Landsat-8 Operational Land Imager Satellite Research Articles

Investigating the effectiveness of Landsat-8 OLI and Sentinel-2 MSI satellite data in monitoring the effects of drought on surface water resources in the Western Cape Province, South Africa

Drought frequency and magnitude are increasing, posing significant risks to surface water bodies, particularly in arid and semi-arid regions. Leveraging on moderate resolution satellite data, such as Landsat-8 Operational Land Imager (OLI) and Sentinel-2 MultiSpectral Instrument (MSI), offers unique opportunities for monitoring surface water availability and drought impacts. In this study, we assessed the extent to which Landsat-8 OLI and Sentinel-2 MSI satellite data can be used to characterise and monitor the impacts of drought on water resources in the Western Cape, South Africa. Multispectral indices including Normalised Difference Vegetation Index (NDVI), Vegetation Condition Index (VCI), Normalised Difference Water Index (NDWI), Modified Normalised Difference Water Index (MNDWI), and the Land Surface Water Index (LSWI+5) were computed to determine the most suitable method for surface water detection and drought monitoring. Sentinel-2-derived NDVI emerged as the most suitable index for mapping surface waterbodies, demonstrating an overall accuracy of 77.27%. However, LSWI+5 exhibited limitations, misclassifying built-up and mountainous areas as surface waterbodies. SPI, VCI, and WRSI effectively identified drought periods and correlated well with climate data, revealing low rainfall and high evapotranspiration rates during the 2016–2018 drought period. These findings provide valuable insights into surface water variability and the impacts of drought on water resources. Overall, our study underscores the feasibility of utilizing moderate resolution datasets to assess drought impacts on surface water resources, enabling improved water resource management, drought detection, and preparedness efforts in resource limited environments.

Responses of phytoplankton community structure and association to variability in environmental drivers in a tropical coastal lagoon

Spatial and seasonal heterogeneity in phytoplankton communities are governed by many biotic and abiotic drivers. However, the identification of long-term spatial and temporal trends in abiotic drivers, and their interdependencies with the phytoplankton communities' structure is understudied in tropical brackish coastal lagoons. We examined phytoplankton communities' spatiotemporal dynamics from a 5-year dataset (n = 780) collected from 13 sampling stations in Chilika Lagoon, India, where the salinity gradient defined the spatial patterns in environmental variables. Generalized additive models showed a declining trend in phytoplankton biomass, pH, and dissolved PO4 in the lagoon. Hierarchical modelling of species communities revealed that salinity (44.48 ± 28.19%), water temperature (4.37 ± 5.65%), and season (4.27 ± 0.96%) accounted for maximum variation in the phytoplankton composition. Bacillariophyta (Indicator Value (IV): 0.74) and Dinophyta (IV: 0.72) emerged as top indicators for polyhaline regime whereas, Cyanophyta (IV: 0.81), Euglenophyta (IV: 0.79), and Chlorophyta (IV: 0.75) were strong indicators for oligohaline regime. The responses of Dinophyta and Chrysophyta to environmental drivers were much more complex as random effects accounted for ~70–75% variation in their abundances. Prorocentrum minimum (IV: 0.52), Gonyaulax sp. (IV: 0.52), and Alexandrium sp. (IV: 0.51) were potential indicators of P-limitation. Diploneis weissflogii (IV: 0.43), a marine diatom, emerged as a potential indicator of N-limitation. Hierarchical modelling revealed the positive association between Cyanophyta, Chlorophyta, and Euglenophyta whereas, Dinophyta and Chrysophyta showed a negative association with Cyanophyta, Chlorophyta, and Euglenophyta. Landsat 8-Operational Land Imager satellite models predicted the highest and lowest Cyanophyta abundances in northern and southern sectors, respectively, which were in accordance with the near-coincident field-based measurements from the lagoon. This study highlighted the dynamics of phytoplankton communities and their relationships with environmental drivers by separating the signals of habitat filtering and biotic interactions in a monsoon-regulated tropical coastal lagoon.

Lithological and alteration mineral mapping for alluvial gold exploration in the south east of Birao area, Central African Republic using Landsat-8 Operational Land Imager (OLI) data

