The overview of REDD+ is presented to facilitate discussion of the special issue on REDD+. REDD+ is an international program intended to mitigate CO₂ emissions by conserving forests in developing countries. After 10 years' negotiation, the Paris agreement encouraged to take action to implement and support for REDD+ in 2017. The scientific and historical background of the REDD+ is shown at first, then the framework of REDD+ is illustrated. National Forest Monitoring System (NFMS) and Forest Reference Emission Levels and Forest Reference Levels (FREL/FRL) are focused as important technical issues of REDD+. Remote sensing is expected as a key technique for transparent monitoring of forest carbon. While methodologies for REDD+ have reached the implementation phase, negotiation on REDD+ finance is ongoing under the Green Climate Fund.

This paper describes the role of remote sensing for REDD+ MRV. As MRV activities is related to national GHG accounting, a usage of remote sensing data is implemented under the 2006 IPCC guideline and the useful guidelines which remote sensing community developed. In the actual MRV business scene, cloud computing service began to be used for satellite data processing in MRV activity by international donor agency like FAO, as fee of using high performance computing and large storage is becoming low cost.

This paper introduces forest monitoring and remote sensing (RS) utilization in the case of Vietnam. Vietnam has two forest monitoring schemes including annual field based monitoring and RS and systematic sampling method in every five years. In order to improve the accuracy and quality of the monitoring data, the utilization of tablet-PC with forest monitoring mobile application has started and expanded. Additionally, the trial of Quality Assurance and Quality Control (QA/QC) based on satellite images has been implemented. It is expected to use more RS technologies with the combination of developing RS tools including SAR and drones.

I studied the forest inventory method utilizing Drone and SfM technology, and examined the possibility of application to Deforestation and Forest Degradation monitoring. Forest inventory and aerial photography using Drone were conducted with Myanmar as the study site. Orthophoto, DSM, DTM and Point Cloud data were generated by Three dimensional reconstruction software, and compared with the forest inventory result. Orthophoto and Point Cloud data are useful for observing of forest stand condition, forest type interpretation, monitoring and so on. Drone is excellent in portability, it can carry and taking photo at the time of inventory and easily check the forest stand condition. Tree Height Measurement by Point Cloud data and Forest Carbon Stock (Biomass) Estimation by DSM and DTM also had highly correlation with the forest inventory result as a truth data, confirming the feasibility for forest inventory and monitoring utilizing Drone.

When implementing REDD+ projects, measurement, reporting, and verification (MRV) is particular emphasized. For measurement and verification, combination of ground surveys and remote sensing technology is required for estimating biomass with high accuracy. In this study, we tried to estimate carbon stock changes of mangrove forests using chronological satellite imageries and airborne LiDAR located in the South Sumatra state, Indonesia. As a result, the biomass and the amount of carbon stock changes can be estimated with high accuracy by combining the spatial volume based on airborne LiDAR data with the tree species classification based on satellite imagery. The result showed that combining airborne LiDAR data with satellite imagery is one of the effective methods of monitoring for REDD+ projects.

At COP21 held in Paris in 2015, the Paris Agreement was adopted, in which REDD-plus was confirmed as one of the most important climate change mitigation measures. In this paper, I explain the status of forest in developing countries, which implement REDD-plus activities, and the use of remote sensing in the REDD-plus scheme. Next, I consider issues of the use of remote sensing for REDD-plus implementation. Finally, I describe prospects of the monitoring to archive REDD-plus using remote sensing.

Advanced Land Observing Satellite (ALOS)/Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) measured triplet images at forward, nadir and backward view directions, and the digital surface model (DSM) is generated from set of triplet images. Existing methods generate multiple DSMs from individual triplet images, and eliminate the effects of outliers by taking average. The proposed method in this paper solves multiple observation equations from all triplet images, and simultaneously determines bias parameters contaminated in the rational polynomial coefficient (RPC) model and ground coordinates. The experimental results showed that errors of the coordinates estimated by using multi-temporal triplet images were almost as small as those estimated with manually determined bias parameters in case of 4 or more sets of triplet images. As a result, we conclude that the proposed method is effective for stably generating accurate DSMs from multi-temporal triplet images.

