scholarly journals Correction of spectral radiance of optical satellite image for mountainous terrain for studying land surface cover changes

2014 ◽  
Vol 63 (1) ◽  
pp. 39-53 ◽  
Author(s):  
Luong Chinh Ke ◽  
Tran Ngoc Tuong ◽  
Nguyen Van Hung
Keyword(s):  
Land Surface ◽  
Satellite Image ◽  
Spectral Radiance ◽  
Image Study ◽  
Mountain Area ◽  
Correction Model ◽  
Surface Cover ◽  
Before And After ◽  
Terrain Elevation ◽  
Spot 5

Abstract Qualitative and quantitative results of high terrain elevation effect on spectral radiance of optical satellite image which affect the accuracy in retrieving of land surface cover changes is given. The paper includes two main parts: correction model of spectral radiance of satellite image affected by high terrain elevation and assessment of impacts and variation of land cover changes before and after correcting influence of high terrain elevation to the spectral radiance of the image. Study has been carried out with SPOT 5 in Hoa Binh mountain area of two periods: 2007 and 2010. Results showed that appropriate correction model is the Meyer’s one. The impacts of correction spectral radiance to 7 classes of classified images fluctuate from 15% to 400%. The varying changes before and after correction of image radiation fluctuate over 7 classes from 5% to 100%.

PRISMA Hyperspectral – First insights in the performance in urban surface cover and coastal seascape analysis

2021 ◽  
Author(s):  
Dimitris Poursanidis ◽  
Nektarios Chrysoulakis
Keyword(s):  
Land Surface ◽  
Satellite Image ◽  
Surface Characteristics ◽  
Spectral Width ◽  
Earth Observation ◽  
Hyperspectral Data ◽  
Spectral Information ◽  
Geospatial Information ◽  
Surface Cover ◽  
Spectral Channels

<p>The characterization of the Earth’s surface cover based on predefined classes is among the fundamental activities in the domain of satellite image analysis image since the early 70s. It was the joint NASA/ U.S. Geological Survey Landsat series of Earth Observation satellites that start to continuously acquired images of the Earth's land surface, providing uninterrupted data to help land managers and policymakers make informed decisions about natural resources and the environment. However, in 2020, the collected data even if are of continuous flow in terms volume of terrabytes per day from various optical and radar systems, are limited in terms of spectral resolution since almost all sensors are limited to a maximum of 25 spectral channels in the visible, near-and-shortwave-and-thermal infrared spectrum. The need of denser spectral information has been highlighted in early 80s and the first satellite-based hyperspectral sensor, AVIRIS, start to provide data allowing the extraction information on material composition and precise surface cover information. Since then few attempt appear but more are undergoing for launching. In 2019, the Italian Space Agency launch the PRISMA hyperspectral satellite which collect spectral data in the 400-2500nm spectrum; in total 250 spectral channels with a spectral width of ~ 12nm, at 30m pixel size. Here we present first results of the use of Level 2D PRISMA hyperspectral data in mapping the surface characteristics of the urban and periurban area of Heraklion city along with the coastal zone of the urban front aiming at the simultaneous creation of a land-and-coastal cover map along with the extraction of coastal bathymetry information using artificial intelligence approaches within open access platforms. The use of hyperspectral information allow the separation of urban surfaces based on material signatures, while the availability of dense spectral information in the blue-green spectrum allow the more accurate retrieval of coastal seascape characteristics. It is envisaged that hyperspectral missions soon to be the normal in Earth Observation, allowing the accurate creation of geospatial information for further use in several applications.</p>

scholarly journals Depicting changes in land surface cover at Al-Hassa oasis of Saudi Arabia using remote sensing and GIS techniques

PLoS ONE ◽  
2019 ◽  
Vol 14 (11) ◽  
pp. e0221115 ◽  
Author(s):  
Abdulrahman Mohamed Almadini ◽  
Abdalhaleem Abdalla Hassaballa
Keyword(s):  
Remote Sensing ◽  
Saudi Arabia ◽  
Land Surface ◽  
Remote Sensing And Gis ◽  
Surface Cover ◽  
Gis Techniques

scholarly journals Coseismic displacements of the 14 November 2016 Mw7.8 Kaikoura, New Zealand, earthquake using an optical cubesat constellation

