In this study, we investigated the effect of clouds on night light using the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) in East Asian urban areas. Focusing on the Mie scattering phenomenon that occurs when night light passes through thin clouds, we investigated the effects of clouds using the Gray Level Co-occurrence Matrix (GLCM) features of texture analysis.

A multi-dimensional approach is an upcoming technology in disaster risk to understand a real risk. In the case of flooding, dimensions of time and elevation (height) should be considered to produce rapid and accurate risk information. Dynamic spatio-temporal flood prediction is an urgent issue for the risk management of short- and long-duration flooding. The main objective of this paper is to propose a step-wise process for dynamic flood detection using the synchronized floodwater index (SfWI 2 ) coupled with 5-day lead-time forecasting data and in-situ water-level data with the primary focus on daily change in flood extent. We mainly employed the 2015 time-series Moderate Resolution Imaging Spectrometer (MODIS) data acquired over the study area along the Brahmaputra River where flood events occur annually. Based on scenario-based forecasting water-levels, flood extent maps were created and show the possibility of the proposed multi-dimensional approach as a useful tool for short-duration flood prediction.

Urban and Peri-urban Agriculture (UPA) has recently come into sharp focus as a valuable source of food for urban populations. High population density and competing land use demands lend a spatiotemporally dynamic and heterogeneous nature to urban and peri-urban croplands. For the provision of information to stakeholders in agriculture and urban planning and management, it is necessary to characterize UPA by means of regular mapping. In this study, partially cloudy, intermittent moderate resolution Landsat images were acquired for an area adjacent to the Tokyo Metropolis, and their Normalized Difference Vegetation Index (NDVI) was computed. Daily MODIS 250 m NDVI and intermittent Landsat NDVI images were then fused, to generate a high temporal frequency synthetic NDVI data set. The identification and distinction of upland croplands from other classes (including paddy rice fields), within the year, was evaluated on the temporally dense synthetic NDVI image time-series, using Random Forest classification. An overall classification accuracy of 91.7% was achieved, with user’s and producer’s accuracies of 86.4% and 79.8%, respectively, for the cropland class. Cropping patterns were also estimated, and classification of peanut cultivation based on post-harvest practices was assessed. Image spatiotemporal fusion provides a means for frequent mapping and continuous monitoring of complex UPA in a dynamic landscape.

: Day and Night Band (DNB) of Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-Orbiting Partnership (S-NPP)has exhibited a capability to detect the fishery lights in the night time. The distribution of fishery lights, which are corresponding to the distribution of fishery resources, has a significant information for fishery industries and resource managements.Unfortunately,it is difficult to distinguish the fishery lights on DNB images because the lunar lights reflected by clouds are observed simultaneously.In this study,the artificial neural network (ANN)was developed to detect the fishery lights apart from the lunar lights reflected by clouds.The ANN was trained to simulate the DNB lights with the brightness temperature at 3.7 μ m (BT (cid:50878)(cid:50911)(cid:50882) )and the fraction of Moon illumination in date and location.The fishery lights were given as the absolute error between the predicted and the observed DNB.The errors were compared between the pixel based ANN and the convolutional NN (CNN),and the pixel based ANN was superior to the CNN due to convolution of the cloud pixels.

Flood detection algorithms are able to generate near real-time flood proxy maps on regional and global scales on a daily basis through the use of multiple spaceborne sensors. In this chapter, we introduce a new algorithm for detecting annual floodwater changes using a hybrid conditional process that is incorporated into the Synchronized Multiple-Floodwater Index (SfWI2), a threshold-based statistical flood detection approach, coupled with in situ hydrological data. SfWI2 was applied to the 2010 Indus flood event, a recent extreme flood case, in order to achieve more accurate flood detection at a transboundary river-basin level and an effective emergency response in the early stage of a flood disaster. The resultant flood map shows good agreement between the MODIS-derived flood proxy map and high-resolution satellite images at the representative barrage stations.

