Remote Sensing of Broom Snakeweed (Gutierrezia Sarothrae) with Noaa-10 Spectral Image Processing

1992 ◽  
Vol 6 (4) ◽  
pp. 1015-1020 ◽  
Author(s):  
Albert J. Peters ◽  
Bradley C. Reed ◽  
Marlen D. Eve ◽  
Kirk C. McDaniel
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
New Mexico ◽  
Spatial Resolution ◽  
Satellite Imagery ◽  
Spectral Image ◽  
Late Season ◽  
Shortgrass Prairie ◽  
Gutierrezia Sarothrae ◽  
Meteorological Satellite

Low-spatial resolution satellite imagery from the NOAA-10 polar-orbiting meteorological satellite was analyzed to determine if central New Mexico grasslands infested by broom snakeweed could be discriminated from unaffected areas. Distinctive phenological characteristics of broom snakeweed, including an early season growth flush and late season flowering, enable moderate to heavily infested areas to be separated from grasslands having few or no weeds present. The procedure used shows promise as a tool for locating and monitoring brown snakeweed and other weeds growing on shortgrass prairie.

scholarly journals Ground Data Inputs To Image Processing for Estimating Terrain Characteristics for Glacio-Hydrological Analysis

1987 ◽  
Vol 9 ◽  
pp. 45-49 ◽  
Author(s):  
M.J. Clark ◽  
A.M. Gurnell ◽  
P.J. Hancock
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Satellite Imagery ◽  
Geometric Correction ◽  
Data Set ◽  
Glacial Environments ◽  
Methodological Problems ◽  
Terrain Characteristics ◽  
Radiometric Data

Remote-sensing research in glacial and pro-glacial environments raises several methodological problems relating to the handling of ground and satellite radiometric data. An evaluation is undertaken of the use of ground radiometry to elucidate properties of relevant surface types in order to interpret satellite imagery. It identifies the influence that geometric correction and re-sampling have on the radiometric purity of the resulting data set. Methodological problems inherent in deriving catchment terrain characteristics are discussed with reference to currently glacierized and pro-glacial zones of south-western Switzerland.

Multi-Spectral Image Fusion Using Intensity Modulation and Non-Separable Wavelet Frame

2005 ◽  
Author(s):  
Xuhong Yang ◽  
Zhongliang Jing ◽  
Jian-Xun Li
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Spatial Resolution ◽  
Intensity Modulation ◽  
Spectral Information ◽  
Wavelet Frame ◽  
Remote Sensing Images ◽  
Spectral Image ◽  
Panchromatic Image ◽  
Fusion Approach

A fusion approach is proposed to refine the resolution of multi-spectral images using the corresponding high-resolution panchromatic images. The technique is based on intensity modulation and non-separable wavelet frame. The high-resolution panchromatic image is decomposed by the non-separable wavelet frame. Then the wavelet coefficients are used as the factor of modulating to modulate the multi-spectral image. Experimental results indicate that, comparing with the traditional methods, the proposed method can efficiently preserve the spectral information while improving the spatial resolution of remote sensing images.

scholarly journals Remote Sensing Phenology of Antarctic Green and Red Snow Algae Using WorldView Satellites

2021 ◽  
Vol 12 ◽  
Author(s):  
Andrew Gray ◽  
Monika Krolikowski ◽  
Peter Fretwell ◽  
Peter Convey ◽  
Lloyd S. Peck ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Cell Density ◽  
Spatial Resolution ◽  
Satellite Imagery ◽  
Algal Blooms ◽  
Vegetation Index ◽  
Net Primary Productivity ◽  
Snow Algae ◽  
Antarctic Snow ◽  
Sentinel 2

