Spectral Remote Sensing Research Articles

Conversion and Display of the Calculated Data of Spectral Remote Sensing Data on the Basis of GeoServer Extensions and Distributed Storage Technologies

The article presents methods for caching and displaying data from spectral satellite images using libraries of distributed computing systems that are part of the Apache Hadoop ecosystem, and GeoServer extensions. The authors gave a brief overview of existing tools that provide the ability to present remote sensing data using distributed information technologies. A distinctive feature is the way to convert remote sensing data inside Apache Parquet files for further display. This approach allows you to interact with the distributed file system via the Kite SDK libraries and switch on additional data processors based on Apache Hadoop technology as external services. A comparative analysis of existing tools, such as: GeoMesa, GeoWawe, etc is performed. The following steps are described: extracting data from Apache Parquet via the Kite SDK, converting this data to GDAL Dataset, iterating the received data, and saving it inside the file system in BIL format. In this article, the BIL format is used for the GeoServer cache. The extension was implemented and published under the Apache License on the GitHub resource. In conclusion, you will find instructions for installing and using the created extension.

Impact of Monsoon-Transported Anthropogenic Aerosols and Sun-Glint on the Satellite-Derived Spectral Remote Sensing Reflectance in the Indian Ocean

Spectral remote sensing reflectance (Rrs(λ), sr−1) is one of the most important products of ocean color satellite missions, where accuracy is essential for retrieval of in-water, bio-optical, and biogeochemical properties. For the Indian Ocean (IO), where Rrs(λ) accuracy has not been well documented, the quality of Rrs(λ) products from Moderate Resolution Imaging Spectroradiometer onboard both Terra (MODIS-Terra) and Aqua (MODIS-Aqua), and Visible Infrared Imaging Radiometer Suite onboard the Suomi National Polar-Orbiting Partnership spacecraft (VIIRS-NPP), is evaluated and inter-compared based on a quality assurance (QA) system, which can objectively grade each individual Rrs(λ) spectrum, with 1 for a perfect spectrum and 0 for an unusable spectrum. Taking the whole year of 2016 as an example, spatiotemporal pattern of Rrs(λ) quality in the Indian Ocean is characterized for the first time, and the underlying factors are elucidated. Specifically, QA analysis of the monthly Rrs(λ) over the IO indicates good quality with the average scores of 0.93 ± 0.02, 0.92 ± 0.02 and 0.92 ± 0.02 for VIIRS-NPP, MODIS-Aqua, and MODIS-Terra, respectively. Low-quality (~0.7) data are mainly found in the Bengal Bay (BB) from January to March, which can be attributed to the imperfect atmospheric correction due to anthropogenic absorptive aerosols transported by the northeasterly winter monsoon. Moreover, low-quality (~0.74) data are also found in the clear oligotrophic gyre zone (OZ) of the south IO in the second half of the year, possibly due to residual sun-glint contributions. These findings highlight the effects of monsoon-transported anthropogenic aerosols, and imperfect sun-glint removal on the Rrs(λ) quality. Further studies are advocated to improve the sun-glint correction in the oligotrophic gyre zone and aerosol correction in the complex ocean–atmosphere environment.

Hyper Spectral Remote Sensing for Mapping Species and Characteristics of Mangroves in Krishna Delta Region

Mangroves are globally classified as eastern and western groups of which 40% are found along Asian coasts. The largest identified mangroves are found in Indonesia, Brazil, Sunder bans of India and Bangladesh. Odum 1971 in his research referred mangroves serve as Juvenile stock and form most valuable Biomass. In the state of Andhra Pradesh the mangroves are concentrated in the deltas of Krishna and Godavari which add a healthy ecosystem. An extensive research in monitoring the nature and changes of Godavari delta mangroves using Remote sensing technologies. The mangroves are vastly reserves of different species of flora and are classified as single vegetative class in traditional multispectral imagery, where there is a possibility of losing information due to specific narrow band widths. Hence an attempt is being made for species level classification and characterization of Krishna delta region using hyper spectral remote sensing imagery. Hyper spectral remote sensing overcomes the limitation of extensive field work which is labor intensive and costly. The current research paper describes the species level classification of flora in Krishna delta using Hyper spectral remote sensing imagery.

