Accurate estimation of forest biomass is essential for the quantification of global carbon stocks and the assessment of climate change impacts. In the last 3 decades, remote sensing technologies, both airborne and spaceborne, have become the primary source for biomass estimation at large scales. Either optical, LiDAR or SAR sensors are commonly used to retrieve biomass estimation with each sensor type having its advantages and disadvantages. LiDAR delivers highly accurate results but cannot observe forest continuously over large areas, while optical sensors and SAR have a very good coverage but lack accuracy when the biomass exceeds the saturation level. To overcome these problems, in recent years great attention has been paid to the development of methods for fusing two or more sensors, and several global- to continental-scale forest biomass maps are developed. This paper also discusses several planned LiDAR and SAR satellite missions which are expected to greatly advance the global biomass research in the near future.

Pine wilt disease is one of the most destructive disease of pine forests. It is important to detect and exterminate infected trees for preservation of the forest. We demonstrated a novel method combining individual tree detection (ITD) and classification by logistic regression using unmanned aerial vehicle (UAV) images for the mapping of infected trees. In the ITD phase, 50 % and 84 % of damaged trees were automatically detected from the 3D point cloud generated from the UAV images using the local maximum filter. These rates of detection were comparable to previous studies that used UAV imagery. Subsequently, five vegetation indices calculated from multispectral and visible color (RGB) images were used. Among the vegetation indices, normalized difference vegetation index (NDVI), normalized difference red edge index (NDRE), and vegetation atmospherically resistant index (VARI) were preferable explanatory variable in the logistic regression to divide damaged and undamaged trees. The accuracy of these models ranged from 98 % to 100 % and the F-measure ranged from 94 % to 100 %. The best model, the logistic regression model using VARI as the explanatory variable, was then tested using five datasets to evaluate general performance. Each model showed explicitly high accuracy ranging from 95 % to 100 %. The best accuracy when considering the ITD and classification was 84 %. To map pine wilt disease, the proposed method is suitable for practical use due to its high-efficient and low-cost.

Regarding to global carbon cycle accurate measurements of forest biomass are important to evaluate its contribution as a CO₂ absorption source. Forest biomass correlates with forest canopy heights, therefore global measurements of canopy heights lead to a better understanding of the global carbon cycle. Space-borne lidar has the unique capability of measuring forest canopy height. A vegetation lidar named MOLI (Multi-footprint Observation Lidar and Imager) has been designed to observe canopy heights more accurately, and MOLI is currently being studied in the Japan Aerospace Exploration Agency. This paper introduces an overview of MOLI and its current status.

The Forestry Agency plans to revise afforestation grant program rules to allow to use various remote sensing data in application and inspection of the grant procedure. Under the new rules, grant applicants can submit, for example, UAV orthophotos and GIS data instead of printed pictures or paper maps. Inspection is conducted with the submitted data. Efficient grant operation and GIS application in forest management and forestry are expected.

In October 2015, Typhoon 201523 created very strong winds in central Hokkaido, Japan, causing windfall forest damage in Hokkaido prefectural forests. To assess the windfall damage quickly, we applied remote-sensing techniques using LANDSAT8 images (resolution 30 m) to estimate the distribution and area of the windfall damage and gave the results to relevant organizations. We could evaluate large-scale (≥1 ha) windfall damage, and we found that the LANDSAT8 analysis results helped to reduce the effort required in field surveys.

In this study, we introduce a CNN (convolutional neural network) which mimics professional interpreters’ manual techniques. Using simultaneously acquired airborne imageries and LiDAR data, we attempt to reproduce the 3D knowledge of tree shape, which interpreters potentially make use of. Geospatial features which support interpretation are also used as inputs to the CNN. Inspired by the interpreters’ techniques, we propose a unified approach that integrates these datasets in a shallow layer. With the proposed CNN, we show that the CNN works robustly.