Background: Aboveground biomass (AGB) is a fundamental indicator of forest ecosystem productivity and health and hence plays an essential role in evaluating forest carbon reserves and supporting the development of targeted forest management plans. Methods: Here, we proposed a random forest/co-kriging framework that integrates the strengths of machine learning and geostatistical approaches to improve the mapping accuracies of AGB in northern Guangdong Province of China. We used Landsat time-series observations, Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) data, and National Forest Inventory (NFI) plot measurements, to generate the forest AGB maps at three time points (1992, 2002 and 2010) showing the spatio-temporal dynamics of AGB in the subtropical forests in Guangdong, China. Results: The proposed model was capable of mapping forest AGB using spectral, textural, topographical variables and the radar backscatter coefficients in an effective and reliable manner. The root mean square error of the plot-level AGB validation was between 15.62 and 53.78 t∙ha^− 1, the mean absolute error ranged from 6.54 to 32.32 t∙ha^− 1, the bias ranged from − 2.14 to 1.07 t∙ha^− 1, and the relative improvement over the random forest algorithm was between 3.8% and 17.7%. The largest coefficient of determination (0.81) and the smallest mean absolute error (6.54 t∙ha^− 1) were observed in the 1992 AGB map. The spectral saturation effect was minimized by adding the PALSAR data to the modeling variable set in 2010. By adding elevation as a covariable, the co-kriging outperformed the ordinary kriging method for the prediction of the AGB residuals, because co-kriging resulted in better interpolation results in the valleys and plains of the study area. Conclusions: Validation of the three AGB maps with an independent dataset indicated that the random forest/co-kriging performed best for AGB prediction, followed by random forest coupled with ordinary kriging (random forest/ordinary kriging), and the random forest model. The proposed random forest/co-kriging framework provides an accurate and reliable method for AGB mapping in subtropical forest regions with complex topography. The resulting AGB maps are suitable for the targeted development of forest management actions to promote carbon sequestration and sustainable forest management in the context of climate change.

Habitat evaluation is essential for managing wildlife populations and formulating conservation policies. With the rise of innovative powerful statistical techniques in partnership with Remote Sensing, GIS and GPS techniques, spatially explicit species distribution modeling (SDM) has rapidly grown in conservation biology. These models can help us to study habitat suitability at the scale of the species range, and are particularly useful for examining the overlapping habitat between sympatric species. Species presence points collected through field GPS observations, in conjunction with 13 different topographic, vegetation related, anthropogenic, and bioclimatic variables, as well as a land cover map with seven classification categories created by support vector machine (SVM) were used to implement Maxent and GARP ecological niche models. With the resulting ecological niche models, the suitable habitat for asiatic black bear (Ursus thibetanus) and red panda (Ailurus fulgens) in Nepal Makalu Barun National Park (MBNP) was predicted. All of the predictor variables were extracted from freely available remote sensing and publicly shared government data resources. The modeled results were validated by using an independent dataset. Analysis of the regularized training gain showed that the three most important environmental variables for habitat suitability were distance to settlement, elevation, and mean annual temperature. The habitat suitability modeling accuracy, characterized by the mean area under curve, was moderate for both species when GARP was used (0.791 for black bear and 0.786 for red panda), but was moderate for black bear (0.857), and high for red panda (0.920) when Maxent was used. The suitable habitat estimated by Maxent for black bear and red panda was 716 km² and 343 km² respectively, while the suitable area determined by GARP was 1074 km² and 714 km² respectively. Maxent predicted that the overlapping area was 83% of the red panda habitat and 40% of the black bear habitat, while GARP estimated 88% of the red panda habitat and 58% of the black bear habitat overlapped. The results of land cover exhibited that barren land covered the highest percentage of area in MBNP (36.0%) followed by forest (32.6%). Of the suitable habitat, both models indicated forest as the most preferred land cover for both species (63.7% for black bear and 61.6% for red panda from Maxent; 59.9% black bear and 58.8% for red panda from GARP). Maxent outperformed GARP in terms of habitat suitability modeling. The black bear showed higher habitat selectivity than red panda. We suggest that proper management should be given to the overlapping habitats in the buffer zone. For remote and inaccessible regions, the proposed methods are promising tools for wildlife management and conservation, deserving further popularization.

The accurate estimation of spatially explicit forest aboveground biomass (AGB) provides an essential basis for sustainable forest management and carbon sequestration accounting, especially in Myanmar, where there is a lack of data for forest conservation due to operational limitations. This study mapped the forest AGB using Sentinel-2 (S-2) images and Shuttle Radar Topographic Mission (SRTM) based on random forest (RF), stochastic gradient boosting (SGB) and Kriging algorithms in two forest reserves (Namhton and Yinmar) in Myanmar, and compared their performance against AGB measured by the traditional methods. Specifically, a suite of forest sample plots were deployed in the two forest reserves, and forest attributes were measured to calculate the plot-level AGB based on allometric equations. The spectral bands, vegetation indices (VIs) and textures derived from processed S-2 data and topographic parameters from SRTM were utilized to statistically link with field-based AGB by implementing random forest (RF) and stochastic gradient boosting (SGB) algorithms. Followed by an evaluation of the algorithmic performances, RF-based Kriging (RFK) models were employed to determine the spatial distribution of AGB as an improvement of accuracy against RF models. The study’s results showed that textural measures produced from wavelet analysis (WA) and vegetation indices (VIs) from Sentinel-2 were the strongest predictors for evergreen forest reserve (Namhton) AGB prediction and spectral bands and vegetation indices (VIs) showed the highest sensitivity to the deciduous forest reserve (Yinmar) AGB prediction. The fitted models were RF-based ordinary Kriging (RFOK) for Namhton forest reserve and RF-based co-Kriging (RFCK) for Yinmar forest reserve because their respective R², whilst the RMSE values were validated as 0.47 and 24.91 AGB t/ha and 0.52 and 34.72 AGB t/ha, respectively. The proposed random forest Kriging framework provides robust AGB maps, which are essential to estimate the carbon sequestration potential in the context of REDD+. From this particular study, we suggest that the protection/disturbance status of forests affects AGB values directly in the study area; thus, community-participated or engaged forest utilization and conservation initiatives are recommended to promote sustainable forest management.

