scholarly journals A MULTI-TEMPORAL APPROACH FOR DETECTING SNOW COVER AREA USING GEOSTATIONARY IMAGERY DATA

2016 ◽  
Vol XLI-B8 ◽  
pp. 511-512
Author(s):  
Hwa-Seon Lee ◽  
Kyu-Sung Lee
Keyword(s):  
Snow Cover ◽  
Near Infrared ◽  
Detection Method ◽  
Temporal Variations ◽  
High Temporal Resolution ◽  
Snow Cover Area ◽  
Swir Band ◽  
Multi Temporal ◽  
The Difference ◽  
Shortwave Infrared

In this study, we attempt to detect snow cover area using multi-temporal geostationary satellite imagery based on the difference of spectral and temporal characteristics between snow and clouds. The snow detection method is based on sequential processing of simple thresholds on multi-temporal GOCI data. We initially applied a simple threshold of blue reflectance and then root mean square deviation (RMSD) threshold of near infrared (NIR) reflectance that were calculated from time-series GOCI data. Snow cover detected by the proposed method was compared with the MODIS snow products. The proposed snow detection method provided very similar results with the MODIS cloud products. Although the GOCI data do not have shortwave infrared (SWIR) band, which can spectrally separate snow cover from clouds, the high temporal resolution of the GOCI was effective for analysing the temporal variations between snow and clouds.

scholarly journals A MULTI-TEMPORAL APPROACH FOR DETECTING SNOW COVER AREA USING GEOSTATIONARY IMAGERY DATA

2016 ◽  
Vol XLI-B8 ◽  
pp. 511-512
Author(s):  
Hwa-Seon Lee ◽  
Kyu-Sung Lee
Keyword(s):  
Snow Cover ◽  
Near Infrared ◽  
Detection Method ◽  
Temporal Variations ◽  
High Temporal Resolution ◽  
Snow Cover Area ◽  
Swir Band ◽  
Multi Temporal ◽  
The Difference ◽  
Shortwave Infrared

In this study, we attempt to detect snow cover area using multi-temporal geostationary satellite imagery based on the difference of spectral and temporal characteristics between snow and clouds. The snow detection method is based on sequential processing of simple thresholds on multi-temporal GOCI data. We initially applied a simple threshold of blue reflectance and then root mean square deviation (RMSD) threshold of near infrared (NIR) reflectance that were calculated from time-series GOCI data. Snow cover detected by the proposed method was compared with the MODIS snow products. The proposed snow detection method provided very similar results with the MODIS cloud products. Although the GOCI data do not have shortwave infrared (SWIR) band, which can spectrally separate snow cover from clouds, the high temporal resolution of the GOCI was effective for analysing the temporal variations between snow and clouds.

Prediction Changes for Nonstationary Multi-Temporal Satellite Images using HMM

2017 ◽  
pp. 387-406
Author(s):  
Ali Ben Abbes ◽  
Imed Riadh Farah
Keyword(s):  
Land Use ◽  
Land Use Changes ◽  
Satellite Image ◽  
Remotely Sensed ◽  
High Temporal Resolution ◽  
Remotely Sensed Data ◽  
Vegetation Monitoring ◽  
Area Of Interest ◽  
Multi Temporal ◽  
The Difference

Due to the growing advances in their temporal, spatial, and spectral resolutions, remotely sensed data continues to provide tools for a wide variety of environmental applications. This chapter presents the benefits and difficulties of Multi-Temporal Satellite Image (MTSI) for land use. Predicting land use changes using remote sensing is an area of interest that has been attracting increasing attention. Land use analysis from high temporal resolution remotely sensed images is important to promote better decisions for sustainable management land cover. The purpose of this book chapter is to review the background of using Hidden Markov Model (HMM) in land use change prediction, to discuss the difference on modeling using stationary as well as non-stationary data and to provide examples of both case studies (e.g. vegetation monitoring, urban growth).

