Landsat-8 OLI Imagery Research Articles

Application of remote sensing method for geological interpretation of Sokoto Plain, Nigeria

Landsat-8 OLI imagery of Sokoto, Nigeria, was processed to emphasize the geology features and mineral potential of the area. Band ratios (4/2,5/6,6/7) were assigned to RGB. Band ratio 4/2 highlights ferric ion minerals, 5/6 emphasizes ferrous minerals, and 6/7 distinguishes iron oxide minerals from carbonate minerals. In a second technique, band ratio 6/7 was replaced with 7/5 in order to accentuate clay minerals with high reflectance within band 7. The last technique evaluated in this study used spectral information from minimum noise fraction image to map surface geology. Supervised classification training sites were selected using five classes (clay, ironstone, alteration zone, water and vegetation). The band ratio classification using maximum likelihood classification was fairly accurate and matched the geologic map of the area, also showing an alteration zone that coincided with the migmatite-quartz/mica schist contact. The classified image was finally passed through a filtering effect for generalization of the data. This filtering effect was helpful in discriminating the pixels of ironstone and those of the alteration zone on the classified map. This study shows the distribution of classified earth-surface materials in Sokoto plain with the aid of supervised classification of Landsat-8 multispectral bands interpreted to reflect in-situ features.Keywords: Landsat-8 imagery, Sokoto, mineral potential, supervised classification

Preliminary comparative assessment of various spectral indices for built-up land derived from Landsat-8 OLI and Sentinel-2A MSI imageries

ABSTRACT Urbanization in China has been rapid over the past three decades causing substantial replacement of the natural landscape by built-up land. In this paper, we present a comparison of Sentinel-2A MSI (S2A) and Landsat-8 OLI (L8) data in the retrieval of five built-up indices, namely Urban Index (UI), Normalized Difference Built-up Index (NDBI), Index-based Built-up Index (IBI) and two visible based indices, i.e. VgNIR-BI and VrNIR-BI. All the built-up indices maps water-masked were classified into built-up and non-built-up land using Otsu’s method. Simultaneously, the support vector machine (SVM) algorithm was employed to classify the two imageries into three respective classes. The accuracy assessment results show that all built-up indices had higher Overall Accuracy for S2A (up to 98.14% for VrNIR-BI) and L8 (up to 98.42% for VrNIR-BI) imageries compared to SVM. The percentage differences demonstrate that L8 estimates higher built-up area compared to S2A between 1.48% and 8.45% via the built-up indices and 13.40% compared to the SVM. Cross-checking with the Statistical Yearbook, S2A is superior to L8 in built-up land mapping capability, especially utilizing built-up indices. The difference caused by spatial resolution and spectral response functions should be taken into consideration in synergistic scientific application.

Improving flood monitoring in rural areas using remote sensing

Abstract Precise information on the extent of inundated land is required for flood monitoring, relief, and protective measures. In this paper, two spectral indices, Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI), were used to identify inundated areas during heavy rainfall events in the Tarwin catchment, Victoria, Australia, using Landsat-8 OLI imagery. By integrating the assessed condition of levees, this research also explains the inefficiency of the flood control measures of this region of Australia. NDWI and MNDWI indices performed well, but water features were enhanced better in the NDWI-derived image, with an accuracy of 96.04% and Kappa coefficient of 0.83.

