Forest Resource Inventory Research Articles

Improving the Accuracy of Estimating Forest Carbon Density Using the Tree Species Classification Method

The accurate and effective estimation of forest carbon density is an essential basis for effectively responding to climate change and achieving the goal of carbon neutrality. Aiming at the problem of the significant differences in the forest carbon model parameters of different tree species, this study used the tree forest in Yueyang City, Hunan Province, China, as the study object and used the random forest classification algorithm through the Google Earth Engine platform to classify the dominant tree species within the forested range of the study area based on the image elements. The overall accuracy in the forest/non-forest classification (primary classification) was 93.79% with a Kappa of 0.9145. The overall accuracy in the dominant species classification (secondary classification) was 87.30% with a Kappa of 0.7747. Based on the classification, a multiple linear regression (MLR), support vector machine (SVM), and random forest (RF) were constructed for different dominant tree species by combining some Forest Resource Inventory data and remote sensing data. The results showed that the RF model had a significantly higher coefficient of determination (R2 = 0.4054–0.7602) than the MLR (R2 = 0.0900–0.4070) and SVM (R2 = 0.1650–0.4450) as well as a substantially lower RMSE and MAE; its spatial distribution of forest carbon density ranged from 3.06 to 62.80 t·hm−2. Compared with the spatial distribution of the forest carbon density (4.64 to 31.96 t·hm−2) without the classification of dominant species, the method eliminated the problems of severe overfitting and significant underestimation of peak values when estimating under unclassified conditions. The method provides a reference for the remote sensing inversion of forest carbon density on a large scale.

Improving Heterogeneous Forest Height Maps by Integrating GEDI-Based Forest Height Information in a Multi-Sensor Mapping Process

Forests are one of the key elements in ecological transition policies in Europe. Sustainable forest management is needed in order to optimise wood harvesting, while preserving carbon storage, biodiversity and other ecological functions. Forest managers and public bodies need improved and cost-effective forest monitoring tools. Research studies have been carried out to assess the use of optical and radar images for producing forest height or biomass maps. The main limitations are the quantity, quality and representativeness of the reference data for model training. The Global Ecosystem Dynamics Investigation (GEDI) mission (full waveform LiDAR on board the International Space Station) has provided an unprecedented number of forest canopy height samples from 2019. These samples could be used to improve reference datasets. This paper aims to present and validate a method for estimating forest dominant height from open access optical and radar satellite images (Sentinel-1, Sentinel-2 and ALOS-2 PALSAR-2), and then to assess the use of GEDI samples to replace field height measurements in model calibration. Our approach combines satellite image features and dominant height measurements, or GEDI metrics, in a Support Vector Machine regression algorithm, with a feature selection process. The method is tested on mixed uneven-aged broadleaved and coniferous forests in France. Using dominant height measurements for model training, the cross-validation shows 7.3 to 11.6% relative Root Mean Square Error (RMSE) depending on the forest class. When using GEDI height metrics instead of field measurements for model training, errors increase to 12.8–16.7% relative RMSE. This level of error remains satisfactory; the use of GEDI could allow the production of dominant height maps on large areas with better sample representativeness. Future work will focus on confirming these results on new study sites, improving the filtering and processing of GEDI data, and producing height maps at regional or national scale. The resulting maps will help forest managers and public bodies to optimise forest resource inventories, as well as allow scientists to integrate these cartographic data into climate models.

Does Classification-Based Forest Management Promote Forest Restoration? Evidence from China’s Ecological Welfare Forestland Certification Program

Classification-based forest management (CFM) is generally regarded as an important political means of achieving sustainable forest development. However, in the upsurge of publicly managed forest devolution, the impact of CFM policies on forestland restoration remains uncertain and needs to be explored. This study contributes to the scant literature on this topic in China, where CFM has long been implemented based on the ecological welfare forestland (EWF) certification program. We use provincial data from China to examine the relationship between EWF-certified areas and forest restoration. Based on inter-provincial panel data from the third to the ninth consecutive forest resource inventories in China (1984–2018), we use a dynamic spatial autoregressive model to analyze the impact of forest classification management on forest restoration. The results show that, contrary to appearances, increasing EWF-certified areas promotes forest restoration. However, after controlling for other possible influencing factors, increasing EWF-certified areas plays a minimal role in promoting forest restoration and regrowth by inhibiting investment in forest management and even has a negative impact on forest restoration in the southern collective forest area.

