Potential of hyperspectral AVIRIS-NG data for vegetation characterization, species spectral separability, and mapping

Ahmad, Shahbaz; Pandey, Arvind Chandra; Kumar, Amit; Lele, Nikhil

doi:10.1007/s12518-021-00355-6

Cited by 11 publications

(6 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These similar findings suggest that the environmental factors, microclimates, plant leaves, solar angles, and changing morphological and spectral properties are different at every growth stage, which will influence weed detection [29]. Humans are unable to differentiate between these plants features using the naked eye; thus, by using spectral signatures, the plants can be differentiated and classified using statistical analysis and machine learning [30][31][32].…”

Section: Classification and Validationmentioning

confidence: 81%

“…Changes in cellulose and leaf water content determine the levels of these characteristics, which can be leveraged to classify plant species [30]. Several wavelengths, including 491, 541, 641, 722, 772, 852, 942, 1047, 1132, 1443, and 2475 nm, were selected and calculated for the purpose of identifying plants species using an artificial neural network (ANN) and support vector machine (SVM) [31]. We found that ANN and SVM provided high levels of accuracy of up to 86%.…”

Section: Band Identificationmentioning

confidence: 99%

See 1 more Smart Citation

Assessment of Weed Classification Using Hyperspectral Reflectance and Optimal Multispectral UAV Imagery

2021

View full text Add to dashboard Cite

Weeds compete with crops and are hard to differentiate and identify due to their similarities in color, shape, and size. In this study, the weed species present in sorghum (sorghum bicolor (L.) Moench) fields, such as amaranth (Amaranthus macrocarpus), pigweed (Portulaca oleracea), mallow weed (Malva sp.), nutgrass (Cyperus rotundus), liver seed grass (Urochoa panicoides), and Bellive (Ipomea plebeian), were discriminated using hyperspectral data and were detected and analyzed using multispectral images. Discriminant analysis (DA) was used to identify the most significant spectral bands in order to discriminate weeds from sorghum using hyperspectral data. The results demonstrated good separation accuracy for Amaranthus macrocarpus, Urochoa panicoides, Malva sp., Cyperus rotundus, and Sorghum bicolor (L.) Moench at 440, 560, 680, 710, 720, and 850 nm. Later, the multispectral images of these six bands were collected to detect weeds in the sorghum crop fields using object-based image analysis (OBIA). The results showed that the differences between sorghum and weed species were detectable using the six selected bands, with data collected using an unmanned aerial vehicle. Here, the highest spatial resolution had the highest accuracy for weed detection. It was concluded that each weed was successfully discriminated using hyperspectral data and was detectable using multispectral data with higher spatial resolution.

show abstract

Section: Classification and Validationmentioning

confidence: 81%

Section: Band Identificationmentioning

confidence: 99%

Assessment of Weed Classification Using Hyperspectral Reflectance and Optimal Multispectral UAV Imagery

2021

View full text Add to dashboard Cite

show abstract

“…Hyperspectral data is one promising remote sensing resource that has been used for mapping vegetation and its attributes with high accuracy in many different parts of the world [14][15][16][17][18][19][20]. In the Florida Everglades, by combining hyperspectral with lidar data Zhang et al were able to map wetland vegetation with an 86% accuracy [20].…”

Section: Introductionmentioning

confidence: 99%

“…NASA's latest Airborne Visible/Infrared Imaging Spectrometer Next-Generation (AVIRIS-NG) hyperspectral camera has 425 spectral bands within the wavelength range 400-2500 nm, and higher pixel resolution 5-10 m depending on the flying height [21]. AVIRIS-NG camera has been flown in many parts of the world to study natural vegetation, crop health, and ecosystem processes [14,16,18,19,22]. In an agricultural setting AVIRIS-NG has been used to quantify plant chlorophyll and identify spectral differences in crop types [19], and for mapping crops species [18].…”

