Forests

Remote sensing and deep learning are increasingly used across the forest-fire management lifecycle in China, but the evidence differs substantially in task, sensor, model, and validation design. We screened 146 candidate records published during 2015–2025 using predefined criteria requiring a China-specific empirical component, forest/wildland-fire relevance, substantive remote-sensing input, substantive deep learning, and primary empirical evaluation. Thirty-four studies met all criteria, and 82.4% were published during 2023–2025. Active-fire detection and monitoring represented 38.2% of the evidence, followed by pre-fire risk and preparedness (32.4%), post-fire assessment (23.5%), and spread/behaviour modelling (5.9%). Sensor use was strongly task-dependent: UAVs supported fine-scale detection and fuel assessment, geostationary satellites supported frequent active-fire monitoring, and multisource satellite/geospatial inputs supported risk and post-fire analysis. Operational evidence remained limited: 11.8% of studies reported multi-region or multi-country validation, 8.8% runtime or latency, 11.8% false-alarm information, 5.9% interpretability, and none reported explicit uncertainty in the study-level evidence extracted for this review. The evidence does not support one universally superior architecture. Progress will depend more on task-matched sensing, independent geographic and event validation, calibrated uncertainty, false-alarm control, and clearer links between model outputs and management decisions. These priorities are relevant beyond China wherever remote-sensing fire systems must transfer across heterogeneous landscapes, sensors, and operating conditions.

Quantifying current topsoil organic matter (SOM) differences associated with cropland retirement is challenging because soil observations are spatially uneven and historical SOM maps are scarce. We integrated 10,262 measurements of 0–20 cm SOM collected in 2023 with remote sensing and environmental covariates and annual land-cover histories from 1999 to 2023 in western Shanxi, China. Random forest (RF), support vector regression, and multiple linear regression were compared using five-fold cross-validation. Ten bootstrap RF prediction runs were used to derive a mean SOM map and a pixel-wise standard deviation layer representing between-run prediction variability. RF achieved the best performance (R2 = 0.52; RMSE = 5.43 g kg−1; MAE = 3.41 g kg−1). Strict one-to-one matching showed that predicted SOM was 3.084 g kg−1 higher in converted croplands than in cropland reference areas. The mean difference was larger for forestland-covered converted croplands (4.960 g kg−1) than for grassland-covered converted croplands (3.019 g kg−1). Earlier conversion cohorts generally showed larger spatial contrasts. A complementary local comparison yielded a mean difference of 3.004 g kg−1 and revealed substantial spatial heterogeneity. These results represent current predicted spatial differences associated with reconstructed land-cover histories rather than directly measured temporal SOM gains or causal effects of the Grain-for-Green Program.

Forest greening may vary considerably within the growing season, particularly in seasonally water-limited ecosystems. This study examined how long-term greening varies from spring to summer across the Zagros forests of western Iran and whether its environmental associations change seasonally. MODIS NDVI time series (2003–2024) were analyzed for March–August and separately for March–April, May–June, and July–August. Trends in land surface temperature, precipitation, and soil moisture were assessed, and generalized additive models were used to examine their associations with seasonal NDVI trends and topography. More than 93% of the forest area showed increasing NDVI during March–August, but greening weakened markedly from spring to summer. Mean NDVI trends declined from 0.0293 decade−1 in March–April to 0.0121 in May–June and 0.0101 in July–August. Model explanatory power also declined from 45.4% to 34.0% and 28.2%, respectively. Temperature-related effects were more prominent early and mid-season, while topographic influences became relatively more important toward summer. Although increasing heat and water limitation later in the season is consistent with other seasonally dry ecosystems, our results show that widespread Zagros greening masks substantial within-season differences in both trend magnitude and environmental associations, which are not apparent from growing-season trends alone.

Heterobasidion and Armillaria may infect tree roots and cause internal decay without visible changes in the crown. Decay is therefore often discovered only after windthrow or harvesting. Conventional remote-sensing observations do not identify the causal fungus directly. They record changes in trees or stand conditions that may be associated with root rot. We reviewed these indicators and the technologies used in studies of Heterobasidion and Armillaria, with particular attention to their possible use in forest management. Three semantic searches in OpenAlex and Semantic Scholar identified 1500 records on disease indicators, sensing technologies and research gaps. Screening retained 106 records, representing 84 unique publications. Information on disease stage, sensing method, reference data and validation was compiled. Site, stand and management data were mainly used to estimate the probability of root rot occurrence. LiDAR and aerial images were effective when damage was already visible as crown decline, mortality or canopy gaps. RGB images were used to identify decay exposed after harvesting. Detecting internal decay in standing trees was more difficult. Hyperspectral studies reported moderate and mainly site-specific results. Small affected classes, inconsistent reference standards and limited independent validation made it difficult to determine whether the results would apply in other forests. Remote sensing could not reliably distinguish root rot from other causes of tree stress from the evaluation data and is currently better suited to locating areas for field inspection than to confirming the causal pathogen.

Forest management objectives have shifted from timber-only production to multifunctional management. From this, a divergence has emerged between the private and social optimal rotation decisions pursued by forest farmers and the government, respectively. Identifying this divergence and improving eco-compensation mechanisms accordingly are critical to promoting multifunctional plantation management. Based on a constructed multi-service Faustmann–Hartman model, this study uses the examples of Picea asperata, Pinus yunnanensis, and Pinus massoniana in Sichuan Province to compare changes in the optimal rotation period and land expectation value (LEV) across three progressive scenarios: timber management, carbon sink management, and multi-objective management. It also estimates eco-compensation needs based on staged opportunity costs. The results show that incorporating non-market ecosystem services delays the optimal rotation period and increases LEV. These systems’ contribution to rotation extension exceeds that of the carbon sink. Sensitivity analysis indicates that timber price and discount rate are important parameters affecting LEV and the optimal rotation period, with the discount rate exerting the most pronounced effect; by contrast, although carbon price increases LEV, its effect on extending the rotation period is relatively limited. Based on management feasibility boundaries, Picea asperata, Pinus yunnanensis, and Pinus massoniana are classified as ecological value-dependent, carbon-enhanced, and management-robust types, respectively. Staged compensation estimates show that carbon sink revenues cover the opportunity costs of switching from timber management to carbon sink management; however, transitioning further to multifunctional management still requires fiscal compensation. Using the existing compensation standard for eligible non-state-owned public welfare forests in Sichuan Province as a policy benchmark, the estimated annual compensation requirements for Pinus yunnanensis and Pinus massoniana exceed this standard by 81.46 and 12.99 CNY/(ha·year), respectively, whereas the requirement for Picea asperata is lower than the benchmark. Therefore, plantation eco-compensation policies should shift from uniform compensation to differentiated mechanisms that account for species heterogeneity and the transition stages included in management objectives.