Many post-fire on-site factors, including fire severity, management strategies, topography, and local climate, are concerns for forest managers and recovery ecologists to formulate forest vegetation recovery plans in response to climate change. We used the Vegetation Change Tracker (VCT) algorithm to map forest disturbance in the Daxing’anling area, Northeastern China, from 1987 to 2016. A support vector machine (SVM) classifier and historical fire records were used to separate burned patches from disturbance patches obtained from VCT. Afterward, stepwise multiple linear regression (SMLR), SVM, and random forest (RF) were applied to assess the statistical relationships between vegetation recovery characteristics and various influential factors. The results indicated that the forest disturbance events obtained from VCT had high spatial accuracy, ranging from 70% to 86% for most years. The overall accuracy of the annual fire patches extracted from the proposed VCT-SVM algorithm was over 92%. The modeling accuracy of post-fire vegetation recovery was excellent, and the validation results confirmed that the RF algorithm provided better prediction accuracy than SVM and SMLR. In conclusion, topographic variables (e.g., elevation) and meteorological variables (e.g., the post-fire annual precipitation in the second year, the post-fire average relative humidity in the fifth year, and the post-fire extreme maximum temperature in the third year) jointly affect vegetation recovery in this cold temperate continental monsoon climate region.

Spatially explicit knowledge of aboveground biomass (AGB) in large areas is important for accurate carbon accounting and quantifying the effect of forest disturbance on the terrestrial carbon cycle. We estimated AGB from 1990 to 2011 in northern Guangdong, China, based on a spatially explicit dataset derived from six years of national forest inventory (NFI) plots, Landsat time series imagery (1986–2011) and Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radars (PALSAR) 25 m mosaic data (2007–2010). Four types of variables were derived for modeling and assessment. The random forest approach was used to seek the optimal variables for mapping and validation. The root mean square error (RMSE) of plot-level validation was between 6.44 and 39.49 (t/ha), the normalized root-mean-square error (NRMSE) was between 7.49% and 19.01% and mean absolute error (MAE) was between 5.06 and 23.84 t/ha. The highest coefficient of determination R² of 0.8 and the lowest NRMSE of 7.49% were reported in 2006. A clear increasing trend of mean AGB from the lowest value of 13.58 t/ha to the highest value of 66.25 t/ha was witnessed between 1988 and 2000, while after 2000 there was a fluctuating ascending change, with a peak mean AGB of 67.13 t/ha in 2004. By integrating AGB change with forest disturbance, the trend in disturbance area closely corresponded with the trend in AGB decrease. To determine the driving forces of these changes, the correlation analysis was adopted and exploratory factor analysis (EFA) method was used to find a factor rotation that maximizes this variance and represents the dominant factors of nine climate elements and nine human activities elements affecting the AGB dynamics. Overall, human activities contributed more to short-term AGB dynamics than climate data. Harvesting and human-induced fire in combination with rock desertification and global warming made a strong contribution to AGB changes. This study provides valuable information for the relationships between forest AGB and climate as well as forest disturbance in subtropical zones.

Key message By integrating vegetation change tracker (VCT), spatial analysis (SA), and random forest regression (RF), the spectral-temporal patterns of forest stand age were mapped for three typical plantations in Southern China. The spectral-temporal distribution of age structure indicated that the plantation stands in the study area were increasingly aging. Context Plantations play a major role in China for ecosystem restoration and carbon sequestration. Mapping plantation stand age distributions is essential for developing sustainable plantation forest management plans. Aims The purpose of this study was to propose two new remote sensing based models for mapping plantation ages and to test the model feasibility and accuracy in determining the spectral-temporal patterns of forest ages. Methods We first integrated vegetation change tracker (VCT) algorithm and spatial analysis (VCT-SA) for the pixels that were disturbed at least once from 1987 to 2017, and integrated VCT and random forest (VCT-RF) for the pixels were not disturbed during the study period. Then the forest age of these two parts were merged separately to generate annual forest age. Results The spectral-temporal (30-m resolution, from 1987 to 2017) of forest age for the three typical plantations in Lishui were generated. The results indicated that the plantation stands in our large study area were increasingly aging. Conclusion Our results revealed that it is reasonable to derive the distribution of plantation stand ages from combined remote sensing models. Besides, we confirmed that the stand ages of the plantations in our large study area of Lishui City are on the rise as the result of forest protection policies.