Landsat Data Continuity Mission Research Articles

Correlation analysis of Land Surface Temperature (LST) measurement using DJI Mavic Enterprise Dual Thermal and Landsat 8 Satellite Imagery (case study: Surabaya City)

Landsat 8 Satellite Imagery (Landsat Data Continuity Mission, LDCM) is a satellite product made by Orbital Science Corporation, which launched with The Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) instruments as the latest features. One of the Thermal Infrared Sensor (TIRS) instruments is called Band 10, that provide temperature information on the earth’s surface. As many research conduct the temperature comparison between satellite imagery analysis and land cover temperature has been come with positive correlation for both of the variable. As to prove the temperature relationship, it is necessary to validate the actual temperature values on the earth’s surface by conduct the temperature survey in the area using the temperature measurement tools. One of the tools is DJI Mavic Enterprise Dual Thermal camera as the camera that capable to take samples data of particular objects categories that included urban areas, waters, vegetation, open land, settlements, and industrial factories. Using the satellite imagery’s temperature data and the land cover temperature data survey, comparing and accuration assessment are needed to see how close the value of both variable. The data processing carried out that both of the data have a positive correlation as the relationship, which have a Pearson correlation value of 0.892 and sig. (2-tailed) at the number 0.000000068. This correlation value showed that the relationship between both data is acceptable as the both data can represent each other to conduct any research. However, as the satellite imagery contains 29,85% of cloud cover, the temperature obtained lower in the Landsat 8 satellite image rather than the actual temperature on the earth’s surface.

Discriminative Feature Learning Constrained Unsupervised Network for Cloud Detection in Remote Sensing Imagery

Cloud detection is a significant preprocessing step for increasing the exploitability of remote sensing imagery that faces various levels of difficulty due to the complexity of underlying surfaces, insufficient training data, and redundant information in high-dimensional data. To solve these problems, we propose an unsupervised network for cloud detection (UNCD) on multispectral (MS) and hyperspectral (HS) remote sensing images. The UNCD method enforces discriminative feature learning to obtain the residual error between the original input and the background in deep latent space, which is based on the observation that clouds are sparse and modeled as sparse outliers in remote sensing imagery. The UNCD enforces discriminative feature learning to obtain the residual error between the original input and the background in deep latent space, which is based on the observation that clouds are sparse and modeled as sparse outliers in remote sensing imagery. First, a compact representation of the original imagery is obtained by a latent adversarial learning constrained encoder. Meanwhile, the majority class with sufficient samples (i.e., background pixels) is more accurately reconstructed than the clouds with limited samples by the decoder. An image discriminator is used to prevent the generalization of out-of-class features caused by latent adversarial learning. To further highlight the background information in the deep latent space, a multivariate Gaussian distribution is introduced. In particular, the residual error with clouds highlighted and background samples suppressed is applied in the cloud detection in deep latent space. To evaluate the performance of the proposed UNCD method, experiments were conducted on both MS and HS datasets that were captured by various sensors over various scenes, and the results demonstrate its state-of-the-art performance. The sensors that captured the datasets include Landsat 8, GaoFen-1 (GF-1), and GaoFen-5 (GF-5). Landsat 8 was launched at Vandenberg Air Force Base in California on 11 February 2013, in a mission that was initially known as the Landsat Data Continuity Mission (LDCM). China launched the GF-1 satellite. The GF-5 satellite captures hyperspectral observations in the Chinese Key Projects of High-Resolution Earth Observation System. The overall accuracy (OA) values for Images I and II from the Landsat 8 dataset were 0.9526 and 0.9536, respectively, and the OA values for Images III and IV from the GF-1 wide field of view (WFV) dataset were 0.9957 and 0.9934, respectively. Hence, the proposed method outperformed the other considered methods.

