China
Africa
Explore chapters and articles related to this topic
Sustainability in the Space Industry
Published in Mark W. McElroy, The Space Industry of the Future, 2023
Remote sensing data can also be used to provide warning and insight for problems such as drought or insect infestations that could cause shortages or crop disease. In the case of fish farms, remote sensing data can track pollution emitted or pollution that threatens the farm. Authorities use this kind of information in order to plan for regional food distribution. In the most severe cases, agricultural remote sensing data can be used to provide warning of famine. The Famine Early Warning System Network, originally founded by the US Agency for International Development (USAID) in 1985, offers this service using, in part, remote sensing data.
Review of Conceptual Models of Estimating the Spatio-Temporal Variations of Water Depth Using Remote Sensing and GIS for the Management of Dams and Reservoirs
Published in Shruti Kanga, Suraj Kumar Singh, Gowhar Meraj, Majid Farooq, Geospatial Modeling for Environmental Management, 2022
There are basically two distinctive groups of methods developed for water depth mapping, namely, those that use active and those that are based on passive remote sensing data, respectively. In passive remote sensing, the sensor detects incoming solar radiation reflected or scattered from the surface of the Earth, while active remote sensing uses artificially generated energy sources to receive signals reflected from objects.
Remote Sensing Technique
Published in Ajai, Rimjhim Bhatnagar, Desertification and Land Degradation, 2022
The first requirement in remote sensing is to have a source of electromagnetic radiation emanating from the earth surface. Depending on the source of energy used for remote sensing the remote sensing technology can be broadly classified as passive remote sensing and active remote sensing. In passive remote sensing, the source of electromagnetic radiation is from the sun or self-emitted radiation. In active remote sensing, the sensor carries a specific band of electromagnetic radiation to illuminate the terrain and the scattered radiation from the target is studied. Since this chapter deals mostly with passive remote sensing, further discussions will primarily deal with passive remote sensing.
Near real-time mapping of jute (Corchorus sp.) area using multi-temporal Sentinel-1 intensity data over the central part of West Bengal, India
Published in Journal of Spatial Science, 2023
Nilimesh Mridha, Biplab Saha, Tanumoy Bera, Saptarshi Sarkar, Koushik Manna
Traditional ground survey methods are difficult for acquiring annual crop information accurately in real time due to low economic efficiency, large coverage, and strong seasonal and spatial heterogeneity. Technological advances make remote sensing a significant tool for classifying crops and measuring crop area for effective monitoring of agricultural crops. Time-series optical data have successfully discriminated soybeans, rice, corn and other major crops, which has improved the efficiency of crop classification (Miao et al. 2011). However, more than 66% of the Earth’s surface is regularly covered by clouds, according to global cloud cover data from the International Satellite Cloud Climatology Project (Zhang et al. 2004). As jute is grown in the kharif season, optical remote sensing cannot provide adequate information needed for accurate area estimation and proper monitoring due to intensive cloud cover.
Towards precision irrigation management: A review of GIS, remote sensing and emerging technologies
Published in Cogent Engineering, 2022
Erion Bwambale, Zita Naangmenyele, Parfait Iradukunda, Komi Mensah Agboka, Eva A. Y. Houessou-Dossou, Daniel A. Akansake, Michael E. Bisa, Abdoul-Aziz H. Hamadou, Joseph Hakizayezu, Oluwaseun Elijah Onofua, Sylvester R. Chikabvumbwa
Remote sensing is the art, science and technology of obtaining information about objects especially using aircraft or satellites (NOAA, 2021). Remote sensors collected data by detecting energy from the earth. The energy can be light or any other form of electromagnetic radiation, force fields, or acoustic energy (Tempfli et al., 2009). A remote sensor can be a conventional camera. Light reflected from an object passes through the lens and the light-sensitive film detects it, and a latent image is recorded. A photograph is then developed and generated in the photo lab for interpretation (Tempfli et al., 2009). RS defined as above, is applied in many fields, including architecture (2021), archaeology (Villa, 2011), medicine (Suzuki et al., 2012), industrial quality control (Barsi et al., 2019), robotics (Guo & Wang, 2012), extra-terrestrial mapping (Nina et al., 2021), etc.
An improved coverage-oriented retrieval algorithm for large-area remote sensing data
Published in International Journal of Digital Earth, 2022
Xuejing Yan, Shibin Liu, Wei Liu, Qin Dai
As an important means of Earth observation, remote sensing has been widely applied in the fields of land resource surveying, disaster monitoring, crop yield estimation, weather forecasting, and mineral resource exploration (Nemni et al. 2020; Zhu et al. 2021a; Shi et al. 2022). The demand for large-area data change analysis has also increased significantly, e.g. for large-area environmental monitoring (Gu and Wei 2021), monitoring of economic development in the Belt and Road Initiative (Xiao et al. 2020), and assessing changes in forest coverage (Lossou, Owusu-Prempeh, and Agyemang 2019). In response to the rapid generation of large-area regional time-series images, an optimal image combination should first be selected from large-area remote sensing data under time and space constraints. The images nearest the required time should be selected to reduce the difference in features between adjacent images; this serves the subsequent image mosaic tasks. Data retrieval is difficult and complex; manual selection is time-consuming and labor-intensive, and it cannot be integrated into the existing data service system. However, it is very important to quickly and accurately find the data of interest within the massive amount of available data. Therefore, fast and accurate data-retrieval methods using metadata are becoming a popular research topic in the field of remote sensing data management (He 2017).