Petrology, palynology, coal facies, and depositional environments of an Upper Carboniferous coal seam, Minto Coalfield, New Brunswick, Canada

Coal petrology and palynology of the Minto coal seam enable depositional environments of the precursor mire to be established in terms of facies-critical maceral ratios, maceral assemblages, and spore and pollen assemblages. The overall petrographic composition indicates a vitrinite-rich coal (mean 67%), followed by inertinite (mean 27%) and liptinite (mean 7%). Pyrite is common to abundant (maximum 15%). Lithotype logs demonstrate a dominance of dull lithotypes (dull and banded dull). Petrographic composition at the lithotype and seam subsection level is highly variable. Vitrinite maceral assemblages are enriched in brighter lithotypes (banded bright and bright), whereas liptinite and inertinite maceral assemblages are enriched in dull and banded dull lithotypes. The duller lithotypes are enriched by mineral matter. Based on spores, the seam is assigned to the Vestispora Zone of Atlantic Canada, with the basal Torispora securis-Torispora laevigata (SL) Zone of western Europe and the lower Torispora securis-Vestispora fenestrata (SF) of the Illinois Basin. This indicates an early Bolsovian (Westphalian C) age. Based on the Tissue Preservation Index - Gelification Index facies concept, the seam was deposited in an upper delta plain. At the seam subsection level, facies-critical maceral ratios (Groundwater Influence Index, Vegetation Index) and maceral assemblages suggest both limnic (open moor) conditions and somewhat drier conditions. Relative low Vegetation Indices suggest mainly herbaceous source material, which is partly supported by the rare to common occurrence of small lycopsid spores and arboreous lycopods. The abundant sphenopsids, including Calamites, and rare gymnosperms may have grown outside the mire.

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Uncertainty of Vegetation Green-Up Date Estimated from Vegetation Indices Due to Snowmelt at Northern Middle and High Latitudes

Remote Sensing ◽

10.3390/rs12010190 ◽

2020 ◽

Vol 12 (1) ◽

pp. 190 ◽

Cited By ~ 3

Author(s):

Ruyin Cao ◽

Yan Feng ◽

Xilong Liu ◽

Miaogen Shen ◽

Ji Zhou

Keyword(s):

Western Europe ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Spatial Scales ◽

Vegetation Indices ◽

High Latitudes ◽

Large Spatial Scale ◽

Arid Grasslands ◽

Seasonal Snow Cover ◽

Spring Snowmelt

Vegetation green-up date (GUD), an important phenological characteristic, is usually estimated from time-series of satellite-based normalized difference vegetation index (NDVI) data at regional and global scales. However, GUD estimates in seasonally snow-covered areas suffer from the effect of spring snowmelt on the NDVI signal, hampering our realistic understanding of phenological responses to climate change. Recently, two snow-free vegetation indices were developed for GUD detection: the normalized difference phenology index (NDPI) and normalized difference greenness index (NDGI). Both were found to improve GUD detection in the presence of spring snowmelt. However, these indices were tested at several field phenological camera sites and carbon flux sites, and a detailed evaluation on their performances at the large spatial scale is still lacking, which limits their applications globally. In this study, we employed NDVI, NDPI, and NDGI to estimate GUD at northern middle and high latitudes (north of 40° N) and quantified the snowmelt-induced uncertainty of GUD estimations from the three vegetation indices (VIs) by considering the changes in VI values caused by snowmelt. Results showed that compared with NDVI, both NDPI and NDGI improve the accuracy of GUD estimation with smaller GUD uncertainty in the areas below 55° N, but at higher latitudes (55°N-70° N), all three indices exhibit substantially larger GUD uncertainty. Furthermore, selecting which vegetation index to use for GUD estimation depends on vegetation types. All three indices performed much better for deciduous forests, and NDPI performed especially well (5.1 days for GUD uncertainty). In the arid and semi-arid grasslands, GUD estimations from NDGI are more reliable (i.e., smaller uncertainty) than NDP-based GUD (e.g., GUD uncertainty values for NDGI vs. NDPI are 4.3 d vs. 7.2 d in Mongolia grassland and 6.7 d vs. 9.8 d in Central Asia grassland), whereas in American prairie, NDPI performs slightly better than NDGI (GUD uncertainty for NDPI vs. NDGI is 3.8 d vs. 4.7 d). In central and western Europe, reliable GUD estimations from NDPI and NDGI were acquired only in those years without snowfall before green-up. This study provides important insights into the application of, and uncertainty in, snow-free vegetation indices for GUD estimation at large spatial scales, particularly in areas with seasonal snow cover.