Landsat 8 Observation of the Internal Solitary Waves in the Lombok Strait

Landsat 8, Landsat Data Continuity Mission (LDCM) satellite, was launched on 11 February 2013 with Operation Land Imager (OLI) sensors. Tis sensor has better radiometric performance than the previous mission, which is quantized in the 12-bit dynamic range due to an increase in the signal-to-noise (SNR) ratio. In this analysis, the spatio-temporal distribution of the propagation of the internal solitary wave (ISW) in the Lombok Strait was extracted from the Landsat 8 images described for the first time. Tere were 14 ISW events studied for period 2014 - 2015 using Landsat 8. Te manifestations of ISW recorded on Landsat 8 images were then extracted using digitization method to investigate and measure several parameters and ISW distribution in the Lombok Strait. Te estimation results of the average ISW phase velocity in this study are 2.05 ms-1 with the direction of propagation heading north at an average angle of 19.08°. Tis study has shown that Landsat 8 can be used to monitor and analyze several internal wave parameters in the ocean.

Retrieval of Land Surface Temperature of Lahore Through Landsat-8 TIRS Data

Land surface temperature (LST) is an important parameter in global climate change and urban thermalenvironmental studies. The significance of land surface temperature is being acknowledged gradually and interest isincreasing in developing methodologies for the retrieval of LST from Satellite Remote Sensing (SRS) data. ThermalInfrared Sensor (TIRS) of Landsat-8 is the newest TIR sensor for the Landsat Data Continuity Mission (LDCM),offering two adjacent thermal infrared bands (10, 11), having significant beneficiary for the land surface temperatureinversion. The spectral radiance can be estimated through TIR bands 10 and 11 of Landsat-8 OLI_TIRS satellite image.In the present study, the radiative transfer equation-based method has been employed in estimating LST of Lahore andthe analysis demonstrated that estimated LST has the highest accuracy from the radiative transfer method through band10. Land Surface Emissivity (LSE) was derived with the aid of the NDVI’s threshold technique. The present studyresults show that as the built-up area increases and vegetation cover decreases in urban surface, they are linked toincrease in urban land surface temperature and conversely larger vegetation cover associated with lower urbantemperature. The output exposed that LST was high in built-up and barren land, whereas it was low in the area wherethere were more vegetation cover and water.

Retrieval of Land Surface Temperature of Lahore Through Landsat-8 TIRS Data

Land surface temperature (LST) is an important parameter in global climate change and urban thermalenvironmental studies. The significance of land surface temperature is being acknowledged gradually and interest isincreasing in developing methodologies for the retrieval of LST from Satellite Remote Sensing (SRS) data. ThermalInfrared Sensor (TIRS) of Landsat-8 is the newest TIR sensor for the Landsat Data Continuity Mission (LDCM),offering two adjacent thermal infrared bands (10, 11), having significant beneficiary for the land surface temperatureinversion. The spectral radiance can be estimated through TIR bands 10 and 11 of Landsat-8 OLI_TIRS satellite image.In the present study, the radiative transfer equation-based method has been employed in estimating LST of Lahore andthe analysis demonstrated that estimated LST has the highest accuracy from the radiative transfer method through band10. Land Surface Emissivity (LSE) was derived with the aid of the NDVI’s threshold technique. The present studyresults show that as the built-up area increases and vegetation cover decreases in urban surface, they are linked toincrease in urban land surface temperature and conversely larger vegetation cover associated with lower urbantemperature. The output exposed that LST was high in built-up and barren land, whereas it was low in the area wherethere were more vegetation cover and water.

Evaluation of land use/land cover changes due to gold mining activities from 1987 to 2017 using landsat imagery, East Cameroon

Gold mining activities began around 1934 at the artisanal stage and became semi-mechanized around 2004 in eastern Cameroon, with the emergence of exploration and mining companies in the region. This paper aims at monitoring Land Use and Land Cover (LULC) changes between 1987 and 2017 among Betare-Oya, Ngoura and Batouri Districts which are witnessing extensive gold mining activities, assessing the dynamics between LULC types and understanding the anthropogenic impact of gold mining activities during this period. A series of Landsat images acquired in 1987, 2000, and 2016/2017 were used to examine LULC change trajectories at per-pixel scale with the post-classification change detection techniques based on the matrix of changes. A supervised classification by the maximum likelihood algorithm composed of five classes—Bare land, Settlements, Water bodies, Vegetation and Mine activities, was designed for this study, in order to classify Landsat images into thematic maps. This research revealed spatio-temporal change patterns, various composition and rates among the three study areas. Also, it shows the strong appearance and emergence of mining activities between 2000 and 2017 are coupled with increase in settlement surfaces and major changes in environment in the study areas. The LULC change analysis over time for the study areas have provide the current change trends. This study stresses the usefulness of Landsat TM/ETM + and Landsat Data Continuity Mission (LDCM) data and highlights the data processing methods for long-term monitoring of artisanal mining activities impacts on the environment.

