Accurate,reliable,and regularly updated information is necessary for targeted management of forest stands.This information is usually obtained from sample-based field inventory data.Due to the time-consuming and costl...Accurate,reliable,and regularly updated information is necessary for targeted management of forest stands.This information is usually obtained from sample-based field inventory data.Due to the time-consuming and costly procedure of forest inventory,it is imperative to generate and use the resulting data optimally.Integrating field inventory information with remote sensing data increases the value of field approaches,such as national forest inventories.This study investigated the optimal integration of forest inventory data with aerial image-based canopy height models(CHM)for forest growing stock estimation.For this purpose,fixed-area and angle-count plots from a forest area in Austria were used to assess which type of inventory system is more suitable when the field data is integrated with aerial image analysis.Although a higher correlation was observed between remotely predicted growing stocks and field inventory values for fixed-area plots,the paired t-test results revealed no statistical difference between the two methods.The R2 increased by 0.08 points and the RMSE decreased by 7.7 percentage points(24.8m^(3)·ha^(−1))using fixed-area plots.Since tree height is the most critical variable essential for modeling forest growing stock using aerial images,we also compared the tree heights obtained from CHM to those from the typical field inventory approach.The result shows a high correlation(R^(2)=0.781)between the tree heights extracted from the CHM and those measured in the field.However,the correlation decreased by 0.113 points and the RMSE increased by 4.2 percentage points(1.04m)when the allometrically derived tree heights were analyzed.Moreover,the results of the paired t-test revealed that there is no significant statistical difference between the tree heights extracted from CHM and those measured in the field,but there is a significant statistical difference when the CHM-derived and the allometrically-derived heights were compared.This proved that image-based CHM can obtain more accurate tree height information than field inventory estimations.Overall,the results of this study demonstrated that image-based CHM can be integrated into the forest inventory data at large scales and provide reliable information on forest growing stock.The produced maps reflect the variability of growth conditions and developmental stages of different forest stands.This information is required to characterize the status and changes,e.g.,in forest structure diversity,parameters for volume,and can be used for forest aboveground biomass estimation,which plays an important role in managing and controlling forest resources in mid-term forest management.This is of particular interest to forest managers and forest ecologists.展开更多
Remote sensing(RS)facilitates forest inventory across a wide range of variables required by the UNFCCC as well as by other agreements and processes.The Conventional model-based(CMB)estimator supports wall-to-wall RS d...Remote sensing(RS)facilitates forest inventory across a wide range of variables required by the UNFCCC as well as by other agreements and processes.The Conventional model-based(CMB)estimator supports wall-to-wall RS data,while Hybrid estimators support surveys where RS data are available as a sample.However,the connection between these two types of monitoring procedures has been unclear,hindering the reconciliation of wall-to-wall and non-wall-to-wall use of RS data in practical applications and thus potentially impeding cost-efficient deployment of high-end sensing instruments for large area monitoring.Consequently,our objectives are to(1)shed further light on the connections between different types of Hybrid estimators,and between CMB and Hybrid estimators,through mathematical analyses and Monte Carlo simulations;and(2)compare the effects and explore the tradeoffs related to the RS sampling design,coverage rate,and cluster size on estimation precision.Primary findings are threefold:(1)the CMB estimator represents a special case of Hybrid estimators,signifying that wallto-wall RS data is a particular instance of sample-based RS data;(2)the precision of estimators in forest inventory can be greater for stratified non-wall-to-wall RS data compared to wall-to-wall RS data;(3)otherwise costprohibitive sensing,such as LiDAR and UAV,can support large scale monitoring through collecting RS data as a sample.These conclusions may reconcile different perspectives regarding choice of RS instruments,data acquisition,and cost for continuous observations,particularly in the context of surveys aiming at providing data for mitigating climate change.展开更多
Background： National forest resource assessments Inventories （NFI＇s）, constitute an important nationa and monitoring, commonly known as National Forest information infrastructure in many countries. Methods： This ...Background： National forest resource assessments Inventories （NFI＇s）, constitute an important nationa and monitoring, commonly known as National Forest information infrastructure in many countries. Methods： This study presents details about developments of the NFI in China, including sampling and plot design and the uses of alternative data sources, and specifically · reviews the evolution of the national forest inventory in China through the 20th and 21st centuries, with some reference to Europe and the US; · highlights the emergence of some common international themes： consistency of measurement; sampling designs; implementation of improved technology; expansion of the variables monitored more efficient scientific transparency;· presents an example of how China＇s expanding NFI exemplifies these global trends. Results： Main results and important changes in China＇s NFI are documented, both to support continued trend analysis and to provide data users with historical perspective. Conclusions： New technologies and data needs ensure that the Chinese NFI, like the national inventories in other countries, will continue to evolve. Within the context of historical change and current conditions, likely directions for this evolution are suggested.展开更多
Background:The age of forest stands is critical information for forest management and conservation,for example for growth modelling,timing of management activities and harvesting,or decisions about protection areas.Ho...Background:The age of forest stands is critical information for forest management and conservation,for example for growth modelling,timing of management activities and harvesting,or decisions about protection areas.However,area-wide information about forest stand age often does not exist.In this study,we developed regression models for large-scale area-wide prediction of age in Norwegian forests.For model development we used more than 4800 plots of the Norwegian National Forest Inventory(NFI)distributed over Norway between latitudes 58°and 65°N in an 18.2 Mha study area.Predictor variables were based on airborne laser scanning(ALS),Sentinel-2,and existing public map data.We performed model validation on an independent data set consisting of 63 spruce stands with known age.Results:The best modelling strategy was to fit independent linear regression models to each observed site index(SI)level and using a SI prediction map in the application of the models.The most important predictor variable was an upper percentile of the ALS heights,and root mean squared errors(RMSEs)ranged between 3 and 31 years(6%to 26%)for SI-specific models,and 21 years(25%)on average.Mean deviance(MD)ranged between^(−1) and 3 years.The models improved with increasing SI and the RMSEs were largest for low SI stands older than 100 years.Using a mapped SI,which is required for practical applications,RMSE and MD on plot level ranged from 19 to 56 years(29%to 53%),and 5 to 37 years(5%to 31%),respectively.For the validation stands,the RMSE and MD were 12(22%)and 2 years(3%),respectively.Conclusions:Tree height estimated from airborne laser scanning and predicted site index were the most important variables in the models describing age.Overall,we obtained good results,especially for stands with high SI.The models could be considered for practical applications,although we see considerable potential for improvements if better SI maps were available.展开更多
Estimating the volume growth of forest ecosystems accurately is important for understanding carbon sequestration and achieving carbon neutrality goals.However,the key environmental factors affecting volume growth diff...Estimating the volume growth of forest ecosystems accurately is important for understanding carbon sequestration and achieving carbon neutrality goals.However,the key environmental factors affecting volume growth differ across various scales and plant functional types.This study was,therefore,conducted to estimate the volume growth of Larix and Quercus forests based on national-scale forestry inventory data in China and its influencing factors using random forest algorithms.The results showed that the model performances of volume growth in natural forests(R^(2)=0.65 for Larix and 0.66 for Quercus,respectively)were better than those in planted forests(R^(2)=0.44 for Larix and 0.40 for Quercus,respectively).In both natural and planted forests,the stand age showed a strong relative importance for volume growth(8.6%–66.2%),while the edaphic and climatic variables had a limited relative importance(<6.0%).The relationship between stand age and volume growth was unimodal in natural forests and linear increase in planted Quercus forests.And the specific locations(i.e.,altitude and aspect)of sampling plots exhibited high relative importance for volume growth in planted forests(4.1%–18.2%).Altitude positively affected volume growth in planted Larix forests but controlled volume growth negatively in planted Quercus forests.Similarly,the effects of other environmental factors on volume growth also differed in both stand origins(planted versus natural)and plant functional types(Larix versus Quercus).These results highlighted that the stand age was the most important predictor for volume growth and there were diverse effects of environmental factors on volume growth among stand origins and plant functional types.Our findings will provide a good framework for site-specific recommendations regarding the management practices necessary to maintain the volume growth in China's forest ecosystems.展开更多