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Petrography and depositional environment of coal seams in the Carboniferous Morien Group, Sydney Coalfield, Nova Scotia

Canadian Journal of Earth Sciences ◽

10.1139/e96-065 ◽

1996 ◽

Vol 33 (6) ◽

pp. 863-874 ◽

Cited By ~ 10

Author(s):

D. Marchioni ◽

M. Gibling ◽

W. Kalkreuth

Keyword(s):

Nova Scotia ◽

Depositional Environments ◽

Water Levels ◽

Coal Seams ◽

Peat Accumulation ◽

Petrographic Composition ◽

Delta Plain ◽

Stratigraphic Position ◽

Depositional Setting ◽

Bar Formation

The Morien Group (Westphalian A – Stephanian) of the Sydney Basin, Nova Scotia, contains numerous coal seams, many of which can be traced across the onshore part of the basin. Depositional environments during coal formation range from proximal, fluvially dominated to intercalated distal fluvial, to restricted marine. Most of the coal samples analyzed in this study come from the Bras d'Or section, a near-continuous coastal exposure of the Sydney Mines Formation. Petrographic compositions of the samples are discussed in the context of the depositional setting of the enclosing strata. The coal seams have a high vitrinite content (70–90 vol. %), with moderate inertinite and low liptinite. Petrographic indices suggest that most coals of the Sydney Mines Formation are similar to seams deposited in interdistributary or lower delta plain settings in other basins and provide support for the interpreted sedimentological setting of this sequence. The coal seams of the Bras d'Or section show little variation in gross petrographic composition relative to their stratigraphic position, although a slight trend of increasing vitrinite content towards the upper part of the sequence may indicate peat accumulation in a more distal setting. Coal seams that developed in the lower part of the section have relatively high amounts of detrital macerals, indicative of higher water levels and a higher degree of water circulation within the mires. The common occurrence of red beds in the upper part of the section and the increase of structured macerals, including fusinite, suggest somewhat drier conditions during peat accumulation. Petrographic compositions of underlying coals of the South Bar Formation (Gardiner and Tracy seams) indicate deposition in a somewhat more proximal setting than most coals from the Sydney Mines Formation, reflecting the overall retrogradational development of the Morien Group.

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Characterization of Coal Quality Based On Ash Content From M2 Coal-Seam Group, Muara Enim Formation, South Sumatra Basin

Journal of Geoscience Engineering Environment and Technology ◽

10.24273/jgeet.2017.2.3.292 ◽

2017 ◽

Vol 2 (3) ◽

pp. 203

Author(s):

Frillia Putri Nasution ◽

Stevanus Nalendra

Keyword(s):

