Nitrogen (N) is a key nutrient for sustaining ecosystem productivity and agricultural sustainability; however, achieving high-precision monitoring in wetlands with highly heterogeneous surface types remains challenging. This study focuses on Caohai, a representative karst plateau wetland in China, and integrates Sentinel-2 multispectral and Zhuhai-1 hyperspectral remote sensing data to develop a soil nitrogen inversion model based on spectral indices, texture features, and their integrated combinations. A comparison of four machine learning models (RF, SVM, PLSR, and BPNN) demonstrates that the SVM model, incorporating Zhuhai-1 hyperspectral data with combined spectral and texture features, yields the highest inversion accuracy. Incorporating land-use type as an auxiliary variable further enhanced the stability and generalization capability of the model. The study reveals the spatial enrichment of soil nitrogen content along the wetland margins of Caohai, where remote sensing inversion results show significantly higher nitrogen levels compared to surrounding areas, highlighting the distinctive role of wetland ecosystems in nutrient accumulation. Using Caohai Wetland on the Chinese karst plateau as a case study, this research validates the applicability of integrating spectral and texture features in complex wetland environments and provides a valuable reference for soil nutrient monitoring in similar ecosystems.

Precisely estimating the position, diameter at breast height (DBH), and height of trees is essential in forest resource inventory. Augmented reality (AR)-based devices help overcome the issue of inconsistent global point cloud data under thick forest canopies with insufficient Global Navigation Satellite System (GNSS) coverage. Although monocular simultaneous localization and mapping (SLAM) is one of the current mainstream systems, there is still no monocular SLAM solution for forest resource inventories, particularly for the precise measurement of inclined trees. We developed a forest plot survey system based on monocular SLAM that utilizes array cameras and Inertial Measurement Unit (IMU) sensors provided by smartphones, combined with augmented reality technology, to achieve a real-time estimation of the position, DBH, and height of trees within forest plots. Our results from the tested plots showed that the tree position estimation is unbiased, with an RMSE of 0.12 m and 0.11 m in the x-axis and y-axis directions, respectively; the DBH estimation bias is −0.17 cm (−0.65%), with an RMSE of 0.83 cm (3.59%), while the height estimation bias is −0.1 m (−0.95%), with an RMSE of 0.99 m (5.38%). This study will be useful in designing an algorithm to estimate the DBH and position of inclined trees using point clouds constrained by sectional planes at the breast height of the trunk, developing an algorithm to estimate the height of inclined trees utilizing the relationship between rays and plane positions, and providing observers with visual measurement results using augmented reality technology, allowing them to judge the accuracy of the estimates intuitively. Clearly, this system has significant potential applications in forest resource management and ecological research.

将内嵌有面阵相机及IMU 的智能手机作为硬件系统,单目SLAM 技术获取多视图几何深度图、位姿等为数据源,构建了单目SLAM 增强现实森林测树系统.设计了基于平滑度高鲁棒性过滤胸高圆柱体表面点云及切线的方法;然后,基于点到圆柱体表面距离及圆柱体切线到圆柱体表面距离构建了胸径与立木位置精确估计算法;最后,以该算法为基础在智能手机端开发了增强现实测树系统,即利用智能手机实时测树、并通过增强现实场景实时人工监督测量结果.新型测树系统在5 块32 m ×32 m 方形样地中进行了测试,以评估新型测树系统的测量精度;此外,每块样地使用了单次观测、正交观测、对称观测及环绕观测4 种不同的观测方法对立木胸高圆柱体观测,以评估不用观测方式对测树精度的影响.结果显示:立木位置估计值在X、Y轴方向的平均误差范围为-0.014～0.020 m,X、Y轴方向均方根误差范围为0.04～0.08 m;立木胸径估计值偏差为-0.85～-0.03 cm(相对偏差为-3.60％～-0.04％),均方根误差为1.32～2.51 cm(相对均方根误差为6.41％～12.33％);相比于单次观测方法,其他观测方法获取位置及胸径估计精度均有提高(特别是不可近似为圆柱体的立木树干),从精度与效率角度而言,正交观测及对称观测为最佳观测方法.结果表明,单目SLAM 增强现实测树系统是一种可精确进行森林样地调查的潜在解决方案.

