Landsat-8 Operational Land Imager Data Research Articles

Space based Earth observation approach for exploring hydrocarbon prospects in an intracratonic rift basin – A study in north Cambay basin, Gujarat

In the present study, we carried out a conjugate analysis of Landsat-8 Operational Land Imager (OLI), Sentinel-2B Multi-Spectral Instrument (MSI), Advanced Land Observation Satellite Phased Array L-band SAR (ALOS PALSAR), and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data over a study area of northern Cambay Basin to delineate prospective areas for hydrocarbon exploration. In this regard, we utilized feature-oriented principal component (PC) images of Landsat OLI data, PC images of Sentinel-2B visible and near-infrared (VNIR)-short-wave infrared (SWIR) bands, thresholded Cartosat-1 Digital Elevation Model (DEM), and polarization image composite of ALOS PALSAR data to delineate geomorphic and lineament features in the study area. Most of the geomorphic features, such as scroll plain, gullies, elevated pediment, circular drainage anomalies, etc., indicate localized structural uplift. Geomorphic anomalies and lineaments, essential for identifying structural controls of near-surface hydrocarbon accumulation, were identified using enhanced satellite data. Geophysical anomalies derived from gravity datasets supplemented the presence and down-depth continuity of geological structures. Spectral profiles of calcrete-bearing soil samples were used to identify calcrete-rich zones in the optical remote sensing image data as a proxy owing to field evidence of close association of calcrete-rich soils with known subsurface hydrocarbon accumulations. Sub-pixel mapping algorithm Constrained Energy Minimization (CEM) applied to SWIR bands of ASTER data was used to delineate surface proxies of near-surface hydrocarbon seepage. Isopach maps of shallowest reservoir formations (Mid-/ Late Eocene) were used as indicators of favorable near-surface hydrocarbon accumulation.Earth observation-based geomorphic, lineament, and spectral anomalies were integrated with supplementary datasets, such as reservoir isopach and absorbed gas anomalies, as controls using the weighted summation method for quantitative deduction of the hydrocarbon potential map of the study area. The weights for input variables or parameters (geomorphic, spectral, geochemical, etc.) were inferred based on spatial associations of these anomalous units/variables with the existing oil & gas well locations. The identified potential map is in broad synergy with the field observations on localized uplift, geo-botanical anomalies, and calcrete-rich zones within the extent of high & moderate hydrocarbon potential areas.

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.

Enhanced Estimate of Chromophoric Dissolved Organic Matter Using Machine Learning Algorithms from Landsat-8 OLI Data in the Pearl River Estuary

Chromophoric Dissolved Organic Matter (CDOM) plays a critical role in the carbon and biogeochemical cycles within aquatic ecosystems. Satellite imagery can be employed to determine aquatic CDOM concentrations, highlighting the need for effective and precise algorithms for this task. In this study, a cruise survey dataset containing CDOM absorption coefficients and water-leaving radiances in the Pearl River estuary (PRE) was utilized to develop machine learning algorithms for CDOM retrieval from Landsat-8 Operational Land Imager (OLI) observations. Based on OLI wavelength bands, five bands and six band-ratios were chosen as input parameters for the machine learning models. Six machine learning models were trained to develop CDOM algorithms, including Support Vector Regression (SVR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Multi-Layer Perceptron (MLP), and Convolutional Neural Network (CNN). The results indicated that, among the six machine learning models, the XGBoost algorithm performed best, with the highest R2 value of 0.9 and the lowest CDOM root mean square error (RMSE) of 0.37 m−1, outperforming empirical algorithms. The XGBoost algorithm identified B4/B1 as the most critical input parameter, contributing 71%, followed by B3/B2 with a 16% contribution, where B1, B2, B3, and B4 are the wavelength bands of the OLI. These two band-ratios accounted for most of the contributions, suggesting their significant role in CDOM retrieval from Landsat OLI images. By employing the developed XGBoost algorithm, CDOM spatial patterns at six instances were derived from Landsat-8 OLI image reflectance, illustrating CDOM variations in the PRE influenced by various factors. Further analysis revealed that, in the PRE, tides and winds are the primary driving forces behind the spatial and temporal variability of CDOM. At present, the exploration of employing machine learning algorithms to infer CDOM concentrations in this region remains relatively limited; therefore, with a higher R2 value, the machine learning model we established unveils fresh and novel results.

