Environmental Drivers of NDVI-Based Vegetation Phenology in Central Asia

Abstract

Abstract:Through the application and use of geospatial data, this study aimed to detect and characterize some of the key environmental drivers contributing to landscape-scale vegetation response patterns in Central Asia. The objectives of the study were to identify the variables driving the year-to-year vegetation dynamics in three regional landscapes (desert, steppe, and mountainous); and to determine if the identified environmental drivers can be used to explain the spatial-temporal variability of these spatio-temporal dynamics over time. It was posed that patterns of change in terrestrial phenology, derived from the 8 km bi-weekly time series of Normalized Difference Vegetation Index (NDVI) data acquired by the Advanced Very High Resolution Radiometer (AVHRR) satellites , can be explained through a multi-scale analysis of a suite of environmental drivers. Multiple linear stepwise regression analyses were used to test the hypotheses and address the objectives of the study. The annually computed phenological response variables or pheno-metrics time (season start, season length, and an NDVI-based productivity metric) were modeled as a function of ten environmental factors relating to soil, topography, and climate. Each of the three studied regional landscapes was shown to be governed by a distinctive suite of environmental drivers. The phenological responses of the steppe landscapes were affected by the year-to-year variation in temperature regimes. The phenology of the mountainous landscapes was influenced primarily by the elevation gradient. The phenological responses of desert landscapes were demonstrated to have the greatest variability over time and seemed to be affected by soil carbon content and OPEN ACCESSRemote Sens. 2011, 3 204 year-to-year variation of both temperature regimes and winter precipitation patterns. Amounts and scales of observed phenological variability over time (measured through coefficient of variation for each pheno-metric time ) in each of the regional landscapes were interpreted in terms of their resistance and resilience capacities under existing and projected environmental settings.

Bridging Ridge-to-Reef Patches: Seamless Classification of the Coast Using Very High Resolution Satellite

“…As previously mentioned, the value of the red edge band might be strongly associated with the detection of reflectance/absorbance peaks of chlorophylls-a and -b and the utilization of NIR2, contiguous to the short-wavelength IR (SWIR). The resulting NDVI might optimize the detection of chlorophyll reflectance in avoiding the detection in the very NIR of vegetation absorbance attendant with its phenology and/or vigor [39]. Table 7.…”

How Normalized Difference Vegetation Index (NDVI) Trendsfrom Advanced Very High Resolution Radiometer (AVHRR) and Système Probatoire d’Observation de la Terre VEGETATION (SPOT VGT) Time Series Differ in Agricultural Areas: An Inner Mongolian Case Study

“…Vegetation properties, such as length of growing season, onset date of greenness, and date of maximum photosynthetic activity are often derived from NDVI time series for monitoring changes in agricultural systems [29][30][31][32]. These phenological indicators emphasize different characteristics of terrestrial ecosystems to gain a better understanding of structure and function of land cover and associated changes [7,33,34]. Phenology, i.e., the timing of recurring life cycle events, may for example shift in response to natural or anthropogenic disturbances in agricultural ecosystems [35,36].…”

Quantifying the Impacts of Environmental Factors on Vegetation Dynamics over Climatic and Management Gradients of Central Asia

“…For the mountainous sub-region, the peak temperature variable had significant effect on natural vegetation (η 2 = 3), non-irrigated cropland (η 2 = 3) and irrigated cropland (η 2 = 6) compared to the other variables. In the mountainous sub-region vegetation patterns are distributed along altitude gradients and temperature thresholds are the main constraint on vegetation growth [27]. Overall fit of the models was high (R 2 = 0.81) for the natural vegetation class and equal to or only marginally below values calculated for the northern sub-region (R 2 = 0.81), the central sub-region and (R 2 = 0.87) and the mountainous sub-region (R 2 = 0.85).…”

“…The study area includes three distinct sub-regional landscapes based on the Köppen climate classification as described by Kariyeva and van Leeuwen [27]: (1) northern semi-steppe and steppe sub-region; (2) mountainous sub-region and (3) central semi-desert/desert sub-region. Rainfed agriculture predominates in the northern and mountainous sub-regions, while large-scale irrigated agriculture is common in the central region.…”

“…Optimal observations are designated by evaluating overall pixel quality in terms of aerosol content, low view angle, and absence of clouds and/or cloud shadows [26]. In order to further reduce data noise levels resulting from cloud and cloud shadow contamination, the MODIS time-series datasets were smoothed with a Whittaker smoother algorithm [27].…”