IntroductionForest spatial structures are the foundations of the structure and function of forest ecosystems. Quantitative descriptions and analyses of forest spatial structure have recently become common tools for digitalized forest management. Therefore, the accuracy and intelligence of acquiring forest spatial structure information are of great significance.MethodsIn this study, we developed a forest measurement system using a mobile phone. Through this system, the following tree measurements can be achieved: (1) point cloud of tree and chest diameter circle to measure tree diameter at breast height (DBH) and position coordinates of tree by using simultaneous localization and mapping (SLAM) technology, (2) virtual boundary creation of the sample plot, and the auxiliary measurement function of tree with the augmented reality (AR) interactive module, and (3) position coordinates and single-tree volume factor to calculate the spatial structural parameters of the forest (e.g., Mingling degree, Dominance index, Uniform angle index, and Crowdedness index).The system was tested in three 32 x 32 martificial forest plots.ResultsThe average DBH estimations showed BIAS of -0.47 to 0.45 cm and RMSEs of 0.57 to 0.95 cm. Its accuracy level met the requirements of forestry sample surveys. The tree position estimates for the three plots had relatively small RMSEs with 0.17 to 0.22 m on the x-axis and 0.16 to 0.26 m on the y-axis. The spatial structural parameters were as follows: the mingling degree of plot 1 was 0.32, and the overall mixing degree of tree species was low. The trees in plots 2 and 3 were all single species, and the mixing degree of both plots was 0. The dominance index of the three plots was 0.56, 0.51, and 0.51, indicating that the competitive advantage of the whole orest species was not obvious. The uniform angle index of the three plots was 0.55, 0.59, and 0.61, indicating that the positions of trees in the three plots were randomly distributed. The crowdedness index of plot 1 was 1.03, indicating that the degree of aggregation of the trees was low and showed a random distribution trend. The crowdedness index of the other plots were 1.36 and 1.40, indicating that the trees in the plots show a trend of uniform distribution, and the uniformity of plot 3 is higher than that of plot 2, but the overall uniformity is relatively weak.DiscussionThe findings of this study provide support for the optimization of forest structures and improve our conceptual understanding of forest community succession and restoration, in addition to the informatization and precision of forest spatial structure surveys.

森林中线、面特征较少等,导致LOAM算法去畸变及配准精度低、鲁棒性差,很难将该算法直接用于森林调查.为此以LOAM算法为基础设计了 LiDAR SLAM森林样地调查系统,在SLAM系统工作流程中剔除了遮挡线特征,避免视点与立木切线点作为线特征参与运算;引入二次去畸变、二次配准等模块提高了去畸变、配准的鲁棒性及精度;该系统将激光雷达测量精度、位姿估计精度等先验信息引入去畸变及配准优化算法中,提高去畸变及配准精度.使用32线激光雷达扫描了 4块32 m×32 m的森林样地,利用LiDAR SLAM森林样地调查系统完成样地建图,利用该点云提取的立木位置及胸径与参考数据对比,完成了新型SLAM样地调查系统在森林中建图精度的间接评估.结果显示:立木位置估计值在x、y轴方向的平均误差分别为-0.004 m和-0.011 m,x、y轴方向均方根误差分别为0.081 m和0.083 m;胸径估计值的偏差为0.25 cm(相对偏差为1.18％),均方根误差为1.03 cm(相对均方根误差为5.53％);经与LOAM估计结果相比,改进系统获取的立木位置及胸径精度均提高.结果表明,所设计的LiDAR SLAM森林样地调查系统可用于多线激光雷达扫描森林样地数据的处理,是一种可精确进行森林样地调查的解决方案.

