How Normalized Difference Vegetation Index (NDVI) Trendsfrom Advanced Very High Resolution Radiometer (AVHRR) and Système Probatoire d’Observation de la Terre VEGETATION (SPOT VGT) Time Series Differ in Agricultural Areas: An Inner Mongolian Case Study

Continuous satellite datasets are widely used in tracking vegetation responses to climate variability. Start of season (SOS), for example, can be derived using a number of methods from the time series of satellite reflectance data; however, various methods often produce different SOS measures which limit the application of satellite data in phenological studies. Therefore, we employed five methods to estimate SOS from the Advanced Very High Resolution Radiometer (AVHRR)/normalized difference vegetation index (NDVI) dataset. Subsequently, we compared the SOS with the ground-based first leaf date (FLD) of 12 deciduous broadleaved plant species at 12 sites of the Chinese Phenological Observation Network (CPON). The results show that the latitudinal patterns of five satellite-derived SOS measures are similar to each other but different from the pattern of ground phenology. For individual methods, the variability of SOS time series is significantly different from ground phenology except for HANTS, Polyfit, and Midpoint methods. The SOS calculated using the Midpoint method showed significant correlations with ground phenophases most frequently (in 47.1% of cases). Using the SOS derived from the Midpoint method, significantly earlier trends in SOS were detected in 50.7% of the natural vegetation area from 1982 to 2006.

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URBAN VEGETATION CLASSIFICATION WITH NDVI THRESHOLD VALUE METHOD WITH VERY HIGH RESOLUTION (VHR) PLEIADES IMAGERY

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-w16-237-2019 ◽

2019 ◽

Vol XLII-4/W16 ◽

pp. 237-240 ◽

Cited By ~ 4

Author(s):

H. Hashim ◽

Z. Abd Latif ◽

N. A. Adnan

Keyword(s):

Land Use ◽

High Resolution ◽

Land Cover ◽

Urban Area ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Urban Vegetation ◽

Land Use Land Cover ◽

Sensing Data ◽

Very High

Abstract. Recently the sensing data for urban mapping used is in high demand together with the accessible of very high resolution (VHR) satellite data such as Worldview and Pleiades. This article presents the use of very high resolution (VHR) remote sensing data for urban vegetation mapping. The research objectives were to assess the use of Pleiades imagery to extricate the data of urban vegetation in urban area of Kuala Lumpur. Normalized Difference Vegetation Index (NDVI) were employs with VHR data to find Vegetation Index for classification process of vegetation and non-vegetation classes. Land use classes are easily determined by computing their Normalized Difference Vegetation Index for Land use land cover classification. Maximum likelihood was conducted for the classification phase. NDVI were extracted from the imagery to assist the process of classification. NDVI method is use by referring to its features such as vegetation at different NDVI threshold values. The result showed three classes of land cover that consist of low vegetation, high vegetation and non-vegetation area. The accuracy assessment gained was then being implemented using the visual interpretation and overall accuracy achieved was 70.740% with kappa coefficient of 0.5. This study gained the proposed threshold method using NDVI value able to identify and classify urban vegetation with the use of VHR Pleiades imagery and need further improvement when apply to different area of interest and different land use land cover characteristics. The information achieved from the result able to help planners for future planning for conservation of vegetation in urban area.

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Time Series Land Cover Mapping and Change Detection Analysis Using Geographic Information System and Remote Sensing, North and West of Africa

Proceedings ◽

10.3390/proceedings2019039003 ◽

2019 ◽

Vol 39 (1) ◽

pp. 3

Author(s):

Malak Henchiri ◽

Wilson Kalisa ◽

Zhang Sha ◽

Jiahua Zhang

Keyword(s):

Land Use ◽

Time Series ◽

Land Cover ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Temporal Dynamics ◽

Land Resources ◽

Detection Analysis ◽

Spatio Temporal ◽

Very High

Land use planners require a time series land resources information and changing pattern for future management. Therefore, it is essential to assess changes in land cover. This study was to quantify the spatio-temporal dynamics of land use change over North and West Africa between 1985 and 2015 using the Normalized Difference Vegetation Index (NDVI) from the Very High Resolution Radiometer (AVHRR). The total investigated area was determined by 17,328,557.16 km2. The class of Urban and Built-up, Barren or sparsely vegetated, Savannas and Deciduous Broadleaf Forests increases considerably during the last three decades. In contrast, the class of Open Shrublands, Woody Savannas and water decrease notably during the three decades. The class of croplands decreases from 1985 to 1995 and increased from 1995 to 2015. The class of grasslands recorded a first increase from 1985 to 1995, and then decreased from 1995 to 2015. The class of permanent wetlands first decrease from 1985 to 1995, then increase from 1995 to 2005, followed by a decreasing trend during the last decade. The class of evergreen broadleaf forests decreased in the first two decades, from 1985 to 2005, and increased over the last decade.

