Assessing spatiotemporal variations of forest carbon density using bi-temporal discrete aerial laser scanning data in Chinese boreal forests

Assessing the changes in forest carbon stocks over time is critical for monitoring carbon dynamics,estimating the balance between carbon uptake and release from forests,and providing key insights into climate change mitigation.In this study,we quantitatively characterized spatiotemporal variations in aboveground carbon density(ACD)in boreal natural forests in the Greater Khingan Mountains(GKM)region using bi-temporal discrete aerial laser scanning(ALS)data acquired in 2012 and 2016.Moreover,we evaluated the transferability of the proposed design model using forest field plot data and produced a wall-to-wall map of ACD changes for the entire study area from 2012 to 2016 at a grid size of 30 m.In addition,we investigated the relationships between carbon dynamics and the dominant tree species,age groups,and topography of undisturbed forested areas to better understand ACD variations by employing heterogeneous forest canopy structural characteristics.The results showed that the performance of the temporally transferable model(R^(2)=0.87,rRMSE=18.25%),which included stable variables,was statistically equivalent to that obtained from the model fitted directly by the 2016 field plots(R^(2)=0.87,rRMSE=17.47%).The average rate of change in carbon sequestration across the entire study region was 1.35 Mg⋅ha^(-1)⋅year^(-1) based on the changes in ALS-based ACD values over the course of four years.The relative change rates of ACD decreased as the elevation increased,with the highest and lowest ACD growth rates occurring in the middle-aged and mature forest stands,respectively.The Gini coefficient,which represents forest canopy surface structure heterogeneity,is sensitive to carbon dynamics and is a reliable predictor of the relative change rate of ACD.This study demonstrated the applicability of bi-temporal ALS for predicting forest carbon dynamics and fine-scale spatial change patterns.Our research contributed to a better understanding of the in-fluence of remote sensing-derived environmental variables on forest carbon dynamic patterns and the development of context-specific management approaches to increase forest carbon stocks.

Spatial heterogeneity of fine root biomass of Pinus massoniana forests in the Three Gorges Reservoir Area, China

Environmental heterogeneity is a constant presence in the natural world that significantly affects plant behavior at a variety of levels of complexity. In order to estimate the spatial pattern of fine root biomass in the Three Gorges Reservoir Area, the spatial heterogeneity of fine root biomass in the upper layer of soils （0-10 cm） in three Mas- son pine （Pinus massoniana） stands in the Three Gorges Reservoir Area, China, was studied in 30 m x 30 m plots with geostatistical analysis. The results indicate that 1） both the live and dead fine root biomass of stand 2 were less than those of other stands, 2） the spatial variation of fine roots in the three stands was caused together by structural and ran- dom factors with moderate spatial dependence and 3） the magnitude of spatial heterogeneity of live fine roots ranked as： stand 3 〉 stand 1 〉 stand 2, while that of dead fine roots was similar in the three stands. These findings suggested that the range of spatial autocorrelation for fine root biomass varied considerably in the Three Gorges Reservoir Area, while soil properties, such as soil bulk density, organic matter and total nitrogen, may exhibit great effect on the spatial distribution of fine roots. Finally, we express our hope to be able to carry out further research on the quantitative relation- ship between the spatial heterogeneous patterns of plant and soil properties.

Large-scale forest inventories of the United States and China reveal positive effects of biodiversity on productivity

Background： With the loss of species worldwide due to anthropogenic factors, especially in forested ecosystems, it has become more urgent than ever to understand the biodiversity-ecosystem functioning relationship （BEFR）. BEFR research in forested ecosystems is very limited and thus studies that incorporate greater geographic coverage and structural complexity are needed. Methods： We compiled ground-measured data from approx, one half million forest inventory sample plots across the contiguous United States, Alaska, and northeastern China to map tree species richness, forest stocking, and productivity at a continental scale. Based on these data, we investigated the relationship between forest productivity and tree species diversity, using a multiple regression analysis and a non-parametric approach to account for spatial autocorrelation. Results： In general, forests in the eastern United States consisted of more tree species than any other regions in the country. The highest forest stocking values over the entire study area were concentrated in the western United States and Central Appalachia. Overall, 96.4 % of sample plots （477,281） showed a significant positive effect of species richness on site productivity, and only 3.6 % （17,349） had an insignificant or negative effect. Conclusions： The large number of ground-measured plots, as well as the magnitude of geographic scale, rendered overwhelming evidence in support of a positive BEFR. This empirical evidence provides insights to forest management and biological conservation across different types of forested ecosystems. Forest timber productivity may be impaired by the loss of species in forests, and biological conservation, due to its potential benefits on maintaining species richness and productivity, can have profound impacts on the functioning and services of forested ecosystems.