Identification of Seed Maize Fields With High Spatial Resolution and Multiple Spectral Remote Sensing Using Random Forest Classifier

Seed maize and common maize plots have different planting patterns and variety types. Identification of seed maize is the basis for seed maize growth monitoring, seed quality and common maize seed supply. In this paper, a random forest (RF) classifier is used to develop an approach for seed maize fields’ identification, using the time series vegetation indexes (VIs) calculated from multispectral data acquired from Landsat 8 and Gaofen 1 satellite (GF-1), field sample data, and texture features of Gaofen 2 satellite (GF-2) panchromatic data. Huocheng and Hutubi County in the Xinjiang Uygur Autonomous Region of China were chosen as study area. The results show that RF performs well with the combination of six VIs (normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), triangle vegetation index (TVI), ratio vegetation index (RVI), normalized difference water index (NDWI) and difference vegetation index (DVI)) and texture features based on a grey-level co-occurrence matrix. The classification based on “spectrum + texture” information has higher overall, user and producer accuracies than that of spectral information alone. Using the “spectrum + texture” method, the overall accuracy of classification in Huocheng County is 95.90%, the Kappa coefficient is 0.92, and the producer accuracy for seed maize fields is 93.91%. The overall accuracy of the classification in Hutubi County is 97.79%, the Kappa coefficient is 0.95, and the producer accuracy for seed maize fields is 97.65%. Therefore, RF classifier inputted with high-resolution remote-sensing image features can distinguish two kinds of planting patterns (seed and common) and varieties types (inbred and hybrid) of maize and can be used to identify and map a wide range of seed maize fields. However, this method requires a large amount of sample data, so how to effectively use and improve it in areas lacking samples needs further research.

Estimate Land Surface Temperature in Relation to Land Use Types and Geological Formations Using Spectral Remote Sensing Data in Northeast Jordan

Land Surface Temperature (LST) is one of the important indicators to understand the spatial changes and surface processes on the earth surface that leads to actual assessment of environmental quality from local to global scales. The relation between spatial analysis of the land surface temperature and existing land use/land cover changes is important to evaluate the climate processes. Monitoring of this relation in the arid and semi-arid regions is necessary to make an appropriate decision about Land surface temperature and environmental status. In this paper, generally the split-window algorithm is used to estimate LST from thermal bands of the Landsat Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) using remote sensing and Geographic Information System (GIS) techniques as well as meteorological data through Moderate Resolution Imaging Spectroradiometer (MODIS). The results show the relationships between land use types and land surface temperature. MODIS data were analyzed. The relationship between MODIS and Landsat data temperature is moderate relation and the (R2 = 0.5109) according on 200 random points were selected. This research concludes that the maximum temperatures of the land use types in MODIS and Landsat data for the rock formation are 59° and 45° respectively, whereas the maximum temperatures of the geological formation in MODIS and Landsat data for the basalt are 59° and 45° respectively. In conclusion, the MODIS and Landsat OLI and TIRS Data have high ability to distinguish the land use types. The correlation coefficient of the relation between the surface temperature with rock was (R2 = 0.6197). Therefore, it is found that there is an ability to monitor the spatial and temporal changes for land surface temperature and thus it can be useful to environmental studies.

