scholarly journals Analysis of Urban Built-up Expansion Based on Combination of Spectral Indices in Surabaya City

Geoid ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. 21
Author(s):  
Fendra Dwi Ramadhan ◽  
Teguh Hariyanto ◽  
Hepi Hapsari Handayani
Keyword(s):  
Spectral Indices

Spectral Indices of Centimeter Continuum Sources in Star-forming Regions: Implications on the Nature of the Outflow Exciting Sources

1998 ◽  
Vol 116 (6) ◽  
pp. 2953-2964 ◽  
Author(s):  
Guillem Anglada ◽  
Eva Villuendas ◽  
Robert Estalella ◽  
Maria T. Beltrán ◽  
Luis F. Rodríguez ◽  
...  
Keyword(s):  
Spectral Indices ◽  
Star Forming ◽  
Star Forming Regions

scholarly journals Assessing Wildfire Burn Severity and Its Relationship with Environmental Factors: A Case Study in Interior Alaska Boreal Forest

2021 ◽  
Vol 13 (10) ◽  
pp. 1966
Author(s):  
Christopher W Smith ◽  
Santosh K Panda ◽  
Uma S Bhatt ◽  
Franz J Meyer ◽  
Anushree Badola ◽  
...  
Keyword(s):  
Boreal Forest ◽  
Ground Truth ◽  
Burn Severity ◽  
Classification Methods ◽  
Spectral Indices ◽  
Ground Truth Data ◽  
Burn Scar ◽  
Interior Alaska ◽  
Remote Sensing Methods ◽  
The Relationship

In recent years, there have been rapid improvements in both remote sensing methods and satellite image availability that have the potential to massively improve burn severity assessments of the Alaskan boreal forest. In this study, we utilized recent pre- and post-fire Sentinel-2 satellite imagery of the 2019 Nugget Creek and Shovel Creek burn scars located in Interior Alaska to both assess burn severity across the burn scars and test the effectiveness of several remote sensing methods for generating accurate map products: Normalized Difference Vegetation Index (NDVI), Normalized Burn Ratio (NBR), and Random Forest (RF) and Support Vector Machine (SVM) supervised classification. We used 52 Composite Burn Index (CBI) plots from the Shovel Creek burn scar and 28 from the Nugget Creek burn scar for training classifiers and product validation. For the Shovel Creek burn scar, the RF and SVM machine learning (ML) classification methods outperformed the traditional spectral indices that use linear regression to separate burn severity classes (RF and SVM accuracy, 83.33%, versus NBR accuracy, 73.08%). However, for the Nugget Creek burn scar, the NDVI product (accuracy: 96%) outperformed the other indices and ML classifiers. In this study, we demonstrated that when sufficient ground truth data is available, the ML classifiers can be very effective for reliable mapping of burn severity in the Alaskan boreal forest. Since the performance of ML classifiers are dependent on the quantity of ground truth data, when sufficient ground truth data is available, the ML classification methods would be better at assessing burn severity, whereas with limited ground truth data the traditional spectral indices would be better suited. We also looked at the relationship between burn severity, fuel type, and topography (aspect and slope) and found that the relationship is site-dependent.

scholarly journals Monitoring the Foliar Nutrients Status of Mango Using Spectroscopy-Based Spectral Indices and PLSR-Combined Machine Learning Models

2021 ◽  
Vol 13 (4) ◽  
pp. 641
Author(s):  
Gopal Ramdas Mahajan ◽  
Bappa Das ◽  
Dayesh Murgaokar ◽  
Ittai Herrmann ◽  
Katja Berger ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Support Vector ◽  
Spectral Indices ◽  
Learning Models ◽  
Leaf Nutrients ◽  
Machine Learning Models

