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Urban Land Use change detection Using Multisensor Satellite Images

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Pedosphere 2009年第1期19卷 96-103页

作者： DENG Jin-Song WANG Ke LI Jun DENG Yan-Hua Institute of Agricultural Remote Sensing and Information Technology Zhejiang University Hangzhou 310029 （China） Zhejiang Environmental Scientific Institute Hangzhou 310028 （China） Changsha Environmental Protection College Changsha 410004 （China）

Due to inappropriate planning and management, accelerated urban growth and tremendous loss in land, especially cropland, have become a great challenge for sustainable urban development in China, especially in developed urban area in the coastal regions; therefore, there is an urgent need to effectively detect and monitor the land use changes and provide accurate and timely information for planning and management. In this study a method combining principal component analysis (PCA) of multisensor satellite images from SPOT (systeme pour l'observation de la terre or earth observation satellite)-5 multispectral (XS) and Landsat-7 enhanced thematic mapper (ETM) panchromatic (PAN) data, and supervised classification was used to detect and analyze the dynamics of land use changes in the city proper of Hangzhou. The overall accuracy of the land use change detection was 90.67% and Kappa index was 0.89. The results indicated that there was a considerable land use change (10.03% of the total area) in the study area from 2001 to 2003, with three major types of land use conversions: from cropland into built-up land, construction site, and water area (fish pond). changes from orchard land into built-up land were also detected. The method described in this study is feasible and useful for detecting rapid land use change in the urban area.

关键词： change detection land use multisensor satellite image principal component analysis (PCA) urban area

Determination of land salinization causes via land cover and hydrological process change detection in a typical part of Songnen Plain

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Journal of Geographical Sciences 2018年第8期28卷 1099-1112页

作者：王志勇李丽娟 Institute of Geographic Sciences and Natural Resources Research CAS University of Chinese Academy of Sciences

Causes of land salinization were determined via land cover and hydrological process change detection in a typical part of Songnen Plain. The area of saline land increased from 4627 km2 in 1980 to 5416 km2 in 2000, and then decreased to 5198 km2 in 2015. The transformation between saline land and other land covers happened mainly before 2000, and saline land had transformation relationship mainly with cropland, grassland, and water body. From 1979 to 2007, groundwater depth fluctuated to increase and was mainly deeper than 3.3 m. Spatially, the area of the region where groundwater depth was deeper than 3.3 m increased from 46.7% in 1980 to 84% in 2000, while the area of the region almost occupied the whole region after 2000. Precipitation and evaporation changed little, while runoff decreased substantially. Shallow groundwater, change of cropland, grassland, and water body induced from human activities and decrease of runoff and increase of irrigation and water transfer from outer basin were the main reasons for land salinization before 2000. After 2000, groundwater with relatively great depth could not exert great influence on land salinization. Protection of grassland and wetland prevented the increase of the area of saline land.

关键词： change detection hydrological process land cover land salinization Songnen Plain

Updating digital elevation models via change detection and fusion of human and remote sensor data in urban environments

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International Journal of Digital Earth 2015年第2期8卷 153-171页

作者： Carolin Klonner Christopher Barron Pascal Neis Bernhard Hofle Department of Geography University of HeidelbergHeidelbergGermany

OpenStreetMap(OSM)currently represents the most popular project of Volunteered Geographic Information(VGI):geodata are collected by common people and made available for public *** Laser Scanning(ALS)enables the acquisition of high-resolution digital elevation models that are used for many *** study combines the advantages of both ALS and OSM,offering a promising new approach that enhances data quality and allows change detection:the mainly up-to-date 2D data of OSM can be combined with the high-resolution–but rarely updated–elevation information provided by *** case study investigates building objects of OSM and ALS data of the city of Bregenz,*** quality of OSM is discerned by the comparison of building footprints using different true positive definitions(*** area).High quality of OSM data is revealed,yet also limitations of each method with respect to heterogeneous regions and building outlines are *** the first time,an up-to-date Digital Surface Model(DSM)combining 2D OSM and ALS data is achieved.A multitude of applications such as flood simulations and solar potential assessments can directly benefit from this data combination,since their value and reliability strongly depend on an up-to-date DSM.

关键词： OpenStreetMap airborne laser scanning change detection 3D modelling up-to-date digital surface models

Coupling ground-level panoramas and aerial imagery for change detection

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维普期刊数据库

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Geo-Spatial Information Science 2016年第3期19卷 222-232页

作者： Nehla Ghouaiel Sébastien Lefèvre Institute of Research in Computer Science and Random Systems Universitéde Bretagne-SudUMR 6074 IRISAVannesFrance