Evaluating soil nutrients of Dacrydium pectinatum in China using machine learning techniques

Background: The accurate estimation of soil nutrient content is particularly important in view of its impact on plant growth and forest regeneration. In order to investigate soil nutrient content and quality for the natural regeneration of Dacrydium pectinatum communities in China, designing advanced and accurate estimation methods is necessary.Methods: This study uses machine learning techniques created a series of comprehensive and novel models from which to evaluate soil nutrient content. Soil nutrient evaluation methods were built by using six support vector machines and four artificial neural networks.Results: The generalized regression neural network model was the best artificial neural network evaluation model with the smallest root mean square error(5.1), mean error(-0.85), and mean square prediction error(29). The accuracy rate of the combined k-nearest neighbors(k-NN) local support vector machines model(i.e. k-nearest neighbors-support vector machine(KNNSVM)) for soil nutrient evaluation was high, comparing to the other five partial support vector machines models investigated. The area under curve value of generalized regression neural network(0.6572) was the highest, and the cross-validation result showed that the generalized regression neural network reached 92.5%.Conclusions: Both the KNNSVM and generalized regression neural network models can be effectively used to evaluate soil nutrient content and quality grades in conjunction with appropriate model variables. Developing a new feasible evaluation method to assess soil nutrient quality for Dacrydium pectinatum, results from this study can be used as a reference for the adaptive management of rare and endangered tree species. This study, however, found some uncertainties in data acquisition and model simulations, which will be investigated in upcoming studies.

Trees as hotspots:Using forests,trees,and agroforestry to foster diverse sustainable landscapes

Forests,trees,and agroforestry(FTA)are ecosystem hotspots.They exemplify the contributions of biodiversity to sustainable and resilient landscapes,green circular economy and to sustainable agriculture and food systems for healthy diets.However,most research on these topics have been performed separately and lack comparison.The International FTA-Kunming Conference'Forests,trees and agroforestry for diverse sustainable landscapes'22nd–24th June 2021,focused on these contributions,brought together scientists NGOs,and policy makers to further the understanding of tree diversity;provided a communication platform for scientists to share their research results;evaluated the role of tree diversity in agroecology and circular agriculture;assessed benefits of landscape restoration;and explored applied research in mountain ecosystems and food security.The goals were to gather evidence that ground the design of solutions that can contribute to the implementation of the post 2020 Global Biodiversity Framework and towards the UN Food Systems Summit,and the overall implementation of the SDGs.This paper summarizes the outcomes of the international FTA Conference in Kunming 2021 and points out the highlights of research involved in six major themes.

Calorific values and ash contents of different parts of Masson pine trees in southern China

Calorific value of plants is an important parameter for evalu- ating and indexing material cycles and energy conversion in forest eco- systems. Based on mensuration data of 150 sample sets, we analyzed the calorific value （CV） and ash content （AC） of different parts of Masson pine （Pinus massoniana） trees in southern China using hypothesis testing and regression analysis. CV and AC of different tree parts were almost significantly different （P〈0.05）. In descending order, ash-free calorific value （AFCV） ranked as foliage 〉 branch 〉 stem bark 〉 root 〉 stem wood, and AC ranked as foliage 〉 stem bark 〉 root 〉 branch 〉 stem wood. CV and AC of stem wood from the top, middle and lower sections of trees differed significantly. CV increased from the top to the lower sections of the tnmk while AC decreased. Mean gross calorific value （GCV） and AFCV of aboveground parts were significantly higher than those of belowground parts （roots）. The mean GCV, AFCV and AC of a whole tree of Masson pine were 21.54 kJ/g, 21.74 kJ/g and 0.90%, re- spectively. CV and AC of different tree parts were, to some extent, cor- related with tree diameter, height and origin.

