Forests play a crucial role in the global carbon cycle, climate regulation, and biodiversity conservation, making them essential for understanding ecosystem responses to environmental change. However, the spatiotemporal dynamics of forest vegetation and their responses to climate change have yet to be fully explored. This study assessed the spatiotemporal dynamics and adaptation of forest vegetation from Northern China by extracting changes in forest vegetation and phenological characteristics from 2001 to 2023 with the time-series MODIS Normalized Difference Vegetation Index (NDVI) data and analyzing the impact of climate variables on these changes. The linear regression analysis method and the four-parameter double logistic model were employed to assess forest vegetation changes and identify forest vegetation phenological phases, respectively. Partial correlation analysis was used to assess the relationship between forest vegetation and climate variables. The results of this study indicate that over the past two decades, the annual mean NDVI of forest vegetation has exhibited a slow increasing trend of approximately 0.002 yr−1, with a spatial distribution pattern that gradually decreases from south to north, showing a significant correlation with latitude. The magnitude of annual mean NDVI changes varies considerably among different forest vegetation types. However, except for evergreen broadleaf forests, the NDVI of all other forest types has shown a significant increasing trend. Additionally, central North China and southeastern Tibet exhibit higher NDVI values in both spring (>0.55) and autumn (>0.65) than other areas, while the NDVI values in Northeast China and North China are higher in summer (>0.8) compared to other areas. The study reveals substantial spatial heterogeneity in the average phenological phases and NDVI values of forest vegetation across different regions, influenced by latitude, altitude, and regional climatic conditions. The spatial distribution patterns of NDVI during the green-up and senescence phases remain relatively consistent, yet significant regional differences exist within the same phenological phase. Partial correlation analysis indicates that forest vegetation in different regions responds distinctly to meteorological factors. These findings contribute to a deeper understanding of the spatiotemporal dynamics of vegetation change and its complex interactions with climate change, offering valuable insights for forest ecosystem management and climate adaptation of forest vegetation.

Tea is a high value-added economic crop with extremely high economic value.It is the main starting point for rural revitalization in mountainous areas of China.However,due to destructive behaviors such as deforestation and planting tea,forest resources are destroyed,and ecological and environmental problems such as soil erosion are caused.Acquiring the spatial distribution of tea plantations quickly and accurately is very important for government supervision and the planning and development of the tea industry.However,due to the rainy weather in the study area and the scattered distribution of tea plantations,which are close to the spectrum of vegetation such as forests,the extraction based on satellite imagery has become a problem.Tea plantations are challenging.In order to find out the spatial distribution of tea plantations in Yingde,this paper systematically analyzes the application potential of medium and high-resolution multispectral Sentinel-2 image data combined with multi-time-series and multi-feature information in tea garden extraction.Taking the whole territory of Yingde as the research area,this paper selects 9 phases of Sentinel-2 image data from 2019 to 2021 to analyze the phenological characteristics of tea tree growth in detail and further explore the characteristics changes of tea plantations and other land types in multiple time series,using the Relief algorithm to sort the importance of all features.According to the result of feature sorting,the feature factors weighted by 90％of the feature weight value are selected,namely 7 vegetation index features and 2 texture features,and 9 kinds of tea garden classification scenes are constructed through different combination rankings,and the RF algorithm is used to evaluate the accuracy of all classification scenes.To select the best classification scene and further discuss the feasibility of the RF classification algorithm and SVM classification algorithm for tea garden extraction.The results show that:(1)When extracting tea plantations in Yingde,February and October are the best combinations to construct multiple characteristics of tea plantations using multi-temporal phases.(2)Compared with the SVM classification method,the RF classification method has high accuracy.Its overall accuracy reaches 91.56％,the Kappa coefficient is 0.89,and the producer accuracy and user accuracy are 80.22％and 84.56％,respectively.This study provides an efficient method for quickly and efficiently obtaining the spatial distribution information of tea plantations in Yingde and provides data support for the government to plan and manage the tea industry.