A NOVEL METHOD FOR WATER AND WATER CANAL EXTRACTION FROM LANDSAT-8 OLI IMAGERY

Abstract. Constituents of hydrologic network, River and water canals play a key role in Agriculture for cultivation, Industrial activities and urban planning. Remote sensing images can be effectively used for water canal extraction, which significantly improves the accuracy and reduces the cost involved in mapping using conventional means. Using remote sensing data, the water Index (WI), Normalized Difference Water Index (NDWI) and Modified NDWI (MNDWI) are used in extracting the water bodies. These techniques are aimed at water body detection and need to be complemented with additional information for the extraction of complete water canal networks. The proposed index MNDWI-2 is able to find the water bodies and water canals as well from the Landsat-8 OLI imagery and is based on the SWIR2 band. In this paper, we use Level-1 precision terrain corrected OLI imagery at 30 meter spatial resolution. The proposed MNDWI-2 index is derived using SWIR2 (B7) band and Green (B3) band. The usage of SWIR2 band over SWIR1 results in very low reflectance values for water features, detection of shallow water and delineation of water features with rest of the features in the image. The computed MNDWI-2 index values are threshold by making the values greater than zero as 1 and less than zero as zero. The binarised values of 1 represent the water bodies and 0 represent the non-water body. This normalized index detects the water bodies and canals as well as vegetation which appears in the form of noise. The vegetation from the MNDWI-2 image is removed by using the NDVI index, which is calculated using the Top of Atmosphere (TOA) corrected images. The paper presents the results of water canal extraction in comparison with the major available indexes. The proposed index can be used for water and water canal extraction from L8 OLI imagery, and can be extended for other high resolution sensors.

Automated Geological lineaments mapping for groundwater exploration in the basement complex terrain of Akoko-Edo area, Edo-State Nigeria using remote sensing techniques

This study aims to map geological structures (lineaments) which are indications of sustainable groundwater supply in a hard rock terrain of Akoko-Edo local government area of Edo State, Nigeria. Landsat-OLI imagery, topographical map and geological map of Nigeria were used for this study. The methodology involves subjecting the imagery to preprocessing algorithms, linear/edge enhancement, directional filtering, contrast stretching using ENVI 4.7 software. The lineaments were extracted automatically using PCI Geomatica software while ArcGIS and Rockworks software were used to plot the lineament density map and lineament trend respectively. The result shows the total number and length of lineaments to be 197 and 278.85 km respectively. Shorter lineaments constitute about 40% of the total number of lineaments. The lineaments density varies from 0 to 2.40 km/sq.km, and areas with 1.32–2.40 km/sq.km and 0.82 km/sq.km reflect high and moderate degree of rock fracturing which makes these areas suitable target for groundwater exploitation as they possess more lineaments. The lineament trend results showed two prominent trends E–W, N–S and ENE–WSW. Other minor trends are also observed in the study area. These trends validate with results of earlier local studies and with directions of prominent geological structures and features of the study area. The discovery and delineation of the geological structures (lineaments) will serve as direct guides for decision makers to accurately site productive boreholes that will solve the problem of water scarcity in the study area, thereby promoting good developmental policy.

Analysis of Landsat-8 OLI Imagery for Estimating Exposed Bedrock Fractions in Typical Karst Regions of Southwest China Using a Karst Bare-Rock Index

Karst rocky desertification (KRD) has become the primary ecoenvironmental problem in the karst regions of southwest China. The rapid and efficient acquisition of exposed bedrock fractions (EBF) is crucial for the monitoring and assessment of KRD degree and distribution within the highly heterogeneous landscapes. Remote-sensing indices provide a useful method for the quick mapping of the EBF at large scales. The currently available rock indices, however, are faced with insensitivity to bedrock change characteristics, which greatly limits their performances and suitability. To address this problem, we proposed a novel karst bare-rock index (KBRI) that applies shortwave-infrared (SWIR) and near-infrared (NIR) bands from Landsat-8 OLI imagery to maximally distinguish between exposed bedrock and other land cover types in southwest China. A linear regression model was thus established between KBRI and the EBF derived from in situ measurements. The model developed here was then validated with an independent experiment and applied over a large geographic area to produce regional maps of EBF in southwest China. Experimental results showed good performance on root mean square error (5.59%), mean absolute error (4.63%), root mean absolute percentage error (13.59%), and coefficient of determination (0.72), respectively. The advantages of the proposed method are reflected in its simplicity and minimal requirements for auxiliary data while still achieving comparatively better accuracy than existing related indices. Thus, the KBRI has the great potential for the application in other regions around the world with the similar geological backgrounds, thereby helping to address the similar or other related environmental issues. Results of this study provide baseline data for the KRD assessment and karst-ecosystem management in southwest China.