Comparison between Parametric and Non-Parametric Supervised Land Cover Classifications of Sentinel-2 MSI and Landsat-8 OLI Data

The present research aims at verifying whether there are significant differences between Land Use/Land Cover (LULC) classifications performed using Landsat 8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI) data—abbreviated as L8 and S2. To comprehend the degree of accuracy between these classifications, both L8 and S2 scenes covering the study area located in the Basilicata region (Italy) and acquired within a couple of days in August 2017 were considered. Both images were geometrically and atmospherically corrected and then resampled at 30 m. To identify the ground truth for training and validation, a LULC map and a forest map realized by the Basilicata region were used as references. Then, each point was verified through photo-interpretation using the orthophoto AGEA 2017 (spatial resolution of 20 cm) as a ground truth image and, only in doubtful cases, a direct GPS field survey. MLC and SVM supervised classifications were applied to both types of images and an error matrix was computed using the same reference points (ground truth) to evaluate the classification accuracy of different LULC classes. The contribution of S2′s red-edge bands in improving classifications was also verified. Definitively, ML classifications show better performance than SVM, and Landsat data provide higher accuracy than Sentinel-2.

Conjunctive Use of Landsat-8 OLI and MODIS Data for Delineation of Burned Areas

For regional-level monitoring of burned areas, Moderate Resolution Imaging Spectroradiometer (MODIS ) MCD64A1 and MCD45A1 products have been operationally used. However, because of their coarser spatial resolution, such products do not allow for detection of small patches (<50 ha) of burned areas, which are very important for modeling gas emissions. In order to bridge this gap, we undertook a study to evaluate the synergy of MCD64A1 and Landsat-8 Operational Land Imager (OLI ) data for delineating burned areas in part of the mountainous terrain of the Himalayas, northern India. The approach involves generating a differenced normalized burn ratio image from Landsat-8 OLI data before and after fire events, refining the burned areas thus delineated using the MCD64A1 burned-area product and field data, and estimating accuracy. An overall mapping accuracy of 90.0% was achieved using Landsat-8 OLI data. Inclusion of the MODIS MCD64A1 burned-area product resulted in marginal improvement in overall accuracy (to 94.0%).

The contribution of landsat 8 oli multispectral data to the lithological mapping of the Ait Ourir basin (Western High Atlas, Morocco)

The present work consists in identifying the geological facies and defining the dominant orientation of the lineaments in order to improve the knowledge on the local geodynamics in the Northern Sub-Atlasic zone of the Marrakesh High Atlas, the Ait Ourir basin region, The latter is characterized by a post-Hercynian Mesozoic cover folded into broad synclines with flat and subhorizontal bottom, separated by ejective and sharp anticlines. For this purpose, multispectral data from Landsat 8 operational land imager (OLI) have been applied in a platform of ArcGIS software and through the treatments offered by this software, namely, the principal component analysis (PCA), the band ratios (BR) and the transformation of the minimum noise fraction (MNF), a better lithological discrimination and description was brought to the set of geological formations that form the studied basins. In parallel, the results obtained by the method of classification by Support Vector Machine (SVM), which is based on the joint use of geomorphic features, textures and multispectral data from Landsat 8 satellite, showed an excellent correlation. With the simplified geological map of the northern slope of the High Atlas of Marrakesh (1/10000) and field data from the study area. The result shows an overall accuracy of classification of Landsat8 OLI data by SVM which is 96.64% and the Kappa coefficient which is 0.98 and a positive correlation is shown between lineament structures and dominant orientations of the extracted lineaments. The highest densities obtained are N-S, NE-SW and NNE-SSW oriented with the predominance of the first direction.