Dynamics of Maximum Snow Cover Area and Snow Line Altitude Across Nepal (2003-2018) Using Improved MODIS Data

2020 ◽  
Vol 25 (2) ◽  
pp. 17-24
Author(s):  
Nitesh Khadka ◽  
Nitesh Khadka ◽  
Shravan Kumar Ghimire ◽  
Xiaoqing Chen ◽  
Sudeep Thakuri ◽  
...  
Keyword(s):  
Snow Cover ◽  
Monsoon Season ◽  
Temporal Variations ◽  
Vital Role ◽  
Eastern Region ◽  
Snow Cover Area ◽  
Post Monsoon ◽  
Snow Line ◽  
Elevation Zone ◽  
Spatio Temporal

Snow is one of the main components of the cryosphere and plays a vital role in the hydrology and regulating climate. This study presents the dynamics of maximum snow cover area (SCA) and snow line altitude (SLA) across the Western, Central, and Eastern Nepal using improved Moderate Resolution Imaging Spectroradiometer (MODIS; 500 m) data from 2003 to 2018. The results showed a heterogeneous behavior of the spatial and temporal variations of SCA in different months, seasons, and elevation zones across three regions of Nepal. Further, the maximum and minimum SCA was observed in winter (December-February) and post-monsoon (October-November) seasons, respectively. The inter-annual variation of winter SCA showed an overall negative trend of SCA between 2003 to 2018 at the national and regional scales. The SLA was assessed in the post-monsoon season. At the national scale, the SLA lies in an elevation zone of 4500-5000 m, and the approximate SLA of Nepal was 4750 m in 2018. Regionally, the SLA lies in an elevation zone of 4500-5000 m in the Western and Central regions (approx. SLA at 4750 m) and 5000-5500 m in the Eastern region (approx. SLA at 5250 m) in 2018. The SLA fluctuated with the changes in SCA, and the spatio-temporal variations of SLAs were observed in three regions of Nepal. We observed an upward shift of SLA by 33.3 m yr-1 in the Western and Central Nepal and by 66.7 m yr-1 in Eastern Nepal. This study will help to understand the impacts of climate change on snow cover, and the information will be useful for the hydrologist and water resource managers.

Prediction Changes for Nonstationary Multi-Temporal Satellite Images Using HMM

2019 ◽  
pp. 1178-1197
Author(s):  
Ali Ben Abbes ◽  
Imed Riadh Farah
Keyword(s):  
Land Use ◽  
Land Use Changes ◽  
Satellite Image ◽  
Remotely Sensed ◽  
High Temporal Resolution ◽  
Remotely Sensed Data ◽  
Vegetation Monitoring ◽  
Area Of Interest ◽  
Multi Temporal ◽  
The Difference

Due to the growing advances in their temporal, spatial, and spectral resolutions, remotely sensed data continues to provide tools for a wide variety of environmental applications. This chapter presents the benefits and difficulties of Multi-Temporal Satellite Image (MTSI) for land use. Predicting land use changes using remote sensing is an area of interest that has been attracting increasing attention. Land use analysis from high temporal resolution remotely sensed images is important to promote better decisions for sustainable management land cover. The purpose of this book chapter is to review the background of using Hidden Markov Model (HMM) in land use change prediction, to discuss the difference on modeling using stationary as well as non-stationary data and to provide examples of both case studies (e.g. vegetation monitoring, urban growth).

Snow cover mapping using IRS-P6 AWiFS data and the relationships between some climatic factors with snowpack in the northwest of Iran

2015 ◽  
Vol 7 (2) ◽  
pp. 415-429
Author(s):  
M. Seyedielmabad ◽  
H. R. Moradi
Keyword(s):  
Snow Cover ◽  
Climatic Factors ◽  
Unsupervised Classification ◽  
Wide Field ◽  
Snow Cover Area ◽  
Multi Temporal ◽  
Moderate Resolution ◽  
Field Sensor ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Normalized Difference Snow Index

In this study, we explored the potential of the multispectral and multi-temporal IRS Advanced Wide Field Sensor (AWiFS) data for mapping of the snow cover in the northwest regions of Iran. The AWiFS snow cover maps, based on the unsupervised classification method, were compared with the estimates of snow cover area derived from the moderate resolution imaging spectroradiometer (MODIS) images based on the normalized difference snow index. Good concurrence was observed with respect to the snow area between the AWiFS features and the MODIS features; however, the snow spatial distribution of the AWiFS features differed from those of the MODIS based on the nonentity of the temporal accordance between two types of features. Also, we explored the relationships between some climatic and topographic factors with the snowpack in the northwest part of Iran. Relationships between some climatic factors with snowpack specifications were obtained, which showed significant correlation only between the components of daily temperature and snow density. The other results showed that the amounts of snowpack depth have significant correlations with the height of the stations and the height classes in 1% surface and snowpack depths showed significant differences together within the different height classes.