Conventional methods of plant nutrient estimation for nutrient management need a huge number of leaf or tissue samples and extensive chemical analysis, which is time-consuming and expensive. Remote sensing is a viable tool to estimate the plant’s nutritional status to determine the appropriate amounts of fertilizer inputs. The aim of the study was to use remote sensing to characterize the foliar nutrient status of mango through the development of spectral indices, multivariate analysis, chemometrics, and machine learning modeling of the spectral data. A spectral database within the 350–1050 nm wavelength range of the leaf samples and leaf nutrients were analyzed for the development of spectral indices and multivariate model development. The normalized difference and ratio spectral indices and multivariate models–partial least square regression (PLSR), principal component regression, and support vector regression (SVR) were ineffective in predicting any of the leaf nutrients. An approach of using PLSR-combined machine learning models was found to be the best to predict most of the nutrients. Based on the independent validation performance and summed ranks, the best performing models were cubist (R2 ≥ 0.91, the ratio of performance to deviation (RPD) ≥ 3.3, and the ratio of performance to interquartile distance (RPIQ) ≥ 3.71) for nitrogen, phosphorus, potassium, and zinc, SVR (R2 ≥ 0.88, RPD ≥ 2.73, RPIQ ≥ 3.31) for calcium, iron, copper, boron, and elastic net (R2 ≥ 0.95, RPD ≥ 4.47, RPIQ ≥ 6.11) for magnesium and sulfur. The results of the study revealed the potential of using hyperspectral remote sensing data for non-destructive estimation of mango leaf macro- and micro-nutrients. The developed approach is suggested to be employed within operational retrieval workflows for precision management of mango orchard nutrients.

scholarly journals Hindcast and Near Real-Time Monitoring of Green Macroalgae Blooms in Shallow Coral Reef Lagoons Using Sentinel-2: A New-Caledonia Case Study

2021 ◽  
Vol 13 (2) ◽  
pp. 211
Author(s):  
Maële Brisset ◽  
Simon Van Wynsberge ◽  
Serge Andréfouët ◽  
Claude Payri ◽  
Benoît Soulard ◽  
...  
Keyword(s):  
Green Algae ◽  
Algal Blooms ◽  
New Caledonia ◽  
Spectral Indices ◽  
Green Macroalgae ◽  
Spectral Bands ◽  
Algae Blooms ◽  
Trade Offs ◽  
Sentinel 2

Despite the necessary trade-offs between spatial and temporal resolution, remote sensing is an effective approach to monitor macroalgae blooms, understand their origins and anticipate their developments. Monitoring of small tropical lagoons is challenging because they require high resolutions. Since 2017, the Sentinel-2 satellites has provided new perspectives, and the feasibility of monitoring green algae blooms was investigated in this study. In the Poé-Gouaro-Déva lagoon, New Caledonia, recent Ulva blooms are the cause of significant nuisances when beaching. Spectral indices using the blue and green spectral bands were confronted with field observations of algal abundances using images concurrent with fieldwork. Depending on seabed compositions and types of correction applied to reflectance data, the spectral indices explained between 1 and 64.9% of variance. The models providing the best statistical fit were used to revisit the algal dynamics using Sentinel-2 data from January 2017 to December 2019, through two image segmentation approaches: unsupervised and supervised. The latter accurately reproduced the two algal blooms that occurred in the area in 2018. This paper demonstrates that Sentinel-2 data can be an effective source to hindcast and monitor the dynamics of green algae in shallow lagoons.

scholarly journals A Comparison of the X-ray Spectra of Quasars and BL Lac Objects

1989 ◽  
Vol 134 ◽  
pp. 197-198
Author(s):  
D. M. Worrall ◽  
B. J. Wilkes
Keyword(s):  
Power Law ◽  
Radio Spectrum ◽  
Spectral Indices ◽  
Mean Power ◽  
Bl Lac Objects ◽  
X Ray ◽  
Bl Lac ◽  
Infrared Frequencies

Quasars with similar core-compact radio properties can be classified by their differences at optical and infrared frequencies. Their X-ray properties might be expected to be similar if the synchrotron self-Compton mechanism relates their radio and X-ray emission. We have compared the 0.2–3.5 keV mean power-law energy spectral indices, , for 4 quasar classes: 12 Highly Polarized QSOs (HPQs), 19 Flat Radio Spectrum, core-compact, low-polarization, QSOs (FRS QSOs), 24 radio-selected BL Lac objects, and 7 X-ray-selected BL Lac objects.

