence: 99%

Comparison of the Photosynthetic Capacity of Phragmites australis in Five Habitats in Saline‒Alkaline Wetlands

Liu

Wen

et al. 2020

Plants

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Water shortages have an important impact on the photosynthetic capacity of Phragmites australis. However, this impact has not been adequately studied from the perspective of photosynthesis. An in-depth study of the photosynthetic process can help in better understanding the impact of water shortages on the photosynthetic capacity of P. australis, especially on the microscale. The aim of this study is to explore the photosynthetic adaptation strategies to environmental changes in saline‒alkaline wetlands. The light response curves and CO2 response curves of P. australis in five habitats (hygrophilous, xerophytic, psammophytic, abandoned farmland, paddy field drainage) in saline‒alkaline wetlands were measured at different stages of their life history, and we used a nonrectangular hyperbolic model to fit the data. It was concluded that P. australis utilized coping strategies that differed between the growing and breeding seasons. P. australis in abandoned farmland during the growing season had the highest apparent quantum efficiency (AQE) and photosynthetic utilization efficiency for weak light because of the dark environment. The dark respiration rate of P. australis in the drainage area of paddy fields was the lowest, and it had the highest values for photorespiration rate, maximum photosynthetic rate (Pmax), photosynthetic capacity (Pa), biomass, maximum carboxylation rate (Vcmax), and maximum electron transfer rate (Jmax). The light insensitivity of P. australis increased with the transition from growing to breeding season, and the dark respiration rate also showed a downward trend. Moreover, Vcmax and Jmax would decline when Pmax and Pa showed a declining trend, and vice versa. In other words, Vcmax and Jmax could explain changes in the photosynthetic capacity to some extent. These findings contribute to providing insights that Vcmax and Jmax can directly reflect the variation in photosynthetic capacity of P. australis under water shortages in saline‒alkaline wetlands and in other parts of world where there are problems with similarly harmful environmental conditions.

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Section: Methodsmentioning

confidence: 99%

Comparison of the Photosynthetic Capacity of Phragmites australis in Five Habitats in Saline‒Alkaline Wetlands

Liu

Wen

et al. 2020

Plants

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“…Grasshoppers, with strong explosive and destructive power, are one of the major insects contributing to forage loss, which affects the development of human economies [1]. During their migration, swarms of grasshoppers can cause serious damage to the ecological environment by reducing and even completely destroying agricultural and livestock production, which directly impacts regional economic development [2][3][4]. In order to avoid or reduce losses, countries and regions at risk of locust and grasshopper outbreak have promoted and applied many effective monitoring and forecasting measures.…”

Section: Introductionmentioning

confidence: 99%

“…Field survey data on vegetation, soil, meteorology, and other geospatial factors were comprehensively taken into account. The objectives of this study were to: (1) calculate the suitability index of grassland grasshoppers and analyze the spatial and temporal distribution characteristics of grasshopper suitability zones over the past 13 years in Hulunbuir grassland; (2) analyze the influence of multiple factors on the grasshopper suitability index and determine the main influencing factors;…”

Section: Introductionmentioning

confidence: 99%

Spatial and Temporal Variability of Grassland Grasshopper Habitat Suitability and Its Main Influencing Factors

