Landsat-8 OLI Research Articles

Prelaunch Spectral Characterization of the Operational Land Imager-2

The Landsat-9 satellite, launched in September 2021, carries the Operational Land Imager-2 (OLI-2) as one of its payloads. This instrument is a clone of the Landsat-8 OLI and its mission is to continue the operational land imaging of the Landsat program. The OLI-2 instrument is not significantly different from OLI though the instrument-level pre-launch spectral characterization process was much improved. The focal plane modules used on OLI-2 were manufactured as spares for OLI and much of the spectral characterization of the components was performed for OLI. However, while the spectral response of the fully assembled OLI was characterized by a double monochromator system, the OLI-2 spectral characterization made use of the Goddard Laser for Absolute Measurement of Radiance (GLAMR). GLAMR is a system of tunable lasers that cover 350–2500 nm which are fiber-coupled to a 30 in integrating sphere permanently monitored by NIST-traceable radiometers. GLAMR allowed the spectral characterization of every detector of the OLI-2 focal plane in nominal imaging conditions. The spectral performance of the OLI-2 was, in general, much better than requirements. The final relative spectral responses (RSRs) represent the best characterization any Landsat instrument spectral response. This paper will cover the results of the spectral characterization from the component-level to the instrument-level of the Landsat-9 OLI-2.

Assessing water quality at Jakkur Lake: a comparative analysis of Landsat-8 OLI and Sentinel-2 Satellite Imagery

Jakkur lake is one of the biggest lakes of Bangalore and is located on the northern side of the city. It derives its name from the name of the locality, Jakkur. The waterbody area is about 160 acres and length is about 1.5 km with several islands. Because of urbanization and increase in population, the catchment location is highly encroached. The amount of the stormwater achieving the lake has decreased making the streams dry. Remote sensing is a tool used for monitoring water bodies to present better spatial and temporal resolution. Remote sensing is also used for continuous monitoring and assessment of water bodies. Landsat-8 OLI and Sentinel-2 are recently launched multispectral sensors that have the potential to be used for the effective monitoring of the Jakkur Lake's water quality. Landsat-8 OLI and Sentinel-2 are similar in many approaches. Sentinel-2, however, has higher spatial resolution. The aim of this research was to find out if Sentinel-2 produces better results than Landsat-8 OLI in testing water quality parameters in the Jakkur lake. The parameters of interest were chlorophyll_a and turbidity. The results show that Sentinel-2 generates better results. For Sentinel-2, the R2 values for chlorophyll_a and turbidity were 0.975 and 0.845 respectively whilst for Landsat-8, the chlorophyll_a and turbidity R2 values were 0.911 and 0.747 respectively.

Remote monitoring of water clarity in coastal oceans of the Guangdong-Hong Kong-Macao Greater Bay Area, China based on machine learning

The development of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), one of the most developed and densely populated regions in China, has posed an increasing threat to the health of the water environment in adjacent coastal oceans. However, the spatiotemporal variations of water clarity in the coastal oceans of the GBA (COGBA) have not been well-documented. Therefore, this study aims to develop a remote sensing retrieval algorithm for assessing water clarity, represented by Secchi disk depth (ZSD), based on Landsat-8 OLI imagery for the COGBA using a machine learning method. Cross-validation demonstrated that the algorithm performed exceptionally well, with an R2 value greater than 0.8. By applying the algorithm to 263 Landsat-8 OLI images, we obtained a time series of ZSD for the period of 2013–2021 over the COGBA. Results indicate that the Pearl River Estuary (PRE) exhibited the highest turbidity, followed by the Daya Bay and the Mirs Bay. Generally, ZSD increased from the northwest to the southeast of the COGBA. Seasonal, interannual, and long-term variations in ZSD were also observed with long-term increases in the PRE and the Daya Bay. The interannual variations in ZSD during fall were primarily regulated by different factors in each region. In the PRE, the negative effects of sediment discharge and wind speed played a significant role. In Mirs Bay, wind speed and sediment discharge had a negative impact on ZSD. In Daya Bay, precipitation and wind speed were the key factors influencing ZSD. The development and findings of our algorithm contribute to the protection and management of the water environment in the COGBA, facilitating effective governance measures.

