scholarly journals Land-Cover Vegetation Change Detection based on Harmonic Analysis of MODIS NDVI Time Series Data

2013 ◽  
Vol 29 (4) ◽  
pp. 351-360 ◽  
Author(s):  
Myunghee Jung ◽  
Eunmi Chang
2011 ◽  
Vol 8 (3) ◽  
pp. 507-511 ◽  
Author(s):  
W. Kleynhans ◽  
J. C. Olivier ◽  
K. J. Wessels ◽  
B. P. Salmon ◽  
F. van den Bergh ◽  
...  

2011 ◽  
Vol 8 (11) ◽  
pp. 3359-3373 ◽  
Author(s):  
C. Höpfner ◽  
D. Scherer

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.


2011 ◽  
Vol 8 (2) ◽  
pp. 3953-3998 ◽  
Author(s):  
C. Höpfner ◽  
D. Scherer

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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