Guangrong Shen

Spatial–temporal land-use/land-cover dynamics and their impacts on surface temperature in Chongming Island of Shanghai, China

Land-use/land-cover (LULC) changes are occurring at rapid rates on the Chongming Island of Shanghai, China, giving rise to a major concern about environmental impacts. We herein carried out a sound analysis of the LULC dynamics, the conversions among different LULC classes, and land-surface temperature (LST) distribution using remote-sensing data from Landsat Multispectral Scanner (MSS), Thematic Mapper (TM), and Enhanced Thematic Mapper Plus (ETM+) time series spanning the last 35 years (1979–2014). Based on LULC class information and LST, we constructed a temperature/vegetation index space to study the temporal variability of thermal data, vegetation cover, and LULC. The results showed that the LULC change dynamics in Chongming Island have strongly impacted the LST in the recent decade. The spatial position conversion and quantitative change of vegetation cover totalled about 44.4% of LULC-type areas over the Island, and the comprehensive LULC dynamicity changed from 2.97 to 3.95 during the investigated period. Accordingly, significant LST changes took place in the portion of the Chongming Island showing normal temperature range, which accounted for 85.94% of the whole Island’s area as of 1 August 2000 and that decreased to 50.79% on 6 May 2009, while the surface extents under low- and with ultra-high-temperature ranges increased, respectively, both from 0 of 2000 to 6.67% and 0.41% of 2009. The results indicate that the pixel classes including vegetation cover, wetland, and waterbody, which have larger dynamicity and maximum change vector magnitudes, played a large role in alleviating the effect of the land-surface thermal environment, and were key driving factors contributing to the increasing trend of non-normal temperature range ratio over time. Our findings are expected to provide valuable information for decision-making regarding the development and construction of Chongming Island into an eco-region.

Application of Texture Analysis in Land Cover Classification of High Resolution Image

The present paper deals with the land cover classification of high resolution Quickbird images using the texture feature analysis. The study area covers the wider region of the urbanized environment of Chania, Greece. Different textural features including Entropy and Asm (angular second moment) were extracted based on GLCM (Grey Level Co-occurrence Matrix) texture feature and used as the distinct feature value in classification procedures. The classification was performed on the texture image that was produced by the synthesis of the original image with vegetation index BRI (band ratio index) extracted from the original datasets. Results indicate that the proposed approach brings significant improvement of the classification rate based on the different texture feature images of various bands, allowing a better discrimination and mapping of mixed land cover types.

Remote sensing of urban growth and landscape pattern changes in response to the expansion of Chongming Island in Shanghai, China

Abstract Chongming Island in China is currently undergoing a rapid urbanization and an increasing environmental pressure due to its fast-paced social economic development. Owing to natural silts deposit phenomenon as well as artificial land reclamation practices, the Island is also experiencing an expansion phenomenon. The synergy between these natural and artificial phenomena results in a rapidly changing landscape on Chongming Island. Consequently, the tools and methods for a rapid and cost-effective detection and assessment of related issues are urgently needed to ensure a harmonious and sustainable development of the Island. We herein investigate the urban growth and the landscape pattern change in relation to the Island’s expansion phenomenon and the associated complexity of land use/cover change. Our investigation is based on a time series of Landsat satellite images spanning the past 34 years. The methodological approach adopted in the present study combines vegetation indices, images textural features and social statistics data in an object-oriented classification framework. With Chongming Island expanding by an annual rate of 0.9% between 1979 and 2013, we found that the proportion of vegetation area to the total area decreased from 71 to 45%, whereas the proportion of built-up area to the total area increased from 5 to 19.9%. The urban area expanded about six times from 1979 to 2013, and during the same period, the Island’s population did not change significantly. The urban spatial expansion of Chongming Island caused distinct expansion intensity index for each intervals, and significant fragmentation and diversity in the landscape pattern between 1979 and 2013. It was also found that the rapid urbanization process took place at the expense of landscape pattern changes at any time within the study period. This is a strong indication that besides the natural geographic element, economic development and policy orientation were the dominant driving factors. If the current rate of urban expansion is to be maintained and the vegetation cover is to keep decreasing at an annual rate of 0.1% (period 1979–2013), their combined effects would profoundly alter the ecological environment in the long term. These findings provide a basic objective and scientific information for knowledgeable decision-making and policy formulation regarding regional planning and management to ensure harmonious transition of Chongming towards ecologically oriented development.

Application of Wavelet Transform in De-noising Geophysical Data

This paper presents a denoising scheme based on the wavelet transform for geophysical projecting data which are contaminated with various levels and types of local cultural noise which were responsible for hindering the valuable information obtained through shallow depth geophysical exploration of archaeological sites. Wavelet transform techniques were tested as a method for decomposing the original geophysical data in order to eliminate the noise levels inherent to the geophysical measurements. Unsupervised classification techniques were employed for the final fusion of different datasets originating from various surveys or processing procedures. The resulting images were able to enhance the subsurface targets, eliminating the noise levels and exploiting fully the properties of the geophysical techniques used. The scheme is particularly useful in minimizing the cultural noise as much as possible in order to allow the data to be interpreted accurately.

Heavy metal contamination in arable land of Chongming based on GIS and Geostatistics

Soil heavy metal pollution has increasing applied pressure on land resources with the intensive effects of local geochemistry or human activities in recent years. This investigation was conducted to understand the spatial variability of the soil heavy metal elements, to evaluate the relationship between heavy metals and their probable sources and to identify or forewarn the probable heavy metal contamination area in Chongming based on Geostatistics and GIS. The effective interpolation and analysis strategy was explored by combing the interpolating model derived from dataset excluding outlier with the performance of Universal Kriging on dataset including outlier to meet this need. The experimental results showed that the spatial variability characteristics and pollution situation of heavy metals accorded to that was investigated through field work, which suggested the scheme concerned the outlier was reasonable and effective. The further improvement of the approach is needed for more extensive application.

Applications of Regression Kriging and GIS in Detecting the Variation in Leaf Nitrogen and Phosphorus of Spruce in Europe

The leaf nitrogen and phosphorus are considered the major limitations of the photosynthetic process, reflecting the quality and suitability of habitation. The multidimensional factors of ecosystem have been a great barrier to discover the biochemical reaction of plants to their environment. In this research, a novel approach integrated with regression kriging and GIS is applied to explore the pattern of these leaf minerals in relation to spatial variability in climate and landscape (urban radiation and forest shield). Europe was chosen as the study area owing to the availability of spruce leaf data and ancillary grids. The advantage of this method is based on the fact that a map-based orthogonal space and universal kriging improve the accuracy and resolution in mapping the spatial distribution of leaf minerals.