Forest biomass is critically important for forest dynamics in the carbon cycle. However, large-scale AGB mapping applications from remote sensing data still carry large uncertainty. In this study, an AGB estimation model was first established with three different remote sensing datasets of GF-2, Sentinel-2 and Landsat-8. Next, the optimal scale estimation result was considered as a reference AGB to obtain the relative true AGB distribution at different scales based on the law of conservation of mass, and the error of the scale effect of AGB estimation at various spatial resolutions was analyzed. Then, the information entropy of land use type was calculated to identify the heterogeneity of pixels. Finally, a scale conversion method for the entropy-weighted index was developed to correct the scale error of the estimated AGB results from coarse-resolution remote sensing images. The results showed that the random forest model had better prediction accuracy for GF-2 (4 m), Sentinel-2 (10 m) and Landsat-8 (30 m) AGB mapping. The determination coefficient between predicted and measured AGB was 0.5711, 0.4819 and 0.4321, respectively. Compared to uncorrected AGB, R² between scale-corrected results and relative true AGB increased from 0.6226 to 0.6725 for Sentinel-2, and increased from 0.5910 to 0.6704 for Landsat-8. The scale error was effectively corrected. This study can provide a reference for forest AGB estimation and scale error reduction for AGB production upscaling with consideration of the spatial heterogeneity of the forest surface.

Vegetation phenology is an important indicator of vegetation dynamics. The boreal forest ecosystem is the main part of terrestrial ecosystem in the Northern Hemisphere and plays an important role in global carbon balance. In this study, the dynamic threshold method combined with the ground-based phenology observation data was applied to extract the forest phenological parameters from MODIS NDVI time-series. Then, the spatiotemporal variation of forest phenology is discussed and the relationship between phenological change and climatic factors was concluded in the northeast China from 2011 to 2020. The results indicated that the distribution of the optimal extraction threshold has spatial heterogeneity, and the changing rate was 3% and 2% with 1° increase in latitude for SOS (the start of the growing season) and EOS (the end of the growing season). This research also notes that the SOS had an advanced trend at a rate of 0.29 d/a while the EOS was delayed by 0.47 d/a. This variation of phenology varied from different forest types. We also found that the preseason temperature played a major role in effecting the forest phenology. The temperature in winter of the previous year had a significant effect on SOS in current year. Temperature in autumn of the current year had a significant effect on EOS.

Forest biomass estimation is undoubtedly one of the most pressing research subjects at present. Combining multi-source remote sensing information can give full play to the advantages of different remote sensing technologies, providing more comprehensive and rich information for aboveground biomass (AGB) estimation research. Based on Landsat 8, Sentinel-2A, and ALOS2 PALSAR data, this paper takes the artificial coniferous forests in the Saihanba Forest of Hebei Province as the object of study, fully explores and establishes remote sensing factors and information related to forest structure, gives full play to the advantages of spectral signals in detecting the horizontal structure and multi-dimensional synthetic aperture radar (SAR) data in detecting the vertical structure, and combines environmental factors to carry out multivariate synergistic methods of estimating the AGB. This paper uses three variable selection methods (Pearson correlation coefficient, random forest significance, and the least absolute shrinkage and selection operator (LASSO)) to establish the variable sets, combining them with three typical non-parametric models to estimate AGB, namely, random forest (RF), support vector regression (SVR), and artificial neural network (ANN), to analyze the effect of forest structure on biomass estimation, explore the suitable AGB of artificial coniferous forests estimation of machine learning models, and develop the method of quantifying saturation value of the combined variables. The results show that the horizontal structure is more capable of explaining the AGB compared to the vertical structure information, and that combining the multi-structure information can improve the model results and the saturation value to a great extent. In this study, different sets of variables can produce relatively superior results in different models. The variable set selected using LASSO gives the best results in the SVR model, with an $R^2$ values of 0.9998 and 0.8792 for the training and the test set, respectively, and the highest saturation value obtained is 185.73 t/ha, which is beyond the range of the measured data. The problem of saturation in biomass estimation in boreal medium- and high-density forests was overcome to a certain extent, and the AGB of the Saihanba area was better estimated.