Tea tree is an economically important crop. The rapid and efficient mapping of the distribution and dynamic changes in tea plantations informs decision making for government departments. It also plays an important role in rational tea planting and environmental governance. This study used the 10 m Sentinel-2 image data to map the spatial distribution of tea plantations in Yingde City, China. In this article, we analyzed the differences in vegetation and texture characteristics between the new and mature tea plantations. We found that the texture features of the new and mature tea plantations were significantly different in contrast, which can be used as an index for tea plantation extraction. Moreover, we selected machine learning classifiers, including support vector machine (SVM) and random forests (RF) method, which were utilized to extract the preliminary classification to complete spatial distribution mapping of tea plantations. The overall accuracy of SVM and RF was 90.79% and 89.42%, respectively, and the kappa coefficient was 0.88 and 0.86, respectively. SVM had the highest overall accuracy in terms of tea plantation distribution at the regional scale. These results demonstrate that: (1) separating tea plantations into mature and new tea plantations, taking into account vegetation and texture features such as soil brightness index and contrast, will help improve the accuracy of tea plantation classification and (2) using multi-period images combined with machine learning classification methods can improve the efficiency and accuracy of tea plantation identification.

Tea is an economically important crop. Evaluating the suitability of tea can better optimize the regional layout of the tea industry and provide a scientific basis for tea planting plans, which is also conducive to the sustainable development of the tea industry in the long run. Driving force analysis can be carried out to better understand the main influencing factors of tea growth. The main purpose of this study was to evaluate the suitability of tea planting in the study area, determine the prioritization of tea industry development in this area, and provide support for the government’s planning and decision making. This study used Sentinel image data to obtain the current land use data of the study area. The results show that the accuracy of tea plantation classification based on Sentinel images reached 86%, and the total accuracy reached 92%. Then, we selected 14 factors, including climate, soil, terrain, and human-related factors, using the analytic hierarchy process and spatial analysis technology to evaluate the suitability of tea cultivation in the study area and obtain a comprehensive potential distribution map of tea cultivation. The results show that the moderately suitable area (36.81%) accounted for the largest proportion of the tea plantation suitability evaluation, followed by the generally suitable area (31.40%), the highly suitable area (16.91%), and the unsuitable area (16.23%). Among these areas, the highly suitable area is in line with the distribution of tea cultivation at the Yingde municipal level. Finally, to better analyze the contribution of each factor to the suitability of tea, the factors were quantitatively evaluated by the Geodetector model. The most important factors affecting the tea cultivation suitability evaluation were temperature (0.492), precipitation (0.367), slope (0.302), and elevation (0.255). Natural factors influence the evaluation of the suitability of tea cultivation, and the influence of human factors is relatively minor. This study provides an important scientific basis for tea yield policy formulation, tea plantation site selection, and adaptation measures.

以1995—2015年5期Landsat系列遥感影像为主要数据源,依据改进的当量因子法对龙门山地区生态系统服务价值进行核算并进行了敏感性分析.结果表明:(1)1995—2015年各类生态系统中森林生态系统服务价值最高,其次是草地生态系统,荒漠生态系统服务价值最低;(2)1995—2015年不同区域单位面积生态系统服务价值具有显著空间差异,山区、高原区远大于丘陵、平原区;不同区域生态系统服务总价值呈现出山区>平原区>高原区>丘陵区的特点;(3)各类生态系统其服务价值及不同区域生态系统服务价值均与生态系统服务总价值基本呈现出一致的变化规律:1995—2000年趋于下降,2000—2010年趋于上升;2010—2015年基本呈下降趋势;整体上,1995—2015年生态系统服务价值总量下降了19.75％;(4)1995—2015年同一项生态系统服务价值在不同时期所占比重趋于稳定;同一时期各项生态系统服务价值所占比重呈现出显著差异性,11种生态服务类型中,气候调节价值对生态系统服务总价值的贡献最大,其次是水文调节价值,水资源供给价值所占比例最小;(5)生态系统服务价值对生态价值系数缺乏弹性,研究结果可信.该区域在今后生态建设与保护及区域国土空间规划等工作中应继续坚持可持续发展道路,谋求经济与生态环境的高度协调发展.

