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

Institute of Biology, University of Tromsø, N-9037 Tromsø, Norway
Remote Sensing of Environment (Impact Factor: 6.39). 02/2006; 100(3):321-334. DOI: 10.1016/j.rse.2005.10.021


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.

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    • "The presence of healthy vegetation, and continuous vegetative cover, is essential to training land availability and access as it reduces soil erosion, prevents formation of dangerous gullies, and provides natural cover for soldiers and vehicles during training exercises. Degradation of vegetation health results from gradual or abrupt changes in the level of vegetation activity over time (de Jong et al., 2011), which can be routinely monitored by collecting and analyzing time-series Normalized Difference Vegetation Index (NDVI) data from medium and coarse spatial resolution satellite sensors (Beck et al., 2006; Dash et al., 2010; Julien and Sobrino, 2009; Verbesselt et al., 2010a, 2010b). Time series datasets using NDVI products from the Moderate Resolution Imaging Spectrometer (MODIS) sensor have been successfully used to quantify vegetation activity and vegetation dynamics (Alh et al., 2006; Jacquin et al., 2010; Zhang et al., 2003). "
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    ABSTRACT: Given the significant land holdings of the U.S. Department of Defense, and the importance of those lands to support a variety of inherently damaging activities, application of sound natural resource conservation principles and proactive monitoring practices are necessary to manage military training lands in a sustainable manner. This study explores a method for, and the utility of, analyzing vegetation condition and trends as sustainability indicators for use by military commanders and land managers, at both the national and local levels, in identifying when and where vegetation-related environmental impacts might exist. The BFAST time series decomposition method was applied to a ten-year MODIS NDVI time series dataset for the Fort Riley military installation and Konza Prairie Biological Station (KPBS) in northeastern Kansas. Imagery selected for time-series analysis were 16-day MODIS NDVI (MOD13Q1 Collection 5) composites capable of characterizing vegetation change induced by human activities and climate variability. Three indicators related to gradual interannual or abrupt intraannual vegetation change for each pixel were calculated from the trend component resulting from the BFAST decomposition. Assessment of gradual interannual NDVI trends showed the majority of Fort Riley experienced browning between 2001 and 2010. This result is supported by validation using high spatial resolution imagery. The observed versus expected frequency of linear trends detected at Fort Riley and KPBS were significantly different and suggest a causal link between military training activities and/or land management practices. While both sites were similar with regards to overall disturbance frequency and the relative spatial extents of monotonic or interrupted trends, vegetation trajectories after disturbance were significantly different. This suggests that the type and magnitude of disturbances characteristic of each location result in distinct post-disturbance vegetation responses. Using a remotely-sensed vegetation index time series with BFAST and the indicators outlined here provides a consistent and relatively rapid assessment of military training lands with applicability outside of grassland biomes. Characterizing overall trends and disturbance responses of vegetation can promote sustainable use of military lands and assist land managers in targeting specific areas for various rehabilitation activities. Copyright © 2014 Elsevier Ltd. All rights reserved.
    Full-text · Article · Mar 2015 · Journal of Environmental Management
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    • "The EVI data from the MODIS instruments mounted on the EOS TERRA and AQUA satellite platforms (launched in 1999 and 2002, respectively) have been the primary data source for LSP characterizations since 2000. LSP is often characterized by fitting mathematical curves such as double logistic models (Beck et al., 2006; Eklundh and J€ onsson, 2010; Zhang et al., 2003, 2006), asymmetric Gaussian models (Beck et al., 2006; Eklundh and J€ onsson, 2010) or Fourier and Wavelet transformations (Moody and Johnson, 2001) to vegetation index greenness time series. A limiting factor in the application of existing techniques on the Australian continent is that the majority of LSP algorithms have been developed to characterize ecosystems in the mid and high latitudes of the Northern Hemisphere where LSP cycles reoccur annually (de Beurs and Henebry, 2010; Eklundh and J€ onsson, 2010). "
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    ABSTRACT: Land surface phenology (LSP) characterizes episodes of greening and browning of the vegetated land surface from remote sensing imagery. LSP is of interest for quantification and monitoring of crop yield, wildfire fuel accumulation, vegetation condition, ecosystem response and resilience to climate variability and change. Deriving LSP represents an effort for end users and existing global products may not accommodate conditions in Australia, a country with a dry climate and high rainfall variability. To fill this information gap we developed the Australian LSP Product in contribution to AusCover/Terrestrial Ecosystem Research Network (TERN). We describe the product's algorithm and information content consisting of metrics that characterize LSP greening and browning episodes of the vegetated land surface. Our product allows tracking LSP metrics over time and thereby quantifying inter- and intraannual variability across Australia. We demonstrate the metrics' response to ENSO-driven climate variability. Lastly, we discuss known limitations of the current product and future development plans.
    Full-text · Article · Feb 2015 · Environmental Modelling and Software
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    • "Season length was then computed as the difference between end and start values. Since this procedure gave results consistent with field data for phenological monitoring in mountain areas (Beck et al., 2006; Busetto et al., 2010; Colombo et al., 2011), it was regarded as validated. "
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    Full-text · Article · Oct 2014 · International Journal of Applied Earth Observation and Geoinformation
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