Coral reefs are threatened ecosystems, with global and local stressors such as excessive sea-surface temperature contributing to their decline. We review what kind of stressors can have an impact on coral bleaching and how open data can be utilized in predicting model of coral bleaching for local and regional reef management.
Development of accuracy of phenology observations and land uses and land cover classification by near-surface and satellite remote sensing is an important but challenging task to evaluate the spatio-temporal variability of ecosystem functions and service, and biodiversity. Towards this aim, we require abundant ground-truth in multiple points for a broad scale. Recently, in Japan, we can use open-access data on the web sites such as phenological information for tourisms and land uses and land cover information collected by avocational volunteers. Here, we present a recent new approach by integrating analysis between remote sensing observations and open-access data, and then discuss usability, issues, and outlook of this approach.
Resolution enhancement is a key technology that can expand the range of potential applications for spaceborne hyperspectral missions. This paper provides an overview of resolution enhancement techniques for hyperspectral imagery, namely, hyperspectral superresolution, subpixel mapping, hyperspectral pansharpening, and hyperspectral and multispectral data fusion. The paper describes the potential and limitations of each technique from the viewpoint of practicality.
This commentary focuses on the role of data assimilation in the context of satellite remote sensing observation. Our work consists of 4 chapters: Introduction, Outline of data assimilation, Data assimilation research using satellite-based observation, and Conclusions and perspectives.
Using the machine learning technique with the wider availability of various observation data, the integration of ground observation data and satellite observation data has been advancing. In recent years, these techniques were applied to studies of terrestrial energy, water, and carbon cycles to estimate their spatial and temporal variations by upscaling. In this article, we provide an overview of the upscaling technique using machine learning and discuss potential applications of the dataset to terrestrial biosphere studies.
Global climate models and Earth system models have been used to project climate change. The Coupled Model Intercomparison Project Phase 6 (CMIP6) is now preparing to provide datasets for the climate projections simulated by the models. Observation datasets including remote sensing products are now intensively used for studies on climate projection.
Google Earth Engine (GEE) is a cloud-based geospatial data analysis platformfor educational and research purposes. Without familiarity with super-computingarchitecture, users are able to process massive geospatial data sets on GEE. This article describes the characteristics of GEE and some examples of Landsatdata analysis.
This paper presents a super resolution (SR) method for satellite imagery based on deep learning. From this preliminary study, we found that the performance of SR strongly depends on the variety of the training data set. A new strategy that combines SR and selection is proposed to improve the accuracy and flexibility of the method.
High-spatial-resolution satellite imagery over urban areas includes small and complicated shadows generated by buildings, trees and so on. These shadows affect applications such as land cover classification and traffic surveillance, but also can be used for estimating building heights, as reported by several investigators. In the present paper, we propose a novel method of estimating building heights based on the consistency between a shadow map generated from a high-resolution satellite image and that created by iterative 3D modelling with building contour data and a digital elevation model (DEM). In checking the consistency between them, tree shadows which may reduce accuracy are excluded by shadow tracking with a vegetation index map. The method was tested using a WorldView-2 image and a 5 m-resolution DEM at the Hitachi Campus of Ibaraki University (Area A), a low-rise residential area (Area B), and a low-to-mid-rise residential area (Area C) in Hitachi City, Ibaraki, Japan, and the results were compared with heights from drawings (only Area A) and laser survey data (Areas A, B, and C). In Area A, the root-mean-square (RMS) errors for 31 buildings were 1.32 m (drawings) and 1.65 m (laser survey), and those for 29 buildings without a large error were 0.78 m (drawings) and 1.18 m (laser survey). In Area B, the RMS error for 87 buildings was 1.95 m, and that for 79 buildings without a large error was 1.54 m (laser survey). In Area C, the RMS error for 99 buildings was 4.01 m, and that for 65 buildings without a large error was 1.50 m (laser survey). Major error factors in these tests were shadow disturbances caused by objects excluded in 3D modeling, such as trees and cars, and merging of shadows due to crowded buildings. Thus, the method should be applied to selected buildings but not to all buildings, but it has some robustness. In our next study we aim to predict the reliability of the estimated height, to detect errors in building GIS data, and to apply the method to foreign cities with neither building GIS data nor aerial laser survey data.
Owing to the remarkable improvements in radar sensors, it is now possible to obtain information regarding a single structure from high-resolution SAR images. In our previous research, we proposed a method for detecting the heights of low-rise buildings automatically using 2D GIS data and a single high-resolution TerraSAR-X intensity image. However, it was difficult to apply this method to high-rise buildings due to their backscattering characteristics. In this study, a new method was developed for estimating the heights of high-rise buildings based on the results from an Interferometric SAR (InSAR) analysis. The potential layover areas were extracted using both amplitude and phase characteristics. First, the proposed method for low-rise buildings was used to extract the layovers from one intensity image. The phase characteristics in the InSAR result were then investigated and used to extract potential layover areas. Finally, heights were estimated based on the layover lengths obtained from both the intensity and phase images. The developed method was tested on two TerraSAR-X image sets of central Tokyo, Japan, in the HighSpot mode. The results were verified by comparison with a digital surface model obtained by stereoscopic photogrammetry. The detected heights were found to be reasonable.
Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) has 14 spectral bands in the VNIR, SWIR and TIR regions and their spectral bandpasses were designed for geological mapping. This study developed a method of showing the spatial distribution of hydrothermal alteration areas and carbonate rocks by combining the spectral indices derived from the multispectral ASTER data of the different wavelength regions. The HSV color model was employed to combine the spectral indices to generate a single integrated color image. Spectral indices indicating mineral species and amounts were allocated to the Hue (H) and Saturation (S) elements respectively, and topographic information was added to the image as a Value (V) element. It was easy to interpret the meanings of colors on this image, as they were determined in advance. This method was tested using the ASTER data of the Cuprite and Goldfield areas in Nevada (USA) and we confirmed that the HSV color image clearly showed the distribution and zonal structures of the hydrothermal alteration areas. In addition, their spatial distribution was also exhibited by integrating topographic information. This new method allows us to generate a color image that can be easily interpreted for geologic mapping even by a user who is not familiar with remote sensing.
The ability to measure the amount of water dispersed inside a solid object is highly desirable in a number of fields such as agriculture, forestry, and civil engineering. In this study, the authors’ goal was to measure the water content inside phenolic foam columns using only microwave backscattering measurements by a scatterometer developed for airborne synthetic aperture radar (SAR). The experiment was carried out in an anechoic chamber using scatterometers three frequency bands: L, X, and Ku. The column irradiated with microwaves was a cylinder of phenolic foam capable of holding various volumes of water. Four objects with a different volume of water permeating were irradiated by microwaves, and the backscattering was measured. In consideration of the influence of microwave fading, the columns were placed on a turntable and rotated one revolution (i.e., 360°) while taking about 75,000 continuous measurements of the entire surface. The measurements were then evaluated based on variance and median. As a result of measuring the microwave backscattering, it was found that the higher the water content in the column, the higher the radar cross-section (RCS) median, average, and maximum values for that object in all three bands. Regarding the L band, it was clearly shown that it was possible to distinguish when the volume content of water was 25 % and 50 %. Also, when the water content of the column was relatively small, the range of dispersion was large, and when the water content exceeded a certain value, the dispersion widths began to converge. This indicates the possibility that analyzing the variance of the microwave backscattering may be a clue to knowing the dispersion state of the water content of the object. In this experiment, the microwave backscattering was continuously measured while rotating the object one time, and a statistical method was used to analyze the results. This measurement method is new, and it could add a new approach to measuring moisture content noninvasively.