True Color Composites Research Articles

Spatiotemporal variability of dust storm source susceptibility during wet and dry periods: The Tigris-Euphrates River Basin

This study develops a framework for spatiotemporal modeling of dust storm source susceptibility in a critical case study, the Tigris-Euphrates River Basin, as a significant source of dust storms in the Middle East. The study period was divided into four periods, 2000–2004 (hydrological dry year), 2005–2007 (hydrological wet year), 2008–2012 (hydrological dry year), and 2013–2021 (hydrological wet year) representing hydrological conditions in the study area. Initially, visual interpretations of true color composites of the MODIS satellite images were conducted to spot dust storm sources in the studied periods. Topographical, hydrological, soil texture, and vegetation health datasets were prepared to model dust storm source susceptibility in each period. The random forest algorithm was implemented on the four study periods’ datasets. For each period, 70% of dust and non-dust storm sources and conditioning factors were used for training the models. The models were then validated using the validation datasets (remaining 30%), and the importance of the variables was determined for each study period. In the 2008–2012 period, experiencing an extensive drought in the region, a higher number of dust storm sources were detected, and 383 locations (pixels) in the area were considered highly susceptible to dust storm sources. In all study periods, as well as in the ensemble model (integrating the results of four study periods into one overall model), high susceptibility to dust storms was detected in areas where lakes and marshlands had dried up due to climate factors, inappropriate water management strategies, and land use policies. The results also depicted that elevation, wind speed, precipitation, vegetation coverage, slope degree, distance from rivers, and soil texture had high impacts on the susceptibility of land to be a dust storm source.

The Thermal Manifestation Monitoring Program at Muara Labuh Case Studies

The thermal manifestations at the Muara Labuh field are monitored regularly to see their behaviors in response to the field production. The monitoring results are then used to identify and mitigate any potential hazards associated with the thermal area changes. This paper presents the monitoring results on the main thermal features Idung Mancung (IM) fumaroles complex, located in the vicinity of the steam cap production zone in the Northeast (NE) Sector of the field. Due to its proximity to the main production area, this thermal complex is considered to be susceptible to any reservoir changes. The thermal manifestations characteristics monitored are areal extent, numbers of the thermal funnels, the activities, physical and chemical characteristics, and geophysical signatures. Baseline data were taken from previous remote sensing studies, geology maps, and thermal manifestation geochemistry surveys prior to production periods. Remote sensing studies using true color composite (TCC) and NDVI suggest that the IM fumarole area covers the wider area prior to stage-1 project development in 2017. After production, periodic aerial photo analysis suggests no changes in IM fumarole thermal area growth. The fact is supported by the periodic Thermal Infra-Red Image (TIR), which shows no sign of thermal anomaly still contained inside the IM fumarole baseline area. IM fumarole field checks show that lack of thermal area changes and no sign that the manifestations have become more aggressive. Temperature and flow rate observation on several manifestation funnels indicates that rainfall rate variation affects the manifestation activities and temperature. As for the fumarole gas chemistry, the IM main fumarole shows a decreasing trend in NCG and CO2 contents from 2016 to 2021 and increasing in H2S, which indicates the return of degassing fluid. After one year of field operation, the microgravity and leveling survey in the IM fumarole area show slight changes from -0.1 to -0.14 mGal. The changes correspond with the area subsidence rate of -10 to -15 mm/yr. These changes are still considered low compared to other operating geothermal fields and did not directly affect the thermal manifestation activity.

