Improving the quantification of waterfowl migration with remote sensing and bird tracking

Accurately quantifying waterfowl migration patterns is pertinent to monitor ecosystem health and control bird-borne infectious diseases. In this review, we summarize the current understanding of the environmental mechanisms that drive waterfowl migration and then investigate the effect of intra- and inter-annual change in food supply and temperature(e.g., climate change) on their migration patterns. Recent advances in remote sensing and animal tracking techniques make it possible to monitor these environmental factors over a wide range of scales and record bird movements in detail. The synergy of these techniques will facilitate substantial progress in our understanding of the environmental drivers of bird migration. We identify prospects for future studies to test existing hypotheses and develop models integrating up-todate knowledge, high-resolution remote sensing data and high-accuracy bird tracking data. This will allow us to predict when waterfowl will be where, in response to shortand long-term global environmental change.

Monitoring of winter wheat distribution and phenological phases based on MODIS time-series： A case study in the Yellow River Delta, China

Accurate winter wheat identification and phenology extraction are essential for field management and agricultural policy making. Here, we present mechanisms of winter wheat discrimination and phenological detection in the Yellow River Delta（YRD） region using moderate resolution imaging spectroradiometer（MODIS） time-series data. The normalized difference vegetation index（NDVI） was obtained by calculating the surface reflectance in red and infrared. We used the Savitzky-Golay filter to smooth time series NDVI curves. We adopted a two-step classification to identify winter wheat. The first step was designed to mask out non-vegetation classes, and the second step aimed to identify winter wheat from other vegetation based on its phenological features. We used the double Gaussian model and the maximum curvature method to extract phenology. Due to the characteristics of the time-series profiles for winter wheat, a double Gaussian function method was selected to fit the temporal profile. A maximum curvature method was performed to extract phenological phases. Phenological phases such as the green-up, heading and harvesting phases were detected when the NDVI curvature exhibited local maximum values. The extracted phenological dates then were validated with records of the ground observations. The spatial patterns of phenological phases were investigated. This study concluded that, for winter wheat, the accuracy of classification is 87.07%, and the accuracy of planting acreage is 90.09%. The phenological result was comparable to the ground observation at the municipal level. The average green-up date for the whole region occurred on March 5, the average heading date occurred on May 9, and the average harvesting date occurred on June 5. The spatial distribution of the phenology for winter wheat showed a significant gradual delay from the southwest to the northeast. This study demonstrates the effectiveness of our proposed method for winter wheat classification and phenology detection.

Spatial and temporal variation of vegetation phenology and its response to climate changes in Qaidam Basin from 2000 to 2015

Based on TIMESAT 3.2 platform, MODIS NDVI data(2000–2015) of Qaidam Basin are fitted, and three main phenological parameters are extracted with the method of dynamic threshold, including the start of growth season(SGS), the end of growth season(EGS) and the length of growth season(LGS). The spatial and temporal variation of vegetation phenology and its response to climate changes are analyzed respectively. The conclusions are as follows:(1) SGS is mainly delayed as a whole. Areas delayed are more than the advanced in EGS, and EGS is a little delayed as a whole. LGS is generally shortened.(2) With the altitude rising, SGS is delayed, EGS is advanced, and LGS is shortened and phenophase appears a big variation below 3000 m and above 5000 m.(3) From 2000 to 2015, the temperature appears a slight increase along with a big fluctuation, and the precipitation increases evidently.(4) Response of phenophase to precipitation is not obvious in the low elevation humid regions, where SGS arrives early and EGS delays; while, in the upper part of the mountain regions, SGS delays and EGS advances with temperature rising, SGS arrives early and EGS delays with precipitation increasing.