A possibility to monitor the reclamation activities by remote sensing was discussed. The lights observed in the night time by Day Night Band (DNB) of Visible Infrared Imaging Radiometer Suite (VIIRS), ocean color observed in the day time by visible bands of VIIRS were the tools to monitor the surface activities, and the Automated Information System (AIS) was used to verify the types and number of vessels associated with the reclamation activities. The lights as the radiance from the surface were monitored by the object based analysis, where the object was defined as a radius of 5 km from the center of the Mischief Reef in the South China Sea (SCS). The time history of surface lights exhibited the increase of the radiance from January to May 2015 and the radiance was kept in the certain level to December 2016 with some variations. The ocean color, chlorophyll-a concentration as a proxy of sediments, showed an increase from February to June 2015 and returned to a low concentration in August 2015. According to the historical data of AIS, the number of dredgers has increased from February to August 2015 and the maximum number of dredgers was recorded in June 2015. The timing of increase of lights from surface, increase of chlorophyll-a concentration, and increase of number of vessels are consistent.

For high accuracy flood mapping, an algorithm that integrates multiple satellite data sources is essential to maximize the sensor ability and compensate the limitations of optical and SAR data. The main objective of this study is to propose an algorithm of dynamic flood detection using optical and Synthetic Aperture Radar (SAR) images that compares and combines two different statistical thresholding approaches. To improve the flood detection accuracy, image fusion technique was investigated to maximize the utilization of calibrated and optimized flood maps as the integrated flood detection approach. To showcase the advantages of the proposed methodology, we employ MODIS, Landsat-8 and Sentinel-IA images acquired over a challenging area along the Brahmaputra River where flood events often occur.

A temporal change of sediment distribution during the dredging activities on coral reefs in the South China Sea (SCS) was detected in 2015 as a proxy of chlorophyll-a concentration, observed by the Visible Infrared Imaging Radiometer (VIIRS) on Suomi-NPP. Mackin et al. [1] reported that the dredging activities was detected as an increase of surface lights in the night by the Day Night Band (DNB) on VIIRS in a way of objective analysis, where an increase of lights intensity was monitored within the buffer including coral reef and waters. Based on the previous research, the chlorophyll-a concentration was monitored to discuss a possibility to detect the distribution of sediments and to evaluate the negative impacts of sediments to coral reefs, where an increase of sediments was expected for the dredging period. As a result, the sediment had increased from the beginning of dredging in the Mischief Reef in January of 2015. The most significant scatters of sediment in the Mischief Reef were observed in May of 2015, when the dredging was in the final stage. Then, the sediments had decreased by the termination of dredging in July of 2015. The combination between DNB and chlorophyll-a concentration revealed the dredging period.

Floods is the greatest natural disasters that affect human society than any other type of natural disaster. With the availability of satellite rainfall analyses at fine time and space resolution, it has also become possible to mitigate such hazards on a near-global basis. In this research, we construct a real-time flood monitoring system using White Object Index (WOI) composite. The efficiency of the WOI was tested by comparing the MVC and MOD35.

Normalized Difference Water Index (NDWI), Green-Red Vegetation Index (GRVI), and other indices of remotely detected spectrum combinations have been proposed in order to evaluate the vegetation changes and distributions. The training area of artificial Japanese larch forest and deciduous broad leaved forest (Japanese white birch and Japanese oak) in Hokkaido were extracted from MODIS data based on RapidEye data and GIS data of Vegetation Survey on 6/7th National Basic Survey on Natural Environment (Biodiversity Center of Japan, Ministry of the Environment (http://www.biodic.go.jp/index_e.html)), the mean elevation, and the monthly mean temperature. The NDWI and GRVI were obtained for each training area from MODIS data. As a result, i) the MODIS GRVI of artificial Japanese larch forest were around -0.1, while the MODIS NDWI were greater than 0.5, ii) the MODIS GRVI of deciduous broad leaved forest were around 0.0, while the MODIS NDWI were less than 0.5, iii) these MODIS GRVI of Japanese larch and deciduous broad leaved forest were similar to GRVI of yellow and red leaves of larch and leafless birch calculated from the camera image of the canopy.