Developing individual tree-based models for estimating aboveground biomass of five key coniferous species in China

Estimating individual tree biomass is critical to forest carbon accounting and ecosystem service modeling.In this study,we developed one-（tree diameter only） and two-variable（tree diameter and height） biomass equations,biomass conversion factor（BCF） models,and an integrated simultaneous equation system（ISES） to estimate the aboveground biomass for five conifer species in China,i.e.,Cunninghamia lanceolata（Lamb.） Hook.,Pinus massoniana Lamb.,P.yunnanensis Faranch,P.tabulaeformis Carr.and P.elliottii Engelm.,based on the field measurement data of aboveground biomass and stem volumes from 1055 destructive sample trees across the country.We found that all three methods,including the one-and two-variable equations,could adequately estimate aboveground biomass with a mean prediction error less than 5%,except for Pinus yunnanensis which yielded an error of about 6%.The BCF method was slightly poorer than the biomass equation and the ISES methods.The average coefficients of determination（R^2） were 0.944,0.938 and 0.943 and the mean prediction errors were 4.26,4.49 and 4.29% for the biomass equation method,the BCF method and the ISES method,respectively.The ISES method was the best approach for estimating aboveground biomass,which not only had high accuracy but also could estimate stocking volumes simultaneously that was compatible with aboveground biomass.In addition,we found that it is possible to develop a species-invariant one-variable allometric model for estimating aboveground biomass of all the five coniferous species.The model had an exponent parameter of 7/3 and the intercept parameter a_0 could be estimated indirectly from stem basic density（a_0= 0.294 q）.

Generalized or general mixed-effect modelling of tree morality of Larix gmelinii subsp.principis-rupprechtii in Northern China

Tree mortality models play an important role in predicting tree growth and yield,but existing mortality models for Larix gmelinii subsp.principis-rupprechtii,an important species used for regeneration and afforestation in northern China,have overlooked potential regional influences on tree mortality.This study used data acquired from 102 temporary sample plots(TSPs)in natural stands of Prince Rupprecht larch in the state-owned Guandi Mountain Forest(n=67)and state-owned Boqiang Forest(n=35)in northern China.To model stand-level tree mortality,we compared seven model forms of county data.Three continuous(dominant height,plot mean diameter,and basal area per hectare)and one dummy variable with two levels(region)were used as fixed effects variables.Tree morality variations caused by forest blocks were accounted for using forest blocks as a random effect in selected models.Results showed that tree mortality significantly positively correlated with stand basal area and dominant height,but negatively correlated with stand mean diameter.Incorporating both the dummy variables and random effects into the tree mortality models significantly increased the fitting improvements,and Hurdle Poisson mixed-effects model showed the most attractive fit statistics(largest R^(2)and smallest RMSE)when employing leave-one-out cross-validation.These mixed-effects dummy variable models will be useful for accurately predicting Larix tree mortality in different regions.

Periodic Solutions of a Cooperative System with State Feedback Control

In this paper, we propose a semi-continuous dynamical system to study the cooperative system with feedback control. Based on geometrical analysis and the analogue of Poincare criterion, the existence and stability of the positive order one periodic solutions are given. Numerical results are carried out to illustrate the feasibility of our main results.

Research Progress of Natural Regeneration Barrier of Forest

After reviewing a large quantity of literatures at home and abroad,the natural regeneration barrier mechanisms of forest were described,including lack of seed,animal eating and trespass,plants allelopathy,microbial pathogenesis, unusual state of ecological factors like light, temperature,humidity and rainfall,physical obstruct of understory groundcover and litters,natural and human disturbance and difference forest community characteristics.The paper finally came up with the problems existing in the current research and the development idea of the research.

林产工业循环经济标准体系框架研究

标准体系框架设计是林产工业循环经济标准体系构建的基础。以国家标准GB/T 13016—2009《标准体系表编制原则和要求》以及相关法律、法规为依据,再结合中国林产工业循环经济产业特征,参考国外林产工业循环经济标准,提出了中国林产工业循环经济标准体系建设的原则、方法,构建了标准体系框架。林产工业循环经济标准体系框架划分为基础与综合、节材、节水、节能、清洁生产、有害物质控制、再利用、废固资源化、废水及工业余热资源化9个子体系。

Modeling compatible single-tree aboveground biomass equations for masson pine(Pinus massoniana) in southern China

Because of global climate change, it is necessary to add forest biomass estimation to national forest resource monitoring. The biomass equations developed for forest biomass estimation should be compatible with volume equations. Based on the tree volume and aboveground biomass data of Masson pine （Pinus massoniana Lamb.） in southern China, we constructed one-, two- and three-variable aboveground biomass equations and biomass conversion functions compatible with tree volume equations by using error-in-variable simultaneous equations. The prediction precision of aboveground biomass estimates from one variable equa- tion exceeded 95%. The regressions of aboveground biomass equations were improved slightly when tree height and crown width were used together with diameter on breast height, although the contributions to regressions were statistically insignificant. For the biomass conversion function on one variable, the conversion factor decreased with increasing diameter, but for the conversion function on two variables, the conversion factor increased with increasing diameter but decreased with in- creasing tree height.

