Derivative Parameters of Hyperspectral NDVI and Its Application in the Inversion of Rapeseed Leaf Area Index

Qiu, Chun-Rong; Liao, Guiping; Tang, Hongyuan; Liu, Fan; Liao, Xiaoyi; Zhang, Rui; Zhao, Zanzhong

doi:10.3390/app8081300

Cited by 11 publications

(6 citation statements)

References 14 publications

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“…The index value higher than zero is considered to be water surface and lower than zero is non-water surface (McFeeters, 2013). NDVI shows the crop growth, nutrient information, chlorophyll and protein assessment (Qiu et al, 2018). The range of index value for both NDVI and NDWI lies between −1 to1.…”

Section: Methodsmentioning

confidence: 99%

Land Use/Land Cover Change Detection in Pokhara Metropolitan, Nepal Using Remote Sensing

Raut¹,

Chaudhary²,

Thapa³

2020

GEP

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Land use and land cover are essential for maintaining and managing the natural resources on the earth surface. A complex set of economic, demographic, social, cultural, technological, and environmental processes usually result in the change in the land use/land cover change (LULC). Pokhara Metropolitan is influenced mainly by the combination of various driving forces: geographical location, high rate of population growth, economic opportunity, globalization, tourism activities, and political activities. In addition to this, geographically steep slope, rugged terrain, and fragile geomorphic conditions and the frequency of earthquakes, floods, and landslides make the Pokhara Metropolitan region a disaster-prone area. The increment of the population along with infrastructure development of a given territory leads towards the urbanization. It has been rapidly changing due to urbanization, industrialization and internal migration since the 1970s. The landscapes and ground patterns are frequently changing on time and prone to disaster. Here a study has been carried to study on LULC for the last 18 years (2000-2018). The supervised classification on Landsat Imagery was performed and verified the classification through computing the error matrix. Besides, the water bodies and vegetation area were extracted through the Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDWI) respectively. This research shows that during the last 18 years the agricultural areas diminishing by 15.66% while urban area is increasing by 13.2%. This research is beneficial for preparing the plan and policy in the sustainable development of Pokhara Metropolitan.

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Section: Methodsmentioning

confidence: 99%

Land Use/Land Cover Change Detection in Pokhara Metropolitan, Nepal Using Remote Sensing

Raut¹,

Chaudhary²,

Thapa³

2020

GEP

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“…This may accentuate the plant and non-plant (i.e., soil) through the differential within RBNDVI and thus provide the best option for delineating tree canopies and for tracking tree mortality events. Qiu et al [72] suggests the finding of the RBNDVI index being more sensitive for this application than traditional NDVI.…”

Section: Evalution Of Spatial Methods For Monitoring Vegetation Declinementioning

confidence: 99%

Investigating Banksia Coastal Woodland Decline Using Multi-Temporal Remote Sensing and Field-Based Monitoring Techniques

Bell

Callow

2020

Remote Sensing

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Coastal woodlands, notable for their floristic diversity and ecosystem service values, are increasingly under threat from a range of interacting biotic and abiotic stressors. Monitoring these complex ecosystems has traditionally been confined to field-scale vegetation surveys; however, remote sensing applications are increasingly becoming more viable. This study reports on the application of field-based monitoring and remote sensing/(Geographic Information System) GIS to interrogate trends in Banksia coastal woodland decline (Kings Park, Perth and Western Australia) and documents the patterns, and potential drivers, of tree mortality over the period 2012–2016. Application of geographic object-based image analysis (GEOBIA) at a park scale was of limited benefit within the closed-canopy ecosystem, with manual digitisation methods feasible only at the smaller transect scale. Analysis of field-based identification of tree mortality, crown-specific spectral characteristics and park-scale change detection imagery identified climate-driven stressors as the likely primary driver of tree mortality in the woodland, with vegetation decline exacerbated by secondary factors, including water stress and low system resilience occasioned by the inability to access the water table and competition between tree species. The results from this paper provide a platform to inform monitoring efforts using airborne remote sensing within coastal woodlands.

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“…It is continuously obtained by imaging spectrometer for remote sensing of ground objects, including dozens or even hundreds of narrow bands [1]. At present, they have been widely used in national defense construction [2], geological exploration [3], ecological environment [4], agricultural science [5] and many other fields. As an extremely important part of hyperspectral images processing, hyperspectral images classification has always been a hot topic in domain of remote sensing [6].…”

Section: Introductionmentioning

confidence: 99%

Kmeans-CM Algorithm With Spectral Angle Mapper for Hyperspectral Image Classification

Lin

Yin

et al. 2023

IEEE Access

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Hyperspectral images possess the characteristics of high dimensionality, which causes "dimensional disaster" and low classification accuracy, in response to the problems, based on traditional kmeans algorithm and considering the importance of different bands for classification, and also combining both intra-class and inter-class information, a Kmeans-CM (K-means with correlation coefficient and maximize inter-class distance) algorithm with spectral angle mapper for hyperspectral image classification is proposed. First, weights of bands are defined by introducing coefficient of variation and spectral angle mapping, which measures the importance of bands for classification. Second, in order to intensify correlation between pixels in the same category, correlation coefficient is introduced to reset intra-class distance. Then, inter-class information is introduced for clustering by maximizing the distance between class centers and global center to reduce effect of local optimum of clustering effect. Finally, the K-means clustering objective function is redefined according to the band weights and intra-class, inter-class information, and also solving optimally. The overall classification accuracy of the method reached 84.47%, 90.08% and 80.45% on classical hyperspectral data sets Pavia University, Salinas and Botswana respectively, and the results show that the proposed algorithm owns good classification performance.INDEX TERMS band weighting, correlation coefficient, classification, hyperspectral images, K-means algorithm, spectral angle mapper.

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Derivative Parameters of Hyperspectral NDVI and Its Application in the Inversion of Rapeseed Leaf Area Index

Cited by 11 publications

References 14 publications

Land Use/Land Cover Change Detection in Pokhara Metropolitan, Nepal Using Remote Sensing

Land Use/Land Cover Change Detection in Pokhara Metropolitan, Nepal Using Remote Sensing

Investigating Banksia Coastal Woodland Decline Using Multi-Temporal Remote Sensing and Field-Based Monitoring Techniques

Kmeans-CM Algorithm With Spectral Angle Mapper for Hyperspectral Image Classification

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