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.

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A Google Earth Engine application for mapping volcanic thermal anomalies at a global scale by means of Sentinel 2 MSI and Landsat 8 OLI data

10.5194/egusphere-egu2020-4683 ◽

2020 ◽

Author(s):

Nicola Genzano ◽

Francesco Marchese ◽

Alfredo Falconieri ◽

Giuseppe Mazzeo ◽

Nicola Pergola

Keyword(s):

Near Infrared ◽

Global Scale ◽

Google Earth ◽

Landsat 8 ◽

Landsat 8 Oli ◽

Thermal Anomalies ◽

Google Earth Engine ◽

Infrared Radiances ◽

Shortwave Infrared ◽

Sentinel 2

NHI (Normalized Hotspot Indices) is an original multichannel algorithm recently developed for mapping volcanic thermal anomalies in daylight conditions by means of infrared Sentinel 2 MSI and Landsat 8 OLI data. The algorithm, which uses two normalized indices analyzing SWIR (Shortwave Infrared) and NIR (Near Infrared) radiances, was tested with success in different volcanic areas, assessing results by means of independent ground and satellite-based observations.Here we present and describe the NHI-based tool, which exploits the high computation capabilities of Google Earth Engine to perform the rapid mapping of hot volcanic features at a global scale. The tool allows the users to retrieve information also about changes of thermal volcanic activity, giving the opportunity of performing time series analysis of hotspot pixel number and total SWIR radiance. Advantages of using the NHI tool as a complement to current satellite-based volcanoes monitoring systems are then analysed and discussed, such as its future upgrades.

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A Google Earth Engine Tool to Investigate, Map and Monitor Volcanic Thermal Anomalies at Global Scale by Means of Mid-High Spatial Resolution Satellite Data

Remote Sensing ◽

10.3390/rs12193232 ◽

2020 ◽

Vol 12 (19) ◽

pp. 3232

Author(s):

Nicola Genzano ◽

Nicola Pergola ◽

Francesco Marchese

Keyword(s):

Spatial Resolution ◽

Satellite Data ◽

Hot Spot ◽

Global Scale ◽

Google Earth ◽

High Temporal Resolution ◽

Landsat 8 ◽

Thermal Anomalies ◽

Google Earth Engine ◽

Sentinel 2

Several satellite-based systems have been developed over the years to study and monitor thermal volcanic activity. Most of them use high temporal resolution satellite data, provided by sensors like the Moderate Resolution Imaging Spectroradiometer (MODIS) that if on the one hand guarantee a continuous monitoring of active volcanic areas on the other hand are less suited to map thermal anomalies, and to provide accurate information about their features. The Multispectral Instrument (MSI) and the Operational Land Imager (OLI), respectively, onboard the Sentinel-2 and Landsat-8 satellites, providing Short-Wave Infrared (SWIR) data at 20 m (MSI) and 30 m (OLI) spatial resolution, may make an important contribution in this area. In this work, we present the first Google Earth Engine (GEE) App to investigate, map and monitor volcanic thermal anomalies at global scale, integrating Landsat-8 OLI and Sentinel-2 MSI observations. This open tool, which implements the Normalized Hot spot Indices (NHI) algorithm, enables the analysis of more than 1400 active volcanoes, with very low processing times, thanks to the high GEE computational resources. Performance and limitations of the tool, such as its next upgrades, aiming at increasing the user-friendly experience and extending the temporal range of data analyses, are analyzed and discussed.

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Introducing WIW for Detecting the Presence of Water in Wetlands with Landsat and Sentinel Satellites

Remote Sensing ◽

10.3390/rs11192210 ◽

2019 ◽

Vol 11 (19) ◽

pp. 2210 ◽

Cited By ~ 2

Author(s):

Lefebvre ◽

Davranche ◽

Willm ◽

Campagna ◽

Redmond ◽

...

