Publisher：Natural Hazards  

Landslides cause significant economic, social, and environmental impacts worldwide. However, selecting the most suitable model and factors for landslide susceptibility mapping (LSM) remains challenging due to the diverse factors influencing landslides and the unique environmental settings in which they occur. Here, we conducted a systematic literature review from 2001 to 2021 to identify the main core-base factors and models used in LSM and highlight areas for future research. We found that there is a need for increased research collaboration with leading knowledge-producing countries and research efforts in underrepresented regions such as Africa, Central America, and South America. Of the 31 most used landslide susceptibility factors, we identified the core-base factors slope, elevation, lithology, land use/land cover, and distance from road, which were the most used, top-ranked predictors and commonly used together when mapping landslide susceptibility. Although aspect was the third most used factor, it ranked among the eight least effective predictors of LSM. Among the core-base factors of LSM, road density, elevation, and slope exhibited the least ranking variability as LSM predictors. The most used methods in LSM were random forest, logistic regression, support vector machine, and artificial neural network, with hybrid, ensemble, and deep learning methods currently trending. Random forest was the most accurate of the four most commonly used models, followed by artificial neural networks. However, artificial neural networks exhibited the least performance variability, followed by support vector machines. This comprehensive review provides valuable insights for researchers in selecting appropriate factors and models for LSM and identifies potential areas for future collaboration and research.

DOI： 10.1007/s11069-024-06697-9

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Language：Japanese   Publisher：Japan Society of Forest Planning  

<p>Shugo Inoue, Tetsuji Ota and Nobuya Mizoue: Searching for the optimal acquisition month for bamboo area detection using satellite constellation images. Jpn. J. For. Plann. 57: 45～51, 2024 We verified the optimal month of data acquisition for detecting bamboo areas from satellite remote sensing data. The study area was the area around the former Tachibana Village in Fukuoka Prefecture, and a total of 11 PlanetScope data were obtained from January to December 2022. Machine learning models were applied to each of the 11 satellite data sets to classify them into two classes: bamboo area and non-bamboo area. Classification accuracy was then compared. The overall accuracy was highest in June (91.2%) and lowest in April (74.6%). The overall accuracy was low in January, gradually increased, and then peaked in May and June, followed by a decrease. In the bamboo area detection using data acquired in June, the red band had the highest importance, and the vegetation indices using the red band, green, and near-infrared band also had high importance. We concluded that data acquired in May-June by satellites with red, green, and near-infrared bands should be used to detect bamboo forests.</p>

DOI： 10.20659/jjfp.57.2_45

CiNii Research

Language：English   Publisher：Ambio  

Unsustainable land use practices have led to increased forest loss rates. Implementing cacao agroforestry can reduce forest loss by preventing the clear-cutting of forests for monoculture plantations. However, research is needed on its effectiveness in preventing forest loss and the factors influencing its adoption between full-time and part-time farmers. Here, we address these gaps in the Maya Golden Landscape, Belize, by using Mahalanobis distance matching to compare forest loss in cacao agroforestry concession, forest reserve, and de-reserve areas and analyzing social data of 187 households. The results suggest that the odds of forest loss in the cacao agroforestry concession area are approximately 16% higher than in the Maya Mountain North Forest Reserve. In comparison, they are 85% lower than in the de-reserved areas. We also report differences in the factors influencing agroforestry adoption between part-time and full-time farmers. Successful cacao agroforestry adoption requires considering the differences that exist between farmers' categories.

DOI： 10.1007/s13280-024-02106-4

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Language：Others   Publishing type：Research paper (scientific journal)   Publisher：Journal of Environmental Management  

Ecosystems around the globe are enduring wildfires with greater frequency, intensity, and severity and this trend is projected to continue as a result of climate change. Climate-smart agriculture (CSA) has been proposed as a strategy to prevent wildfires and mitigate climate change impacts; however, it remains poorly understood as a strategy to prevent wildfires. Therefore, the authors propose a multimethod approach that combines mapping of wildfire susceptibility and social surveys to identify priority areas, main factors influencing the adoption of CSA practices, barriers to their implementation, and the best CSA practices that can be implemented to mitigate wildfires in Belize's Maya Golden Landscape (MGL). Farmers ranked slash and mulch, crop diversification, and agroforestry as the main CSA practices that can be implemented to address wildfires caused by agriculture in the MGL. In order to reduce wildfire risk, these practices should, be implemented in agricultural areas near wildlands with high wildfire susceptibility and during the fire season (February–May), in the case of slash and mulch. However, socio-demographic and economic characteristics, together with a lack of training and extension services support, inadequate consultation by agencies, and limited financial resources, hinder the broader adoption of CSA practices in the MGL. Our research produced actionable and valuable information that can be used to design policies and programs to mitigate the impacts of climate change and wildfire risk in the MGL. This approach can also be used in other regions where wildfires are caused by agricultural practices to identify priority areas, barriers and suitable CSA practices that can be implemented to mitigate wildfires.

DOI： 10.1016/j.jenvman.2023.118562

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Language：Others   Publishing type：Research paper (scientific journal)   Publisher：Oxford University Press (OUP)  

Abstract

Community forests (CFs) have been widely established in tropical countries as a tool to achieve forest conservation. Many studies have shown that CFs can contribute to the reduction of deforestation, yet studies that evaluate the contribution of CFs to reducing forest degradation and facilitating forest recovery remain scarce. We investigated the ability of CFs to prevent deforestation and forest degradation and to facilitate forest recovery by using a country-scale longitudinal tree canopy cover and forest cover dataset in Cambodia. We found that CFs can prevent both forest degradation and deforestation, but we did not observe a forest recovery effect. We also found that recently established CFs are not effective for forest conservation compared with older CFs. We conclude that, to date, CFs are an effective forest conservation tool; however, this does not necessarily mean that new CFs will be as effective as established ones.

DOI： 10.1093/pnasnexus/pgad320

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Language：Others   Publishing type：Research paper (scientific journal)   Publisher：Journal of Forest Research  

Assessing the impact of forest type and age on shallow landslide susceptibility is important for managing protective functions of forests. Previous studies have examined the correlation between forest types and ages, and shallow landslide susceptibility, but causal effects of forest type and age on shallow landslide susceptibility are yet not clear. This study investigated the causal effect of forest type and age on shallow landslide susceptibility using a propensity score method by combining existing geo-data sets. Here, we focus on shallow landslides caused by an extreme rainfall event on 5–6 July 2017, in the mountainous areas of Asakura City and Toho Village, Fukuoka Prefecture, Japan. Randomly located samples of coniferous forest, broadleaved forest, and young forest exposed to shallow landslide events were analyzed. The inverse probability of treatment weighting with the propensity score was applied to the samples to compare the differences in shallow landslide susceptibility among the three forest classes. Young forest had increased shallow landslide susceptibility, compared with coniferous forest and broadleaved forest, by 3.70 × 10−2 and 4.12 × 10−2, respectively, which corresponded to a two to three times increase in shallow landslide susceptibility when the forest changes from coniferous or broadleaved forest to young forest. No significant difference in shallow landslide susceptibility between coniferous forest and broadleaved forest was observed. These differences in shallow landslide susceptibility should be considered when implementing forest management schemes.