Effect of the Temporal Gradient of Vegetation Indices on Early-Season Wheat Classification Using the Random Forest Classifier

Early-season area estimation of the winter wheat crop as a strategic product is important for decision-makers. Multi-temporal images are the best tool to measure early-season winter wheat crops, but there are issues with classification. Classification of multi-temporal images is affected by factors such as training sample size, temporal resolution, vegetation index (VI) type, temporal gradient of spectral bands and VIs, classifiers, and values missed under cloudy conditions. This study addresses the effect of the temporal resolution and VIs, along with the spectral and VIs gradient on the random forest (RF) classifier when missing data occurs in multi-temporal images. To investigate the appropriate temporal resolution for image acquisition, a study area is selected on an overlapping area between two Landsat Data Continuity Mission (LDCM) paths. In the proposed method, the missing data from cloudy pixels are retrieved using the average of the k-nearest cloudless pixels in the feature space. Next, multi-temporal image analysis is performed by considering different scenarios provided by decision-makers for the desired crop types, which should be extracted early in the season in the study areas. The classification results obtained by RF improved by 2.2% when the temporally-missing data were retrieved using the proposed method. Moreover, the experimental results demonstrated that when the temporal resolution of Landsat-8 is increased to one week, the classification task can be conducted earlier with slightly better overall accuracy (OA) and kappa values. The effect of incorporating VIs along with the temporal gradients of spectral bands and VIs into the RF classifier improved the OA by 3.1% and the kappa value by 6.6%, on average. The results show that if only three optimum images from seasonal changes in crops are available, the temporal gradient of the VIs and spectral bands becomes the primary tool available for discriminating wheat from barley. The results also showed that if wheat and barley are considered as single class versus other classes, with the use of images associated with 162 and 163 paths, both crops can be classified in March (at the beginning of the growth stage) with an overall accuracy of 97.1% and kappa coefficient of 93.5%.

A new approach for land surface emissivity estimation using LDCM data in semi-arid areas: exploitation of the ASTER spectral library data set

ABSTRACTIn this research, a new approach called non-vegetated based emissivity estimation method (NV-method) for estimating land surface emissivity (LSE) on Landsat-8 (known as Landsat Data Continuity Mission, LDCM) data has been proposed for semi-arid areas. At first, a simulation of channel emissivities and reflective bands of basic classes in vegetation and non-vegetated areas is accomplished based on convolving Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) spectral Library with LDCM spectral response functions. Then, four main classes in non-vegetated areas are defined to determine separate emissivity estimate model as a function of reflective bands from basic spectra associated with the main class. The LSEs in mixed and vegetation areas are adopted from the simplified normalized difference vegetation index (NDVI)-based emissivity threshold method (N-methodTHM), namely SN-methodTHM and improved N-methodTHM (IN-methodTHM) methods, respectively. The NV-method is empirically tested using LDCM data and the obtained LSEs were compared with two scenes of LSE product of the ASTER. The root mean square error (RMSE) values of computed LSEs by NV-method are 0.46% and 0.81%, for band 10 and 11, respectively, in the first examined scene. While, for the second scene, the RMSE are 0.36% and 0.56% for band 10 and 11, respectively. Moreover, the NV-method were compared with N-methodTHM, SN-methodTHM, and IN-methodTHM in non-vegetated areas. Generally, the obtained results of LSEs by NV-method are better than that of results from the compared methods in non-vegetated areas in terms of statistical measures. Except in rocky class, for which N-methodTHM provides better results, the NV-method achieved superior results in soil texture and man-made classes, which are dominating classes in the study area.

