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Remote Sensing Dedicated to Geographical Conditions Monitoring

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (30 September 2014) | Viewed by 70818

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Prof. Dr. Jixian Zhang

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Guest Editor

Research Center of Geographical Conditions Monitoring, Chinese Academy of Surveying and Mapping, Beijing 100830, China
Interests: photogrammetry and remote sensing, geographical conditions monitoring, etc.

Special Issue Information

Dear Colleagues,

Geographical conditions are parts of national conditions. The objective for proposing this concept is to emphasize recognizing the national situation from a geographical perspective, and to advocate study of the national situation based on the integrative analytics of the geographical–social–economic comprehensive data, so as to reveal the spatial–temporal evolution pattern and the inherent variation relationships concerning natural, economic and social development at different scales in China.

The geographical conditions cover such aspects as the territorial and geographical characteristics of the country, topography and geomorphology, road networks, distribution of rivers and lakes, land cover, urban layout and expansion, environmental and ecological conditions, and the spatial features of productivity. Geographical conditions monitoring (GeoCM) aims to monitor all kinds of indexes for every aspect mentioned above in a dynamic and quantitative way, and to analyze the changes of indexes from the quantity and frequency, distribution characteristics, regional differences and trends, thereby achieving objective, comprehensive and geographicaldescriptionsof the spatial distributions and spatio-temporal changes of natural, economic and social factors.

Until now, many countries have carried out projects related to GeoCM. The United States Geological Survey (USGS) launched a five-year plan entitled “Geographic Analysis and Monitoring Program” (GAM) in 2002 which is still running now. On 12 March 2013, the European Parliament adopted regulations establishing the Copernicus Programme, known as the European programme for the establishment of a European capacity for Earth Observation. In Japan, in addition to developing basic surveying and mapping services, authorities are also responsible for disaster monitoring, urban landscape monitoring, ground movement monitoring, and land use monitoring, as key projects and distribute the results through thematic maps, internet maps, and reports. Besides the above GeoCM activities, several continental or even global-scale monitoring activities have been conducted in recent years. This special issue will provide some exploratory papers in relation to the theory, methodology, techniques and applications of GeoCM. Relative aspects and topics include the following:.

Progress-visions for GeoCM frameworks, policies, and standards;
Multi-source data fusion for GeoCM;
Geographical information extraction algorithms and methodologies;
Large area land cover mapping;
Land over change detection from multi-temporal data sets;
High performance computing algorithms applied for GeoCM;
lReliable analysis and quality control of GeoCM;
Geo-statistical analysis and assessment;
Spatio-temporal modeling and analysis;
Typical GeoCM, such as ecology and environment monitoring, urban sprawl monitoring, etc.;
Geo-visualization for GeoCM

Prof. Dr. Jixian Zhang
Guest Editor

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Keywords

geographical conditions monitoring
image classification
land cover and land use
feature extraction
data fusion
change detection (including 3D change detection)
urban sprawl
statistical analysis
spatio -temporal data
spatial data mining
decision modeling
data quality and reliable analysis
high-performance computation

Published Papers (10 papers)

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Research

37001 KiB

Open AccessArticle

Polarimetric Calibration of CASMSAR P-Band Data Affected by Terrain Slopes Using a Dual-Band Data Fusion Technique

by Lu Liao, Jie Yang, Pingxiang Li and Fenfen Hua

Remote Sens. 2015, 7(4), 4784-4803; https://doi.org/10.3390/rs70404784 - 20 Apr 2015