Keyword(s):

Vegetation Cover ◽

Near Infrared ◽

Ground Truth ◽

Short Wave ◽

Water Dynamics ◽

High Temporal Resolution ◽

Landsat 8 ◽

Model Combining ◽

Wide Range ◽

Sentinel 2

Many wetlands are characterized by a vegetation cover of variable height and density over time. Tracking spatio-temporal changes in inundation patterns of these wetlands remains a challenge for remote sensing. Water In Wetlands (WIW) was predicted using a dichotomous partitioning of reflectance values encoded based on ground-truth (n = 4038) and optical-space derived (n = 7016) data covering all land cover types (n = 17) found in the Rhône delta, southern France. The models were developed with spectral data from Sentinel 2, Landsat 7, and Landsat 8 sensors, hence providing a monitoring tool that covers a 35-year period (same sensor for Landsat 5 and 7). A single model combining the near infrared (NIR ≤ 0.1558 to 0.1804, depending on sensors) and short-wave infrared (SWIR2 ≤ 0.0871 to 0.1131) wavelengths was identified by three independent analyses, each one using a different satellite. Overall accuracy of water maps ranged from 89% to 94% for the training samples and from 90% to 94% for the validation samples, encompassing standard water indices that systematically underestimate flooding duration under vegetation cover. Sentinel 2 provided the highest performance with a kappa coefficient of 0.82 for both samples. Such tool will be most useful for monitoring the water dynamics of seasonal wetlands, which are particularly sensitive to climate change while providing multiple services to humankind. Considering the high temporal resolution of Sentinel 2 (every 5 days), cumulative water maps built with the WIW logical rule could further be used for mapping a wide range of wetlands which are either periodically or permanently flooded.

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IDENTIFICATION OF MANGROVE FORESTS USING MULTISPECTRAL SATELLITE IMAGERIES

International Journal of Remote Sensing and Earth Sciences (IJReSES) ◽

10.30536/j.ijreses.2019.v16.a3097 ◽

2019 ◽

Vol 16 (1) ◽

pp. 63

Author(s):

Anang Dwi Purwanto ◽

Wikanti Asriningrum

Keyword(s):

Mangrove Forest ◽

Near Infrared ◽

Image Data ◽

Short Wave ◽

Landsat 8 ◽

Mangrove Forests ◽

Visual Identification ◽

Red Edge ◽

Swir Band ◽

Sentinel 2A

The visual identification of mangrove forests is greatly constrained by combinations of RGB composite. This research aims to determine the best combination of RGB composite for identifying mangrove forest in Segara Anakan, Cilacap using the Optimum Index Factor (OIF) method. The OIF method uses the standard deviation value and correlation coefficient from a combination of three image bands. The image data comprise Landsat 8 imagery acquired on 30 May 2013, Sentinel 2A imagery acquired on 18 March 2018 and images from SPOT 6 acquired on 10 January 2015. The results show that the band composites of 564 (NIR+SWIR+Red) from Landsat 8 and 8a114 (Vegetation Red Edge+SWIR+Red) from Sentinel 2A are the best RGB composites for identifying mangrove forest, in addition to those of 341 (Red+NIR+Blue) from SPOT 6. The near-infrared (NIR) and short-wave infrared (SWIR) bands play an important role in determining mangrove forests. The properties of vegetation are reflected strongly at the NIR wavelength and the SWIR band is very sensitive to evaporation and the identification of wetlands.

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Radiometric Correction of Landsat-8 and Sentinel-2A Scenes Using Drone Imagery in Synergy with Field Spectroradiometry

Remote Sensing ◽

10.3390/rs10111687 ◽

2018 ◽

Vol 10 (11) ◽

pp. 1687 ◽

Cited By ~ 20

Author(s):

Joan-Cristian Padró ◽

Francisco-Javier Muñoz ◽

Luis Ávila ◽

Lluís Pesquer ◽

Xavier Pons

Keyword(s):

