Forest canopy height is an indispensable forest vertical structure parameter for understanding the carbon cycle and forest ecosystem services. A variety of studies based on spaceborne Lidar, such as ICESat, ICESat-2 and airborne Lidar, were conducted to estimate forest canopy height at multiple scales. However, while a few studies have been conducted based on ICESat-2 simulated data from airborne Lidar data, few studies have analyzed ATL08 and ATL03 products derived from the ATLAS sensor onboard ICESat-2 for regional vegetation canopy height mapping. It is necessary and promising to explore how data obtained by ICESat-2 can be applied to estimate forest canopy height. This study proposes a new means to estimate forest canopy height, defined as the mean height of trees within a given forest area, using a combination of ICESat-2 ATL08 and ATL03 data and ZY-3 satellite stereo images. Five procedures were used to estimate the forest canopy height of the city of Nanning in China: (1) Processing ground photons in a 30 m × 30 m grid; (2) Extracting a digital surface model (DSM) using ZY-3 stereo images; (3) Calculating a discontinuous canopy height model (CHM) dataset; (4) Validating the DSM and ground photon height using GEDI data; (5) Estimating the regional wall-to-wall forest canopy height product based on the backpropagation artificial neural network (BP-ANN) model and Landsat 8 vegetation indices and independent accuracy assessments with field measured plots. The validation shows a root mean square error (RMSE) of 3.34 m to 3.47 m and a coefficient of determination R² = 0.51. The new method shows promise and can be used for large-scale forest canopy height mapping at various resolutions or in combination with other data, such as SAR images. Finally, this study analyzes resolutions and how to filter effective data when ATL08 data are directly used to generate regional or global vegetation height products, which will be the focus of future research.

Land degradation reflected by vegetation is a commonly used practice to monitor desertification. To retrieve important information for ecosystem management accurate assessment of desertification is necessary. The major factors that drive vegetation dynamics in arid and semi-arid regions are climate and anthropogenic activities. Progression of desertification is expected to exacerbate under future climate change scenarios, through precipitation variability, increased drought frequency and persistence of dry conditions. This study examined spatiotemporal vegetation dynamics in arid regions of Sindh, Pakistan, using annual and growing season Normalized Difference Vegetation Index (NDVI) data from 2000 to 2017, and explored the climatic and anthropogenic effects on vegetation. Results showed an overall upward trend (annual 86.71% and growing season 82.7%) and partial downward trend (annual 13.28% and growing season 17.3%) in the study area. NDVI showed the highest significant increase in cropland region during annual, whereas during growing season the highest significant increase was observed in savannas. Overall high consistency in future vegetation trends in arid regions of Sindh province is observed. Stable and steady development region (annual 48.45% and growing 42.80%) dominates the future vegetation trends. Based on the Hurst exponent and vegetation dynamics of the past, improvement in vegetation cover is predicted for a large area (annual 44.49% and growing 30.77%), and a small area is predicted to have decline in vegetation activity (annual 0.09% and growing 3.04%). Results revealed that vegetation growth in the study area is a combined result of climatic and anthropogenic factors; however, in the future multi-controls are expected to have a slightly larger impact on annual positive development than climate whereas positive development in growing season is more likely to continue in future under the control of climate variability.

Accurate information on tree species is in high demand for forestry management and further investigations on biodiversity and environmental monitoring. Over regional or large areas, distinguishing tree species at high resolutions faces the challenges of a lack of representative features and computational power. A novel methodology was proposed to delineate the explicit spatial distribution of six dominant tree species (Pinus tabulaeformis, Quercus mongolia, Betula spp., Populus spp., Larix spp., and Armeniaca sibirica) and one residual class at 10 m resolution. Their spatial patterns were analyzed over an area covering over 90,000 km² using the analysis-ready large volume of multisensor imagery within the Google Earth engine (GEE) platform afterwards. Random forest algorithm built into GEE was used together with the 20th and 80th percentiles of multitemporal features extracted from Sentinel-1/2, and topographic features. The composition of tree species in natural forests and plantations at the city and county-level were performed in detail afterwards. The classification achieved a reliable accuracy (77.5% overall accuracy, 0.71 kappa), and the spatial distribution revealed that plantations (Pinus tabulaeformis, Populus spp., Larix spp., and Armeniaca sibirica) outnumber natural forests (Quercus mongolia and Betula spp.) by 6% and were mainly concentrated in the northern and southern regions. Arhorchin had the largest forest area of over 4500 km², while Hexingten and Aohan ranked first in natural forest and plantation area. Additionally, the class proportion of the number of tree species in Karqin and Ningcheng was more balanced. We suggest focusing more on the suitable areas modeling for tree species using species’ distribution models and environmental factors based on the classification results rather than field survey plots in further studies.

