The Global Ecosystem Dynamics Investigation (GEDI) system provides essential data for estimating forest canopy height on a global scale. However, factors such as complex topography and dense canopy can significantly reduce the accuracy of GEDI canopy height estimations. We selected the South Taihang region of Henan Province, China, as our study area and proposed an optimization framework to improve GEDI canopy height estimation accuracy. This framework includes correcting geolocation errors in GEDI footprints, screening and analyzing features that affect estimation errors, and combining two regression models with feature selection methods. Our findings reveal a geolocation error of 4 to 6 m in GEDI footprints at the orbital scale, along with an overestimation of GEDI canopy height in the South Taihang region. Relative height (RH), waveform characteristics, topographic features, and canopy cover significantly influenced the estimation error. Some studies have suggested that GEDI canopy height estimates for areas with high canopy cover lead to underestimation, However, our study found that accuracy increased with higher canopy cover in complex terrain and dense vegetation. The model’s performance improved significantly after incorporating the canopy cover parameter into the optimization model. Overall, the R² of the best-optimized model was improved from 0.06 to 0.61, the RMSE was decreased from 8.73 m to 2.23 m, and the rRMSE decreased from 65% to 17%, resulting in an accuracy improvement of 74.45%. In general, this study reveals the factors affecting the accuracy of GEDI canopy height estimation in areas with complex terrain and dense vegetation cover, on the premise of minimizing GEDI geolocation errors. Employing the proposed optimization framework significantly enhanced the accuracy of GEDI canopy height estimates. This study also highlighted the crucial role of canopy cover in improving the precision of GEDI canopy height estimation, providing an effective approach for forest monitoring in such regions and vegetation conditions. Future studies should further improve the classification of tree species and expand the diversity of sample tree species to test the accuracy of canopy height estimated by GEDI in different forest structures, consider the distortion of optical remote sensing images caused by rugged terrain, and further mine the information in GEDI waveforms so as to enhance the applicability of the optimization framework in more diverse forest environments.

The Henan Yellow River Basin is an ecological support belt for the entire basin. It holds a significant position in high-quality development and ecological conservation within the Yellow River Basin. However, due to improper development activities, such as urban expansion and deforestation of farmland, certain areas of the region have encountered a series of ecological issues, posing significant challenges to ecosystem services. The scientific foundation for the sustainable development of the ecological environment in the Henan Yellow River Basin is established by research on the evolution characteristics and driving factors of ecosystem service functions. This study focuses on the Henan Yellow River Basin, by introducing remote sensing data and biomass data, assessing the spatiotemporal variations in ecosystem service by the InVEST model—including carbon stock, water yield, and soil conservation—from 2000 to 2020. It analyzes the ecosystem service functions of different land use types. It employs the Geodetector to identify the dominant driving factors behind the changes in these functions based on the improved InVEST model evaluated results. The findings reveal that from 2000 to 2020, total carbon stock increased by 1.86%, carbon stock per unit area rose by 1.81%, and the spatial distribution remained largely stable. The high-value regions were clustered in the west and part of the north, primarily consisting of forest land. Carbon stock capacity in other regions, mainly farmland and construction land, was poor, with forest land having the strongest carbon sequestration capacity, followed by grassland. Total water yield decreased by 20.08%, and water yield per unit area decreased by 20.03%, with a spatial distribution closely following the trend of precipitation distribution. The high-value regions were clustered in the south, primarily in forest land and farmland. The total amount of soil conservation decreased by 19.96%, and soil conservation per unit area decreased by 19.93%, with spatial distribution patterns similar to those of carbon stock and water yield. The high-value regions were concentrated in the southwestern and northern forested regions, while soil conservation capacity in areas primarily consisting of farmland and construction land was weaker. The divergence of carbon stock was most influenced by population density, water yield by precipitation, and soil conservation by slope. In conclusion, during the study period, while carbon storage increased, the significant decline in water yield and soil conservation highlighted critical issues in the ecosystem service functions of the region. These findings indicate the need for targeted conservation measures and sustainable development strategies to address the decline in ecosystem services and mitigate adverse environmental impacts, ensuring the long-term health of the region’s ecosystems. This study offers an in-depth understanding of the differentiation of ecosystem service functions and their driving factors, enabling precise assessment of regional ecosystem services, and providing a theoretical foundation for formulating effective regional ecological conservation policies.

