Monitoring vegetation greenness with satellite data

Burgan, Robert E.; Hartford, Roberta A.

doi:10.2737/int-gtr-297

Cited by 105 publications

(59 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Two phenological parameters, the SOS and the EOS were extracted from the NDVI time series using the dynamic threshold method developed by Burgan and Hartford [41]. This method has been widely accepted by ecologists and phenologists for use in phenology studies [5,42,43].…”

Section: Determination Of Phenological Parametersmentioning

confidence: 99%

Variability and climate change trend in vegetation phenology of recent decades in the Greater Khingan Mountain area, Northeastern China

Tang

Zhu

et al. 2015

Remote Sensing

View full text Add to dashboard Cite

Vegetation phenology has been used in studies as an indicator of an ecosystem's responses to climate change. Satellite remote sensing techniques can capture changes in vegetation greenness, which can be used to estimate vegetation phenology. In this study, a long-term vegetation phenology study of the Greater Khingan Mountain area in Northeastern China was performed by using the Global Inventory Modeling and Mapping Studies (GIMMS) normalized difference vegetation index version 3 (NDVI3g) dataset from the years 1982-2012. After reconstructing the NDVI time series, the start date of the growing season (SOS), the end date of the growing season (EOS) and the length of the growing season (LOS) were extracted using a dynamic threshold method. The response of the variation in phenology with climatic factors was also analyzed. The results showed that the phenology in the study area changed significantly in the three decades between 1982 and 2012, including a 12.1-day increase in the entire region's average LOS, a 3.3-day advance in the SOS and an 8.8-day delay in the EOS. However, differences existed between the steppe, forest and agricultural regions, with the LOSs of the steppe region, forest region and OPEN ACCESSRemote Sens. 2015, 7 11915 agricultural region increasing by 4.40 days, 10.42 days and 1.71 days, respectively, and a later EOS seemed to more strongly affect the extension of the growing season. Additionally, temperature and precipitation were closely correlated with the phenology variations. This study provides a useful understanding of the recent change in phenology and its variability in this high-latitude study area, and this study also details the responses of several ecosystems to climate change.

show abstract

Section: Determination Of Phenological Parametersmentioning

confidence: 99%

Variability and climate change trend in vegetation phenology of recent decades in the Greater Khingan Mountain area, Northeastern China

Tang

Zhu

et al. 2015

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…The model requires the NDVI to compute the Relative Greenness, meteorological data (air temperature, relative humidity, cloudiness and rainfall) for estimating the Ten Hours Time Lag Fuel Moisture (FM10hr) and a fuel map to estimate the percentage of dead vegetation. The relative greenness (RG) or vegetation stress index represents how much green is a pixel, with reference to the range of historical observation of the NDVI used (Burgan, 1993). This quantity allows the estimate of the percentage of green fuel, as function of the fuel model assigned to each pixel.…”

Section: Resultsmentioning

confidence: 99%

PREFER FP7 project for the management of the pre- and post-fire phases: presentation of the products

Laneve¹,

Bonis²,

Fusilli³

et al. 2014

Advances in Forest Fire Research

View full text Add to dashboard Cite

A navegação consulta e descarregamento dos títulos inseridos nas Bibliotecas Digitais UC Digitalis, UC Pombalina e UC Impactum, pressupõem a aceitação plena e sem reservas dos Termos e Condições de Uso destas Bibliotecas Digitais, disponíveis em https://digitalis.uc.pt/pt-pt/termos.Conforme exposto nos referidos Termos e Condições de Uso, o descarregamento de títulos de acesso restrito requer uma licença válida de autorização devendo o utilizador aceder ao(s) documento(s) a partir de um endereço de IP da instituição detentora da supramencionada licença.Ao utilizador é apenas permitido o descarregamento para uso pessoal, pelo que o emprego do(s) título(s) descarregado(s) para outro fim, designadamente comercial, carece de autorização do respetivo autor ou editor da obra. Na medida em que todas as obras da UC Digitalis se encontram protegidas pelo Código do Direito de Autor e Direitos Conexos e demais legislação aplicável, toda a cópia, parcial ou total, deste documento, nos casos em que é legalmente admitida, deverá conter ou fazer-se acompanhar por este aviso.

show abstract

“…Negative values are indicative of clouds, snow, water and other non vegetated, non-reflective surfaces, while positive values denote vegetated or reflective surfaces (Burgan and Hartford, 1993). And these values (range: -1 to +1) now days are used in many drought monitoring tools (VCI) as spatial and temporal changes of drought because NDVI product are available on decadal time basis data.…”

Section: Introductionmentioning

confidence: 99%

“…Such data allow for the production of maps, which indicate visual greenness and can be extremely valuable to land managers and researchers in determining changes in vegetation over time. The NDVI is the difference of near-infrared and visible red reflectance values normalized over reflectance (Burgan 1993 (1)…”

Section: Introductionmentioning

confidence: 99%

Vegetation condition index pattern (2002-2007) over Indian agro-climate regions, using of GIS and SPOT sensor NDVI data

Rawat

Mishra

Kumar

et al. 2012

JANS

View full text Add to dashboard Cite

This study describes the Vegetation Condition Index in the near-real-time with help of SPOT based Normalized Difference Vegetation Index (NDVI) for Agro climatic-region of India and gave the development pattern in last six year (2002)(2003)(2004)(2005)(2006)(2007) over the study area of India using decadal time data set from SPOT satellite sensor for 2002-2007 time periods. The each Agro-climatic region of study, 1°x1° degree in area, part of India agro-climate regions, has been taken for analysis using remote sensing and Geographical Information System (RS and GIS) methods, SPOT satellite sensor NDVI data, and from processed data set (geo-referenced data set), cut out 1°x1°d egree of area by preparing a layers representing Agro-climatic region of India as base mapping units (BMU),The results indicated that NDVI index is only water stress over vegetation while VCI is an appropriate index for vegetation pattern monitoring over study area. As satellite observations provide better spatial and temporal coverage, the VCI based system will provide efficient tools for management of the improvement of agricultural planning. This system will serve as a prototype in the other parts of the world where ground observations are limited or not available.

show abstract

Monitoring vegetation greenness with satellite data

Cited by 105 publications

References 9 publications

Variability and climate change trend in vegetation phenology of recent decades in the Greater Khingan Mountain area, Northeastern China

Variability and climate change trend in vegetation phenology of recent decades in the Greater Khingan Mountain area, Northeastern China

PREFER FP7 project for the management of the pre- and post-fire phases: presentation of the products

Vegetation condition index pattern (2002-2007) over Indian agro-climate regions, using of GIS and SPOT sensor NDVI data

Contact Info

Product

Resources

About