As climate change negotiations progress,monitoring biomass and carbon stocks is becoming an important part of the current forest research.Therefore,national governments are interested in developing forest-monitoring s...As climate change negotiations progress,monitoring biomass and carbon stocks is becoming an important part of the current forest research.Therefore,national governments are interested in developing forest-monitoring strategies using geospatial technology.Among statistical methods for mapping biomass,there is a nonparametric approach called k-nearest neighbor(kNN).We compared four variations of distance metrics of the kNN for the spatially-explicit estimation of aboveground biomass in a portion of the Mexican north border of the intertropical zone.Satellite derived,climatic,and topographic predictor variables were combined with the Mexican National Forest Inventory(NFI)data to accomplish the purpose.Performance of distance metrics applied into the kNN algorithm was evaluated using a cross validation leave-one-out technique.The results indicate that the Most Similar Neighbor(MSN)approach maximizes the correlation between predictor and response variables(r=0.9).Our results are in agreement with those reported in the literature.These findings confirm the predictive potential of the MSN approach for mapping forest variables at pixel level under the policy of Reducing Emission from Deforestation and Forest Degradation(REDD+).展开更多
Background： We explore the factors affecting the optimal plot design （size and type as well as the subsample tree selection strategies within a plot） and their relative importance in defining the optimal plot desig...Background： We explore the factors affecting the optimal plot design （size and type as well as the subsample tree selection strategies within a plot） and their relative importance in defining the optimal plot design in amultipurpose forest inventory. The factors include time used to lay out the plot and to make the tree measurements within the plot, the between-plot variation of each of the variables of interest in the area, and the measurement and model errors for the different variables. Methods： We simulate different plot types and sizes and subsample tree selection strategies on measuredtest areas from North Lapland. The plot types used are fixed-radius, concentric and relascope plots. Weselect the optimal type and size first at plot level using a cost-plus-loss approach and then at cluster level byminimizing the weighted standard error with fixed budget. Results： As relascope plots are ve~/efficient at the plot level for volume and basal area, and fixed-radius plots for stems per ha, the optimal plot type strongly depends on the relative importance of these variables. The concentric plot seems to be a good compromise between these two in many cases. The subsample tree selection strategy was more important in selecting optimal plot than many other factors. In cluster level, the most important factor is the transfer time between plots. Conclusions： While the optimal radius of plots and other parameters were sensitive to the measurement times and other cost factors, the concentric plot type was optimal in almost all studied cases. Subsample tree measurement strategies need further studies, as they were an important cost factor. However, their importance to the precision was not as clear.展开更多
Background: Under ongoing climate and land-use change, biodiversity is continuously decreasing and monitoring biodiversity is becoming increasingly important. National Forest Inventory(NFI) programmes provide valuable...Background: Under ongoing climate and land-use change, biodiversity is continuously decreasing and monitoring biodiversity is becoming increasingly important. National Forest Inventory(NFI) programmes provide valuable timeseries data on biodiversity and thus contribute to assessments of the state and trends in biodiversity, as well as ecosystem functioning. Data quality in this context is of paramount relevance, particularly for ensuring a meaningful interpretation of changes. The Swiss NFI revisits about 8%–10% of its sample plots regularly in repeat surveys to supervise the quality of fieldwork.Methods: We analysed the relevance of observer bias with equivalence tests, examined data quality objectives defined by the Swiss NFI instructors, and calculated the pseudo-turnover(PT) of species composition, that is, the percentage of species not observed by both teams. Three attributes of woody species richness from the latest Swiss NFI cycles(3 and 4) were analysed: occurrence of small tree and shrub species(1) on the sample plot and(2) at the forest edge, and(3) main shrub and trees species in the upper storey.Results: We found equivalent results between regular and repeat surveys for all attributes. Data quality, however,was significantly below expectations in all cases, that is, as much as 20%–30% below the expected data quality limit of 70%–80%(proportion of observations that should not deviate from a predefined threshold). PT values were about 10%–20%, and the PT of two out of three attributes decreased significantly in NFI4. This type of uncertainty –typically caused by a mixture of overlooking and misidentifying species – should be considered carefully when interpreting change figures on species richness estimates from NFI data.Conclusions: Our results provide important information on the data quality achieved in Swiss NFIs in terms of the reproducibility of the collected data. The three applied approaches proved to be effective for evaluating the quality of plot-level species richness and composition data in forest inventories and other biodiversity monitoring programmes. As such, they could also be recommended for assessing the quality of biodiversity indices derived from monitoring data.展开更多
Background: Forest inventories have always been a primary information source concerning the forest ecosystem state. Various applied survey approaches arise from the numerous important factors during sampling scheme pl...Background: Forest inventories have always been a primary information source concerning the forest ecosystem state. Various applied survey approaches arise from the numerous important factors during sampling scheme planning. Paramount aspects include the survey goal and scale, target population inherent variation and patterns,and available resources. The last factor commonly inhibits the goal, and compromises have to be made. Airborne laser scanning(ALS) has been intensively tested as a cost-effective option for forest inventories. Despite existing foundations, research has provided disparate results. Environmental conditions are one of the factors greatly influencing inventory performance. Therefore, a need for site-related sampling optimization is well founded.Moreover, as stands are the basic operational unit of managed forest holdings, few related studies have presented stand-level results. As such, herein, we tested the sampling intensity influence on the performance of the ALSenhanced stand-level inventory.Results: Distributions of possible errors were plotted by comparing ALS model estimates, with reference values derived from field surveys of 3300 sample plots and more than 300 control stands located in 5 forest districts. No improvement in results was observed due to the scanning density. The variance in obtained errors stabilized in the interval of 200–300 sample plots, maintaining the bias within +/-5% and the precision above 80%. The sample plot area affected scores mostly when transitioning from 100 to 200 m2. Only a slight gain was observed when bigger plots were used.Conclusions: ALS-enhanced inventories effectively address the demand for comprehensive and detailed information on the structure of single stands over vast areas. Knowledge of the relation between the sampling intensity and accuracy of ALS estimates allows the determination of certain sampling intensity thresholds. This should be useful when matching the required sample size and accuracy with available resources. Site optimization may be necessary, as certain errors may occur due to the sampling scheme, estimator type or forest site, making these factors worth further consideration.展开更多
