Spaceborne LiDAR has been widely used to obtain forest canopy heights over large areas, but it is still a challenge to obtain spatio-continuous forest canopy heights with this technology. In order to make up for this deficiency and take advantage of the complementary for multi-source remote sensing data in forest canopy height mapping, a new method to estimate forest canopy height was proposed by synergizing the spaceborne LiDAR (ICESat-2) data, Synthetic Aperture Radar (SAR) data, multi-spectral images, and topographic data considering forest types. In this study, National Geographical Condition Monitoring (NGCM) data was used to extract the distributions of coniferous forest (CF), broadleaf forest (BF), and mixed forest (MF) in Hua’ nan forest area in Heilongjiang Province, China. Accordingly, the forest canopy height estimation models for whole forest (all forests together without distinguishing types, WF), CF, BF, and MF were established, respectively, by Radom Forest (RF) and Gradient Boosting Decision Tree (GBDT). The accuracy for established models and the forest canopy height obtained based on estimation models were validated consequently. The results showed that the forest canopy height estimation models considering forest types had better performance than the model grouping all types of forest together. Compared with GBDT, RF with optimal variables had better performance in forest canopy height estimation with Pearson’s correlation coefficient (R) and the root-mean-squared error (RMSE) values for CF, BF, and MF of 0.72, 0.59, 0.62, and 3.15, 3.37, 3.26 m, respectively. It has been validated that a synergy of ICESat-2 with other remote sensing data can make a crucial contribution to spatio-continuous forest canopy height mapping, especially for areas covered by different types of forest.

In recent years, quantity and quality of cultivated land in black soil region in Northeast China have changed with the continuous strengthening of the breadth and depth of land development, directly affecting food security in China. In this study, land-use data based on geo-information Tupu in 1990, 2000, 2010 and 2020 were selected and the spatiotemporal evolution of cultivated land in Northeast China was analyzed. Meanwhile, driving factors affecting the changes of cultivated land over the past 30 years was also explored. The results indicate that the net increase of cultivated land in the study area is 127.72 km² (accounting for 0.038% of the study area) from 1990 to 2020, and a trend of “increase-decrease-increase” over the past 30 years can be obtained. The change of cultivated land mainly due to the mutual conversion between cultivated land and forest (net increase: 6024.76 km²), grassland (net increase: 734.08 km²) and construction land (net decrease: 7393. 42 km²). The conversions of cultivated land and forest, grassland and construction land are mainly located in the northeastern, mid-western and southern, and eastern of the study area, and the center of cultivated land within the study area shifted towards the southeast from 1990 to 2010, while the center showed a convoluted trend (moving towards the northwest) during the 2010–2020 period, but the migration speed gradually slowed down. The spatiotemporal pattern changes of cultivated land in the study area from 1990 to 2020 are the comprehensive effects of natural environmental and socio-economic factors, among which slope, elevation, and annual precipitation are the main contributing factors. The conclusion of this study will provide scientific reference for the study of cultivated land utilization and protection in the black soil region in Northeast China.