Open AccessThis article is
- freely available
Applicability of Green-Red Vegetation Index for Remote Sensing of Vegetation Phenology
Graduate School of Life and Environment Sciences, University of Tsukuba, 1-1-1 Tennoudai, Tsukuba, Ibaraki 305-8572, Japan
National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki 305-8506, Japan
National Institute of Advanced Industrial Science and Technology, 1-1-1 Umezono, Tsukuba, Ibaraki, 305-8568, Japan
* Author to whom correspondence should be addressed.
Received: 25 August 2010; in revised form: 26 September 2010 / Accepted: 9 October 2010 / Published: 15 October 2010
Abstract: We evaluated the use of the Green-Red Vegetation Index (GRVI) as a phenological indicator based on multiyear stand-level observations of spectral reflectance and phenology at several representative ecosystems in Japan. The results showed the relationships between GRVI values and the seasonal change of vegetation and ground surface with high temporal resolution. We found that GRVI has the following advantages as a phenological indicator: (1) “GRVI = 0” can be a site-independent single threshold fordetection of the early phase of leaf green-up and the middle phase of autumn coloring, and (2) GRVI can show a distinct response to subtle disturbance and the difference of ecosystem types.
Keywords: phenology; remote sensing; spectral reflectance; digital camera; Green-Red Vegetation Index; phenological eyes network
Citations to this Article
Cite This Article
MDPI and ACS Style
Motohka, T.; Nasahara, K.N.; Oguma, H.; Tsuchida, S. Applicability of Green-Red Vegetation Index for Remote Sensing of Vegetation Phenology. Remote Sens. 2010, 2, 2369-2387.
Motohka T, Nasahara KN, Oguma H, Tsuchida S. Applicability of Green-Red Vegetation Index for Remote Sensing of Vegetation Phenology. Remote Sensing. 2010; 2(10):2369-2387.
Motohka, Takeshi; Nasahara, Kenlo Nishida; Oguma, Hiroyuki; Tsuchida, Satoshi. 2010. "Applicability of Green-Red Vegetation Index for Remote Sensing of Vegetation Phenology." Remote Sens. 2, no. 10: 2369-2387.