Conservation and enhancement of old-growth forests are key in forest planning and policies.In order to do so,more knowledge is needed on how the attributes traditionally associated with old-growth forests are distribu...Conservation and enhancement of old-growth forests are key in forest planning and policies.In order to do so,more knowledge is needed on how the attributes traditionally associated with old-growth forests are distributed in space,what differences exist across distinct forest types and what natural or anthropic conditions are affecting the distribution of these old-growthness attributes.Using data from the Third Spanish National Forest Inventory(1997–2007),we calculated six indicators commonly associated with forest old-growthness for the plots in the territory of Peninsular Spain and Balearic Islands,and then combined them into an aggregated index.We then assessed their spatial distribution and the differences across five forest functional types,as well as the effects of ten climate,topographic,landscape,and anthropic variables in their distribution.Relevant geographical patterns were apparent,with climate factors,namely temperature and precipitation,playing a crucial role in the distribution of these attributes.The distribution of the indicators also varied across different forest types,while the effects of recent anthropic impacts were weaker but still relevant.Aridity seemed to be one of the main impediments for the development of old-growthness attributes,coupled with a negative impact of recent human pressure.However,these effects seemed to be mediated by other factors,specially the legacies imposed by the complex history of forest management practices,land use changes and natural disturbances that have shaped the forests of Spain.The results of this exploratory analysis highlight on one hand the importance of climate in the dynamic of forests towards old-growthness,which is relevant in a context of Climate Change,and on the other hand,the need for more insights on the history of our forests in order to understand their present and future.展开更多
The United Nations Framework Convention on Climate Change （UNFCCC） requires reporting net carbon stock changes and anthropogenic greenhouse gas emissions, including those related to forests. This paper describes the...The United Nations Framework Convention on Climate Change （UNFCCC） requires reporting net carbon stock changes and anthropogenic greenhouse gas emissions, including those related to forests. This paper describes the status of carbon stocks in sub tropical forests of Pakistan. There are two major sub types in subtropical forests of Pakistan viz a viz Subtropical Chir Pine and Subtropical broadleaved forests. A network of sample plots was laid out in four selected site. Two sites were selected from sub tropical Chir Pine （Pinus roxburghii） forests and two from Subtropical broadleaved forests. Measurement and data acquisition protocols were developed specifically for the inventory car- ried out from 2005 to 2010. In total 261 plots （each of lha.） were established. Estimation of diameter, basal area, height, volume and biomass was carried out to estimate carbon stocks in each of the four carbon pools of above- and below-ground live biomass. Soil carbon stocks were also determined by doing soil sampling. In mature （-100 years old） pine forest stand at Ghoragali and Lehterar sites, a mean basal area of 30.38 and 26.11 m2.ha-1 represented mean volume of 243 and 197 m3·ha-1, respectively. The average biomass （t.ha-1） was 237 in Ghoragali site and 186 tha-1 in Lehterar site, which is equal to 128 and 100 t·ha-1 including soil C. However, on average basis both the forests have 114.5± 2.26 t.ha-1 of carbon stock which comprises of 92% in tree biomass and only 8% in the top soils. In mixed broadleaved evergreen forests a mean basal area （m2.ha-1）was 3.06 at Kherimurat with stem volume of 12.86 and 2.65 at Sohawa with stem volume of 11.40 m3.ha-1. The average upper and under storey biomass （t·ha-1） was 50.93 in Kherimurat site and 40.43 t.ha-1 in Sohawa site, which is equal to 31.18 and 24.36 t ·ha-1 including soil C stocks. This study provides a protocol monitoring biomass and carbon stocks and valuable baseline data for in Pakistan＇s managed and unmanaged sub-tropical forests.展开更多
Background: The importance of structurally diverse forests for the conservation of biodiversity and provision of a wide range of ecosystem services has been widely recognised. However, tools to quantify structural div...Background: The importance of structurally diverse forests for the conservation of biodiversity and provision of a wide range of ecosystem services has been widely recognised. However, tools to quantify structural diversity of forests in an objective and quantitative way across many forest types and sites are still needed, for example to support biodiversity monitoring. The existing approaches to quantify forest structural diversity are based on small geographical regions or single forest types, typically using only small data sets.Results: Here we developed an index of structural diversity based on National Forest Inventory(NFI) data of BadenWurttemberg, Germany, a state with 1.3 million ha of diverse forest types in different ownerships. Based on a literature review, 11 aspects of structural diversity were identified a priori as crucially important to describe structural diversity. An initial comprehensive list of 52 variables derived from National Forest Inventory(NFI) data related to structural diversity was reduced by applying five selection criteria to arrive at one variable for each aspect of structural diversity. These variables comprise 1) quadratic mean diameter at breast height(DBH), 2) standard deviation of DBH, 3) standard deviation of stand height, 4) number of decay classes, 5) bark-diversity index, 6) trees with DBH ≥ 40 cm, 7) diversity of flowering and fructification, 8) average mean diameter of downed deadwood, 9) mean DBH of standing deadwood, 10) tree species richness and 11) tree species richness in the regeneration layer. These variables were combined into a simple,additive index to quantify the level of structural diversity, which assumes values between 0 and 1. We applied this index in an exemplary way to broad forest categories and ownerships to assess its feasibility to analyse structural diversity in large-scale forest inventories.Conclusions: The forest structure index presented here can be derived in a similar way from standard inventory variables for most other large-scale forest inventories to provide important information about biodiversity relevant forest conditions and thus provide an evidence-base for forest management and planning as well as reporting.展开更多
Aerial photographs arc important information sources for forest inventory. With the development of science and technology, an automated approach to aerial photograph interpretation has been sought. This paper introduc...Aerial photographs arc important information sources for forest inventory. With the development of science and technology, an automated approach to aerial photograph interpretation has been sought. This paper introduces an experimental computer system for the auto-interpretation of aerial photographs. On thebasis of the experimental system, several algorithms are developed for the interpretation of density, crown closure, working groups and species composition of forest stands. Experiments show that auto—interpretation works well for some forest inventory data.展开更多
The combined use of LiDAR(Light Detection And Ranging)scanning and field inventories can provide spatially continuous wall-to-wall information on forest characteristics.This information can be used in many ways in for...The combined use of LiDAR(Light Detection And Ranging)scanning and field inventories can provide spatially continuous wall-to-wall information on forest characteristics.This information can be used in many ways in forest mapping,scenario analyses,and forest manage-ment planning.This study aimed to find the optimal way to obtain continuous forest data for Catalonia when using kNN imputation(kNN stands for“k nearest neighbors”).In this method,data are imputed to a certain location from k field-measured sample plots,which are the most similar to the location in terms of LiDAR metrics and topographic variables.Weighted multidimensional Euclidean distance was used as the similarity measure.The study tested two different methods to optimize the distance measure.The first method optimized,in the first step,the set of LiDAR and topographic variables used in the measure,as well as the transformations of these variables.The weights of the selected variables were optimized in the second step.The other method optimized the variable set as well as their transformations and weights in one single step.The two-step method that first finds the variables and their transfor-mations and subsequently optimizes their weights resulted in the best imputation results.In the study area,the use of three to five nearest neighbors was recommended.Altitude and latitude turned out to be the most important variables when assessing the similarity of two locations of Catalan forests in the context of kNN data imputation.The optimal distance measure always included both LiDAR metrics and topographic variables.The study showed that the optimal similarity measure may be different for different regions.Therefore,it was suggested that kNN data imputation should always be started with the optimization of the measure that is used to select the k nearest neighbors.展开更多
The use of mobile laser scanning to survey forest ecosystems is a promising,scalable technology to describe forest 3D structures at high resolution.To confirm the con-sistency in the retrieval of forest structural par...The use of mobile laser scanning to survey forest ecosystems is a promising,scalable technology to describe forest 3D structures at high resolution.To confirm the con-sistency in the retrieval of forest structural parameters using hand-held laser scanning(HLS),before operationalizing the method,confirming the data is crucial.We analyzed the per-formance of tree-level mapping based on HLS under differ-ent phenology conditions on a mixed forest in western Spain comprising Pinus pinaster and two deciduous species,Alnus glutinosa and Quercus pyrenaica.The area was surveyed twice during the growing season(July 2022)and once in the deciduous season(February 2022)using several scan-ning paths.Ground reference data(418 trees,15 snags)was used to calibrate the HLS data and to assess the influence of phenology when converting 3D data into tree-level attrib-utes(DBH,height and volume).The HLS-based workflow was robust at isolating tree positions and recognizing stems despite changes in phenology.Ninety-six percent of all pairs matched below 65 cm.For DBH,phenology barely altered estimates.We observed a strong agreement when comparing HLS-based tree height distributions.The values exceeded 2 m when comparing height measurements,confirming height data should be carefully used as reference in remote sensing-based inventories,especially for deciduous species.Tree volume was more precise for pines(r=0.95,and rela-tive RMSE=21.3–23.8%)compared to deciduous species(r=0.91–0.96,and relative RMSE=27.3–30.5%).HLS data and the forest structural complexity tool performed remark-ably,especially in tree positioning considering mixed forests and mixed phenology conditions.展开更多
