伊尾木 慶子

In tropical regions, expanding human activities have become increasingly threatening to the ecological integrity of protected areas. Shifting cultivation and other agricultural activities around the protected areas by rural communities often lead to increased carbon emissions, wildlife habitat destruction and increasing hunting pressure. Land use planning, with the participation of local communities in the buffer zones, is being considered to strengthen the implementation of the Man and the Biosphere Program at Crocker Range Park, Sabah, Malaysia. As part of the European Union's ‘Tackling Climate Change Through Sustainable Forest Management and Community Development’ program, we emphasized the participatory geographic information systems (PGIS) approach to support village-scale land use planning that considers the needs of multiple stakeholders in the community. The PGIS was applied within a multi-criteria framework to determine the location of a potential community conservation area (CCA) and to plan future land use activities in the village. Key informant interviews were followed by a participatory mapping workshop, attended by various stakeholders and experts, which was convened to discuss and elicit local knowledge to generate the environmental and resource indicators for determining potential land use activities within the village (e.g., agriculture, tourism and recreation, and forest restoration). Based on the discussions and spatial analyses, a land use zoning map with a potential CCA was presented at a follow-up land use decision making workshop. The villagers and external stakeholders reached a consensus on the land use zoning; leading to the designation process of the CCA. The PGIS-based land use planning has effectively supported the community forest conservation and is potentially applicable to other Southeast Asia regions with similar environmental and socio-economic settings.

Developing a robust and cost-effective method for accurately estimating tropical forest's carbon pool over large area is a fundamental requirement for the implementation of Reducing Emissions from Deforestation and forest Degradation (REDD +). This study aims at examining the independent and combined use of airborne LiDAR and Landsat 8 Operational Land Imager (OLI) data to accurately estimate the above-ground biomass (AGB) of primary tropical rainforests in Sabah, Malaysia. Thirty field plots were established in three types of lowland rainforests: alluvial, sandstone hill and heath forests that represent a wide range of AGB density and stand structure. We derived the height percentile and laser penetration variables from the airborne LiDAR and calculated the vegetation indices, tasseled cap transformation values, and the texture measures from Landsat 8 OLI data. We found that there are moderate correlations between the AGB and laser penetration variables from airborne LiDAR data (r = -0.411 to -0.790). For Landsat 8 OLI data, the 6 vegetation indices and the 46 texture measures also significantly correlated with the AGB (r = 0.366-0.519). Stepwise multiple regression analysis was performed to establish the estimation models for independent and combined use of airborne LiDAR and Landsat 8 OLI data. The results showed that the model based on a combination of the two remote sensing data achieved the highest accuracy (R-adj(2) = 0.81, RMSE = 17.36%) whereas the models using Landsat 8 OLI data airborne LiDAR data independently obtained the moderate accuracy (R-adj(2) = 0.52, RMSE = 24.22% and R-adj(2) = 0.63, RMSE = 25.25%, respectively). Our study indicated that texture measures from Landsat 8 OLI data provided useful information for AGB estimation and synergistic use of Landsat 8 OLI and airborne LiDAR data could improve the AGB estimation of primary tropical rainforest.

Accurately quantifying the above-ground carbon stock of tropical rainforest trees is the core component of "Reduction of Emissions from Deforestation and Forest Degradation-plus" (REDD+) projects and is important for evaluating the effects of anthropogenic global change. We used high-resolution optical imagery (IKONOS-2) to identify individual tree crowns in intact and degraded rainforests in the mountains of Northern Borneo, comparing our results with 50 ground-based plots dispersed in intact and degraded forests, within which all stems > 10 cm in diameter were measured and identified to species or genus. We used the dimensions of tree crowns detected in the imagery to estimate above-ground biomasses (AGBs) of individual trees and plots. To this purpose, preprocessed IKONOS imagery was segmented using a watershed algorithm; stem diameter values were then estimated from the cross-sectional crown areas of these trees using regression relationships obtained from ground-based measurements. Finally, we calculated the biomass of each tree (AGBT, in kg), and the AGB of plots by summation (AGB(P), in Mg ha(-1)). Remotely sensed estimates of mean AGBT were similar to ground-based estimates in intact and degraded forests, even though small trees could not be detected from space-borne sensors. The intact and degraded forests not only had different AGB but were also dissimilar in biodiversity. A tree-centric approach to carbon mapping based on high-resolution optical imagery, could be a cheap alternative to airborne laser-scanning.

