Forestry Data Research Articles

Monitoring Forest Loss in ALOS/PALSAR Time-Series with Superpixels

We present a flexible methodology to identify forest loss in synthetic aperture radar (SAR) L-band ALOS/PALSAR images. Instead of single pixel analysis, we generate spatial segments (i.e., superpixels) based on local image statistics to track homogeneous patches of forest across a time-series of ALOS/PALSAR images. Forest loss detection is performed using an ensemble of Support Vector Machines (SVMs) trained on local radar backscatter features derived from superpixels. This method is applied to time-series of ALOS-1 and ALOS-2 radar images over a boreal forest within the Laurentides Wildlife Reserve in Québec, Canada. We evaluate four spatial arrangements including (1) single pixels, (2) square grid cells, (3) superpixels based on segmentation of the radar images, and (4) superpixels derived from ancillary optical Landsat imagery. Detection of forest loss using superpixels outperforms single pixel and regular square grid cell approaches, especially when superpixels are generated from ancillary optical imagery. Results are validated with official Québec forestry data and Hansen et al. forest loss products. Our results indicate that this approach can be applied to monitor forest loss across large study areas using L-band radar instruments such as ALOS/PALSAR, particularly when combined with superpixels generated from ancillary optical data.

Forest Measurements Using ICESat- Geoscience Laser Altimeter System -A Review

Forests play a vital role in carbon cycle and in maintaining climate balance. The measurement of three dimensional forest structural attributes has a profound impact on the forest ecosystem management. Active remote sensing systems provide the capability to quantify the three dimensional structural of forests by measuring both the vertical as well as horizontal structure of forests. Vertical forest measurements can be directly and accurately estimated by means of the space borne Light Detection and Ranging (LiDAR) and it has been often used to estimate the forest structural attributes. This paper provides an introductory review of application of the Geoscience Laser Altimeter System (GLAS) onboard NASA’s Ice Cloud and Land Elevation Satellite (ICESat), for the three dimensional structural attribute measurements of forests. The review attempted to briefly explain the characteristics of ICESat/GLAS instruments, data processing and certain applications from the wide range of applications of GLAS data in forestry.

A Survey of Big Data Analytics for Smart Forestry

Accurate and reliable forestry data can be obtained by means of continuous monitoring of forests using advanced technologies, which provides a major opportunity for the development of smart forestry. However, with the improvement of the precision and acquisition speed of data, the traditional data analysis, and storage technology cannot meet the performance requirements of current applications. Forestry big data has brought a new solution to the difficulties encountered in the course of forestry development, which refers to the application of big data technology to forestry data processing. In this paper, we summarize the research and work of the big data in smart forestry in recent years. First, we review the history of the emergence and development of forestry big data, and then briefly summarize the opportunities brought to the forestry by big data technology. One of the most important tasks of forestry big data is to organize the massive data reasonably and effectively and to calculate fast. Therefore, we propose a five-layer architecture model of forestry big data and summarize the related work of data storage, query, analysis, and application. Finally, the challenges of forestry big data are analyzed, and the trend of future development has prospected from three aspects.

Current achievements to reduce deforestation in Kalimantan

Indonesia has developed its forest reference emission level (FREL), using a historical reference period of 1990-2012. Based on official Ministry of Environment and Forestry (MoEF) data, this paper analyses gross deforestation rates and emissions from deforestation in the five provinces of the island of Kalimantan which occurred in the time after 2012, i.e. 2013 until 2015, and puts them in relation to the average annual deforestation and emission rates of each province in the reference period. Even though the overall linear trend of deforestation and emission rates in Kalimantan from 1990 until 2015 goes down, this trend is not reflected in all of the five provinces equally. West and North Kalimantan’s rates even seem to be on the rise. The potentials to achieve emission reduction targets thus remain unequal for each province in Kalimantan Island.

