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.

Forest aboveground biomass (AGB) plays a key role in assessing forest productivity. In this study, we extracted individual tree structural parameters using backpack LiDAR, assessed their accuracy using terrestrial laser scanning (TLS) data and field measurements as reference values, and reconstructed 3D models of trees based on parameter-optimized quantitative structural models (AdQSM). The individual tree AGB was estimated based on individual tree volumes obtained from the tree model reconstruction, combined with the basic wood density values of specific tree species. In addition, the AGB calculated using the allometric biomass models was validated to explore the feasibility of nondestructive estimation of individual tree AGB by backpack LiDAR. We found that (1) the backpack LiDAR point cloud extracted individual tree diameter at breast height (DBH) with high accuracy. In contrast, the accuracy of the tree height extraction was low; (2) the optimal parameter values of the AdQSM reconstruction models for Larix gmelinii and Betula platyphylla were HS = 0.4 m and HS = 0.6 m, respectively; (3) the individual tree AGB estimated based on the backpack LiDAR and AdQSM fit well with the reference values. Our study confirms that backpack LiDAR can nondestructively estimate individual tree AGB, which can provide a reliable basis for further forest resource management and carbon stock estimation.

Grassland fires are major disturbances to ecosystems and economies around the world. Therefore, research on the spatial patterns of grassland fires is important for understanding the dynamics of fire occurrence and providing evidence for fire prevention and management. One of the problems in grassland fire risk analysis is that historically observed fire data are generally in the point format, with imprecise positions, whereas other influencing factors are often expressed in continuous areal units. To minimise the influences of inaccurate locations and grid size, density estimates can be produced using kernel density estimation (KDE) – a nonparametric statistical method for estimating probability densities. This method has been widely used to convert historical fire data into continuous surfaces. In this study, KDE was applied to grassland fire events in the eastern Inner Mongolia of China, based on Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua daily active fire data from 2001 to 2014. The bandwidth choice was based on the mean random distance method. Annual and seasonal kernel density maps were produced, showing that the spatial patterns of grassland fire events remained temporally consistent. These results were used to create grassland fire risk zones on the basis of the mean density values in the study area. Grassland fire prevention and planning may focus on high-risk areas identified using this method.

The leaf area index (LAI) is a key biophysical parameter that determines the state of plant growth. A global LAI has been routinely produced by the Moderate Resolution Imaging Spectro-radiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR). However, the MODIS and AVHRR LAI products cannot be synchronized with the same spatial and temporal resolution. The LAI features are not discernible when a global LAI product is implemented at the regional scale because it has low resolution and different land cover types. To obtain high spatial and temporal resolution of LAI products, an empirical model based on the pixel scale was developed. The approach to generate a long (multi-decade) time series of a 1-km spatial resolution LAI normally integrates both AVHRR and MODIS datasets for different land cover types. In this paper, a regression-based model for generating a vegetation LAI was developed using the AVHRR Global Inventory Modelling and Mapping Studies Normalized Difference Vegetation Index (NDVI), MODIS LAI and land cover as input data; the model was evaluated by using relevant data from the same period data from 2000 to 2006. The results of this method show a good consistency in LAI values retrieved from the AVHRR NDVI and MODIS LAI. This simple method has no specific-limited data requirements and can provide improved spatial and temporal resolution in a region without ground data.

Discrete point cloud data from unmanned aerial vehicle laser scanning (UAV-LS) can provide information on the three-dimensional structure of a forest, the leaf area index (LAI) at the landscape or sample plot scales, the distribution of the vertical forest structure at a fine resolution, and other information. The retrieved parameters, however, may be affected in a non-negligible way by the inclusion of scan angle information. In this study, we introduced a relational model that encompasses the angular effect, predicted the mechanism of this effect, and extracted the vegetation structure indices that the angular effect might influence. Second, we quantified the direct and indirect effects, particularly the magnitude of the angular effect in broadleaf forests, and used mediated effects to investigate the components and processes that influence the angular effect. The findings demonstrate that some of the differences between the LAIe extracted by UAV-LS and the Decagon LAIe considering the angular effect of UAV-LS can be explained by adjusting physical LiDAR parameters (aerial height, laser divergence fraction, and scanning angle) and vertical forest structure variables. Along continuous and closed forest vertical gradients, the indirect angle impact is negative for the upper canopy and positive for the understory. Three-dimensional vegetation measurements were created using multiangle LiDAR data. In conclusion, this article (1) addresses the angular effect in UAV-LS; and (2) discusses how the angular effect affects 3D vegetation parameters such as LAIe, demonstrates the nonlinear trend of the angular effect, and demonstrates how multiangle LiDAR data can be used to obtain 3D vegetation parameters. This study serves as a reference for reducing the uncertainty in simulations of the angular effect and vegetation light transmission, in addition to the uncertainty in analyses of the vegetation characteristics determined by UAV-LS (e.g., the uncertainty of LAIe).