• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.456-461
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• 1999

It is very important for monitoring the interannual variability of sea ice extents in the Okhotsk Sea because the global warming has firstly appeared around the Okhotsk Sea, locating around the southernmost region of sea ice cover in the Northern Hemisphere. In order to develop the sea ice concentration algorithm by microwave sensors onboard satellite, electromagnetic properties of sea ice in the Okhotsk Sea, therefore, were observed by airborne microwave radiometer (AMR), which has the same frequencies as AMSR (Advanced Microwave Scanning Radiometer), ADEOS-II, launching on November, 2000. On this study, it is discussed how to make the image of AMR-EFOV and the video image with nadir angle under flight at the same time, and superimpose the brightness temperature data by AMR-EFOV on the video mosaiced images. For comparing SPOT image, it is clearly that the variation of brightness temperature is small in 89GHz V-pol without the sea ice types and increase at the lower frequency-band.

• The KIPS Transactions:PartD
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• v.9D no.5
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• pp.939-944
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• 2002

In this paper, we propose and apply new classification method to the remotely sensed image acquired from airborne multi-spectral scanner. This is a neuro-fuzzy image classifier derived from the generic model of a 3-layer fuzzy perceptron. We implement a classification software system with the proposed method for land cover image classification. Comparisons with the proposed and maximum-likelihood classifiers are also presented. The results show that the neuro-fuzzy classification method classifies more accurately than the maximum likelihood method. In comparing the maximum-likelihood classification map with the neuro-fuzzy classification map, it is apparent that there is more different as amount as 7.96% in the overall accuracy. Most of the differences are in the "Building" and "Pine tree", for which the neuro-fuzzy classifier was considerably more accurate. However, the "Bare soil" is classified more correctly with the maximum-likelihood classifier rather than the neuro-fuzzy classifier.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.325-328
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• 2007

We discuss the Normalized Difference Vegetation Index (NDVI) of the forest canopy and floor separately based on airborne spectral reflectance measurements and simultaneous airborne land surface images acquired around Yakutsk, Siberia in 2000. The aerial land surface images were visually classified into four forest types: no-green canopy and snow floor (Type-1), green canopy and snow floor (Type-2), no-green canopy and no-snow floor (Type-3), and green canopy and no-snow floor (Type-4). The mean NDVI was calculated for these four types. Although Type-2 had green canopy, the NDVI was rather small (0.17) because of high reflection from the snow cover on the floor. Type-3, which had no green canopy, indicated considerably large NDVI (0.45) due to the greenness of the floor. Type-4 had the largest NDVI (0.75) because of the greenness of both the canopy and floor. These results reveal that the NDVI depends considerably on forest floor greenness and snow cover in addition to canopy greenness.

• Current Optics and Photonics
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• v.6 no.3
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• pp.313-322
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• 2022

Remote-sensing optical payloads, especially hyperspectral imagers, have particular issues with stray light because they often encounter high-contrast target/background conditions, such as sun glint. While developing an optical payload, we usually apply several stray-light analysis methods, including forward and backward analyses, separately or in combination, to support lens design and optomechanical design. In addition, we often characterize the stray-light response over a full field to support calibration, or when developing an algorithm to correct stray-light errors. For this purpose, we usually use forward analysis across the entire field, but this requires a tremendous amount of computational time. In this paper, we propose a sequence of forward-backward-forward analyses to more effectively investigate the through-field response of stray light, utilizing the combined advantages of the individual methods. The application is an airborne hyperspectral imager for creating hyperspectral maps from 900 to 1700 nm in a 5-nm-continuous band. With the proposed method, we have investigated the through-field response of stray light to an effective accuracy of 0.1°, while reducing computation time to 1/17^th of that for a conventional, forward-only stray-light analysis.

