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.

SAR data have a longer wavelength and stronger penetrating power compared with traditional optical remote sensing. Therefore, SAR data are more suitable for the estimation of the above-ground biomass (AGB) of forests. This study was aimed at evaluating the sensitivity of L-band full polarization data to AGB. L-band data were improved to estimate the saturation point produced by AGB, and were found to be suitable for estimating a wide range of AGB. This study extracted backscattering coefficients, polarization decomposition variables, and terrain factors. New parameters were constructed from these variables, and their performance in predicting AGB was evaluated. Significant variables found with AGB were added to the multivariate linear model. A statistical analysis showed the presence of multicollinearity between the variables. Therefore, ridge regression, random forest method (RF), and principal component analysis (PCA) were introduced to solve the problem of collinearity. In all the three methods, the saturation of the ridge regression model was low, reaching it at 150 t/ha. Better accuracy was obtained with the RF model. No obvious saturation incident was detected in the model established using the principal component analysis. This could be attributed to the low biomass levels observed in our study area. This model provided accurate results (adjusted r² = 0.90 rmse = 14.24 t/ha), indicating that L-band data have the potential to estimate AGB. Additionally, suitable variables and models were selected in this study, with the principal component analysis being more helpful in combining various SAR parameters. The achievement of these accurate results could be attributed to the synergy among variables.

Spaceborne polarimetric synthetic aperture radar interferometry (PolInSAR) has the potential to deal with large-scale forest height inversion. However, the inversion is influenced by strong temporal decorrelation interference resulting from a large temporal baseline. Additionally, the forest canopy induces phase errors, while the smaller vertical wavenumber ($k_z$) enhances the sensitivity of the inversion to temporal decorrelation, which limits the efficiency in forest height inversion. This research is based on the random volume over ground (RVoG) model and follows the assumptions of the three-stage inversion method, to quantify the impact of repeat-pass spaceborne PolInSAR temporal decorrelation on the relative error of retrieval height, and develop a semi-empirical improved inversion model, using ground data to eliminate the interference of coherence and phase error caused by temporal decorrelation. Forest height inversion for temperate forest in northern China was conducted using repeat-pass spaceborne L-band ALOS2 PALSAR data, and was further verified using ground measurement data. The correction of temporal decorrelation using the improved model provided robust inversion for mixed conifer-broad forest height retrieval as it addressed the over-sensitivity to temporal decorrelation resulting from the inappropriate $k_z$ value. The method performed height inversion using interferometric data with temporal baselines ranging from 14 to 70 days and vertical wavenumbers ranging from 0.015 to 0.021 rad/m. The R² and RMSE reached 0.8126 and 2.3125 m, respectively.

Forest density affects the inversion of forest height by influencing the penetration and attenuation of synthetic aperture radar (SAR) signals. Traditional forest height inversion methods often fail in low-density forest areas. Based on L-band single-baseline polarimetric SAR interferometry (PolInSAR) simulation data and the BioSAR 2008 data, we proposed a forest height optimization model at the stand scale suitable for various forest densities. This optimization model took into account shortcomings of the three-stage inversion method by employing height errors to represent the mean penetration depth and SINC inversion method. The relationships between forest density and extinction coefficient, penetration depth, phase, and magnitude were also discussed. In the simulated data, the inversion height established by the optimization method was 17.35 m, while the RMSE value was 3.01 m when the forest density was 100 stems/ha. This addressed the drawbacks of the conventional techniques including failing at low forest density. In the real data, the maximum RMSE of the optimization method was 2.17 m as the stand density increased from 628.66 stems/ha to 1330.54 stems/ha, showing the effectiveness and robustness of the optimization model in overcoming the influence of stand density on the inversion process in realistic scenarios. This study overcame the stand density restriction on L-band single baseline PolInSAR data for forest height estimation and offered a reference for algorithm selection and optimization. The technique is expected to be extended from the stand scale to a larger area for forest ecosystem monitoring and management.