Estimating annual investment returns from forestry and agriculture in New Zealand

This paper describes a method of estimating annual investment returns from forest growing businesses in New Zealand, using financial statement data. The method is also applied to survey data (derived from farm financial statements) published by New Zealand pastoral farming sector organisations. After data are adjusted for inflation, the total investment return is calculated as the sum of the cash return on assets, and the percentage change in asset value. The paper demonstrates that the total investment return calculated in this way is equivalent to the internal rate of return (IRR), for a single period of one year. The direct equivalence between this measure and the single-period IRR (calculated using discounted cash-flow analysis) is demonstrated algebraically and by example.Calculated values for forestry show a wide range of investment returns for different businesses in any one year, although there has been a general trend for total returns to increase for forestry businesses since about 2008. Investment returns for dairy and sheep and beef generally follow similar trends, with dairy providing a higher return on average than sheep and beef over the period studied. The data for forestry and agriculture are not directly comparable, since the forestry data are extracted directly from the financial statements of individual businesses and the farming data are derived through a process of averaging financial returns from a large number of farms, with the objective of providing typical returns for benchmarking. The scale of the operations are also significantly different, with the benchmark dairy farm being 148 ha, the sheep farm 764 ha and the forest businesses ranging from 21,000 ha to 150,000 ha (most recent year in all cases).The measure of total return used in this paper is attractive because it means the same measure can be used for evaluating existing businesses as would have been used to justify the initial set up of the business, or any projects that contributed to the business along the way. This provides a measure for ex post evaluation of existing investments that is consistent with one of the ex-ante decision criteria used at the time investment decisions are made. The method also provides separate estimates of the income or productive return, and the (usually) unrealised change in asset value. The paper demonstrates that the method can be applied to either individual company or industry average financial data equally easily. This method provides a viable alternative to using survey data to estimate annual investment returns.

Discount rates for corporate forest valuations

ABSTRACTThis paper reviews and identifies the discount rates and associated provisions appropriate to corporate forest valuations using deterministic or stochastic analyses. In stochastic analyses, these provisions include the recognition of time-varying discount rates that stem from annual variation in bond rates. Four different components (the risk-free discount rate, the discount rate for debt, the discount rate for equity, and a final weighted average cost of capital (WACC)) are involved in developing final discount rates. We reject using the traditional Capital Asset Pricing Model (CAPM) analysis for want of sufficient forestry data, and use instead industry evidence, based on separate but mutually consistent paths for deterministic and stochastic analyses.Based on a hypothetical major company, with substantial investment in more than 40 000 ha of radiata pine plantations, and an equity to debt ratio around 60:40, the empirical evidence available for the four components indicated a WACC of 7.4% y−1 was appropriate for deterministic analyses. For stochastic analyses, an initial WACC of 5.0% y−1 was deemed appropriate, subject to random annual variation with a standard deviation of ±2.63% y−1.These estimates are illustrative and are not intended to be specific to a particular company. Safety margins may still have to be applied to account for unpredictable events. Companies and independent valuers need to place more emphasis on estimating the components appropriate to their own setting, rather than relying on the CAPM approach.

Геоботаническая карта Пинежского заповедника

The Pinega State Nature Reserve is located in the European part of Russia, Arkhangelsk Region, in northern taiga subzone. The geobotanical studies were carried out on the territory of the reserve in 2007–2014. The classification of plant communities and the geobotanical map of the reserve were made. Ecologic-phytocoenotic classification (the Russian “dominant-determinant” approach) was conducted based on 618 relevés. The relevés were grouping into classes of associations on the basis of dominant bioecological groups, and within the classes of associations - into groups of associations by the prevailing ecologic-coenotic groups of plants. Associations were distinguished on the basis of phytocoenotic significance of species (species with the highest occurrence and abundance) within the groups of associations. The significance of syntaxa was verified by the method of discriminant analysis. 35 groups of associations and 71 associations were revealed in total. The geobotanical map was compiled using Landsat 8 satellite images for 2013 and 2014. 26 indices reflecting the productivity, saturation of the vegetation moisture and soil properties were calculated for each Landsat image. The morphometric characteristics of the relief were used as well. The obtained spatial database was classified by the method of step-by-step discriminant analysis (SDA) with using training sample. The training sample was created on the basis of field descriptions and forestry data. The verification of the map is based on the significance of the discriminant axes and the quality table for discriminant analysis. The article presents the principle stages of mapping using SDA. The vegetation map shows the different vegetation types over the territory of the Reserve.

