Infrared Radiometer Type Research Articles

Monitoring spatial and temporal variability of suspended sediment in Indus River by means of remotely sensed data

Remote sensing has emerged as a useful tool for monitoring spatial and temporal distribution of sediment fluxes in the water bodies. The large quantity of sediment transported by the river waters create a number of water resources and environmental problems and could shorten the useful life of many downstream reservoirs and dams. The objective of the present article is to quantify the distribution of suspended sediment by means of Advanced Land Observing Satellite/Advanced Visible and Near Infrared Radiometer type 2 (ALOS/AVNIR-2) and Systeme Pour l'Observation de la Terre (SPOT) satellite data. Multispectral remotely sensed algorithm was developed to investigate the distribution of suspended sediment in the Indus River, Pakistan, and Tarbela dam reservoir. Reflectance of ALOS/AVNIR-2 band 3 and band 4 and SPOT-2 satellite band 2 and band 3 was found to be the best predictor of suspended sediment concentration in the surface waters. To deal with the lack of in situ data, remotely sensed data are coupled with optical modeling and the desired parameter is derived by model inversion technique. The developed methodology is an effective and efficient tool for monitoring erosion, deposition areas, and sediment distribution in large rivers.

Improved Biomass Estimation Using the Texture Parameters of Two High-Resolution Optical Sensors

Accurate forest biomass estimation is essential for greenhouse gas inventories, terrestrial carbon accounting, and climate change modeling studies. Unfortunately, no universal and transferable technique has been developed so far to quantify biomass carbon sources and sinks over large areas because of the environmental, topographic, and biophysical complexity of forest ecosystems. Among the remote sensing techniques tested, the use of multisensors and the spatial as well as the spectral characteristics of the data have demonstrated a strong potential for forest biomass estimation. However, the use of multisensor data accompanied by spatial data processing has not been fully investigated because of the unavailability of appropriate data sets and the complexity of image processing techniques in combining multisensor data with the analysis of the spatial characteristics. This paper investigates the texture parameters of two high-resolution (10 m) optical sensors (Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) and SPOT-5) in different processing combinations for biomass estimation. Multiple regression models are developed between image parameters extracted from the different stages of image processing and the biomass of 50 field plots, which was estimated using a newly developed “allometric model” for the study region. The results demonstrate a clear improvement in biomass estimation using the texture parameters of a single sensor ( <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$r^{2} = 0.854$</tex></formula> and <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$ \hbox{rmse} = 38.54$</tex></formula> ) compared to the best result obtained from simple spectral reflectance <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$(r^{2} = 0.494)$</tex></formula> and simple spectral band ratios <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$(r^{2} = 0.59)$</tex></formula> . This was further improved to obtain a very promising result using the texture parameter of both sensors together ( <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$r^{2} = 0.897$</tex></formula> and <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$\hbox{rmse} = 32.38$</tex></formula> ), the texture parameters from the principal component analysis of both sensors ( <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$r^{2} = 0.851$ </tex></formula> and <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$\hbox{rmse} = 38.80$</tex></formula> ), and the texture parameters from the averaging of both sensors ( <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$r^{2} = 0.911$</tex></formula> and <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$\hbox{rmse} = 30.10$</tex></formula> ). Improvement was also observed using the simple ratio of the texture parameters of AVNIR-2 ( <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$r^{2} = 0.899$</tex> </formula> and <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$\hbox{rmse} = 32.04$</tex></formula> ) and SPOT-5 <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$(r^{2} = 0.916)$</tex></formula> , and finally, the most promising result ( <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$r^{2} = 0.939$</tex></formula> and <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$ \hbox{rmse} = 24.77$</tex></formula> ) was achieved using the ratios of the texture parameters of both sensors together. This high level of agreement between the field and image data derived from the two novel techniques (i.e., combination/fusion of the multisensor data and the ratio of the texture parameters) is a very significant improvement over previous work where agreement not exceeding <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$r^{2} = 0.65$</tex> </formula> has been achieved using optical sensors. Furthermore, biomass estimates of up to 500 t/ha in our study area far exceed the saturation levels observed in other studies using optical sensors.

