Remote sensing
Remote sensing is the acquisition of information about an object or phenomenon without making physical contact with the object, in contrast to in situ or on-site observation. The term is applied especially to acquiring information about Earth and other planets. Remote sensing is used in numerous fields, including geophysics, geography, land surveying and most Earth science disciplines (e.g. exploration geophysics, hydrology, ecology, meteorology, oceanography, glaciology, geology). It also has military, intelligence, commercial, economic, planning, and humanitarian applications, among others.
Not to be confused with remote viewing.In current usage, the term remote sensing generally refers to the use of satellite- or aircraft-based sensor technologies to detect and classify objects on Earth. It includes the surface and the atmosphere and oceans, based on propagated signals (e.g. electromagnetic radiation). It may be split into "active" remote sensing (when a signal is emitted by a satellite or aircraft to the object and its reflection is detected by the sensor) and "passive" remote sensing (when the reflection of sunlight is detected by the sensor).[1][2][3][4]
Applications[edit]
Satellite images provide very useful information to produce statistics on topics closely related to the territory, such as agriculture, forestry or land cover in general. The first large project to apply Landsata 1 images for statistics was LACIE (Large Area Crop Inventory Experiment), run by NASA, NOAA and the USDA in 1974–77.[32][33] Many other application projects on crop area estimation have followed, including the Italian AGRIT project and the MARS project of the Joint Research Centre (JRC) of the European Commission.[34] Forest area and deforestation estimation have also been a frequent target of remote sensing projects,[35][36] the same as land cover and land use[37]
Groud truth or reference data to train and validate image classification require a field survey if we are targetting annual crops or individual forest species, but may be substituted by photointerpretation if we look at wider classes that can be reliably identified on aerial photos or satellite images. It is relevant to highlight that probabilistic sampling is not critical for the selection of training pixels for image classification, but it is necessary for accuracy assessment of the classified images and area estimation.[38][39][40] Additional care is recommended to ensure that training and validation datasets are not spatially correlated.[41]
We suppose now that we have classified images or a land cover map produced by visual photo-interpretation, with a legend of mapped classes that suits our purpose, taking again the example of wheat. The straightforward approach is counting the number of pixels classified as wheat and multiplying by the area of each pixel. Many authors have noticed that estimator is that it is generally biased because commission and omission errors in a confusion matrix do not compensate each other [42][43][44]
The main strength of classified satellite images or other indicators computed on satellite images is providing cheap information on the whole target area or most of it. This information usually has a good correlation with the target variable (ground truth) that is usually expensive to observe in an unbiased and accurate way. Therefore it can be observed on a probabilistic sample selected on an area sampling frame. Traditional survey methodology provides different methods to combine accurate information on a sample with less accurate, but exhaustive, data for a covariable or proxy that is cheaper to collect. For agricultural statistics, field surveys are usually required, while photo-interpretation may better for land cover classes that can be reliably identified on aerial photographs or high resolution satellite images. Additional uncertainty can appear because of imperfect reference data (ground truth or similar).[45][46]
Some options are: ratio estimator, regression estimator,[47] calibration estimators[48] and small area estimators[37]
If we target other variables, such as crop yield or leaf area, we may need different indicators to be computed from images, such as the NDVI, a good proxy to chlorophyll activity.[49]
Training and education[edit]
Remote Sensing has a growing relevance in the modern information society. It represents a key technology as part of the aerospace industry and bears increasing economic relevance – new sensors e.g. TerraSAR-X and RapidEye are developed constantly and the demand for skilled labour is increasing steadily. Furthermore, remote sensing exceedingly influences everyday life, ranging from weather forecasts to reports on climate change or natural disasters. As an example, 80% of the German students use the services of Google Earth; in 2006 alone the software was downloaded 100 million times. But studies have shown that only a fraction of them know more about the data they are working with.[63] There exists a huge knowledge gap between the application and the understanding of satellite images.
Remote sensing only plays a tangential role in schools, regardless of the political claims to strengthen the support for teaching on the subject.[64] A lot of the computer software explicitly developed for school lessons has not yet been implemented due to its complexity. Thereby, the subject is either not at all integrated into the curriculum or does not pass the step of an interpretation of analogue images. In fact, the subject of remote sensing requires a consolidation of physics and mathematics as well as competences in the fields of media and methods apart from the mere visual interpretation of satellite images.
Many teachers have great interest in the subject "remote sensing", being motivated to integrate this topic into teaching, provided that the curriculum is considered. In many cases, this encouragement fails because of confusing information.[65] In order to integrate remote sensing in a sustainable manner organizations like the EGU or Digital Earth[66] encourage the development of learning modules and learning portals. Examples include: FIS – Remote Sensing in School Lessons,[67] Geospektiv,[68] Ychange,[69] or Spatial Discovery,[70] to promote media and method qualifications as well as independent learning.
Remote Sensing with gamma rays[edit]
There are applications of gamma rays to mineral exploration through remote sensing. In 1972 more than two million dollars were spent on remote sensing applications with gamma rays to mineral exploration. Gamma rays are used to search for deposits of uranium. By observing radioactivity from potassium, porphyry copper deposits can be located. A high ratio of uranium to thorium has been found to be related to the presence of hydrothermal copper deposits. Radiation patterns have also been known to occur above oil and gas fields, but some of these patterns were thought to be due to surface soils instead of oil and gas.[71]