Estimation of carbon emission from peatland fires using Landsat-8 OLI imagery in Siak District, Riau Province

The study was conducted in three land cover conditions (secondary peat forest, shrub land, and palm plantation) that were burned in the Siak District, Riau Province, Indonesia year 2015. Measurement and calculation carbon emission from soil and vegetation of peatland should be done accurately to be implemented on climate change mitigation or greenhouse gases mitigation. The objective of the study was to estimate the carbon emission caused peatland fires in the Siak District, Riau Province, Indonesia year 2015. Estimated carbon emissions were performed using visual method and digital method. The visual method was a method that uses on-screen digitization assisted by hotspot data, the presence of smoke, and fire suppression data. The digital method was a method that uses the Normalized Burn Ratio (NBR) index. The estimated carbon emissions were calculated using the equation that was developed from IPCC 2006 in Verified Carbon Standard 2015. The results showed that the estimation of carbon emissions from fires from above the peat soil surface were higher than the carbon emissions from the peat soil. Carbon emissions above the peat soil surface of 1376.51 ton C/ha were obtained by visual method while 3984.33 ton C/ha were obtained by digital method. Peatland carbon emissions of 6.6 x 10-4 ton C/ha were obtained by visual method, whereas 2.84 x 10-3 ton C/ha was obtained by digital method. Visual method and digital method using remote sensing must be combined and developed in order to carbon emission values will be more accurate.

Comparasion of NDBI and NDVI as Indicators of Surface Urban Heat Island Effect in Landsat 8 Imagery: A Case Study of Iasi

Abstract This study compares the normalized difference built-up index (NDBI) and normalized difference vegetation index (NDVI) as indicators of surface urban heat island effects in Landsat-8 OLI imagery by investigating the relationships between the land surface temperature (LST), NDBI and NDVI. The urban heat island (UHI) represents the phenomenon of higher atmospheric and surface temperatures occurring in urban area or metropolitan area than in the surrounding rural areas due to urbanization. With the development of remote sensing technology, it has become an important approach to urban heat island research. Landsat data were used to estimate the LST, NDBI and NDVI from four seasons for Iasi municipality area. This paper indicates than there is a strong linear relationship between LST and NDBI, whereas the relationship between LST and NDVI varies by season. This paper suggests, NDBI is an accurate indicator of surface UHI effects and can be used as a complementary metric to the traditionally applied NDVI.

Ensemble-based canonical correlation forest (CCF) for land use and land cover classification using sentinel-2 and Landsat OLI imagery

ABSTRACTClassifying spectrally similar crop types in fragmented landscapes is a difficult task due to the low spectral and spatial resolution of satellite imagery. The objective of this study is twofold: (I) to evaluate the performance of a recent ensemble methodology, namely canonical correlation forest (CCF), and (ii) to investigate the potential of recently launched Sentinel-2 (S-2) image for crop classification. The algorithm is based on building a forest-type ensemble model using multiple canonical correlation trees. Its performance was compared to widely-used random forest (RF) and rotation forest (RotFor) for the classification of 4-band and 10-band S-2 and Landsat-8 (L-8) OLI datasets. In addition to overall accuracy estimations, results of the methods were assessed using recently introduced map-level and category-level disagreement measures. Results showed that the CCF and RotFor algorithms produced statistically similar results for the S-2 datasets but statistically different results for the OLI image whilst outperforming the RF algorithm for all cases. The CCF algorithm was found less sensitive to the ensemble size (i.e. number of trees) compared to RF algorithm. Also, for the S-2 data inclusion of six spectral bands at 20 m resolution resulted in a significant increase in classification accuracy (up to 6%).

GEOBIA-based identification of alluvial fans and bajadas through geomorphometry, image analysis and fuzzy ontology

ABSTRACTAlluvial fan and bajada mapping is important for practical and economic importance to society. In this study, advanced geomorphometric, image processing and knowledge representation methods were investigated, enhanced and developed, towards the simultaneous identification of alluvial fans and bajadas. The developed method was based on the landform-pattern element approach. Data used included the 10 m spatial resolution National Elevation Dataset, provided by USGS and a pan-sharpened 15 m Landsat OLi imagery. The study area was located in the Death Valley, Nevada, USA. Digital Terrain Model (DTM) processing included noise-removal filtering and depression treatment. A two-phase GEographic Object-Based Image Analysis approach involving multi-scale segmentation and fuzzy ontology-based reasoning was designed. On the first phase, topographic forms were identified, by examining their morphometry and spatial relationships. On the second phase, the alluvial fans and bajadas were identified based on drainage pattern texture and topographic features such as landform shape. Due to a systematic omission on the fan toe outline, each fan and bajada spectral signature was taken into account to determine the omitted areas through region growing on the multispectral imagery. Accuracy assessment indicated satisfactory results, since a 90.3% completeness and 88.8% quality were achieved for both alluvial fans and bajadas.

