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A multi-source forest inventory method is applied to the estimation of forest resources in the area of the Hebei Forest Bureau in Heilongjiang province in North-East China. A stratified systematic cluster sampling design was utilised in field measurements. The design was constructed on the basis of information from earlier stand-level inventories,...
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... value is used instead of the mean for vari- ables of nominal or ordinal scales. Examples of the map themes are growing stock volumes by tree species, dominant tree species or tree species groups, e.g., on the basis of the tree stem volume (Fig. 3), increments by tree species, site fertility, and mean age of the growing ...
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... parameters, the value of k and the maximum distance from the pixel to be analysed to the potential nearest neighbours were judged using a pixel level cross- validation technique. The chosen value of k was 3 and the maximum distance 30 km. Complete tables of the results are given in Tomppo et al. (1994). Only few examples are given here. Fig. 3 shows an example of a thematic map produced by means of the multi-source ...
Context 3
... of white birch (Betula platyphylla) dominated forests from 9% to 48% and the proportion of pine dominated forests from 0% to 17%. Coniferous tree spe- cies dominated 15% of the productive forest area. Coniferous dominated forests were located mainly in the northern part and white birch domi- nated forests in the western part of the Bureau area (Fig. 3, Table 3 and Fig. ...
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Citations
... It is, therefore, becoming one of the most popular methods applied for forest inventory using large-area remote sensing data (Meng et al., 2009). kNN has been found as a useful tool for forest mapping over a large geographic area using a fine spatial resolution (Tokola, Pitkanen, Partinen, & Muinonen, 1996;Tomppo, Goulding, & Katila, 1999;Tomppo, Korhonen, Heikkinen, & Yli-Kojola, 2001;Holmström & Fransson, 2001;Tanaka et al., 2015;Lang, Gulbe, Traškovs, & Stepčenko, 2016). Although numerous studies of relationships between spectral responses and forest parameters have been conducted during the past several decades, conclusions about these relationships vary, depending on the characteristics of the study areas and the used data . ...
The research on building a flood mapping in Lai Giang Basin of Binh Dinh Province in 2016 on the use of Sentinel-1 radar image and GIS initially determines the construction of flooded square in the studied area based on the results of the process of scattering water value threshold, extracting flooded areas on the radar image. This flood mapping is combined with the depth measured from the flood in 2016 traces to apply GIS techniques to create of flood depth map. The results of the study provide a new way of quickly creating flood maps from radar satellite images.
... It is, therefore, becoming one of the most popular methods applied for forest inventory using large-area remote sensing data (Meng et al., 2009). kNN has been found as a useful tool for forest mapping over a large geographic area using a fine spatial resolution (Tokola, Pitkanen, Partinen, & Muinonen, 1996;Tomppo, Goulding, & Katila, 1999;Tomppo, Korhonen, Heikkinen, & Yli-Kojola, 2001;Holmström & Fransson, 2001;Tanaka et al., 2015;Lang, Gulbe, Traškovs, & Stepčenko, 2016). Although numerous studies of relationships between spectral responses and forest parameters have been conducted during the past several decades, conclusions about these relationships vary, depending on the characteristics of the study areas and the used data . ...
This two-volume set presents the conference papers from the 1st International Conference on Economics, Development and Sustainability (EDESUS 2019), organized by the University of Economics and Business, Vietnam National University, Hanoi. The collection addresses global changes and sustainable development in Vietnam and other emerging market economies in Asia, and covers wider topics such as economics and business (e.g. economic theory, national and international income distribution, macroeconomic policies, sectors of economy, productivity developments, financial market, business governance, bank financing), development and sustainability (e.g. developing process, development policy, public policy, sustainable growth, sustainability tools, sustainable livelihood, sustainable tourism, green growth), and resources and global change (e.g. human resources, natural resources, climate change, globalization, global challenges). The books are of interest to professors, researchers, lecturers, and students in economics and geography, consultants, and decision makers interested in global changes and sustainable development.
Volume 2 focuses on global changes and sustainable development in Vietnam and other emerging market economies in Asia. This covers topics such as sustainability (e.g. sustainable growth, sustainability tools, sustainable livelihood, sustainable tourism), and change in resources globally (e.g. human resources, natural resources, climate change, globalization, global challenges).
