Jonggeol Park

Validation of temporal BRDFs of paddy fields estimated from MODIS reflectance data

The effect of the bidirectional reflectance distribution function (BRDF) is one of the most important factors in correcting and validating the reflectance obtained from remotely sensed data. While the importance of BRDF has become widely recognized, bidirectional reflectance factor (BRF) data measured for correction and validation are insufficient because of the technical difficulty of the measurement. The primary objective of the present research is to estimate BRDF effects from Moderate Resolution Imaging Spectroradiometer (MODIS) data. Temporal ground-based BRDFs of rice paddy fields were estimated from ground measurements conducted in June and August 2002. MODIS-derived BRDFs obtained from MODIS reflectance data and ground-based BRDFs were estimated using the reciprocal form of the RossThick and LiSparse (RossThick-LiSparse-R) kernels, a semiempirical BRDF model adopted for the operational MODIS BRDF product. The MODIS-derived band 1 (620-680 nm) and band 2 (841-876 nm) BRDFs were compared with the ground-based BRDFs corresponding to the same waveband, respectively. The comparison results demonstrate that BRDFs of paddy fields change in accordance with paddy growth and that MODIS-derived BRDFs are closely related to ground-based BRDFs in most of the cases. It was also revealed that MODIS-derived BRDFs can be estimated to a high degree of accuracy when MODIS data necessary for the estimation are available.

Estimating floodwater from MODIS time series and SRTM DEM data

Real-time flood mapping with an automatic flood-detection technique is important in emergency response efforts. However, current mapping technology still has limitations in accurately expressing information on flood areas such as inundation depth and extent. For this reason, the authors attempt to improve a floodwater detection method with a simple algorithm for a better discrimination capacity to discern flood areas from turbid floodwater, mixed vegetation areas, snow, and clouds. The purpose of this study was to estimate a flood area based on the spatial distribution of a nationwide flood from the Moderate Resolution Imaging Spectroradiometer (MODIS) time series images (8-day composites, MOD09A1, 500-m resolution) and a digital elevation model (DEM). The results showed the superiority of the developed method in providing instant, accurate flood mapping by using two algorithms, which modified land surface water index from MODIS image and eight-direction tracking algorithm based on DEM data.

Monitoring Landscape Changes in Japan Using Classification of Modis Data Combined with a Landscape Transformation Sere (LTS) Model

Abstract Japan, with over 75% forest cover, is one of the most heavily forested countries in the world. Various types of climax forest are distributed according to latitude and altitude. At the same time, human intervention in Japan has historically been intensive, and many forest habitats show the influence of various levels of disturbance. Furthermore, Japanese landscapes are changing rapidly, and a system of efficient monitoring is needed. The aim of this research was to identify major historical trends in Japanese landscape change and to develop a system for identifying and monitoring patterns of landscape change at the national level. To provide a base for comparison, Warmth Index (WI) climatic data was digitalized and utilized to map potential climax vegetation for all of Japan. Extant Land Use Information System (LUIS) data were then modified and digitalized to generate national level Land Use/Land Cover (LU/LC) distribution maps for 1900, 1950 and 1985. In addition, MODIS data for 2001 acquired by the Tokyo University of Information Sciences were utilized for remote LU/LC classification using an unsupervised method on multi-temporal composite data. Eight classification categories were established using the ISODATA (cluster analyses) method; alpine plant communities, evergreen coniferous forest, evergreen broad-leaved forest, deciduous broad-leaved forest, mixed forest, arable land (irrigated rice paddy, non-irrigated, grassland), urban area, river and marsh. The results of the LUIS analyses and MODIS classifications were interpreted in terms of a Landscape Transformation Sere model assuming that under increasing levels of human disturbance the landscape will change through a series of stages. The results showed that overall forest cover in Japan has actually increased over the century covered by the data; from 72.1% in 1900 to 76.9% in 2001. Comparison of the actual vegetation and the potential vegetation as predicted by WI, however, indicated that in many areas the climax vegetation has been replaced by secondary forests such as conifer timber plantations. This trend was especially strong in the warm and mid temperate zones of western Japan. This research also demonstrated that classification of moderate resolution remote sensing data, interpreted within a LTS framework, can be an effective tool for efficient and repeat monitoring of landscape changes at the national level. In the future, the authors plan to continue utilizing this approach to track rapidly occurring changes in Japanese landscapes at the national level.

Near Real-Time Flood Volume Estimation From MODIS Time-Series Imagery in the Indus River Basin

Satellite images have been widely applied in near real-time flood inundation maps in many cases. Such images have significant potential to predict the time, place and scale of a flood event, and can be very useful in emergency response efforts. The detection of floodwaters and the estimation of flood volumes are important to determine a hazard in flood risk. In this study, we conducted surface water detection based on the spatial distribution of the 2010 Indus River flood, which affected the entire Pakistan area. A modified surface water index derived from near-real-time Moderate Resolution Imaging Spectrometer (MODIS) images coupled with a digital elevation model (DEM) was used. We also developed and applied a simplified algorithm to extract the 3D volume of floodplain surface water considering surface heights. The results found that the MODIS-DEM combined approach was feasible for automatic, instant flood detection. This approach shows a methodological possibility as an integrated algorithm for producing flood maps at local to global scales.