Fuel type mapping using object-based image analysis of DMC and Landsat-8 OLI imagery

Efficient forest fire management requires precise and up-to-date knowledge regarding the composition and spatial distribution of forest fuels at various spatial and temporal scales. Fuel-type maps are essential for effective fire prevention strategies planning, as well as the alleviation of the environmental impacts of potential wildfire events. The aim of this study was to assess and compare the potential of Disaster Monitoring Constellation and Landsat-8 OLI satellite images (Operational Land Imager), combined with Object-Based Image Analysis (GEOBIA), in operational mapping of the Mediterranean fuel types at a regional scale. The results showcase that although the images of both sensors can be used with GEOBIA analysis for the generation of accurate fuel-type maps, only the OLI images can be considered as applicable for regional mapping of the Mediterranean fuel types on an operational basis.

Geospatial Analysis of Land Loss, Land Cover Change, and Landuse Patterns of Kutubdia Island, Bangladesh

This study utilizes geospatial tools of remote sensing, geographical information systems (GIS), and global positioning system (GPS) to examine the land loss, land cover (LC) change, landuse of Kutubdia Island, Bangladesh. Multi-spectral Scanner (MSS), Thematic Mapper (TM), and Landsat8 OLI imageries were used for land cover change. For assessing the landuse patterns of 2012, spatial video data were collected by using contour GPS camera. Using remote sensing analysis three different land cover classes (water, trees and forest, and agriculture) were identified and land cover changes were detected from 1972 to 2013. The results show from 1972 to 2013, an estimated 9 km2 of land has been lost and significant changes have taken place from 1972 to 2013. Only an estimated .35 km2 area of accretion has taken place during the study period. Using GIS eight different landuse patterns were identified based on spatial video data.

Mapping orangutan habitat and agricultural areas using Landsat OLI imagery augmented with unmanned aircraft system aerial photography

ABSTRACTConservation of the Sumatran orangutans’ (Pongo abelii) habitat is threatened by change in land use/land cover (LULCC), due to the logging of its native primary forest habitat, and the primary forest conversion to oil palm, rubber tree, and coffee plantations. Frequent LULCC monitoring is vital to rapid conservation interventions. Due to the costs of high-resolution satellite imagery, researchers are forced to rely on cost-free sources (e.g. Landsat), those, however, provide images at a moderate-to-low resolution (e.g. 15–250 m), permitting identification only general LULC classes, and limit the detection of small-scale deforestation or degradation. Here, we combine Landsat imagery with very high-resolution imagery obtained from an unmanned aircraft system (UAS). ​The UAS imagery was used as ‘drone truthing’ data to train image classification algorithms. Our results show that UAS data can successfully be used to help discriminate similar land-cover/use classes (oil palm plantation vs. reforestation vs. logged forest) with consistently high identification of over 75% on the generated thematic map, where the oil palm detection rate was as high as 89%. Because UAS is employed increasingly in conservation proWjects, this approach can be used in a large variety of them to improve land-cover classification or aid-specific mapping needs.