2017 ◽  
Author(s):  
Andreas Kääb ◽  
Bas Altena ◽  
Joseph Mascaro
Keyword(s):  
New Zealand ◽  
Land Surface ◽  
Near Infrared ◽  
Sar Interferometry ◽  
Optical Methods ◽  
Optical Data ◽  
Surface Displacements ◽  
Before And After ◽  
Optical Satellite Images ◽  
Sentinel 2

Abstract. Satellite measurements of coseismic displacements are typically based on Synthetic Aperture Radar (SAR) interferometry or amplitude tracking, or based on optical data such as from Landsat, Sentinel-2, SPOT, ASTER, very-high resolution satellites, or airphotos. Here, we evaluate a new class of optical satellite images for this purpose – data from cubesats. More specific, we investigate the PlanetScope cubesat constellation for horizontal surface displacements by the 14 November 2016 Mw7.8 Kaikoura, New Zealand, earthquake. Single PlanetScope scenes are 2–4 m resolution visible and near-infrared frame images of approximately 20–30 km × 9–15 km in size, acquired in continuous sequence along an orbit of approximately 375–475 km height. From single scenes or mosaics from before and after the earthquake we observe surface displacements of up to almost 10 m and estimate a matching accuracy from PlanetScope data of up to ±0.2 pixels (~ ±0.6 m). This accuracy, the daily revisit anticipated for the PlanetScope constellation for the entire land surface of Earth, and a number of other features, together offer new possibilities for investigating coseismic and other Earth surface displacements and managing related hazards and disasters, and complement existing SAR and optical methods. For comparison and for a better regional overview we also match the coseismic displacements by the 2016 Kaikoura earthquake using Landsat8 and Sentinel-2 data.

scholarly journals Atmospheric correction of multispectral satellite imagery

2020 ◽  
pp. 4-11
Author(s):  
Leonid Katkovsky
Keyword(s):  
Land Surface ◽  
Satellite Image ◽  
Atmospheric Correction ◽  
Remote Sensing Data ◽  
Sensor Data ◽  
Physical Parameters ◽  
Landsat 8 ◽  
Optical Parameters ◽  
Outgoing Radiation ◽  
Correction Technique

Atmospheric correction is a necessary step in the processing of remote sensing data acquired in the visible and NIR spectral bands.The paper describes the developed atmospheric correction technique for multispectral satellite data with a small number of relatively broad spectral bands (not hyperspectral). The technique is based on the proposed analytical formulae that expressed the spectrum of outgoing radiation at the top of a cloudless atmosphere with rather high accuracy. The technique uses a model of the atmosphere and its optical and physical parameters that are significant from the point of view of radiation transfer, the atmosphere is considered homogeneous within a satellite image. To solve the system of equations containing the measured radiance of the outgoing radiation in the bands of the satellite sensor, the number of which is less than the number of unknowns of the model, it is proposed to use various additional relations, including regression relations between the optical parameters of the atmosphere. For a particular image pixel selected in a special way, unknown atmospheric parameters are found, which are then used to calculate the reflectance for all other pixels.Testing the proposed technique on OLI sensor data of Landsat 8 satellite showed higher accuracy in comparison with the FLAASH and QUAC methods implemented in the well-known ENVI image processing software. The technique is fast and there is using no additional information about the atmosphere or land surface except images under correction.

scholarly journals Estimation and Validation of Land Surface Temperature by using Remote Sensing & GIS for Chittoor District, Andhra Pradesh

2021 ◽  
Vol 12 (5) ◽  
pp. 607-617
Author(s):  
A. Rajani, Dr. S.Varadarajan
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Andhra Pradesh ◽  
Temperature Data ◽  
Sensor Data ◽  
Spectral Radiance ◽  
Landsat 8 ◽  
Thermal Band

Land Surface Temperature (LST) quantification is needed in various applications like temporal analysis, identification of global warming, land use or land cover, water management, soil moisture estimation and natural disasters. The objective of this study is estimation as well as validation of temperature data at 14 Automatic Weather Stations (AWS) in Chittoor District of Andhra Pradesh with LST extracted by using remote sensing as well as Geographic Information System (GIS). Satellite data considered for estimation purpose is LANDSAT 8. Sensor data used for assessment of LST are OLI (Operational Land Imager) and TIR (Thermal Infrared). Thermal band  contains spectral bands of 10 and 11 were considered for evaluating LST independently by using algorithm called Mono Window Algorithm (MWA). Land Surface Emissivity (LSE) is the vital parameter for calculating LST. The LSE estimation requires NDVI (Normalized Difference Vegetation Index) which is computed by using Band 4 (visible Red band) and band 5 (Near-Infra Red band) spectral radiance bands. Thermal band images having wavelength 11.2 µm and 12.5 µm of 30th May, 2015 and 21st October, 2015 were processed for the analysis of LST. Later on validation of estimated LST through in-suite temperature data obtained from 14 AWS stations in Chittoor district was carried out. The end results showed that, the LST retrieved by using proposed method achieved 5 per cent greater correlation coefficient (r) compared to LST retrieved by using existing method which is based on band 10.