Reliable forest resource information is needed to assess the forest development status and design management plans for forest maintenance and conservation. The forest field sample inventory is a vital forest resource inventory method. Thus, forest inventory reliability depends on tree attribute estimation accuracy and the quantity and quality of field samples. Simultaneous localization and mapping (SLAM)-based mobile laser scanners (MLSs) are convenient inventory tools due to their mobility and global navigation satellite system (GNSS) signal independence. However, such scanners may be heavy, expensive and unable to verify results on-site. With the improved SLAM algorithm and chip computing capabilities, a smartphone can deploy an online SLAM system, which allows the smartphone to perform the relative positioning in forests without GNSS signals. Previous research studies have demonstrated this simple, portable, and economical device for estimating the tree position and diameter at breast height (DBH) through tree-by-tree measurements in real time. However, the device might face a challenge in large-scale forest inventories because the image-feature-based backend may not work well in forests that are not well constructed for traditional SLAM systems. In this paper, an online trunk-based backend was designed to accurately estimate tree position and correct pose drift in large-scale forest inventories in real time. Specifically, a trunk-based loop closure detection algorithm was designed for detecting whether an earlier observed tree is re-observed to provide nodes and constraints for tree position graph optimization; this algorithm uses the provided nodes and constraints to build and optimize the tree position graph and then correct the current pose based on the optimized globally consistent tree position graph. This new backend was integrated with the previous work as an executable program that can be deployed on a smartphone with an online RGB-D SLAM system. The method was tested in 5 field sample plots (32 x 32 m), and the reference tree positions were collected using terrestrial laser scanning (TLS) through multi-scan mode. The distance mean between the estimated and reference tree positions was 0.133 m when using our new backend, and it was 0.759 m when estimated with the RTAB-Map. The tree position estimates were unbiased and had root mean square errors (RMSEs) of less than 0.09 m in the x-axis, y-axis and z-axis directions when using the trunk-based backend. However, the estimates had biases of -0.125 m, -0.261 m and 0.262 m and RMSEs of more than 0.30 m in the x-axis, y-axis and z-axis directions without the new backend. The results showed that the designed trunk-based backend allows a smartphone with an online SLAM system to function as an accurate and efficient tool for large-scale forest inventories. However, the method was tested only in 32 x 32 m square field sample plots. More tests must be performed in larger plots in the future, although enough loop-closure constraints can theoretically guarantee the accuracy of the tree position graph and current pose.

Above-ground biomass (AGB) plays a pivotal role in assessing a forest’s resource dynamics, ecological value, carbon storage, and climate change effects. The traditional methods of AGB measurement are destructive, time consuming and laborious, and an efficient, relatively accurate and non-destructive AGB measurement method will provide an effective supplement for biomass calculation. Based on the real biophysical and morphological structures of trees, this paper adopted a non-destructive method based on terrestrial laser scanning (TLS) point cloud data to estimate the AGBs of multiple common tree species in boreal forests of China, and the effects of differences in bark roughness and trunk curvature on the estimation of the diameter at breast height (DBH) from TLS data were quantitatively analyzed. We optimized the quantitative structure model (QSM) algorithm based on 100 trees of multiple tree species, and then used it to estimate the volume of trees directly from the tree model reconstructed from point cloud data, and to calculate the AGBs of trees by using specific basic wood density values. Our results showed that the total DBH and tree height from the TLS data showed a good consistency with the measured data, since the bias, root mean square error (RMSE) and determination coefficient (R2) of the total DBH were −0.8 cm, 1.2 cm and 0.97, respectively. At the same time, the bias, RMSE and determination coefficient of the tree height were −0.4 m, 1.3 m and 0.90, respectively. The differences of bark roughness and trunk curvature had a small effect on DBH estimation from point cloud data. The AGB estimates from the TLS data showed strong agreement with the reference values, with the RMSE, coefficient of variation of root mean square error (CV(RMSE)), and concordance correlation coefficient (CCC) values of 17.4 kg, 13.6% and 0.97, respectively, indicating that this non-destructive method can accurately estimate tree AGBs and effectively calibrate new allometric biomass models. We believe that the results of this study will benefit forest managers in formulating management measures and accurately calculating the economic and ecological benefits of forests, and should promote the use of non-destructive methods to measure AGB of trees in China.