Fires are frequent in boreal forests affecting forest areas. The detection of forest disturbances and the monitoring of forest restoration are critical for forest management. Vegetation phenology information in remote sensing images may interfere with the monitoring of vegetation restoration, but little research has been done on this issue. Remote sensing and the geographic information system (GIS) have emerged as important tools in providing valuable information about vegetation phenology. Based on the MODIS and Landsat time-series images acquired from 2000 to 2018, this study uses the spatio-temporal data fusion method to construct reflectance images of vegetation with a relatively consistent growth period to study the vegetation restoration after the Greater Hinggan Mountain forest fire in the year 1987. The influence of phenology on vegetation monitoring was analyzed through three aspects: band characteristics, normalized difference vegetation index (NDVI) and disturbance index (DI) values. The comparison of the band characteristics shows that in the blue band and the red band, the average reflectance values of the study area after eliminating phenological influence is lower than that without eliminating the phenological influence in each year. In the infrared band, the average reflectance value after eliminating the influence of phenology is greater than the value with phenological influence in almost every year. In the second shortwave infrared band, the average reflectance value without phenological influence is lower than that with phenological influence in almost every year. The analysis results of NDVI and DI values in the study area of each year show that the NDVI and DI curves vary considerably without eliminating the phenological influence, and there is no obvious trend. After eliminating the phenological influence, the changing trend of the NDVI and DI values in each year is more stable and shows that the forest in the region was impacted by other factors in some years and also the recovery trend. The results show that the spatio-temporal data fusion approach used in this study can eliminate vegetation phenology effectively and the elimination of the phenology impact provides more reliable information about changes in vegetation regions affected by the forest fires. The results will be useful as a reference for future monitoring and management of forest resources.

Accurate information regarding forest volume plays an important role in estimating afforestation, timber harvesting, and forest ecological services. Traditionally, operations on forest growing stock volume using field measurements are labor-intensive and time-consuming. Recently, remote sensing technology has emerged as a time-cost efficient method for forest inventory. In the present study, we have adopted three procedures, including samples expanding, feature selection, and results generation and evaluation. Extrapolating the samples from Light Detection and Ranging (LiDAR) scanning is the most important step in satisfying the requirement of sample size for nonparametric methods operation and result in accuracy improvement. Besides, mean decrease Gini (MDG) methodology embedded into Random Forest (RF) algorithm served as a selector for feature measure; afterwards, RF and K-Nearest Neighbor (KNN) were adopted in subsequent forest volume prediction. The results show that the retrieval of Forest volume in the entire area was in the range of 50–360 m³/ha, and the results from the two models show a better consistency while using the sample combination extrapolated by the optimal threshold value (2 × 10⁻⁴), leading to the best performances of RF (R² = 0.618, root mean square error, RMSE = 43.641 m³/ha, mean absolute error, MAE = 33.016 m³/ha), followed by KNN (R² = 0.617, RMSE = 43.693 m³/ha, MAE = 32.534 m³/ha). The detailed analysis that is discussed in the present paper clearly shows that expanding image-derived LiDAR samples helps in refining the prediction of regional forest volume while using satellite data and nonparametric models.

The combination of natural environment changes and human activities affects the growth of grasslands. In order to quantitatively assess the causes of spatial and temporal variation of grasslands in Xilingol, this study assessed the spatial and temporal evolution patterns of grassland health based on MOD13A1 long time series Normalized Difference Vegetation Index (NDVI) data from 2000–2019 using trend analysis. The geodetector model was used to explore the dominant drivers of spatial variation in grassland NDVI, combined with 34 factors covering natural environmental changes and human disturbances over the same period. The results show that the grasslands of Xilingol showed an overall recovery trend from 2000 to 2019, with an average annual NDVI growth rate of 0.0028/a, a monthly increasing rate of 0.0005/month, and 68.06% of the grassland at an average recovery level. Moisture-dominated natural climate change factors, such as Growing Season Precipitation (Prep2), Annual Mean Water Vapor Pressure (WVP), and Annual Mean Relative Humidity (RH), were the underlying cause of grassland health changes during the study period, with the highest explanatory factor being growing season precipitation (q value of 0.59 on a multi-year average). The influence of primary production value among human activities was greater, and the explanatory factor of tertiary production value showed an increasing trend. The interactions among natural and anthropogenic factors significantly enhances their explanatory credibility for NDVI, with the type of interaction dominated by the two-factor enhancement. Risk detection of the top 10 dominant drivers in terms of q statistic were carried out to obtain the threshold range of each driver in the high zone of grassland NDVI, which can provide a scientific reference for the sustainable restoration of grassland.