Analysis of various crown variables on stem form for Cunninghamia lanceolata based on ANN and taper function

Crown, with many dimensions greatly influences the stem taper of a tree. However, few taper models have accounted for its impact on diameter estimation. In order to investigate and quantify the effects of various crown factors on stem taper and develop new taper models incorporating crown information for Cunninghamia lanceolata in Southeast China, a sample data of 1100 taper measurements from 108 trees and two different modeling methods were utilized. A set of traditional non-linear regression (NLR) models with linear and non-linear functions composed of crown factors introduced respectively, were developed for stem diameter prediction, as well as artificial neural network (ANN) models based on different input variables. ANN technology was applied to variable screening prior to developing models, and the evaluation statistics and graphics were used to assess the models. The results showed that crown length (CL) and height to live crown base (HCB) had larger influence on the model accuracy than other crown variables, demonstrating that variables screening based on ANN is feasible and efficient when numerous potential variables are available. The accuracy of taper model was improved when incorporating different crown variables or their combinations in terms of higher R2 and lower RMSE, however, the degree of improvement varied, depending on the variables added and the modelling approach. The inclusion of CR and HCB presented the highest improvement, whether using ANN or NLR modelling methods. The ANN model decreased 13.96 % in RMSE and 17.00 % in MAE. Similarly, the NLR model reduced 2.1 % in RMSE and 1.78 % in MAE. This study indicated that the refined models with crown variables included were more in line with the biological logic of nature and the accuracy has been improved, although the improvement of nonlinear regression models was not as significant as expected. In addition, it also suggested that in forest resource inventory, ANN was a recommended technique for variable screening and an alternative method for model development.

Use of Sentinel-2 Data to Improve Multivariate Tree Species Composition in a Forest Resource Inventory

Aerial-photo interpreted inventories of forest resources, including tree species composition, are valuable in forest resource management, but are expensive to create and can be relatively inaccurate. Because of differences among tree species in their spectral properties and seasonal phenologies, it might be possible to improve such forest resource inventory information (FRI) by using it in concert with multispectral satellite information from multiple time periods. We used Sentinel-2 information from nine spectral bands and 12 dates within a two-year period to model multivariate percent tree species composition in >51,000 forest stands in the FRI of south-central Ontario, Canada. Accuracy of random forest (RF) and convolutional neural network (CNN) predictions were tested using species-specific basal area information from 155 0.25-ha field plots. Additionally, we created models using the Sentinel-2 information in concert with the field data and compared the accuracy of these models and the FRI-based models by use of basal areas from a second (13.7-ha) field data set. Based on average R2 values across species in the two field data sets, the Sentinel-FRI models outperformed the FRI, showing 1.5- and 1.7-fold improvements relative to the FRI for RF and 2.1- and 2.2-fold improvements for CNN (mean R2: 0.141–0.169 (FRI); 0.217–0.295 (RF); 0.307–0.352 (CNN)). Models created with the field data performed even better: improvements relative to the FRI were 2.1-fold for RF and 2.8-fold for CNN (mean R2: 0.169 (FRI); 0.356 (RF); 0.469 (CNN)). As predicted, R2 values between FRI- and field-trained predictions were higher than R2 values with the FRI. Of the 21 tree species evaluated, 8 relatively rare species had poor models in all cases. Our multivariate approach allowed us to use more FRI stands in model creation than if we had been restricted to stands dominated by single species and allowed us to map species abundances at higher resolution. It might be possible to improve models further by use of tree stem maps and incorporation of the effects of canopy disturbances.

