The causes of a greening trend detected in the Arctic using the normalized difference vegetation index (NDVI) are still poorly understood. Changes in NDVI are a result of multiple ecological and social factors that affect tundra net primary productivity. Here we use a 25 year time series of AVHRR-derived NDVI data (AVHRR: advanced very high resolution radiometer), climate analysis, a global geographic information database and ground-based studies to examine the spatial and temporal patterns of vegetation greenness on the Yamal Peninsula, Russia. We assess the effects of climate change, gas-field development, reindeer grazing and permafrost degradation. In contrast to the case for Arctic North America, there has not been a significant trend in summer temperature or NDVI, and much of the pattern of NDVI in this region is due to disturbances. There has been a 37% change in early-summer coastal sea-ice concentration, a 4% increase in summer land temperatures and a 7% change in the average time-integrated NDVI over the length of the satellite observations. Gas-field infrastructure is not currently extensive enough to affect regional NDVI patterns. The effect of reindeer is difficult to quantitatively assess because of the lack of control areas where reindeer are excluded. Many of the greenest landscapes on the Yamal are associated with landslides and drainage networks that have resulted from ongoing rapid permafrost degradation. A warming climate and enhanced winter snow are likely to exacerbate positive feedbacks between climate and permafrost thawing. We present a diagram that summarizes the social and ecological factors that influence Arctic NDVI. The NDVI should be viewed as a powerful monitoring tool that integrates the cumulative effect of a multitude of factors affecting Arctic land-cover change. ReferencesBelopukova E B, Leibman M O and Tukacheva L A 1989 The effect of climatic parameters on ground temperature regime and active layer depth on the Yamal Peninsula Engineering and Engineering Construction Studies in the Yakutia Republic (Yakutsk: NTO) Bhatt U S, Walker D A, Raynolds M and Comiso J 2007 The relationship between sea ice variability and Arctic tundra on the pan-Arctic, regional, and site scales Eos Trans. AGU 88 (52) Fall Meet. Suppl. Abstract U41C-0612 Bhatt U S, Walker D A, Raynolds M K and Comiso J 2008 Circumpolar and regional analysis of the relationship between sea-ice variability, summer land-surface temperatures, Arctic tundra greenness and large-scale climate drivers

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Aboveground biomass is a key indicator of a grassland ecosystem. Accurate estimation from remote sensing is important for understanding the response of grasslands to climate change and disturbance at a large scale. However, the precision of remote sensing inversion is limited by a lack in the ground truth and scale mismatch with satellite data. In this study, we first tried to establish a grassland aboveground biomass estimation model at 1 m 2 quadrat scale by conducting synchronous experiments of unmanned aerial vehicle (UAV) and field measurement in three different grassland ecosystems. Two flight modes (the new QUADRAT mode and the commonly used MOSAIC mode) were used to generate point clouds for further processing. Canopy height metrics of each quadrat were then calculated using the canopy height model (CHM). Correlation analysis showed that the mean of the canopy height model (CHM_mean) had a significant linear relationship with field height (R 2 = 0.90, root mean square error (RMSE) = 19.79 cm, rRMSE = 16.5%, p < 0.001) and a logarithmic relationship with field aboveground biomass (R 2 = 0.89, RMSE = 91.48 g/m 2 , rRMSE = 16.11%, p < 0.001). We concluded our study by conducting a preliminary application of estimation of the aboveground biomass at a plot scale by jointly using UAV and the constructed 1 m 2 quadrat scale estimation model. Our results confirmed that UAV could be used to collect large quantities of ground truths and bridge the scales between ground truth and remote sensing pixels, which were helpful in improving the accuracy of remote sensing inversion of grassland aboveground biomass.

Permafrost on the Qinghai-Tibetan Plateau (QTP) has degraded over the last few decades. Its ecological effects have attracted great concern. Previous studies focused mostly at plot scale, and hypothesized that degradation of permafrost would cause lowering of the water table and drying of shallow soil and then degradation of alpine grassland. However, none has been done to test the hypothesis at basin scale. In this study, for the first time, we investigated the relationships between land surface temperature (LST) and fractional vegetation cover (FVC) in different types of permafrost zone to infer the limiting condition (water or energy) of grassland growth on the source region of Shule River Basin, which is located in the north-eastern edge of the QTP. LST was obtained from MODIS Aqua products at 1 km resolution, while FVC was upscaled from quadrat (50 cm) to the same resolution as LST, using 30 m resolution NDVI data of the Chinese HJ satellite. FVC at quadrat scale was estimated by analyzing pictures taken with a multi-spectral camera. Results showed that (1) retrieval of FVC at quadrat scale using a multi-spectral camera was both more accurate and more efficient than conventional methods and (2) the limiting factor of vegetation growth transitioned from energy in the extreme stable permafrost zone to water in the seasonal frost zone. Our study suggested that alpine grassland would respond differently to permafrost degradation in different types of permafrost zone. Future studies should consider overall effects of permafrost degradation, and avoid the shortcomings of existing studies, which focus too much on the adverse effects.