Snow cover area identification by using a change detection method applied to COSMO-SkyMed images

2014 ◽  
Vol 8 (1) ◽  
pp. 084684 ◽  
Author(s):  
Simone Pettinato ◽  
Emanuele Santi ◽  
Simonetta Paloscia ◽  
Bruno Aiazzi ◽  
Stefano Baronti ◽  
...  
Keyword(s):  
Change Detection ◽  
Snow Cover ◽  
Detection Method ◽  
Snow Cover Area

scholarly journals Determination of the TROPOMI-SWIR instrument spectral response function

2018 ◽  
Vol 11 (7) ◽  
pp. 3917-3933 ◽  
Author(s):  
Richard M. van Hees ◽  
Paul J. J. Tol ◽  
Sidney Cadot ◽  
Matthijs Krijger ◽  
Stefan T. Persijn ◽  
...  
Keyword(s):  
Response Function ◽  
Near Infrared ◽  
Spectral Response ◽  
Monochromatic Light ◽  
Imaging Spectrometer ◽  
Swir Band ◽  
Shortwave Infrared ◽  
Single Instrument

Abstract. The Tropospheric Monitoring Instrument (TROPOMI) is the single instrument on board the ESA Copernicus Sentinel-5 Precursor satellite. TROPOMI is a nadir-viewing imaging spectrometer with bands in the ultraviolet and visible, the near infrared and the shortwave infrared (SWIR). An accurate instrument spectral response function (ISRF) is required in the SWIR band where absorption lines of CO, methane and water vapor overlap. In this paper, we report on the determination of the TROPOMI-SWIR ISRF during an extensive on-ground calibration campaign. Measurements are taken with a monochromatic light source scanning the whole detector, using the spectrometer itself to determine the light intensity and wavelength. The accuracy of the resulting ISRF calibration key data is well within the requirement for trace-gas retrievals. Long-term in-flight monitoring of SWIR ISRF is achieved using five on-board diode lasers.

scholarly journals Evaluation of Methods for Mapping the Snow Cover Area at High Spatio-Temporal Resolution with VENμS

2020 ◽  
Vol 12 (18) ◽  
pp. 3058
Author(s):  
Mohamed Wassim Baba ◽  
Simon Gascoin ◽  
Olivier Hagolle ◽  
Elsa Bourgeois ◽  
Camille Desjardins ◽  
...  
Keyword(s):  
Machine Learning ◽  
Snow Cover ◽  
Temporal Resolution ◽  
Near Infrared ◽  
Snow Accumulation ◽  
Learning Method ◽  
Snow Cover Area ◽  
High Atlas ◽  
Detection Algorithms ◽  
Sentinel 2

The VENμS mission launched in 2017 provides multispectral optical images of the land surface with a 2-day revisit time at 5 m resolution for over 100 selected sites. A few sites are subject to seasonal snow accumulation, which gives the opportunity to monitor the variations of the snow cover area at unprecedented spatial and temporal resolution. However, the 12 spectral bands of VENμS only cover the visible and near-infrared region of the spectra while existing snow detection algorithms typically make use of a shortwave infrared band to determine the presence of snow. Here, we evaluate two alternative snow detection algorithms. The first one is based on a normalized difference index between the near-infrared and the visible bands, and the second one is based on a machine learning approach using the Theia Sentinel-2 snow products as training data. Both approaches are tested using Sentinel-2 data (as surrogate of VENμS data) as well as actual VENμS in the Pyrenees and the High Atlas. The results confirm the possibility of retrieving snow cover without SWIR with a slight loss in performance. As expected, the results confirm that the machine learning method provides better results than the index-based approach (e.g., an RMSE equal to the learning method 1.35% and for the index-based method 10.80% in the High Atlas.). The improvement is more evident in the Pyrenees probably due to the presence of vegetation which complicates the spectral signature of the snow cover area in VENμS images.