Feature analysis of LIDAR waveforms from forest canopies

Airborne light detection and ranging (LIDAR) can detect the three-dimensional structure of forest canopies by transmitting laser pulses and receiving returned waveforms which contain backscatter from branches and leaves at different heights.We established a solid scatterer model to explain the widened durations found in analyzing the relationship between laser pulses and forest canopies,and obtained the corresponding rule between laser pulse duration and scatterer depth.Based on returned waveform characteristics,scatterers were classified into three types:simple,solid and complex.We developed single-peak derivative and multiple-peak derivative analysis methods to retrieve waveform features and discriminate between scatterer types.Solid scatterer simulations showed that the returned waveforms were widened as scatterer depth increased,and as space between sub-scatterers increased the returned waveforms developed two peaks which subsequently developed into two separate sub-waveforms.There were slight differences between the durations of simulated and measured waveforms.LIDAR waveform data are able to describe the backscatter characteristics of forest canopies,and have potential to improve the estimation accuracy of forest parameters.

Empowering fall webworm surveillance with mobile phone-based community monitoring: a case study in northern China

Recent advances in information and communication technologies, such as mobile Internet and smart- phones, have created new paradigms for participatory environment monitoring. The ubiquitous mobile phones with capabilities such as a global positioning system, camera, and network access, offer opportunities to estab- lish distributed monitoring networks that can perform a wide range of measurements for a landscape. This study examined the potential of mobile phone-based community monitoring of fall webworm （Hyphantria cunea Drury）. We built a prototype of a participatory fall webworm monitoring System based on mobile devices that stream- lined data collection, transmission, and visualization. We also assessed the accuracy and reliability of the data collected by the local community. The system performance was evaluated at the Ziya commune of Tianjin municipality in northern China, where fall webworm infestation has occurred. The local community provided data with accuracy comparable to expert measurements （Willmott＇s index of agreement 〉0.85）. Measurements by the local community effectively complemented remote sensing images in both temporal and spatial resolution.

Fine scale optical remote sensing experiment of mixed stand over complex terrain(FOREST)in the Genhe Reserve Area:objective,observation and a case study

Optical remote sensing allows to efficiently monitor forest ecosystems at regional and global scales.However,most of the widely used optical forward models and backward estimation methods are only suitable for forest canopies in flat areas.To evaluate the recent progress in forest remote sensing over complex terrain,a satellite-airborne-ground synchronous Fine scale Optical Remote sensing Experiment of mixed Stand over complex Terrain(FOREST)was conducted over a 1 km×1 km key experiment area(KEA)located in the Genhe Reserve Areain 2016.Twenty 30 m×30 m elementary sampling units(ESUs)were established to represent the spatiotemporal variations of the KEA.Structural and spectral parameters were simultaneously measured for each ESU.As a case study,we first built two 3D scenes of the KEA with individual-tree and voxel-based approaches,and then simulated the canopy reflectance using the LargE-Scale remote sensing data and image Simulation framework over heterogeneous 3D scenes(LESS).The correlation coefficient between the LESS-simulated reflectance and the airborne-measured reflectance reaches 0.68-0.73 in the red band and 0.56-0.59 in the near-infrared band,indicating a good quality of the experiment dataset.More validation studies of the related forward models and retrieval methods will be done.

Comparing Land Degradation and Regeneration Trends in China Drylands

The aim of this paper is to offer a statistically sound method to make a precise account of the speed of land degradation and regeneration processes.Most common analyses of land degradation focus instead on the extent of degraded areas,rather than on the intensity of degradation processes.The study was implemented for the Potential Extent of Desertification in China(PEDC),composed by arid,semi-arid,and dry sub-humid regions and refers to the period 2002 to 2012.The metrics were standard partial regression coefficients from stepwise regressions,fitted using Net Primary Productivity as the dependent variable,and year number and aridity as predictors.The results indicate that:①the extension of degrading lands(292896 km 2 or 9.12%of PEDC)overcomes the area that is recovering(194560 km 2 or 6.06%of PEDC);and②the intensity of degrading trends is lower than that of increasing trends in three land cover types(grassland,desert,and crops)and in two aridity levels(semi-arid and dry sub-humid).Such an outcome might pinpoint restoration policies by the Chinese government,and document a possible case of hysteresis.