DOI： 10.1080/13416979.2023.2227820

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Authorship：Corresponding author   Language：English   Publisher：Japan Society of Forest Planning  

<p>Semi-captive Asian elephants (<i>Elephas maximus</i>) engaged in forestry activities in Myanmar account for 20% of all captive and semi-captive Asian elephants in the world, and are important for both forestry and the conservation of Asian elephant populations. Understanding moving behavior of the semi-captive elephants is required to sustain them. Our specific goals are 1) to determine the moving range during free time, and 2) to determine the hourly moving distance during skidding and when off duty. Three elephants were fitted with handheld global navigation satellite systems with the signals of global positioning system to collect data on their movements. The elephants were generally located between 0.534 and 0.875 km from the camp with temporary housing of the elephant handler when not skidding (i.e., free time) and between 1.365 and 1.372 km when skidding (i.e., work time). The hourly moving distance during free time (0.622–0.655 km) and work time (1.522 and 1.629 km) did not differ greatly from the hourly moving distance of wild Asian elephants (0.010–1.500 km). The elephants remained within 0.875 km of the camp of the elephant handler, and some variation in movements among individuals was observed during free time. Thus, the conservation of forest in areas near the camp is important for the well-being of these elephants.</p>

DOI： 10.20659/jfp.2022.002

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Journal of Forest Research  

There has been growing interest in the practice of reduced-impact logging (RIL) to enhance various ecological services of selectively logged tropical forests. It is thus important to evaluate the effectiveness of different logging operations between RIL and conventional logging (CON). The Myanmar Selection System (MSS) has a long history and is well known for its use of elephants for skidding, which is considered one form of RIL. However, the difference in the impacts of logging operations between the MSS using elephants for skidding and machine-only-based operations commonly adopted in other countries is unknown. The present study evaluated ground disturbance along elephant skid trails, along logging roads and at log landings in four compartments logged under the MSS, comparing with reported values for CON and RIL in the other countries. The ground disturbance in the MSS compartments was, respectively, 2.1% and 0.4% in average for logging roads and log landings; it is not significantly different from that for CON and RIL (p > 0.05). In contrast, the disturbed area along elephant skid trails (0.9%) is much lower than that for CON (5.2%) and RIL (4.7%) (p < 0.05). A large difference in the width of skid trails was found between elephant skidding (1.0 m) and machinery (CON: 5.5 m, RIL: 4.6 m) (p < 0.0001). We conclude that elephant skidding can largely reduce ground disturbance due to much narrower width of the skid trails as compared with machine skidding, while MSS does not differ from the other countries in ground disturbance at logging roads and log landings.

DOI： 10.1080/13416979.2022.2067618

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：International Journal of Digital Earth  

Mapping of deforestation, forest degradation, and recovery is essential to characterize country-level forest change and formulate mitigation actions. Previous studies have mainly used a simple forest/non-forest classification after forest disturbance to identify deforestation and forest degradation. However, a more flexible approach that is applicable to different forest conditions is desirable. In this study, we examined an approach for mapping deforestation, forest degradation, and recovery using disturbance types and tree canopy cover estimates from annual Landsat time-series data from 1988 to 2020 across Cambodia. We developed models to estimate both disturbance types and tree canopy cover based on a random forest algorithm using predictor variables derived from a trajectory-based temporal segmentation approach. The estimated disturbance types and canopy cover in each year were then used in a rule-based classification of deforestation, forest degradation, and recovery. The producer’s and user’s accuracies ranged from 59.1% to 72.9% and 60.8% to 91.6%, respectively, for the forest change classes of mapping deforestation, forest degradation, and recovery. The approach developed here can be adjusted for different definitions of deforestation, forest degradation, and recovery according to research objectives and thus has the potential to be applied to other study areas.

DOI： 10.1080/17538947.2022.2061618

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Language：English   Publishing type：Research paper (scientific journal)  

Community forestry, which is how local communities are involved in forest conservation and utilization activities, is an important forestry program in developing tropical countries. We evaluated the importance of geographical factors and community characteristics in the deforestation of community forests between 2000 and 2019 in the buffer zone of Inlay Lake Biosphere Reserve, Myanmar, using a mixed-effects logistic regression model. Distance to the nearest village, slope, and distance to the community forestry boundary were the most important variables explaining deforestation in community forests. Forests closer to human settlements and with gentle slopes faced higher risks of deforestation, presumably because such forests are more accessible. In addition, forests located far from the boundaries of community forests were more vulnerable to deforestation. Community characteristics were less important compared with geographical factors. Leadership was the most important variable among community characteristics, although not statistically significant. We conclude that deforestation depends more on forest accessibility. This indicates that the locations at which new community forests are established should receive increased consideration.

DOI： 10.3390/f12050541

Language：English   Publishing type：Research paper (scientific journal)  

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. The restoration of degraded forests is the focus of global attention. Effective restoration requires information on the condition of degraded forests. This study aimed to understand the conditions of illegally logged stands that had also experienced inappropriately short rotations between legal logging cycles in natural production forests in Myanmar. Four rectangular plots (each 0.64 ha) were established in 2013. The plots included illegally logged stumps in three compartments where the latest legal logging was conducted in 2011 after very short rotations between legal logging cycles (up to five harvests between 1995 and 2011, compared with a recommended 30-year logging cycle). Using data from the field measurements in 2013 on the legal and illegal stumps and living trees, we reconstructed stand structure just before and after legal logging in 2011. Before the legal logging in 2011, there were variations in stand structure and the composition of commercial species among four plots. Illegal logging (14–31 trees ha−1 ) was much higher than legal logging (0–11 trees ha−1 ). Illegal logging targeted six to nine species that were suitable for high-quality charcoal from various sized trees, while legal logging targeted one or two timber species with a diameter at breast height (DBH) larger than 58 cm. The number of remaining trees in 2013 ranged from 33 to 181 trees ha−1 . There was a negative relationship with the number of bamboo clumps, which varied from 6 to 145 clumps ha−1 . Bamboo-dominated stands with a low remaining stock of commercial trees may need active restoration such as bamboo cutting and replanting of commercial species. Bamboo cutting could generate income for the local community.

DOI： 10.3390/f12020115

Language：Others   Publishing type：Research paper (scientific journal)  

© 2020 The Author(s) The Myanmar selection system (MSS) has a long tradition for timber production in natural forests, mainly using elephants for skidding. We aimed to evaluate the levels of MSS disturbance to standing trees and the ground as compared with those reported for other tropical countries, and to identify possible ways to improve MSS operations. At each of four study sites, we established a 9-ha rectangular plot with nine 1-ha subplots. We measured all the living trees >10 cm diameter at breast height (DBH) in two of the subplots before felling, damage to residual trees immediately after felling operations and ground disturbance about 3 months after skidding operations in all nine subplots. Harvesting intensity varied from 0 to 18 trees ha−1 (143.7 m3 ha−1) with the mean of 5.2 trees ha−1 (39.0 m3 ha−1) among a total of 36 1-ha subplots. The harvesting intensity was linearly related to felling damage to residual trees (&#37; trees) and bamboo clumps (&#37; clumps), and to ground disturbance (&#37; area) (roads, log landings, skid trails, and machine-disturbed areas). The damage and disturbance increasing with harvesting intensity were at the lowest level of, or not significantly different from, those reported in other studies. The lowest level of ground disturbance is because of the use of elephants for skidding, resulting in no visible ground disturbance only a few months after the operation. However, some felled trees were too large (>100 cm DBH) for elephant skidding, and so a bulldozer was used instead. The aid of the machine for skidding resulted in a greater proportion of disturbed area (2.4&#37; of the area at Site 4). To minimize disturbance to residual trees and the ground, we suggest to limit the maximum harvesting intensity and avoid harvesting trees too big for elephants to drag. Retaining such large trees may also be beneficial to provide seed sources and for biodiversity conservation.

DOI： 10.1016/j.gecco.2020.e01214

Language：English   Publishing type：Research paper (scientific journal)  

© 2019, Northeast Forestry University. This study quantified the effect of weeding frequency and weeding schedules on weeding operation time in a sugi (Cryptomeria japonica) plantation stand. A weeding operation time estimation model was developed; then the cumulative weeding operation time after six growing seasons was simulated using the developed model. The developed model included weed height, relative height of weeds to sugi, and initial planting density. The simulated cumulative weeding operation time decreased approximately 6&#37; for each one-treatment decrease in weeding frequency. Under a three-treatment weeding frequency scenario, the simulated cumulative operation time when weeding was conducted during non-consecutive years was longer than that when weeding was conducted during three consecutive years. The results suggest that carrying out weeding treatment during consecutive years is the more effective for reduction of weeding costs. We conclude that weeding schedule as well as weeding frequency must be considered for reduction of weeding operation time.

