In this paper, the random forests method and the support vector machine in machine learning are explored and compared to the traditional statistical-based maximum likelihood method with 126 features from Sentinel-2A images. The spectral reflectance of 12 bands, 96 texture parameters, 7 vegetation indices, and 11 phenological parameters are successfully extracted from Sentinel-2A images in 2017. The classification result shows that the optimal combination of 13 features yields overall accuracies of traditional classification and machine learning classification of 88.96% and 98%, respectively. Short-wave infrared information shows a significant effect on distinguishing rice, corn, and soybean. The water vapor band plays a significant role in distinguishing between corn and rice. In the multiclassification problem, the machine learning methods have robustness with the identification accuracy of greater than 95% for each crop type, whereas the traditional classification result shows imbalanced accuracies for different crops.

Vegetation phenology is a key biological indicator for monitoring terrestrial ecosystems and global change, and regions with the most obvious phenological changes in vegetation are primarily located at high latitudes and altitudes. Over the past three decades, investigations of obvious phenological changes in vegetation at middle and high latitudes in the Northern Hemisphere have provided significant contributions to understanding global climate change. In this study, phenological parameters were extracted from the Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) to analyze the spatial and temporal characteristics of vegetation phenological changes above 40°N in the Northern Hemisphere from 1982–2013. The results showed that the start of season (SOS) was significantly advanced (−2.2 ± 0.6 days·decade⁻¹, p < 0.05) and that the end of season (EOS) was slightly delayed (0.78 ± 0.6 days·decade⁻¹, p = 0.21) over the entire study area in the initial 21 years (1982–2002). When the time scale was extended to 2013, the change rate of the SOS and EOS was significantly reduced; in addition, the SOS was delayed (3.2 ± 1.7 days·decade⁻¹, p < 0.05), and the EOS was advanced (4.5 ± 0.9 days·decade⁻¹, p < 0.05) over the entire study area in the last 11 years (2003–2013). The trends of advanced SOS and delayed EOS over the past three decades were slower than those over the initial two decades on a hemispheric scale. The change trends showed obvious variability with different vegetation types and were greater for woody plants than for herbaceous plants. For broad-leaved forest, the SOS was significantly advanced (2 ± 0.5 days·decade⁻¹, p < 0.05) and the EOS was significantly delayed (2.7 ± 0.6 days·decade⁻¹, p < 0.05) from 1982–2013. The trend of delayed EOS was greater than that of advanced SOS for different vegetation types. With respect to the spatial distribution of phenological trends in the Northern Hemisphere, the trends of advanced SOS and delayed EOS were strongest in Europe followed by North America, and the trends were least significant in Asia. Coniferous forest, shrub forest, grassland, and the entire study area have the same change trends for the two time periods (1982–2002 and 2003–2013), and the increased rate of the phenology parameters has decelerated over the most recent decade. The length of season (LOS) of broad-leaved forest and mixed forest over the past 32 years shows a strong increased trend, and simultaneously, the SOS and EOS show an advanced trend and a delayed trend, respectively

Net primary productivity (NPP) is the total amount of organic matter fixed by plants from the atmosphere through photosynthesis and is susceptible to the influences of climate change and human activities. In this study, we employed actual NPP (ANPP), potential NPP (PNPP), and human activity-induced NPP (HNPP) based on the Hurst exponent and statistical analysis to analyze the characteristics of vegetation productivity dynamics and to evaluate the effects of climate and human factors on vegetation productivity in Northeast China (NEC). The increasing trends in ANPP, PNPP, and HNPP accounted for 81.62%, 94.90%, and 89.63% of the total area, respectively, and ANPP in 68.64% of the total area will continue to increase in the future. Climate change played a leading role in vegetation productivity dynamics, which promoted an increase in ANPP in 71.55% of the area, and precipitation was the key climate factor affecting ANPP. The aggravation of human activities, such as increased livestock numbers and intensified agricultural activities, resulted in a decrease in ANPP in the western grasslands, northern Greater Khingan Mountains, and eastern Songnen Plain. In particular, human activities led to a decrease in ANPP in 53.84% of deciduous needleleaf forests. The impact of climate change and human activities varied significantly under different topography, and the percentage of the ANPP increase due to climate change decreased from 71.13% to 53.9% from plains to urgent slopes; however, the percentage of ANPP increase due to human activities increased from 3.44% to 21.74%, and the effect of human activities on the increase of ANPP was more obvious with increasing slope. At different altitudes, the difference in the effect of these two factors was not significant. The results are significant for understanding the factors influencing the vegetation productivity dynamics in NEC and can provide a reference for governments to implement projects to improve the ecosystem.