SECTION-BASED TREE SPECIES IDENTIFICATION USING AIRBORNE LIDAR POINT CLOUD

Abstract. The application of LiDAR data in forestry initially focused on mapping forest community, particularly and primarily intended for largescale forest management and planning. Then with the smaller footprint and higher sampling density LiDAR data available, detecting individual tree overstory, estimating crowns parameters and identifying tree species are demonstrated practicable. This paper proposes a section-based protocol of tree species identification taking palm tree as an example. Section-based method is to detect objects through certain profile among different direction, basically along X-axis or Y-axis. And this method improve the utilization of spatial information to generate accurate results. Firstly, separate the tree points from manmade-object points by decision-tree-based rules, and create Crown Height Mode (CHM) by subtracting the Digital Terrain Model (DTM) from the digital surface model (DSM). Then calculate and extract key points to locate individual trees, thus estimate specific tree parameters related to species information, such as crown height, crown radius, and cross point etc. Finally, with parameters we are able to identify certain tree species. Comparing to species information measured on ground, the portion correctly identified trees on all plots could reach up to 90.65 %. The identification result in this research demonstrate the ability to distinguish palm tree using LiDAR point cloud. Furthermore, with more prior knowledge, section-based method enable the process to classify trees into different classes.

Temporal changes of forest species composition studied by compositional data approach

Abstract: Many ecological data are compositional and different quantitative techniques have been used to analyze such data, albeit some of them being methodologically wrong. The aim of this contribution is to apply the compositional data approach to forestry data and demonstrate the strengths of this method for percentage or relative data with infrequent zero values. Basal areas of three dominant tree species (Abies alba, Picea abies, Fagus sylvatica) in 119 forest compartments in some of the Omphalodo-Fagetum forests in Slovenia in 1954 and 2004 were used to investigate the dynamics of forest species composition over a 50-year period. For the investigated period some additional data about geomorphology and harvesting rates within the compartments were used as explanatory variables of compositional change. The species composition of each forest compartment was subjected to several methods within a compositional analysis framework: descriptive, ternary diagram-based graphical presentations, significance of compositional differences between management classes, significance of perturbation differences (the indicator of forest compositional change) and relation of the compositional change with the explanatory variables by means of compositional linear model. Results indicated that the silver fir was the dominant species in both years, but a clear reduction in silver fir proportion was observed after 50 years. The perturbation differences indicated comparatively large relative increase in the proportion of Norway spruce between 1954 and 2004. Subsequently, the perturbation differences were subjected to isometric log-transformation (ilr) and two derived ilr coordinates were further used as dependent variables in the multivariate linear model. The initial stand structure correlated well with the perturbation differences. These were also significantly correlations with salvage cutting, a consequence of silver fir decline in the 1954-2004 period. This study demonstrated that the compositional data approach can be successfully used to study forest dynamics yielding some insights into data which are not possible or even not valid using some alternative methods.

A Landscape Reconstruction Algorithm and pedoanthracological data reveal Late Holocene woodland history in the lowlands of the NE Czech Republic

We aim to obtain composition of the regional vegetation from the pollen record in the high mountains, to use it in the interpretation of the pollen record from the lowlands via a Landscape Reconstruction Algorithm (LRA), and to then compare the pedoanthracological data with the LRA result based on dissimilarity coefficient.We used five pollen sequences from summits of the Eastern Sudetes (NE Czech Republic), and two pollen cores were analysed in the adjacent Litovelské Pomoraví lowlands. The same lowland woodland was sampled in six pedoanthracological sections. All records cover the Late Holocene period.Site-to-site variability of the pollen spectra from the mountains was found to be low. Regional vegetation inferred from shallowest pollen samples corresponds to present-day vegetation inferred from forestry data. Mountain sites represent regional components and can substitute for large sites in an LRA.Mean LRA estimates were more similar to mean charcoal spectra than they were to mean pollen percentages. LRA estimates of Quercus and Fraxinus in the lowlands match the pedoanthracological evidence regarding individual sites. LRA estimates and charcoal spectra both show that the main dominant (>50%) in the woodland studied was Quercus.Mismatching evidence resided in a small number of the determined charcoals and there were differences between some assumptions of the LRA and the real-world conditions. Alnus and Tilia produced higher pollen signals on the expanse of Fagus and Abies, which were recorded by charcoals within the Relevant Source Area of Pollen (RSAP), which varied from a 35 to a 255meter radius, and ranged with the value from forest hollows.Comparability of LRA vegetation estimates and charcoal percentages is still at the level of complementary evidence. However, a quantitative approach shifts the character to substitutable evidence, with potential to provide the same vegetation estimates by both proxies.