Mapping of Aluminous Rich Laterite Depositions through Hyper Spectral Remote Sensing

Increased dimensionality of the satellite data proves to be very useful for discriminating features with very close spectral matching. Present study concentrates on the retrieval of reflectance spectra from the level one radiometrically corrected data in Koraput district (Orissa) for the Bauxite ore. In the present study, atmospheric correction model FLAASH has been used to retrieve reflectance from the radiance data. Preprocessing of the dataset has been done before applying atmospheric correction on the dataset. Spectral subsetting of noise prone bands has been successfully done. Local destriping of the affected bands has been done using a 3*3 local mean filter. Spectral signatures of samples were derived from the processed data. Spectral signature of each sample and derived features vectors were correlated with the satellite image of the area and distribution of each feature was demarcated. Spatial abundance of each feature was used in preparation of mineral abundance map. Accuracy of the map was assessed using training sets of representative geological units. The mineral abundance mapping using the spectral analysis of the reflectance image involves the endmember collection using the N-Dimensional visualizer tool in ENVI software. Laterite, Bauxite, Iron and silica rich Aluminous laterite soil, Alluvium and Forest were selected as the end members after understanding the geology and analysis of the reflectance image. Various mapping techniques were applied to generate the final classified mineral abundance Map, Linear Spectral Unmixing, Mixture Tune Matched Filtering, Spectral Feature Fitting, Spectral Angle Mapper were the techniques used. Results have revealed the ability of Hyper spectral Remote sensing data for the identification and mapping of Hydrothermal altered products like Bauxite, Aluminous Laterite. This technology can be utilized for targeting minerals in the altered zone.

Modeling Soil Salinity and Mapping Using Spectral Remote Sensing Data in the Arid and Semi-arid Region

Modeling Soil Salinity and Mapping Using Spectral Remote Sensing Data in the Arid and Semi-arid Region

Study on Characteristic Chart of Multi\|spectral Remote Sensing Image for Typical Earthquake Secondary Geological Disasters

Study on Characteristic Chart of Multi\|spectral Remote Sensing Image for Typical Earthquake Secondary Geological Disasters

Deciduous Forest Structure Estimated with LIDAR-Optimized Spectral Remote Sensing

Coverage and frequency of remotely sensed forest structural information would benefit from single orbital platforms designed to collect sufficient data. We evaluated forest structural information content using single-date Hyperion hyperspectral imagery collected over full-canopy oak-hickory forests in the Ozark National Forest, Arkansas, USA. Hyperion spectral derivatives were used to develop machine learning regression tree rule sets for predicting forest neighborhood percentile heights generated from near-coincident Leica Geosystems ALS50 small footprint light detection and ranging (LIDAR). The most successful spectral predictors of LIDAR-derived forest structure were also tested with basal area measured in situ. Based on the machine learning regression trees developed, Hyperion spectral derivatives were utilized to predict LIDAR forest neighborhood percentile heights with accuracies between 2.1 and 3.7 m RMSE. Understory predictions consistently resulted in the highest accuracy of 2.1 m RMSE. In contrast, hyperspectral prediction of basal area measured in situ was only found to be 6.5 m2/ha RMSE when the average basal area across the study area was ~12 m2/ha. The results suggest, at a spatial resolution of 30 × 30 m, that orbital hyperspectral imagery alone can provide useful structural information related to vegetation height. Rapidly calibrated biophysical remote sensing techniques will facilitate timely assessment of regional forest conditions.

Analysis and Evaluation of Spectral Calibration Model for Imaging Spectral Remote Sensing

Analysis and Evaluation of Spectral Calibration Model for Imaging Spectral Remote Sensing

Comment on Fusion Theory for Different Spatial and Spectral Remote Sensing Images

Multi\|source remote sensing images fusion is an important method to acquire a higher spatial and spectral image resolution of the same object and can also provide a better tool for its application. Some different fusion methods are reviewed in this paper, they are classified into pixel\|level, feature\|level and decision\|level three categories, all of which are explored in detail respectively and compared each other. Finally, future trends are put forward for discussion.