Comparative analysis of AVIRIS-NG and Landsat-8 OLI data for lithological mapping in parts of Sittampundi layered complex, Tamil Nadu, India

The present study addresses a comparative analysis of the potentials of Airborne Visible and infrared imaging spectrometer -next generation (AVIRIS-NG) data, with respect to Landsat-8 Operational Land Imager (OLI) data for mapping of sporadic exposures of mafic cumulates within the anorthosite intrusion and delineation of major lithological units of Sittampundi layered complex. AVIRIS-NG and Landsat-8 OLI data were initially compared using scene-based relative signal-to-noise ratio (SNR) analysis. Subsequently, spectral anomaly maps were generated to map different lithological rock units using the constrained energy minimization (CEM) approach. Spectral maps derived using CEM were further filtered by low-pass filtering to control the false-positive result. Spectral angle mapper (SAM) classification technique was applied to analyze the suitability of AVIRIS-NG and Landsat-8 OLI data for lithological mapping. Confusion matrix analysis was carried out to assess the SAM classified maps. The first vertical derivative (FVD) map of magnetic anomaly and field evidence/geological exposures were used to validate the results. SNR analysis indicates the superior signal strength of AVIRIS-NG data than Landsat-8 OLI data. Moreover, combined analysis based on CEM and SAM of remote sensing data and FVD of magnetic anomaly reveals that the false-positive area indicating metagabbro/mafic cumulate is lower in AVIRIS-NG data than Landsat-8 OLI data. This is because of the relatively poor SNR, spectral and spatial resolutions of Landsat-8 OLI data; however, it can be good enough for mapping of metagabbro/mafic cumulate when AVIRIS-NG data is unavailable.

International geological significance of the potential Al-Medina volcanic UNESCO Global Geopark Project in Saudi Arabia revealed from multi-satellite remote sensing data

UNESCO Global Geoparks should be of international geological significance in terms of their scientific quality, rarity, aesthetic appeal and tourism value. At least nine Cenozoic volcanic fields are developed in the western Saudi Arabia, with a total area of 180,000 km2. In this paper, the geological and geomorphological features of these volcanic fields induced by the expansion of the Red Sea are interpreted and displayed using multi-source satellite images, such as Landsat-8 OLI (Operational Land Imager) and Gaofen-2 data. Our results show that the Al-Medina volcanic field (AMVF) has great prospects as the aspiring volcanic Geopark in Saudi Arabia. This study indicates that: (1) AMVF has international geoscientific significance and rare natural attribute because the volcanic fields are induced by the rising mantle plume and rare examples of white volcanoes comprised mainly from felsic rock; (2) AMVF has abundant volcanic landscapes, including completely preserved cones and craters and multi-phase lava flows from different eruption stages, which have great aesthetic appreciation and tourism values to attract the general public; (3) AMVF is close to the second holy city of Islam-Medina, which has convenient transportation and is suitable to develop tourism to promote the development of the local economy; (4) AMVF had erupted repeatedly (the latest eruption was in 1256 AD), establishment of the UNESCO Global Geopark Project can not only prevent potential geological hazard risks to the people living in Medina city from future volcanic eruption events, but also provide better protection and conservation to geoheritage sites being damaged by human activities, for example setting protected boundaries of AMVF to avoid the destruction of volcanic landscape integrity caused by urban expansion.

Mt. Etna Paroxysms of February–April 2021 Monitored and Quantified through a Multi-Platform Satellite Observing System

On 16 February 2021, an eruptive paroxysm took place at Mt. Etna (Sicily, Italy), after continuous Strombolian activity recorded at summit craters, which intensified in December 2020. This was the first of 17 short, but violent, eruptive events occurring during February–April 2021, mostly at a time interval of about 2–3 days between each other. The paroxysms produced lava fountains (up to 1000 m high), huge tephra columns (up to 10–11 km above sea level), lava and pyroclastic flows, expanding 2–4 km towards East and South. The last event, which was characterised by about 3 days of almost continuous eruptive activity (30 March–1 April), generated the most lasting lava fountain (8–9 h). During some paroxysms, volcanic ash led to the temporary closure of the Vincenzo Bellini Catania International Airport. Heavy ash falls then affected the areas surrounding the volcano, in some cases reaching zones located hundreds of kilometres away from the eruptive vent. In this study, we investigate the Mt. Etna paroxysms mentioned above through a multi-platform satellite system. Results retrieved from Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS), starting from outputs of the Robust Satellite Techniques for Volcanoes (RSTVOLC), indicate that the 17th paroxysm (31 March–1 April) was the most intense in terms of radiative power, with values estimated around 14 GW. Moreover, by the analysis of SEVIRI data, we found that the 5th and 17th paroxysms were the most energetic. The Multispectral Instrument (MSI) and the Operational Land Imager (OLI), providing shortwave infrared (SWIR) data at 20/30 m spatial resolution, enabled an accurate localisation of active vents and the mapping of the areas inundated by lava flows. In addition, according to the Normalized Hotspot Indices (NHI) tool, the 2nd (17–18 February) and 7th (28 February) paroxysm generated the largest thermal anomaly at Mt. Etna after April 2013, when Landsat-8 OLI data became available. Despite the impact of clouds/plumes, pixel saturation, and other factors (e.g., satellite viewing geometry) on thermal anomaly identification, the used multi-sensor approach allowed us to retrieve quantitative information about the 17 paroxysms occurring at Mt. Etna. This approach could support scientists in better interpreting changes in thermal activity, which could lead to future and more dangerous eruptions.