Coal Seam ◽

Research Area ◽

Ash Content ◽

Depositional Environments ◽

Higher Plant ◽

Delta Plain ◽

Quality Aspects ◽

Burning Coal ◽

Coal Residue ◽

Coal Seam Thickness

Muara Enim Formation is well known as coal-bearing formation in South Sumatra Basin. As coal-bearing formation, this formation was subjects of many integrated study. Muara Enim Formation can be divided into four coal-seam group, M1, M2, M3, and M4. The M2 group comprising of Petai (C), Suban (B), Lower Mangus (A2), and Upper Mangus (A1). Depositional environments of Group M2 is transitional lower delta plain with sub-depositional are crevasse splay and distributary channel. The differentiation of both sub-depositional environments can be caused the quality of coal deposit. One of quality aspects is ash content. This research conducted hopefully can give better understanding of relationship between depositional environments to ash content. Group M2 on research area were found only Seam C, Seam B, and Seam A2, that has distribution from north to central so long as 1400 m. Coal-seam thickness C ranged between 3.25-9.25 m, Seam B range 7.54-13.43 m, and Seam C range 1.53-8.37 m, where all of coal-seams thickening on the central part and thinning-splitting to northern part and southern part. The ash content is formed from burning coal residue material. Ash contents on coal seam caused by organic and inorganic compound which resulted from mixing modified material on surrounded when transportation, sedimentation, and coalification process. There are 27 sample, consists of 9 sample from Seam C, 8 sample from Seam B, and 10 sample from Seam A2. Space grid of sampling is 100-150 m. Ash content influenced by many factors, but in research area, main factor is existence of inorganic parting. Average ash content of Seam C is 6,04%, Seam B is 5,05%, and Seam A2 is 3,8%. Low ash content influenced by settle environment with minor detrital material. High ash content caused by oxidation and erosional process when coalification process. Ash content on coal in research area originated from detritus material carried by channel system into brackish area or originated from higher plant in brackish area. The high ash content also can be caused by after the coal deposited. It had originated from overburden horizon which infill in cleat of coal seam.

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Macerals Analysis Seam M2 Muaraenim Formation, : Implication Toward Coal Facies and Coal Rank in Kendi Hill, South Sumatra

Journal of Geoscience Engineering Environment and Technology ◽

10.24273/jgeet.2018.3.2.670 ◽

2018 ◽

Vol 3 (2) ◽

pp. 94 ◽

Cited By ~ 1

Author(s):

Yonathan Mangatur Rajagukguk ◽

Stevanus Nalendra Jati

Keyword(s):

Coal Seam ◽

Selection Process ◽

Vitrinite Reflectance ◽

Research Area ◽

Mineral Matter ◽

Coal Seams ◽

Delta Plain ◽

Southern Central ◽

Coal Facies ◽

Index Value

The Muaraenim Formation is a coal bearing formation in South Sumatra Basin of the Late Miocene – Pliocene. Shell (1978) divides this formation based on coal seam content are: M1, M2, M3, and M4. Coal seam in this area include in seam M2 member, with the general characteristics of the presence of silicified coal on the roof and floor of coal seams as a marker. The administration of the research area is located in east Kendi Hill, South Sumatra. The Kendi Hill is composed of adesite igneous rocks that intruded Muaraenim Formation in unconformity at the time of Pleistocene. This study aims to determine the environment of coal depositional based on maceral analysis and determine the factors influence the physical characteristics of coal seams in Kendi Hill. Data that has been obtained from the field, then conducted a selection process. The number of samples analyzed were nine sample based on near and far to the Kendi Hill spread from the southern, central, and northern of the location. The result of the analysis will display the maceral diagram. Megascopically, coal seam in the Kendi Hill are black, dull with bright, uneven – subchoncoidal, blackish brown in streak, have a pyrite and resin. The thickness of the coal seams ranges from 0,45 to 14 meters. Based on the maceral analysis, the coal seam in the Muaraenim Formation is composed dominated by vitrinite, then liptinite, inertinite and mineral matter pyrite (1,6-6,6%). Vitrinite reflectance of coal in the research area ranges from (0,37-0,48%) that included to the Sub bituminous – High Volatile Bituminous C. From the results of Tissue Preservation Index and Gelification Index value indicated that the environment of coal depositional in the research area was a limnic to wet forest swamp. Whereas the deposition of the Muaraenim Formation in deltaic environment (Transitional lower delta plain).