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Advanced Very High Resolution Radiometer Normalized Difference Vegetation Index Composites

Fact Sheet ◽

10.3133/fs20053114 ◽

2005 ◽

Author(s):

Keyword(s):

High Resolution ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Very High

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Normalized difference vegetation index measurements from the advanced very high resolution radiometer

Remote Sensing of Environment ◽

10.1016/0034-4257(91)90017-z ◽

1991 ◽

Vol 35 (2-3) ◽

pp. 257-277 ◽

Cited By ~ 350

Author(s):

Samuel N. Goward ◽

Brian Markham ◽

Dennis G. Dye ◽

Wayne Dulaney ◽

Jingli Yang

Keyword(s):

High Resolution ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Very High

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Integração de imagens NOAA/AVHRR: rede de cooperação para monitoramento nacional da safra de soja

Revista CERES ◽

10.1590/s0034-737x2013000200007 ◽

2013 ◽

Vol 60 (2) ◽

pp. 194-204 ◽

Cited By ~ 4

Author(s):

Anibal Gusso

Keyword(s):

High Resolution ◽

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Rio Grande ◽

Rio Grande Do Sul ◽

Noaa Avhrr ◽

Very High

Uma avaliação inicial das condições do desenvolvimento da safra nacional, enquanto as plantas ainda estão nos campos, é altamente necessária para o cálculo correto das projeções na tomada de decisão e políticas relacionadas com o planejamento governamental e segurança alimentar. O objetivo deste trabalho foi avaliar a adequação dos dados NOAA/AVHRR (National Oceanic and Atmospheric Administration / Advanced Very High Resolution Radiometer) em detectar mudanças nas condições da vegetação, devidas à ocorrência de estresse hídrico, na soja, por meio de uma combinação do índice NDVI (Normalized Difference Vegetation Index) e da LST (Land Surface Temperature). Os dados LST e NDVI foram combinados e comparados pixel a pixel, sobre uma área de cultivo de soja, no Rio Grande do Sul. A relação teórica inversa prevista na combinação de LST e NDVI foi detectada. Foi observado que ocorre um aumento médio na LST em uma safra de ciclo normal (de 301,02 K para 308,36 K), quando comparada a uma safra sob condição de estresse hídrico, no desenvolvimento da cultura. Uma redução média do NDVI foi observada no ciclo normal (de 0,65 para 0,53), comparada com uma safra sob efeitos ocasionados pela estiagem no desenvolvimento da cultura. Foi observado maior correlação da produtividade municipal com LST (R2=0,78) do que com o NDVI (R2 = 0,59). Os resultados obtidos indicam que a integração de imagens do sensor AVHRR, proveniente de diferentes instituições, proporciona a adequada combinação espacial e temporal dos dados LST e NDVI, a fim de detectar a ocorrência de estresse hídrico, bem como sua intensidade, caracterizando as condições do ciclo de desenvolvimento da soja.

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Crop Monitoring Using Sentinel-1 Data: A Case Study from The Netherlands

Remote Sensing ◽

10.3390/rs11161887 ◽

2019 ◽

Vol 11 (16) ◽

pp. 1887 ◽

Cited By ~ 14

Author(s):

Saeed Khabbazan ◽

Paul Vermunt ◽

Susan Steele-Dunne ◽

Lexy Ratering Arntz ◽

Caterina Marinetti ◽

...

Keyword(s):

Time Series ◽

The Netherlands ◽

Precision Agriculture ◽

Growth And Development ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Temporal Variations ◽

Sudden Increase ◽

Potential Value

Agriculture is of huge economic significance in The Netherlands where the provision of real-time, reliable information on crop development is essential to support the transition towards precision agriculture. Optical imagery can provide invaluable insights into crop growth and development but is severely hampered by cloud cover. This case study in the Flevopolder illustrates the potential value of Sentinel-1 for monitoring five key crops in The Netherlands, namely sugar beet, potato, maize, wheat and English rye grass. Time series of radar backscatter from the European Space Agency’s Sentinel-1 Mission are analyzed and compared to ground measurements including phenological stage and height. Temporal variations in backscatter data reflect changes in water content and structure associated with phenological development. Emergence and closure dates are estimated from the backscatter time series and validated against a photo archive. Coherence data are compared to Normalized Difference Vegetation Index (NDVI) and ground data, illustrating that the sudden increase in coherence is a useful indicator of harvest. The results presented here demonstrate that Sentinel-1 data have significant potential value to monitor growth and development of key Dutch crops. Furthermore, the guaranteed availability of Sentinel-1 imagery in clouded conditions ensures the reliability of data to meet the monitoring needs of farmers, food producers and regulatory bodies.

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