Key messageIntegrating airborne laser scanning and satellite time series data across the forest rotation enhances decision-making in precision forestry. This review supports forest managers by illustrating practical applications of these remote sensing technologies at different stages of intensive forest plantation management-such as site assessment, monitoring, and silviculture-helping improve productivity, sustainability, and operational efficiency.ContextIntensively managed forest plantations depend on high-resolution, timely data to guide silviculture and promote sustainability.AimsThis review explores how airborne laser scanning (ALS) and satellite time series data support precision forestry across key stages, including site assessment, establishment, monitoring, inventory updates, growth tracking, silvicultural interventions, and harvest planning.ResultsThe review highlights several key applications. ALS-derived digital elevation models and canopy metrics improve site productivity estimation by capturing micro-topographic variables and soil formation factors. Combining ALS with multispectral data enhances monitoring of seedling survival and health, although distinguishing seedlings from non-living components remains a challenge. ALS-based Enhanced Forest Inventories provide spatially detailed forest metrics, while satellite time series and vegetation indices support continuous monitoring of growth and early detection of drought, fire, and pest stress. ALS individual tree detection models offer insights into competition, stand structure, and spatial variability, informing thinning and fertilization decisions by identifying trees under stress or with high growth potential. These models also help mitigate drought and wind damage by guiding density and canopy structure management. ALS terrain data further support harvest planning by optimizing machinery routes and reducing environmental impacts.ConclusionDespite progresses, challenges remain in refining predictive models, expanding remote sensing applications, and developing tools that translate complex data into field operations. A major barrier is the technical expertise needed to interpret spatial data and integrate remote sensing into workflows. Continued research is needed to improve accessibility and operational relevance. High-resolution data still offer strong potential for adaptive management and sustainability.

Rubber-based intercropping is a recommended practice due to its ecological and economic benefits. Understanding the implications of ecophysiological changes in intercropping farms on the production and technological properties of Hevea rubber is still necessary. This study investigated the effects of seasonal changes in the leaf area index (LAI) and soil moisture content (SMC) of rubber-based intercropping farms (RBIFs) on the latex biochemical composition, yield, and technological properties of Hevea rubber. Three RBIFs: rubber-bamboo (RB); rubber-melinjo (RM); rubber-coffee (RC), and one rubber monocropping farm (RR) were selected in a village in southern Thailand. Data were collected from September to December 2020 (S1), January to April 2021 (S2), and May to August 2021 (S3). Over the study period, RB, RM, and RC exhibited significantly high LAI values of 1.2, 1.05, and 0.99, respectively, whereas RR had a low LAI of 0.79. The increasing SMC with soil depths was pronounced in all RBIFs. RB and RM expressed less physiological stress and delivered latex yield, which was on average 40% higher than that of RR. With higher molecular weight distributions, their rheological properties were comparable to those of RR. However, the latex Mg content of RB and RM significantly increased to 660 and 742 mg/kg, respectively, in S2. Their dry rubbers had an ash content of more than 0.6% in S3.

The study applies the Minimum Impact Design Standards (MIDS) calculator to assess urban trees' effectiveness in reducing surface runoff along five flood-prone streets in Hue City, analyzing evapotranspiration, rainfall interception, and infiltration, along with Leaf Area Index (LAI), Canopy Projection (CP), tree pit size, and soil structure. Results show that urban trees retain 1,132.39 m(3) of stormwater, but runoff reduction is not solely dependent on tree quantity. Although tree numbers vary 1.56 to 3.8 times, runoff reduction differs only 1.39 to 1.79 times. Evapotranspiration plays the largest role, contributing 2.8 times more than interception and 2.6 times more than infiltration. Small tree pits and compacted soil limit infiltration, while pruning and height reduction decrease Pc and LAI, reducing flood mitigation benefits. Annual storm damage further weakens this capacity. To enhance effectiveness, the study suggests prioritizing storm-resistant species, increasing tree numbers, enlarging tree pits, and using structured soil. Implementing these measures can improve urban flood resilience and maximize trees' hydrological benefits. Future research should focus on optimizing tree selection and planting strategies for long-term flood management in urban areas, ensuring sustainable solutions that enhance both stormwater control and environmental resilience.

Vegetation indices (VIs) are widely applied to estimate leaf area index (LAI) for monitoring vegetation vigor and growth dynamics. However, the saturation issues in VIs caused by crown closure during the growing season pose significant challenges to the application of VIs in LAI estimation, particularly at the individual tree level. To address this, the feasibility of common VIs for LAI estimation at the individual tree level throughout the growing season was analyzed using data from digital hemispherical photography (DHP) and Unmanned Aerial Vehicle (UAV) acquisition. Additionally, the physical mechanisms underlying a generic VI-based estimation model were explored using the PROSAIL model and Global Sensitivity Analysis (GSA). Furthermore, the relationships between observed LAI derived from DHP and UAV-based VIs across different phenological development phases throughout the growing season were analyzed. The results suggested that the normalized difference vegetation index (NDVI) and its faster substitute infrared percentage vegetation index (IPVI) exhibited the best capabilities for LAI estimation (R2 = 0.55 and RMSE = 0.77 for both) across the entire growing season. The LAI-VI relationship varied seasonally due to the saturation issues on VIs, with R2 values increasing from the leaf budburst to the growing stage, decreasing during maturation, and rising again in the senescence stage. This indicated that seasonal effects induced by phenological changes should be considered when estimating LAI using VIs. Additionally, the saturation of VIs was influenced by soil background, leaf properties (especially leaf chlorophyll content [Cab] and dry matter content [Cm]), and canopy structures (especially average leaf inclination angle, ALA). Compared to satellites, UAV-based sensors were more effective at mitigating spectral saturation at finescale due to their finer spatial resolution and narrower bandwidth. The drone-based VIs used in this study provided reliable estimates and effectively described temporal variability in LAI, contributing to a better understanding of VI saturation effects.

