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Mangrove Species Identification: Comparing WorldView-2 with Aerial Photographs

REMOTE SENSING 2014年第7期6卷 6064-6088页

作者： Heenkenda, Muditha K. Joyce, Karen E. Maier, Stefan W. Bartolo, Renee Charles Darwin Univ Res Inst Environm & Livelihoods Casuarina NT 0909 Australia Dept Environm Environm Res Inst Supervising Scientist Darwin NT 0801 Australia

Remote sensing plays a critical role in mapping and monitoring mangroves. Aerial photographs and visual image interpretation techniques have historically been known to be the most common approach for mapping mangroves and species discrimination. However, with the availability of increased spectral resolution satellite imagery, and advances in digital image classification algorithms, there is now a potential to digitally classify mangroves to the species level. This study compares the accuracy of mangrove species maps derived from two different layer combinations of WorldView-2 images with those generated using high resolution aerial photographs captured by an UltraCamD camera over Rapid Creek coastal mangrove forest, Darwin, Australia. Mangrove and non-mangrove areas were discriminated using object-based image classification. Mangrove areas were then further classified into species using a support vector machine algorithm with best-fit parameters. Overall classification accuracy for the WorldView-2 data within the visible range was 89%. Kappa statistics provided a strong correlation between the classification and validation data. In contrast to this accuracy, the error matrix for the automated classification of aerial photographs indicated less promising results. In summary, it can be concluded that mangrove species mapping using a support vector machine algorithm is more successful with WorldView-2 data than with aerial photographs.

关键词： mangrove species mapping object-based image analysis support vector machine WorldView-2 aerial photographs

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object-based extraction of bark beetle (Ips typographus L.) infestations using multi-date LANDSAT and SPOT satellite imagery

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PROGRESS IN PHYSICAL GEOGRAPHY-EARTH AND ENVIRONMENT 2014年第6期38卷 755-785页

作者： Latifi, Hooman Fassnacht, Fabian E. Schumann, Bastian Dech, Stefan Univ Wurzburg D-97074 Wurzburg Germany Karlsruhe Inst Technol D-76021 Karlsruhe Germany German Aerosp Ctr DLR Cologne Germany

As major agents of biological disturbances, bark beetle infestations have been reported to account for a large portion of damage that occur in European forest stands. As a result, accurate spatiotemporal characterization of the vulnerable areas is crucial for subsequent post-infestation management. Remote sensing-assisted mapping of bark beetle-induced forest mortality has been an important research focus during the last decade. Due to the occurrence of mostly small- to medium-scale infestation patches in European stands, high-resolution optical data is commonly applied for mapping mortality. Despite this, we hypothesize the widely available satellite products to be potentially advantageous due to their multitemporal availability and reasonable costs. Here, we combined multi-date LANDSAT and SPOT scenes across an 11-year time span in which various epidemic and non-epidemic infestations occurred within the Bavarian Forest National Park in Germany. The aim was to map temporally adjacent mortality classes. The spectral, geometric and textural metrics extracted from the segmented imagery were applied to perform a full object-based classification, for which a digital terrain model was additionally employed. A number of potentially influential factors were also explored, including the spatial aggregation of image segments and the spatial enhancement of the multispectral imagery. The analysis resulted in a nearly perfect separation of non-infested and dead trees, while different levels of confusion were observed when classifying the transitional mortality classes. While the pan-sharpening of selected image scenes contributed to the stability of mapping results for non-infested and dead trees, no explicit trend was observed when aggregating small image segments prior to classification. Furthermore, combining the metrics from image objects and the digital terrain model suggested an obviously improved classification compared to the previously achieved pixel-based results acro

关键词： European bark beetle forest mortality multi-date imagery object-based image analysis spatial modelling

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Incorporating road and parcel data for object-based classification of detailed urban land covers from NAIP images

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GISCIENCE & REMOTE SENSING 2014年第5期51卷 498-520页

作者： Qiu, Xiaomin Wu, Shuo-Sheng Miao, Xin Missouri State Univ Dept Geog Geol & Planning Springfield MO 65897 USA

A map showing various urban features, such as buildings, roads, and vegetation, is useful for a variety of urban planning applications. The objective of this study was to incorporate road and parcel GIS data as well as relevant expert knowledge to classify different urban land covers from 1-meter, 4-band NAIP images. based on a hybrid simultaneous-classification and one-by-one-classification approach, a total of 14 urban classes are classified. The classification map has an overall accuracy of 90%, demonstrating a noticeable improvement over past comparable studies on detailed urban land cover classification.

关键词： OBIA urban land cover classification object-based image analysis Feature Analyst((R)) NAIP

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Significance analysis of Different Types of Ancillary Geodata Utilized in a Multisource Classification Process for Forest Identification in Germany

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2014年第6期52卷 3453-3463页

作者： Foerster, Michael Kleinschmit, Birgit Tech Univ Berlin Geoinformat Environm Planning Lab Inst Landscape Architecture & Environm Planning D-10623 Berlin Germany

Ancillary geodata can supply information to enhance classification accuracy for a variety of remote-sensing applications. To understand the integration of different data into a knowledge-based multisource classification process, this paper evaluates the significance of geodata for the classification accuracy of a very high spatial resolution satellite image for the identification of forest types in Germany. The approach utilizes a fuzzy-logic classifier for the integration of a knowledge base, which combines spectral information with ancillary data layers. The results of the classification were used to test a method for evaluating the influence of the integration of single geodata, the effects on different classes, and the impacts of the applied rules. A microarray significance analysis (MSA) was used to evaluate the significance of the classification results, whereas an ISODATA clustering was utilized for visualizing. A sequence of 50 accuracy assessments of classifications with possible combinations of geodata and rules for the identified classes was derived. The resulting microarray of accuracy percentages of single classes and the overall classification was used for further investigation. The MSA supplies the measure of significance, called relative difference d(i). The MSA identified 11 classifications of positive significance (d(i) greater than 1.44) and three classifications of negative significance (d(i) lower than -2.87). In particular, classifications that contain all rules were rated as positive significant.