Cited by 2 | Viewed by 5762

Abstract

For airborne synthetic aperture radar (SAR) polarimetric calibration (PolCAL) based on distributed targets, it is important to ensure the removal of both the polarimetric distortion and terrain slope effect. This paper proposes a new technique for PolCAL in mountainous areas, without the use of corner reflectors (CRs). The technique based on dual-band data fusion consists of two steps. First, the polarization orientation angle shift (POAS), as a priori asymmetry information, is derived from X-band interferometry and applied to P-band fully-polarimetric data. Second, the crosstalk and cross-polarization (cross-pol) channel imbalance are iteratively determined using the POAS after dual-band data fusion. The performance and feasibility of the technique was evaluated by CRs. It was demonstrated that the proposed technique is capable of deriving the distortion parameters and performs better than the methods presented in Quegan and Ainsworth et al. The signal-to-noise ratio (SNR) and pedestal height have been investigated in polarimetric signatures. The proposed technique is useful for PolCAL in mountainous areas and for monitoring systems without CRs in long-term operation. Full article

(This article belongs to the Special Issue Remote Sensing Dedicated to Geographical Conditions Monitoring)

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28155 KiB

Open AccessArticle

Woodland Extraction from High-Resolution CASMSAR Data Based on Dempster-Shafer Evidence Theory Fusion

by Lijun Lu, Wenjun Xie, Jixian Zhang, Guoman Huang, Qiwei Li and Zheng Zhao

Remote Sens. 2015, 7(4), 4068-4091; https://doi.org/10.3390/rs70404068 - 07 Apr 2015

Cited by 6 | Viewed by 5594

Abstract

Mapping and monitoring of woodland resources is necessary, since woodland is vital for the natural environment and human survival. The intent of this paper is to propose a fusion scheme for woodland extraction with different frequency (P- and X-band) polarimetric synthetic aperture radar (PolSAR) and interferometric SAR (InSAR) data. In the study area of Hanjietou, China, a supervised complex Wishart classifier based on the initial polarimetric feature analysis was first applied to the PolSAR data and achieved an overall accuracy of 88%. An unsupervised classification based on elevation threshold segmentation was then applied to the InSAR data, with an overall accuracy of 90%. After Dempster-Shafer (D-S) evidence theory fusion processing for the PolSAR and InSAR classification results, the overall accuracy of fusion result reached 95%. It was found the proposed fusion method facilitates the reduction of polarimetric and interferometric SAR classification errors, and is suitable for the extraction of large areas of land cover with a uniform texture and height. The woodland extraction accuracy of the study area was sufficiently high (producer’s accuracy of 96% and user’s accuracy of 96%) enough that the woodland map generated from the fusion result can meet the demands of forest resource mapping and monitoring. Full article

(This article belongs to the Special Issue Remote Sensing Dedicated to Geographical Conditions Monitoring)

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18855 KiB

Open AccessArticle

Use of Sub-Aperture Decomposition for Supervised PolSAR Classification in Urban Area

by Lei Deng, Ya-nan Yan and Chen Sun

Remote Sens. 2015, 7(2), 1380-1396; https://doi.org/10.3390/rs70201380 - 27 Jan 2015

Cited by 20 | Viewed by 6212

Abstract

A novel approach is proposed for classifying the polarimetric SAR (PolSAR) data by integrating polarimetric decomposition, sub-aperture decomposition and decision tree algorithm. It is composed of three key steps: sub-aperture decomposition, feature extraction and combination, and decision tree classification. Feature extraction and combination is the main contribution to the innovation of the proposed method. Firstly, the full-resolution PolSAR image and its two sub-aperture images are decomposed to obtain the scattering entropy, average scattering angle and anisotropy, respectively. Then, the difference information between the two sub-aperture images are extracted, and combined with the target decomposition features from full-resolution images to form the classification feature set. Finally, C5.0 decision tree algorithm is used to classify the PolSAR image. A comparison between the proposed method and commonly-used Wishart supervised classification was made to verify the improvement of the proposed method on the classification. The overall accuracy using the proposed method was 88.39%, much higher than that using the Wishart supervised classification, which exhibited an overall accuracy of 69.82%. The Kappa Coefficient was 0.83, whereas that using the Wishart supervised classification was 0.56. The results indicate that the proposed method performed better than Wishart supervised classification for landscape classification in urban area using PolSAR data. Further investigation was carried out on the contribution of difference information to PolSAR classification. It was found that the sub-aperture decomposition improved the classification accuracy of forest, buildings and grassland effectively in high-density urban area. Compared with support vector machine (SVM) and QUEST classifier, C5.0 decision tree classifier performs more efficient in time consumption, feature selection and construction of decision rule. Full article

