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Regional hydromorphological characterization with continuous and automated remote sensing analysis based on VHR imagery and low-resolution LiDAR data

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EARTH SURFACE PROCESSES AND LANDFORMS 2017年第3期42卷 531-551页

作者： Demarchi, Luca Bizzi, Simone Piegay, Herve European Commiss Joint Res Ctr Inst Environm & Sustainabil Water Resources Unit Via E Fermi 2749 I-21027 Ispra Italy Warsaw Univ Life Sci Fac Civil & Environm Engn Dept Hydraul Engn Nowoursynowska 166 Warsaw Poland Politecn Milan Dept Elect Informat & Bioengn Piazza Leonardo da Vinci Milan Italy Univ Lyon UMR CNRS EVS 5600 ISIG Platform Site ENS Lyon15 Parvis Rene Descartes F-69362 Lyon France

Recent developments in remote sensing (RS) technologies lead the way in characterizing river morphology at regional scales and inferring potential channel responses to human pressures. In this paper, a unique regional database of continuous hydromorphological variables (HyMo DB) based on areal and topographic data has been generated from RS analysis. Key riverscape units with specific geomorphic meaning have been automatically mapped for 1700 km(2) of river floodplains from simultaneous very-high-resolution (VHR) near-infrared aerial imagery and low-resolution LiDAR-derived products. A multi-level, geographical object-based architecture (GEOBIA) was employed to integrate both spectral and topographic information and generate a regional classifier able to automatically map heterogeneous fluvial patterns in different geographical and topographical contexts of the Piedmont Region (Italy). This HyMo-generated DB offers a unique set of tools for hydromorphologists and can be exploited for different purposes. For the first time, topographic information can be exploited regionally per riverscape unit class, allowing for quantitative analysis of their regional spatial and statistical variability. In this manner, river types can be automatically characterized and classified using objective and repeatable hydromorphological variables. We discuss the potential of quantifying functional links between riverscape units and their driving processes, a valuable source of information to start assessing and highlighting the entity of potential channel adjustments at the regional scale to human pressures. The HyMo DB can also be integrated with historical, field-based information to better comprehend current fluvial changes at a local scale. In view of future RS acquisitions, the present approach will result in a suitable procedure for quantitative, objective and continuous monitoring of river evolutions over large scales. This type of hydromorphological characterization will allow reg

关键词： VHR and LiDAR data object-based classification regional scale hydromorphological characterization

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DECISION TREE REPOSITORY AND RULE SET based MINGJIANG RIVER ESTUARINE WETLANDS CLASSIFACTION

DECISION TREE REPOSITORY AND RULE SET BASED MINGJIANG RIVER ...

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The ISPRS Technical Commission III Midterm Symposium on "Developments, Technologies and Applications in Remote Sensing"

作者： Wenyan Zhang Xintong Li Wei Xiao College of Geography Fujian Normal University

The increasing urbanization and industrialization have led to wetland losses in estuarine area of Mingjiang River over past three decades. There has been increasing attention given to produce wetland inventories using remote sensing and GIS technology. Due to inconsistency training site and training sample, traditionally pixel-based image classification methods can't achieve a comparable result within different organizations. Meanwhile, object-oriented image classification technique shows grate potential to solve this problem and Landsat moderate resolution remote sensing images are widely used to fulfill this ***, the standardized atmospheric correct, spectrally high fidelity texture feature enhancement was conducted before implementing the object-oriented wetland classification method in e Cognition. Secondly, we performed the multi-scale segmentation procedure, taking the scale, hue, shape, compactness and smoothness of the image into account to get the appropriate parameters,using the top and down region merge algorithm from single pixel level, the optimal texture segmentation scale for different types of features is ***, the segmented object is used as the classification unit to calculate the spectral information such as Mean value, Maximum value, Minimum value, Brightness value and the Normalized value. The Area, length, Tightness and the Shape rule of the image object Spatial features and texture features such as Mean, Variance and Entropy of image objects are used as classification features of training samples. based on the reference images and the sampling points of on-the-spot investigation,typical training samples are selected uniformly and randomly for each type of ground objects. The spectral, texture and spatial characteristics of each type of feature in each feature layer corresponding to the range of values are used to create the decision tree repository. Finally, with the help of high resolution reference images, the rando

关键词： Landsat Satellite Image Texture Feature Enhancement object-based classification Wetland Mapping Repository Rule Set

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THE IDENTIFICATION OF LAND UTILIZATION IN COASTAL RECLAMATION AREAS IN TIANJIN USING HIGH RESOLUTION REMOTE SENSING IMAGES

THE IDENTIFICATION OF LAND UTILIZATION IN COASTAL RECLAMATIO...

