Article

Improved monitoring of vegetation dynamics at very high latitudes: A new method using MODIS NDVI

Institute of Biology, University of Tromsø, N-9037 Tromsø, Norway; International Institute for Geo-Information Science and Earth Observation (ITC), P.O. Box 6, 7500 AA Enschede, The Netherlands; NORUT IT AS, N-9291 Tromsø, Norway
Remote Sensing of Environment (Impact Factor: 5.1). 02/2006; DOI: 10.1016/j.rse.2005.10.021

ABSTRACT Current models of vegetation dynamics using the normalized vegetation index (NDVI) time series perform poorly for high-latitude environments. This is due partly to specific attributes of these environments, such as short growing season, long periods of darkness in winter, persistence of snow cover, and dominance of evergreen species, but also to the design of the models. We present a new method for monitoring vegetation activity at high latitudes, using Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI. It estimates the NDVI of the vegetation during winter and applies a double logistic function, which is uniquely defined by six parameters that describe the yearly NDVI time series. Using NDVI data from 2000 to 2004, we illustrate the performance of this method for an area in northern Scandinavia (35 × 162 km2, 68° N 23° E) and compare it to existing methods based on Fourier series and asymmetric Gaussian functions. The double logistic functions describe the NDVI data better than both the Fourier series and the asymmetric Gaussian functions, as quantified by the root mean square errors. Compared with the method based on Fourier series, the new method does not overestimate the duration of the growing season. In addition, it handles outliers effectively and estimates parameters that are related to phenological events, such as the timing of spring and autumn. This makes the method most suitable for both estimating biophysical parameters and monitoring vegetation phenology.

1 Bookmark
 · 
92 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: In satellite remote sensing, irregular temporal sampling is a common feature of geophysical and biological process on the earth's surface. Lee (2008) proposed a feed-back system using a harmonic model of single period to adaptively reconstruct observation image series contaminated by noises resulted from mechanical problems or environmental conditions. However, the simple sinusoidal model of single period may not be appropriate for temporal physical processes of land surface. A complex model of multiple periods would be more proper to represent inter-annual and inner-annual variations of surface parameters. This study extended to use a multi-periodic harmonic model, which is expressed as the sum of a series of sine waves, for the adaptive system. For the system assessment, simulation data were generated from a model of negative errors, based on the fact that the observation is mainly suppressed by bad weather. The experimental results of this simulation study show the potentiality of the proposed system for real-time monitoring on the image series observed by imperfect sensing technology from the environment which are frequently influenced by bad weather.
    Korean Journal of Remote Sensing. 01/2010; 26(6).
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: [1] Warming and changing fire regimes in the northern (≥45°N) latitudes have consequences for land-atmosphere carbon feedbacks to climate change. A terrestrial carbon flux model integrating satellite Normalized Difference Vegetation Index and burned area records with global meteorology data was used to quantify daily vegetation gross primary productivity (GPP) and net ecosystem CO2 exchange (NEE) over a pan-boreal/Arctic domain and their sensitivity to climate variability, drought, and fire from 2000 to 2010. Model validation against regional tower carbon flux measurements showed overall good agreement for GPP (47 sites: R = 0.83, root mean square difference (RMSD) = 1.93 g C m−2 d−1) and consistency for NEE (22 sites: R = 0.56, RMSD = 1.46 g C m−2 d−1). The model simulations also tracked post-fire NEE recovery indicated from three boreal tower fire chronosequence networks but with larger model uncertainty during early succession. Annual GPP was significantly (p < 0.005) larger in warmer years than in colder years, except for Eurasian boreal forest, which showed greater drought sensitivity due to characteristic warmer, drier growing seasons relative to other areas. The NEE response to climate variability and fire was mitigated by compensating changes in GPP and respiration, though NEE carbon losses were generally observed in areas with severe drought or burning. Drought and temperature variations also had larger regional impacts on GPP and NEE than fire during the study period, though fire disturbances were heterogeneous, with larger impacts on carbon fluxes for some areas and years. These results are being used to inform development of similar operational carbon products for the NASA Soil Moisture Active Passive (SMAP) mission.
    Journal of Geophysical Research: Biogeosciences 06/2013; 118(2). · 3.02 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: The aim of the present research is to monitor changes in herbage production during the grazing season in the Semirom and Brojen regions, Iran, using multitemporal Moderate Resolution Imaging Spectroradiometer (MODIS) data. At first, various preprocessing steps were applied to a topography map. The atmospheric and topographic corrections were applied using subtraction of the dark object method and the Lambert method. Image processing, including false-color composite, principal component analysis, and vegetation indices were employed to produce land use and pasture production maps. Vegetation sampling was carried out over a period of 4 months during June–September 2008, using a stratified random sampling method. Twenty random sampling points were selected, and herbage production was estimated and verified with the double-checking method. Four MODIS data sets were used in this study. The models for image processing and integrating ground data with satellite images were processed, and the resulting images were categorized into seven classes. Finally, the land covers were verified for accuracy. A postclassification analysis was carried out to verify the seven class change detections. The results confirmed that Normalized Difference Vegetation Index (NDVI) and Soil-Adjusted Vegetation Index (SAVI) maps had a close relationship with the field data. The indices produced with shortwave infrared bands had a close relationship with field data where the ground cover and yields were high. The R 2 value observed was 0.85. The changes in the pasture vegetation were high during the growing season in more than 90 % of the pastures. During the growing season, most changes in the pastures belonged to class 5 and 2 in the NDVI and SAVI index maps, respectively.
    Arabian Journal of Geosciences · 1.15 Impact Factor

Full-text

Download
45 Downloads
Available from
Jun 1, 2014