Impact of the Spatial Resolution of Satellite’s Data for Mapping Land use Land Cover in Karmala Tehsil, Solapur, India

Land use and land cover (LULC) classification mapping is important for evaluating, monitoring, protecting and planning for land resources. A key factor in extracting desired information from satellite images is choosing the right the spatial resolution. The scale of a pixel on the ground is known as spatial resolution. A pixel is the smallest ‘dot' that makes up an optical satellite image which defines the level of detail as in image. In this paper estimation of the areal extent of water, built up, barren land, vegetation land and fallow land classes with its classification accuracy were reviewed particularly for January 2013 and November 2016 in Karmala tehsil of Solapur district, India. LULC is implied by different spatial resolution images of Advanced Wide Field Sensor (AWiFS), Linear Imaging Self Scanning Sensor (LISS-III), Landsat-8 Operational Land Imager (OLI) and Sentinel-2A imageries in QGIS environment while the classification was carried out using the maximum likelihood algorithm (MLA). The classified maps obtained from AWiFS and LISS-III sensors, as well as Sentinel-2A and Landsat-8 OLI data sets, were compared separately. Spatial analysis depicts that the Kappa coefficient of Sentinal-2A, Landsat-8, LISS III and AWiFS was found 96.96%, 91.64%, 87.30% and 89.36%. Furthermore, overall accuracy of was found to be 99.07%, 94.49%, 89.84% and 94.08% respectively. The accuracy of the classified image with higher spatial resolution (Sentinal-2A) proved more informative than that of lower resolution (AWiFS) sensor. On the response, the finer spatial resolution of Sentinal-2A (10 m) delivered more precise details and enhanced LULC classification accuracy most reliably than the coarser spatial resolution of Landsat-8 (30m), LISS III (23m) and AWiFS (56m) image. A perusal of data revealed that the overall accuracy and Kappa coefficient was found proportionate to spatial resolution of satellite imageries. The higher resolution spatial data also greatly reduces the mixed-pixel problem. The study revealed that the spatial resolution plays an important role and affects classification details and accuracy of LULC level.

Integration of remote-sensing data for mapping lithological and structural features in the Esh El-Mallaha area, west Gulf of Suez, Egypt

Esh El-Mallaha west of the Gulf of Suez is a potential area for hydrocarbon resources. In this article, Landsat-8 Operational Land Imager (OLI), Shuttle Radar Topography Mission (SRTM), and Advanced Land Observing Satellite (ALOS) Phased Array Type L‐band Synthetic Aperture Radar (PALSAR) data were utilized to identify the geological and structural features of this area which represents a part of the North Eastern Desert (NED), Egypt. The use of band ratios (BR) and principal component analysis (PCA) allowed characterizing the different varieties of rock units. These approaches allowed discriminating the various rock units e.g., metavolcanics, granitoids including the two phases of the older granites and younger granites along with the third phase which represented by elongated ridges cut the rocks and clearly allowed tracing the contacts of different lithological units of the Neoproterozoic basement and Phanerozoic sedimentary rocks. Applying the fused Landsat-8 OLI and ALOS/PALSAR data improved the structural elements that difficult to reveal by optical images. Combination of Landsat-8 OLI, ALOS/PALSAR, and SRTM data effectively display the structural and lithological contacts. The lineament extraction and interpretation indicated that the area is clearly shaped by the NW-SE graben and controlled by the Gulf of Suez tectonic trend. Such structures allowed the accumulation of thick sediments in the faulted basin that represents the promise area for oil, gas, and water resources. Lineament analysis revealed that the major tectonic trends controlling the area are NW-SE and NE-SW directions with less effect of N-S and E-W trends. The NW-SE sinistral and NE-SW dextral lateral movements are recognized. Overall results revealed that the integration of optical and radar remotely sensed data is an effective tool in deciphering geological and structural features in the arid regions.

