Rapid assessment of large scale vegetation change based on multi-temporal phenological analysis

Abstract

Detecting vegetation change is critical for earth system and sustainability science. The existing methods, however, show several limitations, including inevitable selection of imagery acquisition dates, affection from vegetation related noise on temporal trajectory analysis, and assumptions due to vegetation classification model. This paper presents a multitemporal phenological frequency analysis over a relatively short period (MTPFA-SP) methodology to detect vegetation changes. This MTPFA-SP methodology bases on the amplitude components of fast Fourier transforming (FFT) and is implemented with two steps. First, NDVI time series over two periods are transformed with FFT into frequency domain, separately. Second, amplitude components with phenological information from Step 1 are selected for further change comparison. In this methodology, component selection shows physical meanings of natural vegetation process in frequency domain. Comparisons among those selected components help enhance the ability to rapidly detect vegetation changes. To validate this MTPFA-SP methodology, we detect changes between two periods (2001-2005 and 2006-2010) in the eastern Tibet Plateau area and make two kinds of assessments. The first is for a larger scale, including statistic analysis of altitudinal zonality and latitudinal zonality. The second assessment is for rapid detection of vegetation change location. Landsat TM image were employed to validate the result.