Mathematics in Utilizing Remote Sensing Data for Investigating and Modelling Environmental Problems

Copyright © 2017 Hasi Bagan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Remote sensing data have already proven useful for environmental monitoring in a timely, detailed, and cost-effective manner to assist various planning and management activities. Remotely sensed data collected over a span of years can be used to identify and characterize both natural and anthropogenic changes over large areas of land at a variety of spatial and temporal scales [1–3]. As climate change and population growth place increasing pressures on many parts of the world, improved methods for monitoring urban growth across a range of spatial and temporal scales will be vital for understanding and addressing the impacts of urbanization on our natural resources [4, 5]. With the advance of machine learning algorithms and computing facilities, many investigations on their real applications are taking place. Combining remote sensing data and mathematics techniques to quantitatively analyze environmental change is a topic growing in importance [6]. The meaningful interpretation of remote sensing data and in situ observations require implementation and analysis using advanced mathematics and statistical techniques. The objective of this special issue is to provide a snapshot of status, potentials, challenges, and achievements of mathematical application in using remote sensing data to address environmental issues. This special issue includes thirteen papers that cover four major topics: image processing methods, land use/land cover change analysis, land degradation, urbanization, and vegetation cover. A brief description of these 13 works is detailed below

Extended Averaged Learning Subspace Method for Hyperspectral Data Classification

Averaged learning subspace methods (ALSM) have the advantage of being easily implemented and appear to outperform in classification problems of hyperspectral images. However, there remain some open and challenging problems, which if addressed, could further improve their performance in terms of classification accuracy. We carried out experiments mainly by using two kinds of improved subspace methods (namely, dynamic and fixed subspace methods), in conjunction with the [0,1] and [-1,+1] normalization methods. We used different performance indicators to support our experimental studies: classification accuracy, computation time, and the stability of the parameter settings. Results are presented for the AVIRIS Indian Pines data set. Experimental analysis showed that the fixed subspace method combined with the [0,1] normalization method yielded higher classification accuracy than other subspace methods. Moreover, ALSMs are easily applied: only two parameters need to be set, and they can be applied directly to hyperspectral data. In addition, they can completely identify training samples in a finite number of iterations

Land-cover change analysis in 50 global cities by using a combination of Landsat data and analysis of grid cells

Global urban expansion has created incentives to convert green spaces to urban/built-up area. Therefore, understanding the distribution and dynamics of the land-cover changes in cities is essential for better understanding of the cities’ fundamental characteristics and processes, and of the impact of changing land-cover on potential carbon storage. We present a grid square approach using multi-temporal Landsat data from around 1985–2010 to monitor the spatio-temporal land-cover dynamics of 50 global cities. The maximum-likelihood classification method is applied to Landsat data to define the cities’ urbanized areas at different points in time. Subsequently, 1 km ^2 grid squares with unique cell IDs are designed to link among land-cover maps for spatio-temporal land-cover change analysis. Then, we calculate land-cover category proportions for each map in 1 km ^2 grid cells. Statistical comparison of the land-cover changes in grid square cells shows that urban area expansion in 50 global cities was strongly negatively correlated with forest, cropland and grassland changes. The generated land-cover proportions in 1 km ^2 grid cells and the spatial relationships between the changes of land-cover classes are critical for understanding past patterns and the consequences of urban development so as to inform future urban planning, risk management and conservation strategies

Multiscale mapping of local climate zones in Tokyo using airborne LiDAR data, GIS vectors, and Sentinel-2 imagery

Multisource remote sensing and geographic information system (GIS) data have contributed powerfully to the large-scale automated mapping of local climate zones (LCZs). However, the accessibility of high-resolution height data, the applicability of standard thresholds to local contexts, and the dependence of mapping scales have limited LCZ classification studies. In this study, we combined airborne LiDAR data, Sentinel-2 imagery, and GIS vector (buildings and roads) data to develop a multiscale automated LCZ classification scheme in the 23 special wards of Tokyo. Based on the optimized thresholds of seven LCZ properties, GIS-based LCZ mapping was implemented using fuzzy logic classifiers at the block scale and at different grid-cell scales (100 m–1000 m). In addition to assessing accuracy using reference samples, multidate thermal infrared data (Landsat-8 and ASTER data) were used to understand the LCZ-LST (land surface temperature) relationship at multiple scales. The results showed that the overall accuracies of LCZs could be significantly increased by threshold optimization at all scales. Significant differences in LCZs and LSTs among different mapping units were observed. The highest overall accuracy was greater than 80% at the 100-m grid-cell scale. As the size of grid cells increased, the overall accuracy of LCZ classification decreased. For each LCZ, the mean daytime/nighttime LST exhibited more variation by date than by scale. This study provides a promising picture of GIS-based LCZ mapping and LCZ-LST relationships at multiple scales

Land-cover change in the Wulagai grassland, Inner Mongolia of China between 1986 and 2014 analysed using multi-temporal Landsat images

The Inner Mongolian steppe is a vast grassland ecosystem that has long been home to nomadic pastoralists. However, this steppe is experiencing grassland degradation as well as more frequent sand storms. The objective of this study was to detect land-cover changes in the Wulagai grassland of Inner Mongolia using multi-temporal Landsat images from 1986 to 2014, and to determine the factors driving these changes and their impacts. Land-cover maps for 1986, 1995, 2000, 2006 and 2014 were produced using the Support Vector Machine method. Subsequently, 300 m × 300 m grid-cell vector map which covered Wulagai grassland was made to detect land-cover changes and correlations between land-cover classes. The results show degradation trend from 1986 to 2014. Grid-cell-based spatial correlation analysis confirmed a strong negative correlation between grassland and barren, indicating that grassland degradation in this region is due to the regional modernization over the past 28 years