DOI： 10.1007/s11676-019-01017-5

Language：English   Publishing type：Research paper (scientific journal)  

© 2020 The Author(s) Firewood is a major energy source, especially in developing countries. Because firewood is often collected from forest, forest quality may affect firewood consumption patterns. Here, we evaluated the impact of forest degradation on firewood consumption patterns in a rural area of Myanmar. Household interviews were conducted for a total of 143 households from 20 villages, and per capita consumption rates for each household were calculated. The per capita consumption rates of households that only used firewood for cooking were regressed against several potentially important factors, including an index of forest degradation. Approximately 85&#37; of sample households used firewood for cooking. The average per capita annual firewood consumption rate for cooking was 530 kg for households that used exclusively firewood for cooking. The regression analysis clearly showed that open forest area ratio, an indicator of forest degradation, had a negative effect on per capita annual firewood consumption rate. In addition to open forest ratio, household size, elevation, and the consumption rate of firewood for drying cigar leaves were strongly related to per capita annual firewood consumption rate. However, the nearest distance to forest had a weak relationship with per capita annual firewood consumption, although previous studies have suggested that the nearest distance to forest negatively affects the firewood consumption rate. Combined with previous studies showing that fuelwood collection causes forest degradation, we conclude that forest degradation and decreases in firewood consumption mutually affected each other. Additional studies are needed to assess the role of forest degradation in maintaining local livelihoods.

DOI： 10.1016/j.gecco.2020.e01340

Language：Others   Publishing type：Research paper (scientific journal)  

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. In many tropical regions, rural households often depend on forests for cash income, but there is still little knowledge on how forest use strategies differ among people living in different locations. This study aimed to detect differences in forest use strategies and forest cash income dependency between households living outside and inside selectively logged production forests, known as Reserved Forests (RFs), in Bago Township, Myanmar. A questionnaire survey was conducted with 146 and 48 households living outside and inside the RFs, respectively. The inside-households (encroachers) had a much higher forest cash income dependency (83&#37;), with charcoal production as the main forest use activity, than the outside-households (32&#37;), with bamboo cutting as the main activity. Higher forest dependency was found for outside-households that had less farmland, better accessibility to forest and more recognition of prohibited access to RFs in forest law. This study revealed evidence of substantial forest use for commercial purposes in RFs by households living both inside and outside the RFs, despite local recognition of the illegality of the use. Implementing community forestry practices for local communities may be a better option to reduce illegal dependence on selectively logged production forests.

DOI： 10.3390/f11121263

Language：English   Publishing type：Research paper (scientific journal)  

© 2020 by the authors. Forest disturbances are generally estimated using globally available forest change maps or locally calibrated disturbance maps. The choice of disturbance map depends on the trade-offs among the detection accuracy, processing time, and expert knowledge. However, the accuracy differences between global and local maps have still not been fully investigated; therefore, their optimal use for estimating forest disturbances has not been clarified. This study assesses the annual forest disturbance detection of an available Global Forest Change map and a local disturbance map based on a Landsat temporal segmentation algorithm in areas dominated by harvest disturbances. We assess the forest disturbance detection accuracies based on two reference datasets in each year. We also use a polygon-based assessment to investigate the thematic accuracy based on each disturbance patch. As a result, we found that the producer's and user's accuracies of disturbances in the Global Forest Change map were 30.1-76.8&#37; and 50.5-90.2&#37;, respectively, for 2001-2017, which corresponded to 78.3-92.5&#37; and 88.8-97.1&#37;, respectively in the local disturbance map. These values indicate that the local disturbance map achieved more stable and higher accuracies. The polygon-based assessment showed that larger disturbances were likely to be accurately detected in both maps; however, more small-scale disturbances were at least partially detected by the Global Forest Change map with a higher commission error. Overall, the local disturbance map had higher forest disturbance detection accuracies. However, for forest disturbances larger than 3 ha, the Global Forest Change map achieved comparable accuracies. In conclusion, the Global Forest Change map can be used to detect larger forest disturbances, but it should be used cautiously because of the substantial commission error for small-scale disturbances and yearly variations in estimated areas and accuracies.

DOI： 10.3390/RS12152438

Language：Japanese   Publishing type：Research paper (scientific journal)  

The influence of sampling grid resolution and understory on forest structure estimation from terrestrial laser scanning
<p>Naoki Suematsu, Tetsuji Ota, Katsuto Shimizu, Keiko Fukumoto, Nobuya Mizoue, Akio Inoue, Haruka Kitazato,Hideo Kusano, Hirofumi Kai and Yasushi Omasa: The influence of sampling grid resolution and understory on forest structure estimation from terrestrial laser scanning. Jpn. J. For. Plann. 54: 45~54, 2020 This study investigated the effects of sampling grid resolution and understory on tree stem detection and diameter at breast height（DBH）, tree height,and stem volume estimation from terrestrial laser scanning（TLS）. We established six rectangular plots. Within three plots,we removed the understory before scanning. The TLS data were acquired using a 5 m sampling grid. Four sampling-grid resolutions were tested by changing the combinations of TLS data used for registration of scans. The results showed that the understory and/or a coarser grid resolution negatively affected the registration of TLS, detection of stems, and estimation of DBH, tree height, and volume.</p>

DOI： 10.20659/jjfp.54.1_45

Language：English   Publishing type：Research paper (scientific journal)  

© 2020 Elsevier Ltd Considerable proportions of the remaining global forest areas are currently harboured in tropical countries. Reducing deforestation in this region is important to help mitigate climate change. Effective forest conservation approach is needed to reduce deforestation and degradation in these countries. Here, we investigated the forest conservation effect of community forests and protected areas using country scale data in Cambodia. In addition to these two forest conservation approaches, we also evaluated the effectiveness of protected forests, which are similar to protected areas but managed by different authorities. We compared deforestation between 2006 and 2016 in areas under the three forest conservation approaches and a non-conserved area by applying the inverse probability of treatment weighting with the propensity score minimizing confounding effects. The results showed that community forest, protected areas, and protected forest significantly decreased deforestation compared with non-conserved forests. Out of the three forest conservation approaches, protected forest was the most effective and community forest was the least effective. We conclude that all of the policies we evaluated are effective for forest conservation but the effectiveness varies depending on the approach. Our findings also suggest that the authority managing the given approach plays an important part in its effectiveness.

DOI： 10.1016/j.landusepol.2020.104597

Language：Others   Publishing type：Research paper (scientific journal)  

© 2020 by the authors. Developing accurate methods for estimating forest structures is essential for efficient forest management. The high spatial and temporal resolution data acquired by CubeSat satellites have desirable characteristics for mapping large-scale forest structural attributes. However, most studies have used a median composite or single image for analyses. The multi-temporal use of CubeSat data may improve prediction accuracy. This study evaluates the capabilities of PlanetScope CubeSat data to estimate canopy height derived from airborne Light Detection and Ranging (LiDAR) by comparing estimates using Sentinel-2 and Landsat 8 data. Random forest (RF) models using a single composite, multi-seasonal composites, and time-series data were investigated at different spatial resolutions of 3, 10, 20, and 30 m. The highest prediction accuracy was obtained by the PlanetScope multi-seasonal composites at 3 m (relative root mean squared error: 51.3&#37;) and Sentinel-2 multi-seasonal composites at the other spatial resolutions (40.5&#37;, 35.2&#37;, and 34.2&#37; for 10, 20, and 30 m, respectively). The results show that RF models using multi-seasonal composites are 1.4&#37; more accurate than those using harmonic metrics from time-series data in the median. PlanetScope is recommended for canopy height mapping at finer spatial resolutions. However, the unique characteristics of PlanetScope data in a spatial and temporal context should be further investigated for operational forest monitoring.