The unchecked and unplanned expansion of urban areas has led to the conversion of millions of green areas to gray areas. The recent urban growth patterns of Pakistan’s metropolitan twin cities, Islamabad and Rawalpindi, is a matter of concern for the surrounding green areas. The present study aimed to categorize and quantify the land-use and land-cover change (LULCC) patterns and the corresponding impacts on the forest carbon dynamics around Islamabad and Rawalpindi. Multispectral satellite images for the year 1990 (Landsat 5 TM) and 2020 (Landsat 8 OLI) were used to determine, quantify, and compare the LULCC inside and around the twin metropolitan cities. Field inventory surveys in the reserved forests of Rawalpindi and Islamabad were also conducted to determine the amount of stored carbon in these forests. Our results showed an accelerated annual urban expansion (i.e., an increase in the built-up area) of 16.49% and 26.72% in Rawalpindi and Islamabad, respectively, during the study period. Similarly, the amount of barren land and agricultural land was reduced at an annual rate of 2.08% and 2.18%, respectively, in Rawalpindi and 0.25% and 1.04% in Islamabad. A reduction in the area of barren mountains also occurred at an annual of 2.26% in Islamabad, while it increased by 4.16% in Rawalpindi. The amount of carbon stored in the reserved forests of Islamabad stood at 139.17 ± 12.15 Mg C/ha while that of Rawalpindi was 110.4 ± 13.79 Mg C/ha. In addition, total stored forest carbon was found to have decreased from 544.70 Gg C to 218.05 Gg C in Rawalpindi, while in Islamabad it increased from 2779.64 Gg C to 3548.16 Gg C. Investment in ecological urban planning, sustainable cities, and appropriate land-use planning is recommended to curb the degradation and conversion of the surrounding green areas of Rawalpindi and Islamabad.

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.

Based on four-thermal-resistance-capacitance network within a borehole, an improved thermal-resistance-capacitance model (TRCM), which takes into account the effect of nonuniform temperature distribution along the borehole perimeter, is proposed for vertical single U-tube ground heat exchanger. For a given geometric and physical parameters of ground heat exchanger, the numerical simulations of the conventional TRCM based on three-thermal-resistance-capacitance network within borehole, the improved TRCM based on four-thermal-resistance-capacitance network within borehole and three-dimensional (3D) finite volume computational fluid dynamics (CFD) model by using FLUENT software were conducted, respectively. Through the comprehensive comparisons of simulation results between these above-mentioned three models for vertical single U-tube ground heat exchanger, it could be concluded that the proposed improved TRCM could not only provide relatively high accurate results, but also remarkably decrease the solving time as compared to the benchmark 3D finite volume CFD model. Since the proposed TRCM has better performance than the one based on three-thermal-resistance-capacitance network within borehole and 3D finite volume CFD model, a new reliable and feasible TRCM for vertical single U-tube ground heat exchanger could be available for the design and optimization of ground heat exchanger, the data interpretation of thermal response test (TRT) and other applications of ground heat exchanger in real industrial engineering.

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％).

Located at the foothills of the Himalayan Mountains, subtropical and moist temperate forests of Pakistan are very rich in flora and fauna. However, due to increased illegal and uncontrolled harvesting of wood, agricultural activities, and urbanization, these forests are fast disappearing. The recent expansion of human activities resulting illegal and uncontrolled harvesting, agricultural activities, and urbanization is a cause for concern. Using Landsat imagery, Markov Chain and Cellular Automata, this study focused on the quantitative assessment of spatiotemporal land use and land cover changes during 1998, 2008, 2018 and a simulation of 2028. In addition, a forest inventory survey of biomass and carbon sink were respectively calculated for these subtropical broad-leaved evergreen, subtropical chirpine and moist temperate forests. Results showed biomass was 560.56 +/- 104.33 Mg ha(-1), 350.95 +/- 104.33 Mg ha(-1) and 153.63 +/- 104.33 Mg ha(-1) in moist temperate, subtropical chirpine and subtropical broad-leaved forests respectively. Meanwhile, carbon was 313.94 +/- 44.78 Mg C ha(-1), 221.34 +/- 44.78 Mg C ha(-1) and 131.77 +/- 44.78 Mg C ha(-1) in moist temperate, subtropical chirpine and subtropical broad-leaved forests respectively. During the study period, land-use and land cover changes showed forest land changed from 40936.77 ha to 36709.23 ha, agricultural land from 4220.46 to 10374.64 ha, and built-up area from 1497.60 to 5395.12 ha. The average annual biomass and carbon loss were respectively 50.34 Gg ha(-1) yr(-1) and 31.33 Gg C ha(-1) yr(-1). The information derived from this study could assist in the development of appropriate sustainable forest management policies in Pakistan. (C) 2019 The Authors. Published by Elsevier B.V.