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HEALTHINESS OF OIL PALM PLANTATION TOWARDS SUSTAINABILITY OF ENVIRONMENT

Malaysian Journal of Sustainable Environment ◽

10.24191/myse.v7i1.8905 ◽

2020 ◽

Vol 7 (1) ◽

pp. 21

Author(s):

Faradina Marzukhi ◽

Nur Nadhirah Rusyda Rosnan ◽

Md Azlin Md Said

Keyword(s):

Oil Palm ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Satellite Image ◽

Vegetation Indices ◽

Soil Nutrient ◽

Oil Palm Plantation ◽

Sustainable Environment ◽

Moisture Analysis ◽

The Relationship

The aim of this study is to analyse the relationship between vegetation indices of Normalized Difference Vegetation Index (NDVI) and soil nutrient of oil palm plantation at Felcra Nasaruddin Bota in Perak for future sustainable environment. The satellite image was used and processed in the research. By Using NDVI, the vegetation index was obtained which varies from -1 to +1. Then, the soil sample and soil moisture analysis were carried in order to identify the nutrient values of Nitrogen (N), Phosphorus (P) and Potassium (K). A total of seven soil samples were acquired within the oil palm plantation area. A regression model was then made between physical condition of the oil palms and soil nutrients for determining the strength of the relationship. It is hoped that the risk map of oil palm healthiness can be produced for various applications which are related to agricultural plantation.

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Modeling of Durum Wheat Yield Based on Sentinel-2 Imagery

Agronomy ◽

10.3390/agronomy11081486 ◽

2021 ◽

Vol 11 (8) ◽

pp. 1486

Author(s):

Chris Cavalaris ◽

Sofia Megoudi ◽

Maria Maxouri ◽

Konstantinos Anatolitis ◽

Marios Sifakis ◽

...

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Vegetation Indices ◽

Model Development ◽

Wheat Yield ◽

Growth Period ◽

Yield Data ◽

Water Index ◽

Normalized Difference Water Index ◽

Sentinel 2

In this study, a modelling approach for the estimation/prediction of wheat yield based on Sentinel-2 data is presented. Model development was accomplished through a two-step process: firstly, the capacity of Sentinel-2 vegetation indices (VIs) to follow plant ecophysiological parameters was established through measurements in a pilot field and secondly, the results of the first step were extended/evaluated in 31 fields, during two growing periods, to increase the applicability range and robustness of the models. Modelling results were examined against yield data collected by a combine harvester equipped with a yield-monitoring system. Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were examined as plant signals and combined with Normalized Difference Water Index (NDWI) and/or Normalized Multiband Drought Index (NMDI) during the growth period or before sowing, as water and soil signals, respectively. The best performing model involved the EVI integral for the 20 April–31 May period as a plant signal and NMDI on 29 April and before sowing as water and soil signals, respectively (R2 = 0.629, RMSE = 538). However, model versions with a single date and maximum seasonal VIs values as a plant signal, performed almost equally well. Since the maximum seasonal VIs values occurred during the last ten days of April, these model versions are suitable for yield prediction.

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Aboveground Biomass Mapping of Crops Supported by Improved CASA Model and Sentinel-2 Multispectral Imagery

Remote Sensing ◽

10.3390/rs13142755 ◽

2021 ◽

Vol 13 (14) ◽

pp. 2755

Author(s):

Peng Fang ◽

Nana Yan ◽

Panpan Wei ◽

Yifan Zhao ◽

Xiwang Zhang

Keyword(s):