Geographic landscapes in all over the world may be subject to rapid changes induced,for instance,by urban,forest,and agricultural *** such kind of changes is usually achieved through remote ***,obtaining regular and up-to-date aerial or satellite images is found to be a high costly process,thus preventing regular updating of land cover ***,in this paper,we propose a low-cost solution based on the use of groundlevel geo-located landscape panoramic photos providing high spatial resolution information of the *** photos can be acquired from various sources:digital cameras,smartphone,or even web ***,since the acquisition is performed at the ground level,the users’immediate surroundings,as sensed by a camera device,can provide information at a very high level of precision,enabling to update the land cover type of the geographic *** the described herein method,we propose to use inverse perspective mapping(inverse warping)to transform the geo-tagged ground-level 360◦photo onto a top-down view as if it had been acquired from a nadiral aerial *** re-projected,the warped photo is compared to a previously acquired remotely sensed image using standard techniques such as *** differences in orientation,resolution,and geographical extent between the top-down view and the aerial image are addressed through specific processing steps(***).Experiments on publicly available data-sets made of both ground-level photos and aerial images show promising results for updating land cover maps with mobile ***,the proposed approach contributes to the crowdsourcing efforts in geo-information processing and mapping,providing hints on the evolution of a landscape.

关键词： Image analysis remote sensing change detection crowdsourcing mobile mapping panoramic photos

Land use/land cover classification and its change detection using multi-temporal MODIS NDVI data

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Journal of Geographical Sciences 2015年第12期25卷 1479-1506页

作者： M USMAN R LIEDLI M A SHAHID A ABBAS Institute for Groundwater Management TU Dresden 01069 Dresden Germany University of Agriculture Faisalabad Pakistan Leibniz Centre for Agricultural Landscape Research (ZALF) Eberswalder Str. 84 15374 Mencheberg Germany

Detailed analysis of Land Use/Land Cover （LULC） using remote sensing data in complex irrigated basins provides complete profile for better water resource management and planning. Using remote sensing data, this study provides detailed land use maps of the Lower Chenab Canal irrigated region of Pakistan from 2005 to 2012 for LULC change detection. Major crop types are demarcated by identifying temporal profiles of NDVI using MODIS 250 m × 250 m spatial resolution data. Wheat and rice are found to be major crops in rabi and kharif seasons, respectively. Accuracy assessment of prepared maps is performed using three dif- ferent techniques： error matrix approach, comparison with ancillary data and with previous study. Producer and user accuracies for each class are calculated along with kappa coeffi- cients （K）. The average overall accuracies for rabi and kharif are 82.83% and 78.21%, re- spectively. Producer and user accuracies for individual class range respectively between 72.5% to 77% and 70.1% to 84.3% for rabi and 76.6% to 90.2% and 72% to 84.7% for kharif. The K values range between 0.66 to 0.77 for rabi with average of 0.73, and from 0.69 to 0.74 with average of 0.71 for kharif. LULC change detection indicates that wheat and rice have less volatility of change in comparison with both rabi and kharif fodders. Transformation be- tween cotton and rice is less common due to their completely different cropping conditions. Results of spatial and temporal LULC distributions and their seasonal variations provide useful insights for establishing realistic LULC scenarios for hydrological studies.

关键词： land use/land cover remote sensing normalized difference vegetation index accuracy assessment change detection hydrological modeling

Classification and Spatio-Temporal change detection of Land Use/Land Cover Using Remote Sensing and Geographic Information System in the Manouba Region, NE Tunisia

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Journal of Geographic Information System 2023年第6期15卷 652-668页

作者： Nadia Trabelsi Ibtissem Triki Imen Hentati Nizar Rachdi Laboratoire Eau Energie Environnement Ecole Nationale d’Ingénieurs de Sfax Sfax Tunisia Faculte des Sciences de Gafsa Campus Universitaire Gafsa Tunisia Geoengine Geomatics and Geosciences Engineering Rue du lac de Constance Tunis Tunisia

Land use/land cover (LULC) mapping and change detection are fundamental aspects of remote sensing data application. Therefore, selecting an appropriate classifier approach is crucial for accurate classification and change assessment. In the first part of this study, the performance of machine learning classification algorithms was compared using Landsat 9 image (2023) of the Manouba government (Tunisia). Three different classification methods were applied: Maximum Likelihood Classification (MLC), Support Vector Machine (SVM), and Random Trees (RT). The classification aimed to identify five land use classes: urban area, vegetation, bare area, water and forest. A qualitative assessment was conducted using Overall Accuracy (OA) and the Kappa coefficient (K), derived from a confusion matrix. The results of the land cover classification demonstrated a high level of accuracy. The SVM method exhibited the best performance, with an overall accuracy of 93% and a kappa accuracy of 0.9. The ML method is the second-best classifier with an overall accuracy of 92% and a kappa accuracy of 0.88. The Random Trees method yielded the lowest accuracy among the three approaches, with an overall accuracy of 91% and a kappa accuracy of 0.87. The second part of the study focused on analyzing LULC changes in the study area. Based on the classification results, the SVM method was chosen to classify the Landsat 7 image acquired in 2000. LULC changes from 2000 to 2023 were investigated using change detection comparison. The findings indicate that over the last 23 years, vegetation land and urban areas in the study area have experienced significant increases of 31.94% and 5.47%, respectively. This study contributed to a better understanding of the classification process and dynamic LULC changes in the Manouba region. It provided valuable insights for decision-makers in planning land conservation and management.