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.

森林生态系统生产力在区域生态发展和气候变化研究中具有重要的现实意义,而植被净初级生产力(NPP)的计算是生态系统生产力研究的重要内容.基于遥感和GIS技术,利用MODIS遥感产品MOD17A3数据和气象数据,研究了2000—2014年间NPP的分布及其与气候因子的关系.采用相关分析的方法分别从空间上与时间上研究森林植被NPP与气候因子的相关关系,探究京津冀地区近15 a NPP时空分布变化规律及其对气候变化的响应规律.从空间分布整体情况来看,气温和降水量均对NPP有正相关影响.从时间相关性来看,NPP与年降水量为正相关关系,NPP随年降水量的增大而增大.并根据所得结论为京津冀地区生态发展提出提高植被生产力的相关建议和对生态现状的改进措施.

Accurate estimation of tree position, diameter at breast height (DBH), and tree height measurements is an important task in forest inventory. Mobile Laser Scanning (MLS) is an important solution. However, the poor global navigation satellite system (GNSS) coverage under the canopy makes the MLS system unable to provide globally-consistent point cloud data, and thus, it cannot accurately estimate the forest attributes. SLAM could be an alternative for solutions dependent on GNSS. In this paper, a mobile phone with RGB-D SLAM was used to estimate tree position, DBH, and tree height in real-time. The main aims of this paper include (1) designing an algorithm to estimate the DBH and position of the tree using the point cloud from the time-of-flight (TOF) camera and camera pose; (2) designing an algorithm to measure tree height using the perspective projection principle of a camera and the camera pose; and (3) showing the measurement results to the observer using augmented reality (AR) technology to allow the observer to intuitively judge the accuracy of the measurement results and re-estimate the measurement results if needed. The device was tested in nine square plots with 12 m sides. The tree position estimations were unbiased and had a root mean square error (RMSE) of 0.12 m in both the x-axis and y-axis directions; the DBH estimations had a 0.33 cm (1.78%) BIAS and a 1.26 cm (6.39%) root mean square error (RMSE); the tree height estimations had a 0.15 m (1.08%) BIAS and a 1.11 m (7.43%) RMSE. The results showed that the mobile phone with RGB-D SLAM is a potential tool for obtaining accurate measurements of tree position, DBH, and tree height.

Forest resources inventory includes individual tree DBH (diameter at breast height),individual tree height,individual tree volume,stand average DBH,stand average height,stand density and stand volume.The theoretical basis was based upon the principle of photogrammetry,imageprocessing technology and forest measurement,and the forest intelligent dendrometer was developed which was composed of the self-developed R&D PDA module,EDM module and rotational station.There were four modular procedures which were compiled in the Java language and developed in Android Studio 2.1 systems development environment,and five measuring functions,such as tree height,DBH,threeelement volume calculation,3D angle gauge plot and basic measurement,and they were implemented by getting dip angle,azimuth,distance,image information and other parameters.Validated by experiments,the measuring accuracy of tree height was as high as 97.13％,DBH was 97.08％,volume was 94.52％,stand average height was 98.09％,stand average DBH was 98.05％,stand density was 96.59％,and stand volume measurement accuracy was 95.72％.Thus the equipment was in line with the accuracy requirement of national forest inventory (NFI),which can be promoted to be used in the forestry inventory.