Forest classification using synthetic GF-1/WFV time series and phenological parameters

High-accuracy forest type mapping is crucial for forest resource inventory to support forest management, conservation biology, and ecological restoration. The Chinese GF-1 satellite can provide high-spatial resolution and low-cost data for forestry monitoring. However, it is difficult to obtain time-series GF-1 images with high spatial resolution and high temporal frequency because of technical constraints and cloud contamination, which affects the accuracy of forest type extraction. We proposed a method for mapping forest type using Chinese GF-1/widefield view (WFV) multi-spectral data, the fused GF-1/WFV normalized difference vegetation index (NDVI) time series, and the phenological parameters. We used the enhanced spatial and temporal adaptive reflectance fusion model algorithm to fuse Chinese GF-1/WFV and Moderate Resolution Imaging Spectroradiometer NDVI to generate high-spatiotemporal resolution NDVI time-series image data and extract phenological parameters in the northeastern part of Hubei Province, China. We then designed four different combinations of GF-1/WFV spectral features, the fused NDVI time-series features, and the phenological parameters. Finally, a random forest algorithm is used to classify forest types in the study area. The results showed that the proposed method can obtain satisfactory results. The overall accuracy based on the combination of GF-1/WFV spectral features, high-resolution time-series NDVI, and phenological features was 89.68%. Compared with the classification using only GF-1/WFV spectral data, the overall accuracy was improved by 9.60% points. These results demonstrate that the proposed method can support forest type survey and mapping at the regional or global scale.

A Novel Method for Estimating Spatial Distribution of Forest Above-Ground Biomass Based on Multispectral Fusion Data and Ensemble Learning Algorithm

Optical remote sensing technology has been widely used in forest resources inventory. Due to the influence of satellite orbits, sensor parameters, sensor errors, and atmospheric effects, there are great differences in vegetation spectral information captured by different satellite sensor images. Spectral fusion technology can couple the advantages of different multispectral sensor images to produce new multispectral data with high spatial and spectral resolution, it has great potential for improving the spectral sensitivity of forest vegetation and alleviating the spectral saturation. However, how to quickly and effectively select the multi-spectral fusion data suitable for forest above-ground biomass (AGB) estimation is a very critical issue. This study proposes a scheme (RF-S) to comprehensively evaluate multispectral fused images and develop the appropriate model for forest AGB estimation, on the basis of random forest (RF) and the stacking ensemble algorithm. First, four classic fusion methods are used to fuse the preprocessed GaoFen-2 (GF-2) multispectral image with Sentinel-2 image to generate 12 fused Sentinel-like images. Secondly, we apply a comprehensive evaluation method to quickly select the optimal fused image for the follow-up research. Subsequently, two feature combination optimization methods are used to select feature variables from the three feature sets. Finally, the stacking ensemble algorithm based on model dynamic integration and hyperparameter automatic optimization, as well as some classic machine learners, are used to construct the forest AGB estimation model. The results show that the fused image NND_B3 (based on nearest neighbor diffusion pan sharpening method and Band3_Red) selected by the evaluation method proposed in this study has the best performance in AGB estimation. Using the stacking ensemble method and NND_B3 image, we get the highest estimation accuracy, with the adjusted R2 and relative root mean square error (RMSEr) of 0.6306 and 15.53%, respectively. The AGB estimation RMSEr of NND_B3 is 19.95% and 24.90% lower than those of GF-2 and Sentinel-2, respectively. We also found that the multi-window texture factor has better performance in the area with low AGB, and it can suppress the overestimation significantly. The AGB spatial distribution estimated using the NND_B3 image matches the field observations well, indicating that the multispectral fusion image combined with the Stacking algorithm can increase the accuracy and saturation of the AGB estimates.

Correlation between non-spatial structure distribution characteristics and productivity of natural coniferous and broad-leaved mixed forests in Zhejiang