Wheat Monitoring by Using Kernel Based Possibilistic c-Means Classifier: Bi-sensor Temporal Multi-spectral Data

The existence of mixed pixels in the satellite images has always been an area of concern. Adding to the challenge is an occurrence of non-linearity between the classes, which is generally overlooked. The study makes an attempt to solve the two frequently occurring problems by kernel based fuzzy approach. This research work deals with Possibilistic c-Means (PCM) classifier with local, global, spectral angle and hyper tangent kernels for wheat crop (Triticum aestivum) identification in Haridwar, Uttarakhand, India. The multi-temporal vegetation index data of Formosat-2 have been used which covers the whole phenology of wheat crop. The additional sensor Landsat-8 OLI imagery has been filled the crucial gap of Formosat-2 temporal datasets. Nine types of proposed kernels based PCM classifier have been applied on three temporal datasets (four, five and six date combinations) to classify two classes early sown and late sown wheat crop. These test results have been concluded that at optimized weighted constant KMOD and polynomial kernel was found effective to separate wheat crop. The five and six date combination were sufficient to discriminate early sown and late sown wheat crop.

Soil erosion and its correlation with vegetation cover: An assesment using multispectral imagery and pixel-based geographic information system in Gesing Sub-Watershed, Central Java, Indonesia

Soil erosion in caused by five factors: rainfall erosivity, soil erodibility, slope and slope length, crop management, and land conservation practices. In theory, vegetation as one of the affecting factors has inversed correlation with soil erosion. This research is aimed to: (1) model RUSLE using pixel-based GIS, and (2) prove whether or not vegetation really has the said correlation with the soil erosion that occurs in Gesing Watershed. The method used in this research is divided into two: the use of RUSLE to estimate the soil erosion rate; and the use of fractional vegetation cover (FVC) formula to estimate the vegetation density in the area. Both methods used Landsat-8 OLI imagery, which is used to extract the RUSLE parameters as well as to derive the vegetation density through NDVI, and pixel-based GIS. The mapping of soil erosion rate distribution done in this research demonstrated that pixel-based modeling is able to represent a much more detailed and logical distribution of a phenomenon. The distribution of soil erosion rate in Gesing Watershed showed that the erosion rate in this area is relatively minor. About 1425.99 hectares and 1587.57 hectares of the total area have erosion rate of 0 – 15 tons/ha/yr (very mild) and 15 – 60 tons/ha/yr (mild) respectively.

Mapping Distinct Forest Types Improves Overall Forest Identification Based on Multi-Spectral Landsat Imagery for Myanmar’s Tanintharyi Region

We investigated the use of multi-spectral Landsat OLI imagery for delineating mangrove, lowland evergreen, upland evergreen and mixed deciduous forest types in Myanmar’s Tanintharyi Region and estimated the extent of degraded forest for each unique forest type. We mapped a total of 16 natural and human land use classes using both a Random Forest algorithm and a multivariate Gaussian model while considering scenarios with all natural forest classes grouped into a single intact or degraded category. Overall, classification accuracy increased for the multivariate Gaussian model with the partitioning of intact and degraded forest into separate forest cover classes but slightly decreased based on the Random Forest classifier. Natural forest cover was estimated to be 80.7% of total area in Tanintharyi. The most prevalent forest types are upland evergreen forest (42.3% of area) and lowland evergreen forest (21.6%). However, while just 27.1% of upland evergreen forest was classified as degraded (on the basis of canopy cover <80%), 66.0% of mangrove forest and 47.5% of the region’s biologically-rich lowland evergreen forest were classified as degraded. This information on the current status of Tanintharyi’s unique forest ecosystems and patterns of human land use is critical to effective conservation strategies and land-use planning.

Landsat-8 imagery to estimate clarity in near-shore coastal waters: Feasibility study - Chabahar Bay, Iran