DOI： 10.3390/rs12111876

Language：Japanese   Publishing type：Research paper (scientific journal)  

Language：Others   Publishing type：Research paper (scientific journal)  

© 2020 by the authors. Understanding the stand dynamics of tropical production forests is essential for determining the sustainability of a polycyclic selective logging system, but limited related studies have addressed the impacts of illegal logging over time. Myanmar faces the extensive degradation of traditional production forests with a 160-year logging history, but the cause of this degradation and how to balance legal and/or illegal disturbances with recovery in over-logged forests remain unclear. The present study investigated stand structural changes over 5 years after official legal logging operations using two 1-ha (100 × 100 m) sample plots. For 5 years after logging, the volume of trees with a diameter at breast height (DBH) ≥ 20 cm decreased by 46.0&#37; from 121 to 65.1 m3 ha-1, with a significant loss of the first-and second-grade species group (Tectona grandis Linn. f. and Xylia xylocarpa (Roxb.) Taub.) from 48.3 to 6.8 m3 ha-1. The total tree loss owing to official logging operations, mainly targeting the second-and fourth-grade species group, was 29.3 m3 ha-1. A similar level of total tree loss (28.0 m3 ha-1) was attributed to illegal logging that targeted the first-and second-grade species group. The mean annual recruitment rate of 3.1&#37; was larger than the reported values for tropical forests, but there were no and only 1.5 trees ha-1 recruitments s for T. grandis and X. xylocarpa, respectively. The mean annual mortality rate of 2.5&#37; was within the values reported in the related literature, and the volume loss from the mortality was relatively similar to the gain from the increment of living trees for all species groups. We concluded that the effects of illegal disturbances for 5 years post-harvest were equivalent to those of legal disturbances and larger than those of natural change, and are a major cause of the substantial reduction in stocking levels, especially for commercial species.

DOI： 10.3390/f11020195

Language：Others   Publishing type：Research paper (scientific journal)  

© 2019 by the authors. Comprehensive forest cover mapping is essential for making policy and management decisions. However, creating a forest cover map from raw remote sensing data is a barrier for many users. Here, we investigated the effects of different tree cover thresholds on the accuracy of forest cover maps derived from the Global Forest Change Dataset (GFCD) across different ecological zones in a country-scale evaluation of Myanmar. To understand the effect of different thresholds on map accuracy, nine forest cover maps having thresholds ranging from 10&#37; to 90&#37; were created from the GFCD. The accuracy of the forest cover maps within each ecological zone and at the national scale was assessed. The overall accuracies of ecological zones other than tropical rainforest were highest when the threshold for tree cover was less than 50&#37;. The appropriate threshold for tropical rainforests was 80&#37;. Therefore, different optimal tree cover thresholds were required to achieve the highest overall accuracy depending on ecological zones. However, in the unique case of Myanmar, we were able to determine the threshold across the whole country. We concluded that the threshold for tree cover for creating a forest cover map should be determined according to the areal ratio of ecological zones determined from large-scale monitoring. Our results are applicable to tropical regions having similar ecological zones.

DOI： 10.3390/F10121062

Language：Others   Publishing type：Research paper (scientific journal)  

© 2019 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Landsat time series images are used for the detection of forest disturbance and the classification of causal agents. Various studies have classified disturbance agents with respect to forest disturbance detected using Landsat time series images. However, the accuracy of the finally classified disturbance agents in different approaches is rarely evaluated. In this study, we investigated the effectiveness of using ensemble classification, and multiple spectral and spatio-temporal information for the accuracy of the classification of disturbance agents in two-stage prediction (i.e., disturbance agents are classified with respect to the detected disturbance) and direct prediction (i.e., disturbance agents are directly classified from Landsat temporal information). Predictor variables were derived from the results of the trajectory-based temporal segmentation of five spectral indices using an annual Landsat time series (2000–2018). We compared six approaches of classifying disturbance agents. For two-stage prediction, we investigated four disturbance detection approaches: threshold-based detection with a single spectral index, random forest (RF) model with a single spectral index, RF model with multiple spectral indices, and RF model with spatio-temporal variables. The detected disturbance pixels were aggregated to disturbance patches and classified into disturbance agents. For direct prediction, two RF models one with only temporal variables and the other with spatio-temporal variables were constructed to classify pixel-based disturbance agents. The overall accuracy of the RF model using spatio-temporal variables for direct prediction was 92.4&#37; and significantly higher than that of the RF model for two-stage prediction (90.9&#37;). The use of an RF model based only on a single spectral index in disturbance detection was not effective for improving accuracy compared with threshold-based detection; however, the use of an RF model based on multiple spectral indices in disturbance detection improved the accuracy of the final classification of disturbance agents. Introducing spatial variables in RF models was effective for improving the overall classification accuracy in pixel-based direct prediction. However, it was not necessary in two-stage prediction because of spatial information contained in the patches. Although a spatially discontinuous appearance was observed for the RF model for directly classifying disturbance agents, this could be an alternative approach to two-stage prediction when considering the relative classification performance and simplicity of implementation.

DOI： 10.1016/j.isprsjprs.2019.10.004

Language：Others   Publishing type：Research paper (scientific journal)  

© 2019 by the authors. Siem Reap River has played a crucial role in maintaining the Angkor temple complex and livelihood of the people in the basin since the 12th century. Land use in this watershed has changed considerably over the last few decades, which is thought to have had an influence on river. This study was carried out as part of assessing the land use and climate change on hydrology of the upper Siem Reap River. The objective was to reconstruct patterns of annual deforestation from 1988 to 2018 and to explore scenarios of land use 40 and 80 years into the future. A supervised maximum likelihood classification was applied to investigate forest cover change in the last three decades. Multi-layer perceptron neural network-Markov chain (MLPNN-MC) was used to forecast land use and land cover (LULC) change for the years 2058 and 2098. The results show that there has been a significantly decreasing trend in forest cover at the rate 1.22% over the last three decades, and there would be a continuous upward trend of deforestation and downward trend of forest cover in the future. This study emphasizes the impacts of land use change on water supply for the Angkor temple complex (World Heritage Site) and the surrounding population.

DOI： 10.3390/hydrology6030064

Language：Others   Publishing type：Research paper (scientific journal)  

© 2019 Foundation for Environmental Conservation. Increasing attention has been given to evaluating the effectiveness of forest conservation projects, but it is not well known whether different methods yield similar results when evaluating changes in forest resources. The present study compares forest cover maps and local people's perceptions for evaluating the effectiveness of the Chambok community-based ecotourism (CBET) programme in Cambodia. We assessed forest cover changes from 2000 to 2012 using published global maps and used a covariate matching method to compare forest sites in CBET and non-CBET areas. We also analysed local people's perceptions of forest resource changes by interviewing 174 households. The forest cover maps showed that the Chambok CBET was effective at reducing deforestation, although the outcome was not completely robust to unobserved heterogeneity. Local people's perceptions concurred with the effectiveness observed in the forest cover maps, in that 64&#37; of the people perceived that forest resources increased and 75&#37; thought that the local community could protect its own forest resources. We conclude that the Chambok CBET performed effectively for forest conservation and suggest that mixed-method approaches are essential for evaluating the effectiveness of conservation programmes.

DOI： 10.1017/S0376892918000462

Language：Japanese   Publishing type：Research paper (bulletin of university, research institution)  

Language：Others   Publishing type：Research paper (scientific journal)  

© 2018 Elsevier B.V. Selective logging is one of the factors contributing to deforestation and forest degradation in tropical forests. A low-cost methodology to monitor selective logging is clearly required. However, this poses a challenge because only a few trees are felled at a given time. Here, we investigate the potential of using repeatedly acquired digital aerial photographs (DAPs) from a lightweight unmanned aerial vehicle (UAV) to detect selective logging in tropical forests in Myanmar. Selective logging was conducted within two 9-ha plots. DAPs were acquired immediately before and after selective logging using a lightweight UAV in this case study. The aboveground biomass (AGB) change related to selective logging was regressed against metrics expressing forest changes calculated at a 0.25-ha resolution from a photogrammetric point cloud created using the DAPs before and after selective logging. The root-mean-square error and coefficient of determination were 0.77 and 9.32 Mg/ha, respectively. This study demonstrates that repeated DAPs taken from a lightweight UAV can be used to estimate changes in the AGB linked to selective logging. This method could be used to quantify the impacts of both legal selective logging and illegal logging in tropical forests.