(This article belongs to the Special Issue Remote Sensing Dedicated to Geographical Conditions Monitoring)

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5283 KiB

Open AccessArticle

Monitoring Groundwater Variations from Satellite Gravimetry and Hydrological Models: A Comparison with in-situ Measurements in the Mid-Atlantic Region of the United States

by Ruya Xiao, Xiufeng He, Yonglei Zhang, Vagner G. Ferreira and Liang Chang

Remote Sens. 2015, 7(1), 686-703; https://doi.org/10.3390/rs70100686 - 12 Jan 2015

Cited by 66 | Viewed by 9797

Abstract

Aimed at mapping time variations in the Earth’s gravity field, the Gravity Recovery and Climate Experiment (GRACE) satellite mission is applicable to access terrestrial water storage (TWS), which mainly includes groundwater, soil moisture (SM), and snow. In this study, SM and accumulated snow water equivalent (SWE) are simulated by the Global Land Data Assimilation System (GLDAS) land surface models (LSMs) and then used to isolate groundwater anomalies from GRACE-derived TWS in Pennsylvania and New York States of the Mid-Atlantic region of the United States. The monitoring well water-level records from the U.S. Geological Survey Ground-Water Climate Response Network from January 2005 to December 2011 are used for validation. The groundwater results from different combinations of GRACE products (from three institutions, CSR, GFZ and JPL) and GLDAS LSMs (CLM, NOAH and VIC) are compared and evaluated with in-situ measurements. The intercomparison analysis shows that the solution obtained through removing averaged simulated SM and SWE of the three LSMs from the averaged GRACE-derived TWS of the three centers would be the most robust to reduce the noises, and increase the confidence consequently. Although discrepancy exists, the GRACE-GLDAS estimated groundwater variations generally agree with in-situ observations. For monthly scales, their correlation coefficient reaches 0.70 at 95% confidence level with the RMSE of the differences of 2.6 cm. Two-tailed Mann-Kendall trend test results show that there is no significant groundwater gain or loss in this region over the study period. The GRACE time-variable field solutions and GLDAS simulations provide precise and reliable data sets in illustrating the regional groundwater storage variations, and the application will be meaningful and invaluable when applied to the data-poor regions. Full article

(This article belongs to the Special Issue Remote Sensing Dedicated to Geographical Conditions Monitoring)

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5822 KiB

Open AccessArticle

A Hierarchical Approach to Persistent Scatterer Network Construction and Deformation Time Series Estimation

by Rui Zhang, Guoxiang Liu, Zhilin Li, Guo Zhang, Hui Lin, Bing Yu and Xiaowen Wang

Remote Sens. 2015, 7(1), 211-228; https://doi.org/10.3390/rs70100211 - 24 Dec 2014

Cited by 7 | Viewed by 5799

Abstract

This paper presents a hierarchical approach to network construction and time series estimation in persistent scatterer interferometry (PSI) for deformation analysis using the time series of high-resolution satellite SAR images. To balance between computational efficiency and solution accuracy, a dividing and conquering algorithm (i.e., two levels of PS networking and solution) is proposed for extracting deformation rates of a study area. The algorithm has been tested using 40 high-resolution TerraSAR-X images collected between 2009 and 2010 over Tianjin in China for subsidence analysis, and validated by using the ground-based leveling measurements. The experimental results indicate that the hierarchical approach can remarkably reduce computing time and memory requirements, and the subsidence measurements derived from the hierarchical solution are in good agreement with the leveling data. Full article

(This article belongs to the Special Issue Remote Sensing Dedicated to Geographical Conditions Monitoring)