Change detection of an earthquake-induced barrier lake based on remote sensing image classification

The earthquake-induced barrier lakes that are caused by landslides are at risk of bursting after excessive rainfall, and they are a serious threat to the surrounding residents. This paper proposes a change detection approach of the barrier lake using the method of post-classification comparison combined with background subtraction. The case studied in the paper is the detection of the Tangjiashan barrier lake, which is one of the largest lakes induced by the Wenchuan Earthquake on 12 May 2008. Both the pre- (31 March 2007) and post-earthquake (16 May 2008) images of the Advanced Land Observing Satellite (ALOS) Advanced Visible and Near Infrared Radiometer type 2 (AVINER-2) were collected in the paper. The results show that the river widened through the Zhangping area, increasing from 90 m pre-earthquake to 545 m post-earthquake, and the surface area of the barrier lake increased from 0.945 km2 to 1.471 km2.

Co-seismic landslides induced by the 2008 Wenchuan magnitude 8.0 Earthquake, as revealed by ALOS PRISM and AVNIR2 imagery data

The magnitude 8.0 Wenchuan Earthquake occurred on 12 May 2008 in Sichuan Province, China, resulting in numerous landslides along the marginal zone between the Tibetan Plateau and Sichuan Basin. We used Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) and Advanced Visible and Near Infrared Radiometer type 2 (AVNIR 2) data from the Advanced Land Observing Satellite (ALOS) and Landsat Enhanced Thematic Mapper (ETM) images acquired before and after the earthquake to identify co-seismic landslides. Interpretations of remote sensing images, combined with fieldwork, reveal that co-seismic landslides are mainly restricted to a corridor of <18 km in width about the co-seismic surface rupture zone along a length of >285 km along pre-existing active faults of the Longmenshan Thrust Belt. The landslides mainly occurred upon steep slopes (dips of 30–75°). The distribution and topographic features of co-seismic landslides indicate a close relationship with seismic slip along the co-seismic surface rupture zone. The locations of landslides are controlled by the tectonic topography developed along pre-existing active faults of the Longmenshan Thrust Belt. Our results demonstrate that remote sensing techniques provide a powerful tool for identifying co-seismic landslides produced in intermountain regions by strong earthquakes.

Remote Sensing of Suspended Particulate Matter in Himalayan Lakes

Abstract This study presents satellite data and in situ measurements to estimate the concentration of suspended solids in high-altitude and remote lakes of the Himalayas. Suspended particulate matter (SPM) concentrations measured in 13 lakes to the south of Mount Everest (Nepal) in October 2008 and reflectance values of the Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) onboard ALOS, acquired a few days after the fieldwork activities concluded, were combined to build a relationship (R2 = 0.921) for mapping SPM concentrations in lakes of the Mount Everest region. The satellite-derived SPM concentrations were compared with in situ data (R2 = 0.924) collected in the same period in 4 additional lakes, located to the north of Mount Everest (Tibet, China). The 13 water samples collected in lakes in Nepal were also used to investigate the absorption coefficients of particles ap(λ) and colored, dissolved organic matter aCDOM(λ), with the aim of parameterizing a bio-optical model. An accurate m...