Unprecedented deforestation and forest degradation in recent decades have severely depleted the carbon storage in Borneo. Estimating aboveground biomass (AGB) with high accuracy is crucial to quantifying carbon stocks for Reducing Emissions from Deforestation and Forest Degradation-plus implementation (REDD+). Airborne Light Detection and Ranging (LiDAR) is a promising remote sensing technology that provides fine-scale forest structure variability data. This paper highlights the use of airborne LiDAR data for estimating the AGB of a logged-over tropical forest in Sabah, Malaysia. The LiDAR data was acquired using an Optech Orion C200 sensor onboard a fixed wing aircraft. The canopy height of each LiDAR point was calculated from the height difference between the first returns and the Digital Terrain Model (DTM) constructed from the ground points. Among the obtained LiDAR height metrics, the mean canopy height produced the strongest relationship with the observed AGB. This single-variable model had a root mean squared error (RMSE) of 80.02 t ha(-1) or 22.31% of the mean AGB, which performed exceptionally when compared with recent tropical rainforest studies. Overall, airborne LiDAR did provide fine-scale canopy height measurements for accurately and reliably estimating the AGB in a logged-over forest in Sabah, thus supporting the state's effort in realizing the REDD+ mechanism.

Human activities in tropical forests, such as logging or shifting cultivation, largely affect forest biodiversity. These disturbances often create small patches of successional vegetation and heterogeneous spatial patterns. Airborne LiDAR can detect small-scale disturbances, which are undetectable by conventional remote sensing. Here, our aim was to evaluate the similarity in community composition of tree assemblages after human disturbances in a tropical rainforest in northern Borneo using small-footprint airborne LiDAR. We derived 16 variables related to the height distribution and canopy characteristics from airborne LiDAR data. The similarity in community composition among plots was calculated using ordination analysis (nonmetric multidimensional scaling) based on the number of trees of each species. LiDAR-derived variables were significantly correlated with the similarity in community composition; the strongest correlation was with the canopy laser penetration rate down to 1-m height above ground (r(s) = 0.81, p < 0.001). The predictive model for similarity in community composition developed using the canopy laser penetration rate at 0-m height and the maximum height had R-adj(2) = 0.71 (p < 0.001). We applied this equation to the entire study area and compared the output with the human disturbance histories. A predictive map based on the equation suggested that the similarity in community composition changes in proportion to the degree of human disturbances. Our findings indicate that the similarity attributed to human disturbances in tropical forests can be predicted and monitored by means of airborne LiDAR. This approach could be combined with ground-based monitoring data to map the patterns of biodiversity in tropical rainforest. (C) 2015 Elsevier Inc. All rights reserved.

Inter-annual canopy growth is one of the key indicators for assessing forest conditions, but the measurements require laborious field surveys. Up-to-date LiDAR remote sensing provides sufficient three-dimensional morphological information of the ground to monitor canopy heights on a broad scale. Thus, we attempted to use multi-temporal airborne LiDAR datasets in the estimation of vertical canopy growth, across various types of broad-leaved trees in a large urban park. The growth of broad-leaved canopies in the EXPO '70 urban forest in Osaka, Japan was assessed with 19 plots at the stand level and 39 selected trees at the individual-tree level. Airborne LiDAR campaigns repeatedly observed the park in the summers of 2004, 2008, and 2010. We acquired canopy height models (CHMs) for each year from the height values of the uppermost laser returns at every 0.5 m grid. The annual canopy growth was calculated by the differences in CHMs and validated with the annual changes in field-measured basal areas and tree heights. LiDAR estimations revealed that the average annual canopy growth from 2004 to 2010 was 0.26 +/- 0.11 mm(-2) yr(-1) at the plot level and 0.26 +/- 0.10 m m(-2) yr(-1) at the individual-tree level. This result showed that growing trends were consistent at different scales through 2004 to 2010 despite uncertainty in estimating short-term growth for small crown areas at the individual-tree level. This LiDAR-estimated canopy growth shows a moderate relation to field-measured increase of basal areas and average heights. The estimation uncertainties seem to result from the complex canopy structure and irregular crown shape of broad-leaved trees. Challenges still remain on how to incorporate the growth of understory trees, growth in the lateral direction, and gap dynamics inside the canopy, particularly in applying multi-temporal LiDAR datasets to the large-scale growth assessment. (C) 2016 Elsevier GmbH. All rights reserved.

