Development of mobile GIS system for forest resources second-class inventory

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.

The national forest inventory in China:history-results-international context

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.

Forest biomass carbon storage from multiple inventories over the past 30 years in Gansu Province, China: implications from the age structure of major forest types

We used the forest inventory data of Gansu Province, China to quantify carbon storage and carbon density changes by regional forest cover and by typical forest types in 1979-2006. Total forest area increased from 1.77 x 106 ha in 1979 to 2.32 x 106 ha in 2006, and the forest carbon storage, estimated by the continuous biomass expansion factor method, increased from 83.14 to 100.66 Tg, equivalent to a carbon accumulation rate of 0.0071 Tg per year during the period. Mean carbon densities were 44.83-48.50 t ha-1 and the values decreased slightly over the time period. Natural forests generated greater car- bon storage and density than did plantations. By regression analysis, forest stand age was an important parameter incarbon density studies. We developed various regression equations between carbon density and stand age for major types of natural forests and plantations in the region. Our results can be used for proper selection of re-forestation species and efficient management of young and middle-aged forests, offering great potential for future carbon sequestra- tion, especially in arid and semi-arid regions.

The inventory of the carbon stocks in sub tropical forests of Pakistanfor reporting under Kyoto Protocol

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.

Quantifying forest structural diversity based on large-scale inventory data:a new approach to support biodiversity monitoring

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.

Mapping forest age using National Forest Inventory,airborne laser scanning,and Sentinel-2 data

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.

Mapping aboveground biomass by integrating geospatial and forest inventory data through a k-nearest neighbor strategy in North Central Mexico

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+).

Optimal plot design in a multipurpose forest inventory

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.

Using machine learning algorithms to estimate stand volume growth of Larix and Quercus forests based on national-scale Forest Inventory data in China

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.

Carbon storage dynamics of subtropical forests estimated with multi-period forest inventories at a regional scale:the case of Jiangxi forests

Temperate and high-latitude forests are carbon sinks and play pivotal roles in offsetting greenhouse gas emissions of CO2.However,uncertainty still exists for subtropical forests,especially in monsoon-prevalent eastern Asia.Earlier studies have depended on remote sensing,ecosystem modeling,carbon fluxes,or single period forest surveys to estimate carbon sequestration capacities,and the results vary significantly.This study was designed to utilize multi-period forest survey data to explore spatial-dynamics of biomass storage in subtropical forests of China.Jiangxi province,a region with over 60%subtropical forest cover,was selected as the case study site and is located in central east China.Based on forest inventory data 1984-2013,and the stock-difference and biomass expansion factor methods,the carbon storage and density,of arboreal forests,economic forests,bamboo forests,woodlands and shrubberies were estimated.The results show that carbon storage increased from 159.1 Tg C in 1988 to 276.1 TgC in 2013,making up 3.1-3.8%of carbon stored throughout China.Among the four types of forests,the amount of carbon stored was as follows:arboreal forest>economic forest>bamboo forest>woodland and shrubbery.Arboreal forests accounted for 64.0-79.4%of the total.Forest carbon density increased from 21.2 Mg C ha-1 in 1984 to26.2 Mg C ha-1 in 2013,equal to 61.2-70.2%of the average carbon density of China’s forests in the same period.Forest carbon storage in Jiangxi will reach 355.5 Tg C and 535.8 Tg C in 2020 and 2030,respectively,and forest carbon density is predicted to be 31.9 Mg C ha-1and 46.4 Mg C ha-1,respectively.As one of the few studies using multi-period data tracking biomass dynamics in Jiangxi province,the findings of this study may be used as a reference for other research.Using Jiangxi as a case study underlies the fact that subtropical forests in China have great carbon sequestration potential and have fundamental significance to offset global environmental change effects.

Thinning inventory technique and its application in forest resource inventory

A brief overview of the system of forest inventory in China and its problems existed is given. Forest planning requires information about the current state of the forest resource. However, generally speaking, the information collected in a periodic inventory may become obsolete already after the first thinning following. Therefore,in order to solve the problem, theory skeleton of thinning inventory technique and its approach in forest inventory,is presented in this paper, with an emphasis on the research and study developing currently thinning inventory.

AN AERIAL PHOTOGRAPH AUTO-INTERPRETATION SYSTEM FOR FOREST RESOURCE INVENTORY

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.

Sample Plot Change in the Seventh Review of Continuous National Forest Inventory in Zigui County

The following qualitative conclusions of forest resources in Zigui can be drawn by the research on 73 plots and 5 vegetation plots:forest area is increasing; forest growing stock is increasing; the adjustment of forest category structure is constantly improved; forest quality has been improving; stand structure is optimized continuously; biodiversity has initially appeared.