Vis-NIR Spectroscopy and Satellite Landsat-8 OLI Data to Map Soil Nutrients in Arid Conditions: A Case Study of the Northwest Coast of Egypt

The mapping of soil nutrients is a key issue for numerous applications and research fields ranging from global changes to environmental degradation, from sustainable soil management to the precision agriculture concept. The characterization, modeling and mapping of soil properties at diverse spatial and temporal scales are key factors required for different environments. This paper is focused on the use and comparison of soil chemical analyses, Visible near infrared and shortwave infrared VNIR-SWIR spectroscopy, partial least-squares regression (PLSR), Ordinary Kriging (OK), and Landsat-8 operational land imager (OLI) images, to inexpensively analyze and predict the content of different soil nutrients (nitrogen (N), phosphorus (P), and potassium (K)), pH, and soil organic matter (SOM) in arid conditions. To achieve this aim, 100 surface samples of soil were gathered to a depth of 25 cm in the Wadi El-Garawla area (the northwest coast of Egypt) using chemical analyses and reflectance spectroscopy in the wavelength range from 350 to 2500 nm. PLSR was used firstly to model the relationship between the averaged values from the ASD spectroradiometer and the available N, P, and K, pH and SOM contents in soils in order to map the predicted value using Ordinary Kriging (OK) and secondly to retrieve N, P, K, pH, and SOM values from OLI images. Thirty soil samples were selected to verify the validity of the results. The randomly selected samples included the spatial diversity and characteristics of the study area. The prediction of available of N, P, K pH and SOM in soils using VNIR-SWIR spectroscopy showed high performance (where R2 was 0.89, 0.72, 0.91, 0.65, and 0.75, respectively) and quite satisfactory results from Landsat-8 OLI images (correlation R2 values 0.71, 0.68, 0.55, 0.62 and 0.7, respectively). The results showed that about 84% of the soils of Wadi El-Garawla are characterized by low-to-moderate fertility, while about 16% of the area is characterized by high soil fertility.

Assessing Climate Change Impact on Soil Salinity Dynamics between 1987–2017 in Arid Landscape Using Landsat TM, ETM+ and OLI Data

This paper examines the climate change impact on the spatiotemporal soil salinity dynamics during the last 30 years (1987–2017) in the arid landscape. The state of Kuwait, located at the northwest Arabian Peninsula, was selected as a pilot study area. To achieve this, a Landsat- Operational Land Imager (OLI) image acquired thereabouts simultaneously to a field survey was preprocessed and processed to derive a soil salinity map using a previously developed semi-empirical predictive model (SEPM). During the field survey, 100 geo-referenced soil samples were collected representing different soil salinity classes (non-saline, low, moderate, high, very high and extreme salinity). The laboratory analysis of soil samples was accomplished to measure the electrical conductivity (EC-Lab) to validate the selected and used SEPM. The results are statistically analyzed (p ˂ 0.05) to determine whether the differences are significant between the predicted salinity (EC-Predicted) and the measured ground truth (EC-Lab). Subsequently, the Landsat serial time’s datasets acquired over the study area with the Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+) and OLI sensors during the last three decades over the intervals (1987, 1992, 1998, 2000, 2002, 2006, 2009, 2013, 2016 and 2017) were radiometrically calibrated. Likewise, the datasets were atmospherically and spectrally normalized by applying a semi-empirical line approach (SELA) based on the pseudo-invariant targets. Afterwards, a series of soil salinity maps were derived through the application of the SEPM on the images sequence. The trend of salinity changes was statistically tested according to climatic variables (temperatures and precipitations). The results revealed that the EC-Predicted validation display a best fits in comparison to the EC-Lab by indicating a good index of agreement (D = 0.84), an excellent correlation coefficient (R2 = 0.97) and low overall root mean square error (RMSE) (13%). This also demonstrates the validity of SEPM to be applicable to the other images acquired multi-temporally. For cross-calibration among the Landsat serial time’s datasets, the SELA performed significantly with an RMSE ≤ ± 5% between all homologous spectral reflectances bands of the considered sensors. This accuracy is considered suitable and fits well the calibration standards of TM, ETM+ and OLI sensors for multi-temporal studies. Moreover, remarkable changes of soil salinity were observed in response to changes in climate that have warmed by more than 1.1 °C with a drastic decrease in precipitations during the last 30 years over the study area. Thus, salinized soils have expanded continuously in space and time and significantly correlated to precipitation rates (R2 = 0.73 and D = 0.85).