Aboveground Biomass ◽

Production Management ◽

Vegetation Index ◽

Net Primary Productivity ◽

Vegetation Indices ◽

Biophysical Parameters ◽

Edge Information ◽

Casa Model ◽

Red Edge ◽

Biomass Mapping

The net primary productivity (NPP) and aboveground biomass mapping of crops based on remote sensing technology are not only conducive to understanding the growth and development of crops but can also be used to monitor timely agricultural information, thereby providing effective decision making for agricultural production management. To solve the saturation problem of the NDVI in the aboveground biomass mapping of crops, the original CASA model was improved using narrow-band red-edge information, which is sensitive to vegetation chlorophyll variation, and the fraction of photosynthetically active radiation (FPAR), NPP, and aboveground biomass of winter wheat and maize were mapped in the main growing seasons. Moreover, in this study, we deeply analyzed the seasonal change trends of crops’ biophysical parameters in terms of the NDVI, FPAR, actual light use efficiency (LUE), and their influence on aboveground biomass. Finally, to analyze the uncertainty of the aboveground biomass mapping of crops, we further discussed the inversion differences of FPAR with different vegetation indices. The results demonstrated that the inversion accuracies of the FPAR of the red-edge normalized vegetation index (NDVIred-edge) and red-edge simple ratio vegetation index (SRred-edge) were higher than those of the original CASA model. Compared with the reference data, the accuracy of aboveground biomass estimated by the improved CASA model was 0.73 and 0.70, respectively, which was 0.21 and 0.13 higher than that of the original CASA model. In addition, the analysis of the FPAR inversions of different vegetation indices showed that the inversion accuracies of the red-edge vegetation indices NDVIred-edge and SRred-edge were higher than those of the other vegetation indices, which confirmed that the vegetation indices involving red-edge information can more effectively retrieve FPAR and aboveground biomass of crops.

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Estimating and Monitoring Land Surface Phenology in Rangelands: A Review of Progress and Challenges

Remote Sensing ◽

10.3390/rs13112060 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2060

Author(s):

Trylee Nyasha Matongera ◽

Onisimo Mutanga ◽

Mbulisi Sibanda ◽

John Odindi

Keyword(s):

Land Surface ◽

Vegetation Index ◽

Critical Role ◽

Spectral Characteristics ◽

Vegetation Indices ◽

Machine Learning Algorithms ◽

Special Focus ◽

Land Surface Phenology ◽

Spectral Vegetation Indices ◽

Satellite Sensors

Land surface phenology (LSP) has been extensively explored from global archives of satellite observations to track and monitor the seasonality of rangeland ecosystems in response to climate change. Long term monitoring of LSP provides large potential for the evaluation of interactions and feedbacks between climate and vegetation. With a special focus on the rangeland ecosystems, the paper reviews the progress, challenges and emerging opportunities in LSP while identifying possible gaps that could be explored in future. Specifically, the paper traces the evolution of satellite sensors and interrogates their properties as well as the associated indices and algorithms in estimating and monitoring LSP in productive rangelands. Findings from the literature revealed that the spectral characteristics of the early satellite sensors such as Landsat, AVHRR and MODIS played a critical role in the development of spectral vegetation indices that have been widely used in LSP applications. The normalized difference vegetation index (NDVI) pioneered LSP investigations, and most other spectral vegetation indices were primarily developed to address the weaknesses and shortcomings of the NDVI. New indices continue to be developed based on recent sensors such as Sentinel-2 that are characterized by unique spectral signatures and fine spatial resolutions, and their successful usage is catalyzed with the development of cutting-edge algorithms for modeling the LSP profiles. In this regard, the paper has documented several LSP algorithms that are designed to provide data smoothing, gap filling and LSP metrics retrieval methods in a single environment. In the future, the development of machine learning algorithms that can effectively model and characterize the phenological cycles of vegetation would help to unlock the value of LSP information in the rangeland monitoring and management process. Precisely, deep learning presents an opportunity to further develop robust software packages such as the decomposition and analysis of time series (DATimeS) with the abundance of data processing tools and techniques that can be used to better characterize the phenological cycles of vegetation in rangeland ecosystems.

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Biomass estimation of cultivated red algae Pyropia using unmanned aerial platform based multispectral imaging

Plant Methods ◽

10.1186/s13007-021-00711-y ◽

2021 ◽

Vol 17 (1) ◽

Author(s):

Shuai Che ◽

Guoying Du ◽

Ning Wang ◽

Kun He ◽

Zhaolan Mo ◽

...

