Integrating spaceborne LiDAR and Sentinel-2 images to estimate forest aboveground biomass in Northern China.

BACKGROUND: Fast and accurate forest aboveground biomass (AGB) estimation and mapping is the basic work of forest management and ecosystem dynamic investigation, which is of great significance to evaluate forest quality, resource assessment, and carbon cycle and management. The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), as one of the latest launched spaceborne light detection and ranging (LiDAR) sensors, can penetrate the forest canopy and has the potential to obtain accurate forest vertical structure parameters on a large scale. However, the along-track segments of canopy height provided by ICESat-2 cannot be used to obtain comprehensive AGB spatial distribution. To make up for the deficiency of spaceborne LiDAR, the Sentinel-2 images provided by google earth engine (GEE) were used as the medium to integrate with ICESat-2 for continuous AGB mapping in our study. Ensemble learning can summarize the advantages of estimation models and achieve better estimation results. A stacking algorithm consisting of four non-parametric base models which are the backpropagation (BP) neural network, k-nearest neighbor (kNN), support vector machine (SVM), and random forest (RF) was proposed for AGB modeling and estimating in Saihanba forest farm, northern China. RESULTS: The results show that stacking achieved the best AGB estimation accuracy among the models, with an R2 of 0.71 and a root mean square error (RMSE) of 45.67 Mg/ha. The stacking resulted in the lowest estimation error with the decreases of RMSE by 22.6%, 27.7%, 23.4%, and 19.0% compared with those from the BP, kNN, SVM, and RF, respectively. CONCLUSION: Compared with using Sentinel-2 alone, the estimation errors of all models have been significantly reduced after adding the LiDAR variables of ICESat-2 in AGB estimation. The research demonstrated that ICESat-2 has the potential to improve the accuracy of AGB estimation and provides a reference for dynamic forest resources management and monitoring.

Spatial Pattern and Dynamic Change of Vegetation Greenness From 2001 to 2020 in Tibet, China.

Due to the cold climate and dramatically undulating altitude, the identification of dynamic vegetation trends and main drivers is essential to maintain the ecological balance in Tibet. The normalized difference vegetation index (NDVI), as the most commonly used greenness index, can effectively evaluate vegetation health and spatial patterns. MODIS-NDVI (Moderate-resolution Imaging Spectroradiometer-NDVI) data for Tibet from 2001 to 2020 were obtained and preprocessed on the Google Earth Engine (GEE) cloud platform. The Theil-Sen median method and Mann-Kendall test method were employed to investigate dynamic NDVI changes, and the Hurst exponent was used to predict future vegetation trends. In addition, the main drivers of NDVI changes were analyzed. The results indicated that (1) the vegetation NDVI in Tibet significantly increased from 2001 to 2020, and the annual average NDVI value fluctuated between 0.31 and 0.34 at an increase rate of 0.0007 year-1; (2) the vegetation improvement area accounted for the largest share of the study area at 56.6%, followed by stable unchanged and degraded areas, with proportions of 27.5 and 15.9%, respectively. The overall variation coefficient of the NDVI in Tibet was low, with a mean value of 0.13; (3) The mean value of the Hurst exponent was 0.53, and the area of continuously improving regions accounted for 41.2% of the study area, indicating that the vegetation change trend was continuous in most areas; (4) The NDVI in Tibet indicated a high degree of spatial agglomeration. However, there existed obvious differences in the spatial distribution of NDVI aggregation areas, and the aggregation types mainly included the high-high and low-low types; and (5) Precipitation and population growth significantly contributed to vegetation cover improvement in western Tibet. In addition, the use of the GEE to obtain remote sensing data combined with time-series data analysis provides the potential to quickly obtain large-scale vegetation change trends.

Extraction of Camellia oleifera crown width based on the method of optimized watershed with multi-scale markers.

