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SummaryIncreasing 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% of the people perceived that forest resources increased and 75% 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.

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% 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.

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 % 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 % 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.

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 e...

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&ndash, 31 trees ha&minus, 1) was much higher than legal logging (0&ndash, 11 trees ha&minus, 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&minus, 1. There was a negative relationship with the number of bamboo clumps, which varied from 6 to 145 clumps ha&minus, 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.

SUMMARY 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% 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.

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.

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.

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%), with charcoal production as the main forest use activity, than the outside-households (32%), 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.

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.

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%, versus 92.9% 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.

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 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.

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 (R2 = 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.

Information about forest biomass distribution is important for sustainable forest management and monitoring fuelwood supply. The objective of this study is to develop an accurate forest biomass map for Kampong Thom Province, Cambodia. We used a new technique (object-based approach) and a conventional technique (pixel-based approach) for the estimation of forest biomass using Landsat Enhanced Thematic Mapper Plus (ETM+). The object-based approach created segments of images, and calculated statistical and textural attributes. Our results showed that estimation accuracy of the object-based approach, with the use of band 1 and an exponential fit, was the best (R2 = 0.76), and this accuracy was comparable to that of the pixel-based approach (R2 = 0.67). Although several textural variables were related to forest biomass, they did not contribute significantly to improvement of estimation accuracy. However, the object-based method can be used for image segmentation so that the image objects are spectrally more homogeneous within individual regions than with their neighbors. Hence, they can be regarded as management units for policy-related spatial decisions. Therefore, it is possible to select either of the two methods depending upon what the situation demands.

Our objective was to investigate the differences in the spectral trajectory of the LANDSAT Thematic Mapper with stand volume development between Japanese larch (Larix kaempferi) and Japanese oak (Quercus mongolica var. crispula) in the experimental forest of Kyushu University, Hokkaido, Japan. We used nonlinear regression and a categorical variable to investigate the relationship between spectral reflectance factor and stand volume for each forest type. This research showed that the reflectance factor of Japanese larch and Japanese oak reached saturation and that both forest types had different spectral trajectories with stand development. In addition, we found that middle of development stage (stand volume was between 100 and 200 m3/ha) of each forest type had similarity in reflectance factor.