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Mangrove Analysis Using ALOS Imagery in Hai Phong city, Vietnam

Mangrove Analysis Using ALOS Imagery in Hai Phong city, Viet...

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Conference on Remote Sensing of the Marine Environment II

作者： Pham Tien Dat Yoshino, Kunihiko HUA CARES Hanoi Vietnam Univ Tsukuba Grad Sch Life & Environm Sci Tsukuba Ibaraki 3058577 Japan Univ Tsukuba Fac Engn Informat & Syst Tsukuba Ibaraki 3058573 Japan

ISBN: (纸本)9780819492647

Mangroves that appear in the inter-tidal zones along the coast in most tropical and semi-tropical countries play a vital role in coastal zones and can defend against the impacts of tsunamis. Nevertheless, these forests are under severe threat because of high population growth, weak governance, poor planning, as well as uncoordinated economic development. Hai Phong city is located on the Northern coast of Vietnam where the mangroves are distributed between zone I and zone II among the four mangrove zones in Vietnam. This city is vulnerable to rising sea levels and tropical cyclones, which are forecasted to become more severe in coming next decades. The objectives of this research were to analyze the current status of mangroves using different ALOS sensors in Hai Phong, Vietnam in 2010 and compare the accuracy of the post satellite image processing of ALOS imagery in mapping mangroves. A combination of object-based and supervised classification was used to generate the land cover maps. The results of this research indicate that the total area of mangrove was approximately 2,549 hectares and mangrove is present in the five coastal districts in Hai Phong. The findings of this research showed that ALOS AVIR-2 provides better accuracy than ALOS PALSAR. This research indicates the potential of utilizing image segmentation associated with supervised method for both optical and SAR images to map mangrove forests in coastal zones

关键词： Mangrove analysis object-based classification supervised classification ALOS imagery Hai Phong

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Fusion of RADARSAT fine-beam SAR and QuickBird data for land-cover mapping and change detection

Fusion of RADARSAT fine-beam SAR and QuickBird data for land...

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15th International Conference on Geoinformatics

作者： Ban, Yifang Hu, Hongtao Rangel, Irene Royal Inst Technol Div Geoinformat Dept Urban Planning & Environm SE-10044 Stockholm Sweden

ISBN: (纸本)9780819469120

The objective of this research is to evaluate multitemporal RADARSAT Fine-Beam C-HH SAR data, QuickBird MS data, and fusion of SAR and MS for urban land-cover mapping and change detection One scene of QuickBird imagery was acquired on July 18, 2002 and five-date RADARSAT fine-beam SAR images were acquired during May to August in 2002. Landsat TM imagery from 1988 was used for change detection. QucikBird images were classified using an object-based and rule-based approach. RADARSAR SAR texture images were classified using a hybrid approach. The results demonstrated that, for identifying 19 land-cover classes, object-based and rule-based classification of Quickbird data yielded an overall classification accuracy of 86.7% (kappa 0.857). For identifying I I land-cover classes, ANN classification of the combined Mean, Standard Deviation and Correlation texture images yielded an overall accuracy: 71.4%, (Kappa: 0.69). The hybrid classification of RADARSAT fine-beam SAR data improved the ANN classification accuracy to 83.56% (kappa: 0.803). Decision level fusion of RADARSAT SAR and QuickBird data improved the classification accuracy of several land cover classes. The post-classification change detection was able to identify the areas of significant change, for example, major new roads, new low-density and high-density, builtup areas and golf courses, even though the change detection results contained large amount of noise due to classification errors of individual images. QuickBrid classification result was able add detailed change information to the major changes identified.

关键词： QuickBird RADARSAT SAR land-cover mapping change detection texture analysis artificial neural network object-based classification data fusion

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SPATIAL INVENTORY OF SOLAR PHOTOVOLTAIC (PV) INSTALLATIONS USING REMOTE SENSING AND MACHINE LEARNING: CASE OF PAMPANGA PROVINCE, PHILIPPINES 8

SPATIAL INVENTORY OF SOLAR PHOTOVOLTAIC (PV) INSTALLATIONS U...

