The study aimed to examine the relationship between pollination service (as an ecosystem service) and the landscape structure of Aya Town, Miyazaki Prefecture, Japan. Our target agricultural crop for the evaluation of pollination service was hyuganatsu (Citrus tamurana), and native (Apis cerana) and managed honey bees (Apis mellifera) were considered the key species of ecosystem service provider. We selected 15 hyuganatsu trees in 5 orchards and counted the number of honey bee visits. We tested local and macro scale landscape indices as explanatory variables to clarify the number of honey bee visits. A model that used the number of flowers, area of adjacent natural forests, and area of agricultural fields within a 1-km radius was selected as the best model. Our results suggest that landscape structure affected the number of honey bee visits to a hyuganatsu tree, which represents the quantity of ecosystem service for the tree, and should be considered in the evaluation of ecosystem services.

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Bangladesh is a poor, partially forested nation located in South Asia. The forests cover an estimated 17.1% of the land surface area of the nation. Rapid human population expansion has increased wood consumption and resource overexploitation, leading to the degradation of forest reserves. We mapped and analyzed forest cover change for the period 1972-2014 using Landsat satellite images of the Madhupur Sal forest captured in 1972, 1991, 2010, and 2014. This forest is a tropical deciduous stand within the Bangladeshi Tangail Forest Division. Forest cover changes were identified and approximately delineated on remotely sensed images. We applied a supervised classification approach to the satellite images using ERDAS IMAGINE ver.10 software. The mapping and analyses of five land-use classes were performed with ArcGIS ver.10 software. Thus, we analyzed the trends in forest cover changes over 42 years. The area under natural forest cover was progressively reduced by 7079.4 ha through anthropogenic activities during the period 1972-2010. However, the natural forest area increased by 202.4 ha between 2010 and 2014 due to the implementation of a revegetation program involving local community groups that was initiated by the national forest department. Our maps are very relevant to forest conservation initiatives and will enable a long-term, integrated approach to forest revegetation operated by the forest department in association with local communities.

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Digital photogrammetry has advanced to the point where digital elevation models (DEMs) can be derived in full automation from stereo images, offering new opportunities in various fields including forestry. However, the performance and limitations of digital photogrammetry need to be carefully investigated in forest environments where both scientific studies and forest management depend on accurate information. We evaluated the performance of a photogrammetric digital surface model (photo-DSM) derived from small-format aerial photographs over approximately 2000 ha of tropical montane forest in northern Borneo, Malaysia. The accuracy of the photo-DSM was evaluated by using a reference dataset derived from airborne laser scanning (ALS) with an approximate density of 15 pulses/m². The vertical accuracy over the total area (18,349,288 pixels) was represented by a mean error of 0.006 m and RMSE of 3.003 m, with 61.1% of all measured heights accurate to within ±1 m, 81.9% accurate to within ±2 m, and 88.7% accurate to within ±3 m. More detailed local accuracy evaluation was conducted at block level: 31 1-ha blocks and one 0.25-ha block located over different forest types and characterized by the mean canopy height (range=8.4­41.1 m) and standard deviation (range=2.0­9.8 m) of the ALS-canopy height model (ALS-CHM). RMSE of the forest blocks ranged from 1.01 to 4.19 m, and this variance in RMSE could be explained by 78.6% of standard deviation of the ALS-CHM. Canopy slope and dark areas also had an effect on the RMSE: in areas of higher canopy slope and in darker areas within the forest blocks, the RMSE increased by up to 8.6 and 5.8 m, respectively. No-data areas accounted for 3.24% in the forest blocks and were also influenced by canopy slope and darker areas. RMSE of non-forest areas was 0.39 m (n=5243 pixels). Research and development on image-matching algorithms (which achieved 86.1% successful alignment of the aerial photographs in our study), cameras, unmanned aerial vehicles, and flight parameters are ongoing; as a result, digital photogrammetry and its capacity for use in various forestry applications are also continuing to improve.

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