... У лісовій інвентаризації історично сформувалося два підходи: один ґрунтується на даних, одержаних статистичними методами, інший, який домінує в країнах пострадянського простору, полягає в агрегуванні даних повидільної таксації лісу. Існує багато доказів того, що другий підхід, як правило, призводить до заниження оцінок площі лісів та запасів деревостанів (Kinnunen et al., 2007;Tomppo et al., 2001;Швиденко & Щепащенко, 2011). У зв'язку з цим у багатьох країнах запроваджено статистичну інвентаризацію лісів, під час якої встановлюються вибіркові оцінки параметрів лісового фонду. ...
In the monograph, an accuracy of four global forest products of 25–30 m spatial resolution has been analysed. Exploring the relationship of tree cover extracted from the Global Forest Change (GFC) data and relative stocking of forest stands, the 40 % tree cover percentage value has been adopted as a threshold for distinction between forested and unforested areas within flat land Ukraine. In comparison with analogous product Landsat Tree Cover Continuous Fields (LTCCF), the GFC map has appeared to be more advanced in terms of accuracy and applicability for forest area dynamics assessment. Analysis of discrete forest masks Global PALSAR-2/PALSAR Forest/Non-Forest Map (JAXA FNF) and GlobeLand30 has revealed a major misclassification of forested areas under severe fragmentation patterns of flat land forest Ukraine. The results of global map of forest biomass GlobBiomass showed that it over approximates the data so that the mapped growing stock volume at pixel level is not agreed with the field measurements data. Nevertheless, the map could be used for preliminary assessment of total and mean values of growing stock volumes for large areas that has been successfully tested on 15 randomly chosen forest enterprises distributed throughout the study region.
To map forested areas in flat land Ukraine using Landsat 8 OLI imagery, the land cover reflectance properties in different spectral bands have been investigated. As a result, phenology-based methods of classification for processing time series of satellite observations have been substantiated, thus more than 1500 scenes of Landsat 8 OLI have been merged into cloudless mosaics for the following four seasons: yearly, summer, autumn and April-October. Google Earth Engine API for cloud-based computation allowed to run high performance algorithms for creation, processing and classification of Landsat 8 OLI seasonal mosaics, while nonparametric method Random Forest was efficient to deal with multidimensional training dataset. Finally, forest mask for flat land of Ukraine has been created having 30 m spatial resolution. Its user’s and producer’s accuracy are 0.910±0.015 and 0.880±0.018 accordingly, albeit for Polissia regions the accuracy is higher, but for Steppe zone it is on the contrary, lower. The forest mask allowed to estimate forested area which for the flat land Ukraine is 9440.5±239.4 thousands hectares. Forest cover for the territory is estimated to be 17.4±0.4 %. The analogous values according to the state forest assessment data are significantly lower, 7766.7 thousand hectares and 14.4 % respectively. The overestimation of forested area can be explained due to the forests that are not included in the state forest assessment data (for example, urban forests).
Basal area of main tree species in forest stands of Sumy region have been mapped using data collected by Centre of national forest inventory during 2013 and custom version of k-NN (k-Nearest Neighbors) algorithm written for Google Earth Engine API. Per-pixel distribution of basal area allowed to map tree species abundance, composition and tree species richness of forest stands. Applying the same distance matrix between nearest neighbors, growing stock volume for each tree species have been mapped for the region. As it was computed, the 95 % confidence interval for mean value of growing stock volume for Sumy region is 329±8 m3·ha-1, while in different districts of the region mean value varies between 268 and 385 m3·ha-1. The estimated values are significantly higher of those in the official forest inventory database. Besides, forested area and total growing stock volume of forest stands for major tree species were assessed.
... To ensure complete spatial coverage, which is not necessarily achieved using simple random sampling, NFIs commonly use systematic sampling designs featuring regular grids of sample plots (Tomppo et al. 2001(Tomppo et al. , 2011Köhl et al. 2006;FAO 2009). In addition, greater efficiency can be realized with respect to travel costs if multiple sample plots are aggregated into groups in relatively close spatial proximity (Matérn et al. 1983;Ranneby et al. 1987). ...