Assessment of Leaf Chlorophyll Content Models for Winter Wheat Using Landsat-8 Multispectral Remote Sensing Data

The leaf chlorophyll content (LCC) is a critical index to characterize crop growth conditions, photosynthetic capacity, and physiological status. Its dynamic change characteristics are of great significance for monitoring crop growth conditions and understanding the process of material and energy exchange between crops and the environment. Extensive research has focused on LCC retrieval with hyperspectral data onboard various sensor platforms. Nevertheless, limited attention has been paid to LCC inversion from multispectral data, such as the data from Landsat-8, and the potentials and capabilities of the data for crop LCC estimation have not been fully explored. The present study made use of Landsat-8 Operational Land Imager (OLI) imagery and the corresponding field experimental data to evaluate their capabilities and potentials for LCC modeling using four different retrieval methods: vegetation indices (VIs), machine learning regression algorithms (MLRAs), lookup-table (LUT)-based inversion, and hybrid regression approaches. The results showed that the modified triangular vegetation index (MTVI2) exhibited the best estimate accuracy for LCC retrieval with a root mean square error (RMSE) of 5.99 μg/cm2 and a relative RMSE (RRMSE) of 10.49%. Several other vegetation indices that were established from red and near-infrared (NIR) bands also exhibited good accuracy. Models established from Gaussian process regression (GPR) achieved the highest accuracy for LCC retrieval (RMSE = 5.50 μg/cm2, RRMSE = 9.62%) compared with other MLRAs. Moreover, red and NIR bands outweighed other bands in terms of GPR modelling. LUT-based inversion methods with the “K(x) = −log (x) + x” cost function that belongs to the “minimum contrast estimates” family showed the best estimation results (RMSE = 8.08 μg/cm2, RRMSE = 14.14%), and the addition of multiple solution regularization strategies effectively improved the inversion accuracy. For hybrid regression methods, the use of active learning (AL) techniques together with GPR for LCC modelling significantly increased the estimation accuracy, and the combination of entropy query by bagging (EQB) AL and GPR had the best accuracy for LCC estimation (RMSE = 12.43 μg/cm2, RRMSE = 21.77%). Overall, our study suggest that Landsat-8 OLI data are suitable for crop LCC retrieval and could provide a basis for LCC estimation with similar multispectral datasets.

Detecting the Burned Area in Volcanic Region by Using Multitemporal Landsat-8 OLI (Case Study: Mt. Sumbing, Central Java)

Biomass burning is one of the natural agents that most change terrestrial ecosystems and has a key ecological role in a huge piece of the Earth’s surface. Biomass burning has numerous socio-economic implications, particularly in developed countries where the developing urbanization of forested regions tends to increase accidents related with extraordinary fire occasions. Wildland fire mapping has been a subject vital for the international community over the recent two decades. Since fire is a major threat to forests and wooded areas in the tropical environment of Kalimantan, methodical regional fire observing is a need. Remote sensing methods are recognized as the efficient source of information for mapping burned areas from regional-national up to global scale. This study investigated the use of multitemporal Landsat-8 OLI (Operational Land Imager) data, to identify the burned area, in the tropical region of Indonesia, during 2019 fire season. A pair of Landsat-8 OLI, collected before and after fires, has been used to delineate the boundaries of sample location of burned area. Then, the difference of reflectance and Normalized Burn Ratio were analyzed. Fire incident causes landcover changes from vegetated land to bareland. This changes can affect the reflectance detected from Landsat-8 OLI. The result showed that biomass burning on volcanic region has caused high decreasing values on band 5 and there were high increasing on band 7. The NBR was better used to extracted burned area rather than NDVI or single spectral band. This method based on multitemporal optical data, creates a valuable tool for identifying and interpreting burned area following a fire event on volcanic region.

