With rapid urbanization, street trees have become an important part of urban greening. To investigate the effects of street trees on PM2.5 and PM10 pollution in summer in Hangzhou, 29 urban roads were selected. Vehicle-mounted LiDAR and a mobile environmental monitoring station were used to synchronously measure street tree parameters, road spatial structure, meteorological conditions, and traffic volume. The results showed significant spatiotemporal variations in PM2.5 and PM10 concentrations: Qiantang District (industrial zone) had the most severe pollution, while Xihu District (scenic region) had the lowest. Their temporal variation curves were highly consistent, indicating common sources. Street Trees exerted significant effects under two scenarios: W/H < 0.6 with traffic volume < 1500 vehicles/2h, and W/H > 1.2 with traffic volume > 1500 vehicles/2h. However, their effects had a threshold: under W/H = 0.6-1.2 and traffic volume > 1500 vehicles/2h, the effect was superior to high tree density only when street tree volume was 8000–16000 m³; excessive volume exacerbated pollution. Linear regression models for PM2.5 (F = 21.591, p < 0.001, adjusted R² = 0.290) and PM10 (F = 21.813, p < 0.001, adjusted R² = 0.292) showed that road volume and fuel vehicle traffic were extremely significantly positively correlated with PM concentrations (p < 0.001), while street tree volume and new energy vehicle traffic were extremely significantly negatively correlated (p < 0.001). These findings highlight conditional and threshold effects of street trees on PM pollution, informing evidence-based road design and urban greening to enhance air quality in urbanizing regions.

Pine forests, particularly Masson pine (Pinus massoniana), are widely distributed across the subtropical regions of China. Understanding the spatial distribution of pine forest growing stock volume (GSV) is essential for effective management and planning of forest resources. However, accurately estimating pine forest GSV over large areas using airborne Lidar is challenging due to the complex stand structure and the influence of environmental conditions. This research employed airborne Lidar data and sample plots from 11 typical sites across the northern, central, and southern subtropical regions of China to explore modeling approaches for pine forest GSV estimation. Ordinary Linear Regression (OLR), Geographically Weighted Regression (GWR), and Hierarchical Bayesian Approach (HBA) were employed to model GSV through comparative analysis of using various sample sizes. The results indicate that: (1) HBA(Site), which models different pine forest types (i.e. pure pine forest (PPF) and mixed pine forest (MXF)) separately, with typical site as a stratification factor, provided the best estimation results with coefficient of determination (R2) of 0.80 and 0.74, root mean square error (RMSE) of 25.15 m3/ha and 23.86 m3/ha for PPF and MXF, respectively. When combining PPF and MXF together to develop GSV models, HBA(Site/Type) with double stratification factors (i.e. typical site and forest type) showed similar performance, with only a minor reduction in RMSE by 0.45 m3/ha for PPF and 0.15 m3/ha for MXF. This modeling approach effectively alleviated the overfitting problem caused by relatively small sample sizes. (2) Compared to OLR which only used global variables, both GWR which incorporated spatial information and HBA which utilized stratification significantly improved modeling performance. (3) The accuracy of OLR and GWR models remained relatively stable with various sample sizes, indicating their low sensitivity to sample sizes, whereas HBA exhibited high sensitivity due to the influence of stratification factors. (4) For a single tree species, a stratification-based modeling method was valuable for GSV estimation. This study provided a quantitative analysis and accurate estimation of pine forest GSV over large areas with different environmental conditions and offered new insights for GSV estimation of other forest types. More research is needed to quantitatively examine different contribution of sample sizes, modeling algorithms, variables from different sources, and stratification factors on modeling results, so that we can design an optimal procedure for GSV modeling using airborne Lidar data.

Forest canopy height (FCH) is a critical parameter for forest management and ecosystem modeling, but there is a lack of accurate FCH distribution in large areas. To address this issue, this study selected Wuyishan National Park in China as a case study to explore the calibration method for mapping FCH in a complex subtropical mountainous region based on ZiYuan-3 (ZY3) stereo imagery and limited Unmanned Aerial Vehicle (UAV) LiDAR data. Pearson’s correlation analysis, Categorical Boosting (CatBoost) feature importance analysis, and causal effect analysis were used to examine major factors causing extraction errors of digital surface model (DSM) data from ZY3 stereo imagery. Different machine learning algorithms were compared and used to calibrate the DSM and FCH results. The results indicate that the DSM extraction accuracy based on ZY3 stereo imagery is primarily influenced by slope aspect, elevation, and vegetation characteristics. These influences were particularly notable in areas with a complex topography and dense vegetation coverage. A Bayesian-optimized CatBoost model with directly calibrating the original FCH (the difference between the DSM from ZY3 and high-precision digital elevation model (DEM) data) demonstrated the best prediction performance. This model produced the FCH map at a 4 m spatial resolution, the root mean square error (RMSE) was reduced from 6.47 m based on initial stereo imagery to 3.99 m after calibration, and the relative RMSE (rRMSE) was reduced from 36.52% to 22.53%. The study demonstrates the feasibility of using ZY3 imagery for regional forest canopy height mapping and confirms the superior performance of using the CatBoost algorithm in enhancing FCH calibration accuracy. These findings provide valuable insights into the multidimensional impacts of key environmental factors on FCH extraction, supporting precise forest monitoring and carbon stock assessment in complex terrains in subtropical regions.

