Satoshi Tsuyuki

Comparison of Mammalian Communities in a Human-Disturbed Tropical Landscape in East Kalimantan, Indonesia

Abstract. To examine the impact of human disturbance on mammalian community in a human-disturbed tropical landscape in Borneo, we conducted a baited camera trapping study in East Kalimantan, Indonesia, from 2005 to 2010. Along a gradient of habitat degradation, we established four camera trapping sites within a 20-km radius, one in Sungai Wain Protection Forest and three in Bukit Soeharto Grand Forest Park. From the camera trapping carried out for 1,017 camera-days, we obtained 3,753 images of 29 mammal species, including an alien species, the domestic dog (Canis lupus familiaris). The trapping efficiency and species composition of mammals recorded differed between the two kinds of bait (banana and shrimp). The number of species decreased, and the species composition changed along the gradient of habitat degradation, suggesting that human-mediated habitat degradation has a significant effect on the mammalian communities. Our results suggest that forest cover is essential for at least 14 out of 29 recorded mammal species to survive, and that the forest fires and habitat isolation strongly affect particular species, i.e. Lariscus insignis and Trichys fasciculata (Rodentia) and Arctogalidia trivirgata (Carnivora).

Simultaneous detection of burned areas of multiple fires in the tropics using multisensor remote-sensing data

Fires associated with recurrent El Niño events have caused severe damage to tropical peat swamp forests. Accurate quantitative information about the frequency and distribution of the burned areas is imperative to fire management but is lacking in the tropics. This article examines a novel method based on principal component analysis (PCA) of the normalized difference water index (NDWI) from multisensor data for simultaneously detecting areas burned due to multiple El Niño–related fires. The principal components of multitemporal NDWI (NDWI-PCs) were able to capture the areas burned in the 1998 and 2003 El Niño fires in NDWI-PC3 and 2, respectively. The proposed method facilitates the reduction of dimensionality in detecting the burned areas. From 22 image bands, the proposed method was able to accurately detect the burned areas of multiple fires with only three NDWI-PCs. The proposed method also shows superior performance to unsupervised classifications of the principal components of combined image bands, multitemporal NDWI, NDWI differencing and post-classification comparison methods. The results show that the 1998 El Niño fire was devastating especially to intact peat swamp forest. For degraded peat swamp forest, there was an increase in the burned area from 1998 to 2003. The proposed method offers the retrieval of accurate and reliable quantitative information on the frequency and spatial distribution of burned areas of multiple fires in the tropics. This method is also applicable to the detection of changes in general as well as the detection of vegetation changes.

A Growth Prediction System for Local Stand Volume Derived from LIDAR Data

Recent advances in light detection and ranging (LIDAR) technology have enabled the estimation of valuable canopy parameters (e.g., crown diameter, leaf area, and canopy structure) that are difficult to obtain through in situ surveys. The objective of this study was to assess the utility of LIDAR-derived measurements of crown and growth parameters to model and predict the growth of sugi (Cryptomeria japonica) stands located in the University of Tokyo Forest, Chiba Prefecture, Japan. Initially, we confirmed that crown lengths and widths of trees in stands of various densities obtained from LIDAR data correlated with those measured in situ. Then, we developed a crown growth model from repeated LIDAR measurements of stands, suggesting that LIDAR data are adequate for this purpose, and indicating that crown surface area and tree volume growth were linearly related (R2 = 0.90; p < 0.01; RMSE tree volume < 0.02 m3). The model also provided robust predictions of the volume growth of local forests in 10 × 10 m plots based on LIDAR-derived estimates of crown surface areas. Future work should test the applicability of this growth model to facilitate practical forest management.