Application of GIS and Remote Sensing Technology to Detect Landscape Change in Hollongapar Gibbon Wildlife Sanctuary, India

The present study stated on an evaluation into the use of remote sensing technology and geographic information systems (GIS) integration to detect land use/cover trajectories in Hollongapar gibbon wildlife sanctuary in Assam, India. Remote sensing technology was used to utilize multi-temporal satellite imagery including Landsat TM (Themetic Mapper) and Landsat OLI (Operational Land Imager) data to perform LU/C change detection from the year 1986 to 2018. The results revealed significant and unequal land conversion in the region of study. The paddy fields and tea gardens in and around the 4 km buffer of the wildlife sanctuary had increased sharply during the period 1986-2018. Remote sensing and GIS integration has been found to be effective in tracking and analyzing trends of LU/C trajectories and assessing the effects of land conversion on biodiversity of the study region.

A Novel Feature-Level Fusion Framework Using Optical and SAR Remote Sensing Images for Land Use/Land Cover (LULC) Classification in Cloudy Mountainous Area

Remote sensing data plays an important role in classifying land use/land cover (LULC) information from various sensors having different spectral, spatial and temporal resolutions. The fusion of an optical image and a synthetic aperture radar (SAR) image is significant for the study of LULC change and simulation in cloudy mountain areas. This paper proposes a novel feature-level fusion framework, in which the Landsat operational land imager (OLI) images with different cloud covers, and a fully polarized Advanced Land Observing Satellite-2 (ALOS-2) image are selected to conduct LULC classification experiments. We take the karst mountain in Chongqing as a study area, following which the features of the spectrum, texture, and space of the optical and SAR images are extracted, respectively, supplemented by the normalized difference vegetation index (NDVI), elevation, slope and other relevant information. Furthermore, the fused feature image is subjected to object-oriented multi-scale segmentation, subsequently, an improved support vector machine (SVM) model is used to conduct the experiment. The results showed that the proposed framework has the advantages of multi-source data feature fusion, high classification performance and can be applied in mountain areas. The overall accuracy (OA) was more than 85%, with the Kappa coefficient values of 0.845. In terms of forest, gardenland, water, and artificial surfaces, the precision of fusion image was higher compared to single data source. In addition, ALOS-2 data have a comparative advantage in the extraction of shrubland, water, and artificial surfaces. This work aims to provide a reference for selecting the suitable data and methods for LULC classification in cloudy mountain areas. When in cloudy mountain areas, the fusion features of images should be preferred, during the period of low cloudiness, the Landsat OLI data should be selected, when no optical remote sensing data are available, and the fully polarized ALOS-2 data are an appropriate substitute.

Detecting the burned area in southern Kalimantan by using the sentinel-1 polarimetric SAR and landsat-8 OLI optic

Synthetic Aperture Radar (SAR) imageries data have turned out to be one of the essential sources for forest fire mapping, especially in tropical region since the smoke haze obstruct data acquisition by optical sensor. Despite these limitations, until now, the use of optical sensors still dominates in monitoring forest and land fires in the world. The Sentinel-1 satellites presently offer unreservedly accessible and freely available, world coverage and fast recurrent time (6-12 days), gives Sentinel-1 images the possibility to be broadly utilized for observing the Earth’s surface, including forest and land fire phenomenon. However, the use of sentinel-1 data for monitoring and mapping forest and land fires in the tropics of Indonesia, is still limited and has not been widely implemented. This study investigated the use of Sentinel-1, synergy with optical Landsat-8 OLI (Operational Land Imager) data, to identify the burned area, in the tropical region of Indonesia, during 2019 fire season. A pair of Landsat-8 OLI, collected before and after fires, has been used to delineate the boundaries of sample location of burned area. Then, the difference of reflectance and Normalized Burn Ratio were analyzed. A series of Sentinel-1 images, collected before and during/after fires, has been utilized to produce the backscatter values among images. Fire incident causes landcover changes from vegetated land to bareland. This changes can affect the reflectance detected from Landsat-8 OLI. This changes also influence the backscatter recognized from SAR sensor. Then analysis of SAR backscatter on the location of the burned area detected from Landsat-8 was performed. The synergy between SAR polarimetric and optical reflected data, creates a valuable tool for identifying and interpreting burned area following a fire event.