Inversion of combustion gas temperature/concentration profile with radiation/turbulence interaction using SRS

The spectral remote sensing (SRS) method is applied to the combustion gas with radiation/turbulence interaction to invert the temperature and concentration profiles. The flame is made and controlled to be optically thin per each fluctuation length so that spatially fluctuating gas layer can be treated equivalently as timewisely fluctuating one sharing identical expression in the radiative transfer equation. Then, the spectral intensity, temperature and concentration distributions are measured for the inversion and as the reference solutions. From the inversion results, we find that SRS can successfully invert the coupled temperature/concentration fluctuation amplitudes and mean values. For the two cases of experiment, inverted values are in good agreement with measured ones. However, SRS cannot find the detailed local fluctuation parameter such as pattern or phase, etc. as far as they do not affect the resulting radiative intensity. So, it is deduced that the pattern or phase is not so influential, whereas the mean value and the fluctuation amplitude of temperature/concentration profile are important parameters for the radiative intensity in the case of the optically thin condition. Further, the radiation/turbulence interaction is verified to play an important role in the radiation.

Application of Spectral Remote Sensing for Agronomic Decisions

Remote sensing has provided valuable insights into agronomic management over the past 40 yr. The contributions of individuals to remote sensing methods have lead to understanding of how leaf reflectance and leaf emittance changes in response to leaf thickness, species, canopy shape, leaf age, nutrient status, and water status. Leaf chlorophyll and the preferential absorption at different wavelengths provides the basis for utilizing reflectance with either broad‐band radiometers typical of current satellite platforms or hyperspectral sensors that measure reflectance at narrow wavebands. Understanding of leaf reflectance has lead to various vegetative indices for crop canopies to quantify various agronomic parameters, e.g., leaf area, crop cover, biomass, crop type, nutrient status, and yield. Emittance from crop canopies is a measure of leaf temperature and infrared thermometers have fostered crop stress indices currently used to quantify water requirements. These tools are being developed as we learn how to use the information provided in reflectance and emittance measurements with a range of sensors. Remote sensing continues to evolve as a valuable agronomic tool that provides information to scientists, consultants, and producers about the status of their crops. This area is still relatively new compared with other agronomic fields; however, the information content is providing valuable insights into improved management decisions. This article details the current status of our understanding of how reflectance and emittance have been used to quantitatively assess agronomic parameters and some of the challenges facing future generations of scientists seeking to further advance remote sensing for agronomic applications.

Spectral Remote Sensing for Furnaces and Flames

The spectral remote sensing (SRS) technique to measure the gas temperature profile in a combustion system is introduced from fundamental concept to lab-size bench test. As the bases of the technique, computationally working models of narrow band emission in the CO2 4.3μ -wide band are tested, and inversion algorithms to convert the spectral intensity data into a temperature profile are proposed. Actual applications to three lab-size test benches are made to confirm the practical applicability of the technique. With the latest inversion methods and instruments, the SRS technique can retrieve the temperature profile within a 20–30 K error for peak temperature of 1500 K within seconds. A scheme called BCIM incorporating base function approach and nodal temperature approach is recommended. It is also shown that a pure concentration problem cannot be handled by SRS. However, SRS can be applied to combustion flames thanks to a strong temperature/concentration correlation.

Characteristics of SRS inversion for measurement of temperature and CO 2 concentration profile of a combustion gas layer

The spectral remote sensing (SRS) inversion technique to determine the complex, line-of-sight temperature distribution from gas emission data at CO 2 4.3 μm is analytically investigated with two proposed algorithms. The modified constrained inversion method (MCIM) and the base function based inversion method (BFIM) are extensively tested to evaluate their convergence characteristics for practical applications. The MCIM shows good applicability only to relatively simple profiles, while the BFIM gives reasonable estimations even for multi-peak profiles provided that appropriate base functions are selected. Further, simultaneous inversion of temperature and concentration is tried and it is found to be accurately achieved when there is an inter-correlation between them.