DOI： 10.1016/j.foreco.2018.10.058

Language：English   Publishing type：Research paper (scientific journal)  

The accurate and timely detection of forest disturbances can provide valuable information for effective forest management. Combining dense time series observations from optical and synthetic aperture radar satellites has the potential to improve large-area forest monitoring. For various disturbances, machine learning algorithms might accurately characterize forest changes. However, there is limited knowledge especially on the use of machine learning algorithms to detect forest disturbances through hybrid approaches that combine different data sources. This study investigated the use of dense Landsat 8 and Sentinel-1 time series data for detecting disturbances in tropical seasonal forests based on a machine learning algorithm. The random forest algorithm was used to predict the disturbance probability of each Landsat 8 and Sentinel-1 observation using variables derived from a harmonic regression model, which characterized seasonality and disturbance-related changes. The time series disturbance probabilities of both sensors were then combined to detect forest disturbances in each pixel. The results showed that the combination of Landsat 8 and Sentinel-1 achieved an overall accuracy of 83.6% for disturbance detection, which was higher than the disturbance detection using only Landsat 8 (78.3%) or Sentinel-1 (75.5%). Additionally, more timely disturbance detection was achieved by combining Landsat 8 and Sentinel-1. Small-scale disturbances caused by logging led to large omissions of disturbances; however, other disturbances were detected with relatively high accuracy. Although disturbance detection using only Sentinel-1 data had low accuracy in this study, the combination with Landsat 8 data improved the accuracy of detection, indicating the value of dense Landsat 8 and Sentinel-1 time series data for timely and accurate disturbance detection.

DOI： 10.3390/rs11161899

Language：Others   Publishing type：Research paper (scientific journal)  

© 2018 Elsevier B.V. Community-based ecotourism (CBET) has become a popular tool, especially in developing countries, for biodiversity conservation and livelihood improvement, but there is a lack of studies that use quantitative data to evaluate the effectiveness of CBET using a before-after-control-intervention (BACI), BA, or CI design. We conducted a case study of the flagship Chambok CBET program in Cambodia to quantify the contribution of CBET to household income and livelihood changes for CBET and non-CBET members. We conducted an interview survey of 173 households (77 CBET and 96 non-CBET members) that were selected systematically from every two households along roads within the same villages. There was no significant difference between the total income of member and non-member households; the median CBET income was 1.22 USD per month, which is only 1.65&#37; of the total income. The inequality of income from CBET among the CBET members was higher than that from other income sources. The perceptions of livelihood changes differed considerably before and after the establishment of the Chambok CBET program, although this difference was not attributed to CBET but rather to general socioeconomic changes in the country. We conclude that it is challenging to employ CBET to achieve poverty reduction and livelihood improvement.

DOI： 10.1016/j.ecolecon.2018.04.036

Language：Others   Publishing type：Research paper (scientific journal)  

© 2018 by the authors. Shifting cultivation is a widely practiced agriculture system in the tropics. Regardless of the dominant land use, the dynamics of shifting cultivation over large areas are of limited knowledge. We conducted patch-based assessments and characterization of shifting cultivation extracted from already developed dataset, which detected shifting cultivation by a trajectory-based analysis using annual Landsat TM/ETM+/OLI time series images from 2000 to 2014 in Myanmar. An accuracy assessment was conducted in terms of the size and number of cleared areas compared with reference polygons of shifting cultivation, which were manually delineated by visual interpretation using Landsat and high-resolution satellite images from Google Earth™ in the selected areas. The producer's and user's accuracies in detecting the number of shifting cultivation patches were 78.1&#37; and 88.4&#37;, respectively. In whole study area, the probability of disturbances caused by shifting cultivation was significantly affected by distance to the nearest village, indicating the importance of accessibility from residences. The number of shifting cultivation patches showed a decreasing trend in this region and it will lead to less cleared forests such as located far from residences. These dynamics of shifting cultivation have possibility to affect the mosaic patterns of landscape and function maintained in the landscape in this region.

DOI： 10.3390/su10093350

Language：Others   Publishing type：Research paper (scientific journal)  

© 2018 by the authors. There is concern over the environmental impact of charcoal use for cooking in urban areas; however, studies have mainly been limited to Africa and South Asia. This investigation aimed to evaluate woodfuel consumption rates and patterns in an urban area in Yedashe Township, Myanmar and compared them with results from a rural area in the same township. From interviews with 66 urban households, it was evident that firewood and charcoal consumption rates in the urban areawere about one-third and one-fourth, respectively, of those in the rural area. These lowconsumption rates were because of multiple-fuel use (mainly woodfuel and electricity) in the urban area in contrast to single-fuel use in the rural area. We estimated the forest area required to meet woodfuel demand of the whole township to be 3738 ha; that could decrease by almost 40&#37; (1592 ha) if the single-fuel use in the rural area switched to the multiple-fuel methods used in the urban area. This study confirms that urbanization with an "energy stack" in multiple-fuel use, rather than an "energy ladder" from firewood to charcoal, could largely reduce the environmental impact on forests.

DOI： 10.3390/f9070429

Language：Others   Publishing type：Research paper (scientific journal)  

© 2018 The Japanese Forest Society. Monitoring forest changes based on numerous satellite images has been recently conducted in the tropics. Preparation of a time series of satellite images, sometimes referred to as preprocessing, is essential for conducting robust detection of forest change. To create consistent and stable conditions in satellite images, the best methods have to be used in each step to correct various sources of noise. This study assessed three atmospheric correction methods, six topographic correction methods, and eight gap-filling methods to produce the best possible time series of Landsat images of tropical seasonal forests. The results showed that the best methods for atmospheric and topographic correction were relative corrections using a Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS), which is based on a 6S radiative transfer model, and the C-correction, respectively. Weighted linear regression and multiple linear regression models were selected as the best models for the filling of data gaps associated with Scan Line Corrector-off and clouds, respectively. This study provided the best possible image preprocessing for trajectory-based change detection using annual Landsat images. Although the best possible preprocessing methods might vary depending on the change detection methods used in different study areas, the results highlight the preferable preprocessing methods, even for different types of time series analysis.

DOI： 10.1080/13416979.2018.1434034

Language：Others   Publishing type：Research paper (scientific journal)  

© 2018 by the authors. Community forestry (CF) is increasingly used in developing countries to achieve both the socioeconomic outcome of poverty reduction and an ecological outcome. There have been many single case studies in a specific region to identify the factors affecting the success or failure of CF. Other studies have used large-N data collected from multiple countries. However, there is a dearth of large-N studies within a single country. In this study, we used a country scale dataset of 197 CF projects, established between 1994 and 2005 across Cambodia, to identify the biophysical factors that affected forest cover changes from 2005 to 2016. A mixed-effects logistic regression model was used for a total of 71,252 randomly sampled data pixels nested in the 197 CF. Results showed that deforestation in CF was likely to increase with increasing size of CF area at lower elevations and on gentler slopes. Deforestation also increased if CF was located close to villages, markets and CF boundaries, but further away from main roads. These findings on biophysical factors can help the government to decide on priority locations for further conservation interventions or for the establishment of new CF projects.