Mapping forest height using photon-counting LiDAR data and Landsat 8 OLI data: A case study in Virginia and North Carolina, USA

• Mapping forest height by combining photon-counting LiDAR data and Landsat OLI data. • Douglas-Peucker algorithm can solve the large turns in photon-counting LiDAR data. • The filtering of forest heights can improve the accuracy of forest height map. Large-scale, accurate and detailed forest height map is worthwhile and necessary for understanding and assessing global carbon cycle and biodiversity. The Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) mission, employing a photon-counting LiDAR (PCL) system, offers an opportunity to map global forest height with high resolution. This study aimed to map forest height at a spatial resolution of 30 m by combining Multiple Altimeter Beam Experimental Lidar (MABEL) data with Landsat 8 Operational Land Imager (OLI) data. There are four key steps to accomplish this goal. First, a segmentation method based on the Douglas-Peucker algorithm was proposed to solve the problem of large turns or calibration maneuvers in MABEL data. Second, we estimated forest height and selected the forest height samples with high accuracy and reliability by developing three filters including signal-to-noise ratio (SNR) filter, slope filter, and canopy photons density (CPD) filter. Third, forest height models based on both random forest (RF) and stepwise multiple regression algorithms were developed to establish relationships between the selected forest height samples and predicator variables of Landsat-derived spectral indices, topographic variables and geographic coordinates. Finally, a wall-to-wall forest height map was generated by applying the developed forest height models to predicator variables, and the accuracy of forest height map was validated using airborne LiDAR-derived forest heights. An area of 160, 000 km 2 in southeast Virginia and east North Carolina was chosen for testing the methods proposed in this study. The results demonstrated that the Douglas-Peucker algorithm can effectively solve the MABEL data overlapping issues caused by large turns and calibration maneuvers in flight lines. The suitable filters for selecting forest heights are SNR > 6, terrain slope < 25°, and 40 < CPD < 170. These developed filters can substantially increase the accuracies of forest height models. The results also indicated that RF-derived forest height models achieved higher modeling accuracy than stepwise regression-derived forest height models. RF-derived forest height models yielded coefficient of determination (R 2 ) values of 0.59, 0.68 and 0.62 and RMSE values of 4.55 m, 3.41 m and 4.08 m for deciduous forest, evergreen forest and mixed forest, respectively. Compared to LiDAR-derived forest height, the forest height map produced in this study has a R 2 value of 0.54 and a RMSE value of 6.85 m, which demonstrates that combination of MABEL data and Landsat 8 OLI data can be used to generate forest height maps with a spatial resolution of 30 m.

A Multi-Channel Algorithm for Mapping Volcanic Thermal Anomalies by Means of Sentinel-2 MSI and Landsat-8 OLI Data

The Multispectral Instrument (MSI) and the Operational Land Imager (OLI), respectively onboard Sentinel-2A/2B and Landsat 8 satellites, thanks to their features especially in terms of spatial/spectral resolution, represents two important instruments for investigating thermal volcanic activity from space. In this study, we used data from those sensors to test an original multichannel algorithm, which aims at mapping volcanic thermal anomalies at a global scale. The algorithm, named Normalized Hotspot Indices (NHI), combines two normalized indices, analyzing near infrared (NIR) and short wave infrared (SWIR) radiances, to identify hotspot pixels in daylight conditions. Results, achieved studying a number of active volcanoes located in different geographic areas and characterized by a different eruptive behavior, demonstrated the NHI capacity in mapping both subtle and more intense volcanic thermal anomalies despite some limitations (e.g., missed detections because of clouds/volcanic plumes). In addition, the study shows that the performance of NHI might be further increased using some additional spectral/spatial tests, in view of a possible usage of this algorithm within a known multi-temporal scheme of satellite data analysis. The low processing times and the straight forth exportability to data from other sensors make NHI, which is sensitive even to other high temperature sources, suited for mapping hot volcanic targets integrating information provided by current and well-established satellite-based volcanoes monitoring systems.