Keitarou Hara

Validation of MODIS Albedo Products of Paddy Fields in Japan

A study was conducted in Chiba, Japan, to validate Moderate Resolution Imaging Spectroradiometer (MODIS) albedo products by taking the field measurements of shortwave band albedos in paddy fields. A large difference in spatial scale, from field-measured point data to 1-km resolution, complicates the validation process. To assess such effect of different spatial scales, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Enhanced Thematic Mapper Plus (ETM+) data were used. Spatial scale effects on the albedo were examined from three viewpoints: 1) comparison between point-based albedo and mean of albedo in homogeneous area; 2) comparison between point-based albedo and 1-km aggregated albedo; and 3) assessment of semivariogram of albedo in homogeneous area. In implementation of viewpoint 2), Liangs regression model was applied to convert ASTER reflectance into shortwave band albedo. The 1-km ASTER albedo was estimated using the point spread function, and in the same manner, 1-km ETM+ albedo was estimated. All results represent that an area around the measurement site can be assumed to be homogeneous, indicating negligible effects of spatial resolution difference during most of the periods. Comparison of ground-point-based albedos with MODIS actual albedo, estimated from MODIS black-sky albedo, white-sky albedo, and a fraction of diffuse skylight, showed that the accuracy of MODIS albedo products for paddy fields in Japan is within approximately 0.026 by absolute value (root-mean-square error) and 15.1% by relative value

Global mapping of urban built-up areas of year 2014 by combining MODIS multispectral data with VIIRS nighttime light data

ABSTRACT An improved methodology for the extraction and mapping of urban built-up areas at a global scale is presented in this study. The Moderate Resolution Imaging Spectroradiometer (MODIS)-based multispectral data were combined with the Visible Infrared Imager Radiometer Suite (VIIRS)-based nighttime light (NTL) data for robust extraction and mapping of urban built-up areas. The MODIS-based newly proposed Urban Built-up Index (UBI) was combined with NTL data, and the resulting Enhanced UBI (EUBI) was used as a single master image for global extraction of urban built-up areas. Due to higher variation of the EUBI with respect to geographical regions, a region-specific threshold approach was used to extract urban built-up areas. This research provided 500-m-resolution global urban built-up map of year 2014. The resulted map was compared with three existing moderate-resolution global maps and one high-resolution map in the United States. The comparative analysis demonstrated finer details of the urban built-up cover estimated by the resultant map.

Developing Superfine Water Index (SWI) for Global Water Cover Mapping Using MODIS Data

Monitoring of water cover and shorelines at a global scale is essential for better understanding climate change consequences and modern human disturbances. The level and turbidity of the surface water, and the background objects in which they interact with, vary significantly at a global scale. The existing water indices applicable to detection and extraction of water cover at local and regional scales cannot work efficiently everywhere in the globe. In this research, a new water index called Superfine Water Index (SWI) was developed for robust detection and discrimination of the surface water at a global scale using MODIS based multispectral data. The SWI was designed in such a way that it provides high contrast between the water and non-water areas. Achieving high contrast is vital for discriminating the surface water mixed with a variety of objects. The sensitivity analysis of the SWI demonstrated its high sensitivity to the surface water compared to the existing water indices. One single-layered global mosaic of a 90-percentile SWI image was used as a master image for global water cover mapping by reducing the large volume of MODIS data available between 2012 and 2014 globally. The random walker algorithm was applied in the SWI image with the support of reference training data for the extraction and mapping of water cover. This research produced an up-to-date global water cover map of the year 2013. The performance of a new map was evaluated with a number of case studies and compared with existing maps. The supremacy of the SWI over the existing water indices, and high performance of the SWI based water map confirmed the reliability of the new water mapping methodology developed. We expect that this methodology can contribute to seasonal and annual change analysis of the global water cover as well.

Production of the Japan 30-m Land Cover Map of 2013–2015 Using a Random Forests-Based Feature Optimization Approach

Achieving more timely, accurate and transparent information on the distribution and dynamics of the world’s land cover is essential to understanding the fundamental characteristics, processes and threats associated with human-nature-climate interactions. Higher resolution (~30–50 m) land cover mapping is expected to advance the understanding of the multi-dimensional interactions of the human-nature-climate system with the potentiality of representing most of the biophysical processes and characteristics of the land surface. However, mapping at 30-m resolution is complicated with existing manual techniques, due to the laborious procedures involved with the analysis and interpretation of huge volumes of satellite data. To cope with this problem, an automated technique was explored for the production of a high resolution land cover map at a national scale. The automated technique consists of the construction of a reference library by the optimum combination of the spectral, textural and topographic features and predicting the results using the optimum random forests model. The feature-rich reference library-driven automated technique was used to produce the Japan 30-m resolution land cover (JpLC-30) map of 2013–2015. The JpLC-30 map consists of seven major land cover types: water bodies, deciduous forests, evergreen forests, croplands, bare lands, built-up areas and herbaceous. The resultant JpLC-30 map was compared to the existing 50-m resolution JAXA High Resolution Land-Use and Land-Cover (JHR LULC) map with reference to Google Earth™ images. The JpLC-30 map provides more accurate and up-to-date land cover information than the JHR LULC map. This research recommends an effective utilization of the spectral, textural and topographic information to increase the accuracy of automated land cover mapping.

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.