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13574 KiB

Open AccessArticle

Delineation of Rain Areas with TRMM Microwave Observations Based on PNN

by Shiguang Xu, Chaoyang Wu, Alemu Gonsamo and Yan Shen

Remote Sens. 2014, 6(12), 12118-12137; https://doi.org/10.3390/rs61212118 - 04 Dec 2014

Cited by 3 | Viewed by 5080

Abstract

False alarm and misdetected precipitation are prominent drawbacks of high-resolution satellite precipitation datasets, and they usually lead to serious uncertainty in hydrological and meteorological applications. In order to provide accurate rain area delineation for retrieving high-resolution precipitation datasets using satellite microwave observations, a probabilistic neural network (PNN)-based rain area delineation method was developed with rain gauge observations over the Yangtze River Basin and three parameters, including polarization corrected temperature at 85 GHz, difference of brightness temperature at vertically polarized 37 and 19 GHz channels (termed as TB_37V and TB_19V, respectively) and the sum of TB_37V and TB_19V derived from the observations of the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI). The PNN method was validated with independent samples, and the performance of this method was compared with dynamic cluster K-means method, TRMM Microwave Imager (TMI) Level 2 Hydrometeor Profile Product and the threshold method used in the Scatter Index (SI), a widely used microwave-based precipitation retrieval algorithm. Independent validation indicated that the PNN method can provide more reasonable rain areas than the other three methods. Furthermore, the precipitation volumes estimated by the SI algorithm were significantly improved by substituting the PNN method for the threshold method in the traditional SI algorithm. This study suggests that PNN is a promising way to obtain reasonable rain areas with satellite observations, and the development of an accurate rain area delineation method deserves more attention for improving the accuracy of satellite precipitation datasets. Full article

(This article belongs to the Special Issue Remote Sensing Dedicated to Geographical Conditions Monitoring)

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2058 KiB

Open AccessArticle

Graph-Based Divide and Conquer Method for Parallelizing Spatial Operations on Vector Data

by Xiaochen Kang and Xiangguo Lin

Remote Sens. 2014, 6(10), 10107-10130; https://doi.org/10.3390/rs61010107 - 22 Oct 2014

Cited by 4 | Viewed by 7070

Abstract

In computer science, dependence analysis determines whether or not it is safe to parallelize statements in programs. In dealing with the data-intensive and computationally intensive spatial operations in processing massive volumes of geometric features, this dependence can be well utilized for exploiting the parallelism. In this paper, we propose a graph-based divide and conquer method for parallelizing spatial operations (GDCMPSO) on vector data. It can represent spatial data dependences in spatial operations through representing the vector features as graph vertices, and their computational dependences as graph edges. By this way, spatial operations can be parallelized in three steps: partitioning the graph into graph components with inter-component edges firstly, simultaneously processing multiple subtasks indicated by the graph components secondly and finally handling remainder tasks denoted by the inter-component edges. To demonstrate how it works, buffer operation and intersection operation under this paradigm are conducted. In a 12-core environment, the two spatial operations both gain obvious performance improvements, and the speedups are more than eight. The testing results suggest that GDCMPSO contributes to a method for parallelizing spatial operations and can greatly improve the computing efficiency on multi-core architectures. Full article

(This article belongs to the Special Issue Remote Sensing Dedicated to Geographical Conditions Monitoring)

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12318 KiB

Open AccessArticle

A Bidimensional Empirical Mode Decomposition Method for Fusion of Multispectral and Panchromatic Remote Sensing Images

by Weihua Dong, Xian'en Li, Xiangguo Lin and Zhilin Li

Remote Sens. 2014, 6(9), 8446-8467; https://doi.org/10.3390/rs6098446 - 05 Sep 2014