Abstract The State Forest Inventory (SFI) in the Russian Federation is a relatively new project that is little known in the English-language scientific literature. Following the stipulations of the Forest Act of 2006, the first SFI sample plots in this vast territory were established in 2007. The 34 Russian forest regions were the basic geographical units for all statistical estimates and served as a first-level stratification, while a second level was based on old inventory data and remotely sensed data. The sampling design was to consist of a simple random sample of 84,700 circular 500 m2 sample plots over forest land. Each sample plot consists of three nested concentric circular subplots with radii of 12.62, 5.64 and 2.82 m and additional subplots for assessing and describing undergrowth, regeneration and ground vegetation. In total, 117 variables were to be measured or assessed on each plot. Although field work has begun, the methodology has elicited some criticism. The simple random sampling design is less efficient than a systematic design featuring sample plot clusters and a mix of temporary and permanent plots. The second-level stratification is mostly ineffective for increasing precision. Qualitative variables, which are not always essential, are dominant, while important quantitative variables are under-represented. Because of very slow progress, in 2018 the original plan was adjusted by reducing the number of permanent sample plots from 84,700 to 68,287 so that the first SFI cycle could be completed by 2020.
... Most of this literature has applied the regression method with linear or nonlinear regression (Trotter et al., 1997;Cohen et al., 2003;Awaya et al., 2004;Rahman, 2004). Recently, kNN has found as a useful tool for forest mapping over a large geographic area using a fine spatial resolution (Tokola et al., 1996;Tomppo et al., 1999Tomppo et al., , 2001Holmström and Fransson, 2003;Makela and Pekkarinen, 2004;Kurzer, 2008, Thesler et al., 2008. Geostatistics models have been used in some cases and gave promising results (Tuominen et al., 2003;Wallerman, 2003;Meng et al., 2009). ...
This paper describes the potential of applying methods to estimate stand volume using remote sensing imagery for the disturbed forest stands in the Central Highlands of Vietnam. The equation of stand volume was defined in the first process based on field data. The methods of regression, k-Nearest neighbor, and regression-kriging were applied to estimate the stand volume using satellite image. The combination of 4 bands SPOT 5 along with normalized difference vegetation index (NDVI) and principal components (PCs) were used in these methods. Validation using independent data indicates that the regression-kriging is the best method for stand volume predictions, simultaneously, it is confirmed that the presence of all SPOT-5 bands improved the prediction result of stand volume. The results show that the more spectral bands included, the lower the root mean square error (RMSE) obtained for all these method.
... To the author at least, the MD view of the population as random and the sample as fixed ( Gregoire, 1998) has more appeal in the context of an FI ( Firth and Bennett, 1998;Elliott and Little, 2000;Mugglin et al., 2000;McRoberts, 2001). In FIs, an analyst may have available a large number of potentially useful auxiliary variables ( Andersen et al., 2011;Tomppo et al., 2001;Tuominen and Poso, 2001;McRoberts and Tomppo, 2007). FIs aided by airborne laser scanner data are a prime example ( Naesset, 2004). ...
Forest inventories (FIs) are increasingly supported by auxiliary variables (X) with a formulated link to a target variable (Y). The tenet forwarded here is that with simple single-stage sampling designs and a census of X, an analyst can choose between a model-assisted and a model-dependent finite population prediction approach to inference
without the risk of important numerical differences in resulting estimates of interest. A small simulation study largely confirmed
the tenet. A suite of FI-related issues regarding the two paradigms and their application in FIs are brought to the attention
and discussed, hopefully triggering a more balanced reflections on the choice of inferential paradigm.
... In Finland and Sweden, nationwide forest databases, including various kinds of information, e.g., stand volume, stand basal area and stand age, have been created by combining NFI field plot data, medium resolution satellite image data and other digital maps using the non-parametric k-nearest neighbor (k-NN) technique [3,6]. In China [7,8], Ireland [9] and Norway [10], the technique was tested in parts of the country to estimate forest information. Some previous studies revealed that the k-NN technique has the potential to increase the precision of NFI estimates by the post-stratification technique [5,[11][12][13]. ...