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The ISPRS Technical Commission III Midterm Symposium on "Developments, Technologies and Applications in Remote Sensing"

作者： Yunshan Meng Yingzhi Cao Hongjun Tian Zhicong Han National Marine Data and Information Service

In recent decades, land reclamation activities have been developed rapidly in Chinese coastal regions, especially in Bohai Bay. The land reclamation areas can effectively alleviate the contradiction between land resources shortage and human needs, but some idle lands that left unused after the government making approval the usage of sea areas are also supposed to pay attention to. Due to the particular features of land coverage identification in large regions, traditional monitoring approaches are unable to perfectly meet the needs of effectively and quickly land use classification. In this paper, Gaofen-1 remotely sensed satellite imagery data together with sea area usage ownership data were used to identify the land use classifications and find out the idle land resources. It can be seen from the result that most of the land use types and idle land resources can be identified precisely.

关键词： Reclamation High resolution images object-based classification Decision tree method

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SENTINEL-1 AND SENTINEL-2 DATA FUSION FOR WETLANDS MAPPING: BALIKDAMI, TURKEY

SENTINEL-1 AND SENTINEL-2 DATA FUSION FOR WETLANDS MAPPING: ...

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The ISPRS Technical Commission III Midterm Symposium on "Developments, Technologies and Applications in Remote Sensing"

作者： Gordana Kaplan Ugur Avdan Anadolu University Remote Sensing and GIS programme Anadolu University Earth and Space Sciences Institute

Wetlands provide a number of environmental and socio-economic benefits such as their ability to store floodwaters and improve water quality, providing habitats for wildlife and supporting biodiversity, as well as aesthetic values. Remote sensing technology has proven to be a useful and frequent application in monitoring and mapping wetlands. Combining optical and microwave satellite data can help with mapping and monitoring the biophysical characteristics of wetlands and wetlands` vegetation. Also, fusing radar and optical remote sensing data can increase the wetland classification accuracy. In this paper, data from the fine spatial resolution optical satellite, Sentinel-2 and the Synthetic Aperture Radar Satellite, Sentinel-1, were fused for mapping wetlands. Both Sentinel-1 and Sentinel-2 images were pre-processed. After the pre-processing, vegetation indices were calculated using the Sentinel-2 bands and the results were included in the fusion data set. For the classification of the fused data, three different classification approaches were used and compared. The results showed significant improvement in the wetland classification using both multispectral and microwave data. Also, the presence of the red edge bands and the vegetation indices used in the data set showed significant improvement in the discrimination between wetlands and other vegetated areas. The statistical results of the fusion of the optical and radar data showed high wetland mapping accuracy, showing an overall classification accuracy of approximately 90% in the object-based classification method. For future research, we recommend multi-temporal image use, terrain data collection, as well as a comparison of the used method with the traditional image fusion techniques.

关键词： Image Fusion Sentinel-1 Sentinel-2 Wetlands object-based classification

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VHR satellite imagery for humanitarian crisis management: a case study 5

VHR satellite imagery for humanitarian crisis management: a ...

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5th International Conference on Remote Sensing and Geoinformation of the Environment (RSCy)

作者： Bitelli, Gabriele Eleias, Magdalena Franci, Francesca Mandanici, Emanuele Univ Bologna DICAM Viale Risorgimento 2 Bologna Italy

ISBN: (纸本)9781510613539;9781510613522

During the last years, remote sensing data along with GIS have been largely employed for supporting emergency management activities. In this context, the use of satellite images and derived map products has become more common also in the different phases of humanitarian crisis response. In this work very high resolution satellite imagery was processed to assess the evolution of Za'atari Refugee Camp, built in Jordan in 2012 by the UN Refugee Agency to host Syrian refugees. Multispectral satellite scenes of the Za'atari area were processed by means of object-based classifications. The main aim of the present work is the development of a semi-automated procedure for multi-temporal camp monitoring with particular reference to the dwellings detection. Whilst in the emergency mapping domain automation of feature extraction is widely investigated, in the field of humanitarian missions the information is often extracted by means of photointerpretation of the satellite data. This approach requires time for the interpretation;moreover, it is not reliable enough in complex situations, where features of interest are often small, heterogeneous and inconsistent. Therefore, the present paper discusses a methodology to obtain information for assisting humanitarian crisis management, using a semi-automatic classification approach applied to satellite imagery.