Satellite-Based Characterization of Convection and Impacts from the Catastrophic 10 August 2020 Midwest U.S. Derecho

Abstract The catastrophic derecho that occurred on 10 August 2020 across the midwestern United States caused billions of dollars of damage to both urban and rural infrastructure as well as agricultural crops, most notably across the state of Iowa. This paper documents the complex evolution of the derecho through the use of low-Earth-orbit passive-microwave imager and GOES-16 satellite-derived products complemented by products derived from NEXRAD weather radar observations. Additional satellite sensors including optical imagers and synthetic aperture radar (SAR) were used to observe impacts to the power grid and agriculture in Iowa. SAR improved the identification and quantification of damaged corn and soybeans, as compared to true-color composites and normalized difference vegetation index (NDVI). A statistical approach to identify damaged corn and soybean crops from SAR was created with estimates of 1.97 million acres of damaged corn and 1.40 million acres of damaged soybeans in the state of Iowa. The damage estimates generated by this study were comparable to estimates produced by others after the derecho, including two commercial agricultural companies.

Land cover classification of spaceborne multifrequency SAR and optical multispectral data using machine learning

This study compares the utility of multifrequency SAR and Optical multispectral data for land-cover classification of Mumbai city and its nearby regions with a special focus on water body mapping. The L-band ALOS-2 PALSAR-2, X-band TerraSAR-X, C-band RISAT-1, and Sentinel-2 datasets have been used in this work. This work is done as a retrospective study for the dual-frequency L and S-band NASA-ISRO Synthetic Aperture Radar (NISAR) mission. The ALOS-2 PALSAR-2 data has been pre-processed before the implementation of machine learning algorithms for image segmentation. Multi-looking is performed on ALOS-2 PALSAR-2 data to generate square pixels of size 5.78 m and then target decomposition is applied to generate a false-color composite RGB image. While in the case of TerraSAR-X and RISAT-1 datasets, no multi-looking was performed and direct target decomposition was applied to generate false-color composite RGB images. Similarly, for the optical dataset that has a resolution of 10 m, a true color composite, and a false color composite RGB image are generated. For the comparative study between ALOS-2 PALSAR-2 and Sentinel-2 dataset, the RGB images are divided into smaller chunks of size 500*500 pixels each to create a training and testing dataset. Ten image patches were taken from the large dataset, out of which eight patches were used to train the machine learning models Random Forest (RF), K Nearest Neighbor (KNN), and Support Vector Machine (SVM), and two patches were kept for testing and validation purpose. For training the machine learning models, feature vectors are generated using the Gabor filter, Scharr filter, Gaussian filter, and Median filter. For patch 1, the mIOU for true-color composite based Optical image varies from 0.2323 to 0.2866 with the RF classifier performing the best and the mIOU for false-color composite based Optical image varies from 0.4130 to 0.4941 with the RF classifier performing the best while for ALOS-2 PALSAR-2 data, the mIOU varies from 0.4033 to 0.4663 with the RF classifier outperforming the KNN and the SVM classifiers. For patch 2, the mIOU for true-color composite based Optical data varies from 0.3451 to 0.4517 with KNN performing the best and the mIOU for false-color composite based Optical image varies from 0.5156 to 0.5832 with the RF classifier performing the best while for ALOS-2 PALSAR-2 data, the mIOU varies from 0.4600 to 0.5178 with the RF classifier outperforming the KNN and the SVM classifiers. The gap between the performance of ALOS-2 PALSAR-2 data and Sentinel-2 optical data is observed when the IOU of water class is compared, with IOUw for the true-color composite based optical image at a maximum of 0.2525 and for false-color composite based optical image at a maximum of 0.7366 while for ALOS-2 PALSAR-2 data a maximum IOUw of 0.7948 is achieved. The better performance of SAR data as compared to true-color composite based optical image data is due to the misclassification of ground and water classes into urban and forest in the case of the true-color composite based optical dataset which can be attributed to the high similarity between water and forest classes in the case of true-color composite based optical data whereas both these classes are easily separable in case of SAR data. This issue is however resolved by using the false color composite based optical image dataset for the classification task which performs slightly better than ALOS-2 PALSAR-2 data in the overall classification task. However, the SAR data works best in water body detection as notable from the high IOU for water class in the case of SAR data. In addition to the comparative analysis between Sentinel-2 optical and ALOS-2 PALSAR-2 data, land-cover classification has been performed on X-band TerraSAR-X and C-band RISAT-1 data on a single patch and it has been found that the RF classifier performs the best, recording the mIOU 0.5815 for X-band TerraSAR-X data, mIOU of 0.4031 for the C-band RISAT-1 data, and mIOU of 0.6153 for the L-band ALOS-2 data.