Association mining of coastline change and land use patterns to enhance conservation

Coastal zones, as interfaces between marine and terrestrial ecosystems, possess great ecological and economic value but are environmentally fragile. This research provides a new perspective for studying development and change patterns in coastal zones, offering insights for land planning and contributing to the ecological preservation and sustainable development of coastal areas. We utilized Landsat TM and OLI sensor imagery (from the years 1990, 2000, 2010, and 2020) to extract coastline and land use information from the coastal zone of Shandong Province. Through the establishment of association mining models between coastline changes and land use patterns, the relationships between spatial layout changes and development processes in different geographical locations are revealed. In the initial stage, weak expansion and moderate changes in the coastline corresponded to units with lower land development intensity sequences. In the intermediate stage, with increasing coastline expansion and intense changes, the sequence of land development intensity was extremely strong > strong > moderate > weak from the sea to the land. This type of association was mostly distributed in the Laizhou Bay salt field and coastal port areas and was mostly focused on aquaculture and port trade, causing “strong” and “extremely strong” units of regional land development intensity. In the later stage, as coastline expansion slowed, the use of ports and docks caused the development intensity of the surrounding land to fluctuate. Farming, fishing, salt field and port construction in the coastal zone of Shandong Province were the major forms of land expansion and change. The association rules between coastline expansion or erosion and coastal development intensity revealed the impact of coastline changes on the spatial pattern from sea to land, which is important for planning layouts, optimizing industrial structures, and maximizing socioeconomic and environmental benefits in coastal areas.

Spatio-temporal analysis of urban expansion using remote sensing data and GIS for the sustainable management of urban land: the case of Burayu, Ethiopia

PurposeThe purpose of this study is to analyze the horizontal expansion of Burayu Town between 1990 and 2020. The study typically acts as a baseline for integrated spatial planning in small- and medium-sized towns, which will help to plan sustainable utilization of land.Design/methodology/approachLandsat5-TM, Landsat7 ETM+, Landsat5 TM and Landsat8 OLI were used in the study, along with other auxiliary data. The LULC map classifications were generated using the Random Forest Package from the Comprehensive R Archive Network. Post-classification, spatial metrics, and per capita land consumption rate were used to understand the manner and rate of expansion of Burayu Town. Focus group discussions and key informant interviews were also used to validate land use classes through triangulation.FindingsThe study found that the built-up area was the most dynamic LULC category (85.1%) as it increased by over 4,000 ha between 1990 and 2020. Furthermore, population increase did not result in density increase as per capita land consumption increased from 0.024 to 0.040 during the same period.Research limitations/implicationsAs a result of financial limitations, there were no high-resolution satellite images available, making it challenging to pinpoint the truth as it is on the ground. Including senior citizens in the study region allowed this study to overcome these restrictions and detect every type of land use and cover.Practical implicationsData on urban growth are useful for planning land uses, estimating growth rates and advising the government on how best to use land. This can be achieved by monitoring and reviewing development plans using satellite imaging data and GIS tools.Originality/valueThe use of Random Forest for image classification and the employment of local knowledge to validate the accuracy of land cover classification is a novel approach to properly customize remote sensing applications.

Tracking rapid and slow ice-dammed lake changes through optical satellites and local knowledge: a case study of Tystigbreen in Norway

Abstract The number of glacial lakes has grown globally concurrently with the retreat of glaciers in the last few decades, increasing the risk of potentially hazardous glacial lake outburst floods (GLOFs) and posing a threat to downstream communities. Norway has several known ice-dammed lakes that produce repeated GLOFs but as we show here, the existing GLOF database is incomplete and needs to be improved through continuous monitoring of glaciers and glacial lakes. This study examines the case of an ice cap in central Norway hosting at least four drainage-prone lakes. We reconstruct the sequence of lake drainage events through a combination of remote sensing, ground-truthing and citizen science while scrutinising the applicability of the PlanetScope imagery vs Sentinel-2 and Landsat-8 OLI products. As opposed to the Landsat imagery that often fails to resolve even the largest glacial lakes of Tystigbreen, both PlanetScope and Sentinel-2 are helpful in identifying previously unrecognised glacial lakes and undocumented drainage events. Our analysis suggests that a fusion of the two satellite products may be beneficial for automated tracking of glacial lake changes. We also demonstrate that local knowledge and systematic involvement of citizens in data collection have a potential to enrich GLOF databases.