关键词： Remote Sensing GIS LULC SVM MLC RT change detection

change detection of Land Use and Riverbank in Mekong Delta, Vietnam Using Time Series Remotely Sensed Data

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Journal of Resources and Ecology 2011年第4期2卷 370-374页

作者：阮林道范巴赫闵阮青潘麦黎蓬晃披越南胡志明市资源地理研究所GIS与遥感研究中心

By taking the advantages of remote sensing technology, changes of land cover in Ca Mau Peninsular （the Southern part of the Mekong delta） and erosion of riverbank of the Mekong River have been defined. change detection using time-series optical and radar satellite images （Landsat, SPOT and Radarsat） is the main approach in this study. Typical land cover in Ca Mau Peninsular is mangrove; its changes are observable by analysing imageries from 1973 to 2008. The results showed that more than half of the mangrove area was converted into shrimp farms. This made forest cover severely declined. Riverbank of the Mekong River also was detected to be eroded and accreted in a complicated form. Locations along the left and right banks of Tien and Hau river are defined as hot spots that are seriously suffered erosions. Topographic maps in 1966-1968 were taken into account as base line data, which was analyzed along with satellite images from 1989 to 2009. In addition, the Digital Shoreline Analysis System （DSAS） package was taken to analyze trend of changes in terms of changing prediction.

关键词： remote sensing change detection land use riverbank erosion

A Novel Unsupervised change detection Method with Structure Consistency and GFLICM Based on UAV Images

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Journal of Geodesy and Geoinformation Science 2022年第1期5卷 91-102页

作者： Wensong LIU Xinyuan JI Jie LIU Fengcheng GUO Zongqiao YU School of Geography Geomatics and PlanningJiangsu Normal UniversityXuzhou 221116China Geological Affairs Center of Tianjin Tianjin 300040China

With the rapid development of Unmanned Aerial Vehicle(UAV)technology,change detection methods based on UAV images have been extensively ***,the imaging of UAV sensors is susceptible to environmental interference,which leads to great differences of same object between UAV *** the discrepancy difference between UAV images is crucial to improving the accuracy of change *** address this issue,a novel unsupervised change detection method based on structural consistency and the Generalized Fuzzy Local Information C-means Clustering Model(GFLICM)was proposed in this *** this method,the establishment of a graph-based structural consistency measure allowed for the detection of change information by comparing structure similarity between UAV *** local variation coefficient was introduced and a new fuzzy factor was reconstructed,after which the GFLICM algorithm was used to analyze difference ***,change detection results were analyzed qualitatively and *** measure the feasibility and robustness of the proposed method,experiments were conducted using two data sets from the cities of Yangzhou and *** experimental results show that the proposed method can improve the overall accuracy of change detection and reduce the false alarm rate when compared with other state-of-the-art change detection methods.

关键词： change detection UAV images graph model structural consistency Generalized Fuzzy Local Information C-means Clustering Model(GFLICM)

Sub-pixel change detection for urban land-cover analysis via multi-temporal remote sensing images

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Geo-Spatial Information Science 2014年第1期17卷 26-38页

作者： Peijun DU Sicong LIU Pei LIU Kun TAN Liang CHENG Key Laboratory for Satellite Mapping Technology and Application of National Administration of Surveying Mapping and GeoinformationNanjing UniversityNanjing CityJiangsu ProvinceP.R.China Department of Information Engineering and Computer Science University of TrentoTrentoItaly Jiangsu Provincial Key Laboratory for Resources and Environment Information Engineering China University of Mining and TechnologyXuzhouJiangsu ProvinceP.R.China

Conventional change detection approaches are mainly based on per-pixel processing,which ignore the sub-pixel spectral variation resulted from spectral *** for medium-resolution remote sensing images used in urban landcover change monitoring,land use/cover components within a single pixel are usually complicated and heterogeneous due to the limitation of the spatial ***,traditional hard detection methods based on pure pixel assumption may lead to a high level of omission and commission errors inevitably,degrading the overall accuracy of change *** order to address this issue and find a possible way to exploit the spectral variation in a sub-pixel level,a novel change detection scheme is designed based on the spectral mixture analysis and decision-level *** spectral mixture model is selected for spectral unmixing,and change detection is implemented in a sub-pixel level by investigating the inner-pixel subtle changes and combining multiple composition *** proposed method is tested on multi-temporal Landsat Thematic Mapper and China–Brazil Earth Resources Satellite remote sensing images for the land-cover change detection over urban *** effectiveness of the proposed approach is confirmed in terms of several accuracy indices in contrast with two pixel-based change detection methods(*** vector analysis and principal component analysis-based method).In particular,the proposed sub-pixel change detection approach not only provides the binary change information,but also obtains the characterization about change direction and intensity,which greatly extends the semantic meaning of the detected change targets.

关键词： change detection sub-pixel level processing multi-temporal images spectral mixture model back propagation neural network remote sensing