Near Infrared ◽

Field Measurements ◽

Systematic Sampling ◽

Landsat 8 ◽

Radiometric Correction ◽

Multispectral Data ◽

Central Hypothesis ◽

Sentinel 2A ◽

Sentinel 2

The main objective of this research is to apply unmanned aerial system (UAS) data in synergy with field spectroradiometry for the accurate radiometric correction of Landsat-8 (L8) and Sentinel-2 (S2) imagery. The central hypothesis is that imagery acquired with multispectral UAS sensors that are well calibrated with highly accurate field measurements can fill in the scale gap between satellite imagery and conventional in situ measurements; this can be possible by sampling a larger area, including difficult-to-access land covers, in less time while simultaneously providing good radiometric quality. With this aim and by using near-coincident L8 and S2 imagery, we applied an upscaling workflow, whereby: (a) UAS-acquired multispectral data was empirically fitted to the reflectance of field measurements, with an extensive set of radiometric references distributed across the spectral domain; (b) drone data was resampled to satellite grids for comparison with the radiometrically corrected L8 and S2 official products (6S-LaSRC and Sen2Cor-SNAP, respectively) and the CorRad-MiraMon algorithm using pseudo-invariant areas, such as reflectance references (PIA-MiraMon), to examine their overall accuracy; (c) then, a subset of UAS data was used as reflectance references, in combination with the CorRad-MiraMon algorithm (UAS-MiraMon), to radiometrically correct the matching bands of UAS, L8, and S2; and (d) radiometrically corrected L8 and S2 scenes obtained with UAS-MiraMon were intercompared (intersensor coherence). In the first upscaling step, the results showed a good correlation between the field spectroradiometric measurements and the drone data in all evaluated bands (R2 > 0.946). In the second upscaling step, drone data indicated good agreement (estimated from root mean square error, RMSE) with the satellite official products in visible (VIS) bands (RMSEVIS < 2.484%), but yielded poor results in the near-infrared (NIR) band (RMSENIR > 6.688% was not very good due to spectral sensor response differences). In the third step, UAS-MiraMon indicated better agreement (RMSEVIS < 2.018%) than the other satellite radiometric correction methods in visible bands (6S-LaSRC (RMSE < 2.680%), Sen2Cor-SNAP (RMSE < 2.192%), and PIA-MiraMon (RMSE < 3.130%), but did not achieve sufficient results in the NIR band (RMSENIR < 7.530%); this also occurred with all other methods. In the intercomparison step, the UAS-MiraMon method achieved an excellent intersensor (L8-S2) coherence (RMSEVIS < 1%). The UAS-sampled area involved 51 L8 (30 m) pixels, 143 S2 (20 m) pixels, and 517 S2 (10 m) pixels. The drone time needed to cover this area was only 10 min, including areas that were difficult to access. The systematic sampling of the study area was achieved with a pixel size of 6 cm, and the raster nature of the sampling allowed for an easy but rigorous resampling of UAS data to the different satellite grids. These advances improve human capacities for conventional field spectroradiometry samplings. However, our study also shows that field spectroradiometry is the backbone that supports the full upscaling workflow. In conclusion, the synergy between field spectroradiometry, UAS sensors, and Landsat-like satellite data can be a useful tool for accurate radiometric corrections used in local environmental studies or the monitoring of protected areas around the world.

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A Multi Sensor Approach to Forest Type Mapping for Advancing Monitoring of Sustainable Development Goals (SDG) in Myanmar

Remote Sensing ◽

10.3390/rs12193220 ◽

2020 ◽

Vol 12 (19) ◽

pp. 3220

Author(s):

Sumalika Biswas ◽

Qiongyu Huang ◽

Anupam Anand ◽

Myat Su Mon ◽

Franz-Eugen Arnold ◽

...