Detecting the Great Pacific Garbage Patch floating plastic litter using WorldView-3 satellite imagery.

We present a direct and proxy-based approach to qualitatively and semi-quantitatively observe floating plastic litter in the Great Pacific Garbage Patch (GPGP) based on a survey in 2018 using very high geo-spatial resolution 8-waveband WorldView-3 imagery. A proxy for the plastics was defined as a waveband difference for anomalies in the top-of-the-atmosphere spectra. The anomalies were computed by subtracting spatially varying reflectance of the surrounding ocean water as background from the top-of-the-atmosphere reflectance. Spectral shapes and magnitude were also evaluated using a reference target of known plastics, The Ocean Cleanup System 001 Wilson. Presence of 'suspected plastics' was confirmed by the similarity in derived anomalies and spectral shapes with respect to the known plastics in the image as well as direct observations in the true color composites. The proposed proxy-based approach is a step towards future mapping techniques of suspected floating plastics with potential operational monitoring applications from the Sentinel-2 that recently started regular imaging over the GPGP that will be supported or validated by numerical solutions and net trawling survey.

Application of remote-sensing techniques in geological and structural mapping of Atalla Shear Zone and Environs, Central Eastern Desert, Egypt

The Atalla Shear Zone (ASZ) is one of the crucial megashears which are geometrically and kinematically akin to the Najd-Shear Corridor in the central Eastern Desert of Egypt. The Landsat-8 and ASTER-based mapping techniques integrated with extensive petrographic and structural-field investigations enhanced the geological and structural maps of the study area. The Neoproterozoic basement complex in the study area discriminated into ditinctive rock suites: Meatiq Group, ophiolitic melange, arc assemblage, syn- to late-tectonic granitoids, Dokhan Volcanics, Hammamat Sediments, post-Hammamat Felsites and post-tectonic granitoids. The true color composites (TCC) (4 3 2 for Landsat-8; 3 2 1 for ASTER), false color composites (FCC) (1 6 4, 1 6 7 and 7 5 1 for Landsat-8; 5 4 3 and 9 6 4 for ASTER), principal component analysis (PCA) (1 2 3, 3 2 1 and 6 4 2 for Landsat-8; 1 2 3 and 5 4 2 for ASTER), minimum noise fraction (MNF) (1 2 3 and 3 5 1 for Landsat-8; 1 2 4 and 3 2 1 for ASTER) and band ratios (BR) (6/2, 6/7, 6/5*4/3 and 7/6, 7/5, 5/3 for Landsat-8; 2/6, 7/5, 7/6 and 4/7, 3/4, 2/1 for ASTER besides the new created ratio 2/6, 7/5, 7/6) are the best combinations that demonstrate efficiency in discrimination of lithologic contacts and structural elements, using the spectral signature of different rock units. Moreover, the BR b7/b5, b5/b7 and b6/b4 of grey scale for Landsat-8 are used as a tool for mineral detection along with the spectral indices created to detect muscovite (b7/b6), ferrous silicates b5/b4 (biotite, chlorite and amphiboles) and ferrous iron b5/b3 (mafic minerals). The automatic lineament extraction utilizing the SRTM data advocated the role of NE- to NNE-trending lineaments in the tectonic framework of the study area. The structural fabric of the study area is evaluated in three main structural domains: Meatiq (MD), Atalla Shear Zone (ASZD) and Wadi Hammamat (WHD). The pronounced structural implication is the transposition of NE-oriented fabric in MD into NW-oriented penetrative shear-related fabric, fully in the ASZD and partly in WHD. The combination of transcurrent shearing with ENE-directed shortening along the ASZ that was resulted in noteworthy transpressive structures resemble in many respects those observed in the Ajjaj Shear Belt in Western Arabia. Among these structures are shear zone-related folds, imbricated thrust sheets, antiformal stacks and thrust duplexes. The deformation history of the investigated area proposed four phases of deformation (D1–D4). It depicts a long lasted deformational event covering the main accretion-collision phases concurrent with the E-W assembly of Gondwana, and the subsequent post-collision Najd-related transpressional phase which resulted in several major left-lateral transcurrent shear zones along with the exhumation of gneissic core complexes in the Arabian-Nubian Shield.