Cited by 33 | Viewed by 8661

Abstract

This article focuses on the image fusion of high-resolution panchromatic and multispectral images. We propose a new image fusion method based on a Hue-Saturation-Value (HSV) color space model and bidimensional empirical mode decomposition (BEMD), by integrating high-frequency component of panchromatic image into multispectral image and optimizing the BEMD in decreasing sifting time, simplifying extrema point locating and more efficient interpolation. This new method has been tested with a panchromatic image (SPOT, 10-m resolution) and a multispectral image (TM, 28-m resolution). Visual and quantitative assessment methods are applied to evaluate the quality of the fused images. The experimental results show that the proposed method provided superior performance over conventional fusion algorithms in improving the quality of the fused images in terms of visual effectiveness, standard deviation, correlation coefficient, bias index and degree of distortion. Both five different land cover types WorldView-II images and three different sensor combinations (TM/SPOT, WorldView-II, 0.5 m/1 m resolution and IKONOS, 1 m/4 m resolution) validated the robustness of BEMD fusion performance. Both of these results prove the capability of the proposed BEMD method as a robust image fusion method to prevent color distortion and enhance image detail. Full article

(This article belongs to the Special Issue Remote Sensing Dedicated to Geographical Conditions Monitoring)

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12752 KiB

Open AccessArticle

L- and X-Band Multi-Temporal InSAR Analysis of Tianjin Subsidence

by Qingli Luo, Daniele Perissin, Yuanzhi Zhang and Youliang Jia

Remote Sens. 2014, 6(9), 7933-7951; https://doi.org/10.3390/rs6097933 - 26 Aug 2014

Cited by 65 | Viewed by 8777

Abstract

When synthetic aperture radar interferometry (InSAR) technology is applied in the monitoring of land subsidence, the sensor band plays an important role. An X-band SAR system as TerraSAR-X (TSX) provides high resolution and short revisit time, but it has no capability of global coverage. On the other side, an L-band sensor as Advanced Land Observing Satellite-Phased Array L-band Synthetic Aperture Radar (ALOS-PALSAR) has global coverage and it produces highly coherent interferograms, but it provides much less details in time and space. The characteristics of these two satellites from different bands can be regarded as complementary. In this paper, we firstly present a possible strategy for X-band optimized acquisition planning combining with L-band. More importantly, we also present the multi-temporal InSAR (MT-InSAR) analysis results from 23 ALOS-PALSAR images and 37 TSX data, which show the complementarity of L- and X-band allows measuring deformations both in urban and non-urban areas. Furthermore, the validation between MT-INSAR and leveling/GPS has been carried out. The combination analysis of L- and X-band MT-InSAR results effectively avoids the limitation of X-band, providing a way to define the shape and the borderline of subsiding center and helps us to understand the subsidence mechanism. Finally, the geological interpretation of the detected subsidence center is given. Full article

(This article belongs to the Special Issue Remote Sensing Dedicated to Geographical Conditions Monitoring)

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1055 KiB

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A Parallel Computing Paradigm for Pan-Sharpening Algorithms of Remotely Sensed Images on a Multi-Core Computer

by Jinghui Yang, Jixian Zhang and Guoman Huang

Remote Sens. 2014, 6(7), 6039-6063; https://doi.org/10.3390/rs6076039 - 27 Jun 2014

Cited by 15 | Viewed by 7147

Abstract

Pan-sharpening algorithms are data-and computation-intensive, and the processing performance can be poor if common serial processing techniques are adopted. This paper presents a parallel computing paradigm for pan-sharpening algorithms based on a generalized fusion model and parallel computing techniques. The developed modules, including eight typical pan-sharpening algorithms, show that the framework can be applied to implement most algorithms. The experiments demonstrate that if parallel strategies are adopted, in the best cases the fastest times required to finish the entire fusion operation (including disk input/output (I/O) and computation) are close to the time required to directly read and write the images without any computation. The parallel processing implemented on a workstation with two CPUs is able to perform these operations up to 13.9 times faster than serial execution. An algorithm in the framework is 32.6 times faster than the corresponding version in the ERDAS IMAGINE software. Additionally, no obvious differences in the fusion effects are observed between the fusion results of different implemented versions. Full article

(This article belongs to the Special Issue Remote Sensing Dedicated to Geographical Conditions Monitoring)

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