引用

8th International Conference on GeoInformation Advances (GeoAdvances)

作者： Dalagan, A. G. Principe, J. A. Univ Philippines Diliman Natl Grad Sch Engn Quezon City Philippines Univ Philippines Diliman Dept Geodet Engn Quezon City Philippines

In recent years, the cost of photovoltaic (PV) technologies significantly declined, boosting the expansion of solar PV installations in the country. This rapid development in solar PV utilization necessitates an effective detection method capable of delineating both utility-scale and distributed PV installations to generate a complete inventory of solar PV installations for status monitoring and implementation of appropriate programs of stakeholders and decision-makers. This study aims to detect and delineate solar PV installations in Pampanga, Philippines using different band combinations of Sentinel-2, Sentinel-1, and indices (NDVI, NDWI, and PVSI) through machine learning with the aid of open-source geographic information system and remote sensing software. Moreover, an alternative approach to identifying solar PVs using the combination of pixel-based classification (PBC) and object-based classification (OBC) was introduced. Training and validation data were acquired from the satellite images. The accuracy of each approach in PV detection was then compared using three classifiers: Support Vector Machine (SVM), Random Forest, and Naive Bayes. Results showed that SVM has the best performance for PBC while Random Forest demonstrated the highest accuracy for OBC. A post-processing procedure was also implemented using a set of spectral rules to further refine the results of image classification. The delineation accuracies of the post-processed data over the ground-truth data showed that the methodology is effective in delineating utility-scale installations for both Sentinel-2 and Planetscope. However, the detection of distributed PV systems showed limitations particularly when dealing with small solar PV installations (less than 45 pixels) due to Sentinel-2's coarse spatial resolution.

关键词： Solar Photovoltaics Utility-scale Distributed systems Pixel-based classification object-based classification Sentinel-2 Planetscope Indices

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Derivation of urban objects and their attributes for large-scale urban areas based on very high resolution UltraCam true orthophotos and nDSM - a case study Berlin, Germany

Derivation of urban objects and their attributes for large-s...

引用

Conference on Earth Resources and Environmental Remote Sensing/GIS Applications IV

作者： Poznanska, Anna Maria Bayer, Steven Bucher, Tilman German Aerosp Ctr DLR eV Inst Opt Sensor Syst Dept Applicat & Sensor Concepts D-12489 Berlin Germany

ISBN: (纸本)9780819497635

The development of automatic extraction methods for urban building and vegetation objects and the realization on a large urban data set have been accomplished within the project 'Derivation of Building-and Vegetation Heights and Structures in Berlin'. This project was executed on behalf of the Senate Department for Urban Development and Environment of the City of Berlin. As input an UltraCamX dataset consisting of the true ortho mosaic with four spectral channels and the normalized digital surface model (nDSM) with 30 cm resolution were used. The size of the area adds up to about 450 km(2). For the delineation of roof tiles additional slope and aspect layers were created. The workflow was developed in eCognition for automatic extraction of elevated urban objects and their height structures. Additionally we used focal statistics in ArcGIS to extend the workflow for the extraction of single tree crowns, since detailed information about the correct position and number of urban trees is relevant and completes the urban geo database. In this way a unique, complete and extensive workflow for an automatic urban objects extraction arises. Within the project methods for robust extraction of buildings with roof tiles as well as vegetation were applied. Greened roofs are automatically extracted and assigned to the class buildings. Furthermore building tiles which are located under trees are extracted by the intersection with the cadastral building data. For the transferability of the complete rule set a multi-layered workflow consisting of automated data import and export, iterating segmentation methods and fuzzy classification as well as object reshaping was developed and applied. The methods are transferable and effectively operate on large data sets. As results complete layers with building and vegetation shapes including single tree tops and crown outlines with comprehensive attributes like height and area are derived. The accuracies are in the range of 85 % (trees) to 95

关键词： urban remote sensing building and roof extraction single tree detection tree crown delineation object-based classification very high spatial resolution data large data handling

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HOTEX: AN APPROACH FOR GLOBAL MAPPING OF HUMAN BUILT-UP AND SETTLEMENT EXTENT 37

HOTEX: AN APPROACH FOR GLOBAL MAPPING OF HUMAN BUILT-UP AND ...

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IEEE International Geoscience & Remote Sensing Symposium

作者： Wang, Panshi Huang, Chengquan Tilton, James C. Tan, Bin de Colstoun, Eric C. Brown Univ Maryland Dept Geog Sci College Pk MD 20742 USA NASA GSFC Computat & Informat Sci & Technol Off Greenbelt MD 20771 USA NASA GSFC Sci Syst & Applicat Inc Greenbelt MD 20771 USA NASA GSFC Biospher Sci Lab Greenbelt MD 20771 USA

ISBN: (纸本)9781509049516

Understanding the impacts of urbanization requires accurate and updatable urban extent maps. Here we present an algorithm for mapping urban extent at global scale using Landsat data. An innovative hierarchical object-based texture (HOTex) classification approach was designed to overcome spectral confusion between urban and nonurban land cover types. VIIRS nightlights data and MODIS vegetation index datasets are integrated as high-level features under an object-based framework. We applied the HOTexmethod to the GLS-2010 Landsat images to produce a global map of human built-up and settlement extent. As shown by visual assessments, our method could effectively map urban extent and generate consistent results using images with inconsistent acquisition time and vegetation phenology. Using scene-level cross validation for results in Europe, we assessed the performance of HOTex and achieved a kappa coefficient of 0.91, compared to 0.74 from a baseline method using per-pixel classification using spectral information.