A special mobile GIS（Geographic Information System） system used for forest resources second-class inventory was developed on the basis of traditional forest resources inventory,remote sensing,GPS（Globe Positioning ...A special mobile GIS（Geographic Information System） system used for forest resources second-class inventory was developed on the basis of traditional forest resources inventory,remote sensing,GPS（Globe Positioning System） and embedded technology.Portable instrument,embedded development and the integration technology of RS（Remote Sensing）,GIS and GPS are all used in this special mobile GIS system.Further,the system composition,key techniques,and current situation of the practical application in China were analyzed in the study.The results are important for applying modern high-tech for the planning and design of digital forest resources to improve the precision and efficiency of inventory and reduce the labor cost and financial investment.展开更多
Since the launch of the Google Earth Engine(GEE)cloud platform in 2010,it has been widely used,leading to a wealth of valuable information.However,the potential of GEE for forest resource management has not been fully...Since the launch of the Google Earth Engine(GEE)cloud platform in 2010,it has been widely used,leading to a wealth of valuable information.However,the potential of GEE for forest resource management has not been fully exploited.To extract dominant woody plant species,GEE combined Sen-tinel-1(S1)and Sentinel-2(S2)data with the addition of the National Forest Resources Inventory(NFRI)and topographic data,resulting in a 10 m resolution multimodal geospatial dataset for subtropical forests in southeast China.Spectral and texture features,red-edge bands,and vegetation indices of S1 and S2 data were computed.A hierarchical model obtained information on forest distribution and area and the dominant woody plant species.The results suggest that combining data sources from the S1 winter and S2 yearly ranges enhances accuracy in forest distribution and area extraction compared to using either data source independently.Similarly,for dominant woody species recognition,using S1 winter and S2 data across all four seasons was accurate.Including terrain factors and removing spatial correlation from NFRI sample points further improved the recognition accuracy.The optimal forest extraction achieved an overall accuracy(OA)of 97.4%and a maplevel image classification efficacy(MICE)of 96.7%.OA and MICE were 83.6%and 80.7%for dominant species extraction,respectively.The high accuracy and efficacy values indicate that the hierarchical recognition model based on multimodal remote sensing data performed extremely well for extracting information about dominant woody plant species.Visualizing the results using the GEE application allows for an intuitive display of forest and species distribution,offering significant convenience for forest resource monitoring.展开更多
Accurately estimating forest net primary productivity (NPP) plays an important role in study of global carbon budget. A NPP model reflecting the synthetic effects of both biotic (forest stand age, A and stem volume, V...Accurately estimating forest net primary productivity (NPP) plays an important role in study of global carbon budget. A NPP model reflecting the synthetic effects of both biotic (forest stand age, A and stem volume, V) and climatic factors (mean annual actual evapotranspiration, E) was developed for Chinese pine (Pinus tabulaeformis) forest by making full use of Forest Inventory Data (FID) and dynamically assessing forest productivity. The NPP of Chinese pine forest was estimated by using this model and the fourth FID (1989–1993), and the spatial pattern of NPP of Chinese pine forest was given by Geography Information System (GIS) software. The results indicated that mean NPP value, of Chinese pine forest was 7.82 t m?2·a?1 and varied at the range of 3.32–11.87 t hm?2·a?1. NPP distribution of Chinese pine forests was significantly different in different regions, higher in the south and lower in the north of China. In terms of the main distribution regions of Chinese pine, the NPPs of Chinese pine forest in Shanxi and Shaanxi provinces were in middle level, with an average NPP of 7.4 t hm?2·a?1, that in the southern and the eastern parts (e.g. Shichuang Hunan, Henan, and Liaoning provinces) was higher (over 7.7 t hm?2·a?1), and that in the northern part and western part (e.g. Neimenggu and Ningxia provinces) was lower (below 5 t hm?2·a?1). This study provides an efficient way for using FID to understand the dynamics of foest NPP and evaluate its effects on global climate change. Keywords Forest NPP - Forest inventory data - Chinese pine forest - Climatic and biotic NPP model - Spatial distribution pattern CLC number S727.22 - S757.2 Document code A Foundation item: This study was supported by the National Natural Science Foundation of China (Nos. 30028001, 49905005), National Key Basic Research Specific Foundation (G1999043407); the Chinese Academy of Sciences (KSC2-1-07).Biography: ZHAO Min (1973-), female, Ph. D. in Laboratory of Quantitative Vegetation Ecology, Institute of Botany, the Chinese Academy of Sciences, Beijing 100093, P. R. China.Responsible editor: Zhu Hong展开更多
Forests are vital ecosystems that play a crucial role in sustaining life on Earth and supporting human well-being.Traditional forest mapping and monitoring methods are often costly and limited in scope,necessitating t...Forests are vital ecosystems that play a crucial role in sustaining life on Earth and supporting human well-being.Traditional forest mapping and monitoring methods are often costly and limited in scope,necessitating the adoption of advanced,automated approaches for improved forest conservation and management.This study explores the application of deep learning-based object detection techniques for individual tree detection in RGB satellite imagery.A dataset of 3157 images was collected and divided into training(2528),validation(495),and testing(134)sets.To enhance model robustness and generalization,data augmentation was applied to the training part of the dataset.Various YOLO-based models,including YOLOv8,YOLOv9,YOLOv10,YOLOv11,and YOLOv12,were evaluated using different hyperparameters and optimization techniques,such as stochastic gradient descent(SGD)and auto-optimization.These models were assessed in terms of detection accuracy and the number of detected trees.The highest-performing model,YOLOv12m,achieved a mean average precision(mAP@50)of 0.908,mAP@50:95 of 0.581,recall of 0.851,precision of 0.852,and an F1-score of 0.847.The results demonstrate that YOLO-based object detection offers a highly efficient,scalable,and accurate solution for individual tree detection in satellite imagery,facilitating improved forest inventory,monitoring,and ecosystem management.This study underscores the potential of AI-driven tree detection to enhance environmental sustainability and support data-driven decision-making in forestry.展开更多
Critical Height Sampling(CHS)estimates stand volume free from any model and tree form assumptions.Despite its introduction more than four decades ago,CHS has not been widely applied in the field due to perceived chall...Critical Height Sampling(CHS)estimates stand volume free from any model and tree form assumptions.Despite its introduction more than four decades ago,CHS has not been widely applied in the field due to perceived challenges in measurement.The objectives of this study were to compare estimated stand volume between CHS and sampling methods that used volume or taper models,the equivalence of the sampling methods,and their relative efficiency.We established 65 field plots in planted forests of two coniferous tree species.We estimated stand volume for a range of Basal Area Factors(BAFs).Results showed that CHS produced the most similar mean stand volume across BAFs and tree species with maximum differences between BAFs of 5-18m^(3)·ha^(−1).Horizontal Point Sampling(HPS)using volume models produced very large variability in mean stand volume across BAFs with the differences up to 126m^(3)·ha^(−1).However,CHS was less precise and less efficient than HPS.Furthermore,none of the sampling methods were statistically interchangeable with CHS at an allowable tolerance of≤55m^(3)·ha^(−1).About 72%of critical height measurements were below crown base indicating that critical height was more accessible to measurement than expected.Our study suggests that the consistency in the mean estimates of CHS is a major advantage when planning a forest inventory.When checking against CHS,results hint that HPS estimates might contain potential model bias.These strengths of CHS could outweigh its lower precision.Our study also implies serious implications in financial terms when choosing a sampling method.Lastly,CHS could potentially benefit forest management as an alternate option of estimating stand volume when volume or taper models are lacking or are not reliable.展开更多
基金supported by grants provided within the research project»EO4Forest:Use of multi-temporal Sentinel-2 and VHR Pleiades stereo data for sustainable forest monitoring and management«funded by the Austrian Federal Ministry for Climate Action,Environ-ment,Energy,Mobility,Innovation and Technology(BMK)within the FFG Austrian Space Applications Program ASAP 12(grant agreement number 854027).