A Comparative Analysis of Random Forest and Support Vector Machines for Classifying Irrigated Cropping Areas in The Upper-Comoé Basin, Burkina Faso

Objectives: This study investigates the performance of two machine-learning algorithms in classifying land areas across the Upper-Comoé basin in Burkina Faso. Methods: Within the Google Earth Engine data processing environment, Support Vector Machine (SVM) and the Random Forest (RF) algorithms were applied to a Landsat-8 OLI image of March 2019, to discriminate agricultural land areas, with an emphasis on irrigated areas. Findings: The results indicated good to excellent classification performance, with overall accuracies and Kappa coefficients between 71% and 99%, and 0.66 and 0.99, respectively. The RF method outperformed the SVM in terms of mapping "accuracy", but in terms of spatial distribution of classes, the SVM method provided a mapping close to reality, due to the density of the classes generated. Novelty: Our findings suggest that remote sensing can constitute a tool fully adapted to the needs of services in charge of agricultural water management in Burkina Faso. Keywords: Irrigation, Random Forest, Support Vector Machine, Google Earth Engine, Burkina Faso

Estimating Soil Erosion and the Effect of Changing Land Use and Land Cover in the Bringi Catchment’s Devalgam Watershed Using RS and GIS

Abstract: One of the main factors contributing to soil degradation worldwide is soil erosion. The geographical variability of erosion at the Devalgam watershed was evaluated using the Revised Universal Soil Loss Equation (RUSLE) model in a GIS environment. DEM ASTER (30 m × 30 m). The model was fed with annual rainfall data from 1990, 2000, 2010, and 2020 as well as soil and LULC maps. In the Devalgam watershed, the mean annual soil loss ranged from 0 to 127.32, 140.34, 146.49, and 187.23 t/ha/yr for the years 1990, 2000, 2010, and 2020. The mean annual sol loss was calculated to be 16.6, 18.3,19.1, and 22.08 t/ha/yr. According to a zonal statistical analysis of soil erosion for various types of land cover, open forests and barren areas were more likely to experience erosion with the least susceptible to erosion were vegetation, built-up areas, orchards, and agriculture, with estimates of 85.12 and 52.35 t/ha/yr, respectively. According to the current study, managing natural resources is increasingly dependent on the LULC change in the Devalgam Watershed. GIS and remote sensing technologies have shown to be useful tools for analyzing LU/LC changes on a watershed-by-watershed basis. Six LU/LC classes were used to categorize the study area: built-up, barren terrain, vegetation, farmland, woodland, and orchards. and it was discovered that, out of the total area, or 2058.618 hectares, the least area was covered by orchards (0.5%), while the highest area was covered by forest (30.5%), followed by barren ground (25.7%). The examination of the overlay of analysis of the changes from 1990 to 2020 was conducted using LANDSAT-5 1990 over LANDSAT-8 OLI 2020. The findings also indicate that whereas the area under agricultural, builtup areas, barren land, and orchards expanded by 123%, 36.5%, 0.5%, and 36.4%, respectively, the area under forests and vegetation dropped by 17.3% and 48%. With a kappa coefficient of 0.84, 0.71, 0.75, and 0.87, respectively, the overall accuracy for the categorized imageries LANDSAT-5 (1990), LANDSAT-7 (2000), LANDSAT-7 TM (2010), and LANDSAT-8 OLI (2020) was determined to be 86.6%, 76.6%, 80%, and 90%.respectively

Changes in water surface area of the Middle Atlas-Morocco lakes: A response to climate and human effects