Keyword(s):

Sustainable Development ◽

Satellite Data ◽

Classification System ◽

Sustainable Development Goals ◽

Near Infrared ◽

Short Wave ◽

Landsat 8 ◽

Forest Types ◽

Development Goals ◽

Sentinel 2

Monitoring forests is important for measuring overall success of the 2030 Agenda because forests play an essential role in meeting many Sustainable Development Goals (SDG), especially SDG 15. Our study evaluates the contribution of three satellite data sources (Landsat-8, Sentinel-2 and Sentinel-1) for mapping diverse forest types in Myanmar. This assessment is especially important because Myanmar is currently revising its classification system for forests and it is critical that these new forest types can be accurately mapped and monitored over time using satellite imagery. Our results show that using a combination of Sentinel-1 and Sentinel-2 yields the highest accuracy (89.6% ± 0.16 percentage point(pp)), followed by Sentinel-2 alone (87.97% ± 0.11 pp) and Landsat-8 (82.68% ± 0.13 pp). The higher spatial resolution of Sentinel-2 Blue, Green, Red, Narrow Near Infrared and Short Wave Infrared bands enhances accuracy by 4.83% compared to Landsat-8. The addition of the Sentinel-2 Near Infrared and three Vegetation Red Edge bands further improve accuracy by 0.46% compared to using only Sentinel-2 Blue, Green, Red, Narrow Near Infrared and Short Wave Infrared bands. Adding the radar information from Sentinel-1 further increases the accuracy by 1.63%. We were able to map the two major forest types, Upper Moist and Upper Dry Mixed Deciduous Forest, which comprise 90% of our study area. Accuracies for these forest types ranged from 77 to 96% depending on the sensors used, demonstrating the feasibility of using satellite data to map forest categories from a newly revised classification system. Our results advance the ongoing development of the National Forest Monitoring System (NFMS) by the Myanmar Forest Department and United Nations-Food and Agriculture Organization (UN-FAO) and facilitates future monitoring of progress towards the SDGs.

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Comparison of Multi-Temporal PlanetScope Data with Landsat 8 and Sentinel-2 Data for Estimating Airborne LiDAR Derived Canopy Height in Temperate Forests

Remote Sensing ◽

10.3390/rs12111876 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1876 ◽

Cited By ~ 1

Author(s):

Katsuto Shimizu ◽

Tetsuji Ota ◽

Nobuya Mizoue ◽

Hideki Saito

Keyword(s):

Time Series ◽

Prediction Accuracy ◽

Time Series Data ◽

Airborne Lidar ◽

Canopy Height ◽

Forest Monitoring ◽

Series Data ◽

Landsat 8 ◽

Multi Temporal ◽

Sentinel 2

Developing accurate methods for estimating forest structures is essential for efficient forest management. The high spatial and temporal resolution data acquired by CubeSat satellites have desirable characteristics for mapping large-scale forest structural attributes. However, most studies have used a median composite or single image for analyses. The multi-temporal use of CubeSat data may improve prediction accuracy. This study evaluates the capabilities of PlanetScope CubeSat data to estimate canopy height derived from airborne Light Detection and Ranging (LiDAR) by comparing estimates using Sentinel-2 and Landsat 8 data. Random forest (RF) models using a single composite, multi-seasonal composites, and time-series data were investigated at different spatial resolutions of 3, 10, 20, and 30 m. The highest prediction accuracy was obtained by the PlanetScope multi-seasonal composites at 3 m (relative root mean squared error: 51.3%) and Sentinel-2 multi-seasonal composites at the other spatial resolutions (40.5%, 35.2%, and 34.2% for 10, 20, and 30 m, respectively). The results show that RF models using multi-seasonal composites are 1.4% more accurate than those using harmonic metrics from time-series data in the median. PlanetScope is recommended for canopy height mapping at finer spatial resolutions. However, the unique characteristics of PlanetScope data in a spatial and temporal context should be further investigated for operational forest monitoring.