文摘Accurate,reliable,and regularly updated information is necessary for targeted management of forest stands.This information is usually obtained from sample-based field inventory data.Due to the time-consuming and costly procedure of forest inventory,it is imperative to generate and use the resulting data optimally.Integrating field inventory information with remote sensing data increases the value of field approaches,such as national forest inventories.This study investigated the optimal integration of forest inventory data with aerial image-based canopy height models(CHM)for forest growing stock estimation.For this purpose,fixed-area and angle-count plots from a forest area in Austria were used to assess which type of inventory system is more suitable when the field data is integrated with aerial image analysis.Although a higher correlation was observed between remotely predicted growing stocks and field inventory values for fixed-area plots,the paired t-test results revealed no statistical difference between the two methods.The R2 increased by 0.08 points and the RMSE decreased by 7.7 percentage points(24.8m^(3)·ha^(−1))using fixed-area plots.Since tree height is the most critical variable essential for modeling forest growing stock using aerial images,we also compared the tree heights obtained from CHM to those from the typical field inventory approach.The result shows a high correlation(R^(2)=0.781)between the tree heights extracted from the CHM and those measured in the field.However,the correlation decreased by 0.113 points and the RMSE increased by 4.2 percentage points(1.04m)when the allometrically derived tree heights were analyzed.Moreover,the results of the paired t-test revealed that there is no significant statistical difference between the tree heights extracted from CHM and those measured in the field,but there is a significant statistical difference when the CHM-derived and the allometrically-derived heights were compared.This proved that image-based CHM can obtain more accurate tree height information than field inventory estimations.Overall,the results of this study demonstrated that image-based CHM can be integrated into the forest inventory data at large scales and provide reliable information on forest growing stock.The produced maps reflect the variability of growth conditions and developmental stages of different forest stands.This information is required to characterize the status and changes,e.g.,in forest structure diversity,parameters for volume,and can be used for forest aboveground biomass estimation,which plays an important role in managing and controlling forest resources in mid-term forest management.This is of particular interest to forest managers and forest ecologists.
基金supported by the National Social Science Fund of China(No.22BTJ005)the Key Project of National Key Research and Development Plan(No.2023YFF1304002-05)+1 种基金supported by the National Natural Science Foundation of China(No.32001252)the International Center for Bamboo and Rattan(Nos.1632022024,1632020029,1632021024).
文摘Remote sensing(RS)facilitates forest inventory across a wide range of variables required by the UNFCCC as well as by other agreements and processes.The Conventional model-based(CMB)estimator supports wall-to-wall RS data,while Hybrid estimators support surveys where RS data are available as a sample.However,the connection between these two types of monitoring procedures has been unclear,hindering the reconciliation of wall-to-wall and non-wall-to-wall use of RS data in practical applications and thus potentially impeding cost-efficient deployment of high-end sensing instruments for large area monitoring.Consequently,our objectives are to(1)shed further light on the connections between different types of Hybrid estimators,and between CMB and Hybrid estimators,through mathematical analyses and Monte Carlo simulations;and(2)compare the effects and explore the tradeoffs related to the RS sampling design,coverage rate,and cluster size on estimation precision.Primary findings are threefold:(1)the CMB estimator represents a special case of Hybrid estimators,signifying that wallto-wall RS data is a particular instance of sample-based RS data;(2)the precision of estimators in forest inventory can be greater for stratified non-wall-to-wall RS data compared to wall-to-wall RS data;(3)otherwise costprohibitive sensing,such as LiDAR and UAV,can support large scale monitoring through collecting RS data as a sample.These conclusions may reconcile different perspectives regarding choice of RS instruments,data acquisition,and cost for continuous observations,particularly in the context of surveys aiming at providing data for mitigating climate change.
文摘Background： National forest resource assessments Inventories （NFI＇s）, constitute an important nationa and monitoring, commonly known as National Forest information infrastructure in many countries. Methods： This study presents details about developments of the NFI in China, including sampling and plot design and the uses of alternative data sources, and specifically · reviews the evolution of the national forest inventory in China through the 20th and 21st centuries, with some reference to Europe and the US; · highlights the emergence of some common international themes： consistency of measurement; sampling designs; implementation of improved technology; expansion of the variables monitored more efficient scientific transparency;· presents an example of how China＇s expanding NFI exemplifies these global trends. Results： Main results and important changes in China＇s NFI are documented, both to support continued trend analysis and to provide data users with historical perspective. Conclusions： New technologies and data needs ensure that the Chinese NFI, like the national inventories in other countries, will continue to evolve. Within the context of historical change and current conditions, likely directions for this evolution are suggested.
文摘Background:The age of forest stands is critical information for forest management and conservation,for example for growth modelling,timing of management activities and harvesting,or decisions about protection areas.However,area-wide information about forest stand age often does not exist.In this study,we developed regression models for large-scale area-wide prediction of age in Norwegian forests.For model development we used more than 4800 plots of the Norwegian National Forest Inventory(NFI)distributed over Norway between latitudes 58°and 65°N in an 18.2 Mha study area.Predictor variables were based on airborne laser scanning(ALS),Sentinel-2,and existing public map data.We performed model validation on an independent data set consisting of 63 spruce stands with known age.Results:The best modelling strategy was to fit independent linear regression models to each observed site index(SI)level and using a SI prediction map in the application of the models.The most important predictor variable was an upper percentile of the ALS heights,and root mean squared errors(RMSEs)ranged between 3 and 31 years(6%to 26%)for SI-specific models,and 21 years(25%)on average.Mean deviance(MD)ranged between^(−1) and 3 years.The models improved with increasing SI and the RMSEs were largest for low SI stands older than 100 years.Using a mapped SI,which is required for practical applications,RMSE and MD on plot level ranged from 19 to 56 years(29%to 53%),and 5 to 37 years(5%to 31%),respectively.For the validation stands,the RMSE and MD were 12(22%)and 2 years(3%),respectively.Conclusions:Tree height estimated from airborne laser scanning and predicted site index were the most important variables in the models describing age.Overall,we obtained good results,especially for stands with high SI.The models could be considered for practical applications,although we see considerable potential for improvements if better SI maps were available.
基金supported by the Major Program of the National Natural Science Foundation of China(No.32192434)the Fundamental Research Funds of Chinese Academy of Forestry(No.CAFYBB2019ZD001)the National Key Research and Development Program of China(2016YFD060020602).
文摘Estimating the volume growth of forest ecosystems accurately is important for understanding carbon sequestration and achieving carbon neutrality goals.However,the key environmental factors affecting volume growth differ across various scales and plant functional types.This study was,therefore,conducted to estimate the volume growth of Larix and Quercus forests based on national-scale forestry inventory data in China and its influencing factors using random forest algorithms.The results showed that the model performances of volume growth in natural forests(R^(2)=0.65 for Larix and 0.66 for Quercus,respectively)were better than those in planted forests(R^(2)=0.44 for Larix and 0.40 for Quercus,respectively).In both natural and planted forests,the stand age showed a strong relative importance for volume growth(8.6%–66.2%),while the edaphic and climatic variables had a limited relative importance(<6.0%).The relationship between stand age and volume growth was unimodal in natural forests and linear increase in planted Quercus forests.And the specific locations(i.e.,altitude and aspect)of sampling plots exhibited high relative importance for volume growth in planted forests(4.1%–18.2%).Altitude positively affected volume growth in planted Larix forests but controlled volume growth negatively in planted Quercus forests.Similarly,the effects of other environmental factors on volume growth also differed in both stand origins(planted versus natural)and plant functional types(Larix versus Quercus).These results highlighted that the stand age was the most important predictor for volume growth and there were diverse effects of environmental factors on volume growth among stand origins and plant functional types.Our findings will provide a good framework for site-specific recommendations regarding the management practices necessary to maintain the volume growth in China's forest ecosystems.