The main objective of this study was to evaluate the effectiveness of adding feature variables, such as forest type information and topographic- and climatic-environmental factors to satellite image data, on the accuracy of stand volume estimates made with the k-nearest neighbor (k-NN) technique in southwestern Japan. Data from the Forest Resources Monitoring Survey-a national plot sampling survey in Japan-was used as in situ data in this study. The estimates obtained from three Landsat Enhanced Thematic Mapper Plus (ETM+) datasets acquired in different seasons with various combinations of additional feature variables were compared. The results showed that although the addition of environmental factors to satellite image data did not always help improve estimation accuracy, the use of summer rainfall (SRF) data had a consistent positive effect on accuracy improvement. Therefore, SRF may be a useful feature variable to consider in stand volume estimation in this study area. Moreover, the use of forest type information is very effective at reducing k-NN estimation errors when using an optimum combination of satellite image data and environmental factors. All of the results indicated that the k-NN technique combined with appropriate feature variables is applicable to nationwide stand volume estimation in Japan.
... Such field data can be aggregated to generate National Forest Inventories (NFI) (Brown et al. 1999;Jenkins et al. 2001;Chojnacky et al. 2004). The NFI dataset currently contains the most accurate biomass estimates in various countries, such as in Finland and Sweden (Tomppo 1991;Reese et al. 2003), Norway (Gjersten 2005), Austria (Koukal et al. 2005), New Zealand (Tomppo et al. 1999), China (Tomppo et al. 2001), Germany (Diemer et al. 2000), Italy (Maselli et al. 2005), and the United States (Franco-Lopez et al. 2001;McRoberts 2006;McRoberts et al. 2002McRoberts et al. , 2007). However, it is challenging to extrapolate plot estimates to unit ground area of high-quality geo-referenced ground-truth (Gibbs et al. 2007;Goetz et al. 2009). ...
Forest biomass reflects sequestration or release of carbon between terrestrial ecosystems and the atmosphere. Measuring the size and complexity of forest biomass over large areas can enable us to better understand the environmental processes, availability of renewable energy, and global carbon cycle. This chapter reviews recent progress in measuring forest biomass from remote sensing. In quantifying forest biomass, forest properties are often characterized from three types of remote sensing data. Passive optical spectral reflectances are sensitive to vegetation structure (leaf area index, crown size and tree density), texture and shadow. Radar data measure dielectric and geometrical properties of forests. Lidar data characterize vegetation vertical structure and height. Because these instruments have their advantages and disadvantages in reflecting forest properties, data fusion techniques can combine data from multiple sensors and related information from associated databases to achieve improved accuracy in biomass estimation. The remote sensing data or derived forest attributes are commonly correlated to forest biomass using empirical regression models, non-parametric methods, and physically-based allometric models. Although forest biomass is widely estimated at various scales from remote sensing data, models tend to underestimate large biomass densities and overestimate small ones because of saturation issues. Finally, the assessment and validation of forest biomass obtained from remote sensing is critical because current biomass estimates at large area are of large uncertainties.
... Reese et al. 2002, Tomppo et al. 2008). MS-NFI techniques have already been applied to estimate traditional forestry variables in Finland, China and New Zealand ( Tomppo et al. 1999, Tomppo et al. 2001, Tuominen et al. 2010) but also to assess biodiversity-related issues such as landscape quality for the three-toed woodpecker (Picoides tridactylus) in a region in southern Finland ( Pakkala et al. 2002). ...
Criteria and indicators (C & I) to evaluate the sustainability of forest management have been proposed by the Ministerial Conference on the Protection of Forests in Europe. Although primarily defined at the national scale, these C & I also have implications at scales ranging from forest stands to the forest management unit. In this paper, we review existing forest growth and ecosystem models from the point of view of applicability to prediction of indicators of sustainable management, focusing on stand scale models and management. To do this, we first present a conceptual framework for understanding the role of models in assessing forest management at the stand level in the context of sustainability criteria and indicators. We classify the criteria into those predictable using models operating at the stand scale, and those derivable either through scaling up or as solutions of a multi-objective management optimisation problem.
... They found it to be a useful tool compared to the traditional inventory method. (Tomppo et al., 2001) utilized the approach to estimate/classify growth, main tree species, and forest type by means of multispectral TM data in China. The authors found the method to be helpful in classifying tree types and stand ages, though the stand-level predictions were reported to underestimate the growing stock. ...