• Journal of Korean Society of Forest Science
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• v.100 no.3
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• pp.315-326
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• 2011

In recent years, domestic and international interests focus on climate change, and importance of forest as carbon sink have been also increased. Particularly REDD+ mechanism expanded from REDD (Reduced Emissions from Deforestation and Degradation) is expected to perform a new mechanism for reducing greenhouse gas in post 2012. To conduct this mechanism, countries which try to get a carbon credit have to certify effectiveness of their activities by MRV (Measuring, Reporting and Verification) system. This study analyzed the approaches for detecting land cover change and estimating carbon stock by remote sensing technology which is considered as the effective method to develop MRV system. The most appropriate remote sensing for detection of land cover change is optical medium resolution sensors and satellite SAR (Synthetic Aperture Radar) according to cost efficiency and uncertainty assessment. In case of estimating carbon stock, integration of low uncertainty techniques, airborne LiDAR (Light Detection and Ranging), SAR, and cost efficient techniques, optical medium resolution sensors and satellite SAR, could be more appropriate. However, due to absence of certificate authority, guideline, and standard of uncertainty, we should pay continuously our attention on international information flow and establish appropriate methods. Moreover, to apply monitoring system to developing countries, close collaboration and monitoring method reflected characteristics of each countries should be considered.

• Journal of Cadastre & Land InformatiX
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• v.46 no.1
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• pp.87-100
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• 2016

The acquisition of the forest resource information has depended on a partial sampling method or aerial photographs which demand a lot of effort and time because of the vast areas and the difficult approach. For the acquisition of the forest resource information, there have been the optical remote-sensing and the multi-spectrum image to offer only horizontal distributions of trees, but a new technological approach, such as Airborne LiDAR, is more necessary to acquire directly three dimensional information related to the forest terrains and trees' features. This paper proposes an algorithm for the forest information extraction such as trees' individual numbers and the heights of trees by using LiDAR data. Especially, this proposed algorithm adopts a region growing method for the extraction of the vegetation-point and extracts the forest information using morphological features of trees.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1374-1376
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• 2003

NASA, NSF and USGS jointly conducted a LIDAR survey over several sites in the Antarctic Dry Valleys and its vicinity, acquiring numerous surface points by NASA's Airborne Topographic Mapper (ATM) conical laser scanning altimetry system. The data set have high blunder ratio, and the conical scanning pattern resulted large variation of the point densities. Hence, to reduce the undesirable effects due to these characteristics and process the huge number of points with reasonable time and resources, we developed a novel approach to generate large-scale and high-resolution DEMs in robust, efficient and nearly automatic manners. Based on this approach we produced DEMs and then verified them with reference data.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.474-477
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• 2005

In tactical theater, it is crucial to detect ground moving targets and to locate them precisely. This problem can be resolved by using SAR (Synthetic Aperture Radar) sensors providing GMTI (Ground Moving Target Indication) capability. In general, to implement a robust GMTI sensor is not simple because of the strong competitions between target signals and clutter signals on the ground, and low speed of moving targets. Contrary to the case that a delay canceller is mostly suitable for ground surveillance radars, DPCA (Displaced Phase Centered Antenna) or STAP (Space Time Adaptive Processing) techniques have been adapted for GMT! function of modem airborne radars. In this paper, anew scheme of DPCA using two passive antennas with vertical separation is proposed, which also provides good clutter cancellation performance. The proposed scheme enables us to scan straight ahead of the carrying platform that is impossible with typical DPCA configuration. Simulations using various conditions have been performed to validate the proposed scheme, and the results are acceptable.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.646-649
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• 2004

LiDAR(Light Detection and Ranging) system can record intensity data as well as range data. Recently, LiDAR intensity data is widely used for landcover classification, ancillary data of feature extraction, vegetation species identification, and so on. Since the intensity return value is associated with several factors, same features is not consistent for same flight or multiple flights. This paper investigated correlation between intensity and range data. Once the effects of range was determined, the single flight line normalization and the multiple flight line normalization was performed by an empirical function that was derived from relationship between range and return intensity

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.405-409
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• 2004

We chose the Kushiro wetland in Hokkaido, Japan, as a test site to monitor wetland areas. Synthetic aperture radar (SAR) can carry out continuous observation in any weather conditions, and can therefore be used to observe high humidity areas such as wetlands. We applied multi-parameter SAR data (dual-frequency, multi-polarization, and multi-incidence angle) to monitoring the wetland forest. To find the optimum incidence angle and polarization for monitoring the wetland biomass, a simple backscattering model of wetland vegetation was developed and applied to estimate backscattering coefficients for different biomass and surface conditions.