In view of the limitation of scale on the spatial structure of ground objects and the problem that traditional watershed segmentation tends to produce crown over-segmentation, we proposed a segmentation method of Camellia oleifera crown based on the optimized watershed with multi-scale markers, with the C. oleifera base in Mingyue Village of Changsha County as the research object. Firstly, the high-resolution unmanned aerial vehicle (UAV) was used to collect images. The image features were analyzed to construct the classification system of C. oleifera, and the distribution area of C. oleifera was extracted. After being extracted by multi-scale region iterative growth, the crown markers were applied to the multi-threshold scale watershed transformation. Combined with Johnson index, the optimal scale of crown marker growth and watershed threshold was used to realize the accurate identification of individual trees. The results showed that the relative error between the method of optimized watershed with multi-scale markers and the visual interpretation of the reference value of tree-crown was 9.4% for the separation of individual trees. The overall identification accuracy of each tree was 89.4%, which was 34.8% higher than that of the traditional watershed segmentation method. The optimal iterative growth scale obtained by Johnson index was 20, while the thre-shold scale of watershed segmentation was 85. Compared with the results of different scale combinations, the crown extraction accuracy under the optimal scale was the highest (R2=0.75). The method of optimized watershed with multi-scale markers could accurately separate C. oleifera crown. Applying this method to UAV image crown segmentation could effectively improve the efficiency of economic forest investigation.

Estimating the aboveground biomass of coniferous forest in Northeast China using spectral variables, land surface temperature and soil moisture.

As a crucial indicator of forest growth and quality, estimating aboveground biomass (AGB) plays a key role in monitoring the global carbon cycle and forest health assessments. Novel methods and applications in remote sensing technology can greatly reduce the investigation time and cost and therefore have the potential to efficiently estimate AGB. Random forest (RF), combined with remote sensing images, is a popular machine learning method that has been widely used for AGB estimation. However, the accuracy of the ordinary linear variable selection method in the AGB estimation of coniferous forests is challenging due to the complexity of these forest biomes. In this study, spectral variables (spectral reflectance and vegetation index), land surface temperature (LST) and soil moisture were extracted from the operational land imager (OLI) and thermal infrared sensor (TIRS) of Landsat 8, and optimized RF regressions were established to estimate the AGB of coniferous forests in the Wangyedian forest farm, Inner Mongolia, Northeast China. We applied one linear (Pearson correlation coefficient (PC)) and four nonlinear (Kendall's τ coefficient (KC), Spearman coefficient (SC), distance correlation coefficient (DC) and the importance index) indices to select variables and establish optimized RF regressions for AGB estimation. The results showed that all the nonlinear indices provided significantly lower estimation errors than the linear index, in which the minimum root mean square error (RMSE) of 40.92 Mg/ha was obtained by the importance index in the nonlinear indices. In addition, the inclusion of LST and soil moisture significantly improved AGB estimation. The RMSE of the models constructed through the five indices decreased by 12.93%, 7.31%, 8.33%, 6.28% and 10.78%, respectively, following the application of the LST variable. In particular, when LST and soil moisture were both added into the model, the RMSE decreased by 31.47%. This study demonstrates that combining the nonlinear variable selection method with optimized RF regression can improve the efficiency of AGB estimation to support regional forest resource management and monitoring.

Estimating the Growing Stem Volume of Coniferous Plantations Based on Random Forest Using an Optimized Variable Selection Method.

Forest growing stem volume (GSV) reflects the richness of forest resources as well as the quality of forest ecosystems. Remote sensing technology enables robust and efficient GSV estimation as it greatly reduces the survey time and cost while facilitating periodic monitoring. Given its red edge bands and a short revisit time period, Sentinel-2 images were selected for the GSV estimation in Wangyedian forest farm, Inner Mongolia, China. The variable combination was shown to significantly affect the accuracy of the estimation model. After extracting spectral variables, texture features, and topographic factors, a stepwise random forest (SRF) method was proposed to select variable combinations and establish random forest regressions (RFR) for GSV estimation. The linear stepwise regression (LSR), Boruta, Variable Selection Using Random Forests (VSURF), and random forest (RF) methods were then used as references for comparison with the proposed SRF for selection of predictors and GSV estimation. Combined with the observed GSV data and the Sentinel-2 images, the distributions of GSV were generated by the RFR models with the variable combinations determined by the LSR, RF, Boruta, VSURF, and SRF. The results show that the texture features of Sentinel-2's red edge bands can significantly improve the accuracy of GSV estimation. The SRF method can effectively select the optimal variable combination, and the SRF-based model results in the highest estimation accuracy with the decreases of relative root mean square error by 16.4%, 14.4%, 16.3%, and 10.6% compared with those from the LSR-, RF-, Boruta-, and VSURF-based models, respectively. The GSV distribution generated by the SRF-based model matched that of the field observations well. The results of this study are expected to provide a reference for GSV estimation of coniferous plantations.