Review and Progress of China’s Forest Continuous Inventory System

China was one of the earliest countries to set up a system to continuously inventory natural forest resources. From the beginning of the 1970s until today, seven forest resource inventories have been carried out. This research summarizes the progress of forest continuous inventories and analyzes the existing deficiencies ofChina’s forest continuous inventory system and forest management plan inventory. As stated above, this research offers corresponding countermeasures and suggestions: establishing a sample plot system for comprehensive national forest inventory and monitoring with each province’s continuous forest inventory based on the foundation of the national sample plot system, able to develop the province as a subset of the overall province-level forest resource inventory according to the actual conditions in each province. Through annual multi-resource/multi-benefit surveying of the forests, the monitoring of forest amounts, quality, functions and benefits will be assisted in its entirety. The further integration of the forest continuous inventory and the forest management plan inventory is also discussed. This research also proposes the varied probability sampling method with sub-compartments as the basic sampling unit (or combinations of sub-compartments). This will also satisfy the requirements of ecological inventory by region.

Analysing the quality of Swiss National Forest Inventory measurements of woody species richness

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.

Influence of sampling intensity on performance of two-phase forest inventory using airborne laser scanning

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.

Comparison of the local pivotal method and systematic sampling for national forest inventories

Background:The local pivotal method(LPM)utilizing auxiliary data in sample selection has recently been proposed as a sampling method for national forest inventories(NFIs).Its performance compared to simple random sampling(SRS)and LPM with geographical coordinates has produced promising results in simulation studies.In this simulation study we compared all these sampling methods to systematic sampling.The LPM samples were selected solely using the coordinates(LPMxy)or,in addition to that,auxiliary remote sensing-based forest variables(RS variables).We utilized field measurement data(NFI-field)and Multi-Source NFI(MS-NFI)maps as target data,and independent MS-NFI maps as auxiliary data.The designs were compared using relative efficiency(RE);a ratio of mean squared errors of the reference sampling design against the studied design.Applying a method in NFI also requires a proven estimator for the variance.Therefore,three different variance estimators were evaluated against the empirical variance of replications:1)an estimator corresponding to SRS;2)a Grafström-Schelin estimator repurposed for LPM;and 3)a Matérn estimator applied in the Finnish NFI for systematic sampling design.Results:The LPMxy was nearly comparable with the systematic design for the most target variables.The REs of the LPM designs utilizing auxiliary data compared to the systematic design varied between 0.74–1.18,according to the studied target variable.The SRS estimator for variance was expectedly the most biased and conservative estimator.Similarly,the Grafström-Schelin estimator gave overestimates in the case of LPMxy.When the RS variables were utilized as auxiliary data,the Grafström-Schelin estimates tended to underestimate the empirical variance.In systematic sampling the Matérn and Grafström-Schelin estimators performed for practical purposes equally.Conclusions:LPM optimized for a specific variable tended to be more efficient than systematic sampling,but all of the considered LPM designs were less efficient than the systematic sampling design for some target variables.The Grafström-Schelin estimator could be used as such with LPMxy or instead of the Matérn estimator in systematic sampling.Further studies of the variance estimators are needed if other auxiliary variables are to be used in LPM.

The Genesis and Paradigm Shift in Forest Inventory: Bangladesh Chapter

Bangladesh (Indo-Bangladesh to independent Bangladesh) has more than 250 years of history in forest inventory. The Boundary demarcation of the Sundarbans forest in 1764 was the first record of forest inventory. A total of 30 inventories have been recorded that were started to form the boundary demarcation to complex biodiversity, biomass, and carbon stock assessment. These inventories used simple cartographic to complex satellite image processing techniques, software base data/information collection methods, and sophisticated statistical procedures in data analysis. Eventually, the history of the forest inventory of Bangladesh is about 100 years older than the history of forest management. This study aimed to classify the inventories into distinct time frames based on outputs and align them with the motive of rulers, existing forest policy, and contemporary global and national issues. The historical records forest inventory has been divided into four distinct periods, e.g., Mid eighteenth to the late nineteenth century: 1764-1876;Early twenty century: 1905-1924;Mid-twenty to late twenty century: 1958-2000;and Early twenty-first century: 2001-present). The objectives/outputs of each inventory were highly linked with the motive of rulers, policy statements, available technologies, and recent issues from national and global perspectives.

Accuracy of tree mapping based on hand-held laser scanning comparing leaf-on and leaf-off conditions in mixed forests

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.

Developing kNN forest data imputation for Catalonia

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.