Using Grid Computing and Satellite Remote Sensing in Evapotranspiration Estimation

2015 ◽  
pp. 145-173
Author(s):  
Cristina Serban ◽  
Carmen Maftei
Keyword(s):  
Remote Sensing ◽  
Grid Computing ◽  
Land Surface ◽  
Essential Role ◽  
Satellite Image ◽  
Vegetation Indices ◽  
Computing Services ◽  
Monitoring And Forecasting ◽  
Forecasting System ◽  
Evapotranspiration Estimation

The most advanced and applicable approach today in the development of environmental monitoring programs is the integration of remote sensing and Grid computing services into a monitoring and forecasting system that helps the analyst to understand the problem without being a remote sensing or computer expert. In this chapter we present the main features of Grid computing and how we can use it in conjunction with remote sensing to develop several applications that will estimate ET (Evapotranspiration), LST (Land Surface Temperature) and some vegetation indices (VI's) directly from a satellite image, these parameters playing an essential role in all activities related to water resources management.

scholarly journals A distributed snowmelt prediction model in mountain areas based on an energy balance method

1994 ◽  
Vol 19 ◽  
pp. 107-113 ◽  
Author(s):  
Takeshi Ohta
Keyword(s):  
Energy Balance ◽  
Prediction Model ◽  
Deciduous Forest ◽  
Balance Method ◽  
Evergreen Forest ◽  
Snowmelt Runoff ◽  
Mountain Areas ◽  
Mountain Area ◽  
Energy Balance Method ◽  
Surface Cover

A distributed snowmelt prediction model was developed for a mountain area. Topography of the study area was represented by a digital map. Cells On the map were divided into three surface-cover types; deciduous forest, evergreen forest and deforested area. Snowmelt rates for each cell were calculated by an energy balance method. Meteorological elements were estimated separately in each cell according to topographical characteristics and surface-cover type. Distributions of water equivalent of snow cover were estimated by the model. Snowmelt runoff in the watershed was also simulated by snowmelt rates calculated by the model. The model showed thai the snowmelt period and snowmelt runoff after timber harvests would be about two weeks earlier than under the forest-covered condition.

scholarly journals Improvement of Clustering Methods for Modelling Abrupt Land Surface Changes in Satellite Image Fusions

2019 ◽  
Vol 11 (15) ◽  
pp. 1759 ◽  
Author(s):  
Detang Zhong ◽  
Fuqun Zhou
Keyword(s):  
Land Surface ◽  
Satellite Image ◽  
Weighted Average ◽  
Clustering Methods ◽  
Fusion Model ◽  
Dynamic Prediction ◽  
New Methods ◽  
Satellite Sensors ◽  
Spectral Mixing ◽  
Surface Changes

A key challenge in developing models for the fusion of surface reflectance data across multiple satellite sensors is ensuring that they apply to both gradual vegetation phenological dynamics and abrupt land surface changes. To better model land cover spatial and temporal changes, we proposed previously a Prediction Smooth Reflectance Fusion Model (PSRFM) that combines a dynamic prediction model based on the linear spectral mixing model with a smoothing filter corresponding to the weighted average of forward and backward temporal predictions. One of the significant advantages of PSRFM is that PSRFM can model abrupt land surface changes either through optimized clusters or the residuals of the predicted gradual changes. In this paper, we expanded our approach and developed more efficient methods for clustering. We applied the new methods for dramatic land surface changes caused by a flood and a forest fire. Comparison of the model outputs showed that the new methods can capture the land surface changes more effectively. We also compared the improved PSRFM to two most popular reflectance fusion algorithms: Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and Enhanced version of STARFM (ESTARFM). The results showed that the improved PSRFM is more effective and outperforms STARFM and ESTARFM both visually and quantitatively.

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