Remote Sensing Estimation of Forest Canopy Density Combined with Texture Features

The development of high-resolution remote sensing imaging technology provides a new way to the large-scale estimation of forest canopy density. The traditional inversion methods for canopy density only use spectral or topographical features of remote sensing images.However,due to the existence of the different thing with same spectrum and the same thing with different spectrum phenomena,it is difficult to improve the estimation accuracy of canopy density.Based on spectrum and other traditional features,this paper combines texture features of remote sensing images to estimate canopy density.Firstly,the gray level co-occurrence matrix (GLCM) texture features are computed using objectbased method.Then,the principal component analysis (PCA) method is applied in correlation analysis and dimension reduction of texture features.Finally, spectrum and topographical features together with texture features are introduced into stepwise regression model to estimate canopy density.The experimental results showed that compared with the traditional method only based on spectrum or topographical features,the method combined with texture features greatly improved the estimation accuracy.The coefficient of determination(adjusted R^2 ) increased from 0.737 to 0.805.The estimation accuracy increased from 81.03%to 84.32%.

Parallelized LEDAPS method for Remote Sensing Preprocessing Based on MPI

Based on Landsat image,the Landsat Ecosystem Disturbance Adaptive Processing System(LEDAPS)uses radiation change detection method for image processing and offers the surface reflectivity products for ecosystem carbon sequestration and carbon reserves.As the accumulation of massive remote sensing data especially for the Landsat image,the traditional serial LEDAPS for image processing has a long cycle that make a lot of difficulties in practical application.For this problem,this paper design a high performance parallel LEDAPS processing method based on MPI.The results not only aimed to improve the calculation speed and save computing time,but also considered the load balance between the flexibly extended computing nodes.Results show that the highest speed ratio of parallelized LEDAPS reached 7.37 when the number of MPI process is 8.It effectively improves the ability of LEDAPS to handle massive remote sensing data and reduces the forest carbon stocks calculation cycle by using the remote sensing images.

Comparison on Estimation of Wood Biomass Using Forest Inventory Data

Based on sixth and seventh national forestry inventory data of the six provinces,including Guangdong,Jiangxi,Guizhou,Shaanxi,Jilin and Beijing,the three methods(IPCC,continuous function for biomass expansion factor and weighted biomass regression model) were selected to estimate wood biomass in this paper.The estimation of the three methods were compared and analyzed from calculating process,method characters,repeatability and verifiability to stability of growth rate of biomass between two periods.The results showed the total biomass estimated by IPCC method with variable BEF2 was large,the total biomass estimated by IPCC method with constant BEF2 was small and the total biomasses estimated by continuous function for biomass expansion factor and weighted biomass regression model were middle.The biomass expansion factor derived from weighted regression model was most stable in the different provinces. Based on the seventh national forestry inventory data, the biomass expansion factors of various kinds of tree species derived from IPCC and the weighted regression model were more stable than the biomass expansion factors derived from continuous function method.The growth rate of biomass between two periods was the same regular pattern as the biomass expansion factors.

Two-stage sequential sampling for two rare species in western Inner Mongolia of China

To evaluate the efficiencies of different sampling methods for a rare and clustered population, we investigated the sampling effects for the two species Tamarix chinensis (Salt cedar) and Elaeagnus angustifolia (Russian olive) in western Inner Mongolia with two-stage sequential sampling, which is a new sampling method, traditional simple random sampling and two-stage sampling. Based on two-stage sequential sampling and two-stage sampling, each population was partitioned into four primary sampling units, and then two of them were randomly selected. Sampling designs were simulated based on the conditions of five secondary sampling unit areas, two criterion values, five initial secondary sampling units and two sequential secondary sampling units in 1000 repetitions. To evaluate the performance of the sampling designs for each method, the variance and relative error of the density estimates were used. The relative sampling efficiencies of the three sampling methods were compared using the same final sampling sizes. We analyzed the sampling efficiency generated by two-stage sequential sampling and found that it yielded smaller variances than those of simple random sampling and two-stage sampling in all sampling designs, and that two-stage sampling was more efficient than simple random sampling. Density estimates from the two-stage sequential sampling were very close to the true values. We also determined the optimum secondary sampling unit areas for the two species in the two-stage sequential sampling. It was best for Tamarix chinensis and Elaeagnus angustifolia when the secondary sampling unit areas were 200 and 100 m2 , respectively.