Natural color representation of Sentinel-2 data

The true color composite is a widely used Earth observation product for displaying satellite imagery. As it is often used in communication with non-expert audiences in various media, it is desirable that such a product approximates the color perceived by the human eye. Additionally, as the Sentinel-2 mission with its high resolution multispectral imagery and short revisit times is delivering unprecedented amounts of data, any algorithm for computing the composite should be efficient. In this paper we define the natural color product, propose two efficient approaches for computing it, analyze the results, and implement the products on a satellite imagery service for interactive use. Our algorithms work on a per-pixel basis and hence parallelize naturally. The presented approaches are general and not limited to Sentinel-2 data.

DETECTION OF FOREST FIRE, SMOKE SOURCE LOCATIONS IN KALIMANTAN DURING THE DRY SEASON FOR THE YEAR 2015 USING LANDSAT 8 FROM THE THRESHOLD OF BRIGHTNESS TEMPERATURE ALGORITHM

Almost every dry season, there are large forest/land fires in several regions in Indonesia, especially in Kalimantan and Sumatra in the dry season of August to September 2015 a forest fire in 6 provinces namely West Kalimantan, Central Kalimantan, South Kalimantan, Riau, Jambi, and South Sumatra. Even some parties proposed that the Government of Indonesia declares them as a national disaster. The low-resolution remote sensing data have been widely used for monitoring the occurrence of forest/land fires (hotspots), and mapping of burnt scars. The hotspot detection was done by utilizing the data of NOAA-AVHRR and MODIS data which have a lower spatial resolution (1 km). In order to increase the level of detail and accuracy of product information, this research is done by using Landsat 8 TIRS (Thermal Infrared Sensor) band which has a greater spatial resolution of 100 m. The purpose of this research is to find and to determine the threshold value of the brightness temperature of the TIRS data to identify the source of fire smoke. The data used is the Landsat 8 of several parts of Borneo during the period of 24 August to 18 September 2015 recorded by the LAPAN's receiving station. Landsat - 8 TIRS band was converted into brightness temperature in degrees Celsius, then dots in a region that is considered the source of the smoke if the temperature of each pixel in the region > 43oC, and given the attributes with the highest temperatures of the pixels in the region. The source of the smoke was obtained through visual interpretation of the objects in the multispectral Natural Color Composite (NCC) and True Color Composite (TCC) images. Analysis of errors (commission error) is obtained by comparing the temperature detected by TIRS band with a visual appearance of the source of the smoke. The result of the experiment showed that there were detected 9 scenes with high temperatures over 43oC from the 27 scenes Kalimantan Landsat 8 data, which include 153 sites. The accuracy (commission error) of identification results using temperature ≥ 51°C is 0%, temperature ≥ 47°C is 10%, and temperature ≥ 43°C is 30.5%.

Regional Changes in Earth's Color and Texture as Observed From Space Over a 15-Year Period

