Land surface temperature (LST) is a key parameter in geophysical fields. The Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra provides an accurate LST dataset with global coverage and monthly series, but the monthly MODIS LST data are often obscured by clouds and other atmospheric disturbances and consequently exhibit significant data gaps at a global scale, resulting in a difficult interpretation of LST trends and climatological characteristics. In this paper, an effective and fast LST reconstruction method to fill data gaps in monthly MODIS LST is presented. The proposal combines the Discrete Cosine Transform (DCT) and the Penalized Least Square approach (PLS) together with the Generalized Cross-Validation (GCV) criterion. It depends only on the spatial high-frequency information from original LST estimates and allows a fast and automatic filling process without the help of any other ancillary data. To analyze its performance, the method is applied to fill data gaps on three continents with synthetic random missing values introduced as validation sets. The statistical evaluation shows that this method is capable of filling a large number of missing values in MODIS LST datasets with very high accuracy. In addition, the trend differences between the original LST and reconstructed LST have assessed the significance by computing 95% confidence intervals for a time series of trend differences is examined. Simulated experiments show that data gaps with large missing counts lead to significant differences in trend patterns and the patterns on validation sets are well estimated by this method, which confirms that the filling process of MODIS LST is necessary and favorable results can be produced for substantial data gaps by the DCT-PLS method.

Russia has experienced population decline in years and the economic development in Russia is largely restricted by labor shortage, particularly for the Far North and East region. In order to explore the migration mechanisms, six socioeconomic factors were selected to explore the influences on the net migration. Data from the 82 regions covering four time periods (2000, 2005, 2010 and 2015) was processed use spatial panel econometric analysis and the time-period fixed effects Spatial Durbin Model (SDM) was selected as the best fit model after tests. The results indicates that, unemployment and infant death rate are significantly negatively associated with net migration, while urbanization rate, urban scale and life expectancy are significantly positively associated with net migration; every 100 USD increase in per capita GRP (Gross Regional Product) is positively related with averagely 5.4 net migrates in the region; every 1 year increase in life expectancy would increase 1052 net migrates; every 1sqm increase in urban scale would increase the net migrates by 11.75 and every 1% increase in unemployment would lead to a decrease of 0.54 net migrates. Spillover effect was also found for per capita GRP and life expectancy, indicating that the increase of per capita GRP and life expectancy in neighboring regions can also increase the attractiveness in one region. It can be concluded that better job market, better economic status and health related wellbeing are all attracting factors for migrates and these factors can even make the neighborhood region more attractive for immigrates. Considering the ambitious development plan for the Russia Far North and East regions, related suggestions on attracting migrates are provided.

Anoplophora glabripennis Motschulsky, 1854 (Asian longhorned beetle) does serious damage to forests. It has a long history and wide distribution area in China and is spreading there and elsewhere. Extreme climate events, such as cold surges and droughts, have had a promotive impact on Anoplophora glabripennis occurrence, but the spatial spillover effect of extreme climate events and other environmental factors on the occurrence of this pest has not yet been clarified. Two indices, namely, Standardized Precipitation Evapotranspiration Index (SPEI) and Low Temperature Index (LTI), were used to quantify the effects of drought and low-temperature freezing damage. Based on spatial panel data modeling, this study calculated the spatial spillover effect of environmental factors on the incidence of Anoplophora glabripennis in 666 counties in China’s central plains from 2002 to 2009. The factors examined included LTI, SPEI, average wind speed, hours of sunlight, Gross Domestic Product (GDP) of regional primary industry, population density, Normalized Difference Vegetation Index (NDVI), and pest control rate. Study results indicated that the impacts of environmental factors on the incidence rate of Anoplophora glabripennis are different. Low-temperature freezing damage, drought, wind speed, and pest control rate had a driving impact on pest incidence rates. Overall, the direct effect accounts for about 85% of the total effect, while the indirect effect accounts for about 15% of the total effect.