以视觉里程计技术恢复连续摄影序列图像位姿,并以恢复位姿的图像为基础构建样地调查系统.该系统通过对图像位姿尺度恢复、定义样地坐标系、标记立木等过程估计样地中立木位置及胸径.用相机对12块半径为7.5m的圆形样地进行连续摄影,获取有序图像序列,并使用构建的样地调查系统对图像序列进行处理,以获取样地中立木位置及胸径.实验结果表明,所有样地立木位置估计值x轴与y轴方向的偏差(BIAS)分别为0.04、-0.03 m,均方根误差(RMSE)分别为0.21、0.17m;样地中立木胸径估计值的BIAS及RMSE分别为0.09 cm(0.51％)和0.88 cm(5.03％).

Light Detection and Ranging (LiDAR) technology has been widely used in forestry surveys in the form of airborne laser scanning (ALS), terrestrial laser scanning (TLS), and mobile laser scanning (MLS). The acquisition of important basic tree parameters (e.g., diameter at breast height and tree position) in forest inventory did not solve the problem of low measurement efficiency or weak GNSS signal under the canopy. A personal laser scanning (PLS) device combined with SLAM technology provides an effective solution for forest inventory under complex conditions with its light weight and flexible mobility. This study proposes a new method for calculating the volume of a cylinder using point cloud data obtained by a PLS device by fitting to a polygonal cylinder to calculate the diameter of the trunk. The point cloud data of tree trunks of different thickness were modeled using different fitting methods. The rate of correct tree trunk detection was 93.3% and the total deviation of the estimations of tree diameter at breast height (DBH) was -1.26 cm. The root mean square errors (RMSEs) of the estimations of the extracted DBH and the tree position were 1.58 cm and 26 cm, respectively. The survey efficiency of the personal laser scanning (PLS) device was 30m2/min for each investigator, compared with 0.91m2/min for the field survey. The test demonstrated that the PLS device combined with the SLAM algorithm provides an efficient and convenient solution for forest inventory.

基于RGB-D SLAM手机构建了森林样地调查系统,该系统实现了样地构建、每木检尺及林分/样地参数的估计功能,并在测量过程中使用增强现实展示测量结果,且提供了重新测量的交互方式,使观测者在观测过程中能够检测结果的可靠性,并保证所获取样地信息的完整性.该系统在18块半径为7.5m的圆形样地中进行了测试.结果 显示,平均胸径估计值的偏差(BIAS)及均方根误差(RMSE)分别为0.36、0.69 cm,平均树高估计值的BIAS及RMSE分别为0.06、0.63 m,蓄积量估计值的BIAS及RMSE分别为8.595 9、25.735 8 m3/hm2,横断面积估计值的BIAS及RMSE分别为0.949 7、1.987 3 m2/hm2,株树密度估计值的BIAS及RMSE分别为-3、13株/hm2,坡度估计值的BIAS及RMSE分别为0.30°、0.88°,坡向估计值的BIAS及RMSE分别为-0.44°、7.61°.其中,坡向估计具有较大的RMSE,是由于当坡度较小时,即使SLAM系统估计位姿有较小漂移,仍会导致该值产生较大偏差,但整体而言坡向仍是无偏的.