Objective The objective of this study is to explore the distribution characteristics of non-spatial structure and the correlation between non-spatial structure and productivity of coniferous and broad-leaved mixed forests in Zhejiang Province, so as to provide theoretical basis and technical support for improving forest quality. Method Based on the data of natural coniferous and broad-leaved mixed forest sample plots and sample wood from the consecutive forest resource inventory in 1999, 2004, and 2009, the diameter at breast height(DBH), tree height, volume, and non-spatial structure factors and accumulation per unit area of conifer species were extracted. Patterns of non-spatial structure distribution in each period was modeled to explore characteristics of change. The continuous function method of biomass conversion factor was used to estimate biomass and productivity, and then the partial correlation analysis between non-spatial structure and productivity was performed. Result There existed regular distribution characteristics of forest non-spatial structure factors in different periods. The non-spatial structure factors and productivity showed a positive correlation. From 1999 to 2009, the proportion of coniferous species in the coniferous and broad-leaved mixed forest in Zhejiang Province increased gradually, and the productivity in the first period (1.39 t·hm−2·a−1) was higher than that in the second period (1.15 t·hm−2·a−1). The mean value of DBH, tree height and volume per plant were approximately normal. Conclusion In the management of coniferous and broad-leaved mixed forests, priority should be given to the proportion of coniferous and broad-leaved mixed forest in order to maintain the high productivity of the forest stand. The proportion of species should be kept above 50% and the mixed forests with a low proportion of coniferous species should be artificially intervened. If DBH is lower than 10.00 cm, tending should be strengthened, and if DBH is higher than 12.00 cm, cutting should be carried out in time. The average volume and tree height of per plant should be maintained at 0.040−0.070 m3·plant−1 and 7.00−10.00 m respectively. [Ch, 6 fig. 5 tab. 34 ref.]

Potentiality of income generation through Non-timber Forest Products: A case study from the Sallipatan Trishakti Community Forest, Bajhang district, Nepal

Non-timber Forest Products are the major source of livelihood support for rural communities in Nepal. A case study was performed to determine the potentiality of income generation through Non-timber Forest Products (NTFPs) in the Sallipatan Trishakti community forest of Bajhang district, Nepal from February to April 2020. The Questionnaire surveys, key informant surveys, focus group discussions and forest resource inventory have been used for the data collection. A total of 37 species of NTFPs from 31 families were identified out of which only five species were found to have high market potential. Xanthoxylum armatum (Timur), Phyllanthus emblica (Amala), and Cinnamomum tamala (Tejpat) were found in good frequency and have a good market but the yearly earnings and the number of households involved were low. Bergenia ciliate (Pakhanbed), Valeriana jatamansi (Sugandhawal), and Rhododendron arboretum (Lali gurans) were present in high density in the forest and have high market value but locals didn’t prefer and market them due to lack of knowledge and processing technology. Pinus roxburghii, Myrica esculenta, Among the total respondents, 59% collect NTFPs for household purposes, 30% for selling purposes, and 11% for traditional uses. Swertia chirayita, Rhododendron arboretum, Cinnamomum tamala, Drepanostachyum sps., Asparagous racemous and Taxus wallichiana would have the potentiality for small scale business from the community forest if they were promoted and managed properly.

Effects of Canopy and Multi-Epoch Observations on Single-Point Positioning Errors of a GNSS in Coniferous and Broadleaved Forests

Global navigation satellite systems (GNSS) can quickly, efficiently, and accurately provide precise coordinates of points, lines, and surface elements, plus complete surveys and determine various boundary lines in forest investigations and management. The system has become a powerful tool for dynamic forest resource investigations and monitoring. GNSS technology plays a unique and important role in estimating timber volume, calculating timber cutting area, and determining the location of virgin forest roads and individual trees in forests. In this study, we quantitatively analyzed the influence of crown size and observation time on the single-point positioning accuracy of GNSS receivers for different forest types. The GNSS located single points for different forest types and crown sizes, enabling the collection of data. The locating time for each tree was more than 10 min. Statistical methods were used to analyze the positioning accuracy of multi-epoch data, and a model was developed to estimate the maximum positioning errors under different forest conditions in a certain positioning time. The results showed that for a continuous positioning time of approximately 10 min, the maximum positioning accuracies in coniferous and broadleaf forests were obtained, which were 12.13 and 15.11 m, respectively. The size of a single canopy had no obvious influence on the single-point positioning error of the GNSS, and canopy density was proven to be closely related to the positioning accuracy of a GNSS. The determination coefficients (R2) in the regression analysis of the general model, coniferous forest model, and broadleaved forest model that were developed in this study were 0.579, 0.701, and 0.544, respectively. These results indicated that the model could effectively predict the maximum positioning error in a certain period of time under different forest types and crown conditions at middle altitudes, which has important guiding significance for forest resource inventories and precise forest management.