DOI： 10.3390/f9050273

Language：Others   Publishing type：Research paper (scientific journal)  

© 2018 by the authors. Developing countries that intend to implement the United Nations REDD-plus (Reducing Emissions from Deforestation and forest Degradation, and the role of forest conservation, sustainable management of forests, and enhancement of forest carbon stocks) framework and obtain economic incentives are required to estimate changes in forest carbon stocks based on the IPCC guidelines. In this study, we developed a method to support REDD-plus implementation by estimating tropical forest aboveground biomass (AGB) by combining airborne LiDAR with very-high-spatial-resolution satellite data. We acquired QuickBird satellite images of Kampong Thom, Cambodia in 2011 and airborne LiDAR measurements in some parts of the same area. After haze reduction and atmospheric correction of the satellite data, we calibrated reflectance values from the mean reflectance of the objects (obtained by segmentation from areas of overlap between dates) to reduce the effects of the observation angle and solar elevation. Then, we performed object-based classification using the satellite data (overall accuracy = 77.0&#37;, versus 92.9&#37; for distinguishing forest from non-forest land). We used a two-step method to estimate AGB and map it in a tropical environment in Cambodia. First, we created a multiple-regression model to estimate AGB from the LiDAR data and plotted field-surveyed AGB values against AGB values predicted by the LiDAR-based model (R2 = 0.90, RMSE = 38.7 Mg/ha), and calculated reflectance values in each band of the satellite data for the analyzed objects. Then, we created a multiple-regression model using AGB predicted by the LiDAR-based model as the dependent variable and the mean and standard deviation of the reflectance values in each band of the satellite data as the explanatory variables (R2 = 0.73, RMSE = 42.8 Mg/ha). We calculated AGB of all objects, divided the results into density classes, and mapped the resulting AGB distribution. Our results suggest that this approach can provide the forest carbon stock per unit area values required to support REDD-plus.

DOI： 10.3390/rs10030438

Language：Others   Publishing type：Research paper (scientific journal)  

© 2017 Universitas Brawijaya. This study evaluated the uncertainty of individual tree biomass estimated by allometric models by both including and excluding tree height independently. Using two independent sets of measurements on the same trees, the errors in the measurement of diameter at breast height and tree height were quantified, and the uncertainty of individual tree biomass estimation caused by errors in measurement was calculated. For both allometric models, the uncertainties of the individual tree biomass estimation caused by the use of a specific allometric model were also calculated. Finally, the overall uncertainty of individual tree biomass by combining the two uncertainties was calculated. The allometric model including tree height was 6 &#37; more accurate than that excluding tree height when the uncertainty caused by allometric models became the only consideration. However, in terms of the uncertainty caused by measurement, the allometric model excluding tree height was three times more accurate than allometric model including tree height. As a result, the allometric model excluding tree height was 5 &#37; more accurate than the allometric model including tree height when both causes of uncertainty, the allometric model and measurement errors were considered. In conclusion, errors in tree height measurement have the potential to increase the error of aboveground biomass estimation.

DOI： 10.17503/agrivita.v40i1.1730

Language：Others   Publishing type：Research paper (scientific journal)  

© 2017 Elsevier Ltd Firewood and charcoal are the main energy sources in developing countries, but much fewer quantitative data have been collected for charcoal consumption. This study compared firewood and charcoal consumption rates and patterns in a rural area of Yedashe Township, Myanmar. Household interviews were conducted for randomly selected households, resulting in 147 firewood users and 34 charcoal users. Forest inventory data was used to estimate forest area needed to meet woodfuel demand. Average per capita consumption rates were 780 and 280 kg year−1 for firewood and charcoal, respectively. Distinct differences were found in fuel sources and sizes; only 16&#37; of firewood was collected from living trees in natural forests, of which 72&#37; was from trees <10 cm diameter, whereas 100&#37; of the wood for charcoal was harvested from this source from 10 to 40 cm diameter. The per capita demand for forest-originated green-wood with ≥10 cm diameter was 1190 kg for charcoal, which is 33 times more than that for firewood. The forest area needed to meet the per capita demand for charcoal was 820 m2, which is 23-fold larger than that for firewood. The estimated forest area to meet the current woodfuel demand from the rural population was 3430 ha year−1, being 3.0&#37; of the forest area within the distance <5 km from the villages. This demand of forest area for woodfuel supply can be increased up to 15,440 ha (13&#37;) along with increasing shares of charcoal users, resulting in a high risk of further forest degradation.

DOI： 10.1016/j.biombioe.2017.12.011

Language：Japanese  

Effect of weeding frequency, slope aspect and initial tree density on the mortality and accidental cutting of planted Sugi (Cryptomeria Japonica) trees : A case study in the Kagoshima University Research Forest, southwestern Japan

Language：English   Publishing type：Research paper (scientific journal)  

Here, we investigated the capabilities of a lightweight unmanned aerial vehicle (UAV) photogrammetric point cloud for estimating forest biophysical properties in managed temperate coniferous forests in Japan, and the importance of spectral information for the estimation. We estimated four biophysical properties: stand volume (V), Lorey's mean height (HL), mean height (HA), and max height (HM). We developed three independent variable sets, which included a height variable, a spectral variable, and a combined height and spectral variable. The addition of a dominant tree type to the above data sets was also tested. The model including a height variable and dominant tree type was the best for all biophysical property estimations. The root-mean-square errors (RMSEs) for the best model for V, HL, HA, and HM, were 118.30, 1.13, 1.24, and 1.24, respectively. The model including a height variable alone yielded the second highest accuracy. The respective RMSEs were 131.74, 1.21, 1.31, and 1.32. The model including a spectral variable alone yielded much lower estimation accuracy than that including a height variable. Thus, a lightweight UAV photogrammetric point cloud could accurately estimate forest biophysical properties, and a spectral variable was not necessarily required for the estimation. The dominant tree type improved estimation accuracy.

DOI： 10.3390/f8090343

Language：English   Publishing type：Research paper (scientific journal)  

In 2016, in response to forest loss, the Myanmar government banned logging operations for 1 year throughout the entire country and for 10 years in specific regions. However, it is unclear whether this measure will effectively reduce forest loss, because disturbance agents other than logging may have substantial effects on forest loss. In this study, we investigated an approach to attribute disturbance agents to forest loss, and we characterized the attribution of disturbance agents, as well as the areas affected by these agents, in tropical seasonal forests in the Bago Mountains, Myanmar. A trajectory-based analysis using a Landsat time series was performed to detect change pixels. After the aggregation process that grouped adjacent change pixels in the same year as patches, a change attribution was implemented using the spectral, geometric, and topographic information of each patch via random forest modeling. The attributed agents of change include "logging", "plantation", "shifting cultivation", "urban expansion", "water invasion", "recovery", "other change", and "no change". The overall accuracy of the attribution model at the patch and area levels was 84.7&#37; and 96.0&#37;, respectively. The estimated disturbance area from the attribution model accounted for 10.0&#37; of the study area. The largest disturbance agent was found to be logging (59.8&#37;), followed by water invasion (14.6&#37;). This approach quantifies disturbance agents at both spatial and temporal scales in tropical seasonal forests, where limited information is available for forest management, thereby providing crucial information for assessing forest conditions in such environments.

DOI： 10.3390/f8060218

Language：English   Publishing type：Research paper (scientific journal)  

Detecting forest disturbances is an important task in formulating mitigation strategies for deforestation and forest degradation in the tropics. Our study investigated the use of Landsat time series imagery combined with a trajectory-based analysis for detecting forest disturbances resulting exclusively from selective logging in Myanmar. Selective logging was the only forest disturbance and degradation indicator used in this study as a causative force, and the results showed that the overall accuracy for forest disturbance detection based on selective logging was 83.0&#37; in the study area. The areas affected by selective logging and other factors accounted for 4.7&#37; and 5.4&#37;, respectively, of the study area from 2000 to 2014. The detected disturbance areas were underestimated according to error assessments; however, a significant correlation between areas of disturbance and numbers of harvested trees during the logging year was observed, indicating the utility of a trajectory-based, annual Landsat imagery time series analysis for selective logging detection in the tropics. A major constraint of this study was the lack of available data for disturbances other than selective logging. Further studies should focus on identifying other types of disturbances and their impacts on future forest conditions.