Global climate change has raised concerns about the relationship between ecosystems and forests, which is a core component of the carbon cycle and a critical factor in understanding and mitigating the effects of climate change. Forest models and sufficient information for predictions are important for ensuring efficient afforestation activities and sustainable forest development. Based on the theory of difference equations and the general rules of tree growth, this study established a difference equation for the relationship between the ratio of tree diameter at breast height (DBH) to the tree height and age of age of China’s main arbor species. A comparison with equations that represent the traditional tree growth models, i.e., Logistic and Richards equations, showed that the difference equations exhibited higher precision for both fitting and verification data. Moreover, the biomass carbon stocks (BCS) of Chinese forests from 2013 to 2050 were predicted by combining the 8th Chinese Ministry of Forestry and partial continuous forest inventory (CFI) data sets. The results showed that the BCS of Chinese forests would increase from 7342 to 11,030 terra grams of carbon (Tg C) in 2013–2050, with an annual biomass C (carbon) sink of 99.68 Tg C year−1, and they indicated that the Chinese land-surface forest vegetation has an important carbon sequestration capability.

Incremental vertical ground movements due to coal mining can increase landslide susceptibility greatly in a short time and have thus triggered a large number of geological disasters, especially in the Karst Region, where a lot of steep slopes are on fractured rocks. Therefore, the landslide susceptibility maps (LSM) in Karst Region should be updated regularly. This paper presents an efficient methodology to update and refine LSM by using Persistent Scatterer Interferometry (PSI) data directly. First, an original LSM was produced by using a support vector machine (SVM) algorithm, and the distribution of coal mining was considered a crucial factor to generate the LSM. Then, the Permanent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR) technique was implemented to retrieve displacement time-series. Finally, the landslide displacement map, produced by the PSInSAR analysis, was projected to the direction of the steepest slope and resampled to the same cell in the LSM, to update the original LSM. This methodology is illustrated with the case study of Bijie in the Karst Region of Southwest China, wherein the ascending RADARSAT-2 and descending Sentinel-1 datasets are processed for the period of 2017–2019. The results show that the susceptibility degree increased in 56.41 km2 of the study area, and 80 percent of the increased susceptibility degree was caused by coal mining. The comparison between original and refined LSM in two specific areas revealed that the proposed method can produce more-reliable landslide susceptibility maps in areas of intense mining activities in the Karst Region.

The accounting model of water conservation quantity is established by ARC GIS 10.1 spatial analysis tools considering influencing factors of ecosystem type,vegetation index,vegetation coverage,development index,runoff coefficient and precipitation to water conservation based on HJ star and ETM+ image data of Guizhou province in different seasons in 2014 and the water conservation value in Guizhou is calculated by the accounting model to explore the health status of ecosystem and provide a reference for resources development and utilization,water and soil conservation,and ecological environmental protection in Guizhou.Results:The distribution status of water conservation value is closely related to distribution of the ecosystem types in Guizhou.Total water conservation value is 44.220 billion yuan in Guizhou.The water conservation value of Qiandongnan Prefecture with a dominant forest ecosystem reaches 9.732 billion Yuan and accounts for 22.01％ of Guizhou Province.The water conservation value of Zunyi City and Qiannan Prefecture is up to 7.086 and 7.083 billion Yuan and accounts for 16.02％ of Guizhou Province respectively.The water conservation value of Guiyang City with a dominant residence and industrial land and Liupanshui City with a dominant dry-land ecosystem is 1.757 and 1.920 billion Yuan and accounts for 3.97％ and 4.34％ of Guizhou Province separately.

Mobile Office Automatic(OA) is a combination of commnication industry and IT industry, both of which are in a fast evolution. With the communication industry, it support the scale of user and convenient of communication. With IT industry, it support the maturity of software and plenty of business content. It has became a new model after the Paperless Office and Internet Office. Mobile Office is also called 3A Office, which means anytime, anywhere and anything. In order to make the emplyees be able to receive and handle information in real-time through mobile devices, this paper made a re-search and comparison of several message push solutions, proposed a solution of message push based on XMPP protocol of Android Platform. This paper also made a research and discuss on the deployment of Mobile OA in enterprises.