Landsat 8: The plans, the reality, and the legacy

Landsat 8, originally known as the Landsat Data Continuity Mission (LDCM), is a National Aeronautics and Space Administration (NASA)-U.S. Geological Survey (USGS) partnership that continues the legacy of continuous moderate resolution observations started in 1972. The conception of LDCM to the reality of Landsat 8 followed an arduous path extending over nearly 13 years, but the successful launch on February 11, 2013 ensures the continuity of the unparalleled Landsat record. The USGS took over mission operations on May 30, 2013 and renamed LCDM to Landsat 8. Access to Landsat 8 data was opened to users worldwide. Three years following launch we evaluate the science and applications impact of Landsat 8. With a mission objective to enable the detection and characterization of global land changes at a scale where differentiation between natural and human-induced causes of change is possible, LDCM promised incremental technical improvements in capabilities needed for Landsat scientific and applications investigations. Results show that with Landsat 8, we are acquiring more data than ever before, the radiometric and geometric quality of data are generally technically superior to data acquired by past Landsat missions, and the new measurements, e.g., the coastal aerosol and cirrus bands, are opening new opportunities. Collectively, these improvements are sparking the growth of science and applications opportunities. Equally important, with Landsat 7 still operational, we have returned to global imaging on an 8-day cycle, a capability that ended when Landsat 5 ceased operational Earth imaging in November 2011. As a result, the Landsat program is on secure footings and planning is underway to extend the record for another 20 or more years.

Fusion Methods for Land Surface Emissivity and Temperature Retrieval of the Landsat Data Continuity Mission Data

In this paper, a new approach based on two fusion schemes is proposed to overcome the uncertainties in land surface emissivity (LSE) estimation and, consequently, land surface temperature (LST) retrieval. The fusion schemes are called image-based weighted methods and knowledge-based weighted methods, in which each of them includes two LSE estimation methods. The effectiveness of the two proposed fusion schemes is empirically tested over two scenes of Landsat-8 (known as Landsat Data Continuity Mission) data sets, and the obtained LSEs by individual and proposed methods were compared to the LSE product of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) by image-based and class-based cross-comparison. In both scenes, the adjusted normalized emissivity method (ANEM) and NDVI-based emissivity method (NBEM) provide appropriate results among five individual methods. In contrast, weighted to median (WMED) achieves superior results among the proposed fusion methods for both scenes. In addition, the root-mean-square error (rmse) values of LSE obtained by ANEM and WMED are 1.48% and 0.87%, which lead to 1.25 K and 0.73 K errors in the LST retrieval by the single-channel algorithm in the first scene, respectively. For the second scene, the error values of NBEM and WMED are 1.10% and 0.52%, which lead to 0.93 K and 0.44 K errors in the LST, respectively. Moreover, the error ranges and rmse of cross-comparison for the obtained LSE in the proposed methods were remarkably decreased. Also, in this research, for LST cross-comparison, an alternative scaling method based on LST products of the Moderate Resolution Imaging Spectroradiometer was proposed. The LST validation results demonstrated that the proposed methods provide better estimates in terms of three accuracy measures in both examined data sets.

LANDSAT 8 MULTISPECTRAL AND PANSHARPENED IMAGERY PROCESSING ON THE STUDY OF CIVIL ENGINEERING ISSUES

Abstract. Scientific and professional interests of civil engineering mainly include structures, hydraulics, geotechnical engineering, environment, and transportation issues. Topics included in the context of the above may concern urban environment issues, urban planning, hydrological modelling, study of hazards and road construction. Land cover information contributes significantly on the study of the above subjects. Land cover information can be acquired effectively by visual image interpretation of satellite imagery or after applying enhancement routines and also by imagery classification. The Landsat Data Continuity Mission (LDCM – Landsat 8) is the latest satellite in Landsat series, launched in February 2013. Landsat 8 medium spatial resolution multispectral imagery presents particular interest in extracting land cover, because of the fine spectral resolution, the radiometric quantization of 12bits, the capability of merging the high resolution panchromatic band of 15 meters with multispectral imagery of 30 meters as well as the policy of free data. In this paper, Landsat 8 multispectral and panchromatic imageries are being used, concerning surroundings of a lake in north-western Greece. Land cover information is extracted, using suitable digital image processing software. The rich spectral context of the multispectral image is combined with the high spatial resolution of the panchromatic image, applying image fusion – pansharpening, facilitating in this way visual image interpretation to delineate land cover. Further processing concerns supervised image classification. The classification of pansharpened image preceded multispectral image classification. Corresponding comparative considerations are also presented.

