Vegetation Dynamics from Denoised NDVI Using Empirical Mode Decomposition
- PDF / 1,131,939 Bytes
- 12 Pages / 547.087 x 737.008 pts Page_size
- 67 Downloads / 203 Views
RESEARCH ARTICLE
Vegetation Dynamics from Denoised NDVI Using Empirical Mode Decomposition Rahul Verma & Subashisa Dutta
Received: 4 April 2012 / Accepted: 18 October 2012 # Indian Society of Remote Sensing 2012
Abstract A novel approach to study vegetation dynamics is introduced, using the Empirical Mode Decomposition (EMD) to analyze NDVI time series. The NDVI time series which is nonlinear and nonstationary can be decomposed by EMD into components called intrinsic mode functions (IMFs), based on inherent temporal scales. The highest frequency component which has been found to represent noise is subtracted from the original NDVI series; thus smoothing the noisy signal. The different key features describing vegetation phenology have been extracted by analyzing the noise free signal. The lowest frequency component (last IMF) is the trend in the NDVI series. The trend in the series has been identified finding the Sen’s slope of last IMF, and the non-parametric seasonal Mann–Kendall test has been used to confirm the significance of the observed trend. The method has been applied on per–pixel basis to the SPOT Vegetation NDVI product covering Northeast India and surrounding regions for the time span of 1998–2009. Results show that the method has performed well in identifying the pixel clusters with significant trends. Hotspot regions with severe vegetation degeneration have been R. Verma : S. Dutta (*) Department of Civil Engineering, Indian Institute of Technology Guwahati, Assam 781039, India e-mail: [email protected] R. Verma e-mail: [email protected]
identified, and the relationship of the observed trends with the expected causative variables such as land use and land cover, topographic relief, and anthropogenic causes has been explored. The spatial locations of these critical regions closely matches with the findings of the previous studies carried out locally in the region, mainly indicating the shifting cultivation practice to be the main cause for land cover change. Keywords EMD (Empirical Mode Decomposition) . NDVI (Normalized Difference Vegetation Index) . Vegetation dynamics . Eastern Himalayas
Introduction The spatial and temporal variability in vegetation cover is mainly the result of anthropogenic stress (cultivation patterns, industrialization, overgrazing, land abandonment) and natural hazard (floods, drought, wind, rain erosion). A systematic study of vegetation dynamics helps to monitor the changes in vegetation cover in response to the natural or anthropogenic causes, and predict their affect on the ecosystem. At present, human activity has profoundly affected ecosystems in terms of habitat destruction and biodiversity reduction, so that a need to detect and predict changes in ecosystem functioning has arisen at different parts of the world (Naeem et al. 1999). Monitoring the environment through remote sensing techniques offers several advantages in terms of
J Indian Soc Remote Sens
synoptic coverage, good spectral resolution, repetitive acquisition of data and cost effectiveness. The
Data Loading...