This study examined the advantages of incorporating the new band of Landsat-8 OLI imagery (band 1: Coastal/Aerosol, 435–451nm) to a model for estimation of Secchi disk depth (SDD) values (as an indicator for transparency) in near-shore coastal waters using multispectral bands. In doing so, Chabahar Bay in the southern part of Iran (north of Gulf of Oman) was selected as the study area. Two approximately four-hour in-situ observations (including 48 and 56 field measured SDD values for each date respectively) were performed in the study area using Secchi disk; this was designed to start about two hours before and end about two hours after the time of satellite overpasses. Thereafter, a model was formed for estimation of SDD values based on the terms including all possible linear and mutual ratio values of Coastal/Aerosol (B1), Blue (B2), Green (B3), and Red bands (B4). In the first step, the correlation between reflectance/ratio reflectance values of these bands and Ln(SDD) values were calculated to indicate higher correlated bands/band ratios with the first field measured SDD values. Consequently, 17 combinations of highest correlated bands/band ratios were selected to estimate SDD values. In this regard, 32 points among the 48 field observations were selected to determine unknown coefficients of models using a multiple linear regression, and the rest 16 points were designated for accuracy assessment the results. Eventually, the measured SDD values in second field observations were utilized for validating the results. Final results demonstrated that combination of linear terms including B1, B2 and B3 bands and band ratio terms including ratio reflectance values of B4/B3, B3/B1, and B2/B1 has led to obtain the highest accuracy (R2=0.866 and RMSE=0.919, SVM feature weight=4.294). This was in agreement with the results obtained from the second observations. Finally, by applying the entire 104 field observed SDD values, the model in form of SDD=0.077exp(1.209RB1−1.739RB2+0.412RB3−5.198RB3/RB1+10.408RB2/RB1) can be used to determine SDD values in near-shore coastal waters of Chabahar Bay using atmospherically corrected reflectance values of Landsat-8 OLI bands.

An effective modified water extraction method for Landsat-8 OLI imagery of mountainous plateau regions

Water body extraction from remote sensing imagery is an efficient way to investigate and monitor water resources. In the study area of this research, a mountainous plateau near Kashgar, China, sparse vegetation and seasonal rivers affect water body extraction. In order to extract water bodies, a modified water body extraction method is proposed in this paper and tested using Landsat-8 OLI imagery. Following this method, binary images are first generated using a classification, a Tasseled Cap transform, and a normalized difference water index, respectively, and then combined to yield a mask. Next, water bodies are delineated by masking the Landsat-8 OLI imagery and then refined by eliminating false areas using a supervised classification. It is demonstrated from the resulting water body maps that terrain related shadows in imagery were effectively eliminated and river tributaries and artificial ditches were precisely delineated, with accuracy up to 94%. Compared with several current water body extraction methods, the modified method yielded water body maps with better visualization and slightly improved accuracy.

Mapping Spatial Distribution of Larch Plantations from Multi-Seasonal Landsat-8 OLI Imagery and Multi-Scale Textures Using Random Forests

The knowledge about spatial distribution of plantation forests is critical for forest management, monitoring programs and functional assessment. This study demonstrates the potential of multi-seasonal (spring, summer, autumn and winter) Landsat-8 Operational Land Imager imageries with random forests (RF) modeling to map larch plantations (LP) in a typical plantation forest landscape in North China. The spectral bands and two types of textures were applied for creating 675 input variables of RF. An accuracy of 92.7% for LP, with a Kappa coefficient of 0.834, was attained using the RF model. A RF-based importance assessment reveals that the spectral bands and bivariate textural features calculated by pseudo-cross variogram (PC) strongly promoted forest class-separability, whereas the univariate textural features influenced weakly. A feature selection strategy eliminated 93% of variables, and then a subset of the 47 most essential variables was generated. In this subset, PC texture derived from summer and winter appeared the most frequently, suggesting that this variability in growing peak season and non-growing season can effectively enhance forest class-separability. A RF classifier applied to the subset led to 91.9% accuracy for LP, with a Kappa coefficient of 0.829. This study provides an insight into approaches for discriminating plantation forests with phenological behaviors.

Study of Optional Bands Combination of Landsat Imagery for Extracting Information of Ice Shelves

For the purpose of extracting the information of ice shelves, the statistical analysis of TM, ETM+ and Landsat8 OLI imagery of the typical area at the southwest corner of Wilkins Ice Shelf is conducted. Basic statistics of single bands were extracted and the correlation coefficients, joint entropy as well as the Optimum Index Factor among multiple bands were calculated. The results of experiments as well as the characteristics of spectrum characteristics of the surface features were taken into consideration comprehensively and the optional bands combinations were investigated. Results show that for TM and ETM + imagery, the combination of band 2, 3 and 4 is the optional bands combination. For L8 OLI imagery, the combination of band 3, 4 and 5 is the optional bands combination. In these two kinds of combinations, the images contain abundant information and moderate brightness. The features are more obvious to distinguish.