DOI： 10.1139/cjfr-2016-0244

Language：English   Publishing type：Research paper (scientific journal)  

We quantified the differences in growth responses between Cryptomeria japonica and Chamaecyparis obtusa trees in approximately 0.1-ha group selection openings in southern Japan. We measured the position, height (H), and diameter at ground level (D) of 6- and 13-year-old C. japonica and C. obtusa trees that were planted in two respective openings. The annual H and D growth of C. japonica increased until trees were 10 and 8 m, respectively, distant from the nearest edge of the opening. For C. obtusa, the annual H and D growth increased slightly until the trees were 6 m distant from the nearest edge, but they did not significantly differ between 6 and 18 m distant from the nearest edge. While the shortest distance from the edge explained 63% and 50% of the variationsal H and D growth, respectively, of C. obtusa. Our result demonstrates that C. obtusa is less sensitive to edge effects than C. japonica. We conclude that C. obtusa is likely to be more suitable than C. japonica for planting within 0.1-ha group openings, as it will result in faster early stage growth, with less variation, within the openings.

DOI： 10.1080/13416979.2017.1283978

Language：English   Publishing type：Research paper (scientific journal)  

This study investigated the effect of weeding on the height growth of planted sugi (Cryptomeria japonica) saplings in southwestern Japan. First, we developed stand-level sugi and weed annual height growth models that accounted for tree spacing, slope aspect, and mean sugi height at the end of prior growing season and mean weed height just after weeding. Then, we simulated sugi heights after the sixth growing season under 64 different weeding schedules, using the developed model to examine the effects of the weeding frequency and the timing of weeding. The selected model for sugi height growth was expressed in terms of mean sugi height, mean weed height, slope aspect, and the relative height of weeds to sugi. In the case of weeds, the selected model was expressed in terms of mean sugi height, slope aspect, and the relative height of weeds to sugi. The simulation showed that sugi height decreased approximately 20% for every one-time reduction in the number of weeding. The simulation also showed that earlier, rather than later, weeding yielded better sugi growth. In conclusion, not only weeding frequency but also weeding schedules are important for sugi height growth.

DOI： 10.1080/13416979.2017.1322347

Language：English   Publishing type：Research paper (scientific journal)  

Logging damage to residual trees is an important parameter when considering the sustainability of selective logging in tropical natural forests. Here, we applied a proposed tree-based approach to evaluate logging damage in a tropical mixed deciduous forest in Bago Yoma, Myanmar and compared the cases with semi-evergreen forests of Cambodia. The logging damage was assessed in twenty 0.1-ha plots, each of which contained the stump and crown of one felled tree, and multinomial logistic regression was used to quantify the probability of the felled tree causing severe, slight, or no damage to residual trees. In both cases of Myanmar and Cambodia, severe damage was dependent on the size of the residual and felled trees, while slight damage was independent of the size of felled trees. There was no slight damage of residual trees with ≥50 cm diameter at breast height (DBH) in Myanmar, whereas slight damage increased with residual tree size in Cambodia and in tropical rain forests of other countries. This difference could be attributed to the sparseness of standing trees (98 trees ha⁻¹) in our study site in Myanmar, which made it easier to control the felling direction to avoid damage to larger residual trees. Additionally, the probability of increasingly severe damage with increasing DBH of the felled trees was higher in Myanmar than in Cambodia; one of the reasons may be the steeper terrain at the Myanmar site.

DOI： 10.1080/13416979.2017.1302545

Language：English   Publishing type：Research paper (scientific journal)  

Reduced-impact logging (RIL) is widely expected to maximize conservation values of selectively logged tropical forests; however, there remains a lack of supporting data to confirm the effectiveness of individual RIL practices. This study evaluates the extent of damage to residual stands and soil caused by directional felling, elephant skidding, and road construction in a tropical mixed deciduous forest under the Myanmar Selection System (MSS). The felling damage number was consistently larger for bamboo clumps than for trees over the range of felled tree size and felling intensity. Soil disturbed by road construction made up 4.6&#37; of the 9-ha study area, but no visible damage to residual trees and soil from elephant skidding was found three months after the operation. Directional felling toward bamboos and elephant skidding of MSS are effective as RIL practices, producing the lowest level of damage to residual trees and soil as compared with other RIL studies.

DOI： 10.1505/146554816819501718

Language：English   Publishing type：Research paper (scientific journal)  

In this study, we evaluated the effects of topographic correction and gap filling of Landsat Enhanced Thematic Mapper Plus (ETM+) images on the accuracy of forest change detection through a trajectory-based approach. Four types of Landsat time series stacks (LTSS) were generated. These stacks resulted from combinations of topographically corrected and uncorrected imagery combined with gap-filled and unfilled stacks. These combinations of stacks were then used as input into a trajectory-based change detection. The results of change detection from trajectory-based analysis using these LTSS were compared in order to assess the effects of both topographic correction and gap-filling procedures on the ability to detect forest disturbances. The results showed that overall accuracies of change detection were improved after gap filling (10.5% and 7.5%), but were only slightly improved after topographic correction (3.6% and 0.6%). Although the gap-filling process introduced some uncertainty that might have caused false change detection, the number of pixels whose detection of disturbance was enhanced after gap filling exceeded those detecting false change. The results also showed that the topographic correction did not contribute much to improve the change detection in this study area. However, topographic correction has a potential to increase the accuracy of change detection in areas of more rugged terrain and steep slopes. This is because a direct relationship between the slope of the topography with topographic correction and an enhanced detection of disturbance in pixels from year to year was observed in this study. For robust change detection, we recommend that a gap-filling process should be included in the trajectory-based analysis procedures such as the one used in this study where a single image per year is used to characterize change. We also recommend that in areas of rugged terrain, a topographic correction in the image pre-processing should be implemented.

DOI： 10.1080/01431161.2016.1201230

Language：English   Publishing type：Research paper (scientific journal)  

Appropriate cutting cycles and annual allowable cuts are crucial to ensure sustainability of tropical selective logging, but there have been limited field data to verify long-term effects of different cutting cycles. This study reveals some evidence of forest degradation in selectively logged production forests of Myanmar, which are subject to inappropriate cutting frequency. We compared stand structure, commercial species composition, and incidence of illegal logging between two compartments with low (LCF; 1 time) and high (HCF; 5 times) cutting frequency over a recent 18 years. Prior to the latest cutting, LCF had 176 trees ha(-1) with an inverted-J shape distribution of diameter at breast height (DBH), including a substantial amount of teak (Tectona grandis) and other commercially important species in each DBH class. HCF prior to the latest cut had only 41 trees ha(-1) without many commercially important species. At HCF, nearly half the standing trees of various species and size were illegally cut following legal operations; this was for charcoal making in nearby kilns. At LCF, two species, teak and Xylia xylocarpa, were cut illegally and sawn for timber on the spot. More extensive and systematic surveys are needed to generalize the findings of forest degradation and illegal logging. However, our study calls for urgent reconsideration of logging practices with high cutting frequency, which can greatly degrade forests with accompanying illegal logging, and for rehabilitating strongly degraded, bamboo-dominated forests. To reduce illegal logging, it would be important to pay more attention on a MSS regulation stating that logging roads should be destroyed after logging operations. (C) 2016 The Authors. Published by Elsevier B.V.

DOI： 10.1016/j.gecco.2016.06.001

Language：English   Publishing type：Research paper (scientific journal)  

We developed an empirical model to estimate aboveground carbon density with variables derived from airborne Light Detection and Ranging (LiDAR) in tropical seasonal forests in Cambodia, and assessed the effects of LiDAR pulse density on the accurate estimation of aboveground carbon density. First, we tested the applicability of variables used for estimating aboveground carbon density with the original LiDAR pulse density data (26 pulse m(-2)). Aboveground carbon density was regressed against variables derived from airborne LiDAR. Three individual height variable models were developed along with a canopy density model, and three other models combined canopy height and canopy density variables. The influence of forest type on model accuracy was also assessed. Next, the relationship between pulse density and estimation accuracy was investigated using the best regression model. The accuracy of the models were compared based on seven LiDAR point densities consisting of 0.25, 1, 2, 3, 4, 5 and 10 pulse m(-2). The best model was obtained using the single mean canopy height (MCH) model (R-2 = 0.92) with the original pulse density data. The relationship between MCH and aboveground carbon density was found to be consistent under different forest types. The differences between predicted and measured residual mean of squares of deviations were less than 1.5 Mg C ha(-1) between each pulse density. We concluded that aboveground carbon density can be estimated using MCH derived from airborne LiDAR in tropical seasonal forests in Cambodia even with a low pulse density of 0.25 pulse m(-2) without stratifying the study area based on forest type.