Snow algae are an important group of terrestrial photosynthetic organisms in Antarctica, where they mostly grow in low lying coastal snow fields. Reliable observations of Antarctic snow algae are difficult owing to the transient nature of their blooms and the logistics involved to travel and work there. Previous studies have used Sentinel 2 satellite imagery to detect and monitor snow algal blooms remotely, but were limited by the coarse spatial resolution and difficulties detecting red blooms. Here, for the first time, we use high-resolution WorldView multispectral satellite imagery to study Antarctic snow algal blooms in detail, tracking the growth of red and green blooms throughout the summer. Our remote sensing approach was developed alongside two Antarctic field seasons, where field spectroscopy was used to build a detection model capable of estimating cell density. Global Positioning System (GPS) tagging of blooms and in situ life cycle analysis was used to validate and verify our model output. WorldView imagery was then used successfully to identify red and green snow algae on Anchorage Island (Ryder Bay, 67°S), estimating peak coverage to be 9.48 × 104 and 6.26 × 104 m2, respectively. Combined, this was greater than terrestrial vegetation area coverage for the island, measured using a normalized difference vegetation index. Green snow algae had greater cell density and average layer thickness than red blooms (6.0 × 104 vs. 4.3 × 104 cells ml−1) and so for Anchorage Island we estimated that green algae dry biomass was over three times that of red algae (567 vs. 180 kg, respectively). Because the high spatial resolution of the WorldView imagery and its ability to detect red blooms, calculated snow algal area was 17.5 times greater than estimated with Sentinel 2 imagery. This highlights a scaling problem of using coarse resolution imagery and suggests snow algal contribution to net primary productivity on Antarctica may be far greater than previously recognized.

scholarly journals Analytical review of remote sensing data sources suitable for mapping of land resource in Chernivtsi region

2013 ◽  
pp. 331-338
Author(s):  
J. Smirnov
Keyword(s):  
Remote Sensing ◽  
Key Words ◽  
Spatial Resolution ◽  
Satellite Imagery ◽  
Remote Sensing Data ◽  
Data Sources ◽  
Land Resource ◽  
Land Resources ◽  
Sensing Data ◽  
Analytical Review

In the article described the sources of remote sensing data and analyzed their suitability for involvement in the process Chernivtsi region land resources mapping. Taken into account space surveying systems of different spatial resolution and aerial photographic surveys. As a result, have been identified the best sources of data that can be used in the Chernivtsi region land resources mapping. Key words: land resources, remote sensing, satellite imagery, mapping of land resources, sources of remote sensing data.

scholarly journals Ground Data Inputs To Image Processing for Estimating Terrain Characteristics for Glacio-Hydrological Analysis

1987 ◽  
Vol 9 ◽  
pp. 45-49
Author(s):  
M.J. Clark ◽  
A.M. Gurnell ◽  
P.J. Hancock
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Satellite Imagery ◽  
Geometric Correction ◽  
Data Set ◽  
Glacial Environments ◽  
Methodological Problems ◽  
Terrain Characteristics ◽  
Radiometric Data

Remote-sensing research in glacial and pro-glacial environments raises several methodological problems relating to the handling of ground and satellite radiometric data. An evaluation is undertaken of the use of ground radiometry to elucidate properties of relevant surface types in order to interpret satellite imagery. It identifies the influence that geometric correction and re-sampling have on the radiometric purity of the resulting data set. Methodological problems inherent in deriving catchment terrain characteristics are discussed with reference to currently glacierized and pro-glacial zones of south-western Switzerland.

scholarly journals Chlorophyll Segmentation Of Satellite Image With Region-Based Active Contours

2020 ◽  
Author(s):  
Herman Kabetta
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Image Segmentation ◽  
Satellite Imagery ◽  
Active Contours ◽  
Satellite Image ◽  
Accurate Method ◽  
Complex Task ◽  
Surrounding Areas

In image processing, segmentation is a complex task that requires the use of an accurate method. Interpretation of satellite image today is important in remote sensing tasks. A defined area of satellite image segmentation to be performed. The purpose of this study was to use a method for extracting boundary Active Contours of chlorophyll from satellite imagery, so it can be known areas that contain chlorophyll and areas that do not contain chlorophyll. The results demonstrate the effectiveness of this method of segmentation between the chlorophyll and the surrounding areas that contain little or no chlorophyll.

scholarly journals MULTIDIMENSIONAL STATISTICAL MODEL FOR DETECTING OIL POLLUTION SITES BASED ON SATELLITE IMAGERY

2021 ◽  
Vol 4 (1) ◽  
pp. 11-16
Author(s):  
Alovsat Shura Guliyev ◽  
Tatiana A. Khlebnikova
Keyword(s):  
Remote Sensing ◽  
Statistical Model ◽  
Spatial Resolution ◽  
Satellite Imagery ◽  
Oil Spills ◽  
Oil Pollution ◽  
Sliding Window ◽  
Synthetic Aperture ◽  
The Earth ◽  
Homogeneous Regions