Northeast China is located at high northern latitudes and is a typical region of relatively high sensitivity to global climate change. Studies of the land surface phenology in Northeast China and its response to climate change are important for understanding global climate change. In this study, the land surface phenology parameters were calculated using the third generation dataset from the Global Inventory Modeling and Mapping Studies (GIMMS 3g) that was collected from 1982 to 2013 were estimated to analyze the variations of the land surface phenology in Northeast China at different scales and to discuss the internal relationships between phenology and climate change. We examined the phonological changes of all ecoregions. The average start of the growing season (SOS) did not exhibit a significant trend throughout the study area; however, the end of the growing season (EOS) was significantly delayed by 4.1 days or 0.13 days/year (p < 0.05) over the past 32 years. The SOS for the Hulunbuir Plain, Greater Khingan Mountains and Lesser Khingan Mountains was earlier, and the SOS for the Sanjing, Songnen and Liaohe Plains was later. In addition, the EOS of the Greater Khingan Mountains, Lesser Khingan Mountains and Changbai Mountains was later than the EOS of the Liaohe Plain. The spring temperature had the greatest impact on the SOS. Precipitation had an insignificant impact on forest SOS and a relatively large impact on grassland SOS. The EOS was affected by both temperature and precipitation. Furthermore, although temperature had a lag effect on the EOS, no significant lag effect was observed for the SOS.

Time series of Normalized Difference Vegetation Index (NDVI) derived from multiple satellite sensors are crucial data to study vegetation dynamics. The Land Long Term Data Record Version 4 (LTDR V4) NDVI dataset was recently released at a 0.05 × 0.05° spatial resolution and daily temporal resolution. In this study, annual NDVI time series that are composited by the LTDR V4 and Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI datasets (MOD13C1) are compared and evaluated for the period from 2001 to 2014 in China. The spatial patterns of the NDVI generally match between the LTDR V4 and MOD13C1 datasets. The transitional zone between high and low NDVI values generally matches the boundary of semi-arid and sub-humid regions. A significant and high coefficient of determination is found between the two datasets according to a pixel-based correlation analysis. The spatially averaged NDVI of LTDR V4 is characterized by a much weaker positive regression slope relative to that of the spatially averaged NDVI of the MOD13C1 dataset because of changes in NOAA AVHRR sensors between 2005 and 2006. The measured NDVI values of LTDR V4 were always higher than that of MOD13C1 in western China due to the relatively lower atmospheric water vapor content in western China, and opposite observation appeared in eastern China. In total, 18.54% of the LTDR V4 NDVI pixels exhibit significant trends, whereas 35.79% of the MOD13C1 NDVI pixels show significant trends. Good agreement is observed between the significant trends of the two datasets in the Northeast Plain, Bohai Economic Rim, Loess Plateau, and Yangtze River Delta. By contrast, the datasets contrasted in northwestern desert regions and southern China. A trend analysis of the regression slope values according to the vegetation type shows good agreement between the LTDR V4 and MOD13C1 datasets. This study demonstrates the spatial and temporal consistencies and discrepancies between the AVHRR LTDR and MODIS MOD13C1 NDVI products in China, which could provide useful information for the choice of NDVI products in subsequent studies of vegetation dynamics.

With climate change, significant fluctuations in wildfires have been observed on the Mongolian Plateau. The ability to predict the distribution of wildfires in the context of climate change plays a critical role in wildfire management and ecosystem maintenance. In this paper, Ripley’s K function and a Random Forest (RF) model were applied to analyse the spatial patterns and main influencing factors affecting the occurrence of wildfire on the Mongolian Plateau. The results showed that the wildfires were mainly clustered in space due to the combination of influencing factors. The distance scale is less than 1/2 of the length of the Mongolian Plateau; that is, it does not experience boundary effects in the study area and it meets the requirements of Ripley’s K function. Among the driving factors, the fraction of vegetation coverage (FVC), land use degree (La), elevation, precipitation (pre), wet day frequency (wet), and maximum temperature (tmx) had the greatest influences, while the aspect had the lowest influence. The likelihood of fire was mainly concentrated in the northern, eastern, and southern parts of the Mongolian Plateau and in the border area between the Inner Mongolia Autonomous Region (Inner Mongolia) and Mongolian People’s Republic (Mongolia), and wildfires did not occur or occurred less frequently in the hinterland area. The fitting results of the RF model showed a prediction accuracy exceeding 90%, which indicates that the model has a high ability to predict wildfire occurrences on the Mongolian Plateau. This study can provide a reference for predictions and decision-making related to wildfires on the Mongolian Plateau.