Since forest is an important part of ecological system, the deforestation is one of global substantive issues. It is generally accepted that the climate change is related to the deforestation. The issue is worse in developing countries because the forest is one of important natural resources. In the case of North Korea, the deforestation is on the rise from forest reclamation for firewood collection and food production. Moreover, a secondary effect from flood intensifies the damage. Also, the political situation in North Korea presents difficulty to have in-situ measurements. It means that the accurate information of North Korea is nearly impossible to obtain. Thus, assessing the current situation of the forest in North Korea by indirect method is required. The objective of this study is to monitor the forest status of North Korea using multitemporal Landsat images, from 1980s to 2010s. Since the deforestation in North Korea is caused by local residents, we selected two study areas of high population density: Pyeongyang and Hyesan. In North Korea, most of clean Landsat images are acquired in fall season. The fall images have an advantage that we can easily distinguish agriculture areas from forest areas, also have an disadvantage that the forests cannot be easily identified because some of trees have turned red. To identify the forests exactly, we proposed a modified Normalized Difference Vegetation Index (mNDVI) value. The deforestation in Pyeongyang and Hyesan was analyzed by using mNDVI. The dimension of forest has decreased approximately 36% in Pyeongyang for 27 years and approximately 25% in Hyesan for 16 years. The results show that the forest areas in Pyeongyang and Hyesan have been steadily reduced.

This paper presents a method to evaluate the performance of subpixel localization operators using target image data. Subpixel localization of edges is important to extract the precise shape of objects from images. In this study, each target image was designed to provide reference lines and edges to which the localization operators can be applied. We selected two types of moment-based operators: Gray-level Moment (GM) operator and Spatial Moment (SM) operator for comparison. The original edge localization operators with kernel size 5 are tested and their extended versions with kernel size 7 are also tested. Target images were collected with varying Camera-to-Object Distance (COD). From the target images, reference lines are estimated and edge profiles along the estimated reference lines are accumulated. Then, evaluation of the performance of edge localization operators was performed by comparing the locations calculated by each operator and by superimposing them on edge profiles. Also, enhancement of edge localization by increasing the kernel size was also quantified. The experimental result shows that the SM operator whose kernel size is 7 provides higher accuracy than other operators implemented in this study.

With the increasing need for high temporal resolution satellite imagery for monitoring land surfaces, this study evaluated the temporal resolution of the NDVI composites from Geostationary Ocean Color Imager (GOCI) data. The GOCI is the first geostationary satellite sensor designed to provide continuous images over a $2,500{\times}2,500km^2$ area of the northeast Asian region with relatively high spatial resolution of 500 m. We used total 2,944 hourly images of the GOCI level 1B radiance data obtained during the one-year period from April 2011 to March 2012. A daily NDVI composite was produced by maximum value compositing of eight hourly images captured during day-time. Further NDVI composites were created with different compositing periods ranging from two to five days. The cloud coverage of each composite was estimated by the cloud detection method developed in study and then compared with the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua cloud product and 16-day NDVI composite. The GOCI NDVI composites showed much higher temporal resolution with less cloud coverage than the MODIS NDVI products. The average of cloud coverage for the five-day GOCI composites during the one year was only 2.5%, which is a significant improvement compared to the 8.9%~19.3% cloud coverage in the MODIS 16-day NDVI composites.

Yangon Mega City is densely populated and most urbanization area of Myanmar. Rapid urbanization is the main causes of Land Use and Land Cover (LULC) change and they impact on Land Surface Temperature (LST). The objectives of this study were to investigate on the LST with respect to LULC of Yangon Mega City. For this research, Landsat satellite images of 1996, 2006 and 2014 of Yangon Area were used. Supervised classification with the region of interest and calculated change detection. Ground check points used 348 points for accuracy assessment. The overall accuracy indicated 89.94 percent. The result of this paper, the vegetation area decreased from $1061.08sq\;km^2$ (24.5%) in 1996 to $483.53sq\;km^2$ (11.2%) in 2014 and built up area clearly increased from $485.33sq\;km^2$ (11.2%) in 1996 to $1435.72sq\;km^2$ (33.1%) in 2014. Although the land surface temperature was higher in built up area and bare land, lower value in cultivated land, vegetation and water area. The results of the image processing pointed out that land surface temperature increased from $23^{\circ}C$, $26^{\circ}C$ and $27^{\circ}C$ to $36^{\circ}C$, $42^{\circ}C$ and $43.3^{\circ}C$ for three periods. The findings of this paper revealed a notable changes of land use and land cover and land surface temperature for the future heat management of sustainable urban planning for Yangon Mega city. The relationship of regression experienced between LULC and LST can be found gradually stronger from 0.8323 in 1996, 0.8929 in 2006 and 0.9424 in 2014 respectively.

This paper presents a set of methods to evaluate the image quality of smartphone cameras as compared with that of a DSLR camera. In recent years, smartphone cameras have been used broadly for many purposes. As the performance of smartphone cameras has been enhanced considerably, they can be considered to be used for precise mapping instead of metric cameras. To evaluate the possibility, we tested the quality of one DSLR camera and 3 smartphone cameras. In the first step, we compare the amount of lens distortions inherent in each camera using camera calibration sheet images. Then, we acquired target sheet images, extracted reference lines from them and evaluated the geometric quality of smartphone cameras based on the amount of errors occurring in fitting a straight line to observed points. In addition, we present a method to evaluate the radiometric quality of the images taken by each camera based on planar fitting errors. Also, we propose a method to quantify the geometric quality of the selected camera using edge displacements observed in target sheet images. The experimental results show that the geometric and radiometric qualities of smartphone cameras are comparable to those of a DSLR camera except lens distortion parameters.

Importance of remote sensing for surface is increased than past. So many countries try to many ways to retrieve surface reflectance. In this study, we study a Bidirectional Reflectance Distribution Function (BRDF) to retrieve surface reflectance. We apply BRDF using observed surface reflectance of SPOT/VEGETATION (VGT-S1) and angular data to get Bidirectional Reflectance Distribution (BRD) coefficients for calculating scattering. And then we apply BRDF in the opposite direction with BRD coefficients and angular data to retrieve Background Surface Reflectance (BSR). The range of BSR is not over $0.4{\mu}m$ (blue), $0.45{\mu}m$ (red), $0.55{\mu}m$ (NIR). And for validation we compare BSR with VGT-S1, there are bias is from 0.0116 to 0.0158 and RMSE is from 0.0459 to 0.0545. As a result, we confirm that BSR is similar to VGT-S1.

The surface reflectance is essential to retrieval various indicators related land properties such as vegetation index, albedo and etc. In this study, we estimated surface reflectance using Himawari-8 / Advanced Himawari Imager (AHI) channel data. In order to estimate surface reflectance from Top of Atmosphere (TOA) reflectance, the atmospheric correction is necessary because all of the TOA reflectance from optical sensor is affected by gas molecules and aerosol in the atmosphere. We used Second Simulation of a Satellite Signal in the Solar Spectrum Vector (6SV) Radiative Transfer Model (RTM) to correct atmospheric effect, and Look-Up Table (LUT) to shorten the calculation time. We verified through comparison Himawri-8 / AHI surface reflectance and Proba-V S1 products. As a result, bias and Root Mean Square Error (RMSE) are calculated about -0.02 and 0.05.