Biomass evaluation by the use of Landsat satellite imagery and forestry data

Satellite imagery allows to estimate vegetation parameters related to large areas and to evaluate biogeochemical cycles and radiative energy transfer processes between soil/vegetation and atmosphere.Moreover, the spectral indices derived from remote sensed data can be used for biomass estimation.This paper focuses on the evaluation of above-ground biomass in the Leonessa Municipality, Latium Region (Italy) by the use of Landsat 7 ETM+ (2001) and Landsat 8-OLI (2015) data. To achieve this goal, Rural Development Programs (PSR) and Forest Management Plans(FMP) (2001-2010) have been analyzed to retrieve the main information related to the different types of wood resources. In particular, dendrometry and prospects of different cultivation classes provide the main data such as the extension (ha), the biomass production (m3/ha), the number of plants, the cuts plan of each Forest Management Unit (FMU). This dataset was organized within a Geographical Information System (GIS) as well as Landsat images.Landsat 7 imagery was classified with two spectral indices, Normalized Difference Vegetation Index (NDVI) and Tasseled Cup, in order to find a correlation between remote sensed data and biomass production in m3/ha. Once obtained the spectral model, the analysis was extended to Landsat 8 and the 2015 biomass map was produced and exported on the web. The results, obtained by the exclusively analysis of open source optical remote sensing data, demonstrate their suitability to update FMPs with lower cost if compared to canonical field methods. Additionally, the analysis allows to extend the investigation to un-analyzed areas by forestry studies, too.

System architecture for mastering machine parameter optimisation

In mobile machines, as well as in manufacturing, the overall productivity is essential for business competitiveness. As the operation of a modern mobile machine is affected by various parameters, they need to be tuned to reach an optimal performance – however, due to machine complexity, parameter optimisation is difficult for a typical operator. To enable parameter optimisation locally in machines, this article presents a system architecture to generate information and knowledge from machine fleet data and to utilise them in machine operations in the field. Measurement data is collected and analysed to discover the associations between machine performance and parameter values. While some results are plain statistical distributions, any resulting more sophisticated domain knowledge is stored as rules. Rule-based reasoning enables a zone of interoperation between the information system and domain experts. Once information and knowledge have been generated, they are made available to machines that run the actual parameter assessment application. Results made with forestry data indicate that the system has a considerable potential to improve machine productivity.

A novel forest state assessment methodology to support conservation and forest management planning

A new forest state assessment methodology to complement existing conservation and forestry data has been developed. The aim is to provide tools for strategic planning including spatial distribution of conservation priorities. The method is point-based using a dense systematic sampling grid and provides more detailed information than vegetation maps or forest subcompartment descriptions, but requires less effort than forest inventories. Indicators include canopy composition and structure, deadwood, herbs, microhabitats, disturbances, shrubs and regeneration. The results can inform managers about the structural and compositional diversity of forest stands in the form of thematic maps and can provide the basis for analysis of habitat suitability for forest-dwelling organisms. A smartphone application has been developed to enable electronic data collection. PostGIS and Python scripts were used in the data flow. In this paper, we outline the development of the assessment protocol, and present the sampling design and the variables recorded. The main advantages of the survey methodology are also shown by case-studies based on data collected during the first field season in 2014. The protocol has been designed for low mountain forests in Hungary, but it can be modified to fit other forest types.