Evapotranspiration (ET) is a critical component of the soil-plant-atmosphere continuum, significantly influencing the water and energy balance of ecosystems. However, existing studies on ET have primarily focused on the growing season or specific years, with limited long-term analyses spanning decades. This study aims to analyse the components of ET within the alpine ecosystem of the Heihe River Basin, specifically investigating the dynamics of vegetation transpiration (T) and soil evaporation (Ev). Utilizing the SPAC model and integrating meteorological observations and eddy covariance data from 2013 to 2022, we investigate the impact of solar radiation and vegetation dynamics on ET and its partitioning (T/ET). The agreement between measured and simulated energy fluxes (net radiation and latent energy flux) and soil temperature underscores the validity of the model's performance. Additionally, a comparison employing the underlying water use efficiency method reveals consistent T/ET values during the growing season, further confirming the model's accuracy. Results indicate that the annual average T/ET during the 10-year study period is 0.41 +/- 0.03, close to the global average but lower than in warmer, humid regions. Seasonal analysis reveals a significant increase in T/ET during the growing season (April to October), particularly in May and June, coinciding with the thawing of permafrost and increased soil moisture. In addition, the study finds that the leaf area index and canopy stomatal conductance exhibit a logarithmic relationship with T/ET, whereas soil temperature and downward longwave radiation show an exponential relationship with T/ET. This study highlights the importance of understanding the stomatal conductance dynamics and their controls of transpiration process within alpine ecosystems. By providing key insights into the hydrological processes of these environments, it offers guidance for adapting to climate change impacts.

The European spruce bark beetle (Ips typographus) is an insect species that causes significant damage to Norway spruce (Picea abies) forests across Europe. Infestation by bark beetles can profoundly impact forest ecosystems, affecting their structure and composition and affecting the carbon cycle and biodiversity, including a decrease in net primary productivity (NPP), a key indicator of forest health. The primary objective of this study is to enhance our understanding of the interplay among NPP, bark beetle infestation, land surface temperature (LST), and soil moisture content as key components influencing the effects of climate change-related events (e.g., drought) during and after a drought event in the Bavarian Forest National Park in southeastern Germany. Earth observation data, specifically Landsat-8 TIR and Sentinel-2, were used to retrieve LST and leaf area index (LAI), respectively. Furthermore, for the first time, we incorporated a time series of high-resolution (20 m) LAI as a remote sensing biodiversity product into a process-based ecological model (LPJ-GUESS) to accurately generate high-resolution (20 m) NPP products. The study found a gradual decline in NPP values over time due to drought, increased LST, low precipitation, and a high rate of bark beetle infestation. We observed significantly lower LST in healthy Norway spruce stands compared to those infested by bark beetles. Likewise, low soil moisture content was associated with minimal NPP value. Our results suggest synergistic effects between bark beetle infestations and elevated LST, leading to amplified reductions in NPP value. This study highlights the critical role of integrating high-resolution remote sensing data with

Context: Grey leaf spot is a main leaf disease of tomato in Mediterranean greenhouses, characterized by warm temperatures and high humidity during the spring and winter seasons, hence suitable for pathogen infection and spore spread. Consequently, the utilization of automatic control and optimization algorithms has emerged as effective means to prevent chemical-oriented disease control and enhancing the overall quality and safety of food and crops. Objective: The aim of this work is to search an optimal strategy for precision management on greenhouse tomato growth environment. So, multi-objective optimization rises to an alternative to achieve this goal. While there were lots of research on determining trajectories to control a desired crop growth, and lacking works that optimize climate conditions for restraining the damage of disease on crop. Methods: Based on the multi-objective genetic algorithm optimization method (MOGA), the solution balances the conflict of two objectives: minimum power cost caused by climate control and maximum health leaves with few effects of grey leaf spot. This study also highlights disease and high temperature impact on tomato growth, which are as inequality constraints of the optimization problems. Results and conclusions: The results showed MOGA strategy performers good, the minimum power cost is only need 0.084*day(-1) in warm weather condition, as well as 3.74 *day(-1) in cold weather condition, the uninfected LAI (m(2)[Leaves](m(-2)[soils]*day(-1))) is the range of [0.14 0.20]. The yearly power cost at least [308 1365].These are able to embed within a control scheme for achieving optimization purpose. Significance: The farmer receives the data necessary for decision-making to establish the setpoints during the crop cycle, modifying the control decisions, lowering production costs, reducing the use of pesticides and increasing the system efficiency to optimize crop growth.