本論文では，異なる長さの波長を用いて取得された合成開口レーダ（SAR）画像を併用して，高精度に地盤沈下を推定する手法を提案する。差分干渉SAR解析に使用できる画像枚数が少なくなるにつれて，波長が短いSAR画像では位相を復元する際に生じるアンラッピングエラーが発生する可能性が高まる。一方，波長が長いSAR画像では地盤面の小さな変動が検出されにくくなる。提案手法では，まず二種類のSAR画像から個別に変動速度を推定する。次に，類似の変動速度を示す領域のクラスタを個別に生成する。その後，二種類のSAR画像におけるクラスタ間で平均地盤変動速度が一致するように補正を加え，最終的に波長の短いSAR画像によって得られた結果を採用する。提案手法をX，LバンドSARであるTerraSAR-X，PALSAR-２の８枚ずつの画像に適用して，関西国際空港の地盤変動速度を推定した結果，各々の単独解析結果よりも高精度に推定できることが判明した。

Depth of field (DOF) is an important problem in close range photogrammetry, in particular for very small distances between the camera and object. The DOF problem can be resolved by tilting the image sensor with respect to the image plane using a tilt-shift lens based on Scheimpflug condition. Therefore, Scheimpflug cameras mounted with tilt-shift lens have been receiving increasing attention as a potential solution to such a problem. However, conventional calibration models are not valid for Scheimpflug cameras because such models are based on pinhole cameras.

Thus, a new camera calibration model has been developed for Scheimpflug cameras in this paper using two Scheimpflug angles and a faithful coordinate transformation describing the relationship of a real sensor array plane with respect to an ideal image plane. Experimental results indicate that the proposed calibration model is applicable not only to Scheimpflug cameras but also to conventional pinhole and zoom lens cameras.

Appearance, aroma and taste are important factors for assessing the quality of tea (Camellia sinensis) and then shading of tea is performed to increase chlorophyll content, which is an important factor for evaluating the appearance and good taste. Although some traditional approaches that require tremendous efforts for the collection of samples and laboratory chemical analyses have been applied, they are not feasible for long-term monitoring. In contrast, hyperspectral remote sensing is proven to be an efficient way for chlorophyll content monitoring. In this study, the three different approaches of kernel-based extreme learning machine (KELM), random forests (RF), and deep belief nets (DBN) were compared to assess the potential for estimating leaf chlorophyll contents from hyperspectral data with existing supervised learning models. Overall, regression models based on KELM yielded the highest performance, achieving a Root Mean Square (RMS) error of 0.20-0.56μg/cm².

In recent years, wildfire has become a global social issue. In this study, by focusing Himawari-8, forest fire detection was carried out. The study site is Buryat Republic in Russia (East side of Lake Baikal). Himawari-8 temporal resolution is 10 min. So every 10 min the target fires were pursued. As a result, AHI-8 ch7 has highest potential for fire detection in AHI IR channel and 0.20 [km²] size forest fire could be detected. It was minimum fire detection. And fire power was calculated by AHI chs. 7, 13. From fire power and ch. 13, the type of fire was estimated. They were “fixed fire”, “advanced fire”and “spreading fire”.

SLATS is the first test satellite of JAXA to demonstrate operations at “super low orbit”below 400 km altitude, almost never used for long-life remote sensing. Lower altitudes will bring benefits for Earth observation such as higher resolution optical imaging and higher Signal/Noise ratio of Synthetic Aperture Radar. Small and High Resolution Optical Sensor (SHIROP) is a panchromatic camera to achieve under 1 meter resolution from super low altitudes. Pointing accuracy and image quality, which are influenced by atmospheric drag, ion engine and Time Delay Integration (TDI), are going to be evaluated. Techniques acquired by SLATS are planned to be used for future regional observation.

Nishi-no-shima volcano restarted its eruptive activity on November 20, 2013. Effusion of huge amount of lava continued for two years and original Nishi-no-shima was merged into the huge amount of new lava flow. After the vulcanian eruption on November 17, 2015, no eruption had been observed. Therefore, the Japan Coast Guard conducted hydrographic survey by survey vessels and aircraft in June 2015 and October 2016. In this report, we introduce the process from the survey results to the issue of new charts.

Volcanic eruption of Nishinoshima Island in the Ogasawara Islands was confirmed in November 2013 and thereafter the area of the island carried on expanding. The Geospatial Information Authority of Japan (GSI) has repeatedly conducted aerial photography to grasp the situation of the expanding island as a part of the national land management. GSI created and updated maps of the island and published them in June 2017, using aerial photos which were taken by GSI while volcanic activities of the island once ceased. This report introduces the GSI's operations such as taking aerial photos and creating new maps of the island during its activities.