Tomomi Takeda

Development of a method to detect coral bleaching using airborne hyperspectral sensor

In recent years, coral bleaching is becoming one of the environmental problems all over the world. In order to monitor this bleaching, remote sensing is expected as a effective tool. Although existing multispectral imager can detect coral bleaching, it cannot detect whether coral became death or living after bleaching. In this study, we used airborne hyperspectral imager and classified bottom type to detect coral death or living. The classification was conducted by ISODATA classification using bottom index and first derivative imageries calculated from hyperspectral data. We verified the accuracy of classification with thirty ground truths and found that twenty-four points were correctly classified and six points were misclassified.

A monitoring method of coral bleaching and recovery by using hyperspectral sensor

Coral bleaching can lead to death of corals and has been increasing in worldwide in recent years. We proposed a method to classify extents of alive and dead corals by using space-bone hyperspectral sensor to monitor the events. The method was composed of two different approaches- Bottom Index and first derivative analysis of reflectance. In this study, examinations for improvement of classification accuracy and understanding a limitation of water depth which the classification can be applied for. After the result, feasibility of the method was evaluated with simulation data of HISUI, space-bone hyperspectral sensor. As a result, it was shown that the developed method was capable to be applied to HISUI data.

A study on detecting the poppy field using hyperspectral remote sensing techniques

This study aims to recognize the difference of spectral characteristics between poppy and the other crops and develop a method to detect illegal poppy fields using a hyperspectral data. As analysis methods, we examined linear discriminant analysis using two bands, red edge position (REP), and partial least square discriminant analysis (PLSDA). As a result, REP is valid for discriminating between poppy and wheat. Furthermore, PLSDA would be possible to detect poppy fields in agricultural area including various crops. Availability of the proposed methods was confirmed using ground spectral data, airborne and simulated satellite-borne hyperspectral data. Our goal is to develop a method for practical use of HISUI (Hyperspectral Imager SUIte). Its expected that HISUI could make a contribution to monitor illegal crop using our methods in combination with the monitoring system established previously.

Evaluation of wheat growth monitoring methods based on hyperspectral data in Western Australia

Approximately 90% of wheat consumed in Japan is imported from such countries as the United States (the U.S.) and Australia. Therefore, Australia plays an important role to ensure food security of Japan. This study attempted to make development of growth monitoring application of Australian wheat using both ground measurement hyperspectral data (FieldSpec) and airborne hyperspectral data (HyMap). As a result, estimation of head moisture and leaf area index (LAI) of the wheat was achieved by using the hyperspectral data acquired in the late grain filling period. This attempt also made it possible to visualize a growth situation of the wheat over a wide area. In this study, the multiple regression analysis achieved the most suitable estimation results among various indices.

A methodology of forest monitoring from hyperspectral images with sparse regularization

This paper presents a methodology to extract information on existing conditions of a forest from hyperspectral images and SAR images for the forest management. To overcome the difficulties in hyperspectral image analysis such as optimal band selection and model overfitting, a machine learning technique called sparse regularization was adopted. Experimental results show the effectiveness of this approach.

Contribution of Hyperspectral Applications to Tropical Peatland Ecosystem Monitoring

Tropical peatland is a valuable and vulnerable ecosystem storing tremendous carbon in the form of above ground biomass and soil carbon. Hokkaido University and collaborative research group with Indonesian experts are conducting a long-term research on tropical peatland ecosystem since 1990s and they concluded that eight key elements need to be monitored for the comprehensive peatland assessment; (1) CO2 flux and concentration, (2) Hotspots detection, (3) Forest degradation and species mapping, (4) Deforestation, forest biomass changes, (5) Ground water level and soil moisture, (6) Peat area and peat property, (7) Peat subsidence and (8) Water soluble organic carbon. As hyperspectral sensors which have more than 100 channels from VNIR (Visible Near Infrared) to SWIR (Short Wave Infrared) regions enable us to extract specific spectral information of various objects including above elements, its applications are expected to contribute to tropical peatland monitoring. Some examples of hyperspectral applications are introduced in this chapter.

Bottom-type classification in coral reef area using hyperspectral bottom index imagery

This paper describes a bottom-types classification method for mapping shallow coral reef area using hyperspectral bottom index (BI) imagery. Monitoring of coral reef degradation, especially coral bleaching, is important because coral reefs offer important ecosystem services. However, it is difficult to monitor coral reefs using spaceborne multispectral sensors due to spectral confusion between coral, algae and seagrass. This study develops a bottom-type area ratio regression model using support vector machine (SVM) and hyperspectral BI imagery. BI imagery is effective in reducing wavelength dependency. The method applied in this study achieved overall accuracies that range from 0.6 to 0.8. In addition, the effectiveness of the applied method was confirmed with sample size reduction. For example, half of the sample size produced an overall accuracy of 0.6. This result shows that the applied method is effective for coral reef monitoring.

Discrimination of peat swamp forest types with hyperspectral data

In tropical peat swamp forest, forest fire and illegal logging are major problems, which cause forest succession from grass just after disturbance to completely recovered forest. In order to distinguish each recovering stage, discrimination of forest types, such as primary forest and secondary forest, is very important. In general cases, a pixel-based classification is one of the most attractive choices for forest monitoring. However, since difference between primary and secondary forest comes in distribution ratio between the number of small-diameter trees and the number of large-diameter trees, only the pixel-based approach for the classification is not sufficient. In this paper, we use both spectral and spatial information from hyperspectral data to develop a high accurate biomass prediction model. Moreover, forest type classification scheme considering spatial distribution of biomass is proposed.

Application studies of HISUI

Japan Space Systems has conducted the application studies for HISUI, the Hyper-spectral Imager SUIte, since FY 2007. Our studies cover various fields such as oil, gas and mineral resources, agriculture and environment. This report summarizes some of the outcomes, including alteration mineral mapping, paddy growth estimation, coral reef monitoring and forest type classification, and introduces the anticipated benefits of HISUI imagery for future data users.

Hyperspectral data application for peat forest monitoring in Central Kalimantan, Indonesia

Peatland is a large CO2 reservoir which accumulates 2000Gt of CO2, which is equal to 30% of global soil carbon. However, it has been becoming a large CO2 emission source because of peat decomposition and fire due to drainage water. This is caused by social activities such as canalizing. Especially, in Indonesia, peat swamp forests cover considerable portions of Kalimantan and 37.5% of CO2 emission source is peatland (DNPI, 2010). To take measures, it is necessary to conduct appropriate assessment of CO2 emission in broad peat swamp forest. Because hyperspectral data possess higher spectral resolutions, it is expected to evaluate the detailed forest conditions. We develop a method to assess carbon emission from peat swamp forest by using hyperspectral data in Central Kalimantan, Indonesia. Specifically, we estimate 1) forestry biomass and 2) underground water level expected as an indicator of CO2 emission from peat. In this research, we use the image taken by HyMAP which is one of the airborne hyperspectral sensors. Since the research area differs in forest types and conditions due to the past forest fire and disturbance, forest types are classified with the sparse linear discriminant analysis. Then, we conduct a biomass estimation using Normalized Difference Spectral Index (NDSI). We also analyze the relationship between underground water level and Normalized Difference Water Index (NDWI), and find the possibility of underground water level estimation with hyperspectral data. We plan to establish a highly developed method to apply hyperspectral sensor to peatland monitoring system.