Earth-observing satellites provide global observations of many geophysical variables. As these variables are derived from measured radiances, the underlying radiance data are the most reliable sources of information for change detection. Here, we identify statistically significant trends in the color and spatial texture of the Earth as viewed from multiple directions from the Multi-angle Imaging SpectroRadiometer (MISR), which has been sampling the angular distribution of scattered sunlight since 2000. Globally, our results show that the Earth has been appearing relatively bluer (up to 1.6% per decade from both nadir and oblique views) and smoother (up to 1.5% per decade only from oblique views) over the past 15 years. The magnitude of the global blueing trends is comparable to that of uncertainties in radiometric calibration stability. Regional shifts in color and texture, which are significantly larger than global means, are observed, particularly over polar regions, along the boundaries of the subtropical highs, the tropical western Pacific, Southwestern Asia, and Australia. We demonstrate that the large regional trends cannot be explained either by uncertainties in radiometric calibration or variability in total or spectral solar irradiance; hence, they reflect changes internal to the Earth's climate system. The 15-year-mean true color composites and texture images of the Earth at both nadir and oblique views are also presented for the first time.

Characterization of harmful algal blooms (HABs) in the Arabian Gulf and the Sea of Oman using MERIS fluorescence data

In this study, MERIS fluorescence data were utilized to monitor a toxin-producing dinoflagellate Cochlodinium bloom in 2008 in the Arabian Gulf and the Sea of Oman. The bloom was characterized using modified fluorescence line height (MFLH), enhanced Red–Green–Blue (ERGB) and true color composites, and the ratio of particulate backscattering (bbp) to MFLH (bbp/MFLH). In addition to high MFLH values and dark colors in ERGB images which are generally observed when blooms happen, it was found that the Cochlodinium bloom indicated species–specific signatures which consisted of reddish brown colors in true color composites and bbp/MFLH values below 0.2mW−1cm2μmm−1sr. Based on these findings, Cochlodinium blooms were successfully distinguished from blooms dominated by other species that were found in the study area, like diatom, Noctiluca, and Trichodesmium. Qualitative analysis showed that the fluorescence-based approach presented better performance than the chlorophyll-a anomaly approach for HAB detection, despite the sensitivity to atmospheric perturbations, benthic vegetation in coastal shallow waters, and variations in environmental conditions. The applicability of the HAB characterization approach tested for the first time over the study area using MERIS data was discussed and can be anticipated with sufficient knowledge of local bloom history. Combing different ocean color products is strongly recommended to improve our understanding of HAB dynamics and enhance our ability to characterize them. This is of great importance for marine environment protection and management and can lead to valuable information for contingency planning.

The Processing Algorithms and EML Modeling of True Color Synthesis for SPOT5 Image

SPOT-5 image is widely used in every trade by its special advantage of high spatial resolution and stereo mapping. SPOT-5 image is difficult to composite true color without blue wavelength channel. This article aims to achieve approximation from RS images colors to natural colors through true-color processing. The experiential arithmetic module of true-color composite is established by Using Erdas Imagine, which is the software for processing RS image, and ERDAS Macro Language (EML), which is modeling language. Loading module and some simulations get more effective images of approximating natural colors by all kinds of surface features of SPOT-5 image.