The lakes represent crucial surface water resources and an integral part of wetlands. The most concerning aspect of the degradation of these areas is the complete drying up of the lakes. In the Mediterranean region, successive changes in land use practices in the context of climate change have strongly influenced wetland areas. In this study, we used Landsat TM, OLI, and OLI-2 satellite images to monitor the water surface area in two representative lakes (Aoua and Ifrah) of the Tabular Middle Atlas and to map land use across the entire study area. To extract information related to lakes and land use, we employed the Support Vector Machine machine learning algorithm, widely used in remote sensing studies. However, we identified drought periods from precipitation data using the Standardized Precipitation Index (SPI) recommended by the World Meteorological Organization (WMO). The results obtained from the processing of Landsat satellite images indicate a significant reduction in the surface area of the lakes, with periods of drying for Aoua lake, endangering their fragile ecosystems and biodiversity. The critical situation of the two lakes is attributed to a combination of natural and anthropogenic factors. The analysis of climatic data shows a significant climate change from the 1980s, with long periods of drought. In parallel, the study area has undergone remarkable modifications in land use patterns, mainly characterized by a significant extension of irrigated agricultural surfaces to the detriment of grazing and rainfed lands. In three decades, the area of irrigated crops has increased from approximately 1300 hectares in 1985 to 7070 hectares in 2022, representing an increase of 542%. The findings presented in this study reveal the extent of lake degradation in the TMA and reflect the alarming decline in groundwater levels. This situation indicates the necessity of formulating a strategy to protect water resources and wetlands in the Middle Atlas.

Study on shoreline migration and island dynamics over the last five decades in the Muriganga River using multi-temporal satellite images.

The Muriganga River, also known as channel creek, underwent morphological changes often since it is an alluvial as well as a tidal river. The present study analyses the morphological changes in the Muriganga River and its islands with the help of the Remote Sensing and Geographical Information System (GIS) and digital shoreline analysis tool (DSAS 5.0). Moreover, the computation of morphological changes was also performed on two islands, i.e. Sagar and Ghoramara, which are situated just outside the river reach. Eight cloud-free satellite images of Landsat MSS (1972-1980), Landsat TM (1988-2011) and Landsat OLI (2017-2021) have been used to investigate the river shoreline shifting and island dynamics of the Muriganga River resulted from the erosion-accretion process during the last 49 years. For the short-term study, the erosion-accretion rates are derived from one Landsat image to the next, whereas for long-term analysis, the erosion-accretion rates are estimated based on the difference between 1972 as the reference image and the succeeding images. Short-term and long-term analysis shows that the average rate of erosion is more than that of accretion in Muriganga River. It is also found that the areas of Sagar, Ghoramara, Mousuni and Pushpa islands are shrinking continuously, whereas the Niogi and Basit islands are expanding enormously. These may indicate that the shoreline erosion results in widening the river and the eroded materials are accumulated in Niogi and Basit islands. The results suggest that there is an urge for a better coastal management strategy for the erosion control scheme. This study also helps in gaining knowledge of maintaining the navigability in the Muriganga River.

Spatiotemporal Variations in Biophysical Water Quality Parameters: An Integrated In Situ and Remote Sensing Analysis of an Urban Lake in Chile

This study aims to develop and implement a methodology for retrieving bio-optical parameters in a lagoon located in the Biobío region, South-Central Chile, by analyzing time series of Landsat-8 OLI satellite images. The bio-optical parameters, i.e., chlorophyll-a (Chl-a, in mg·m−3) and turbidity (in NTU) were measured in situ during a satellite overpass to minimize the impact of atmospheric distortions. To calibrate the satellite images, various atmospheric correction methods (including ACOLITE, C2RCC, iCOR, and LaSRC) were evaluated during the image preprocessing phase. Spectral signatures obtained from the scenes for each atmospheric correction method were then compared with spectral signatures acquired in situ on the water surface. In short, the ACOLITE model emerged as the best fit for the calibration process, reaching R2 values of 0.88 and 0.79 for Chl-a and turbidity, respectively. This underlies the importance of using inversion models, when processing water surfaces, to mitigate errors due to aerosols and the sun-glint effect. Subsequently, reflectance data derived from the ACOLITE model were used to establish correlations between various spectral indices and the in situ data. The empirical retrieval models (based on band combinations) yielding superior performance, with higher R2 values, were subjected to a rigorous statistical validation and optimization by applying a bootstrapping approach. From this process the green chlorophyll index (GCI) was selected as the optimal choice for constructing the Chl-a retrieval model, reaching an R2 of 0.88, while the red + NIR spectral index achieved the highest R2 value (0.79) for turbidity analysis, although in the last case, it was necessary to incorporate data from several seasons for an adequate model training. Our analysis covered a broad spectrum of dates, seasons, and years, which allowed us to search deeper into the evolution of the trophic state associated with the lake. We identified a striking eight-year period (2014–2022) characterized by a decline in Chl-a concentration in the lake, possibly attributable to governmental measures in the region for the protection and conservation of the lake. Additionally, the OLI imagery showed a spatial pattern varying from higher Chl-a values in the northern zone compared to the southern zone, probably due to the heat island effect of the northern urban areas. The results of this study suggest a positive effect of recent local regulations and serve as the basis for the creation of a modern monitoring system that enhances traditional point-based methods, offering a holistic view of the ongoing processes within the lake.