Comparison of Forest Inventory Methods at Plot-Level between a Backpack Personal Laser Scanning (BPLS) and Conventional Equipment in Jeju Island, South Korea

In recent years, light detection and ranging (LiDAR) has been increasingly utilized to estimate forest resources. This study was conducted to identify the applicability of a LiDAR sensor for such estimations by comparing data on a tree’s position, height, and diameter at breast height (DBH) obtained using the sensor with those by existing forest inventory methods for a Cryptomeria japonica forest in Jeju Island, South Korea. For this purpose, a backpack personal laser scanning device (BPLS, Greenvalley International, Model D50) was employed in a protected forest, where cutting is not allowed, as a non-invasive means, simultaneously assessing the device’s field applicability. The data collected by the sensor were divided into seven different pathway variations, or “patterns” to consider the density of the sample plots and enhance the efficiency. The accuracy of estimating the variables of each tree was then assessed. The time spent acquiring and processing real-time data was also analyzed for each method, as well as total time and the time required for each measurement. The findings showed that the rate of detection of standing trees by LiDAR was 100%. Additionally, a high statistical accuracy was observed in pattern 5 (DBH: RMSE 1.22 cm, bias—0.90 cm, Height: RMSE 1.66 m, bias—1.18 m) and pattern 7 (DBH: RMSE 1.22 cm, bias—0.92 cm, Height: RMSE 1.48 m, bias—1.23 m) compared to the results from the typical inventory method. A range of 115–162.5 min/ha was required to process the data using the LiDAR, while 322.5–567.5 min was required for the typical inventory method. Thus, the application of a backpack personal LiDAR can lead to higher efficiency when conducting a forest resource inventory in a coniferous plantation with understory vegetation. Further research in various stands is necessary to confirm the efficiency of using backpack personal laser scanning.

Vegetation Carbon Accumulation Driven by Stand Characteristics and Climatic Factors in Subtropical Forests of Southeastern China

ABSTRACT Subtropical forests act as a carbon sink and play an important role in mitigating global warming. However, the understanding of the effects of stand characteristics and climatic factors on forest carbon sequestration capacity remains limited. Based on multiple regression analyses using structural equation modeling and plot-level data of coniferous and broadleaf forests from the national forest resource inventories, our study quantified vegetation carbon accumulation rate (VCAR) and the underlying influence pattern in Zhejiang Province, Southeastern China. The mean VCAR of coniferous and broadleaf forests was 2.30 Mg C ha−1 yr−1 and 4.53 Mg C ha−1 yr−1 during 2004–2009, and 1.56 Mg C ha−1 yr−1 and 1.98 Mg C ha−1 yr−1 during 2009–2014, respectively. Stand characteristics (i.e., change in tree density and mean diameter at breast height) and climatic factors (i.e., mean annual precipitation (MAP), temperature, sunshine hours, and monthly precipitation fluctuation) were the major driving factors on the change patterns of VCAR. All factors, except precipitation fluctuation, had positive effects on VCAR. The positive effects of MAP decreased with the increase in MAP, but the negative effects of precipitation fluctuation increased with the increase in precipitation fluctuation. Precipitation increased with increasing elevation, while the VCAR was lower at lower elevations. Additionally, we found that broadleaf forests could be able to cope better with the negative effects of precipitation fluctuation. Overall, this study provides new insights into the sensitivity and potential of vegetation carbon accumulation for structural development and climate change response in Zhejiang Province to better promote sustainable forestry development.

