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Mapping Dragon Fruit Croplands from Space Using Remote Sensing of Artificial Light at Night
- Source :
- Remote Sensing, Vol 12, Iss 4139, p 4139 (2020)
- Publication Year :
- 2020
- Publisher :
- MDPI AG, 2020.
-
Abstract
- The nighttime light (NTL) on the surface of Earth is an important indicator for the human transformation of the world. NTL remotely sensed data have been widely used in urban development, population estimation, economic activity, resource development and other fields. With the increasing use of artificial lighting technology in agriculture, it has become possible to use NTL remote sensing data for monitoring agricultural activities. In this study, National Polar Partnership (NPP)-Visible Infrared Imaging Radiometer Suite (VIIRS) NTL remote sensing data were used to observe the seasonal variation of artificial lighting in dragon fruit cropland in Binh Thuan Province, Vietnam. Compared with the statistics of planted area, area having products and production of dragon fruit by district in the Statistical Yearbook of Binh Thuan Province 2018, values of the mean and standard deviation of NTL brightness have significant positive correlations with the statistical data. The results suggest that the NTL remotely sensed data could be used to reveal some agricultural productive activities such as dragon fruits production accurately by monitoring the seasonal artificial lighting. This research demonstrates the application potential of NTL remotely sensed data in agriculture.
- Subjects :
- 010504 meteorology & atmospheric sciences
Science
0211 other engineering and technologies
NPP-VIIRS
02 engineering and technology
01 natural sciences
nighttime light
Resource development
Population estimation
medicine
021101 geological & geomatics engineering
0105 earth and related environmental sciences
Remote sensing
Radiometer
Artificial light
business.industry
Seasonality
medicine.disease
Vietnam
Remote sensing (archaeology)
Agriculture
General Earth and Planetary Sciences
Environmental science
dragon fruit
business
Subjects
Details
- Language :
- English
- ISSN :
- 20724292
- Volume :
- 12
- Issue :
- 4139
- Database :
- OpenAIRE
- Journal :
- Remote Sensing
- Accession number :
- edsair.doi.dedup.....b16e5512ecf8e37fb04ec173688b589b