Vegetation phenology is a comprehensive indictor for the responses of terrestrial ecosystem to climatic and environmental changes. Remote sensing spectrum has been widely used in the extraction of vegetation phenology information. However, there are many differences between phenology extracted by remote sensing and site observations, with their physical meaning remaining unclear. We selected one tile of MODIS data in northeastern China (2000-2014) to examine the SOS and EOS differences derived from the normalized difference vegetation index (NDVI) and the simple ratio vegetation index (SR) based on both the red and near-infrared bands. The results showed that there were significant differences between NDVI-phenology and SR-phenology. SOS derived from NDVI averaged 18.9 days earlier than that from SR. EOS derived from NDVI averaged 19.0 days later than from SR. NDVI-phenology had a longer growing season. There were significant differences in the inter-annual variation of phenology from NDVI and SR. More than 20% of the pixel SOS and EOS derived from NDVI and SR showed the opposite temporal trend. These results caused by the seasonal curve characteristics and noise resistance differences of NDVI and SR. The observed data source of NDVI and SR were completely consistent, only the mathematical expressions were different, but phenology results were significantly different. Our results indicated that vegetation phenology monitoring by remote sensing is highly dependent on the mathematical expression of vegetation index. How to establish a reliable method for extracting vegetation phenology by remote sensing needs further research.
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http://dx.doi.org/10.13287/j.1001-9332.201802.019 | DOI Listing |
Proc Biol Sci
January 2025
Department of Biosciences, Centre for Ecological and Evolutionary Synthesis (CEES), University of Oslo, P.O. Box 1066 Blindern, NO-0316, Oslo, Norway.
The timing of migration is fundamental for species exploiting seasonally variable environments. For ungulates, earlier spring migration is expected with earlier vegetation green-up. However, other drivers, such as access to agricultural farmland and variation in local conditions, are also known to affect migration.
View Article and Find Full Text PDFFront Plant Sci
December 2024
College of Forestry, The Northeast Forestry University, Harbin, China.
Global warming has significantly altered plant phenology by advancing the timing of leaf emergence, impacting vegetation productivity and adaptability. Winter and spring temperatures have commonly been used to explain spring phenology shifts, but we still lack a solid understanding of the effects of interactions between conditions in different seasons. This study utilizes normalized difference vegetation index (NDVI) and meteorological data to examine the effects of changes in winter and spring temperatures and precipitation on the start of the vegetation growing season (SOS) at high latitudes in China from 1982 to 2015.
View Article and Find Full Text PDFFront Vet Sci
November 2024
Department of Animal Science, University of Wyoming, Laramie, WY, United States.
Diet selection and composition of sheep target grazing plains larkspur ( Greene) in northern mixed-grass prairie were evaluated during a drought year (2022). Thirteen Rambouillet ewes (3-to 6-year-old, body weight (BW) 76 kg ± 2.9), 14 Dorper ewes (3-to 6-year-old, BW 47 kg ± 1.
View Article and Find Full Text PDFTree Physiol
December 2024
Institute of Ecology and Earth Sciences, University of Tartu, J. Liivi 2, 50409 Tartu, Estonia.
Scenarios for future climate predict an increase in precipitation amounts and frequency of rain events, resulting in higher air humidity and soil moisture at high latitudes, including in northern Europe. We analysed the effects of artificially elevated environmental humidity (air relative humidity and soil moisture) on leaf gas exchange, water relations, growth and phenology of silver birch (Betula pendula) trees growing at the Free Air Humidity Manipulation (FAHM) experimental site situated in the hemiboreal vegetation zone, in eastern Estonia, with no occurring water deficit to the trees. The environmental humidity manipulation did not significantly affect the water relations traits but did affect some leaf gas exchange parameters, growth and phenology of the trees.
View Article and Find Full Text PDFHuan Jing Ke Xue
November 2024
Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, China.
Timely monitoring of the changes in the ecological quality of arable land and the driving forces is of great significance for maintaining the ecological balance and sustainable development of agriculture. This study used the advanced time-series remote sensing continuous change detection and classification (CCDC) algorithm to synthesize images with the acquisition date of each year, in order to overcome the impacts of cloudy weather and vegetation phenology. Based on this, the reversal process and mechanism for the ecological quality of arable land in Ningbo were precisely identified using the comprehensive ecological evaluation index (CEEI) and geo-detector methods.
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