Detection and mapping vegetation cover based on the Spectral Angle Mapper algorithm using NOAA AVHRR data

Yagoub, Houria; Belbachir, Ahmed Hafid; Benabadji, Noureddine

doi:10.1016/j.asr.2014.03.020

Cited by 11 publications

(7 citation statements)

References 17 publications

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“…Coarse-resolution satellite data obtained, for example from the Advanced Very High Resolution Radiometer (AVHRR) [ 1 ], Systeme Pour l’Observation de la Terre (SPOT) Vegetation (VGT) [ 2 ], and from the Moderate Resolution Imaging Spectroradiometer (MODIS) [ 3 ], are widely used in areas such as land cover and land use mapping [ 4 , 5 ], crop mapping and yield forecasting [ 6 , 7 ], global change [ 8 ], vegetation trends and phenology estimations [ 9 , 10 ], disaster monitoring [ 11 , 12 , 13 , 14 ] and atmospheric environment [ 15 , 16 , 17 ] and water environment monitoring [ 18 ]. The return cycle of these satellites is one to two days, making them suitable for dynamic monitoring of land surface processes, particularly AVHRR data, which provides the longest time series among global satellite measurements [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Combining HJ CCD, GF-1 WFV and MODIS Data to Generate Daily High Spatial Resolution Synthetic Data for Environmental Process Monitoring

Huang

Niu

et al. 2015

IJERPH

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The limitations of satellite data acquisition mean that there is a lack of satellite data with high spatial and temporal resolutions for environmental process monitoring. In this study, we address this problem by applying the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) and the Spatial and Temporal Data Fusion Approach (STDFA) to combine Huanjing satellite charge coupled device (HJ CCD), Gaofen satellite no. 1 wide field of view camera (GF-1 WFV) and Moderate Resolution Imaging Spectroradiometer (MODIS) data to generate daily high spatial resolution synthetic data for land surface process monitoring. Actual HJ CCD and GF-1 WFV data were used to evaluate the precision of the synthetic images using the correlation analysis method. Our method was tested and validated for two study areas in Xinjiang Province, China. The results show that both the ESTARFM and STDFA can be applied to combine HJ CCD and MODIS reflectance data, and GF-1 WFV and MODIS reflectance data, to generate synthetic HJ CCD data and synthetic GF-1 WFV data that closely match actual data with correlation coefficients (r) greater than 0.8989 and 0.8643, respectively. Synthetic red- and near infrared (NIR)-band data generated by ESTARFM are more suitable for the calculation of Normalized Different Vegetation Index (NDVI) than the data generated by STDFA.

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Section: Introductionmentioning

confidence: 99%

Combining HJ CCD, GF-1 WFV and MODIS Data to Generate Daily High Spatial Resolution Synthetic Data for Environmental Process Monitoring

Huang

Niu

et al. 2015

IJERPH

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“…On one hand, similarity measures enable us to discriminate between similar classes from a set of spectra, extracted from images or acquired on the field. Some spectral measures, such as the Spectral Angle Mapper (SAM) are related to the difference of the spectral shape (e.g., Yagoub, H. et al [21] identified forests of the Liege oaks from other forests, grain crops and steppes using the multispectral Advanced Very High Resolution Radiometer (AVHRR) with five bands from 580 nm to 1250 nm, 1 km spatial resolution (Overall Accuracy (OA) = 94.10%, κ = 0.93); Bahri, E.M. et al [22] discriminated between tree species using the multispectral Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor with 9 spectral bands from 520 nm to 2430 nm and a spatial resolution of 15 m or 30 m (κ = 0.66)). Other spectral measures, such as the Spectral Information Divergence (SID) are related to probabilistic behaviour (e.g., Sobhan, I.…”

Section: Introductionmentioning

confidence: 99%

Criteria Comparison for Classifying Peatland Vegetation Types Using In Situ Hyperspectral Measurements