LANDSAT 8 MULTISPECTRAL AND PANSHARPENED IMAGERY PROCESSING ON THE STUDY OF CIVIL ENGINEERING ISSUES

Scientific and professional interests of civil engineering mainly include structures, hydraulics, geotechnical engineering, environment, and transportation issues. Topics included in the context of the above may concern urban environment issues, urban planning, hydrological modelling, study of hazards and road construction. Land cover information contributes significantly on the study of the above subjects. Land cover information can be acquired effectively by visual image interpretation of satellite imagery or after applying enhancement routines and also by imagery classification. The Landsat Data Continuity Mission (LDCM – Landsat 8) is the latest satellite in Landsat series, launched in February 2013. Landsat 8 medium spatial resolution multispectral imagery presents particular interest in extracting land cover, because of the fine spectral resolution, the radiometric quantization of 12bits, the capability of merging the high resolution panchromatic band of 15 meters with multispectral imagery of 30 meters as well as the policy of free data. In this paper, Landsat 8 multispectral and panchromatic imageries are being used, concerning surroundings of a lake in north-western Greece. Land cover information is extracted, using suitable digital image processing software. The rich spectral context of the multispectral image is combined with the high spatial resolution of the panchromatic image, applying image fusion – pansharpening, facilitating in this way visual image interpretation to delineate land cover. Further processing concerns supervised image classification. The classification of pansharpened image preceded multispectral image classification. Corresponding comparative considerations are also presented.

Satellite-based land use mapping: comparative analysis of Landsat-8, Advanced Land Imager, and big data Hyperion imagery

Until recently, Landsat technology has suffered from low signal-to-noise ratio (SNR) and comparatively poor radiometric resolution, which resulted in limited application for inland water and land use/cover mapping. The new generation of Landsat, the Landsat Data Continuity Mission carrying the Operational Land Imager (OLI), has improved SNR and high radiometric resolution. This study evaluated the utility of orthoimagery from OLI in comparison with the Advanced Land Imager (ALI) and hyperspectral Hyperion (after preprocessing) with respect to spectral profiling of classes, land use/cover classification, classification accuracy assessment, classifier selection, study area selection, and other applications. For each data source, the support vector machine (SVM) model outperformed the spectral angle mapper (SAM) classifier in terms of class discrimination accuracy (i.e., water, built-up area, mixed forest, shrub, and bare soil). Using the SVM classifier, Hyperion hyperspectral orthoimagery achieved higher overall accuracy than OLI and ALI. However, OLI outperformed both hyperspectral Hyperion and multispectral ALI using the SAM classifier, and with the SVM classifier outperformed ALI in terms of overall accuracy and individual classes. The results show that the new generation of Landsat achieved higher accuracies in mapping compared with the previous Landsat multispectral satellite series.

Modeling Soil Organic Carbon at Regional Scale by Combining Multi-Spectral Images with Laboratory Spectra.

