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Published in Philip A. Laplante, Comprehensive Dictionary of Electrical Engineering, 2018
satellite imagery the acquisition of pictures of the earth from space. Satellite imagery can be used to enhance maps, collect resource inventories (eg., forestry, water, land use), assess environmental impact, appraise damage following a disaster, and collect information on the activities of humans. Satellite imagery tends to be multi-spectral, including a wide range of optical frequencies and, more recently, infrared and radar. See remote sensing. satistical pattern recognition methods for carrying out the recognition of patterns on the basis of statistical analysis. These methods are typically based on the learning of unknown pattern probability distributions from examples. saturable absorber the nonlinear optical phenomenon in which the absorption coefficient of a material decreases as the intensity of the light used to measure the absorption increases. saturable absorption the effect of there being less absorption in a material for larger values of the incident illumination. saturated gain value of the gain in a saturable amplifier for a particular value of intensity. saturated logic logic gates whose output is fully on or fully off, determined principally by the external circuit.
Satellite Guided Agriculture: Soil Fertility and Crop Management
Published in K. R. Krishna, Push Button Agriculture, 2017
As stated above, satellite imagery is a good option to monitor agricultural crops, map their expanses, rate of spread, and productivity. However, a basic requirement is that the spectral bands used and resolution should be able to distinguish and identify different crops species. The imagery should provide an accurate estimate of cropping area and its fluctuations. Agricultural monitoring and mapping has been attempted using different satellite systems. For example, Chenghai et al. (2008) have shown that high-resolution imagery obtained using SPOT 5 satellite helps in recording crops such as maize, wheat, sorghum and sugarcane and map the areas covered by each of them. These crops grown intensely in the Great Plains of North America could be easily mapped with acceptable accuracy. They have examined the satellite imagery obtained at 20 and 10 Mpixel. They suggest that both coarser images and sharper ones have their utility during studying cropping systems and allocating resources. We should note that these satellite aided crop maps done using computer and satellite connec-tivity works at ‘Push of a Button.’ We can browse the entire agrarian belt or large farm on a computer screen. Compared with it, during yester years, several human scouts had to move across the entire cropping expanse or else costly aircraft imagery had to be procured. At this juncture, we may note that such applications of satellites, reduces human drudgery in agri-cultural zones to very great extent.
Thematic Map Accuracy Assessment Considerations
Published in Russell G. Congalton, Kass Green, Assessing the Accuracy of Remotely Sensed Data, 2019
Russell G. Congalton, Kass Green
Extending the concept of a cluster of pixels to higher-resolution imagery requires knowledge about the positional accuracy of the imagery. As previously stated, common registration (positional) accuracies for Landsat Thematic Mapper and SPOT satellite imagery (with 10–30 meter pixels) are about half a pixel, and the GPS accuracy is 5–15 meters. Therefore, selecting a homogeneous cluster of 3 × 3 pixels as the sample unit ensures that the center of the sample will definitely fall within the 3 × 3 cluster. If the sample is homogeneous, and the collection is performed at the center of the sample, then an error that could be caused by positional issues will be eliminated. Higher–spatial resolution satellite imagery now has pixel sizes from 4 meters to below 1 meter. However, because of the off-nadir acquisition and other issues, the positional accuracy of these data are often in the range of 5–10 meters, and the GPS accuracy is still 3–10 meters. Therefore, the use of a 3 × 3 pixel cluster as the sampling unit would be too small and inappropriate in this case to compensate for the positional error. If the registration accuracy was 5 meters, the GPS accuracy was 10 meters, and the pixel size was 2 meters, then the sample unit cluster would need to be a homogeneous area of at least 8 × 8 pixels to account for this positional error. It is imperative that the positional accuracy of the sensor be considered in the selection of the sample unit cluster size, or else the thematic assessment will be flawed, as the error indicated in the error matrix will be a combination of both thematic and positional error.
Quantitative estimates of collective geo-tagged human activities in response to typhoon Hato using location-aware big data
Published in International Journal of Digital Earth, 2020
Zhang Liu, Yunyan Du, Jiawei Yi, Fuyuan Liang, Ting Ma, Tao Pei
Real-time monitoring of the disaster-induced threats is crucial for disaster mitigation. Over the past 20 years, satellite images have been increasingly used for near real-time monitoring and rapid assessment of global disasters (Voigt et al. 2016). For example, satellite-based emergency mapping (SEM) systems have been used to assess the damages caused by the 2008 Wenchuan earthquakes (Ehrlich et al. 2009; Tong et al. 2012), monitor the 2010 Pakistan flood (Haq et al. 2012), evaluate the effects of the 2017 hurricanes Harvey, Irma, and Maria (Worthem et al. 2017). The SEM offers high-resolution images that allow the emergency responders to closely monitor an ongoing natural disaster and are very helpful in coordinating disaster relief operations (Cervone et al. 2017; Voigt et al. 2016). The SEM system has its advantages in supporting disaster relief when emergency response is impeded by limited, incomplete, and often contradictory ground information. Nevertheless, it suffers two drawbacks. First, it usually takes ∼2.5 days on average to reprogram the satellite systems to acquire needed remote sensing imagery. The time delay makes impossible for the SEM to provide real-time support to disaster monitoring (Voigt et al. 2016). Secondly, it is almost impossible for the SEM to provide the dynamic information about human activities, which, however, is more essential in disaster mitigation (Liu et al. 2015).
Rapid urban flood damage assessment using high resolution remote sensing data and an object-based approach
Published in Geomatics, Natural Hazards and Risk, 2020
Sergio Iván Jiménez-Jiménez, Waldo Ojeda-Bustamante, Ronald Ernesto Ontiveros-Capurata, Mariana de Jesús Marcial-Pablo
Remote sensing is widely used to extract information about the earth’s surface with precision. However, to evaluate the impact caused by floods, spatial and temporal images of high resolution are required before and after the occurrence of the event to compare the prior situation with the post event situation. The use of satellite images has been used to assess the impact of natural hazards and planned emergency responses in various studies (Barnes et al. 2007; Eguchi et al. 2008). Hodgson et al. (2010) mention that state and local agencies involved in emergency response to natural hazards need high resolution images (e.g., Ikonos-2, Quickbird-2, and Orbview-3) of the affected area within three days of the event and, more desirably, within 24 hours of the event; however, they estimated that the probability of obtaining a high resolution image of the affected site within the first 24 hours after the event was 61%; within the first three day the probability increases to 94–100%.
Evaluation of Agriculture Development Projects status in Lake Tana Sub-basin applying Remote Sensing Technique
Published in Water Science, 2019
A. M. I. Abd Elhamid, Rasha H. A. Monem, Marwa M. Aly
Therefore, it is necessary to evaluate periodically the status of the agriculture development projects in Lake Tana sub-basin as it has an undeviating influence on the water resources in the region. In the field of agriculture, there are many uses of satellite imagery such as mapping for agricultural areas, identification, and forecasting of agricultural crops, desertification control, land cover, etc. Vegetation indices can be derived from the spectral channels and are good indicators for the state of vegetation (Tucker, 1979).