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Jin Chen  - - - 
Top co-authors
Alfredo R. Huete

205 shared publications

Ecosystem Dynamics, Health and Resilience, University of Technology Sydney, Ultimo 2007, NSW, Australia

Bunkei Matsushita

42 shared publications

Graduate School of Life and Environmental Studies, University of Tsukuba, Tsukuba, Japan

Guo Yu Qiu

19 shared publications

Key Laboratory for Urban Habitat Environment Science and Technology, School of Environment and Energy, Shenzhen Graduate School of Peking University, Shenzhen 518055, China

Jing Li

7 shared publications

Beijing Normal University

Wei Yang

2 shared publications

115
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Publication Record
Distribution of Articles published per year 
(2004 - 2018)
Total number of journals
published in
 
25
 
Publications See all
Article 0 Reads 1 Citation A simple method to improve the quality of NDVI time-series data by integrating spatiotemporal information with the Savit... Ruyin Cao, Yang Chen, Miaogen Shen, Jin Chen, Ji Zhou, Cong ... Published: 01 November 2018
Remote Sensing of Environment, doi: 10.1016/j.rse.2018.08.022
DOI See at publisher website
Article 0 Reads 0 Citations A Novel Cloud Removal Method Based on IHOT and the Cloud Trajectories for Landsat Imagery Shuli Chen, Xuehong Chen, Xiang Chen, Jin Chen, Xin Cao, Mia... Published: 02 July 2018
Remote Sensing, doi: 10.3390/rs10071040
DOI See at publisher website ABS Show/hide abstract
Cloud removal is a prerequisite for the application of Landsat datasets, as such satellite images are invariably contaminated by clouds. Clouds affect the transmission of radiation signal to different degrees because of their different thicknesses, shapes, heights and distributions. Existing methods utilize pixel replacement to remove thick clouds and pixel correction techniques to rectify thin clouds in order to retain the land surface information in contaminated pixels. However, a major limitation of these methods refers to their deficiency in retrieving land surface reflectance when both thick clouds and thin clouds exist in the images, as the two types of clouds differ in the transmission of radiation signal. As most remotely sensed images show rather complex cloud contamination patterns, an efficient method to alleviate both thin and thick cloud effects is in need of development. To this end, the paper proposes a new method to rectify cloud contamination based on the cloud detection of iterative haze-optimized transformation (IHOT) and the cloud removal of cloud trajectory (IHOT-Trajectory). The cloud trajectory is able to take consideration of signal transmission for different levels of cloud contamination, which characterizes the spectral response of a certain type of land cover under increasing cloud thickness. Specifically, this method consists in four steps. First, the cloud thicknesses of contaminated pixels are estimated by the IHOT. Second, areas affected by cloud shadows are marked. Third, cloud trajectories are fitted with the aid of neighboring similar pixels under different cloud thickness. Last, contaminated areas are rectified according to the relationship between the land surface reflectance and the IHOT. The experimental results indicate that the proposed approach is able to effectively remove both the thin and thick clouds and erase the cloud shadows of Landsat images under different scenarios. In addition, the proposed method was compared with the dark object subtraction (DOS), the modified neighborhood similar pixel interpolator (MNSPI) and the multitemporal dictionary learning (MDL) methods. Quantitative assessments show that the IHOT-Trajectory method is superior to the other cloud removal methods overall. For specific spectral bands, the proposed method performs better than other methods in visible bands, whereas it does not necessarily perform better in infrared bands.
Article 0 Reads 3 Citations The mixed pixel effect in land surface phenology: A simulation study Xiang Chen, Dawei Wang, Jin Chen, Cong Wang, Miaogen Shen Published: 01 June 2018
Remote Sensing of Environment, doi: 10.1016/j.rse.2018.04.030
DOI See at publisher website
Article 0 Reads 0 Citations Inequalities of Nuclear Risk Communication Within and Beyond the Evacuation Planning Zone Xiang Chen, Clayton Frazier, Rejina Manandhar, Zhigang Han, ... Published: 10 May 2018
Applied Spatial Analysis and Policy, doi: 10.1007/s12061-018-9257-7
DOI See at publisher website
Article 0 Reads 0 Citations A new index for mapping the ‘blue steel tile’ roof dominated industrial zone from Landsat imagery Zhengfei Guo, Dedi Yang, Jin Chen, Xihong Cui Published: 22 March 2018
Remote Sensing Letters, doi: 10.1080/2150704x.2018.1452057
DOI See at publisher website
Article 0 Reads 5 Citations Exploring Determinants of Housing Prices in Beijing: An Enhanced Hedonic Regression with Open Access POI Data Yixiong Xiao, Xiang Chen, Qiang Li, Xi Yu, Jin Chen, Jing Gu... Published: 15 November 2017
ISPRS International Journal of Geo-Information, doi: 10.3390/ijgi6110358
DOI See at publisher website ABS Show/hide abstract
The housing market in Chinese metropolises have become inflated significantly over the last decade. In addition to an economic upturn and housing policies that have potentially fueled the real estate bubble, factors that have contributed to the spatial heterogeneity of housing prices can be dictated by the amenity value in the proximity of communities, such as accessibility to business centers and transportation hubs. In the past, scholars have employed the hedonic pricing model to quantify the amenity value in relation to structural, locational, and environmental variables. These studies, however, are limited by two methodological obstacles that are relatively difficult to overcome. The first pertains to difficulty of data collection in regions where geospatial datasets are strictly controlled and limited. The second refers to the spatial autocorrelation effect inherent in the hedonic analysis. Using Beijing, China as a case study, we addressed these two issues by (1) collecting residential housing and urban amenity data in terms of Points of Interest (POIs) through web-crawling on open access platforms; and (2) eliminating the spatial autocorrelation effect using the Eigenvector Spatial Filtering (ESF) method. The results showed that the effects of nearby amenities on housing prices are mixed. In other words, while proximity to certain amenities, such as convenient parking, was positively correlated with housing prices, other amenity variables, such as supermarkets, showed negative correlations. This mixed finding is further discussed in relation to community planning strategies in Beijing. This paper provides an example of employing open access datasets to analyze the determinants of housing prices. Results derived from the model can offer insights into the reasons for housing segmentation in Chinese cities, eventually helping to formulate effective urban planning strategies and equitable housing policies.
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