Wei

Lin

et al. 2022

Remote Sensing

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Grasshoppers are highly destructive pests, and their outbreak can directly damage livestock development. Grasshopper outbreaks can be monitored and forecasted through dynamic analysis of their potential geographic distribution and main influencing factors. By integrating vegetation, edaphic, meteorological, topography, and other geospatial data, this study simulated the grasshopper suitability index in Hulunbuir grassland using maximum entropy species distribution modeling (Maxent). The Maxent model showed high accuracy, with the training area under the curve (AUC) value ranging from 0.897 to 0.973 and the testing AUC ranging from 0.853 to 0.971 for the past 13 years. The results showed that suitable areas, including the most suitable area and moderately suitable area, accounted for a small proportion and were mainly located in the eastern and southern parts of the study area. According to model analysis based on 51 environmental factors, not all factors played a significant role in the grasshopper cycle. Moreover, differences in environmental factors drive the spatial variability of suitable areas for grasshoppers. The monitoring and prediction of potential outbreak areas can be improved by identifying major environmental factors having large variability between suitable and unsuitable areas. Future trends in grasshopper suitability indices are likely to contradict past trends in most of the study area, with only approximately 33% of the study area continuing the past trend. The results are expected to guide future monitoring and prediction of grasshoppers in Hulunbuir grassland.

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“…Moreover, the CWSI, TVDI, and VSWI exhibit certain lags in drought detection, meaning that they take some time to respond [23]. In view of these lagging vegetation indices, hyperspectral remote sensing technology can be used to monitor winter wheat freezing injury and locust disasters [24][25][26]. The DVDI, which is often used in flood disaster and wind disaster monitoring, has a linear relationship with crop yield reduction and is an effective indicator of the degree of vegetation damage [27,28].…”

Section: Introductionmentioning

confidence: 99%

Rapid Extraction of Regional-scale Agricultural Disasters by the Standardized Monitoring Model Based on Google Earth Engine

Liu

Luo

et al. 2020

Sustainability

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Remote sensing has been used as an important tool for disaster monitoring and disaster scope extraction, especially for the analysis of spatial and temporal disaster patterns of large-scale and long-duration series. Google Earth Engine provides the possibility of quickly extracting the disaster range over a large area. Based on the Google Earth Engine cloud platform, this study used MODIS vegetation index products with 250-m spatial resolution synthesized over 16 days from the period 2005–2019 to develop a rapid and effective method for monitoring disasters across a wide spatiotemporal range. Three types of disaster monitoring and scope extraction models are proposed: the normalized difference vegetation index (NDVI) median time standardization model (RNDVI_TM(i)), the NDVI median phenology standardization model (RNDVI_AM(i)(j)), and the NDVI median spatiotemporal standardization model (RNDVI_ZM(i)(j)). The optimal disaster extraction threshold for each model in different time phases was determined using Otsu’s method, and the extraction results were verified by medium-resolution images and ground-measured data of the same or quasi-same period. Finally, the disaster scope of cultivated land in Heilongjiang Province from 2010–2019 was extracted, and the spatial and temporal patterns of the disasters were analyzed based on meteorological data. This analysis revealed that the three aforementioned models exhibited high disaster monitoring and range extraction capabilities, with verification accuracies of 97.46%, 96.90%, and 96.67% for RNDVI_TM(i), RNDVI_AM(i), and (j)RNDVI_ZM(i)(j), respectively. The spatial and temporal disaster distributions were found to be consistent with the disasters of the insured plots and the meteorological data across the entire province. Moreover, different monitoring and extraction methods were used for different disasters, among which wind hazard and insect disasters often required a delay of 16 days prior to observation. Each model also displayed various sensitivities and was applicable to different disasters. Compared with other techniques, the proposed method is fast and easy to implement. This new approach can be applied to numerous types of disaster monitoring as well as large-scale agricultural disaster monitoring and can easily be applied to other research areas. This study presents a novel method for large-scale agricultural disaster monitoring.

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Monitoring Locusta migratoria manilensis damage using ground level hyperspectral data

Cited by 8 publications

References 22 publications

Comparison of the Photosynthetic Capacity of Phragmites australis in Five Habitats in Saline‒Alkaline Wetlands

Comparison of the Photosynthetic Capacity of Phragmites australis in Five Habitats in Saline‒Alkaline Wetlands

Spatial and Temporal Variability of Grassland Grasshopper Habitat Suitability and Its Main Influencing Factors

Rapid Extraction of Regional-scale Agricultural Disasters by the Standardized Monitoring Model Based on Google Earth Engine

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