文摘As climate change negotiations progress,monitoring biomass and carbon stocks is becoming an important part of the current forest research.Therefore,national governments are interested in developing forest-monitoring strategies using geospatial technology.Among statistical methods for mapping biomass,there is a nonparametric approach called k-nearest neighbor(kNN).We compared four variations of distance metrics of the kNN for the spatially-explicit estimation of aboveground biomass in a portion of the Mexican north border of the intertropical zone.Satellite derived,climatic,and topographic predictor variables were combined with the Mexican National Forest Inventory(NFI)data to accomplish the purpose.Performance of distance metrics applied into the kNN algorithm was evaluated using a cross validation leave-one-out technique.The results indicate that the Most Similar Neighbor(MSN)approach maximizes the correlation between predictor and response variables(r=0.9).Our results are in agreement with those reported in the literature.These findings confirm the predictive potential of the MSN approach for mapping forest variables at pixel level under the policy of Reducing Emission from Deforestation and Forest Degradation(REDD+).
文摘Background： We explore the factors affecting the optimal plot design （size and type as well as the subsample tree selection strategies within a plot） and their relative importance in defining the optimal plot design in amultipurpose forest inventory. The factors include time used to lay out the plot and to make the tree measurements within the plot, the between-plot variation of each of the variables of interest in the area, and the measurement and model errors for the different variables. Methods： We simulate different plot types and sizes and subsample tree selection strategies on measuredtest areas from North Lapland. The plot types used are fixed-radius, concentric and relascope plots. Weselect the optimal type and size first at plot level using a cost-plus-loss approach and then at cluster level byminimizing the weighted standard error with fixed budget. Results： As relascope plots are ve~/efficient at the plot level for volume and basal area, and fixed-radius plots for stems per ha, the optimal plot type strongly depends on the relative importance of these variables. The concentric plot seems to be a good compromise between these two in many cases. The subsample tree selection strategy was more important in selecting optimal plot than many other factors. In cluster level, the most important factor is the transfer time between plots. Conclusions： While the optimal radius of plots and other parameters were sensitive to the measurement times and other cost factors, the concentric plot type was optimal in almost all studied cases. Subsample tree measurement strategies need further studies, as they were an important cost factor. However, their importance to the precision was not as clear.
基金funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 787638),granted to Catherine Graham。
文摘Background: Under ongoing climate and land-use change, biodiversity is continuously decreasing and monitoring biodiversity is becoming increasingly important. National Forest Inventory(NFI) programmes provide valuable timeseries data on biodiversity and thus contribute to assessments of the state and trends in biodiversity, as well as ecosystem functioning. Data quality in this context is of paramount relevance, particularly for ensuring a meaningful interpretation of changes. The Swiss NFI revisits about 8%–10% of its sample plots regularly in repeat surveys to supervise the quality of fieldwork.Methods: We analysed the relevance of observer bias with equivalence tests, examined data quality objectives defined by the Swiss NFI instructors, and calculated the pseudo-turnover(PT) of species composition, that is, the percentage of species not observed by both teams. Three attributes of woody species richness from the latest Swiss NFI cycles(3 and 4) were analysed: occurrence of small tree and shrub species(1) on the sample plot and(2) at the forest edge, and(3) main shrub and trees species in the upper storey.Results: We found equivalent results between regular and repeat surveys for all attributes. Data quality, however,was significantly below expectations in all cases, that is, as much as 20%–30% below the expected data quality limit of 70%–80%(proportion of observations that should not deviate from a predefined threshold). PT values were about 10%–20%, and the PT of two out of three attributes decreased significantly in NFI4. This type of uncertainty –typically caused by a mixture of overlooking and misidentifying species – should be considered carefully when interpreting change figures on species richness estimates from NFI data.Conclusions: Our results provide important information on the data quality achieved in Swiss NFIs in terms of the reproducibility of the collected data. The three applied approaches proved to be effective for evaluating the quality of plot-level species richness and composition data in forest inventories and other biodiversity monitoring programmes. As such, they could also be recommended for assessing the quality of biodiversity indices derived from monitoring data.
基金the research project entitled“Remote sensing-based assessment of woody biomass and carbon storage in forests”,which was financially supported by the National Centre for Research and Development(Poland),under the BIOSTRATEG programme(Agreement No.BIOSTRATEG1/267755/4/NCBR/2015)Financial support was also received from the project entitled“Rozbudowa metody inwentaryzacji urządzeniowej stanu lasu z wykorzystaniem efektów projektu REMBIOFOR”(Project No.500463,agreement No.EO.271.3.12.2019 with the Polish State Forests National Forest Holding,signed on 14.10.2019),which constitutes a continuation of the former project.
文摘Background: Forest inventories have always been a primary information source concerning the forest ecosystem state. Various applied survey approaches arise from the numerous important factors during sampling scheme planning. Paramount aspects include the survey goal and scale, target population inherent variation and patterns,and available resources. The last factor commonly inhibits the goal, and compromises have to be made. Airborne laser scanning(ALS) has been intensively tested as a cost-effective option for forest inventories. Despite existing foundations, research has provided disparate results. Environmental conditions are one of the factors greatly influencing inventory performance. Therefore, a need for site-related sampling optimization is well founded.Moreover, as stands are the basic operational unit of managed forest holdings, few related studies have presented stand-level results. As such, herein, we tested the sampling intensity influence on the performance of the ALSenhanced stand-level inventory.Results: Distributions of possible errors were plotted by comparing ALS model estimates, with reference values derived from field surveys of 3300 sample plots and more than 300 control stands located in 5 forest districts. No improvement in results was observed due to the scanning density. The variance in obtained errors stabilized in the interval of 200–300 sample plots, maintaining the bias within +/-5% and the precision above 80%. The sample plot area affected scores mostly when transitioning from 100 to 200 m2. Only a slight gain was observed when bigger plots were used.Conclusions: ALS-enhanced inventories effectively address the demand for comprehensive and detailed information on the structure of single stands over vast areas. Knowledge of the relation between the sampling intensity and accuracy of ALS estimates allows the determination of certain sampling intensity thresholds. This should be useful when matching the required sample size and accuracy with available resources. Site optimization may be necessary, as certain errors may occur due to the sampling scheme, estimator type or forest site, making these factors worth further consideration.
基金supported by the Spanish Ministry of Science and Innovation project GREEN-RISK(Evaluation of past changes in ecosystem services and biodiversity in forests and restoration priorities under global change impacts-PID2020-119933RB-C21)A.C.received a pre-doctoral fellowship funded by the Spanish Ministry of Science and Innovation(PRE2021-099642).