A thorough understanding of carbon storage patterns in forest ecosystems is crucial for forest management to slow the rate of climate change. Here, we explored fine-scale biomass spatial patterns in a secondary warm temperate deciduous broad-leaved forest in north China. A 20-ha plot was established and classified by topographic features into ridge, valley, gentle slope, and steep slope habitats. Total tree biomass varied from 103.8 Mg/ha on the gentle slope habitats to 117.4 Mg/ha on the ridge habitats, with an average biomass of 109.6 Mg/ha across the entire plot. A few species produced the majority of the biomass, with five species contributing 78.4% of the total tree biomass. These five species included Quercus mongolica Fisch. ex Ledeb (41.7 Mg/ha, 38.1%), Betula dahurica Pall. (19.8 Mg/ha, 18.0%), Acer mono Maxim. (12.6 Mg/ha, 11.5%), Betula platyphylla Suk. (7.0 Mg/ha, 6.4%), and Populus davidiana Dode. (4.8 Mg/ha, 4.4%). The five species were also associated with certain habitats; for example, Q. mongolica was positively associated with the ridge habitat and A. mono was positively associated with the valley habitat. Results from this work document the variability in forest biomass across a warm temperate forest ecosystem of north China, with implications for managing and accounting forest carbon.

With the intensification of human activities and climate variability, balancing ecosystem service (ES) supply and demand is critical for regional sustainable development. Existing studies predominantly focus on linear driving effects and lack integrated quantitative frameworks that link the spatiotemporal dynamics of ES supply–demand relationships (ESSDRs) with their nonlinear driving mechanisms, and few have systematically quantified the critical thresholds of driving factors and their interactive effects. To address these research gaps, this study quantified the supply, demand, and supply–demand ratios of four key ESs (food production [FP], carbon sequestration [CS], water yield [WY], and soil retention [SR]) in the Yellow River Basin of Henan Province (2000–2020) using the InVEST model and multi-source data. An analytical framework integrating the Extreme Gradient Boosting (XGBoost) model and Shapley Additive Explanations (SHAP) was established to identify dominant drivers, reveal nonlinear response patterns, and quantify critical thresholds. The results showed that FP and CS supply increased continuously, while WY and SR supply slightly declined; CS and WY demand grew faster than supply, leading to expanding deficits, whereas FP and SR maintained relative balance. Spatially, FP/CS surpluses concentrated in eastern plains and southwestern forests, WY deficits occurred in the northwest, and SR balance prevailed in most regions. Dominant drivers differed by ES type—arable land proportion (FP), population density (CS), precipitation (WY), and slope (SR)—all exhibiting distinct threshold effects (e.g., arable land proportion >0.6, slope >3°). These findings provide novel insights into ESSDR spatial heterogeneity and threshold-based regulation, offering a scientific basis for differentiated ecological management and sustainable spatial planning in the Yellow River Basin and similar ecologically vulnerable regions.

The intensifying conflict between urban–rural development and the natural environment under rapid industrialization and urbanization underscores the necessity to balance ecological preservation with economic growth for sustainable land use. Grounded in the Production–Living–Ecological Space (PLES) theory, this study classifies land use into production, living and ecological functional types to set three scenarios (natural development, production–living priority, ecological priority), and employs the PLUS model with a land use conversion cost matrix and spatial driving factors as input variables to simulate land use changes and their ecological impacts. Analysis of the period 2010–2020 reveals an initial decline followed by a recovery in ecological quality, highlighting the urgency of improved spatial planning. Projections for 2025–2035 based on the PLES theory reveal divergent trajectories across three scenarios simulated by the PLUS model, whereas the production–living priority scenario accelerates urban–industrial expansion and exacerbates environmental degradation by driving irrational land use conversion. In contrast, the ecological priority scenario significantly enhances ecological and environmental quality while minimizing the loss of agricultural land, achieving a more sustainable balance between development and conservation. Through quantitative calculation of the ecological contribution rate of land use transitions, the internal conversion of agricultural production space contributes the most to regional ecological quality improvement (42.50% in 2025–2030 in the ecological priority scenario), and forest PLES land use category has the highest ecological environment index (0.2836 in 2025) among all PLES types, emphasizing the pivotal role of agricultural production space and the high ecological value of forestland in regional ecosystems. These findings demonstrate that prioritizing ecological strategies is essential for realizing a win–win outcome between ecological protection and economic development, offering actionable insights for sustainable land use planning in ecologically fragile transition zones similar to the Southern Taihang region.