et al. 2017

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This study aims to evaluate three classes of methods to discriminate between 13 peatland vegetation types using reflectance data. These vegetation types were empirically defined according to their composition, strata and biodiversity richness. On one hand, it is assumed that the same vegetation type spectral signatures have similarities. Consequently, they can be compared to a reference spectral database. To catch those similarities, several similarities criteria (related to distances (Euclidean distance, Manhattan distance, Canberra distance) or spectral shapes (Spectral Angle Mapper) or probabilistic behaviour (Spectral Information Divergence)) and several mathematical transformations of spectral signatures enhancing absorption features (such as the first derivative or the second derivative, the normalized spectral signature, the continuum removal, the continuum removal derivative reflectance, the log transformation) were investigated. Furthermore, those similarity measures were applied on spectral ranges which characterize specific biophysical properties. On the other hand, we suppose that specific biophysical properties/components may help to discriminate between vegetation types applying supervised classification such as Random Forest (RF), Support Vector Machines (SVM), Regularized Logistic Regression (RLR), Partial Least Squares-Discriminant Analysis (PLS-DA). Biophysical components can be used in a local way considering vegetation spectral indices or in a global way considering spectral ranges and transformed spectral signatures, as explained above. RLR classifier applied on spectral vegetation indices (training size = 25%) was able to achieve 77.21% overall accuracy in discriminating peatland vegetation types. It was also able to discriminate between 83.95% vegetation types considering specific spectral range [350-1350 nm], first derivative of spectral signatures and training size = 25%. Conversely, similarity criterion was able to achieve 81.70% overall accuracy using the Canberra distance computed on the full spectral range [350-2500 nm]. The results of this study suggest that RLR classifier and similarity criteria are promising to map the different vegetation types with high ecological values despite vegetation heterogeneity and mixture.

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“…Only the spectral regions with the distinct features of RWT were utilized (fluorescent excitation, 548 nm; fluorescent emission, 587 nm; red absorption 680 nm), in order to exclude the influence of any unrelated effects, with features in other wavelengths. To quantify the spectral distance, the spectral angle metric was used following the spectral angle mapper (SAM) classification method (Cho et al, 2010;Renza et al, 2017;Yagoub et al, 2014). For calculating the spectral angle, each spectrum is represented as a vector with n dimensions, where n is the number of spectral bands.…”

Section: Tracer Concentration From Hyperspectral Imagerymentioning

confidence: 99%

Imaging Tracer Mixing Concentration 2D in a Stream with Hyperspectral Sensing from an Unmanned Aerial System

Köppl

Lemaire

McKnight

et al. 2023

Preprint

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The release of anthropogenic chemicals to streams, stemming from contaminated sites, direct application (rural/urban) or accidental release, represents a significant threat to water resources and thus the health of humans and aquatic ecosystems. Predicting the transport and fate of chemicals is key to quantify contaminant concentrations and develop environmental quality standards (EQS). Tracer tests are a well-established tool for such hydrological investigations in various water-based systems. In stream settings, such experiments have predominantly investigated longitudinal mixing and flow velocities by measuring the tracer concentration in a few discrete locations; few studies have focused on the transversal mixing properties. Recent progress in hyperspectral remote sensing from unmanned aerial systems (UAS) allows advancing the two-dimensional monitoring of tracer tests, by mapping the tracer concentration with a high spatial resolution in narrow streams with difficult accessibility. So far, such methods have only been demonstrated in controlled settings or in ocean waters, but not in optically complex streams. In this study, we evaluated the performance of a miniaturized hyperspectral imaging system and a consumer grade camera on board of an UAS, to map the concentration of the fluorescent tracer Rhodamine WT in a stream impacted by a contaminated site. In order to estimate tracer concentrations from the remotely sensed data, a band ratio of the red and blue band was used for the photo camera, while a vector based method, estimating the spectral angle in regards to a reference spectrum was applied for the hyperspectral data. The photo camera performed well, but it only mapped reliably the concentration in sections of the stream exposed to direct sunlight (R2: 0.83; nRMSE: 10.2 %), failing to map the concentration in all locations, which included locations where the direct sunlight was blocked by riparian trees (R2: 0.17; nRMSE: 28.7 %). In contrast, the advanced spectral information allowed the hyperspectral-based system to map the concentration well in all sections of the stream (R2: 0.76; nRMSE: 15.1 %), regardless of illumination changes. This demonstrated the advantage of optical cameras measuring water-leaving irradiance from hundreds of contiguous narrow spectral bands that also allow detecting finer spectral absorption and emission features. The results presented here would help to improve the knowledge about mixing of contaminants in streams, i.e. to predict the location of fully transversal mixing for contaminant sites discharging to streams via groundwater-surface interactions, as well as general assumptions behind mixing and dilution models.

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Detection and mapping vegetation cover based on the Spectral Angle Mapper algorithm using NOAA AVHRR data

Cited by 11 publications

References 17 publications

Combining HJ CCD, GF-1 WFV and MODIS Data to Generate Daily High Spatial Resolution Synthetic Data for Environmental Process Monitoring

Combining HJ CCD, GF-1 WFV and MODIS Data to Generate Daily High Spatial Resolution Synthetic Data for Environmental Process Monitoring

Criteria Comparison for Classifying Peatland Vegetation Types Using In Situ Hyperspectral Measurements

Imaging Tracer Mixing Concentration 2D in a Stream with Hyperspectral Sensing from an Unmanned Aerial System

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