Object-based classification of IKONOS data for vegetation mapping in Central Japan

Vegetation mapping using IKONOS data was implemented at a countryside study area in central Japan, where small patches of various plant communities are mixed together in a complicated mosaic pattern. Pixel-based and object-based classifications using only spectral features were implemented and their accuracies were compared. In addition, the object-based classification was also performed on a combination of spectral and textural features, with a stepwise regression model used in the discriminate analysis to select the most relevant features. Classifications were implemented at four levels, the highest of which used seven vegetation categories. The object-based classification proved more accurate than the pixel-based classification. In addition, the addition of textural features generated significant improvements in accuracy. The overall classification accuracy and Kappa coefficients at the highest level were 52.8% and 0.373 for the pixel-based classification; 58.9% and 0.458 for the object-based with spectral features only; and 65.0% and 0.542 for the object-based with additional features. Some problems with misclassification remained, but the overall results demonstrate that object-based classification of very high resolution satellite images using additional features is a practical tool for vegetation mapping in Japan.

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.

Earthquake Damage Visualization (EDV) Technique for the Rapid Detection of Earthquake-Induced Damages Using SAR Data

The damage of buildings and manmade structures, where most of human activities occur, is the major cause of casualties of from earthquakes. In this paper, an improved technique, Earthquake Damage Visualization (EDV) is presented for the rapid detection of earthquake damage using the Synthetic Aperture Radar (SAR) data. The EDV is based on the pre-seismic and co-seismic coherence change method. The normalized difference between the pre-seismic and co-seismic coherences, and vice versa, are used to calculate the forward (from pre-seismic to co-seismic) and backward (from co-seismic to pre-seismic) change parameters, respectively. The backward change parameter is added to visualize the retrospective changes caused by factors other than the earthquake. The third change-free parameter uses the average values of the pre-seismic and co-seismic coherence maps. These three change parameters were ultimately merged into the EDV as an RGB (Red, Green, and Blue) composite imagery. The EDV could visualize the earthquake damage efficiently using Horizontal transmit and Horizontal receive (HH), and Horizontal transmit and Vertical receive (HV) polarizations data from the Advanced Land Observing Satellite-2 (ALOS-2). Its performance was evaluated in the Kathmandu Valley, which was hit severely by the 2015 Nepal Earthquake. The cross-validation results showed that the EDV is more sensitive to the damaged buildings than the existing method. The EDV could be used for building damage detection in other earthquakes as well.

A Biophysical Image Compositing Technique for the Global-Scale Extraction and Mapping of Barren Lands

As the barren lands play a key role in the interaction between land cover dynamics and climate system, an efficient methodology for the global-scale extraction and mapping of the barren lands is important. The discriminative potential of the existing soil/bareness indexes was assessed by collecting globally distributed reference data belonging to major land cover types. The existing soil/bareness indexes parameterized at the local scale did not work satisfactorily everywhere at the global level. A new technique called the Biophysical Image Composite (BIC) is proposed in the research by exploiting time-series of the multi-spectral data to capture global-scale barren land attributes effectively. The BIC is a false color composite image made up of Normalized Difference Vegetation Index (NDVI), short wave infrared reflectance, and green reflectance, which were specially selected from the highest vegetation activity period by avoiding signals from the seasonal snowfall. The drastic contrast between the barren lands and vegetation as exhibited by the BIC provides a robust extraction and mapping of the barren lands, and facilitates its visual interpretation. Random Forests based supervised classification approach was applied on the BIC for the mapping of global barren lands. A new global barren land cover map of year 2013 was produced with high accuracy. The comparison of the resulted map with an existing map of the same year showed a substantial discrepancy between two maps due to methodological variation. To cope with this problem, the BIC based mapping methodology, with a special account of the land surface phenological changes, is suggested to standardize the global-scale estimates and mapping of the barren lands.

Structure and diversity of remnant natural evergreen broad-leaved forests at three sites affected by urbanization in Chongqing metropolis, Southwest China

Evergreen broad-leaved forests (EBLFs, lucidophyllous forests) are vegetation types characteristic of East Asia. The extent of EBLFs has decreased significantly due to long-term anthropogenic disturbance, and remnant EBLFs in urban area are rare and important landscape types contributing to biodiversity and sustainable development. This study focuses on remnant EBLFs on Mt. Gele (GL), Mt. Tieshanping (TSP), and Mt. Jinyun (JY), located from the inner city to outskirts of Chongqing metropolis, Southwest China. Species of Theaceae, Lauraceae, Symplocaceae, and other families, which are essential floristic components of primary EBLFs, were still the main components at the three sites. GL and JY showed higher biodiversity, with richer heliophytes and shade-tolerant species, respectively. Castanopsis carlesii var. spinulosa was the sole dominant species at all three sites in woody layer, with codomination by Pinus massoniana and Cinnamomum camphora at GL and by Machilus pingii and Castanopsis fargesii at JY; these evergreen broad-leaved trees all showed inverse-J or sporadic-shaped size distribution with large numbers of small stems, but the conifer tree Pinus massoniana showed unimodal distribution with small stems at GL. The height growth of tree species, especially Castanopsis carlesii var. spinulosa, was increasingly restricted from JY to TSP to GL. Sprouting is an important life history strategy at community and population level, and differences were exhibited from GL to TSP to JY. A rural–urban gradient from JY to TSP to GL was indicated in this study. Species composition, biodiversity, and stand structure of these remnant EBLFs showed obvious differences along this gradient, and conservation responses to address the effects of urbanization need to be carefully considered.