Section: Introductionmentioning

confidence: 99%

Improved Boreal Forest Wildfire Fuel Type Mapping in Interior Alaska Using AVIRIS-NG Hyperspectral Data

et al. 2021

View full text Add to dashboard Cite

In Alaska the current wildfire fuel map products were generated from low spatial (30 m) and spectral resolution (11 bands) Landsat 8 satellite imagery which resulted in map products that not only lack the granularity but also have insufficient accuracy to be effective in fire and fuel management at a local scale. In this study we used higher spatial and spectral resolution AVIRIS-NG hyperspectral data (acquired as part of the NASA ABoVE project campaign) to generate boreal forest vegetation and fire fuel maps. Based on our field plot data, random forest classified images derived from 304 AVIRIS-NG bands at Viereck IV level (Alaska Vegetation Classification) had an 80% accuracy compared to the 33% accuracy of the LANDFIRE’s Existing Vegetation Type (EVT) product derived from Landsat 8. Not only did our product more accurately classify fire fuels but was also able to identify 20 dominant vegetation classes (percent cover >1%) while the EVT product only identified 8 dominant classes within the study area. This study demonstrated that highly detailed and accurate fire fuel maps can be created at local sites where AVIRIS-NG is available and can provide valuable decision-support information to fire managers to combat wildfires.

show abstract

“…Advancements in airborne hyperspectral remote sensing provide an efficient approach to retrieve essential information for better characterization of forest fuels [14,[19][20][21]. A number of studies have shown that hyperspectral data is much more effective than multispectral data for detailed vegetation mapping at species or stand scales [14,[22][23][24][25][26][27][28][29][30]. The narrower bandwidths and improved spatial resolution of airborne hyperspectral datasets makes them much more effective than multispectral datasets at distinguishing visually similar vegetation classes.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Data Simulation (Sentinel-2 to AVIRIS-NG) for Improved Wildfire Fuel Mapping, Boreal Alaska

et al. 2021

View full text Add to dashboard Cite

Alaska has witnessed a significant increase in wildfire events in recent decades that have been linked to drier and warmer summers. Forest fuel maps play a vital role in wildfire management and risk assessment. Freely available multispectral datasets are widely used for land use and land cover mapping, but they have limited utility for fuel mapping due to their coarse spectral resolution. Hyperspectral datasets have a high spectral resolution, ideal for detailed fuel mapping, but they are limited and expensive to acquire. This study simulates hyperspectral data from Sentinel-2 multispectral data using the spectral response function of the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) sensor, and normalized ground spectra of gravel, birch, and spruce. We used the Uniform Pattern Decomposition Method (UPDM) for spectral unmixing, which is a sensor-independent method, where each pixel is expressed as the linear sum of standard reference spectra. The simulated hyperspectral data have spectral characteristics of AVIRIS-NG and the reflectance properties of Sentinel-2 data. We validated the simulated spectra by visually and statistically comparing it with real AVIRIS-NG data. We observed a high correlation between the spectra of tree classes collected from AVIRIS-NG and simulated hyperspectral data. Upon performing species level classification, we achieved a classification accuracy of 89% for the simulated hyperspectral data, which is better than the accuracy of Sentinel-2 data (77.8%). We generated a fuel map from the simulated hyperspectral image using the Random Forest classifier. Our study demonstrated that low-cost and high-quality hyperspectral data can be generated from Sentinel-2 data using UPDM for improved land cover and vegetation mapping in the boreal forest.

show abstract

Potential of hyperspectral AVIRIS-NG data for vegetation characterization, species spectral separability, and mapping

Cited by 11 publications

References 39 publications

Assessment of Weed Classification Using Hyperspectral Reflectance and Optimal Multispectral UAV Imagery

Assessment of Weed Classification Using Hyperspectral Reflectance and Optimal Multispectral UAV Imagery

Improved Boreal Forest Wildfire Fuel Type Mapping in Interior Alaska Using AVIRIS-NG Hyperspectral Data

Hyperspectral Data Simulation (Sentinel-2 to AVIRIS-NG) for Improved Wildfire Fuel Mapping, Boreal Alaska

Contact Info

Product

Resources

About