scholarly journals A Deep Large area Search for Low Luminosity Stars

1998 ◽  
Vol 11 (1) ◽  
pp. 435-435
Author(s):  
Hugh R.A. Jones ◽  
Mike R.S. Hawkins
Keyword(s):  
Spectral Type ◽  
Low Temperatures ◽  
Current Model ◽  
Brown Dwarfs ◽  
Evolutionary Models ◽  
Spectral Indices ◽  
Late Type ◽  
Brown Dwarf ◽  
Large Area ◽  
M Dwarfs

In a recent survey for faint red stars from a digital stack of Schmidt plates a number of candidate objects were identified. Parallax’s for three of these objects have been reported showing them to have luminosities which interpreted within the available evolutionary models indicate them to be good brown dwarf candidates. Here we examine spectra of these objects and others from the plate stack. Using standard spectral indices we find that for a given spectral type their spectra are more consistent with the Pleiades brown dwarfs (PPL 15, Teide 1 and Calar 3) than with standard late-type M dwarfs. Our interpretation is that this is due to their selection by RF IN colours which at values > 3 preferentially selects objects with relatively low gravities. For late-type M dwarfs and brown dwarfs low gravities are expected to be a reliable indication of youth. We also notice that the stack objects generally have strong FeH absorption for their spectral type. Current model atmospheres suggest that FeH strongly increases in strength toward lower metallicities and lower temperatures. We believe that this is not consistent with the available observational evidence from late-type M dwarfs. It is possible that solid Fe is forming inthe low temperature atmospheres relatively depleting FeH strengths toward lower temperatures. We find some evidence that for dwarfs at low temperatures dust formation is less prevalent in lower gravity objects suggesting that dwarfs at low temperatures stronger FeH may be an indication of youth. In addition to the spectral evidence the three stack objects whose parallax’s have been measured show small tangential velocities which is a further indication of youth.

Spectral assessments of wheat plants grown in pots and containers under saline conditions

2013 ◽  
Vol 40 (4) ◽  
pp. 409 ◽  
Author(s):  
Harald Hackl ◽  
Bodo Mistele ◽  
Yuncai Hu ◽  
Urs Schmidhalter
Keyword(s):  
Water Potential ◽  
Water Content ◽  
Aboveground Biomass ◽  
Leaf Water ◽  
Leaf Water Content ◽  
Spectral Indices ◽  
Fresh Weight ◽  
Normalised Difference Vegetation Index ◽  
Spectral Sensing ◽  
Reflectance Ratio

Spectral measurements allow fast nondestructive assessment of plant traits under controlled greenhouse and close-to-field conditions. Field crop stands differ from pot-grown plants, which may affect the ability to assess stress-related traits by nondestructive high-throughput measurements. This study analysed the potential to detect salt stress-related traits of spring wheat (Triticum aestivum L.) cultivars grown in pots or in a close-to-field container platform. In two experiments, selected spectral indices assessed by active and passive spectral sensing were related to the fresh weight of the aboveground biomass, the water content of the aboveground biomass, the leaf water potential and the relative leaf water content of two cultivars with different salt tolerance. The traits were better ascertained by spectral sensing of container-grown plants compared with pot-grown plants. This may be due to a decreased match between the sensors’ footprint and the plant area of the pot-grown plants, which was further characterised by enhanced senescence of lower leaves. The reflectance ratio R760 : R670, the normalised difference vegetation index and the reflectance ratio R780 : R550 spectral indices were the best indices and were significantly related to the fresh weight, the water content of the aboveground biomass and the water potential of the youngest fully developed leaf. Passive sensors delivered similar relationships to active sensors. Across all treatments, both cultivars were successfully differentiated using either destructively or nondestructively assessed parameters. Although spectral sensors provide fast and qualitatively good assessments of the traits of salt-stressed plants, further research is required to describe the potential and limitations of spectral sensing.

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