Ensuring the sustainability of forest resources for future generations has been a central concern for scientists and managers who have been engaged with the science and practice of forest management. Forest resources provide innumerable ecosystem services that benefit society and the environment. Effective and innovative scientific and practical methods have been developed and implemented to protect important ecosystem functions while meeting increased demands for forest products. Changing societal values demand innovative and/or integrated approaches to forest management that meet social, ecological, and economic goals. New monitoring approaches involving continuous evaluation of harvest-induced and human-induced changes in the forest structure and/or function are needed. Additionally, new approaches to forest management, as well as innovative political measures and people's involvement, are needed to encourage the most efficient and effective use of resources. The objective of this review is to gather state-of-the-art research results and techniques relating to the management and analysis of forest resources. The organizers of FORCOM2011 would like to welcome and invite those who intend to share their ideas and thoughts about current problems in forest management with others from different regions and research areas.

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This paper points out some defects of the current forest register and proposes a concept of next-generation forest register from the view point of GIS application to forest management. Since the fundamental purpose of the GIS is to conduct spatial analysis of multiple thematic maps, one of the basic requirements for the next-generation forest register is the management of forest information on a thematic map basis by using the spatial analysis function of the forest GIS. The main proposals are as follows. The forest register information should be divided into multiple pieces according to thematic maps and their attribute information should be managed separately. The administrative data is an accumulation of thematic maps created every year. Data on forest management should be shared with forestry cooperatives and updated by forestry cooperatives electronically. The issue of personal information can be resolved by dividing the forest register information and managing it separately. Detailed historical information of stands is not necessarily contained in the database; rather, utilizing hyperlinks to the Forest Karte (record). The minimum information of forest GIS may be the thematic map of the forest physiognomy. A forestry experiment station-type forest GIS which is equipped with advanced spatial analysis functions is needed, and analysis and research results obtained from the full use of these functions are provided to the forest GIS used by administrative offices and forestry cooperatives.

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The objectives of this study were to develop a national-level system for simulating the carbon dynamics of planted forests of hinoki, which is the second most important plantation species in Japan, and to discuss the effects of forestry on the carbon dynamics of hinoki planted forests at a national scale. We developed a simulation system consisting of a 1-km-grid national-level forest database and a stand-level carbon cycle model. We simulated the forest carbon dynamics for 2005 to 2050 at a national scale using the developed simulation system with estimated climatic values and harvested timber volumes. The results of simulations indicated that the promotion of forestry leads to a decrease in both total carbon stocks and total carbon sequestration for hinoki planted forests in Japan. Longer-term simulation is required to evaluate the effects of clearcutting and subsequent replanting on the carbon dynamics of Japan's forests.

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The Forest Resource Monitoring Survey (FRMS) started to satisfy not only national requirements but also international ones such as the Montreal Process in 1999. Forest type is an important component from which many indicators of forest biodiversity can be estimated, and the country reports of the Montreal Process must include the forest characteristics of forest types. The current data from FRMS is expected to classify natural forest, but there was almost no data on natural forest types in the second country report of Japan. There are several methods of defining, classifying and identifying forest types and the results differ depending on the method used. Most definitions or classifications of species assemblages and identification of indicators are subjective. This study aimed to classify the forest types of natural FRMS stands and to develop a reproducible classification method. As a result, we classified 74.1% of natural stands which corresponded to climatic area and better than classification of recent country reports, but we could not classify all natural stands.

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When synthetic aperture radar (SAR) is used to obtain remote sensing images of forests, the images can be degraded by double reflections of the radar beam, e.g., by reflecting off water surface and then tree stems or by reflecting off a fallen tree and then by other fallen stems, which together act as a corner reflector. Here, we developed a method for correcting for this effect, called the double bounce effect. To demonstrate the method, we monitored changes in a peat swamp forest around Palangka Raya in Central Kalimantan, Indonesia using ALOS/PALSAR data. Biomass surveys were executed in plots of grass, shrub and forest and above ground biomass (AGB) was calculated. Five Fine Beam Double Polarization PALSAR images, which were obtained between July 2007 and December 2010, were used. The backscattering coef cient (BSC) increased asymptotically to the saturation level with increasing AGB. BSC of HH changed linearly with BSC of HV in areas with different biomass and without double bounce. The linear relationship between the BSC of HH and BSC of HV showed plant succession, and the change ratio of HH BSC to HV BSC show a contribution of vertical stems on BSC, since stem biomass was the main part of AGB in L-band. However, in areas such as riverside forests and re scars where BSC suffered from double bounce (DB areas) no such relationship was observed. Because signal of HH returns much stronger than that of HV by double bounce, we assumed that the double bounce changes the ratio of HH BSC and HV BSC inversely. The double bounce effect on HV BSC was corrected using a linear combination of two vectors which showed the contribution of vertical stems and the contribution of double bounce. In contrast to AGB, the original BSC was higher in DB areas than in high biomass forests. However, when we applied the above correction in DB areas, the BSC anomaly in HV disappeared. Curvilinear regression models were developed to estimate AGB using the corrected HV BSC of an image acquired in July 2007. The estimated AGB was not accurate when it exceeded 100 Mg ha^<-1> due to saturation of BSC, although it was reasonable in DB areas except in the case of an image acquired in December 2010. The method for correcting AGB should be suitable for monitoring of deforestation, regeneration and regrowth of trees in areas with low biomass in peat swamp forest.