There is a great challenge in combining soil proximal spectra and remote sensing spectra to improve the accuracy of soil organic carbon (SOC) models. This is primarily because mixing of spectral data from different sources and technologies to improve soil models is still in its infancy. The first objective of this study was to integrate information of SOC derived from visible near-infrared reflectance (Vis-NIR) spectra in the laboratory with remote sensing (RS) images to improve predictions of topsoil SOC in the Skjern river catchment, Denmark. The second objective was to improve SOC prediction results by separately modeling uplands and wetlands. A total of 328 topsoil samples were collected and analyzed for SOC. Satellite Pour l’Observation de la Terre (SPOT5), Landsat Data Continuity Mission (Landsat 8) images, laboratory Vis-NIR and other ancillary environmental data including terrain parameters and soil maps were compiled to predict topsoil SOC using Cubist regression and Bayesian kriging. The results showed that the model developed from RS data, ancillary environmental data and laboratory spectral data yielded a lower root mean square error (RMSE) (2.8%) and higher R2 (0.59) than the model developed from only RS data and ancillary environmental data (RMSE: 3.6%, R2: 0.46). Plant-available water (PAW) was the most important predictor for all the models because of its close relationship with soil organic matter content. Moreover, vegetation indices, such as the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), were very important predictors in SOC spatial models. Furthermore, the ‘upland model’ was able to more accurately predict SOC compared with the ‘upland & wetland model’. However, the separately calibrated ‘upland and wetland model’ did not improve the prediction accuracy for wetland sites, since it was not possible to adequately discriminate the vegetation in the RS summer images. We conclude that laboratory Vis-NIR spectroscopy adds critical information that significantly improves the prediction accuracy of SOC compared to using RS data alone. We recommend the incorporation of laboratory spectra with RS data and other environmental data to improve soil spatial modeling and digital soil mapping (DSM).

Post-wildfire assessment of vegetation regeneration in Bastrop, Texas, using Landsat imagery

Monitoring post-wildfire vegetation trends is key to the management and sustainability of forest ecosystems. The 2011 Bastrop Complex Wildfire provided a unique opportunity to study the effects of wildfire on vegetation in an infrequently studied locale for wildfire research. Using Landsat 5 Thematic Mapper and Landsat 8 Landsat Data Continuity Mission imagery, we investigated the relationship between burn severity and vegetation regeneration while exploring changes in spatial vegetation patterns resulting from the wildfire. Mean normalized difference vegetation index (NDVI) levels increased substantially in the three years after the fire, indicating that vegetation has recovered rapidly in the Bastrop forest, though the overall course of recovery is still uncertain at these early stages. At an aggregate level, the substantial vegetation regrowth in the early post-wildfire years was broadly consistent with previous research, but NDVI trajectories behaved divergently when evaluated by burn severity level. Heavy and moderate burn severity areas experienced a net decline in NDVI during the third year, while more lightly burned areas demonstrated the greatest degree of overall recovery. Continued research will be required to evaluate the long-term effects of the wildfire and determine whether these trends are temporary or lasting.

Climate Change for Agriculture, Forest Cover and 3d Urban Models

Abstract. This research demonstrates the important role of the remote sensing in finding out the different parameters behind the agricultural crop change, forest cover and urban 3D models. Standalone software is developed to view and analysis the different factors effecting the change in crop productions. Open-source libraries from the Open Source Geospatial Foundation have been used for the development of the shape-file viewer. Software can be used to get the attribute information, scale, zoom in/out and pan the shapefiles. Environmental changes due to pollution and population that are increasing the urbanisation and decreasing the forest cover on the earth. Satellite imagery such as Landsat 5(1984) to Landsat TRIS/8 (2014), Landsat Data Continuity Mission (LDCM) and NDVI are used to analyse the different parameters that are effecting the agricultural crop production change and forest change. It is advisable for the development of good quality of NDVI and forest cover maps to use data collected from the same processing methods for the complete region. Management practices have been developed from the analysed data for the betterment of the crop and saving the forest cover

Land Surface Temperature Retrieval Methods From Landsat-8 Thermal Infrared Sensor Data

The importance of land surface temperature (LST) retrieved from high to medium spatial resolution remote sensing data for many environmental studies, particularly the applications related to water resources management over agricultural sites, was a key factor for the final decision of including a thermal infrared (TIR) instrument on board the Landsat Data Continuity Mission or Landsat-8. This new TIR sensor (TIRS) includes two TIR bands in the atmospheric window between 10 and 12 μm, thus allowing the application of split-window (SW) algorithms in addition to single-channel (SC) algorithms or direct inversions of the radiative transfer equation used in previous sensors on board the Landsat platforms, with only one TIR band. In this letter, we propose SC and SW algorithms to be applied to Landsat-8 TIRS data for LST retrieval. Algorithms were tested with simulated data obtained from forward simulations using atmospheric profile databases and emissivity spectra extracted from spectral libraries. Results show mean errors typically below 1.5 K for both SC and SW algorithms, with slightly better results for the SW algorithm than for the SC algorithm with increasing atmospheric water vapor contents.