Geological field mapping and mineral exploration still remain very laborious in many parts of the inter tropical regions of African plate due to remoteness, low security and high cost. Currently, a number of remote sensing satellite imagery are freely available and can be broadly used for lithological mapping and mineral exploration in inaccessible regions of African plate. The Birao region of the Central African Republic is well known for its alluvial deposits of gold and copper. The Birao region is located in subtropical belt of Africa plate. In this study, Landsat-8 Operational Land Imager (OLI) satellite imagery was used for lithological and alteration mapping in the south-eastern part (inaccessible zone) of Birao region for alluvial gold exploration. Image processing techniques such as False Color Composite (FCC), Band Ratio (BR), Principal component analysis (PCA), minimum noise fraction (MNF) and Spectral Angle Mapper (SAM) were executed to extract spectral information related to lithological units and alteration minerals. Clay and carbonate minerals, iron oxides, ferrous silicates and lithological units, including the Neo-Chadian Formation, the Lower Proterozoic Formations and the Recent Alluvial Formation were identified and discriminated in the study area. The band ratio of 6/7, 4/2 and 6/5 were typically used to map clay, iron oxides and ferrous silicates, respectively. The results of band ratio and PCA techniques were integrated with fuzzy logic modeling that considerably highlighted hydrothermal alteration minerals and high perspective zones for alluvial gold exploration. Additionally, an updated lithological-mineralogical map was generated for south-eastern part of the Birao region by fusing the FCC, MNF and SAM techniques. Comparison with previous lithological map of the study area, virtual verification, fieldwork and laboratory data confirmed good rate of accuracy for the result obtained from the Landsat-8 OLI satellite data. The approach presented in this study is a low-cost technique that highly applicable for lithological mapping and mineral exploration in remote subtropical regions of African plate.

Estimation of aboveground vegetation biomass based on Landsat-8 OLI satellite images in the Guanzhong Basin, China

ABSTRACTThis paper presents a method to estimate the aboveground biomass (AGB) through the selection of different estimation methods based on numerous vegetation types (i.e., broadleaf forest, coniferous forest, shrub and grassland) at a regional scale. The proposed method is based on three models, namely, the stepwise regression, an improved back-propagation neural network (Improved BBPNN) model based on the Gaussian error function, and the support vector machine (SVM) technique, Meanwhile, Landsat 8 Operational Land Imager (OLI)/Thermal Infrared Sensor (TIRS) image data and geo-parameters are employed to select 68 feature variables and optimize 213 data samples. Our results reveal that, the stepwise regression method provides the best AGB estimation performance for broadleaf forests and coniferous forests, while the SVM technique shows the best performance for grasslands and shrubs. Different vegetation types should be selected for additional biomass estimation models that have been proven to enhance the biomass estimation. This study on the AGB not only promotes research on the net primary productivity (NPP), but also plays a key role in global carbon cycle research.

Mapping and monitoring of mangrove along the Odisha coast based on remote sensing and GIS techniques

Digital image processing techniques using multi-temporal satellite imagery are widely used to understand landscape dynamics. In this study, we analysed the spatio-temporal changes of mangrove along the Odisha coast, India using Landsat satellite images of four different time periods, i.e., Landsat-5 Thematic Mapper satellite images of 1990 and 2009, Landsat-7 Enhanced Thematic Mapper Plus satellite images of 2000 and Landsat-8 Operational Land Imager satellite images of 2015. Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI) and Normalized Difference Wetland Index (NDWI) are used to extract and classify the mangroves into three classes such as dense mangrove, sparse mangrove and open mangrove. The study reveals that total mangrove of the study area increased from 18573.49 ha (1990) to 23871.49 ha (2015) during the last three decades. This may be attributed to mangrove plantation, restoration and proper coastal zone management plan. The paper highlights the importance of digital change detection techniques using NDVI, SAVI and NDWI for mangrove mapping and monitoring along the Odisha coast. This study aids Odisha coastal zone management authority to take timely decisions for conservation of mangrove and future mangrove plantation plans with help of satellite image processing in GIS environment.

Monitoring Coastal Chlorophyll-a Concentrations in Coastal Areas Using Machine Learning Models

Harmful algal blooms have negatively affected the aquaculture industry and aquatic ecosystems globally. Remote sensing using satellite sensor systems has been applied on large spatial scales with high temporal resolutions for effective monitoring of harmful algal blooms in coastal waters. However, oceanic color satellites have limitations, such as low spatial resolution of sensor systems and the optical complexity of coastal waters. In this study, bands 1 to 4, obtained from Landsat-8 Operational Land Imager satellite images, were used to evaluate the performance of empirical ocean chlorophyll algorithms using machine learning techniques. Artificial neural network and support vector machine techniques were used to develop an optimal chlorophyll-a model. Four-band, four-band-ratio, and mixed reflectance datasets were tested to select the appropriate input dataset for estimating chlorophyll-a concentration using the two machine learning models. While the ocean chlorophyll algorithm application on Landsat-8 Operational Land Imager showed relatively low performance, the machine learning methods showed improved performance during both the training and validation steps. The artificial neural network and support vector machine demonstrated a similar level of prediction accuracy. Overall, the support vector machine showed slightly superior performance to that of the artificial neural network during the validation step. This study provides practical information about effective monitoring systems for coastal algal blooms.