Selection of Landsat-8 Operational Land Imager (OLI) Optimal Band Combinations for Mapping Alteration Zones

In typical alteration extraction methods, e.g., band math and principal component analysis (PCA), the bands or band combinations unitized to extract altered minerals are usually selected based on empirical models or previous rules. This results in significant differences in the alteration of mineral mapping even in the same area, thus greatly increasing the uncertainty of mineral resource prediction. In this paper, an intelligent alteration extraction approach was proposed in which an optimization algorithm, i.e., a genetic algorithm (GA), was introduced into the PCA; this approach is termed GA-PCA and is used for selecting the optimized band combinations of mineralized alterations. The proposed GA-PCA was employed to map iron oxides and hydroxyl minerals using the most commonly adopted multispectral data, i.e., Landsat-8 OLI data, at the Lalingzaohuo polymetallic deposits, China. The results showed that the spectral characteristics of GA-PCA-selected OLI band combinations in the research area were beneficial for enhancing alteration information and were more capable of suppressing the interference of vegetation information. The mapping alteration zones using the GA-PCA approach had a higher agreement with known ore spots, i.e., 25% and 33.3% in ferrous-bearing and hydroxyl-bearing deposits, compared to the classical PCA. Furthermore, two predicted targets (not shown in the classical PCA results) were precisely obtained via analyzing the GA-PCA alteration maps combined with the ore-forming geological conditions of the mine and its tectonic characteristics. This indicated that the intelligent selection of mineral alteration band combinations increased the reliability of remote sensing-based mineral exploration.

The possibility of spatial mapping of SOC content in olive groves under integrated production using easy-to-obtain ancillary data in a Mediterranean area.

Unlike most of Europe, Andalucía in southern Spain as a Mediterranean area still lacks digital maps of SOC content provided by machine learning algorithms. The wide diversity of climate, geology, hydrology, landscape, topography, vegetation, and micro-relief data as easy-to-obtain covariates facilitated the development of digital soil mapping (DSM). The purpose of this research is to model and map the spatial distribution of SOC at three depths, in an area of approximately 10000 km 2 located in Seville and Cordoba Provinces, and to use R programming to compare two machine learning techniques (cubist and random forest) for developing SOC maps at multiple depths. Environmental covariates used in this research include nine derivatives from digital elevation models (DEM), three climatic variables and finally eighteen remotely-sensed spectral data (band ratios calculated by the acquired Landsat-8 OLI and Sentinel-2A MSI in July 2019). In total, 300 soil samples from 100 points were taken (0-25 cm). The purpose of this research is to model and map the spatial distribution of SOC, in an area with approximately 10000 km2 located in Seville and Cordoba Provinces, and to compare two machine learning techniques (cubist and random forest) by R programming. The findings showed that the novel approach for integrating the indices using Landsat-8 OLI and Sentinel-2A MSI satellite data had a better result. Finally, we obtained evidence that the resolution of satellite images is more important in modelling and digital mapping.