关键词： Refugees camp monitoring object-based classification WorldView-2 Pleiades

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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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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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ESTIMATING CROP COVER FRACTION FROM DIGITAL COLOR IMAGES 4

ESTIMATING CROP COVER FRACTION FROM DIGITAL COLOR IMAGES

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4th International Workshop on Geoinformation Science / 4th ISPRS International Workshop on Multi-Dimensional and Multi-Scale Spatial Data Modeling (GeoAdvances)

作者： Karakus, P. Karabork, H. OKU Engn Fac Dept Geomat Engn TR-80000 Osmaniye Turkey SU Dept Geomat Engn Engn Fac TR-42075 Konya Turkey

The use of automated methods to estimate crop cover fraction from digital color images has increased in recent years. Crop cover fraction can determine accurate, fast and inexpensive with this methods. A digital color images was acquired over each of the 30 sample fields in 2014 year at 2-3 week intervals. Study area has 15 sunflower fields and 15 corn fields. Digital color images were collected during 4 months, namely over the course of the growing season from sowing until harvesting to determine crop cover fraction. We used two approach to estimate crop cover fraction. In first method, the images were transformed from the RGB (red, green, blue) color space to the HSI (hue, intensity, saturation) color space. We used an object-based image analysis approach to classify the images into green vegetation and the other materials. In the second method, The Green Crop Tracker is less labor and time intensive than the object-based classification approach, is a viable alternative to ground-based methods. By comparing object-based classification method and Green Crop Tracker software 2014 growing season, results were obtained: There were high correlations between the estimations obtained by object-based classification method and Green Crop Tracker software (for 2014 R-2=0.89). The relationship between two methods for 2014-23 sunflower field was calculated R-2=0.97.

关键词： Crop Cover Fraction The Green Crop Tracker RGB to HSI Digital Camera object-based classification Corn Sunflower

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Understanding Community and Ecophysiology of Plant Species on the Colorado Plateau

Understanding Community and Ecophysiology of Plant Species o...

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作者： Yokum, Hannah Elizabeth Brigham Young University

学位级别：M.Sc., Master of Science

The intensification of aridity due to anthropogenic climate change is likely to have a large impact on the growth and survival of plant species in the southwestern U.S. where species are already vulnerable to high temperatures and limited precipitation. Global climate change impacts plants through a rising temperature effect, CO2 effect, and land management. In order to forecast the impacts of global climate change, it is necessary to know the current conditions and create a baseline for future comparisons and to understand the factors and players that will affect what happens in the future. The objective of Chapter 1 is to create the very first high resolution, accurate, park-wide map that shows the distribution of dominant plants on the Colorado Plateau and serves as a baseline for future comparisons of species distribution. If we are going to forecast what species have already been impacted by global change or will likely be impacted in the future, we need to know their physiology. Chapter 2 surveys the physiology of the twelve most abundant non-tree species on the Colorado Plateau to help us forecast what climate change might do and to understand what has likely already occurred. Chapter 1. Our objective was to create an accurate species-level classification map using a combination of multispectral data from the World View-3 satellite and hyperspectral data from a handheld radiometer to compare pixel-based and object-based classification. We found that overall, both methods were successful in creating an accurate landscape map. Different functional types could be classified with fairly good accuracy in a pixel-based classification but to get more accurate species-level classification, object-based methods were more effective (0.915, kappa coefficient=0.905) than pixel-based classification (0.79, kappa coefficient=0.766). Although spectral reflectance values were important in classification, the addition of other features such as brightness, texture, number of pi

关键词： Cclimate change Plant sensitivity object-based classification Pixel-based classification Ecophysiology

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Identifying Alpine Wetlands in the Damqu River Basin in the Source Area of the Yangtze River Using object-based classification Method

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Journal of Resources and Ecology 2011年第2期2卷 186-192页

作者：张继平张镱锂刘林山丁明军张学儒中国科学院地理科学与资源研究所北京100101 中国科学院研究生院北京100049 江西师范大学鄱阳湖湿地与流域研究教育部重点实验室江西南昌330022

Alpine wetlands are very sensitive to global change, have great impacts on the hydrological condition of rivers, and are closely related to peoples＇ living in lower reaches. It is essential to monitor alpine wetland changes to appropriately manage and protect wetland resources; however, it is quite difficult to accurately extract such information from remote sensing images due to spectral confusion and arduous field verification. In this study, we identified different wetland types in the Damqu River Basin located in the Yangze River source region from Landsat remote sensing data using the object-based method. In order to ensure the interpretation accuracy of wetland, a digital elevation model （DEM） and its derived data （slope, aspect）, Normalized Difference Vegetation Index （NDVI）, Normalized Difference Water Index （NDWI）, and Kauth-Thomas transformation were considered as the components of the spectral characteristics of wetland types. The spectral characteristics, texture features and spatial structure characteristics of each wetland type were comprehensively analyzed based on the success of image segmentation. The extraction rules for each wetland type were established by determining the thresholds of the spatial, texture and spectral attributes of typical parameter layers according to their histogram statistics. The classification accuracy was assessed using error matrixes and field survey verification data. According to the accuracy assessment, the total accuracy of image classification was 89%.

关键词： alpine wetland remote sensing object-based classification Damqu River Basin

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