文摘Conservation and enhancement of old-growth forests are key in forest planning and policies.In order to do so,more knowledge is needed on how the attributes traditionally associated with old-growth forests are distributed in space,what differences exist across distinct forest types and what natural or anthropic conditions are affecting the distribution of these old-growthness attributes.Using data from the Third Spanish National Forest Inventory(1997–2007),we calculated six indicators commonly associated with forest old-growthness for the plots in the territory of Peninsular Spain and Balearic Islands,and then combined them into an aggregated index.We then assessed their spatial distribution and the differences across five forest functional types,as well as the effects of ten climate,topographic,landscape,and anthropic variables in their distribution.Relevant geographical patterns were apparent,with climate factors,namely temperature and precipitation,playing a crucial role in the distribution of these attributes.The distribution of the indicators also varied across different forest types,while the effects of recent anthropic impacts were weaker but still relevant.Aridity seemed to be one of the main impediments for the development of old-growthness attributes,coupled with a negative impact of recent human pressure.However,these effects seemed to be mediated by other factors,specially the legacies imposed by the complex history of forest management practices,land use changes and natural disturbances that have shaped the forests of Spain.The results of this exploratory analysis highlight on one hand the importance of climate in the dynamic of forests towards old-growthness,which is relevant in a context of Climate Change,and on the other hand,the need for more insights on the history of our forests in order to understand their present and future.
文摘The United Nations Framework Convention on Climate Change （UNFCCC） requires reporting net carbon stock changes and anthropogenic greenhouse gas emissions, including those related to forests. This paper describes the status of carbon stocks in sub tropical forests of Pakistan. There are two major sub types in subtropical forests of Pakistan viz a viz Subtropical Chir Pine and Subtropical broadleaved forests. A network of sample plots was laid out in four selected site. Two sites were selected from sub tropical Chir Pine （Pinus roxburghii） forests and two from Subtropical broadleaved forests. Measurement and data acquisition protocols were developed specifically for the inventory car- ried out from 2005 to 2010. In total 261 plots （each of lha.） were established. Estimation of diameter, basal area, height, volume and biomass was carried out to estimate carbon stocks in each of the four carbon pools of above- and below-ground live biomass. Soil carbon stocks were also determined by doing soil sampling. In mature （-100 years old） pine forest stand at Ghoragali and Lehterar sites, a mean basal area of 30.38 and 26.11 m2.ha-1 represented mean volume of 243 and 197 m3·ha-1, respectively. The average biomass （t.ha-1） was 237 in Ghoragali site and 186 tha-1 in Lehterar site, which is equal to 128 and 100 t·ha-1 including soil C. However, on average basis both the forests have 114.5± 2.26 t.ha-1 of carbon stock which comprises of 92% in tree biomass and only 8% in the top soils. In mixed broadleaved evergreen forests a mean basal area （m2.ha-1）was 3.06 at Kherimurat with stem volume of 12.86 and 2.65 at Sohawa with stem volume of 11.40 m3.ha-1. The average upper and under storey biomass （t·ha-1） was 50.93 in Kherimurat site and 40.43 t.ha-1 in Sohawa site, which is equal to 31.18 and 24.36 t ·ha-1 including soil C stocks. This study provides a protocol monitoring biomass and carbon stocks and valuable baseline data for in Pakistan＇s managed and unmanaged sub-tropical forests.
基金supported by a grant from the Ministry of Science,Research and the Arts of Baden-Württemberg(7533-10-5-78)to Jürgen BauhusFelix Storch received additional support through the BBW ForWerts Graduate Program
文摘Background: The importance of structurally diverse forests for the conservation of biodiversity and provision of a wide range of ecosystem services has been widely recognised. However, tools to quantify structural diversity of forests in an objective and quantitative way across many forest types and sites are still needed, for example to support biodiversity monitoring. The existing approaches to quantify forest structural diversity are based on small geographical regions or single forest types, typically using only small data sets.Results: Here we developed an index of structural diversity based on National Forest Inventory(NFI) data of BadenWurttemberg, Germany, a state with 1.3 million ha of diverse forest types in different ownerships. Based on a literature review, 11 aspects of structural diversity were identified a priori as crucially important to describe structural diversity. An initial comprehensive list of 52 variables derived from National Forest Inventory(NFI) data related to structural diversity was reduced by applying five selection criteria to arrive at one variable for each aspect of structural diversity. These variables comprise 1) quadratic mean diameter at breast height(DBH), 2) standard deviation of DBH, 3) standard deviation of stand height, 4) number of decay classes, 5) bark-diversity index, 6) trees with DBH ≥ 40 cm, 7) diversity of flowering and fructification, 8) average mean diameter of downed deadwood, 9) mean DBH of standing deadwood, 10) tree species richness and 11) tree species richness in the regeneration layer. These variables were combined into a simple,additive index to quantify the level of structural diversity, which assumes values between 0 and 1. We applied this index in an exemplary way to broad forest categories and ownerships to assess its feasibility to analyse structural diversity in large-scale forest inventories.Conclusions: The forest structure index presented here can be derived in a similar way from standard inventory variables for most other large-scale forest inventories to provide important information about biodiversity relevant forest conditions and thus provide an evidence-base for forest management and planning as well as reporting.
文摘Aerial photographs arc important information sources for forest inventory. With the development of science and technology, an automated approach to aerial photograph interpretation has been sought. This paper introduces an experimental computer system for the auto-interpretation of aerial photographs. On thebasis of the experimental system, several algorithms are developed for the interpretation of density, crown closure, working groups and species composition of forest stands. Experiments show that auto—interpretation works well for some forest inventory data.
基金This work was supported by a Juan de la Cierva fellowship of the Spanish Ministry of Science and Innovation(FCJ2020-046387-I)the Spanish Ministry of Science,Innovation and Universities(PID2020-120355RB-IOO).
文摘The combined use of LiDAR(Light Detection And Ranging)scanning and field inventories can provide spatially continuous wall-to-wall information on forest characteristics.This information can be used in many ways in forest mapping,scenario analyses,and forest manage-ment planning.This study aimed to find the optimal way to obtain continuous forest data for Catalonia when using kNN imputation(kNN stands for“k nearest neighbors”).In this method,data are imputed to a certain location from k field-measured sample plots,which are the most similar to the location in terms of LiDAR metrics and topographic variables.Weighted multidimensional Euclidean distance was used as the similarity measure.The study tested two different methods to optimize the distance measure.The first method optimized,in the first step,the set of LiDAR and topographic variables used in the measure,as well as the transformations of these variables.The weights of the selected variables were optimized in the second step.The other method optimized the variable set as well as their transformations and weights in one single step.The two-step method that first finds the variables and their transfor-mations and subsequently optimizes their weights resulted in the best imputation results.In the study area,the use of three to five nearest neighbors was recommended.Altitude and latitude turned out to be the most important variables when assessing the similarity of two locations of Catalan forests in the context of kNN data imputation.The optimal distance measure always included both LiDAR metrics and topographic variables.The study showed that the optimal similarity measure may be different for different regions.Therefore,it was suggested that kNN data imputation should always be started with the optimization of the measure that is used to select the k nearest neighbors.
文摘The use of mobile laser scanning to survey forest ecosystems is a promising,scalable technology to describe forest 3D structures at high resolution.To confirm the con-sistency in the retrieval of forest structural parameters using hand-held laser scanning(HLS),before operationalizing the method,confirming the data is crucial.We analyzed the per-formance of tree-level mapping based on HLS under differ-ent phenology conditions on a mixed forest in western Spain comprising Pinus pinaster and two deciduous species,Alnus glutinosa and Quercus pyrenaica.The area was surveyed twice during the growing season(July 2022)and once in the deciduous season(February 2022)using several scan-ning paths.Ground reference data(418 trees,15 snags)was used to calibrate the HLS data and to assess the influence of phenology when converting 3D data into tree-level attrib-utes(DBH,height and volume).The HLS-based workflow was robust at isolating tree positions and recognizing stems despite changes in phenology.Ninety-six percent of all pairs matched below 65 cm.For DBH,phenology barely altered estimates.We observed a strong agreement when comparing HLS-based tree height distributions.The values exceeded 2 m when comparing height measurements,confirming height data should be carefully used as reference in remote sensing-based inventories,especially for deciduous species.Tree volume was more precise for pines(r=0.95,and rela-tive RMSE=21.3–23.8%)compared to deciduous species(r=0.91–0.96,and relative RMSE=27.3–30.5%).HLS data and the forest structural complexity tool performed remark-ably,especially in tree positioning considering mixed forests and mixed phenology conditions.