Study on forest volume-to-biomass modeling and carbon storage dynamics in China

<p indent="0mm">Estimating forest biomass (in forest volume per unit area) is a significant forest carbon estimation method. However, models of forest volume-to-biomass were established based on the limited tree species at a local scale. Therefore, it is significant to establish regional and national biomass models. According to sampling populations and climate differences in forest resource inventory, China was divided into seven regions. Using the national biomass estimated data obtained in this study, we established forest volume-to-biomass regression models for 25 tree species and 21 forest stands of conifer, broad-leaved, and conifer-broad-leaved mixed forest in China. This study aims to reveal the changing patterns and causes of forest carbon storage and its contribution to carbon sinks across the nation and different regions in the past <sc>70 years.</sc> Based on the established regression models and data of the forest resource inventory and statistics within 11 periods from 1949 to 2018, forest carbon storage and carbon source/sink capacity in China were systematically calculated. Results obtained showed that carbon change in various regions was significantly different. From 1949 to 1976, the carbon in China’s forest resources decreased significantly; the average annual decrease was 0.52%. The total carbon in forest resources was 0.13%, and the reduction lasted for a long time in Northeast and Southwest China. From 1977 to 2018, the standing carbon and total carbon, which was 3.80% and 3.14%, respectively, increased each year, and the growth rate was the largest between 1999 and 2003. In 2018, the total carbon stored increased from 4.050 PgC (1 Pg=<sc>10¹⁵ g)</sc> to 8.362 PgC (an increase of 106.46%), and carbon storage of the forest increased from 3.938 PgC to 7.669 PgC (an increase of 94.74%). The lowest carbon density during 1973–1976 was <sc>35.24 Mg/ha</sc> (1 Mg=10⁶ g), which increased to 42.63 Mg/ha between 2014 and 2018 and continued to increase. In the future, China’s carbon sink potential will be huge.

Examining the influencing factors of forest health, its implications on rural revitalization: A case study of five forest farms in Beijing

Based on first-hand data from the forest resources inventory of five representative forest farms in Beijing, this study quantitatively evaluates forest ecosystem health at sub-compartmental level for the years 2004, 2009 and 2014, using GIS technology, spatial statistical methods and mathematical modelling. The spatio-temporal pattern and evolution features of the health conditions of forest sub-compartments are explored, taking into account different forest types, forest age groups and forest categories. Our results indicate that the health condition of forest sub-compartments improved significantly between 2004 and 2014. The area and number of forest sub-compartments at the “healthy” level showed obvious increases, and most forest sub-compartments that were classed as “unhealthy” and “morbid” improved to a “sub-healthy” level. Most of the forest sub-compartments above the “healthy” level were part of middle-aged forests, near-mature forests, mature forests and over-mature forests, while the sub-compartments below the “unhealthy” level were mainly young forests. The health conditions of the categories shelter forests and special-purpose forests were found to be better than that of economic forests. Further analysis shows that the forest sub-compartments with better health conditions were mainly concentrated in the Ecological Conservation Zone of Beijing, while those in the New Urban Development Zone and the Functional Expansion Zone displayed relatively poor conditions. The spatio-temporal evolution characteristics of forest health in the study area were shown to be mainly affected by geographical location, forest resources, socio-economic development, natural environment and forestry policies. We argue that the research can provide a scientific reference for the sustainable development and scientific management of forest farms in the process of implementing rural revitalization strategy in Beijing.

Conservancy of forest shelter belts in Russia and the Rostov region as one of the factors of transition to a green economy

Today forestry in Russia is going through one of the most difficult periods in its history, which has resulted in decades of extensive wasteful land use. So, change of climate can bring desertification of some territories and various forms of degradation (desertification) cover about 60 percent of the land in the Rostov region, and the rest is potentially dangerous in this regard. The reasons are anthropogenic impact, drought, erosion, deflation. Despite the fact that in recent years Russia has been developing forestry legislation and has taken a course towards the development of sustainable growth and "green" one, the regions are taking measures aimed at protecting lands from degradation and preserving soil fertility, soil-destructive processes and desertification are increasing many times over. An inventory of forest resources in Russia was carried out in 2019 and it showed that the forest cover in Russia tends to decrease, and on the territory of the Rostov region, forest inventory was carried out in 2006, only 40% of all forests were taken into account - about 240 thousand hectares of protective forest plantations, from of them 125.8 thousand hectares are shelter belts. It’s imperative to enact urgent measures for reforestation and landscaping of territories and annually reconstruct forest belts and create new ones to improve environmental conditions in regions with low forest cover, otherwise in 10-15 years their mass natural death may occur.