Keyword(s):

High Throughput ◽

Vegetation Index ◽

Multispectral Imaging ◽

Vegetation Indices ◽

Spectral Imaging ◽

Shielding Effect ◽

Quadratic Model ◽

Biomass Estimation ◽

Marine Aquaculture ◽

Significant Difference

Abstract Background Pyropia is an economically advantageous genus of red macroalgae, which has been cultivated in the coastal areas of East Asia for over 300 years. Realizing estimation of macroalgae biomass in a high-throughput way would great benefit their cultivation management and research on breeding and phenomics. However, the conventional method is labour-intensive, time-consuming, manually destructive, and prone to human error. Nowadays, high-throughput phenotyping using unmanned aerial vehicle (UAV)-based spectral imaging is widely used for terrestrial crops, grassland, and forest, but no such application in marine aquaculture has been reported. Results In this study, multispectral images of cultivated Pyropia yezoensis were taken using a UAV system in the north of Haizhou Bay in the midwestern coast of Yellow Sea. The exposure period of P. yezoensis was utilized to prevent the significant shielding effect of seawater on the reflectance spectrum. The vegetation indices of normalized difference vegetation index (NDVI), ratio vegetation index (RVI), difference vegetation index (DVI) and normalized difference of red edge (NDRE) were derived and indicated no significant difference between the time that P. yezoensis was completely exposed to the air and 1 h later. The regression models of the vegetation indices and P. yezoensis biomass per unit area were established and validated. The quadratic model of DVI (Biomass = − 5.550DVI2 + 105.410DVI + 7.530) showed more accuracy than the other index or indices combination, with the highest coefficient of determination (R2), root mean square error (RMSE), and relative estimated accuracy (Ac) values of 0.925, 8.06, and 74.93%, respectively. The regression model was further validated by consistently predicting the biomass with a high R2 value of 0.918, RMSE of 8.80, and Ac of 82.25%. Conclusions This study suggests that the biomass of Pyropia can be effectively estimated using UAV-based spectral imaging with high accuracy and consistency. It also implied that multispectral aerial imaging is potential to assist digital management and phenomics research on cultivated macroalgae in a high-throughput way.

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Assessing the Effect of Drought on Winter Wheat Growth Using Unmanned Aerial System (UAS)-Based Phenotyping

Remote Sensing ◽

10.3390/rs13061144 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1144

Author(s):

Mahendra Bhandari ◽

Shannon Baker ◽

Jackie C. Rudd ◽

Amir M. H. Ibrahim ◽

Anjin Chang ◽

...

Keyword(s):

Winter Wheat ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Vegetation Indices ◽

Grain Filling ◽

Unmanned Aerial Systems ◽

High Temporal Resolution ◽

Wheat Growth ◽

Spatial Domains ◽

Temporal And Spatial

Drought significantly limits wheat productivity across the temporal and spatial domains. Unmanned Aerial Systems (UAS) has become an indispensable tool to collect refined spatial and high temporal resolution imagery data. A 2-year field study was conducted in 2018 and 2019 to determine the temporal effects of drought on canopy growth of winter wheat. Weekly UAS data were collected using red, green, and blue (RGB) and multispectral (MS) sensors over a yield trial consisting of 22 winter wheat cultivars in both irrigated and dryland environments. Raw-images were processed to compute canopy features such as canopy cover (CC) and canopy height (CH), and vegetation indices (VIs) such as Normalized Difference Vegetation Index (NDVI), Excess Green Index (ExG), and Normalized Difference Red-edge Index (NDRE). The drought was more severe in 2018 than in 2019 and the effects of growth differences across years and irrigation levels were visible in the UAS measurements. CC, CH, and VIs, measured during grain filling, were positively correlated with grain yield (r = 0.4–0.7, p < 0.05) in the dryland in both years. Yield was positively correlated with VIs in 2018 (r = 0.45–0.55, p < 0.05) in the irrigated environment, but the correlations were non-significant in 2019 (r = 0.1 to −0.4), except for CH. The study shows that high-throughput UAS data can be used to monitor the drought effects on wheat growth and productivity across the temporal and spatial domains.

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