The article considers an algorithm for determining the statistical model from several inhomogeneous images of the Earth's surface obtained by different sensors (optoelectronic scanning device, synthetic aperture radar (SAR)) over the sea areas. The object of the study are the methods of remote sensing of the Earth used for detection and mapping of oil spills. The aim of the research was to perform testing for a possible variation of the statistical model inside a non-uniform sliding window based on a semi-automatic approach. The proposed algorithm makes it possible to determine the spatial extent of oil production sites and oil pollution in offshore waters using multi-time RSA data and a multi-zone combined image with a spatial resolution of 10 m. First, homogeneous regions are analyzed in the image, and then the model of the analysis zone is expanded to the more general case of inhomogeneous regions that are observed in the analysis windows.

scholarly journals Combining Optical and Radar Satellite Imagery to Investigate the Surface Properties and Evolution of the Lordsburg Playa, New Mexico, USA

2021 ◽  
Vol 13 (17) ◽  
pp. 3402
Author(s):  
Iyasu G. Eibedingil ◽  
Thomas E. Gill ◽  
R. Scott Van Pelt ◽  
Daniel Q. Tong
Keyword(s):  
New Mexico ◽  
Spatial Resolution ◽  
Satellite Imagery ◽  
Land Surface ◽  
Spectral Unmixing ◽  
Radar Images ◽  
Linear Spectral Unmixing ◽  
Radar Satellite ◽  
Sentinel 2

Driven by erodible soil, hydrological stresses, land use/land cover (LULC) changes, and meteorological parameters, windblown dust events initiated from Lordsburg Playa, New Mexico, United States, threaten public safety and health through low visibility and exposure to dust emissions. Combining optical and radar satellite imagery products can provide invaluable benefits in characterizing surface properties of desert playas—a potent landform for wind erosion. The optical images provide a long-term data record, while radar images can observe land surface irrespective of clouds, darkness, and precipitation. As a home for optical and radar imagery, powerful algorithms, cloud computing infrastructure, and application programming interface applications, Google Earth Engine (GEE) is an invaluable resource facilitating acquisition, processing, and analysis. In this study, the fractional abundance of soil, vegetation, and water endmembers were determined from pixel mixtures using the linear spectral unmixing model in GEE for Lordsburg Playa. For this approach, Landsat 5 and 8 images at 30 m spatial resolution and Sentinel-2 images at 10–20 m spatial resolution were used. Employing the Interferometric Synthetic Aperture Radar (InSAR) techniques, the playa’s land surface changes and possible sinks for sediment loading from the surrounding catchment area were identified. In this data recipe, a pair of Sentinel-1 images bracketing a monsoon day with high rainfall and a pair of images representing spring (dry, windy) and monsoon seasons were used. The combination of optical and radar images significantly improved the effort to identify long-term changes in the playa and locations within the playa susceptible to hydrological stresses and LULC changes. The linear spectral unmixing algorithm addressed the limitation of Landsat and Sentinel-2 images related to their moderate spatial resolutions. The application of GEE facilitated the study by minimizing the time required for acquisition, processing, and analysis of images, and storage required for the big satellite data.

scholarly journals Generating High-Quality and High-Resolution Seamless Satellite Imagery for Large-Scale Urban Regions

2019 ◽  
Vol 12 (1) ◽  
pp. 81 ◽  
Author(s):  
Xinghua Li ◽  
Zhiwei Li ◽  
Ruitao Feng ◽  
Shuang Luo ◽  
Chi Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Spatial Resolution ◽  
Satellite Imagery ◽  
Satellite Remote Sensing ◽  
Large Scale ◽  
High Spatial Resolution ◽  
Remote Sensing Images ◽  
High Quality ◽  
Geographical Maps

Urban geographical maps are important to urban planning, urban construction, land-use studies, disaster control and relief, touring and sightseeing, and so on. Satellite remote sensing images are the most important data source for urban geographical maps. However, for optical satellite remote sensing images with high spatial resolution, certain inevitable factors, including cloud, haze, and cloud shadow, severely degrade the image quality. Moreover, the geometrical and radiometric differences amongst multiple high-spatial-resolution images are difficult to eliminate. In this study, we propose a robust and efficient procedure for generating high-resolution and high-quality seamless satellite imagery for large-scale urban regions. This procedure consists of image registration, cloud detection, thin/thick cloud removal, pansharpening, and mosaicking processes. Methodologically, a spatially adaptive method considering the variation of atmospheric scattering, and a stepwise replacement method based on local moment matching are proposed for removing thin and thick clouds, respectively. The effectiveness is demonstrated by a successful case of generating a 0.91-m-resolution image of the main city zone in Nanning, Guangxi Zhuang Autonomous Region, China, using images obtained from the Chinese Beijing-2 and Gaofen-2 high-resolution satellites.

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