Stand Parameter Estimation of Artificial Evergreen Conifer Forests Using Airborne Images: An Evaluation of Seasonal Difference on Accuracy and Best Wavelength

The relationship between the stand parameters (top layer height (H1) and volume/ha (Vha)) and digital number (DN) were evaluated for evergreen conifer stands using three airborne images with 4-m spatial resolution, which were taken in June 1995, September 1993, and October 1994 using the Compact Airborne Spectrographic Imager (CASI). Estimation accuracy of the stand parameters, their seasonal changes, and suitable wavelength were analyzed using correlation coefficients and a regression analysis. The minimum DN of stands, which showed the darkness of a canopy shadow, had a higher correlation with H1 than the average and maximum DN while the average DN had a higher correlation with Vha. The green channels gave the highest correlation coefficients with H1 and Vha, which exceeded — 0.9 for the September and October images. However, the red channels had a consistently high correlation with the stand parameters for the three images. The near infrared channels gave poor correlations with H1 and Vha for the June image. Spectral variations among trees may affect the relationship between DN and the stand parameters in the leaf maturation period in June. Consequently, the late growing season was better at giving consistent results for the stand parameter studies. There was a linear relationship between the measured and the estimated stand parameters for the validation plots especially for the H1 case of September with sufficient accuracy. Nadir viewing images, which had high spatial resolution and a wide dynamic range such as the CASI images, were necessary to estimate the stand parameters accurately.

Analysis of forest structural complexity using airborne LiDAR data and aerial photography in a mixed conifer–broadleaf forest in northern Japan

Determining forest structural complexity, i.e., a measure of the number of different attributes of a forest and the relative abundance of each attribute, is important for forest management and conservation. In this study, we examined the structural complexity of mixed conifer–broadleaf forests by integrating multiple forest structural attributes derived from airborne LiDAR data and aerial photography. We sampled 76 plots from an unmanaged mixed conifer–broadleaf forest reserve in northern Japan. Plot-level metrics were computed for all plots using both field and remote sensing data to assess their ability to capture the vertical and horizontal variations of forest structure. A multivariate set of forest structural attributes that included three LiDAR metrics (95th percentile canopy height, canopy density and surface area ratio) and one image metric (proportion of broadleaf cover), was used to classify forest structure into structural complexity classes. Our results revealed significant correlation between field and remote sensing metrics, indicating that these two sets of measurements captured similar patterns of structure in mixed conifer–broadleaf forests. Further, cluster analysis identified six forest structural complexity classes including two low-complexity classes and four high-complexity classes that were distributed in different elevation ranges. In this study, we could reliably analyze the structural complexity of mixed conifer–broadleaf forests using a simple and easy to calculate set of forest structural attributes derived from airborne LiDAR data and high-resolution aerial photography. This study provides a good example of the use of airborne LiDAR data sets for wider purposes in forest ecology as well as in forest management.

Monitoring of Deforestation and Fragmentation in Sarawak, Malaysia between 1990 and 2009 Using Landsat and SPOT Images

Sarawak is the largest state in Malaysia that covers 37.5% of the total land area. Multitemporal satellite images of Landsat and SPOT were used to examine deforestation and forest fragmentation in Sarawak between 1990 and 2009. Supervised classification with maximum likelihood classifier was used to classify the land cover types in Sarawak. The overall accuracies of all classifications were more than 80%. Our results showed that forests were reduced at 0.62% annually during the two decades. The peat swamp forest suffered a tremendous loss of almost 50% between 1990 and 2009 especially at coastal divisions due to intensified oil palm plantation development. Fragmentation analysis revealed the loss of about 65% of the core area of intact forest during the change period. The core area of peat swamp forest had almost completely disappeared during the two decades.

Evaluating the Performance of Photogrammetric Products Using Fixed-Wing UAV Imagery over a Mixed Conifer–Broadleaf Forest: Comparison with Airborne Laser Scanning

Unmanned aerial vehicles (UAVs) and digital photogrammetric techniques are two recent advances in remote sensing (RS) technology that are emerging as alternatives to high-cost airborne laser scanning (ALS) data sources. Despite the potential of UAVs in forestry applications, very few studies have included detailed analyses of UAV photogrammetric products at larger scales or over a range of forest types, including mixed conifer–broadleaf forests. In this study, we assessed the performance of fixed-wing UAV photogrammetric products of a mixed conifer–broadleaf forest with varying levels of canopy structural complexity. We demonstrate that fixed-wing UAVs are capable of efficiently collecting image data at local scales and that UAV imagery can be effectively utilized with digital photogrammetric techniques to provide detailed automated reconstruction of the three-dimensional (3D) canopy surface of mixed conifer–broadleaf forests. When combined with an accurate digital terrain model (DTM), UAV photogrammetric products are promising for producing reliable structural measurements of the forest canopy. However, the performance of UAV photogrammetric products is likely to be influenced by the structural complexity of the forest canopy. Furthermore, we highlight the potential of fixed-wing UAVs in operational forest management at the forest management compartment level, for acquiring high-resolution imagery at low cost. A future direction of this research would be to address the issue of how well the photogrammetric products can predict the actual structure of mixed conifer–broadleaf forests.