Employing a Method on SAR and Optical Images for Forest Biomass Estimation

In this paper, we develop a novel method for forest biomass estimation. The intensity values of Advanced Land Observation Satellite-Advanced Visible and Near Infrared Radiometer type 2 and PRISM images and the texture features of the Japanese Earth Resources Satellite 1 image are used in a multilayer perceptron neural network (MLPNN) that relates them to the forest variable measurements on the ground. A proposed speckle noise model is also applied for modeling and reducing the speckle noise in the synthetic aperture radar (SAR) image. Reducing the speckle would improve the discrimination among different land use types and would make the textual classifiers more efficient in SAR images. Ideally, filters will reduce the speckle without loss of information. In the process of the forest biomass estimation, the filters should preserve the backscattering coefficient values and edges between different areas. We investigate both quantitative and qualitative criteria in speckle reduction and texture preservation to evaluate the performance of the proposed filter in the forest biomass estimation. We will also show that the biomass estimation accuracy is significantly improved in an MLPNN when the radar and the optical data are used in combination compared to estimating the biomass by using a single datum only. The root-mean-square error (rmse) value is decreased when the proposed method is used (rmse = 2.175 ton) compared with that of the classic method (rmse = 5.34 ton).

Improvement of AVNIR-2 Radiometric Calibration by Comparison of Cross-Calibration and Onboard Lamp Calibration

The Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) on-orbit radiometric performance has been improved through the comparison of cross-calibration and onboard lamp calibration. We proposed a new cross-calibration scheme which uses top-of-atmosphere reflectance functions of satellite zenith angle at temporally and spatially stable ground sites. Each function is made from Moderate Resolution Imaging Spectroradiometer (MODIS) 500-m resolution data over 16 days, which includes AVNIR-2 observation dates. The results showed that the radiances of AVNIR-2 bands 1 (463 nm), 2 (560 nm), and 3 (652 nm) agreed with the radiances of Aqua and Terra MODIS within 3% accuracy (standard deviation of 2%). AVNIR-2 band 4 (821 nm) had a difference of about 7% (AVNIR-2< MODIS) due to water-vapor absorption which could explain more than half of the 7%. Using many samples from this scheme, we found dependences of radiometric calibration errors within the field of view (FOV) and for different gain modes. The lamp calibration system onboard AVNIR-2 unveiled these dependences over the FOV and time. Furthermore, for the gain mode, consistent results could be retrieved using the cross-calibration scheme. The retrieved radiometric correction factors (over the FOV and gain modes) have been applied to the Japan Aerospace Exploration Agency AVNIR-2 processing scheme. The subsequent validation of the correction showed, for polar snow areas, an improved radiometric performance over the entire FOV.

A Strip Adjustment Approach for Precise Georeferencing of ALOS Optical Imagery

Precise georeferencing is one of the prerequisites for orthoimage generation from high-resolution satellite imagery. This requires the availability of a small number of GCPs that have to be visible in each scene. In this paper, it is shown how the number of GCPs required for the precise georeferencing of Advanced Land Observation Satellite (ALOS) imagery can be reduced by up to 90% using a generic pushbroom sensor model and strip adjustment while still achieving an accuracy of better than 1 pixel. A fully automatic work flow requiring an existing digital orthophoto and a digital elevation model (DEM) is also presented. Using an orthophoto mosaic generated from Landsat-7 panchromatic imagery for automatic GCP measurement, pixel-level accuracy can be achieved for images from the Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) instrument. For images from the Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM), the accuracy of the automated procedure is only about 2 pixels due to the poor resolution of the orthoimage, compared with PRISM.

Alos mission requirement and sensor specifications

Abstract Advanced Land Observing Satellite (ALOS) is an advanced remote sensing satellite of the mission dedicated for the global high resolution earth observation, with compatibility of high resolution and wide swath width observation, and simultaneous observation of three-dimensional land topography, two-dimensional spectral images and geodetic land surface deformation. This paper introduces ALOS mission requirement and corresponding sensor specifications and further potential of data utilization. ALOS will provide global high spatial resolution earth observation data sets from three remote sensing instruments. PRISM (Panchromatic Remote-sensing Instrument for Stereo Mapping); AVNIR-2 (Advanced Visible and Near Infrared Radiometer type 2) and PALSAR (Phased Array type L-band Synthetic Aperture Radar), for the generation of a spatial information infrastructure applicable to the geographic information system (GIS) useful to the various kind of applications.