scholarly journals Radiometric Comparison of 1.6-μm CO2 Absorption Band of Greenhouse Gases Observing Satellite (GOSAT) TANSO-FTS with Suomi-NPP VIIRS SWIR Band

2016 ◽  
Vol 33 (7) ◽  
pp. 1443-1453
Author(s):  
Sirish Uprety ◽  
Changyong Cao
Keyword(s):  
Greenhouse Gases ◽  
Near Infrared ◽  
Infrared Imaging ◽  
Co2 Absorption ◽  
New Methods ◽  
Swir Band ◽  
Long Term Trends ◽  
Global Concern ◽  
Shortwave Infrared

AbstractAn atmospheric CO2 increase has become a progressively important global concern in recent past decades. Since the 1950s, the Keeling curve has documented the atmospheric CO2 increase as well as seasonal variations, which also intrigued scientists to develop new methods for global CO2 measurements from satellites. One of the dedicated satellite missions is the CO2 measurement in the 1.6-μm shortwave infrared spectra by the Greenhouse Gases Observing Satellite (GOSAT) Thermal and Near Infrared Sensor for Carbon Observations–Fourier Transform Spectrometer (TANSO-FTS) instrument. While this spectral region has unique advantages in detecting lower-trophosphere CO2, there are many challenges because it relies on accurate measurements of reflected solar radiance from Earth’s surface. Therefore, the calibration of the TANSO-FTS CO2 has a direct impact on the CO2 retrievals and its long-term trends. Coincidently, the Suomi-NPP Visible Infrared Imaging Radiometer Suite (VIIRS) 1.6-μm band spectrally overlaps with the TANSO-FTS CO2 band, and both satellites are in orbit with periodical simultaneous nadir overpass measurements. This study performs an intercomparison of VIIRS and the TANSO-FTS CO2 band in an effort to evaluate and improve the radiometric consistency. Understanding the differences provides feedback on how well the GOSAT TANSO-FTS is performing over time, which is critical to ensure a well-calibrated, stable, and bias-free CO2 product.

scholarly journals Forest species mapping using airborne hyperspectral APEX data

2016 ◽  
Vol 20 (1) ◽  
pp. 28-33 ◽  
Author(s):  
Giulia Tagliabue ◽  
Cinzia Panigada ◽  
Roberto Colombo ◽  
Francesco Fava ◽  
Chiara Cilia ◽  
...  
Keyword(s):  
Near Infrared ◽  
Hyperspectral Data ◽  
Forest Species ◽  
Blue Band ◽  
Vegetation Development ◽  
Infrared Spectral ◽  
Multi Temporal ◽  
High Resolution Images ◽  
Shortwave Infrared ◽  
Species Mapping

Abstract The accurate mapping of forest species is a very important task in relation to the increasing need to better understand the role of the forest ecosystem within environmental dynamics. The objective of this paper is the investigation of the potential of a multi-temporal hyperspectral dataset for the production of a thematic map of the dominant species in the Forêt de Hardt (France). Hyperspectral data were collected in June and September 2013 using the Airborne Prism EXperiment (APEX) sensor, covering the visible, near-infrared and shortwave infrared spectral regions with a spatial resolution of 3 m by 3 m. The map was realized by means of a maximum likelihood supervised classification. The classification was first performed separately on images from June and September and then on the two images together. Class discrimination was performed using as input 3 spectral indices computed as ratios between red edge bands and a blue band for each image. The map was validated using a testing set selected on the basis of a random stratified sampling scheme. Results showed that the algorithm performances improved from an overall accuracy of 59.5% and 48% (for the June and September images, respectively) to an overall accuracy of 74.4%, with the producer’s accuracy ranging from 60% to 86% and user’s accuracy ranging from 61% to 90%, when both images (June and September) were combined. This study demonstrates that the use of multi-temporal high-resolution images acquired in two different vegetation development stages (i.e., 17 June 2013 and 4 September 2013) allows accurate (overall accuracy 74.4%) local-scale thematic products to be obtained in an operational way.

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