In order to realize the rapid and accurate determination of diameter at breast height (DBH),EAN-13 bar code encoding and decoding rules was taken as template,based on the Android platform and Open CV intelligent image processing technology,an electronic bar code ruler was designed and implemented to realize the automatic measurement of the tree DBH size by single operation.When the bar code encoding was completed,bar code image was scanned by using mobile phone APP,through image preprocessing,bar code identification,location and diameter automatic measurement and recording,data storage and export process,the automatic measurement of DBH in forest survey was realized.By selecting 205 samples of coniferous trees and 200 broadleaf trees,the electronic bar code was used to measure them,at the same time,the measurement results were compared with the precision of the traditional measuring ruler.The experimental results showed that the measurement accuracy of the method can be more than 99.95％,which can meet the precision requirements of continuous logging of national forest resources.Meanwhile,the method of measuring tree breast diameter of each tree was only 11 to complete the work of measuring diameter,and the measuring work efficiency was greatly increased.This method had good application prospect of the tree DBH measurement in forest survey.

In order to establish the one-way and two-way tree volume models for Gansu Province and im-prove the accuracy,the paper took Prunu sarmeniaca and Aspen as the research objects and applied elec-tronic theodolite to obtain precise data of standing tree in a nondestructive way.By using simultaneous e-quation of error variance,double-element tree volume equation,DBH duality standing tree volume equa-tion and tree height diameter model were constructed,which provided scientific basis for the estimation of forest stock volume in relevant tree species.The compatibility of the standing tree volume equation was derived from the classic Yamamoto and Tibetan two-way tree volume models as well as index tree height model,followed by an overall evaluation of the newly-established model with six indexes.It was revealed by the result that both two-way tree volume models could yield good results.The average prediction error of two-way tree volume table and prunus armeniaca DBH unitary-volume table is 2.12% and 2.58% re-spectively.In terms of aspen binary volume table and DBH unitary volume table,the average prediction error is 1.57% and 2.01% respectively.Therefore,the models of this article can be used to estimate the volume of prunus armeniaca and aspen in Gansu Province.

在内蒙古旺业甸林场选取181棵落叶松，分别对树高、胸径进行精确测定。利用1stOpt优化分析软件平台的Levenberg－Marquarat＋通用全局优化算法（ LM－UGO）和 SPSS软件的普通最小二乘法分别对所测数据进行拟合，引入确定系数（R2）、估计值的标准差（SEE）等作为评价指标。结果显示：LM－UGO方法的拟合结果优于传统使用SPSS的拟合结果，经F检验， FLM－UGO＞FSPSS最小二乘法＞F0．05（1，179），且R2＞09．，变动系数CV＜50％，相对误差TRE、平均相对误差MSE值均控制在（－3％，3％）范围内，满足评价指标的值域要求以及林业调查技术的相关规定。

The invention discloses a precise calculation measurement method for small group dynamic growth amount, comprising steps of taking a group as a basic unit, calculating and measuring parameters of a mixed stand ratio, an average tree height, an average chest diameter and density by setting permanent mark points in the small group and utilizing an angle counter, and performing precise measurement and calculation on the small group dynamic growth amount. The invention can perform simple, quick, quantitative and precise measurement and calculation on the small group dynamic growth amount, improves the efficiency and precision of the small group dynamic growth amount and is easy in operation.

The invention provides a shrub biomass measuring method through cellphone shooting. The method includes the following steps: taking a fixed-focus cellphone as a tool and a sighting rod as an auxiliary measuring tool; taking the fixed-focus cellphone to take photos and extracting the diameter of the root and height of the shrub; performing model fitting through root digging, sampling and weighing; performing real-time supposition of the shrub biomass by using the obtained model. According to the method, the way for acquiring data is non-contact type, little in damage and quick, influence on the environment in the traditional destructive method is lessened, so that the environment is effectively protected; the method is simple in measurement, the instrument is simple, convenient and portable, and the efficiency for measuring the shrub biomass can be improved.