Poplar (PopulusL.) is one of the most widely distributed tree species planted in the plains of China and plays an important role in wood products and ecological services. Accurate estimation of poplar Growing Stock Volume (GSV) is crucial for better understanding the ecological functions and economic values in plain regions. However, the striped distribution feature of poplar forests in plain regions makes traditional grid-based GSV modeling methods highly uncertain. This research took Lixin County and Yongqiao District as case studies to examine the advantages of using object-based GSV modeling approach over the traditional grid-based approaches for poplar GSV estimation. The canopy height variables and density variables were extracted from airborne LIDAR-derived Canopy Height Model (CHM) data through different grid sizes and segmentation unit for constructing the poplar GSV estimation models using the linear regression. The results indicate that (1) Significantly linear relationships exist between GSV and height percentile variables; (2) The estimation accuracy in Lixin can be effectively improved by incorporating the CHM density variables into height variables, with the coefficient of determination (R-2) increasing from 0.46 to 0.71 and Root Mean Square Error (RMSE) decreasing from 20.23 to 14.94 m(3)/ha when a grid-based approach was implemented at grid size of 26 m by 26 m (plot size). However, CHM density variables have no effect on estimation modeling in Yongqiao district. The patch sizes and shapes considerably affect the selection of modeling variables and accuracy of modeling prediction; (3) The object-based mapping approach outperforms the grid-based approach in solving the mixed plot problem. This is especially valuable in the study areas with striped forest distribution. This study shows that differences in poplar stand structure affect the selection of modeling variables and GSV modeling performance, and an object-based modeling approach is recommended for GSV estimation in the plain areas.

Forest canopy height (FCH) is an important variable for estimating forest biomass and ecosystem carbon sequestration. Spaceborne LiDAR data have been used to create wall-to-wall FCH maps, such as the forest tree height map of China (FCHChina), Global Forest Canopy Height 2020 (GFCH2020), and Global Forest Canopy Height 2019 (GFCH2019). However, these products lack comprehensive assessment. This study used airborne LiDAR data from various topographies (e.g., plain, hill, and mountain) to assess the impacts of different topographical and vegetation characteristics on spaceborne LiDAR-derived FCH products. The results show that GEDI–FCH demonstrates better accuracy in plain and hill regions, while ICESat-2 ATLAS–FCH shows superior accuracy in the mountainous region. The difficulty in accurately capturing photons from sparse tree canopies by ATLAS and the geolocation errors of GEDI has led to partial underestimations of FCH products in plain areas. Spaceborne LiDAR FCH retrievals are more accurate in hilly regions, with a root mean square error (RMSE) of 4.99 m for ATLAS and 3.85 m for GEDI. GEDI–FCH is significantly affected by slope in mountainous regions, with an RMSE of 13.26 m. For wall-to-wall FCH products, the availability of FCH data is limited in plain areas. Optimal accuracy is achieved in hilly regions by FCHChina, GFCH2020, and GFCH2019, with RMSEs of 5.52 m, 5.07 m, and 4.85 m, respectively. In mountainous regions, the accuracy of wall-to-wall FCH products is influenced by factors such as tree canopy coverage, forest cover types, and slope. However, some of these errors may stem from directly using current ATL08 and GEDI L2A FCH products for mountainous FCH estimation. Introducing accurate digital elevation model (DEM) data can improve FCH retrieval from spaceborne LiDAR to some extent. This research improves our understanding of the existing FCH products and provides valuable insights into methods for more effectively extracting accurate FCH from spaceborne LiDAR data. Further research should focus on developing suitable approaches to enhance the FCH retrieval accuracy from spaceborne LiDAR data and integrating multi-source data and modeling algorithms to produce accurate wall-to-wall FCH distribution in a large area.