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Vegetation mapping provides basic information for forest management and planning. In remote sensing research, the process of creating an accurate vegetation map is an important subject. Recently, there has been growing research interest in the object-oriented image classification techniques. The object-oriented image classification consists of multi-dimensional features including object features and thus requires multi-dimensional image classification approaches. For example, a linear model such as the maximum likelihood method of pixelbased classification cannot characterize the patterns or relations of multi-dimensional data. In multi-dimensional image classification, data mining and ensemble learning have been shown to increase accuracy and exibility. This study examined the use of the object-oriented image classification by Random Forest classification for vegetation mapping. Vegetation maps were also created using the Nearest Neighbor method and the Classification and Regression Tree method for comparison of classification accuracy. The study area was Sado Island in Niigata Prefecture, Japan. SPOT/HRG imagery (June 2007) was used and classified into the following seven classes: broadleaved deciduous forest, Japanese cedar, Japanese red pine, bamboo forest, paddy field, urban/road, and bare land. We employed eCognition software for the object-oriented image classification. We selected 18 object features: the mean, standard deviation, ratio, shape, length, and compactness for each band and normalized difference vegetation index value. High accuracy was found for the vegetation maps produced using the Random Forest and Nearest Neighbor methods. The accuracies of these two methods were significantly different from that of the Classification and Regression Tree method, as shown by kappa analysis. Among the three techniques, the Random Forest method showed the highest classification accuracies when class accuracies such as user's accuracy and producer's accuracy were considered. This study demonstrates that the Random Forest classification is effective for vegetation mapping by multi-dimensional image classification.

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This study examined first-year high school students' perceptions of "forests" in general, "artificial forests," and "natural forests" and investigated factors associated with their knowledge of forests, experience of visiting forests, and gender. A questionnaire was administered in a high school in Kobe, Japan, in 2009. The valid response rate was 97.6% (boys 146, girls 139). There were three main findings: carbon storage was less expected in forests in general and natural forests by those who knew the item "substitute for wood products," but more expected in natural forests by those who knew "management by people"; the factor "experience visiting forests" increased students' expectations regarding rare species protection in natural forests, but decreased their expectations regarding carbon storage in the three types of forest; and boys perceived timber production as a function of artificial forests more than girls did.

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This study examined the impacts of socio-economic factors on forest area change by human development level of countries. Cluster analysis and panel data analysis were combined to solve the problem of heterogeneity in panel data analysis and the problem of data availability to some extent. The results show that many socio-economic factors have negative impacts on forest area in countries at low levels of human development, but their impacts become positive in countries at higher levels of human development, such as rate of rural population, adult literacy rate, and GDP per capita. The findings of this research remind decision makers to pay attention to increasing countries' level of human development when attempting to prevent deforestation, because people in higher human development index countries tend to be more protective of forests.

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Spatially explicit classification is an essential planning technique in managing natural forests having structural heterogeneity on a landscape scale. Operationally, forest planners demarcate classification boundaries on-site, based on their empirical knowledge. This study examined factors affecting spatially explicit classification decisions by expert planners in natural forest management in central Hokkaido, Japan. A case analysis was conducted at the University of Tokyo Hokkaido Forest to identify the criteria used for classification decision-making. We examined a total of 930 inventory plots within four major management types ('softwood selection harvest,' 'hardwood selection harvest,' 'pre-harvest,' and 'regeneration activity required') during 2005 to 2009. We calculated tree basal area (BA) and density, species composition, and diameter distribution in each management type. 'Softwood selection harvest' had high abundance and density of conifers (Abies sachalinensis in particular) at marketable size classes [diameter at breast height (DBH) &ge; 13 cm]. In 'hardwood selection harvest,' a large BA of broad-leaved trees (mainly Tilia japonica and Acer mono) was observed at middle- and large-size classes. 'Pre-harvest' had high densities of both coniferous and broad-leaved trees (mainly A. sachalinensis and Betula ermanii) of small diameter. The BA and tree density were relatively low in 'regeneration activities required.' Results from the classification and regression tree analysis indicated that the amount of marketable timber (25 cm &le; DBH < 59 cm) had the primary effect on spatially explicit classification decisions in natural forest.

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