AUTOMATED CITRUS TREE COUNTING FROM OBLIQUE OR ORTHO IMAGES AND TREE HEIGHT ESTIMATION FROM OBLIQUE IMAGES

The objectives of this research paper were to 1) compare tree counts between oblique and ortho images and 2) determine the accuracy of tree height measurements from oblique images. Accurate determination of tree count and canopy volume can aid growers in planning some orchard operations and estimating yield. The analytical capability of Feature Analyst (FA) to count trees enables the user to quantify the trees in a given area. Eight sample sets of ortho and oblique images were selected from Pictometry aerial imagery software (Pictometry International Corp, 100 Town Center Drive, Suite A, Rochester, NY, 14623) by entering the coordinates of the required area of interest. The images thus obtained were exported to Arc Map in ortho and oblique views. The same training samples randomly selected from the study area in the ortho view were also used for the oblique image in FA to validate/determine the number of trees. The image was represented by a shape file, and an attribute query denoted the quantity of trees. After processing with FA, the majority of the machine counts were below the actual value. Three of the eight ortho images represented over counts, while five represented under count with a total range of -16 to +23%, with a mean error of 8.67%. All the eight oblique images represented under counts with an error range of 0.26 to 27.82% with a mean error of 9.94%. Thus, Oblique images had an error range of 27.56%, while orthogonal images have an error range of 39%, but overall the average error was less for ortho images. An estimate was made of tree height using oblique images in Pictometry. The errors were partially due to the ambiguity in selecting each tree’s base and apex. Assuming there may be 0.15 m error due to resolution in ground area covered and tree height centering contributed an 11% error, overall precision in tree canopy volume measurement was still 85%. INTRODUCTION Feature Analyst (FA) has the ability to count trees in a given acreage. Saraswat et al. (2007) used FA to count the trees in citrus groves in Polk County, Florida. They digitized a few trees in the study area and used these samples to determine the total number of trees in the study area (Saraswat et al., 2008). Attempts were made by previous researchers to determine tree height using Lidar data and digital roof models, but heights could not be determined to 100% accuracy. Imai et al. created a digital height model (DHM) by subtracting Digital Elevation model from a Digital Surface model. Hashiba et al. (2003) generated a digital roof model (DRM) using a typical stereogram concept and basic trigonometric calculations were executed to calculate the tree height. Pictometry is aerial imaging software that has the capabilities to measure the tree height and ground area covered from ortho and oblique images. The aerial images were collected by running flight line mosaics across the study area. These two factors are required to calculate the tree canopy volume. The combination of these factors to determine canopy volume per block then enables estimating the yield with weather factors that determine flowering intensity (Valiente and Albrigo, 2004; Albrigo et al., 2002). Proc. IS on Appl. of Precision Agric. for Fruits and Vegetables Eds.: L.G. Albrigo and R. Ehsani Acta Hort. 824, ISHS 2009 92 MATERIAL The Citrus Research and Education Center groves in Florida were chosen as the study area. The images were randomly selected from four blocks. Eight other sites were identified and ortho and oblique images were obtained for these sites. These were 0.15 m resolution images provided by the Pictometry Visual Intelligence Company. Images were collected by running flight line mosaics across the study area. Each image had three bands (RGB) stacked as a true color composite. Five additional sites were identified and ortho images were extracted from the Pictometry image source. The same site areas (0.3 m resolution) were extracted from a Polk county tax assessor source. All the trees in the study area were citrus.

Comparison of Landsat-7 Enhanced Thematic Mapper Plus <inline-formula><math display="inline" overflow="scroll"><mo>(</mo><mi>ETM</mi><mo>+</mo><mo>)</mo></math></inline-formula> and Earth Observing One (EO-1) Advanced Land Imager

We compare images from the Enhanced Thematic Mapper Plus (ETM+) sensor on Landsat-7 and the Advanced Land Imager (ALI) instrument on Earth Observing One (EO-1) over a test site in Rochester, New York. The site contains a variety of features, ranging from water of varying depths, deciduous/coniferous forest, grass fields, to urban areas. Nearly coincident cloud-free images were collected one minute apart on 25 August 2001. We also compare images of a forest site near Howland, Maine, that were collected on 7 September 2001. We atmospherically corrected each pair of images with the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) atmosphere model, using aerosol optical thickness and water vapor column density measured by in situ Cimel sun photometers within the Aerosol Robotic Network (AERONET), along with ozone density derived from the Total Ozone Mapping Spectrometer (TOMS) on the Earth Probe satellite. We present true-color composites from each instrument that show excellent qualitative agreement between the multispectral sensors, along with gray-scale images that demonstrate a significantly improved ALI panchromatic band. We quantitatively compare ALI and ETM+ reflectance spectra of a grassy field in Rochester and find 6% differences in the visible/near infrared and ~2% differences in the short-wave infrared. Spectral comparisons of forest sites in Rochester and Howland yield similar percentage agreement except for band 1, which has very low reflectance. Principal component analyses and comparison of normalized difference vegetation index histograms for each sensor indicate that the ALI is able to reproduce the information content in the ETM+ but with superior signal-to-noise performance due to its increased 12-bit quantization.