Aboveground biomass removal and canopy opening by selective logging modifies soil moisture in the main root zone, impacting soil aeration and various biogeochemical processes in tropical production forests. This study investigated the relationship between canopy damage and topsoil (10 cm) moisture in two logged forests in Malaysian Borneo, while simultaneously controlling for logging intensity, time elapsed since historical logging, and spatial autocorrelation. Volumetric soil water content (VSWC), canopy height model (CHM), leaf area index (LAI), and historical logging data were collected from 84 transects placed subjectively in 15 sites exhibiting varying canopies. We generated an index (PC1) quantifying the magnitude of canopy structural degradation from canopy structure metrics (CSM) combining CHM and LAI data within a 20-meter buffer for each transect. PC1 was analyzed for its impact on VSWC across logging periods, and contrasted with topography. Spatial autocorrelation of VSWC was examined regarding to canopy conditions. VSWC was significantly higher in all logged forests (over 0.4 m(3) m(-3)) comparing to non-disturbed forests (0.27 m(3) m(-3)). The immediate wetting could be a result of extracting mature individuals of late-successional species holding large biomass, while the persistent wet condition may be due to retarded canopy and biomass recovery. In the study area, canopy structure was a stronger predictor of soil moisture than topography. The high soil moisture underneath the most degraded canopies presented the largest spatial extent of autocorrelation. This study revealed soil wetting after selective logging in humid tropical forests, driven by reduced transpiration from biomass loss rather than increased evaporative demand resulting from canopy opening. The elevation in soil moisture could have disrupted biogeochemical processes in the below-ground system, which in turn impede forest succession and put stress on the overall vulnerability of disturbed tropical rainforests.

Considering the global aggravated agricultural drought condition, the availability of reliable historical agricultural drought information with high spatiotemporal resolution is crucial for accurate drought monitoring, effective water resources management, and sustainable agricultural development. However, the coarse spatiotemporal resolution of available historical agricultural drought datasets imposes great limitations on drought prevention and mitigation. Recognizing the research gap, this study developed a novel approach of leaf area index (LAI) relative thresholds to generate a 500 m-resolution agricultural drought areas dataset in the North China Plain (NCP) spanning the timeframe from 2006 to 2019. A range of key LAI relative thresholds were established to capture various levels of agricultural drought severity. Subsequently, a 500 m-resolution agricultural drought area dataset was generated, encompassing critical parameters of drought-covered area, drought- damaged area, and crop failure area for both summer-harvest and autumn-harvest seasons in each year. The relative thresholds for drought-covered area, drought-damaged area, and crop failure area yielded percentages of 56 %, 51 %, 34 % for summer-harvest crops and 45 %, 41 %, 28 % for autumn-harvest crops, respectively. To validate the credibility of the generated approach, historical agricultural drought areas data from the Bulletin of Flood and Drought Disasters in China were juxtaposed across various harvest seasons and multiple years. The spatial verification in the Hebei Province (one main province of the NCP) revealed a remarkable consistency between the newly generated dataset and the authentic dataset, demonstrating correlation efficiency estimates ranging from 0.70 to 0.83. The developed approach gives insights into the spatial distribution and coherence of agricultural drought impacted areas and sheds light on revealing the inter-annual dynamics of crop growing seasons. It can support for impact-based agricultural drought monitoring and prediction, and subsequently assist in optimizing agricultural water management and ensuring global food security.

The leaf is an important site for energy acquisition and material transformation in plants. Leaf functional traits and their trade-off mechanisms reflect the resource utilisation efficiency and habitat adaptation strategies of plants, and contribute to our understanding of the mechanism by which the distribution pattern of plant populations in arid and semi-arid areas influences the evolution of vegetation structure and function. We selected two natural environments, the tree-shrub community canopy area and the shrub-grass community open area in the transition zone between the Qinghai-Tibet Plateau and the Loess Plateau. We studied the trade-off relationships of leaf area with leaf midvein diameter and leaf vein density in Cotoneaster multiflorus using the standardised major axis (SMA) method. The results show that the growth pattern of C. multiflorus, which has small leaves of high density and extremely small vein diameters, in the open area. The water use efficiency and net photosynthetic rate of plants in the open area were significantly greater than those of plants growing in the canopy area. The adaptability of C. multiflorus to environments with high light and low soil water content reflects its spatial colonisation potential in arid and semiarid mountains. The leaf is an important site for energy acquisition and material transformation in plants. Leaf functional traits and their trade-off mechanisms reflect the resource utilisation efficiency and habitat adaptation strategies. We studied the trade-off relationships of leaf area with leaf midvein diameter and leaf vein density in Cotoneaster multiflorus. The results show the adaptability of C. multiflorus to environments with high light and low soil water content, which explains the expansion in the shrub's geographic distribution.