关键词： object-based classification Hieratical Segmentation Texture Urban Landsat

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Flood mapping using VHR satellite imagery: a comparison between different classification approaches 7

Flood mapping using VHR satellite imagery: a comparison betw...

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SPIE Conference on Earth Resources and Environmental Remote Sensing/GIS Applications VII

作者： Franci, Francesca Boccardo, Piero Mandanici, Emanuele Roveri, Elena Bitelli, Gabriele Univ Bologna DICAM Viale Risorgimento 2 Bologna Italy Politecn Torino DIST Viale Matteoli 39 Turin Italy

ISBN: (数字)9781510604155

ISBN: (纸本)9781510604148;9781510604155

Various regions in Europe have suffered from severe flooding over the last decades. Flood disasters often have a broad extent and a high frequency. They are considered the most devastating natural hazards because of the tremendous fatalities, injuries, property damages, economic and social disruption that they cause. In this context, Earth Observation techniques have become a key tool for flood risk and damage assessment. In particular, remote sensing facilitates flood surveying, providing valuable information, e.g. flood occurrence, intensity and progress of flood inundation, spurs and embankments affected/threatened. The present work aims to investigate the use of Very High Resolution satellite imagery for mapping flood-affected areas. The case study is the November 2013 flood event which occurred in Sardinia region (Italy), affecting a total of 2,700 people and killing 18 persons. The investigated zone extends for 28 km(2) along the Posada river, from the Maccheronis dam to the mouth in the Tyrrhenian sea. A post-event SPOT6 image was processed by means of different classification methods, in order to produce the flood map of the analysed area. The unsupervised classification algorithm ISODATA was tested. A pixel-based supervised technique was applied using the Maximum Likelihood algorithm;moreover, the SPOT 6 image was processed by means of object-oriented approaches. The produced flood maps were compared among each other and with an independent data source, in order to evaluate the performance of each method, also in terms of time demand.

关键词： Flood mapping Sardinia object-based classification pixel-based classification disaster management SPOT 6 2013 flood event

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Concepts of Image Fusion in Remote Sensing Applications

Concepts of Image Fusion in Remote Sensing Applications

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Vijayaraj, Veeraraghavan Younan, Nicolas H. O'Hara, Charles G. Oak Ridge Natl Lab Oak Ridge TN 37831 USA Mississippi State Univ Dept Elect & Comp Engn Mississippi State MS USA Mississippi State Univ GeoResources Inst Mississippi State MS USA

ISBN: (纸本)9780780395091

Earth observation satellites provide data covering different parts of the electromagnetic spectrum at different spatial, spectral, and temporal resolutions. To utilize these different types of image data effectively, a number of image fusion techniques have been developed. Image fusion is defined as "the set of methods, tools, and means of using data from two or more different images to improve the quality of the information" [1]. The fused image has rich information that will improve the performance of image analysis algorithms. This increase in quality of the information leads to better processing (ex: classification, segmentation) accuracies compared to using the information from one type of data alone. In this paper pixel level and feature level image fusion are applied for the classification of a co-registered QuickBird multispectral and panchromatic images.

关键词： image fusion object-based classification

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SATELLITE AND UNMANNED AERIAL VEHICLE DATA FOR THE classification OF SAND DUNE VEGETATION 37

SATELLITE AND UNMANNED AERIAL VEHICLE DATA FOR THE CLASSIFIC...

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37th International Symposium on Remote Sensing of Environment

作者： De Giglio, M. Goffo, F. Greggio, N. Merloni, N. Dubbini, M. Barbarella, M. Univ Bologna Civil Chem Environm & Mat Engn Dept DICAM Viale Risorgimento 2 I-40136 Bologna Italy Univ Bologna Lab IGRG Interdept Ctr Environm Sci Res CIRSA Via S Alberto 163 I-48100 Ravenna Italy Piazza A Costa 15 I-48015 Cervia Ravenna Italy Univ Bologna Geog Sec DiSCi Piazza San Giovanni Monte 2 I-40124 Bologna Italy