Estimation of Above-Ground Biomass of a Tropical Forest in Northern Borneo Using High-resolution Satellite Image

Abstract Estimating above-ground biomass is important in establishing an applicable methodology of Measurement, Reporting and Verification (MRV) System for Reducing Emissions from Deforestation and Forest Degradation-Plus (REDD+). We developed an estimation model of diameter at breast height (DBH) from IKONOS-2 image that led to above-ground biomass estimation (AGB). The IKONOS image was preprocessed with dark object subtraction and topographic effect correction prior to watershed segmentation for tree crown delineation. Compared to the field observation, the overall segmentation accuracy was 64%. Crown detection percent had a strong negative correlation to tree density. In addition, satellite-based crown area had the highest correlation with the field measured DBH. We then developed the DBH allometric model that explained 74% of the data variance. In average, the estimated DBH was very similar to the measured DBH as well as for AGB. Overall, this method can potentially be applied to estimate AGB over a relatively large and remote tropical forest in Northern Borneo.Key Words: tree crown delineation, biomass estimation, IKONOS-2

The use of fixed–wing UAV photogrammetry with LiDAR DTM to estimate merchantable volume and carbon stock in living biomass over a mixed conifer–broadleaf forest

Abstract Remote sensing (RS) data are often used as a complementary data source to acquire accurate quantitative estimations of merchantable volume (V) and carbon stock in living biomass (CST), which are critical for the sustainable use of forest resources. In this study, we investigated the utility of unmanned aerial vehicles (UAVs) and the structure from motion (SfM) technique for estimating and mapping the spatial distributions of V and CST of an uneven–aged mixed conifer–broadleaf forest that had experienced major disturbances (e.g., wind damage and selection harvesting) over time. In addition to the commonly used RS structural metrics, we also calculated an image metric (broadleaf vegetation cover percentage) using a UAV–SfM orthomosaic to use as an explanatory variable. Plot level validation of UAV–SfM–estimated V revealed a root mean square error (RMSE) of 39.8 m3 ha–1 and a relative RMSE of 16.7%, whereas the RMSE and relative RMSE vales for UAV–SfM–estimated CST were 14.3 Mg C ha–1 and 17.4% respectively. Our image metric had a statistically significant association with V and CST, providing additional explanatory power in the regression analysis. Nevertheless, RMSE values did not significantly change after adding the image metric into the regression analysis, e.g., %RMSE was reduced by 1.9% for V estimation, and 1.5% for CST estimation. Furthermore, the UAV–SfM estimates we obtained were comparable to light detection and ranging (LiDAR) estimates (relative RMSE of 16.4% and 16.7% for V and CST, respectively). We also successfully mapped the spatial distributions of V and CST and identified their stand– and landscape–level variations. Therefore, we confirmed the potential of UAV imagery when combined with LiDAR digital terrain model to capture the fine scale spatial variation of V and CST in uneven–aged forests subjected to silvicultural practices and natural disturbances over time.

Mapping Distribution of Dipterocarpus in East Kalimantan, Indonesia

Dipterocarps (Dipterocarpaceae) is a dominant tree family of tropical rainforest in Southeast Asia. Dipterocarps have been exploited for its timber and disappearing fast in East Kalimantan. In this study, we predicted the distribution of dipterocarpus, one of the main dipterocarps genera, by evaluating its habitat suitability using logistic regression analysis with specimen collection points and environmental factors from GIS data. Current distribution of dipterocarpus was generated by combining the habitat suitability classes with an updated forest cover map. Rainfall, soil type, followed by elevation was the main factors that influence the distribution of dipterocarpus in East Kalimantan. Dipterocarpus can be found in a quarter of the current forest cover, which is highly suitable as habitat of Dipterocarpus.