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 km(2)) of the total area of Bangladesh.

A temporal change of lights from islands on the South China Sea was monitored by the Day/Night Band (DNB) of the visible infrared imaging radiometer (VIIRS) on the Suomi-NPP from 2014 to 2016. As the DNB data could be contaminated by the lunar lights reflected by clouds for half of each month, the DNB data around the new moon period were used to focus on detecting lights from the surface. DNB mean intensity from the reef and vessel activities was measured in a circular area with the radius of 5 km around each reef. The DNB mean intensity exhibited a marked increase at the beginning of dredging, a decrease when dredging ended, and then a gradual increase after termination of dredging, as ground facilities were built on Subi Reef and Mischief Reef. Fiery Cross Reef didn't show a clear distinction between dredging operations and construction of ground facilities.

The information on urban land cover distribution and its dynamics is useful for understanding urbanization and its impacts on the hydrological cycle, water management, surface energy balances, urban heat island, and biodiversity. This study utilizes machine learning, texture variables and spectral bands to quantify the urban growth annually. We used multi-temporal Landsat satellite image sets from 2007 to 2016 and Random Forest classification to map urban land-use in Dar es Salaam. We also applied Annual classification approach to detect the spatiotemporal patterns of urban areas. This approach improved classification accuracy and aided in understanding the urban land-use system dynamics operating in our study area. The results pointed out that, the total built-up areas have grown from 318 km2, 388.6 km2 and 634.7 km2 in 2007, 2012 and 2016 respectively. The built up areas growth rate is almost 8%, which makes Dar es Salaam be among the fastest growing cities in Africa. The results indicate that, combining spectral bands, texture variables (NDVI BCI, MNDWI) and annual classification map approach was sufficient to map the urban areas. The approach applied in this research provides a useful guide to the urban growth studies and may also serve as a tool for land management planners.

Irrespective of several attempts to land use/cover mapping at local, regional, or global scales, mapping of vegetation physiognomic types is limited and challenging. The main objective of the research is to produce an accurate nationwide vegetation physiognomic map by using automated machine learning approach with the support of reference data. A time-series of the multi-spectral and multi-indices data derived from Moderate Resolution Imaging Spectroradiometer (MODIS) were exploited along with the land-surface slope data. Reliable reference data of the vegetation physiognomic types were prepared by refining the existing vegetation survey data available in the country. The Random Forests based mapping framework adopted in the research showed high performance (Overall accuracy = 0.82, Kappa coefficient = 0.79) using 148 optimum number of features out of 231 featured used. A nationwide vegetation physiognomic map of year 2013 was produced in the research. The resulted map was compared to the existing MODIS Land Cover Type (MCD12Q1) product of year 2013. A huge difference was found between two maps. Validation with the reference data showed that the MCD12Q1 product did not work satisfactorily in Japan. The outcome of the research highlights the possibility of improving the accuracy of the MCD12Q1 product with special focus on reference data.

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.

The urban heat island (UHI) effect describes the influence of urban surfaces on temperature patterns in urban areas as opposed to surrounding areas. Several indicators have been suggested in different studies. In this study, a procedure is presented to extract value based on polygon and characterize temporal changes using MODIS time's series from two surface variables, NDVI and Land Surface Temperature (LST). The result provides empirical evidence of UHI at Dar es Salaam-City center. The difference of (2°C in Night LST) between City center and outer part area, as well as low NDVI at City Center compared to outer part was observed. 12 years trend pointed out that, all areas of city facing both LST and land use/cover changes. City center showed stronger biophysical changes in terms of NDVI (decrease in NDVI Pearson correlation -0.66 compared to outer part (decrease in NDVI Pearson correlation of -0.30).

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

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.