文摘A special mobile GIS（Geographic Information System） system used for forest resources second-class inventory was developed on the basis of traditional forest resources inventory,remote sensing,GPS（Globe Positioning System） and embedded technology.Portable instrument,embedded development and the integration technology of RS（Remote Sensing）,GIS and GPS are all used in this special mobile GIS system.Further,the system composition,key techniques,and current situation of the practical application in China were analyzed in the study.The results are important for applying modern high-tech for the planning and design of digital forest resources to improve the precision and efficiency of inventory and reduce the labor cost and financial investment.
基金supported by the National Technology Extension Fund of Forestry,Forest Vegetation Carbon Storage Monitoring Technology Based on Watershed Algorithm ([2019]06)Fundamental Research Funds for the Central Universities (No.PTYX202107).
文摘Since the launch of the Google Earth Engine(GEE)cloud platform in 2010,it has been widely used,leading to a wealth of valuable information.However,the potential of GEE for forest resource management has not been fully exploited.To extract dominant woody plant species,GEE combined Sen-tinel-1(S1)and Sentinel-2(S2)data with the addition of the National Forest Resources Inventory(NFRI)and topographic data,resulting in a 10 m resolution multimodal geospatial dataset for subtropical forests in southeast China.Spectral and texture features,red-edge bands,and vegetation indices of S1 and S2 data were computed.A hierarchical model obtained information on forest distribution and area and the dominant woody plant species.The results suggest that combining data sources from the S1 winter and S2 yearly ranges enhances accuracy in forest distribution and area extraction compared to using either data source independently.Similarly,for dominant woody species recognition,using S1 winter and S2 data across all four seasons was accurate.Including terrain factors and removing spatial correlation from NFRI sample points further improved the recognition accuracy.The optimal forest extraction achieved an overall accuracy(OA)of 97.4%and a maplevel image classification efficacy(MICE)of 96.7%.OA and MICE were 83.6%and 80.7%for dominant species extraction,respectively.The high accuracy and efficacy values indicate that the hierarchical recognition model based on multimodal remote sensing data performed extremely well for extracting information about dominant woody plant species.Visualizing the results using the GEE application allows for an intuitive display of forest and species distribution,offering significant convenience for forest resource monitoring.
基金This study was supported by the National Natural Science Foundation of China (Nos. 30028001 49905005)+1 种基金 National Key Basic Re-search Specific Foundation (G1999043407) the Chinese Acade
文摘Accurately estimating forest net primary productivity (NPP) plays an important role in study of global carbon budget. A NPP model reflecting the synthetic effects of both biotic (forest stand age, A and stem volume, V) and climatic factors (mean annual actual evapotranspiration, E) was developed for Chinese pine (Pinus tabulaeformis) forest by making full use of Forest Inventory Data (FID) and dynamically assessing forest productivity. The NPP of Chinese pine forest was estimated by using this model and the fourth FID (1989–1993), and the spatial pattern of NPP of Chinese pine forest was given by Geography Information System (GIS) software. The results indicated that mean NPP value, of Chinese pine forest was 7.82 t m?2·a?1 and varied at the range of 3.32–11.87 t hm?2·a?1. NPP distribution of Chinese pine forests was significantly different in different regions, higher in the south and lower in the north of China. In terms of the main distribution regions of Chinese pine, the NPPs of Chinese pine forest in Shanxi and Shaanxi provinces were in middle level, with an average NPP of 7.4 t hm?2·a?1, that in the southern and the eastern parts (e.g. Shichuang Hunan, Henan, and Liaoning provinces) was higher (over 7.7 t hm?2·a?1), and that in the northern part and western part (e.g. Neimenggu and Ningxia provinces) was lower (below 5 t hm?2·a?1). This study provides an efficient way for using FID to understand the dynamics of foest NPP and evaluate its effects on global climate change. Keywords Forest NPP - Forest inventory data - Chinese pine forest - Climatic and biotic NPP model - Spatial distribution pattern CLC number S727.22 - S757.2 Document code A Foundation item: This study was supported by the National Natural Science Foundation of China (Nos. 30028001, 49905005), National Key Basic Research Specific Foundation (G1999043407); the Chinese Academy of Sciences (KSC2-1-07).Biography: ZHAO Min (1973-), female, Ph. D. in Laboratory of Quantitative Vegetation Ecology, Institute of Botany, the Chinese Academy of Sciences, Beijing 100093, P. R. China.Responsible editor: Zhu Hong
基金funding from Horizon Europe Framework Programme(HORIZON),call Teaming for Excellence(HORIZON-WIDERA-2022-ACCESS-01-two-stage)-Creation of the centre of excellence in smart forestry“Forest 4.0”No.101059985funded by the EuropeanUnion under the project FOREST 4.0-“Ekscelencijos centras tvariai miško bioekonomikai vystyti”No.10-042-P-0002.
文摘Forests are vital ecosystems that play a crucial role in sustaining life on Earth and supporting human well-being.Traditional forest mapping and monitoring methods are often costly and limited in scope,necessitating the adoption of advanced,automated approaches for improved forest conservation and management.This study explores the application of deep learning-based object detection techniques for individual tree detection in RGB satellite imagery.A dataset of 3157 images was collected and divided into training(2528),validation(495),and testing(134)sets.To enhance model robustness and generalization,data augmentation was applied to the training part of the dataset.Various YOLO-based models,including YOLOv8,YOLOv9,YOLOv10,YOLOv11,and YOLOv12,were evaluated using different hyperparameters and optimization techniques,such as stochastic gradient descent(SGD)and auto-optimization.These models were assessed in terms of detection accuracy and the number of detected trees.The highest-performing model,YOLOv12m,achieved a mean average precision(mAP@50)of 0.908,mAP@50:95 of 0.581,recall of 0.851,precision of 0.852,and an F1-score of 0.847.The results demonstrate that YOLO-based object detection offers a highly efficient,scalable,and accurate solution for individual tree detection in satellite imagery,facilitating improved forest inventory,monitoring,and ecosystem management.This study underscores the potential of AI-driven tree detection to enhance environmental sustainability and support data-driven decision-making in forestry.
文摘Critical Height Sampling(CHS)estimates stand volume free from any model and tree form assumptions.Despite its introduction more than four decades ago,CHS has not been widely applied in the field due to perceived challenges in measurement.The objectives of this study were to compare estimated stand volume between CHS and sampling methods that used volume or taper models,the equivalence of the sampling methods,and their relative efficiency.We established 65 field plots in planted forests of two coniferous tree species.We estimated stand volume for a range of Basal Area Factors(BAFs).Results showed that CHS produced the most similar mean stand volume across BAFs and tree species with maximum differences between BAFs of 5-18m^(3)·ha^(−1).Horizontal Point Sampling(HPS)using volume models produced very large variability in mean stand volume across BAFs with the differences up to 126m^(3)·ha^(−1).However,CHS was less precise and less efficient than HPS.Furthermore,none of the sampling methods were statistically interchangeable with CHS at an allowable tolerance of≤55m^(3)·ha^(−1).About 72%of critical height measurements were below crown base indicating that critical height was more accessible to measurement than expected.Our study suggests that the consistency in the mean estimates of CHS is a major advantage when planning a forest inventory.When checking against CHS,results hint that HPS estimates might contain potential model bias.These strengths of CHS could outweigh its lower precision.Our study also implies serious implications in financial terms when choosing a sampling method.Lastly,CHS could potentially benefit forest management as an alternate option of estimating stand volume when volume or taper models are lacking or are not reliable.