Major land-use/land-cover change due to rapid urbanization has been known to increase the land-surface temperature around the world. Consequently, examining the variation of land-surface temperatures and mitigating the related impacts remain a challenge. The present study employed remote-sensing and geoinformational techniques to examine land-use/land-cover change and its effects on land-surface temperature variations in the villages within the Luki Biosphere Reserve, Democratic Republic of Congo. Land-use/land-cover change for the year 2038 was predicted by using the CA–Markov chain. Additionally, focus-group discussions (FGDs) with local communities from different villages were applied to better understand the impact of climate change, considering the increase of land-surface temperature. The results revealed major changes in land-use/land-cover in the four villages from 2002 to 2020, principally the expansion of fallow land and built-up areas, as well as the decline in forest land, and the complex of young secondary and degraded forest. There was an increase in mean LST values over all villages between 2002 and 2020. The highest value was observed in Tsumba kituti (25.12 °C), followed by Kisavu (24.87 °C), Kibuya (23.31 °C) and Kiobo (21.82 °C). Between 2002 and 2020, the mean LST of built-up areas increased from 23.18 to 25.12 °C, 21.55 to 23.38 °C, 21.4 to 25.78 °C and 22.31 to 25.62 °C in Tsumba kituti, Kiobo, Kisavu and Kibuya, respectively. Moreover, the mean LST of fallow land increased from 20.8 to 23.2 °C, 21.13 to 22.12 °C, 21.89 to 23.12 °C and 20.31 to 23.47 °C in Tsumba, Kiobo, Kibuya and Kisavu, respectively. This indicates that built-up and fallow land experienced the highest land-surface temperature compared to other land-use/land-cover categories. Meanwhile, the conversion of all land-use/land-cover categories into built-up areas in all the villages resulted in the increase of the land-surface temperature. FGDs results recognize the recurrent land-use/land-cover change as the major driver of the increase in LST (86%). However, it was predicted that farmland and built-up area will still increase within all the villages, while the forest land will decline. As for the complex of secondary and degraded forest, it will decrease in Tsumba kituti, while, in Kiobo and Kisavu, it is expected to increase. Through a combination of remote-sensing and primary data, this study provides accurate information that will benefit decision-makers to implement appropriate landscape-planning techniques to mitigate the effect of the increased land-surface temperature in the villages.

Villages within the Luki Biosphere Reserve and the surrounding cities have undergone rapid demographic growth and urbanization that have impacted the reserve’s natural landscape. However, no study has focused on the spatiotemporal analysis of its land use/land cover. The present research aims at providing a comprehensive analysis of land use/land cover change in the Luki Biosphere Reserve from the year 1987 to 2020, and to predict its future change for the year 2038. Landsat images were classified in order to provide land use/land cover maps for the years 1987, 2002, 2017 and 2020. Based on these maps, change detection, gradient direction, and landscape metric analyses were performed. In addition, land use/land cover change prediction was carried out using the Multilayer Perceptron Markov model. The results revealed significant land use/land cover changes in the Luki Biosphere Reserve during the study period. Indeed, tremendous changes in the primary forest, which lost around 17.8% of its total area, were noted. Other classes, notably savannah, secondary forest, built-up area, fallow land and fields had gained 79.35, 1150.36, 67.63, 3852.12 hectares, respectively. Based on the landscape metric analysis, it was revealed that built-up areas and fallow land and fields experienced an aggregation trend, while other classes showed disaggregation and fragmentation trends. Analysis further revealed that village expansion has significantly affected the process of land use/land cover change in the Luki Biosphere Reserve. However, the prediction results revealed that the primary forest will continue to increase while built-up area, fallow land and fields will follow a trend similar to a previous one. As for secondary forest and savannah, the forecast revealed a decrease of the extent during the period extending from 2020 to 2038. The present findings will benefit the decision makers, particularly in the sustainable natural resources management of the Luki Biosphere Reserve.

The Mayombe tropical forest has experienced dramatic changes over several decades due to human activities. However, the impact of these changes on tree biodiversity and ecosystem services has not been studied yet. Such a study could advance the current knowledge on tree biodiversity and carbon storage within the Mayombe forest, which is presently under high anthropogenic pressures. This information could benefit decision-makers to design and implement strategies for biodiversity conservation and sustainable natural resource utilization. As such, biodiversity surveys were conducted within the forest under different land utilization regimes. To evaluate the effect of human utilization on tree biodiversity and ecosystem services (carbon storage), land was classified into three categories based on the intensity of human utilization: low utilization, moderate utilization, and high utilization. Additionally, the study evaluated the recovery potential of the disturbed forest under both moderate and high utilization, after abandonment for 10 and 20 years. Tree diameter and height were measured for all trees whose diameter at breast height was greater than or equal to 10 cm. Our findings revealed that forest land with both high and moderate utilization regimes, and having no regulation, resulted in the decline of tree species richness, tree species diversity, and carbon storage. The magnitude of decrease was greater in high utilization compared to moderate utilization regimes. On the other hand, high values of biodiversity indices and carbon storage were observed in the low utilization regime. This study also demonstrated that fallow land that had been left undisturbed for more than 10 years, but had experienced both high and moderate utilization regimes, could reasonably recover carbon storage, and an acceptable level of tree species biodiversity can be achieved. However, there remains a significant difference when compared with the original level in the low utilization regime, suggesting that the Mayombe forest takes longer to recover. Based on the findings on tree biodiversity and carbon storage over the recovery trajectory, this study improves the understanding of the degraded forest restoration process within the Mayombe forest. It is therefore necessary to formulate new strategies to regulate forest land utilization within the Mayombe forest. This will ensure sustainability and availability of all ecosystem services this forest provides to a human population that strongly depends on it for their survival.