Within coastal systems, sand dunes are the only natural barriers able to counteract erosive processes. Since their equilibrium is often threatened by human activities and high vulnerability of the coastal environment, dunes require increasing attention and specific monitoring. Located between the mainland and the sea, dunes are unique residue habitats for some plant and animal species. In particular, their vegetation is important because it has a consolidation function and promotes the vertical dune accretion. A georeferenced vegetation classification can be useful to define the advancements or erosion stage of the dune, usually based only on the geometric reconstruction. The proposed study aims to compare the classifications performed with some combinations of two of the last generation sensors and traditional image processing techniques. High spectral resolution satellite image (WorldView-2) and a multispectral orthophoto, obtained from data acquired by an unmanned aerial vehicle, were used. objects and pixel algorithms were applied and the results were compared by a statistical test. Using the same bands, the findings show that both data are suitable for monitoring the evolutionary dune status. Specifically, the WorldView-2 pixel-based classification and UAV object-based classification provide the same accurate results.

关键词： WorldView2 Unmanned Aerial Vehicle coastal vegetation sand dunes pixel-based classification object-based classification

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Agricultural Land Cover classification using RapidEye Satellite Imagery in South Korea - First Result

Agricultural Land Cover Classification using RapidEye Satell...

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Conference on Remote Sensing for Agriculture, Ecosystems, and Hydrology XIII/18th International Symposium on Remote Sensing

作者： Kim, Hyun Ok Yeom, Jong Min Kim, Youn Soo Korea Aerosp Res Inst Taejon South Korea

ISBN: (纸本)9780819488015

Global climate changes as well as abnormal climate phenomena have affected the agricultural environment on a great scale. Thus, there is a strong need for countermeasures by making full use of agriculture related information. As agricultural lands in South Korea are mostly operated by private farmers on a small parcel level, it is difficult to gather information for an overview on changing crop condition and to construct database necessary for disease management, production estimation and compensation measures on a regional or governmental level. The objective of this study is to evaluate the multispectral reflectance characteristics of RapidEye image data to classify agricultural land cover as well as crop condition in South Korea. As the RapidEye sensor offers the spectral information in red edge range as a first multispectral satellite system, we focus on the usefulness of red edge reflectance for identifying crop species and for interpreting crop growth or stress condition.

关键词： RapidEye red edge spectral vegetation index object-based classification agriculture crop condition

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Multi-scale Correlation-based feature selection and Random forest classification for LULC mapping from the integration of SAR and optical Sentinel images 4

Multi-scale Correlation-based feature selection and Random f...

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Conference on Remote Sensing Technologies and Applications in Urban Environments IV held at SPIE Remote Sensing Conference

作者： Al-Ruzouq, Rami Shanableh, Abdallah Gibril, Mohamed Barakat A. Kalantar, Bahareh Univ Sharjah Dept Civil & Environm Engn Sharjah 27272 U Arab Emirates Univ Sharjah Res Inst Sci & Engn Sharjah 27272 U Arab Emirates RIKEN Ctr Adv Intelligence Project Goal Oriented Technol Res Grp Disaster Resilience Sci Team Tokyo 1030027 Japan

ISBN: (数字)9781510630185

ISBN: (纸本)9781510630178;9781510630185

Reliable and accurate land use/land cover (LULC) map is a crucial data source for the understanding of coupled human-environment systems, monitoring changes, timely low-cost planning, and management of natural resources. Improvements in sensor technologies and machine learning capabilities have shifted the attention of remote sensing community to data complementarity through fusion of multi-sensor data for accurate feature extraction and mapping. Amalgamation of optical and synthetic aperture radar (SAR) images has shown promising advantages in enhancing the accuracy of extracting LULC as such method allows exploitation of information in sensors. This study investigated the potential of using freely available multisource Sentinel images to extract LULC maps in semi-arid environments through multi-scale geographic object-based image analysis (GEOBIA). A multi-scale classification framework that integrates GEOBIA, correlation-based feature selection (CFS), and random forest (RF)-supervised classification was adopted to extract LULC from assimilation of Sentinel multi-sensor products. First, Sentinel-1 and -2 images were pre-processed. Second, optimum multi-scale segmentation levels were selected using F-score segmentation quality measures. Third, 70 features of various spectral indices and derivatives and geometrical features from optical data and multiple ratios and textural features from dual-polarization SAR images were computed, and a CFS based on wrapper approach was used to select the most significant features at multi-scale levels. Finally, a single and multi-scale RF classifier was used to extract LULC classes using the most relevant features extracted from Sentinel SAR and optical images. Results of multi-scale image segmentation optimization showed that scale parameter (SP) values of 40, 60, and 150 were optimal for extraction of LULC classes. Results of feature selection showed that 22, 24, and 27 features were selected at scale SP values of 40, 60, and 150

关键词： Optical sensors SAR data fusion object-based classification image segmentation optimization LULC

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