qi Cheng

Massive remote sensing image data management based on HBase and GeoSOT

Since the traditional approaches of managing remote sensing image data could no longer deal with the massive data challenges nowadays, we present a method to divide remote sensing image data into blocks based on GeoSOT global discrete grid system and store the data blocks into HBase. Also, a distributed processing method using parallel programming model MapReduce, is designed to deal with the image data blocks. In the end, we verifies the feasibility and efficiency of the proposed approaches in HBase cluster.

Analyzing regional economic disparities based on ESDA in Yangtze River Delta, China

The economic growth in each region not only depends on its own characteristics, but also on those close to the region. This paper introduces ESDA(Exploratory Spatial Data Analysis) into analyzing regional economic disparities. By means of global and local spatial autocorrelation analyses, this paper then investigates the spatial dynamics of regional disparities at the city level in Yangtze River Delta by analyzing per capita GDP data, 2001-2011. Experiments show that the level of economic development of the cities in the Yangtze River Delta showed strong spatial autocorrelation as a whole spatial pattern, with similar regions gathering in space distribution and the trend is increasing gradually. But the economy disparity of cities far away is still large, especially Shanghai and the cities in southern or northern Jiangsu or southern Zhejiang.

An Improved Identification Code for City Components Based on Discrete Global Grid System

City components are important elements of a city, and their identification plays a key role in digital city management. Various identification codes have been proposed by different departments and systems over the years, however, their application has been partly hindered by the lack of a unified coding framework. The use of a code identifying a city component for unified management and geospatial computation across systems is still problematic. In this paper, we put forward an improved identification code for city components based on the discrete global grid system (DGGS). According to their spatial location, city components were identified with one-dimensional integer codes. The results illustrated that this identification code could express the location information of city components explicitly, as well as indicate the spatial distance relationship and the spatial direction relationship between different components. The experiment showed that this code performed better than traditional codes in data query and geospatial computation. Therefore, we concluded that this improved identification code was conducive to the more efficient management of city components, and hence might be used to improve digital city management.

Research of spatial topological relation model based on GeoSOT

To solve the disadvantages of high difficulty and complexity in studying the spatial topological relation method on different resolution remote sensing images, this paper proposes a spatial topological relationship model based on GeoSOT, which is one of the global subdivision grids. To express spatial objects on different resolution remote sensing images, this paper introduces the spatial objects subdivision location identification. Then, the model calculates spatial topological relation by comparing location identifications in combined table. At last, test shows that it is feasible to analyze the spatial objects topological relations based on GeoSOT and it has high efficiency operation significantly, especially for complicated spatial objects.

Spatial data management method with GeoSOT grid

This documents put forward an advanced method to manage the rapid increasing spatial data. The core idea of the proposed method is the application of GeoSOT grid. In this geo-information management system, all the spatial data are marked by one or more codes for identification and the mentioned code is simply related to the geographical attributes regardless of the other characteristics. Further, we pay attention to the agricultural service applications and the dwelling service applications, and it turned out that our management method demonstrates the advantages of the traditional methods. Because of the high efficiency and convenience of this method, its application scenario is supposed to be quite broad in the future.

Transferability of Economy Estimation Based on DMSP/OLS Night-Time Light

Despite the fact that economic data are of great significance in the assessment of human socioeconomic development, the application of this data has been hindered partly due to the unreliable and inefficient economic censuses conducted in developing countries. The night-time light (NTL) imagery from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) provides one of the most important ways to evaluate an economy with low cost and high efficiency. However, little research has addressed the transferability of the estimation across years. Based on the entire DN series from 0 to 63 of NTL data and GDP data in 31 provinces of mainland China from 2000 to 2012, this paper aims to study the transferability of economy estimation across years, with four linear and non-linear data mining methods, including the Multiple Linear Regression (MLR), Local Weighted Regression (LWR), Partial Least Squares Regression (PLSR), and Support Vector Machine Regression (SVMR). We firstly built up the GDP estimation model based on the NTL data in each year with each method respectively, then applied each model to the other 12 years for the evaluation of the time series transferability. Results revealed that the performances of models differ greatly across years and methods: PLSR (mean of ) and SVMR (mean of ) are superior to MLR (mean of ) and LWR (mean of ) for model calibration; only PLSR (mean of , mean of ) holds a strong transferability among different years; the frequency of three DN sections of (0–1), (4–16), and (57–63) are especially important for economy estimation. Such results are expected to provide a more comprehensive understanding of the NTL, which can be used for economy estimation across years.

Discussion on linear algorithms for simultaneously retrieving three components of case 2 waters in Yellow Sea and East China Sea

Retrieving water components in case 2 waters by remote sensing is a crucial problem in evaluating ocean first productivity and monitoring various disasters. But it is difficult to accurately and universally develop both bio-optical models and remote-sensing reflectance model because independent temporal and spatial variation of dissolved organic matter (CDOM), chlorophyll and total suspended matter (TSM), high concentration of TSM, as well as the local characters of different regions. Currently Linear algorithms such as principal component analysis (PCA), factor analysis (FA), matrix inversion technique and semi-analytical algorithm are widely used in the field of ocean color. Remote sensing reflectance model is derived from the radiative transfer equation, which is significantly featured by non-linearity and negative feedback. In our study, the chlorophyll absorption model and some other parameters of bio-optical models are adjusted. The adjustment is based on the water components concentration measured simultaneously with remote sensing data in the Yellow Sea and the East Sea of China. Then the equation of remote-sensing reflectance model can be changed into linear matrix of water components and coefficients, we find the spectrum curves of total suspended matter coefficient and chlorophyll coefficient turn out significant negative correlation. As a result, when performing matrix retrieval algorithm, chlorophyll concentration and CDOM concentration are out of required accuracy except some special conditions. Experiment results suggested that the TSM had the greatest influence on the linear model.