Assessing the quality of temporal high-resolution classifications with low-resolution satellite image time series

Upcoming temporally and spatially high-resolution satellites such as Venus, SENTINEL-2, and Landsat Data Continuity Mission (LDCM) will provide very valuable data for land-cover and vegetation monitoring. However, owing to cloud cover and even to some rapid changes, a higher temporal resolution may be needed for some applications. In this work, we propose using the higher temporal resolution of satellites with mid to low spatial resolutions such as the upcoming PROBA-V. The aim of this work is to study how images provided by satellites with a lower spatial resolution but with a higher temporal one can be used to obtain information about the temporal classification derived from satellite image time series (SITS) provided by high-resolution satellites such as Venus, SENTINEL-2, or LDCM.We show that the low-spatial-resolution SITS can be used to inform about the stability and relevance of high-spatial-resolution classification. Experiments include a wide variety of resolution ratios and study the use of each ratio from global to class-specific invalidation of the high-resolution classification map (computed from the high-spatial-resolution SITS).This work contributes to the assessment of the usefulness of the joint use of PROBA-V data and Venus/SENTINEL-2/LDCM images for land-cover monitoring.

Early Analysis of Landsat-8 Thermal Infrared Sensor Imagery of Volcanic Activity

The Landsat-8 satellite of the Landsat Data Continuity Mission was launched by the National Aeronautics and Space Administration (NASA) in April 2013. Just weeks after it entered active service, its sensors observed activity at Paluweh Volcano, Indonesia. Given that the image acquired was in the daytime, its shortwave infrared observations were contaminated with reflected solar radiation; however, those of the satellite’s Thermal Infrared Sensor (TIRS) show thermal emission from the volcano’s summit and flanks. These emissions detected in sensor’s band 10 (10.60–11.19 µm) have here been quantified in terms of radiant power, to confirm reports of the actual volcanic processes operating at the time of image acquisition, and to form an initial assessment of the TIRS in its volcanic observation capabilities. Data from band 11 have been neglected as its data have been shown to be unreliable at the time of writing. At the instant of image acquisition, the thermal emission of the volcano was found to be 345 MW. This value is shown to be on the same order of magnitude as similarly timed NASA Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer thermal observations. Given its unique characteristics, the TIRS shows much potential for providing useful, detailed and accurate volcanic observations in the future.

Increased potential to monitor water quality in the near-shore environment with Landsat’s next-generation satellite

The Operational Land Imager (OLI) is a new sensor developed by the joint USGS-NASA Landsat Data Continuity Mission that should become a valuable tool for studying inland and coastal waters. With upgrades to spectral coverage, 12-bit quantization, and increased signal-to-noise due to its new push-broom design, OLI exhibits the potential to become the first Landsat sensor with the radiometric resolution necessary for retrieval of the three primary constituents in Case 2 waters: chlorophyll, suspended materials, and colored-dissolved organic matter. Considering its traditional 30-m spatial resolution, this next-generation Landsat satellite will be especially useful for monitoring the near-shore environment. This work presents the relevant sensor parameters and results of experiments designed to determine if OLI will have the radiometric sensitivity necessary for water-based research. An OLI sensor model is developed, and its ability to retrieve water constituents from simulated data is compared with that of existing sensors. Results indicate that when atmospheric effects are properly accounted for, OLI introduces retrieval errors of less than 11% of the expected observable range for all three constituents. Furthermore, by spatially averaging a few OLI pixels, noise can be reduced to the Medium Resolution Imaging Spectrometer levels, making this next Landsat instrument an exciting option for monitoring inland and coastal waters.