Remote sensing retrieval and driving analysis of phytoplankton density in the large storage freshwater lake: A study based on random forest and Landsat-8 OLI

Remote sensing monitoring of seasonal changes in phytoplankton density and analyses of the driving factors of phytoplankton densities are necessary for assessing the health of aquatic ecosystems, controlling lake eutrophication, and formulating ecological restoration policies. Building upon the satellite-ground synchronization experiment that involves the in situ aquatic ecological monitoring conducted in Nansi Lake, which is the largest storage lake situated along the eastern route of the South-to-North Water Diversion Project, we developed a phytoplankton density retrieval model utilizing the random forest (RF) method and Landsat-8 OLI data. On this basis, we mapped the seasonal fluctuations and spatial disparities in the phytoplankton densities from 2013 to 2023. Subsequently, we conducted a detailed analysis of the driving factors and considered both the natural and anthropogenic aspects. The results indicate that (1) the RF model, when utilizing three band combinations, yielded favorable results with R2, RMSE and MAE values of 0.67, 1.31 × 106 cells/L and 1.18 × 106 cells/L, respectively. (2) The phytoplankton densities exhibited both seasonal and spatial variations, with higher concentrations in summer and autumn than in spring and winter. Significantly, the northwestern region of Zhaoyang Lake and the southeastern region of Weishan Lake had substantially greater phytoplankton densities than did the other areas. Furthermore, overarching upward trends were observed from 2013 to 2023, reflecting an annual rate of increase of 3.32%. (3) An analysis of the causal factors indicated that temperatures and gross agricultural production levels are the primary drivers influencing the seasonal variations and distributions of phytoplankton densities. In the future, we will delve into the potential of deep learning and utilize various satellite sensors to explore the intricacies of phytoplankton monitoring, as well as the complex mechanisms that influence aquatic ecological health.

Litho-Lineament Mapping of Rocks in Ila Orangun Area Southwestern Nigeria Using Remote Sensing and Aeromagnetic Data: Implications for Mineral Exploration

Ila Orangun is located north of Okemesi and falls within latitudes 7054l and 8000l N and longitudes 40 53l and 50 00l E respectively. Field studies revealed six lithological units in the study area namely quartzites, granites, granite gneiss, porphyritic granites, amphibolite schist and pegmatites. The aim of the research is to elucidate the geology and structure as well as evaluate the metallic mineral potentials of the study area. An integrated multi-technique study approach was adopted for reconnaissance survey and structural interpretation which involved the use of acquired remotely sensed satellite imageries such as Landsat-8 OLI, Shuttle Radar Topographic Mission, Radar Sentinel-2A and a geophysical method involving the use of aeromagnetic data. Results obtained from interpretation of remote sensing data was used to produce different lineament maps which displayed fractures of varying lengths and trend dominantly in the NE-SW directions and subsidiary fractures also orientate in the NW-SE, E-W and N-S directions. Results from the stereographic projection plots showed that the dominant orientation of foliation planes is in the NE-SW direction with the NE and SE sections of the study area recording the highest cluster of the foliation planes. Hence, the eastern section spotted as the mineralization zone.

Estimation of land surface temperature from Landsat-8 OLI: comparative analysis of two periods for Duzce in Turkey

Estimation of land surface temperature from Landsat-8 OLI: comparative analysis of two periods for Duzce in Turkey

Bi-Temporal Analysis of Vegetation Index on Land Surface Temperature in Kottayam, Kerala

The impact of NDVI (Normalized Difference Vegetation Index) on the LST (Land Surface Temperature) as well as on the genesis of surface heat islands in urban areas during two different time periods was assessed in Kottayam district, Kerala, India. Landsat TM, Landsat OLI and TIRS imagery from the years 1988 and 2020 were employed to scrutinize the relationship between NDVI and LST. The area covered under different NDVI classes were quantified. The finding indicated that NDVI of the research region decreased from 0.77 in 1988 to 0.59 in 2020, resulting in an increase in LSTmax from 34.46 °C in 1988 to 40.63 °C in 2020. Decrease in NDVI resulted in an increase in the high UHI class from 20.83 km2 in 1988 to 660.59 km2 and from 7.26 km2 to 181.35km2 in the very high UHI class. An inverse relationship was observed between NDVI and LST, with Pearson coefficients of 0.5737 and 0.5199 for 1988 and 2020, respectively, which indicates that NDVI could serve as a crucial metric for evaluating LST and UHI effects. Future research will explore the effect of seasonal variability in LULCC on LST, day and night time UHI and their impacts on human health and energy consumption.