Estimation of flood volume in Chao Phraya River basin, Thailand, from MODIS images couppled with flood inundation level

River inundation satellite images are restricted to make real-time flood inundation maps in many cases. However, such images have significant potential to predict the time, place and scale of a flooding event, and can be very useful in emergency response efforts. The estimation of water extent boundary and flood volume is important to determine a fundamental hazard in flood risk assessment. In this study, an attempt was made to detect surface water in a severe flood event (the 2011 Thai flood) by applying modified remote sensing indices to near-real-time MODIS images. Flood volumes were also calculated for detected flood areas by using a proposed flood inundation level (FIL) model with the Digital Elevation Model (DEM). FILs were verified through field investigation. The results show that the MODIS-FIL combined approach is feasible for automatic, instant flooding detection.

The potential of high resolution remotely sensed data for urban infrastructure monitoring

The object of this research investigated the urban infrastructure monitoring using high resolution remotely sensed data. Multi-source imagery with high spatial resolution has great potential to improve the performance of detailed urban expansion and infrastructure analysis. These images allowed for continual monitoring of infrastructure needs. Multi-temporal analysis of satellite imagery is effective for urban growth and changes of infrastructure because a high correlation exists between spectral variation in images from different dates and urban land cover change.

Nation-wide flood risk assessment using inundation level model and MODIS time-series imagery

Real-time flood inundation mapping can be useful in emergency response efforts but is limited in capturing a wide area for assessment. In this paper, the authors suggest an improved extraction method of surface water with a simplified algorithm. The purpose of this study was to accurately estimate a flood area based on the spatial distribution of a nation-wide flood using a flood inundation level (FIL) model and a modified LSWI (MLSWI) from near real-time images from MODIS time series. This flood risk assessment study focused on reducing the susceptibility of nation-wide risk damage and improved the accuracy of extracting a vulnerable inundation area.

A determination of the earthquake disaster area by object-based analysis using a single satellite image

The final goal of this study is to create data to support emergency efforts in a disaster affected area by locating damaged buildings shortly after the disaster. In this study, prioritizing the practicality of the method for emergency purposes, we designed a method only to use a single satellite image of an affected area, eliminating the use of complex algorithms and auxiliary data. The uniqueness of our method lies in the application of an object-based region segmentation to images and the use of features of objects obtained from texture, hierarchical and other information in order to extract damaged buildings. Out of 26 features resulting from the analysis of objects, we found one feature and three combinations of two different features that are effective in extracting damaged buildings, such as Rectangular fit, Homogeneity, Number of sub-objects/Area, and Length of longest of edge/Area.

Large flood mapping using syncro water index coupling with hydro data and time series modis images

In this research, we proposed the Synchro Water Index (SWI) to detect widespread inundation extent in a transboundary river basin using the time-series Moderate Resolution Imaging Spectrometer (MODIS) data, a major contributor to progress in international flood monitoring. After removing clouds using the White-object Index (WoI), the multi-temporal processing coupled with in-situ water level data was applied to the 2015 Bangladesh flood for near-real-time nationwide rapid flood monitoring. The preliminary results showed that the maximum inundation area from SWI was underestimated to be smaller than the area from the solo use of modified land surface index, or 32% (29,900 km2) of the total area of Bangladesh.

Optimum Object Analysis Of Islands Activities On South China Sea By DNB On VIIRS

A data processing and analyzing system was designed and made operational with free software to process the big data over the South China Sea (SCS), which are provided by the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (Suomi-NPP) to monitor the light distributions in the night. The VIIRS data are processed from the raw data (level-0) to geophysical data (level-3) using the International Polar Orbiter Processing Package (IPOPP), which are freely available from NASA. The Day Night Band (DNB) of VIIRS is extracted from the level-3 data and is geospatially projected for our region of interest (ROI) on the SCS. For those ingested data, an optimum object analysis in geographic domain was proposed to estimate the reclamation activities on coral reefs in the SCS using GDAL. A pixel base analysis of DNB data is possible to estimate the island activities independently among dredgers, support vessels, or buildings on coral reefs, but not appropriate to analyze the activities for as an integrated system or for the changes in the ROI. Although there is a difficulty to determine the scale of objects on the analysis of DNB data for the reclamation activities, the optimum object scale was empirically determined for different size of coral reefs and was applied for this study. The optimum object analysis determines the reclamation activities with including lights not only from buildings but also dredgers and supporting vessels around coral reefs. Poster Download: http://scholarworks.umass.edu/foss4g/vol17/iss1/25 ∗Corresponding author Email address: asanuma@rsch.tuis.ac.jp (Ichio Asanuma) Submitted to FOSS4G 2017 Conference Proceedings, Boston, USA. September 20, 2017 0 2 4 6 8 10 12 14 16 18 20 D N B R ad ia nc e (n W c m -2 sr -1 ) Date Temporal change of DNB around Subi Reef 0 2 4 6 8 10 12 14 16 18 20 D N B R ad ia nc e (n W c m -2 sr -1 ) Date Temporal change of DNB around Fiery Cross Reef 0 2 4 6 8 10 12 14 16 18 20 D N B R ad ia nc e (n W c m -2 sr -1 ) Date Temporal change of DNB around Mischief Reef A