Discussion on linear algorithms for simultaneously retrieving TSM, chlorophyll and cdom of case 2 waters in Yellow Sea and East China Sea

Retrieving water constituents in case 2 waters by remote sensing is a crucial problem in evaluating ocean first productivity and monitoring various disasters. But it is difficult to accurately and universally develop both bio-optical models and remote-sensing reflectance model because independent temporal and spatial variation of dissolved organic matter (CDOM), chlorophyll and total suspended matter (TSM), high concentration of TSM, as well as the local characters of different regions. Currently Linear algorithms such as principal component analysis (PCA), factor analysis (FA), matrix inversion technique and semi-analytical algorithm are widely used in the field of ocean color. Remote-sensing reflectance model is derived from the radiative transfer equation, which is significantly featured by non-linearity and negative feedback. In our study, the chlorophyll absorption model and some other parameters of bio-optical models are adjusted. The adjustment is based on the water constituents concentration measured simultaneously with remote sensing data in the Yellow Sea and the East Sea of China. Then the equation of remote-sensing reflectance model can be changed into linear matrix of water constituents and coefficients, we find the spectrum curves of total suspended matter coefficient and chlorophyll coefficient turn out significant negative correlation. As a result, when performing matrix retrieval algorithm, chlorophyll concentration and CDOM concentration are out of required accuracy except some special conditions. According to the experiment results, detailed error analysis is presented. And at last, we made conclusions about the research. Keywords-component; case 2 water; remote sensing; linear retrieval algorithms; suspended matter coefficient; chlorophyll coefficient

A linear retrieval algorithm for retrieval of three components of case 2 in coastal oceans of China

Retrieval of concentrations of total suspended matter, chlorophyll, and CDOM constitutes a concern in remote sensing of Case 2 ocean water. Based on former research achievements, this study established a linear model of Reflectance of Remote Sensing (Rrs) of Case 2 coastal water of China. Experimental simulation was also carried out. The results show that the concentrations of TSM and chlorophyll are relatively less sensitive to the model errors, while CDOM is more sensitive. Remote sensing data of Yellow Sea and East Sea of China provided by National Satellite Ocean Application Service of China were applied in this paper. Spectral profiles calculated from the linear model show the similar trend with measured results form the Yellow Sea and the East Sea. The linear relationships between band 443 nm and band 490, and 510 and 555 nm match well with real situations. The spectral curves of TSM and chlorophyll in our model yield some useful information for calculating the concentration of CDOM.

A study of farmland landscape pattern with TM and DEM. Case study in Yongsheng County, Yunnan Province, P.R.China

The application of TM images and digital elevation model (DEM) is rising and promising in the field of landscape ecology and land cover change detection. This study presents it, and GIS-based technology route is recommended in the TM application. The study area is Yongsheng County, Yunnan Province, P. R. China. Six Landsat TM images are acquired over Yongsheng County with geometrical correction, procession and classification. Landscape types, including farmland, urban area, forest, watershed, wasteland and selected farmland are obtained as areas of interest. The elevation and grade maps of Yongsheng County are produced on its digital elevation model (DEM) with a scale of 1:250,000. We group the elevation into five levels: 1,000-1500 m, 1500-2000 m, 2000-2500 m, 2500-3000 m, and above 3000 m; and four levels are determined for grade maps, i.e., 0-6, 6-15, 15-25, and above 25 degrees. Under the help of GIS tools, area of six landscape types on five elevation levels is calculated separately. We also obtain area of farmland on each elevation and grade level through the same methodology. All these data work in the subsequent analysis of landscape pattern. It is found that farmland mostly locates in areas with elevation below 2,000 m and grade less than 15 degrees, for example, candid areas. We can interpret it by the great ease these candid areas provide for farm work. The Index of Component Fragmentation Degree (ICFD) is used to evaluate humans disturbance on farmland. ICFD is relatively small for candid areas due to comparatively large farmland patches, but maximizes itself where elevation is from 2,000 to 2,500 m. It gives a signal of serious human disturbance there. This disturbance is just deforestation for farmland on this belt. We also analyze the landscape pattern of urban areas. Its area reaches a maximum on the belt with elevation between 1,500 and 2,000 m. Its also considerable that urban area decreases as the elevation increases and reaches almost ZERO where elevation is above 3,000 m. Results presented in this paper suggest that farmland landscape pattern shows its vertical variance according to elevation ranges, and humans disturbance weights much in this variance. This study also provides information on deforestation for farmland, and it seems valuable in environmental protection and region regulation