Built-up areas are the results of human activities. Not only are they the real reflection of human and society activities, but also one of the most important input parameters for the simulation of biogeochemical cycle. Therefore, it is very necessary to map the distribution of built-up areas and monitor their changes by using new technologies and methods at high spatiotemporal resolution. By combining technologies of GIS (Geographic Information System) and RS (Remote Sensing), this study mainly explored the expansion and driving factors of built-up areas at the beginning of the 21st century in Zhejiang Province, China. Firstly, it introduced the mapping processes of LULC (Land Use and Land Cover) based on the method which combined object-oriented method and binary decision tree. Then, it analyzed the expansion features of built-up areas in Zhejiang from 2000 to 2005 and 2005 to 2010. In addition to these, potential driving factors on the expansion of built-up areas were also explored, which contained physical geographical factors, railways, highways, rivers, urban centers, elevation, and slop. Results revealed that the expansions of built-up areas in Zhejiang from 2000 to 2005 and from 2005 to 2010 were very obvious and they showed high levels of variation in spatial heterogeneity. Except those, increased built-up areas with distance to railways, highways, rivers, and urban centers could be fitted with power function (y = a*x<sup>b</sup> ), with minimum R<sup>2</sup> of 0.9507 for urban centers from 2000 to 2005; the increased permillages of built-up areas to mean elevation and mean slop could be fitted with exponential functions (y = a*e<sup>bx</sup>), with minimum R<sup>2</sup> of 0.6657 for mean slop from 2005 to 2010. Besides, government policy could also impact expansion of built-up areas. In a nutshell, a series of conclusions were obtained through this study about the spatial features and driving factors of evolution of built-up areas in Zhejiang from 2000 to 2010.
Many factors such as geomorphic features, economic development, expansion of cities, the implementation of new policy, etc. are changing land cover. Therefore, it is necessary to monitor Land-Use and Land-Cover Change (LUCC) by using new technologies and methods at high tempo-spatial resolution. Based on one supervised classification approach combining object-oriented method and binary decision tree, this study mapped land cover of Zhejiang Province, China, at the scale of 1:250000 in 2000, 2005, and 2010. After image segmentation, object features such as normalized difference vegetation index (NDVI), modified normalized difference water index (MNDWI), area, the ratio of length to width, density, etc. were applied to assign each object to specific class. Being verified through confusion matrix, the mapping results were satisfactory. Taking land cover map in 2010 as an example, the lowest user accuracy was 84.14%, with the average of 92.15%; The lowest production accuracy was 62.00%, with the average was 86.69%; The overall accuracy was 0.8928; The Kappa coefficient was 0.8752. Under the influence of geomorphic features and economic development, changes of land cover in Zhejiang were mainly distributed in the areas with lower elevation and higher GDP from 2000 to 2010. Under the influence of natural factors and human activities, many croplands and wetlands were lost from 2000 to 2010. For croplands, there were 2106.608 km<sup>2</sup> croplands changed into other types from 2000 to 2005, and 1897.809 km<sup>2</sup> from 2005 to 2010. Most of croplands were changed into artificial lands, with 1520.601km<sup>2</sup> from 2000 to 2005 and 1446.826 km<sup>2</sup> from 2005 to 2010. For wetland, there were 209.085 km<sup>2</sup> wetlands changed into other types from 2000 to 2005, and 292.975 km<sup>2</sup> from 2005 to 2010. Most of wetlands were changed into croplands, with 134.652 km<sup>2</sup> from 2000 to 2005 and 122.979 km<sup>2</sup> from 2005 to 2010.
Particulate Organic Carbon (POC) plays an important role in sink of atmospheric CO2, global carbon cycle, etc. Around river estuary, POC is sourced from terrestrial ecosystem and aquatic ecosystem; its distribution features might be complex and likely to change with time. Based on in-situ samples from four seasonal cruises, we discussed spatial-temporal distribution and remote sensing monitoring of POC concentration in the Pearl River Estuary (PRE). Being affected by larger discharge from the Pearl River, surface POC concentrations in summer were usually higher than those in other three seasons, similar, in the PRE. However, because of sediment resuspension, POC concentrations at the bottom layer were higher than those at the surface layer. Taking the PRE as an example, remote sensing monitoring of POC concentration in case II water around estuary was also discussed. On the one hand, on the basis of Chlorophyll-a (Chl-a) and Total Suspended Matter (TSM) concentrations inversed by published algorithms, we can estimate surface POC concentration through multiple linear regression equation: POC=0.042*Chl-a+0.014*TSM+0.1595, R=0.9156. On the other hand, great relationships between surface POC concentrations and total particle absorption coefficient at 667nm (TPabs(667)) and 678nm (TPabs(678)) were also found: POC=3.813*TPabs(667)+0.0684, R=0.8769 and POC=3.9175*TPabs(678)+0.0624, R=0.8745. They implied the possibility of estuarine POC monitoring from space through remote sensing reflectance at 667nm or 678nm.