DOI： 10.1007/s10310-015-0504-3

Language：English   Publishing type：Research paper (scientific journal)  

We investigated the capabilities of a canopy height model (CHM) derived from aerial photographs using the Structure from Motion (SfM) approach to estimate aboveground biomass (AGB) in a tropical forest. Aerial photographs and airborne Light Detection and Ranging (LiDAR) data were simultaneously acquired under leaf-on canopy conditions. A 3D point cloud was generated from aerial photographs using the SfM approach and converted to a digital surface model (DSMP). We also created a DSM from airborne LiDAR data (DSML). From each of DSMP and DSML, we constructed digital terrain models (DTM), which are DTMP and DTML, respectively. We created four CHMs, which were calculated from (1) DSMP and DTMP (CHMPP); (2) DSMP and DTML (CHMPL); (3) DSML and DTMP (CHMLP); and (4) DSML and DTML (CHMLL). Then, we estimated AGB using these CHMs. The model using CHMLL yielded the highest accuracy in four CHMs (R-2 = 0.94) and was comparable to the model using CHMPL (R-2 = 0.93). The model using CHMPP yielded the lowest accuracy (R-2 = 0.79). In conclusion, AGB can be estimated from CHM derived from aerial photographs using the SfM approach in the tropics. However, to accurately estimate AGB, we need a more accurate DTM than the DTM derived from aerial photographs using the SfM approach.

DOI： 10.3390/f6113882

Other Link： http://www.mdpi.com/1999-4907/6/11/3882

Language：English   Publishing type：Research paper (scientific journal)  

The quality of diameter measurements for natural old-growth forest was evaluated by comparing repeated field measurements in a diameter census and tree-ring chronologies. The diameter census was repeated three times from 1973 at 10- to 19-year intervals in old-growth Cryptomeria japonica forests on Yakushima Island, Japan. The diameter growth in the three intervals between (1) first and second measurement (15-19 years), (2) second and third measurement (10-13 years), and (3) first and third measurement (28-30 years) were calculated. The diameter growth of each interval was also measured using tree-ring chronologies as a control. There were significant differences in paired t tests for diameter growth calculated from the diameter census data and from the tree-ring chronologies when the interval was < 20 years. Conversely, no significant differences were observed when the measurement interval was > 27 years. The average percentage differences (APDs) were relatively high for every interval and for all diameter classes, but the APDs for the 28- to 30-year intervals were better than the 10- to 19-year intervals. The APDs followed a rising trend with increasing diameter at breast height for every interval. In conclusion, data from an interval of > 27 years is required for accurate estimation of diameter growth. Furthermore, extra care should be taken when measuring large diameter trees. The results from this study demonstrated the difficulty of using a diameter census in natural old-growth forest.

DOI： 10.1007/s10310-014-0476-8

Language：Others   Publishing type：Research paper (scientific journal)  

© 2014 by the authors. licensee MDPI, Basel, Switzerland. The objectives of this study are to: (1) evaluate accuracy of tree height measurements of manual stereo viewing on a computer display using digital aerial photographs compared with airborne LiDAR height measurements; and (2) develop an empirical model to estimate stand-level aboveground biomass with variables derived from manual stereo viewing on the computer display in a Cambodian tropical seasonal forest. We evaluate observation error of tree height measured from the manual stereo viewing, based on field measurements. RMSEs of tree height measurement with manual stereo viewing and LiDAR were 1.96 m and 1.72 m, respectively. Then, stand-level aboveground biomass is regressed against tree height indices derived from the manual stereo viewing. We determined the best model to estimate aboveground biomass in terms of the Akaike's information criterion. This was a model of mean tree height of the tallest five trees in each plot (R2 = 0.78; RMSE = 58.18 Mg/ha). In conclusion, manual stereo viewing on the computer display can measure tree height accurately and is useful to estimate aboveground stand biomass.

DOI： 10.3390/land3041270

Language：English   Publishing type：Research paper (scientific journal)  

In this study, we test and demonstrate the utility of disturbance and recovery information derived from annual Landsat time series to predict current forest vertical structure (as compared to the more common approaches, that consider a sample of airborne Lidar and single-date Landsat derived variables). Mean Canopy Height (MCH) was estimated separately using single date, time series, and the combination of single date and time series variables in multiple regression and random forest (RF) models. The combination of single date and time series variables, which integrate disturbance history over the entire time series, overall provided better MCH prediction than using either of the two sets of variables separately. In general, the RF models resulted in improved performance in all estimates over those using multiple regression. The lowest validation error was obtained using Landsat time series variables in a RF model (R-2 = 0.75 and RMSE = 2.81 m). Combining single date and time series data was more effective when the RF model was used (opposed to multiple regression). The RMSE for RF mean canopy height prediction was reduced by 13.5% when combining the two sets of variables as compared to the 3.6% RMSE decline presented by multiple regression. This study demonstrates the value of airborne Lidar and long term Landsat observations to generate estimates of forest canopy height using the random forest algorithm.

DOI： 10.3390/rs61110750

Language：Others  

Estimation of Growth Rates Based on Tree-ring Analysis of Cryptomeria japonica on Yakushima Island, Japan

Language：English   Publishing type：Research paper (scientific journal)  

We evaluated the influence of texture information from remote sensed data on the accuracy of forest type classification at different spatial resolutions. We used 4-m spatial resolution imagery to create five different sets of imagery with lower spatial resolutions down to 30 m. We classified forest type using spectral information alone, texture information alone, and spectral and texture information combined at each spatial resolution, and compared the classification accuracy at each resolution. The classification and regression tree method was used for classification. The accuracy of all three tests decreased slightly with lower spatial resolution. The accuracy with the combined data was generally higher than with either the spectral or texture information alone. At most resolutions, the lowest accuracy was with texture information alone. However, there was no clear difference in accuracy between the combined data and spectral data alone at 25- and 30-m spatial resolution. These results indicate that adding texture information to spatial information improves the accuracy of forest type classification from very high resolution (4-m spatial resolution) to medium resolution imagery (20-m spatial resolution), but this accuracy improvement does not appear to hold for relatively coarse resolution imagery (25- to 30-m spatial resolution).

DOI： 10.1007/s10310-010-0233-6

Language：Japanese   Publishing type：Research paper (scientific journal)  

Publishing type：Research paper (scientific journal)  

Language：Japanese   Publishing type：Research paper (scientific journal)  

Language：English  

Language：English  

Language：Japanese  

The relationship between the density of Cryptomeria japonica stands and texture statistics derived from very high resolution imagery : The research using simulated very-high-resolution imagery

DOI： 10.4287/jsprs.46.6_4

Language：Others  

Country：Other  

Detection of clear-cut areas using the image differencing method with LANDSAT/TM data
Detecting the occurrence of clear-cutting is a practical use for remotely sensed data, and the image differencing method is one technique that can be used. This method is simple and powerful, but a systematic evaluation of its accuracy and a statistical determination of threshold values have not been established. We examined the accuracy of image differencing methods using a single band or band combinations, or scene combinations, to detect clear-cuts with LANDSAT/ TM data. Furthermore, we examined the relationship between clear-cut size and detection accuracy with a focus on the most effective bands. Results showed that bands 3 and 7 are optimal because they detect clear-cut areas with less error. Accuracy varied with the clear-cut size, and clear-cut regions less than 1 ha in area exhibited more error.