Analysis of Optimal Pathways for Terrestrial LiDAR Scanning for the Establishment of Digital Inventory of Forest Resources

Analysis of Optimal Pathways for Terrestrial LiDAR Scanning for the Establishment of Digital Inventory of Forest Resources

北京市4种城市功能区森林植被涵养水源功能评价及价值估算

本研究对北京市首都功能核心区、城市功能拓展区、城市发展新区和生态涵养发展区进行林分涵养水源功能评价及价值估算，旨在探究提升城市功能区森林水源涵养功能较优林分配置模式。结果表明：（1）北京市森林植被面积2009年为4.00×10⁵ hm²，2014年为4.59×10⁵ hm²，北京市NDVI平均值2009年为0.331，2014年为0.708，北京市森林植被面积总体呈增加趋势。北京市2014年林地绿化调节水量较2009年降低了近1/3，2014年水源涵养功能价值量较2009年分别减少了22.08%和8.24%。（2）生态涵养发展区水源涵养功能年平均能力最强（2.88×10⁸ m³/a），城市发展新区水源涵养功能年平均能力次之（1.72×10⁸ m³/a），而首都功能核心区水源涵养功能年平均能力相对最弱，仅1.05×10⁶ m³/a。在针叶林、阔叶林和混交林三种林分类型中，2014年混交林水源涵养功能价值量较2009年增加5.13%，而阔叶林和针叶林分别减少34.93%和55.55%。（3）培育纯林，提高森林覆盖率，对于调节水土资源和保护生态环境具有非常重要的生态意义，而从森林水源涵养功能实物量和价值量考虑，认为混交林比纯林具有更强的水源涵养功能和较优的经济价值。在城市特定的条件下，建立生态与景观相协调的人工植物群落使城市土地资源的利用达到生态、社会、经济三大效益的最佳结合，是提高城市绿地质量的关键所在。;In this study, an optimal forest configuration for water conservation function was explored, which this optimal forest configuration could guarantee the forest vegetation can be give full play to water and soil conservation function. The remote sensing image data in 2009 to 2014 of Beijing and the data of Beijing forest resources inventory of 2009 and 2014 were used to carry out the evaluation and value estimation of water conservation function of typical forest (coniferous, broad-leaved and mixed forests) with GIS software in this paper, where in capital function core area, urban function development area, new urban development area and ecological conservation development area in Beijing. The evaluated and estimated results show: (1) the forest vegetation area in Beijing was 4.00×10⁵ hm² in 2009 and 4.59×10⁵ hm² in 2014. The average Normalized Difference Vegetation Index (NDVI) in Beijing was 0.331 in 2009 and 0.708 in 2014. The forest vegetation area in Beijing presented an overall increasing trend. The regulated water volume of forest land greening in Beijing in 2014 was nearly 1/3 lower than that in 2009, and the value of water conservation function in 2014 was 22.08% and 8.24% lower than that in 2009, respectively. (2) The annual average capacity of water conservation in the ecological conservation development area is the strongest (2.88×10⁸ m³/a), followed by the annual average capacity of water conservation in the new urban development area (1.72×10⁸ m³/a), while the annual average capacity of water conservation in the capital core area is the weakest (1.05×10⁶ m³/a). Among the coniferous, broad-leaved and mixed forests, the value of water conservation function of mixed forest is increasing, and its value of this forest type increased by 5.13% in 2014 compared with that in 2009, while the value of water conservation function of broad-leaved forest and coniferous forest decreased by 34.93% and 55.55%, respectively. (3) Cultivating pure forest to increase the forest cover rate has a very important ecological significance for regulating soil and water resources and protecting ecological environment, but considering the amount and value of forest water conservation function, this study concluded that the mixed forest had stronger water conservation function and better economic value than pure forest. Under the specific conditions of the city, the artificial plant community coordinated with ecology and landscape can make the utilization of urban land resources to reach the ecological society. The best combination of the three economic benefits is the key to improve the quality of urban green space.