The distribution and transport of suspended sediment in the coastal areas has attracted more and more attention. Monitoring and modeling of the distribution and transport of suspended sediment is significant. The mutual promotion of remote sensing and numerical simulation plays an important role on the coastal water quality study. In this study, a method of coupling derived suspended sediment concentration (SSC) images with numerical model, based on GOCI and COHERENS (COupled Hydrodynamical-Ecological model for REgioNal and Shelf seas) model, is proposed to monitor the suspended sediment dynamics in the East China Sea. The retrieved SSC were extracted from GOCI images, to set as initial condition and employed to calibrate the parameters and validation for the hydrodynamic modeling and sediment transport modeling, respectively. The model is forced by considering tidal surface elevation at open sea boundary, river discharges, surface stress as a function of wind speed, air temperature, relative humidity and cloud coverage, bottom roughness and heat flux through sea surface. The model results are in accord with the in situ measurements. The results show that: (1) Numerical model which initialized with satellite-derived SSC data can quickly response to the changes of sediment concentration in real sense. (2) Remote sensing is helpful to calibrate and validate the model for simulating the suspended sediment concentration distribution. (3)The proposed approach can obtain reasonable simulated results in the East China Sea. (4) It is of great significance to combine remote sensing and numerical simulation together to study the water quality in the coastal areas.
Lake ecological environment is changing, driving by natural and human factors, and in turn influence people's living and
producing. Therefore, dynamic monitoring of lake based on remote sensing technologies will play an important role to the
disaster prevention and reduction work of lakes. In this paper, we expounded a series of work to realized monitor Poyang
Lake dynamically by using HJ-CCD images. First, we did pretreatment to all HJ-CCD images, which mainly contain
geometric correction, atmospheric correlation, image clipping, etc. Then, based on different features between water and
non-water in different index layers, we extracted the covered area by water in different times from the corresponding
HJ-CCD images, and we also extracted the true area through visual interpretation method. After that, by combining the
water boundaries and DEM, we also estimated water level and water capacity in different times. Results of our work
showed that the mean absolute error of water area extracted through remote technologies is 5.57%. The relationship of
remote sensing areas and visual interpretation areas could be described as S<sub>true</sub> = 0.8757*S<sub>interp</sub> + 110.24, with R<sup>2</sup> = 0.9807.
Besides, there was obvious relationship between water area and water capacity of Poyang Lake too, and the relations can
be described with linear function. Based on such results, we can realize the dynamic estimation of Poyang Lake’s area and
capacity from daily gotten HJ-CCD image which covers the District of Poyang Lake. In other words, the results of this
paper can provide decision basis for Poyang Lake’s real-time, dynamic, economic monitoring.
Built-up area is the result of human activities, which is one of the most important input parameters for the simulation of biogeochemical cycle, and has very important significance for the research of earth system science. Therefore, it is very necessary to map the distribution of built-up area and monitor the changes of it by using new technologies and methods at high spatiotemporal resolution. This article mainly explored the changes of built-up areas at the beginning of the 21<sup>st</sup> century in Zhejiang Province, China, based on Geographic Information System (GIS) and Remote Sensing (RS). In the article, we first introduced the mapping processes of built-up areas in the study area based on the method which combined object-oriented method and the mapping precision were high. Then, we analyzed the changes of built-up areas in the study area from 2000 to 2005 and 2005 to 2010. Through the study of this article, we got that most of the changes have distributed at the northeast part of Zhejiang from 2000 to 2005, and all parts of Zhejiang except Lishui have significant changes from 2005 to 2010. There were about 1564.07km<sup>2</sup> and 1607.73km<sup>2</sup> non-built-up areas turned into built-up areas from 2000 to 2005 and 2005 to 2010 respectively. Of course, the contrary conversion also exist which accounted for 22.52km<sup>2</sup> and 96.32 km<sup>2</sup> respectively. Moreover, the city with the greatest increase of built-up areas from 2000 to 2005 and 2005 to 2010 is Ningbo and Jiaxing respectively.