Single tree identification using airborne multibaseline SAR interferometry data

Remote sensing data allowlarge scale observation of forested ecosystems. Forest assessment benefits from information about individual trees. Multibaseline SAR interferometry (InSAR) is able to generate dense point clouds of forest canopies, similar to airborne laser scanning (ALS). This type of point cloud was generated using data from the Ka-bandMEMPHIS system, acquired over a mainly coniferous forest near Vordemwald in the Swiss Midlands. This point cloud was segmented using an advanced clustering technique to detect individual trees and derive their positions, heights, and crown diameters. To evaluate the InSAR point cloud properties and limitations, it was compared to products derived from ALS and stereo-photogrammetry. All point clouds showed similar geolocation accuracies with 0.2–0.3mrelative shifts. Both InSAR and photogrammetry techniques yielded points predominantly located in the upper levels of the forest vegetation, while ALS provided points fromthe top of the canopy down to the understory and forest floor. The canopy height models agreed very well with each other, with R² values between 0.84 and 0.89. The detected trees and their estimated physical and structural parameters were validated by comparing them to reference forestry data. A detection rate of ~90% was achieved for larger trees, corresponding to half of the reference trees. The smaller trees were detected with a success rate of ~50%. The tree height was slightly underestimated, with a R² value of 0.63. The estimated crown diameter agreed on an average sense, however with a relatively low R² value of 0.19. Very high success rates (N90%) were obtained when matching the trees detected fromthe InSAR-datawith those detected fromthe ALS- and photogrammetry-data. There, InSAR tree heightswere in themean 1–1.5mlower,with high R² values ranging between 0.8 and 0.9. Our results demonstrate the use of millimeter wave SAR interferometry data as an alternative to ALS- and photogrammetry-based data for forest monitoring.

Research Trend and Policy Evaluation of Urban Tree Canopy and its Effect on Ecosystem Services

The purpose of the study was to review ecosystem service researches and show the trend to guide researchers who want to study ecosystem service. Ecosystem service has been a theoretical base for conservation of nature such as grand mountains, rivers and so on. Moreover, reviewed studies showed economical, social, environmental values of ecosystem service in nature as well as in urban to support ecosystem service theory. By leading metropolitan governments in America, Canada and european and asian countries, the urban tree canopy(UTC)s’ ecosystem services have been evaluated quantitatively and accumluated as urban forestry data. This global trend has an implication that the study of UTC and its ecosystem service can support the provision of urban forestry and green spaces and especially, Korean cities should realize UTCs’ values on ecosystem services and start to apply them institutionally to enhance their urban environment. Keywords: Urban tree Canopy Cover, Urban forest, Urban green space, Urban ecosystem services

Vers une meilleure valorisation des chênes de qualité secondaire

Creating value from low-grade oak is a constant concern of forest policies. Finding markets for this abundant resource can improve the cost-effectiveness of the timber industry and silviculture. New opportunities in the structural area are currently the focus of interest, because of the strong points of wood for green building. In this area, low-grade oak has a strong technological potential, demonstrated through characterization studies. To realize this potential, the LaBoMap of the Arts et Metiers Cluny centre is working on the non-destructive mechanical grading of timber by machine, by combining different technologies and using different models. The article concludes that the inclusion of forestry data would improve the predictive value of models and better quantify the forest resource for the purposes of defining a business model.

Web GIS Based Spatial Data Infrastructure (SDI) System for Forestry Management in Sri Lanka