Deforestation and degradation of forests have severely depleted carbon storage in tropical countries, whose forests have the most carbon-rich ecosystems in the world. Estimating above-ground biomass (AGB) with high accuracy is critical to quantifying carbon stocks in the tropics. We propose a model to estimate AGB in the tropical montane forests of northern Borneo with different disturbance histories using airborne LiDAR data. The level of forest degradation was determined from species composition and field-observed AGB. Of 50 sample plots established in forests with various levels of degradation, we categorized 20 as highly degraded (AGB: 52.18-229.11 Mg/ha), 16 as moderately degraded (AGB: 136.00-382.59 Mg/ha), and 14 as old-growth forest (AGB: 280.31-622.79 Mg/ha). Height metrics and laser penetration rate (LP) at specific heights from the ground were derived from vertical point profiles of LiDAR data. After testing the performance of single variables, we used stepwise multiple regressions to select variables to include in the model for AGB estimation. The best model with a single variable used the mean height from the laser returns (R-2 = 0.78, RMSE = 65.54 Mg/ha). All LP variables were sensitive to AGB (R-2 > 0.60). The final model from stepwise analysis included the mean height of the canopy height model and LP at 7 m height (adjusted R-2 = 0.81, RMSE = 61.26 Mg/ha). The results confirm the suitability of LP variables for estimating AGB. We suggest that airborne LiDAR data can capture AGB variability at fine spatial scales, which correspond to deforestation and forest degradation caused by human activities and natural disturbances. (C) 2014 Elsevier B.V. All rights reserved.

The use of woody biomass from forest is becoming of interest in Japan, its amount and availability should be discussed in a spatial manner. Airborne laser scanning (ALS) technology enables three-dimensional information of objects on the ground to be obtained, and such data can provide valuable information for forest management that can contribute to biomass utilization planning. This study examines the use of such ALS data for classifying vegetation types in a forest in Kyoto city, Japan. Training sample plots were established based on five different vegetation type classes; five variables were then calculated from each plot's ALS data, and the effectiveness of the variables was quantified. Of the five variables, the coefficient of variation (CV) and the only fraction (OF) were the most effective, and consequently were used in the classification analysis to generate a map of vegetation types across the study area. The accuracy assessment yielded a kappa coefficient of agreement of 0.79. This study demonstrates that ALS data can be successfully used to discriminate between different vegetation types of forests in urban area that have good potential for biomass utilization.

We evaluated the effectiveness of integrating discrete return light detection and ranging (LiDAR) data with high spatial resolution near-infrared digital imagery for object-based classification of land cover types and dominant tree species. In particular we adopted LiDAR ratio features based on pulse attributes that have not been used in past studies. Object-based classifications were performed first on land cover types, and subsequently on dominant tree species within the area classified as trees. In each classification stage, two different data combinations were examined: LiDAR data integrated with digital imagery or digital imagery only. We created basic image objects and calculated a number of spectral, textural, and LiDAR-based features for each image object. Decision tree analysis was performed and important features were investigated in each classification. In the land cover classification, the overall accuracy was improved to 0.975 when using the object-based method and integrating LiDAR data. The mean height value derived from the LiDAR data was effective in separating "trees" and "lawn" objects having different height. As for the tree species classification, the overall accuracy was also improved by object-based classification with LiDAR data although it remained up to 0.484 because spectral and textural signatures were similar among tree species. We revealed that the LiDAR ratio features associated with laser penetration proportion were important in the object-based classification as they can distinguish tree species having different canopy density. We concluded that integrating LiDAR data was effective in the object-based classifications of land cover and dominant tree species.

We estimated leaf area index (LAI) and canopy openness of broad-leaved forest using discrete return and small-footprint airborne laser scanner (ALS) data. We tested four ALS variables, including two newly proposed ones, using three echo types (first, last, and only) and three classes (ground, vegetation, and upper vegetation), and compared the accuracy by means of correlation and regression analysis with seven conventional vegetation indices derived from simultaneously acquired high-resolution near-infrared digital photographs. Among the ALS variables, the ratio of the "only-and-ground" pulse to "only" pulse (OGF) was the best estimator of both LAI (adjusted R-2 = 0.797) and canopy openness (adjusted R-2 = 0.832), followed by the ratio of the pulses that reached the ground to projected lasers (GF). Among the vegetation indices, the normalized differential vegetation index (NDVI) was the best estimator of both LAI (adjusted R-2 = 0.791) and canopy openness (adjusted R-2 = 0.764). Resampling analysis on ALS data to examine whether the estimation of LAI and canopy openness was possible with lower point densities revealed that GF maintained a high adjusted R-2 until a fairly low density of about 0.226 points/m(2), while OGF performed marginally when the point density was reduced to about 1 point/m(2), the standard density of high-density products on the market as of February 2008. Consequently, the ALS variables proposed in the present study, GF and OGF, seemed to have great potential to estimate LAI and canopy openness of broadleaved forest, with accuracy comparable to NDVI, from high-resolution near-infrared imagery.