Extraction of Agricultural Phenological Parameters of Sri Lanka Using MODIS, NDVI Time Series Data

Abstract. Vegetation phenology as well as the current variability and dynamics of vegetation and land cover, including its climatic and human drivers, are examined in a region in north-western Morocco that is nearly 22 700 km2 big. A gapless time series of Normalized Differenced Vegetation Index (NDVI) composite raster data from 29 September 2000 to 29 September 2009 is utilised. The data have a spatial resolution of 250 m and were acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The presented approach allows to compose and to analyse yearly land cover maps in a widely unknown region with scarce validated ground truth data by deriving phenological parameters. Results show that the high temporal resolution of 16 d is sufficient for (a) determining local land cover better than global land cover classifications of Plant Functional Types (PFT) and Global Land Cover 2000 (GLC2000) and (b) for drawing conclusions on vegetation dynamics and its drivers. Areas of stably classified land cover types (i.e. areas that did not change their land cover type) show climatically driven inter- and intra-annual variability with indicated influence of droughts. The presented approach to determine human-driven influence on vegetation dynamics caused by agriculture results in a more than ten times larger area compared with stably classified areas. Change detection based on yearly land cover maps shows a gain of high-productive vegetation (cropland) of about 259.3 km2. Statistically significant inter-annual trends in vegetation dynamics during the last decade could however not be discovered. A sequence of correlations was respectively carried out to extract the most important periods of rainfall responsible for the production of green biomass and for the extent of land cover types. Results show that mean daily precipitation from 1 October to 15 December has high correlation results (max. r2=0.85) on an intra-annual time scale to NDVI percentiles (50 %) of land cover types. Correlation results of mean daily precipitation from 16 September to 15 January and percentage of yearly classified area of each land cover type are medium up to high (max. r2=0.64). In all, an offset of nearly 1.5 months is detected between precipitation rates and NDVI values. High-productive vegetation (cropland) is proved to be mainly rain-fed. We conclude that identification, understanding and knowledge about vegetation phenology, and current variability of vegetation and land cover, as well as prediction methods of land cover change, can be improved using multi-year MODIS NDVI time series data. This study enhances the comprehension of current land surface dynamics and variability of vegetation and land cover in north-western Morocco. It especially offers a quick access when estimating the extent of agricultural lands.

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Applicability of Smoothing Techniques in Generation of Phenological Metrics of Tectona grandis L. Using NDVI Time Series Data

Remote Sensing ◽

10.3390/rs13173343 ◽

2021 ◽

Vol 13 (17) ◽

pp. 3343

Author(s):

Ramandeep Kaur M. Malhi ◽

G. Sandhya Kiran ◽

Mangala N. Shah ◽

Nirav V. Mistry ◽

Viral H. Bhavsar ◽

...

Keyword(s):

Time Series ◽

Plant Species ◽

Time Series Data ◽

Series Data ◽

Individual Plant ◽

Smoothing Techniques ◽

Modis Ndvi ◽

Ndvi Time Series ◽

Phenological Metrics ◽

Year 2013

Information on phenological metrics of individual plant species is meager. Phenological metrics generation for a specific plant species can prove beneficial if the species is ecologically or economically important. Teak, a dominating tree in most regions of the world has been focused on in the present study due to its multiple benefits. Forecasts on such species can attain a substantial improvement in their productivity. MODIS NDVI time series when subjected to statistical smoothing techniques exhibited good output with Tukey’s smoothing (TS) with a low RMSE of 0.042 compared to single exponential (SE) and double exponential (DE). Phenological metrics, namely, the start of the season (SOS), end of the season (EOS), maximum of the season (MAX), and length of the season (LOS) were generated using Tukey-smoothed MODIS NDVI data for the years 2003–2004 and 2013–2014. Post shifts in SOS and EOS by 14 and 37 days respectively with a preshift of 28 days in MAX were observed in the year 2013–2014. Preshift in MAX was accompanied by an increase in greenness exhibiting increased NDVI value.LOS increased by 24 days in the year 2013–2014, showing an increase in the duration of the season of teak. Dates of these satellite-retrieved phenological occurrences were validated with ground phenological data calculated using crown cover assessment. The present study demonstrated the potential of a spatial approach in the generation of phenometrics for an individual plant species, which is significant in determining productivity or a crucial trophic link for a given region.

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Analysis of vegetation and land cover dynamics in north-western Morocco during the last decade using MODIS NDVI time series data

Biogeosciences Discussions ◽

10.5194/bgd-8-3953-2011 ◽

2011 ◽

Vol 8 (2) ◽

pp. 3953-3998 ◽

Cited By ~ 2

Author(s):

C. Höpfner ◽

D. Scherer

Keyword(s):

Time Series ◽

Land Cover ◽

Vegetation Dynamics ◽

Time Series Data ◽

Series Data ◽

Land Cover Types ◽

Modis Ndvi ◽

Ndvi Time Series ◽

Western Morocco ◽

North Western

Abstract. Vegetation phenology as well as current variability and dynamics of vegetation and land cover including its climatic and human drivers are examined in a region in north-western Morocco of nearly 22 700 km2. A gapless time series of Normalized Differenced Vegetation Index (NDVI) composite raster data from 29 September 2000 to 29 September 2009 with a spatial resolution of 250 m and acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor is utilised. The presented approach allows to compose and analyse yearly land cover maps in a widely unknown region with scarce validated ground truth data by deriving phenological parameters. Results show that high temporal resolution of 16 d is sufficient (a) for determining land cover better than global land cover classifications of Plant Functional Types (PFT) and Global Land Cover 2000 (GLC2000), and (b) for drawing conclusions on vegetation dynamics and its drivers. Areas of stably classified land cover types show climatically driven inter- and intra-annual variability with indicated influence of droughts. The presented approach to determine human-driven influence on vegetation dynamics caused by agriculture results in a more than ten times larger area compared to the stably classified areas. Change detection based on yearly land cover maps shows a gain of high-productive vegetation (cropland) of about 259.3 km2. However, statistically significant inter-annual trends in vegetation dynamics during the last decade could not be discovered. A sequence of correlations was done to extract the most important period of rainfall for production of green biomass and for the extent of land cover types, respectively. Results show that mean daily precipitation from 1 October to 15 December has high correlation results (max. r2=0.85) at intra-annual time scale to NDVI percentiles (50%) of land cover types. Correlation results of mean daily precipitation from 16 September to 15 January and percentage of yearly classified area of each land cover type are medium up to high (max. r2=0.64). In all, an offset of nearly 1.5 months is detected between precipitation rates and NDVI in 16 d steps. High-productive vegetation (cropland) is proved to be mainly rain-fed. We conclude that identification, understanding and knowledge about vegetation phenology, and current variability of vegetation and land cover as well as prediction methods of land cover change can be improved using multi-year MODIS NDVI time series data. This study enhances the comprehension of current land surface dynamics and variability of vegetation and land cover in north-western Morocco offering a fast access especially for estimating the extent of agricultural lands.

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