Remote detection of Artemia slicks using multisatellite observations: spectral, spatial and temporal considerations

ABSTRACT Artemia has high commercial and ecological value, but few studies have considered the spectral, spatial and temporal requirements of Artemia multisatellite observations. Adopting Landsat-8 OLI and Sentinel-2 MSI as examples, we mainly analyzed and compared their Artemia slick detection performance considering spectral sensitivity, spatial resolution and revisit cycle. First, the NIR bands of both sensors were most sensitive in Artemia slick detection. Second, compared to that of 2 m GF-6 PMS fusion images, the spatial uncertainty of MSI images was approximately ±16% using the brine shrimp index (BSI) to extract Artemia slicks, better than that of OLI images. The OLI and MSI detection limits under ideal conditions were 0.77% and 1.69%, corresponding to the smallest detectable Artemia slick sizes of approximately 6.93 and 1.69 m2. Third, multisatellite observations could improve dynamic Artemia slick monitoring ability. The OLI and MSI combination could enhance the monthly average observation frequency to 6.58 times. Finally, we examined the influences of the selected near-infrared (or red-edge) bands and Artemia slick width on the extraction results and analyzed detection limit changes and sensor consistency through practical considerations. Overall, these findings could provide support for remote Artemia observation, aiding in fishery management, global mapping and biomass estimation.

Updating glacier inventories on the periphery of Antarctica and Greenland using multi-source data

Abstract Melting and calving of glaciers and ice caps in Antarctica and Greenland could potentially contribute significantly to global sea level rise. Updates to existing outlines that provide critical glacier baseline information in both regions could help in the analysis of particular changes in glacier parameters such as area and volume from time-series inventories. Here we synthesize previously established techniques and apply new multi-source datasets to update glacier outlines in selected test areas of Antarctica and Greenland, as well as to reduce uncertainties and errors during the mapping process. The workflow includes mapping glacier boundaries, subdividing glaciers by watersheds and assigning glacier attributes. Complicated glacier scenarios and updating challenges in polar regions are discussed and demonstrated by representative case studies. For the first time in Antarctica, we analyze the effect of terminus types on mapped glacier areas, and in Greenland we compare the differences with glacier mapping results using Landsat OLI and ETM+. With new data sources, the methods described in this study might help to create glacier outlines on a larger scale in Antarctica and Greenland. Although data sources can be substituted, the enormous amount of manual labor required to update glacier inventories remains a significant challenge.

Lithological mapping of Nidar ophiolite complex, Ladakh using high-resolution data

Ophiolites represents a distinctive variety of igneous rock that comprises fragments of oceanic crust with the underlying mantle portion. Their mineral assemblages and distribution relation, both spatial and temporal are in close association with the complex mantle dynamics of the Earth. Nidar ophiolite complex located towards SE of Ladakh is one of such ophiolite assemblages exposed well in the Indus Suture Zone of Himalayas. It represents parts of Neo-Tethys ocean that existed during Mesozoic-Cenozoic period between continental plates of India and Eurasia. The earlier studies for Indian ophiolites are mostly field and analytical based with a limited work carried out using remote sensing techniques and that adds scope to apply this approach for further exploration of ophiolites. Advanced tools and techniques especially, hyperspectral remote sensing and machine learning based approach can be effective operational tools for exploration of ophiolites that provide assessable terrains for characterization of Earth’s mantle, serve analogs for planetary crustal rocks and also have economic significance. This study has utilized high-resolution multispectral (Landsat OLI and ASTER) and hyperspectral data from various missions including the recent PRISMA mission which has provided the first hyperspectral datasets for lithological mapping of Nidar ophiolite section. The multiple high-resolution datasets were used for identification and mapping of the various lithounits of the Nidar ophiolites together with the underlying and overlying associated rocks based on band ratioing, derived spectral compositional indices and spectral characterization. The differentiated lithounits of the Nidar ophiolites are interpreted based on spectral response of lithological units in different bands. In addition, classification based on machine learning techniques have been applied to the PRISMA data to generate a detailed classification map of the study area where the various lithological units of the ophiolites and associated rock exposures belonging to various geological formations are clearly discernible. The results have been validated from geological field visit to the study area and spectral analysis of the field samples.