Development of the Ontario Forest Resources Inventory: a historical review

The Forest Resources Inventory provides the base layer to support decision-making for a variety of stakeholders in Ontario, Canada. The inventory is used to identify areas of high economic potential, endangered species habitat, and recreational opportunities. As technology evolves, the inventory creation process has changed. From fully manual efforts conducted in the era before planes or roads to hand-drawn sketches made aboard early aircraft, the early inventory program was designed to provide a rough context of timber availability. As society changed, more was asked of the inventory, and ecological and biological features were added. With a land area larger than many European countries to inventory, the creation of an accurate forest inventory in Ontario is a technological and logistical challenge. Although the acquisition technology and strategy have changed over time, the goal of providing an improved understanding into the extent and distribution of natural resources in the province of Ontario has not changed. This review aims to provide a comprehensive look at the history of these inventory efforts in Ontario, as well as provide some context for why the inventory contains the attributes it does today.

Comparison of Modeling Algorithms for Forest Canopy Structures Based on UAV-LiDAR: A Case Study in Tropical China

Knowledge of forest structure is vital for sustainable forest management decisions. Terrestrial laser scanning cannot describe the canopy trees in a large area, and it is unclear whether unmanned aerial vehicle-light detection and ranging (UAV-LiDAR) data have the ability to capture the forest canopy structural parameters in tropical forests. In this study, we estimated five forest canopy structures (stand density (N), basic area (G), above-ground biomass (AGB), Lorey’s mean height (HL), and under-crown height (hT)) with four modeling algorithms (linear regression (LR), bagged tree (BT), support vector regression (SVR), and random forest (RF)) based on UAV-LiDAR data and 60 sample plot data from tropical forests in Hainan and determined the optimal algorithms for the five canopy structures by comparing the performance of the four algorithms. First, we defined the canopy tree as a tree with a height ≥70% HL. Then, UAV-LiDAR metrics were calculated, and the LiDAR metrics were screened by recursive feature elimination (RFE). Finally, a prediction model of the five forest canopy structural parameters was established by the four algorithms, and the results were compared. The metrics’ screening results show that the most important LiDAR indexes for estimating HL, AGB, and hT are the leaf area index and some height metrics, while the most important indexes for estimating N and G are the kurtosis of heights and the coefficient of variation of height. The relative root mean squared error (rRMSE) of five structure parameters showed the following: when modeling HL, the rRMSEs (10.60%–12.05%) obtained by the four algorithms showed little difference; when N was modeled, BT, RF, and SVR had lower rRMSEs (26.76%–27.44%); when G was modeled, the rRMSEs of RF and SVR (15.37%–15.87%) were lower; when hT was modeled, BT, RF, and SVR had lower rRMSEs (10.24%–11.07%); when AGB was modeled, RF had the lowest rRMSE (26.75%). Our results will help facilitate choosing LiDAR indexes and modeling algorithms for tropical forest resource inventories.

Ensuring sustainable development of forestry in the Russian Federation

The study is dedicated to the pressing issues of sustainable development of Russia’s forestry. The relevance of the study is substantiated by the importance of understanding sustainability as a new paradigm for the development of society and the uniqueness of forests as an ecosystem resource. The purpose of the study is to justify a set of measures to ensure sustainable development of forestry at the mega- and macrolevels, while taking into account the economic, environmental and social aspects of this process. The present study used international and Russian research data and publications, and regulatory documents. Based on the provisions of the Concept of Sustainable Development of Forestry and other international and national regulatory documents, we present the results of the analysis and the rationale for the proposed measures to ensure the sustainable development of the forest complex of the Russian Federation in the following areas: reforestation and afforestation; forest conservation and protection; monitoring, inventory and assessment of forests; public administration, services and support; forest exploitation; and forest education and science. The study identified problems of sustainable development of the forestry sector of the Russian Federation that require immediate attention. We provide characteristics of some digital solutions in the forestry of the Russian Federation. We have also analyzed the effectiveness of tools for assessing the status and for inventory of forest resources of the Russian Federation, and presented a comprehensive rationale for digitalization of forestry. We have analysed in detail the level of the development of forestry education and science and proposed the ways for further improvement.