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 (DSM_P). We also created a DSM from airborne LiDAR data (DSM_L). From each of DSM_P and DSM_L, we constructed digital terrain models (DTM), which are DTM_P and DTM_L, respectively. We created four CHMs, which were calculated from (1) DSM_P and DTM_P (CHM_PP); (2) DSM_P and DTM_L (CHM_PL); (3) DSM_L and DTM_P (CHM_LP); and (4) DSM_L and DTM_L (CHM_LL). Then, we estimated AGB using these CHMs. The model using CHM_LL yielded the highest accuracy in four CHMs (R² = 0.94) and was comparable to the model using CHM_PL (R² = 0.93). The model using CHM_PP yielded the lowest accuracy (R² = 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.

High-value timber species such as monarch birch (Betula maximowicziana Regel), castor aralia (Kalopanax septemlobus (Thunb.) Koidz), and Japanese oak (Quercus crispula Blume) play important ecological and economic roles in forest management in the cool temperate mixed forests in northern Japan. The accurate measurement of their tree height is necessary for both practical management and scientific reasons such as estimation of biomass and site index. In this study, we investigated the similarity of individual tree heights derived from conventional field survey, digital aerial photographs derived from unmanned aerial vehicle (UAV-DAP) data and light detection and ranging (LiDAR) data. We aimed to assess the applicability of UAV-DAP in obtaining individual tree height information for large-sized high-value broadleaf species. The spatial position, tree height, and diameter at breast height (DBH) were measured in the field for 178 trees of high-value broadleaf species. In addition, we manually derived individual tree height information from UAV-DAP and LiDAR data with the aid of spatial position data and high resolution orthophotographs. Tree heights from three different sources were cross-compared statistically through paired sample t-test, correlation coefficient, and height-diameter model. We found that UAV-DAP derived tree heights were highly correlated with LiDAR tree height and field measured tree height. The performance of individual tree height measurement using traditional field survey is likely to be influenced by individual species. Overall mean height difference between LiDAR and UAV-DAP derived tree height indicates that UAV-DAP could underestimate individual tree height for target high-value timber species. The height-diameter models revealed that tree height derived from LiDAR and UAV-DAP could be better explained by DBH with lower prediction errors than field measured tree height. We confirmed the applicability of UAV-DAP data for obtaining the individual tree height of large-size high-value broadleaf species with comparable accuracy to LiDAR and field survey. The result of this study will be useful for the species-specific forest management of economically high-value timber species.

High-value timber species play an important economic role in forest management. The individual tree information for such species is necessary for practical forest management and for conservation purposes. Digital aerial photogrammetry derived from an unmanned aerial vehicle (UAV-DAP) can provide fine spatial and spectral information, as well as information on the three-dimensional (3D) structure of a forest canopy. Light detection and ranging (LiDAR) data enable area-wide 3D tree mapping and provide accurate forest floor terrain information. In this study, we evaluated the potential use of UAV-DAP and LiDAR data for the estimation of individual tree location and diameter at breast height (DBH) values of large-size high-value timber species in northern Japanese mixed-wood forests. We performed multiresolution segmentation of UAV-DAP orthophotographs to derive individual tree crown. We used object-based image analysis and random forest algorithm to classify the forest canopy into five categories: three high-value timber species, other broadleaf species, and conifer species. The UAV-DAP technique produced overall accuracy values of 73% and 63% for classification of the forest canopy in two forest management sub-compartments. In addition, we estimated individual tree DBH Values of high-value timber species through field survey, LiDAR, and UAV-DAP data. The results indicated that UAV-DAP can predict individual tree DBH Values, with comparable accuracy to DBH prediction using field and LiDAR data. The results of this study are useful for forest managers when searching for high-value timber trees and estimating tree size in large mixed-wood forests and can be applied in single-tree management systems for high-value timber species.

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.

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.

In Hokkaido, northern Japan, bareroot Sakhalin fir seedlings are conventionally planted in spring and fall, following strip site preparation that alternates managed and unmanaged strips. However, this method requires seven years of weeding due to encroachment of evergreen dwarf bamboo. Given diminishing forest labor availability, a shortage of workers for planting and weeding operations has become a problem in reforestation following clearcutting. We examined whether comprehensive mechanical site preparation (MSP) could reduce weeding frequency by preventing regrowth of dwarf bamboo and whether container seedlings could extend the planting season into summer. Over eight years, the survival and growth of summer-planted bareroot and container seedlings were examined on a fully MSP-treated site with only one weeding operation in the fifth year. Full-site MSP resulted in a shift of the vegetation from dwarf bamboo to deciduous plants, leading to high survival and growth rates of Sakhalin fir seedlings despite minimal weeding. Container seedlings exhibited superior establishment and maintained higher survival rates over eight years than bareroot seedlings. However, planting on the forwarder trail decreased seedling growth, and ultimately decreased survival under rare summer drought. Our findings indicate that container seedling summer planting and full-site MSP may represent an alternative approach to reforestation of Sakhalin fir, potentially reducing the need for weeding and extending the planting season.