Measurement of gas temperature profile using spectral intensity from CO 2 4.3 μm band

Spectral remote sensing (SRS) method for determining the temperature profile along a line-of-sight is investigated experimentally. Quartz tube, within which combustion gas flows, is used as the test section. The inversion procedure is carried out with a line-by-line (LBL) method and a CK-based WNB model. The optimal gas temperature profile that minimizes the error between the measured narrow band intensities around the CO 2 4.3 μm band and the calculated ones is obtained as the result of the inversion process. The gas temperature is also measured with a shielded thermocouple and corrected for the error. The results show that the front, center and back temperatures are within errors of 1%, 4% and 12%, respectively. SRS technique shows poor performance in measuring cold gas temperatures behind a hot layer. The reconstructed temperature profile in the front region is, however, in good agreement with the thermocouple reading. The potential applicability of SRS is positively demonstrated and current technical limitations are also discussed.

Measurement of gas temperature distributions in a test furnace using spectral remote sensing

Measurement of a temperature profile of a hot gas is made using the Spectral Remote Sensing (SRS) method. Emphasis is placed on the accuracy of the SRS method in a test furnace with intense radiation emerging from the gas as well as from the rear wall. Kerosene is used as the fuel and a 2 m long stainless steel (STS) tube is employed as the test furnace. In calculating spectral intensity, the CK-based WNB model is used. By minimizing the error between the measured spectral intensities and the computed ones, the temperature profile along the line-of-sight is recovered. To demonstrate the SRS method, the recovered temperatures are compared with measured temperatures using thermocouples. The recovered temperature profile and measured temperatures are in good agreement within an error of 4%. Especially, the hot rear wall is found to affect the overall performance of the SRS method significantly through relatively heavily weighted Planck distribution there. The SRS method, however, proves to be applicable in determining the temperature distribution of the gas with a reasonable accuracy whether the rear wall is hot or cold.

Conceptual Development of a Eucalypt Canopy Condition Index Using High Resolution Spatial and Spectral Remote Sensing Imagery

Recent advances in remote sensing technologies and image analysis provide a realistic opportunity to develop a reliable, robust indicator of forest health for Australian eucalypt forests. Using these technologies we present the development of a conceptual framework for a new indicator based on eucalypt canopy condition. The indicator, termed the Eucalypt Canopy Condition Index (ECCI), is derived from an assumed hierarchy of canopy decline symptoms, both physiological and structural. Our objective is to identify and discuss possible combinations of spectral, radiometric and spatial (scale) features from remotely sensed data that theoretically and practically correlate with each hierarchical phase of severity in canopy decline. Some of the issues associated with the development and application of the ECCI are discussed, and finally, how the ECCI could be integrated into existing or future operational systems is illustrated.

Error analysis of spectral remote sensing by CO 2 4.3 μm band in various temperature profiles

The inversion error of temperature profiles reconstructed by the spectral remote sensing (SRS) technique is analyzed by using CO 2 4.3 μ m band. Total of 14 narrow bands averaged over 25 cm −1 spectral width is selected as the low-resolution sensing spectra from 2050 to 2400 cm −1 . The HITRAN line-by-line database enhanced with hot line data is utilized as the tool to generate the reference spectral intensity. The medium is assumed to be filled with nonisothermal pure CO 2 gas at 1 atm. Four temperature profiles are tested in this work: three boundary layer types, and one prabolic type. Continuous temperature distribution is assumed by specifying three pointwise temperatures. Through this work, it is investigated how much inversion error of temperature is induced per unit relative error in ‘true’ and ‘measured’ intensities when changing the number of sensing narrow bands, combination of narrow bands, path length, temperature profile shape, temperature level, and temperature gradient. The error analyses show that the SRS inversion with this band gives accurate inversion of temperature within 1% error when both of the measured and the reference intensities have 1% error for any temperature profile up to 2500 K and up to about 10 m path length. It is also found that a cold layer behind a hot layer is not accurately detected for higher path length, so the sensor is better to be located at the colder side.

Mechanism Analysis of Leaf Biochemical Concentration by High Spectral Remote Sensing

Mechanism Analysis of Leaf Biochemical Concentration by High Spectral Remote Sensing