Forest canopy height (FCH) is one of the most important variables for carbon stock estimation. While many studies have focused on extracting FCH from spaceborne LiDAR in regions with spatially continuous and large patch sizes of forested lands, limited research has addressed the challenges of FCH extraction in plain regions with sparse and fragmented forest distributions. In this study, we proposed innovative processing approaches to extract FCH from ICESat-2 photons and GEDI footprints in the plain regions of Anhui Province, China. Specifically, we proposed a sectional photon denoising method for processing ICESat-2 data and a geolocation error correction method for processing GEDI data. Airborne LiDAR data were used to validate the extracted FCH products across typical plain regions. The results demonstrated the effectiveness of the proposed methods in improving FCH extraction accuracy. Evaluation indicated that the directly extracted FCH products from ATL08 and GEDI L2A had Pearson’s correlation coefficients (r) of 0.6 and 0.93, respectively. After processing with the proposed methods, the 2019 FCH products from ICESat-2 exhibited r of 0.82 and relative root mean square error (rRMSE) of 31.11% based on 3,217 ICESat-2 segments, and the products from GEDI showed r of 0.96 and rRMSE of 18.35% based on 4,862 GEDI footprints. Further application of these methods to extract FCH products from GEDI and ICESat-2 for the years 2020, 2021, and 2022 indicated their promise for addressing sparse vegetation coverage in plain regions.

Under the strong influence of climate change and human activities, the frequency and intensity of disturbance events in the forest ecosystem both show significant increasing trends. Pine wood nematode (Bursapherenchus xylophilus, PWN) is one of the major alien invasive species in China, which has rapidly infected the forest and spread. In recent years, its tendency has been to spread from south to north, causing serious losses to Pinus and non-Pinus coniferous forests. It is urgent to carry out remote sensing monitoring and prediction of pine wilt disease (PWD). Taking Anhui Province as the study area, we applied ground survey, satellite-borne optical remote sensing imagery and environmental factor statistics, relying on the Google Earth Engine (GEE) platform to build a new vegetation index NDFI based on time-series Landsat images to extract coniferous forest information and used a random forest classification algorithm to build a monitoring model of the PWD infection stage. The results show that the proposed NDFI differentiation threshold classification method can accurately extract the coniferous forest range, with the overall accuracy of 87.75%. The overall accuracy of the PWD monitoring model based on random forest classification reaches 81.67%, and the kappa coefficient is 0.622. High temperature and low humidity are conducive to the survival of PWN, which aggravates the occurrence of PWD. Under the background of global warming, the degree of PWD in Anhui Province has gradually increased, and has transferred from the southwest and south to the middle and northeast. Our results show that PWD monitoring and prediction at a regional scale can be realized by using long time-series multi-source remote sensing data, NDFI index can accurately extract coniferous forest information and grasp disease information in a timely manner, which is crucial for effective monitoring and control of PWD.

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Forest canopy height is one of the important forest parameters for accurately assessing forest biomass or carbon sequestration. ICESat-2 ATLAS provides the potential for retrieval of forest canopy height at global or regional scale, but the current canopy height product (ATL08) has coarse resolution and high uncertainty compared to airborne LiDAR-derived canopy height (hereafter ALCH) in mountainous regions, and is not ready for such applications as biomass modeling at finer scale. The objective of this research was to explore the approach to accurately retrieve canopy height from ATLAS data by incorporating an airborne-derived digital terrain model (DTM) and a data-filtering strategy. By linking ATLAS ATL03 with ATL08 products, the geospatial locations, types, and (absolute) heights of photons were obtained, and canopy heights at different lengths (from 20 to 200 m at 20-m intervals) of segments along a track were computed with the aid of airborne LiDAR DTM. Based on the relationship between the numbers of canopy photons within the segments and accuracy of ATLAS mean canopy height compared to ALCH, a filtering method for excluding a certain portion of unreliable segments was proposed. This method was further applied to different ATLAS ground tracks for retrieval of canopy heights and the results were evaluated using corresponding ALCH. The results show that the incorporation of high-precision DTM and ATLAS products can considerably improve the retrieval accuracy of forest canopy height in mountainous regions. Using the proposed filtering approach, the correlation coefficients (r) between ATLAS canopy height and corresponding ALCH were 0.61???0.91, 0.65???0.92, 0.68???0.94 for segment lengths of 20, 60, and 100 m, respectively; RMSE were 1.90???4.35, 1.55???3.63, and 1.34???3.23 m for the same segment lengths. The results indicate the necessity of using high-precision DTM and using the proposed filtering method to retrieve accurate canopy height from ICESat-2 ATLAS in mountainous regions with dense forest cover and complex terrain conditions.