Web mapping is the process of using geo-spatial information through the cyberspace. It consists both a service activities and end-user activities. Web GIS emphasises geo-data distribution aspects more involved with design aspects such as data accruing, storage, processing with server software architecture such as data storage and algorithms. Web based system is more advanced than stand-alone system because its provide pathway to overcome geographical boundaries. As a result of this one, any user who has internet facility will be able to access the system. This paper aims to address the role of Spatial Data Infrastructure (SDI) as a framework for the development of a web-based system as a tool for facilitating forestry management by resolving current problems with spatial data. Centralise web GIS tool facilitates to visualisation and update geo-spatial data for forestry management sector in Sri Lanka. This research is a one of node to open the gate to access the cyberspace which consists of SDI. Collected forest data were cross checked with existing GIS data sources and Google Earth before incorporate to the system. Web system was created using HTML5 and PHP language. Google Map class library and Java script library were used to render the Google Map to the web GIS system and Arc GIS cloud database is used as a geo-database for storing forestry data. in addition to that, several software packages have been used such as Adobe Dreamweaver, Adobe Photoshop, PHP editor, MySQL database, Java, Ajax, Arc GIS, Arc server etc. Web administrator will be able to maintain and update the system using the given authentication facility and to maintain and change query function of the geo-database. End user will be able to read the map and gather information using attribute data. In addition to that, the system facilities to get the print out of the selected area with all map components such as legend, map title, scale etc. Keywords: Web GIS, Cloud GIS, Forest management, SDI, GIS modelling

Two-stage ingrowth models for four major tree species in Alberta

A two-stage modelling approach was used for developing ingrowth models for four major tree species in Alberta: aspen, lodgepole pine, black spruce and white spruce. The probability of ingrowth presence was modelled first, followed by the modelling of annual amount of ingrowth conditional on ingrowth being present. To handle variable measurement intervals and plot sizes typical of repeatedly measured forestry data, two generalized logistic models were evaluated at the first stage: a traditional model with measurement interval length and plot size factor incorporated as an exponent of a standard logistic function and an alternative model with measurement interval length and plot size factor incorporated as a denominator. Two criteria were evaluated for defining optimal threshold values for plot classification: criterion 1 to maximize the summation of sensitivity and specificity and criterion 2 to maximize the overall rate of correct plot classification. It was found that criterion 1 was superior to criterion 2 for providing better plot classifications. The two ingrowth probability models behaved similarly based on model fitting and model validation results. Annual amount of ingrowth was modelled at the second stage by exponential functions.

Analysis on the Spatial Distribution and Evolutionary Trends of Farmers’ Forestry Income in China

With regard to the implementation of the Collective Forestry Tenure Reform in China, the proportion of forestry income in farmers’ total family income has gradually increased with rapid increases observed in some provinces. Due to these regional disparities, it is important to analyze the spatial distribution and evolutionary trends of farmers’ forestry income to uncover the underlying factors for the existing distribution and note gradual changes from the perspective of economic geography. When spatial distribution and evolutionary trends of farmers’ forestry income data from 31 provinces from 2000 to 2012 were analyzed, we found that farmers’ forestry income during the 13-yr period grew quickly, but the disparities between the highest and lowest income rates gradually widened as large increases in income within a few provinces constituted the bulk of the overall increase across China. We attribute the observed spatial distribution and evolutionary trends to an imbalanced distribution of forestry resources, varying forest species, complicated forestry ownership, multiple forestry policies, differences of labor, and forestry investment.

Forest fires within a temperate landscape: A decadal and millennial perspective from a sandstone region in Central Europe

In Europe, fire is considered an integral part of forest dynamics only in the Mediterranean and in Fenno-Scandinavia. In Central Europe, the ecological role of fire is largely neglected and deemed unimportant. To fill this knowledge gap, we studied ancient and recent fires in temperate coniferous forests of a sandstone landscape. We used palaeoecological and contemporary forestry data to reveal wildfire events in the present-day landscape and in the distant past. Using linear regression and the ENFA method, we identified the factors influencing fire occurrences in the landscape on two time scales. Analyses of soil charcoal concentrations correspond with contemporary forestry data. The main driving factors affecting the incidence of fires were topographic features, namely the heat load index and presence of rocks. Additional important factors were forest composition features, especially the abundance of Pinus sylvestris. Even though the landscape is populated and attractive to tourists, present-day anthropogenic factors, surprisingly, have only marginal effects. Fires have been occurring in similar fire-prone habitats at least since the Subatlantic period, regardless of whether they were caused by humans or lightning. Our results therefore show that fire affects long-term forest vegetation development also in temperate forests of Central Europe. This has far reaching consequences for forest management because, contrary to prevailing beliefs, fire must be considered a natural driver of forest vegetation patterns even in this temperate region.