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Greenhouse Crop Identification from Multi-Temporal Multi-Sensor Satellite Imagery Using Object-Based Approach: A Case Study from Almería (Spain)

Remote Sensing ◽

10.3390/rs10111751 ◽

2018 ◽

Vol 10 (11) ◽

pp. 1751 ◽

Cited By ~ 10

Author(s):

Abderrahim Nemmaoui ◽

Manuel A. Aguilar ◽

Fernando J. Aguilar ◽

Antonio Novelli ◽

Andrés García Lorca

Keyword(s):

Time Series ◽

Binary Classification ◽

Free Access ◽

Landsat 8 ◽

Horticultural Crops ◽

Command Line Tool ◽

Multi Temporal ◽

Tree Models ◽

Sentinel 2A

A workflow headed up to identify crops growing under plastic-covered greenhouses (PCG) and based on multi-temporal and multi-sensor satellite data is developed in this article. This workflow is made up of four steps: (i) data pre-processing, (ii) PCG segmentation, (iii) binary pre-classification between greenhouses and non-greenhouses, and (iv) classification of horticultural crops under greenhouses regarding two agronomic seasons (autumn and spring). The segmentation stage was carried out by applying a multi-resolution segmentation algorithm on the pre-processed WorldView-2 data. The free access AssesSeg command line tool was used to determine the more suitable multi-resolution algorithm parameters. Two decision tree models mainly based on the Plastic Greenhouse Index were developed to perform greenhouse/non-greenhouse binary classification from Landsat 8 and Sentinel-2A time series, attaining overall accuracies of 92.65% and 93.97%, respectively. With regards to the classification of crops under PCG, pepper in autumn, and melon and watermelon in spring provided the best results (Fβ around 84% and 95%, respectively). Data from the Sentinel-2A time series showed slightly better accuracies than those from Landsat 8.

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Forest Type Identification with Random Forest Using Sentinel-1A, Sentinel-2A, Multi-Temporal Landsat-8 and DEM Data

Remote Sensing ◽

10.3390/rs10060946 ◽

2018 ◽

Vol 10 (6) ◽

pp. 946 ◽

Cited By ~ 29

Author(s):

Yanan Liu ◽

Weishu Gong ◽

Xiangyun Hu ◽

Jianya Gong

Keyword(s):

Random Forest ◽

Forest Type ◽

Landsat 8 ◽

Multi Temporal ◽

Sentinel 2A

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Landsat thermal infrared to detect sub-canopy riparian flooding

10.5194/egusphere-egu21-13762 ◽

2021 ◽

Author(s):

Emanuel Storey ◽

Witold Krajewski ◽

Efthymios Nikolopoulos

Keyword(s):

Open Water ◽

Thermal Infrared ◽

Short Wave ◽

Mapping Technique ◽

Landsat 8 ◽

Infrared Observations ◽

Plant Canopies ◽

Multi Temporal ◽

Flood Detection

Satellite based flood detection can enhance understanding of risk to humans and infrastructures, geomorphic processes, and ecological effects.&#160; Such application of optical satellite imagery has been mostly limited to the detection of water exposed to sky, as plant canopies tend to obstruct water visibility in short electromagnetic wavelengths.&#160; This case study evaluates the utility in multi-temporal thermal infrared observations from Landsat 8 as a basis for detecting sub-canopy fluvial inundation resulting in ambient temperature change.We selected three flood events of 2016 and 2019 along sections of the Mississippi, Cedar, and Wapsipinicon Rivers located in Iowa, Minnesota, and Wisconsin, United States.&#160; Classification of sub-canopy water involved logical, threshold-exceedance criteria to capture thermal decline within channel-adjacent vegetated zones.&#160; Open water extent in the floods was mapped based on short-wave infrared thresholds determined parametrically from baseline (non-flooded) observations.&#160; Map accuracy was evaluated using higher-resolution (0.5&#8211;5.0 m) synchronic optical imagery.Results demonstrate improved ability to detect sub-canopy inundation when thermal infrared change is incorporated: sub-canopy flood class accuracy was comparable to that of open water in previous studies.&#160; The multi-temporal open-water mapping technique yielded high accuracy as compared to similar studies.&#160; This research highlights the utility of Landsat thermal infrared data for monitoring riparian inundation and for validating other remotely sensed and simulated flood maps.

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