Lidar has been regarded as the most accurate data source for forest-growing stock volume (FGSV) estimation, but inconsistent acquisition dates of lidar data with field survey often result in poor FGSV estimation accuracy. Spaceborne stereo imagery is captured at regular intervals, providing new opportunities for mapping and updating FGSV spatial distributions. Digital Surface Model derived from spaceborne stereo imagery and Digital Terrain Model (DTM) derived from airborne lidar can be used together to produce a canopy height model (CHM) (LS-CHM), which can then be used to predict FGSV spatial distributions, but this methodology has yet to be explored. Our research attempts to compare the performance of LS-CHM and lidar-CHM (L-CHM) for FGSV modeling and to explore the advantages of using the hierarchical Bayesian approach (HBA) over traditional linear regression and random forest modeling approaches when sample size is small. Considering different forest types and topographical conditions, as well as the number of sample plots for each forest type, HBA is used to develop the FGSV estimation model, and the results are compared with those from linear regression and random forest approaches. The research results in a northern subtropical forest ecosystem indicate that overall, L-CHM provides better predictions than LS-CHM using the same modeling approaches, and L-CHM is especially valuable when FGSV is small or large, but when FGSV falls within 100–200 m3/ha, LS-CHM–based variables produce better modeling accuracy than L-CHM–based variables using linear regression or HBA. The HBA based on stratification of both forest type and slope aspect provides the best FGSV estimation, using either L-CHM or LS-CHM, and solves the modeling problem due to limited sample sizes for forest types. Our research provides new insights to using the combination of satellite stereo images and lidar-derived DTM for mapping and updating FGSV in a large area.

通过对比分析杭州市森林资源动态监测数据与土地调查成果,探讨林业专项数据中林地及重要森林资源与土地调查成果的差异化程度、差异分布及差异产生的原因,为加快杭州市林业专项纳入现行国土空间规划体系提供科学依据.结果表明,杭州市森林资源动态监测数据与土地调查更新成果的地类一致性整体较高,平均达到88.06％.差异图斑以集体林,人工林,商品林和低保护等级的林地为主,但国有林,天然林,重点公益林等重要森林资源的差异总面积依然庞大,是需要林业部门重点关注的几大类型.

通过对浙江省德清、开化,江西省婺源、南丰四县的林地与国土数据的衔接情况进行分析,得出整体差异情况,并提出协调原则和具体差异图斑的处理建议,以期解决林业部门与国土部门在林地界定上存在的差异问题.

针对国内松材线虫病发生及监测状况,阐述了无人机遥感技术和松材线虫病遥感监测研究概况,并对无人机遥感技术在松材线虫病监测应用前景做出了展望.

结合林地年度变更调查试点工作实践，分析林地年度变更之经营资料收集处理、遥感影像判读、前期数据错误、行政界线变动、变更属性因子填写等环节中的问题及原因，并有针对性地探讨解决办法与对策。

The architecture and implementation technique of WebGIS are always the focus of scholars in its rapid developing process.Aiming at disadvantages of high complexity,poor interactive experience,and low response efficiency in traditional WebGIS application,this paper proposes a rich WebGIS application framework based on RIA/SilverLight and REST technologies which was divided into data access layer(DAL),business logic layer(BLL) and user interface layer(UI).Silverlight is an application framework for creating and delivering rich internet applications(RIA) and media experiences on the Web,which combining with the representational state transfer(REST) software architecture style can significantly remedy these shortages of traditional WebGIS application mentioned above.Firstly,in this paper the detailed description on designing and building steps of spatial databases were given,and the optimization experiences on spatial database were shared.Then the composition of business logic layer and its operating mechanism had been analyzed.To improve the efficiency of the user interface layer,the Model-View-ViewModel(MVVM) architectural pattern had been adopted.And to follow the user's habit,the office ribbon style had been used visually.The code-behind was programmed with.NET C#,and according to different function types,it was designed into event center component,configuration management component,UI interactive component,map container component,control management component,auxiliary function component,and the event center component is the communication hub.Finally,based on this framework,a rich WebGIS application which named LightGIS had been developed.It showed that the applied framework can effectively improve the efficiency,enrich user's experience and enhance the system capability.

The Jiufeng national forest park in Beijing as the research area,Using 3S technology,the fire danger rating map was painted,considering site conditions,topography factors and anthropic factors synthetically.The fire danger rating of Jiufeng forest was divided to 5 levels.After making statistics and analysis,the area of easier to burning zone(the fourth level) and common zone(the third level) is 80%.The area of easiest burning(the fifth level) is 18.66%.The area of difficult to burn area(the first level) and combustible area(second level) is altogether less than 2%.The signification of 3S could provides ideas for the forest fire prevention.

The authors of this paper chose Jidong county as the study area and researched the visual interpretation accuracy of high-resolution remote sensing image in interpreting certificated cutting area.Subsequently,they analyzed the interpretation result using decision tree method,and summarized the relationships between interpretation result and cutting percentage of the cutting area.In the meantime,they discussed how to improve the accuracy in interpreting cutting area with remote sensing technology.