关键词： Additional information fuzzy logic geodata image classification microarray significance analysis (MSA) object-based image analysis QuickBird

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Monitoring large oil slick dynamics with moderate resolution multispectral satellite data

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NATURAL HAZARDS 2014年第2期73卷 473-492页

作者： Maianti, Pieralberto Rusmini, Marco Tortini, Riccardo Dalla Via, Giorgio Frassy, Federico Marchesi, Andrea Nodari, Francesco Rota Gianinetto, Marco Politecn Milan Dept Architecture Built Environm & Construct Engn Lab Remote Sensing L RS I-20133 Milan Italy ERM Italia SpA I-20124 Milan Italy Michigan Technol Univ Dept Geol & Min Engn & Sci Houghton MI 49931 USA

Accidental release of crude oil into the sea due to human activity causes water pollution and heavy damages to natural ecosystems killing birds, fish, mammals and other organisms. A number of monitoring systems are used for tracking the spills and their effects on the marine environment, as well as for collecting data for feeding models. Among them, Earth observation technologies play a crucial role and moderate spatial resolution satellite systems are able to collect images with a very short revisit time or even daily. This paper describes the use of Moderate-Resolution Imaging Spectroradiometer data for monitoring large oil slicks with the fluorescence/emissivity index and object-based image analysis. Two case studies are presented: the Deepwater Horizon (2010) and the Campos Basin (2011) oil spill accidents. Results show that it is possible to track the dynamics of the slick both for massive and long-lasting accidents and for smaller and very quick accidents. The main advantages of the method proposed are a straightforward implementation, a fast and semi-automated data processing and the capability of integration of daytime and nighttime acquisitions, as well as its adaptability to different sensors.

关键词： Oil slick MODIS object-based image analysis

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Utilizing National Agriculture imagery Program Data to Estimate Tree Cover and Biomass of Pinon and Juniper Woodlands

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RANGELAND ECOLOGY & MANAGEMENT 2014年第5期67卷 563-572页

作者： Hulet, April Roundy, Bruce A. Petersen, Steven L. Bunting, Stephen C. Jensen, Ryan R. Roundy, Darrell B. ARS USDA Eastern Oregon Agr Res Ctr Burns OR 97720 USA Brigham Young Univ Dept Plant & Wildlife Sci Provo UT 84602 USA Univ Idaho Dept Forest Rangeland & Fire Sci Moscow ID 83844 USA Brigham Young Univ Dept Geog Provo UT 84602 USA

With the encroachment of pinon (Pinus ssp.) and juniper (Juniperus ssp.) woodlands onto sagebrush steppe rangelands, there is an increasing interest in rapid, accurate, and inexpensive quantification methods to estimate tree canopy cover and aboveground biomass. The objectives of this study were 1) to evaluate the relationship and agreement of pinon and juniper (P-J) canopy cover estimates, using object-based image analysis (OBIA) techniques and National Agriculture imagery Program (NAIP, 1-m pixel resolution) imagery with ground measurements, and 2) to investigate the relationship between remotely-sensed P-J canopy cover and ground-measured aboveground biomass. For the OBIA, we used eCognition (R) Developer 8.8 software to extract tree canopy cover from NAIP imagery across 12 P-J woodlands within the Sagebrush Steppe Treatment Evaluation Project (SageSTEP) network. The P-J woodlands were categorized based on the dominant tree species found at the individual sites for the analysis (western juniper, Utah juniper, and mixed P-J community). Following tree canopy cover extractions, relationships were assessed between remotely-sensed canopy cover and ground-measured aboveground biomass. Our OBIA estimates for P-J canopy cover were highly correlated with ground-measured tree canopy cover (averaged across all regions r=0.92). However, differences between methods occurred for western and Utah juniper sites (P < 0.05), and were more prominent where tree canopy cover was >40%. There were high degrees of correlation between predicted aboveground biomass estimates with the use of remotely-sensed tree canopy cover and ground-measured aboveground biomass (averaged across all regions r=0.89). Our results suggest that OBIA methods combined with NAIP imagery can provide land managers with quantitative data that can be used to evaluate P-J woodland cover and aboveground biomass rapidly, on broad scales. Although some accuracy and precision may be lost when utilizing aerial imagery to

关键词： remote sensing object-based image analysis biomass Pinus Juniperus eCognition (R) Developer

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Uncertainty in urban forest canopy assessment: Lessons from Seattle, WA, USA

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URBAN FORESTRY & URBAN GREENING 2014年第1期13卷 152-157页

作者： Richardson, Jeffrey J. Moskal, L. Monika Univ Washington Sch Environm & Forest Sci Seattle WA 98195 USA

Increasing urbanization around the globe is leading to concern over the loss of tree canopy within cities, but quantifying urban forest canopy cover can be difficult. We discuss methods of assessing canopy cover within cities, and then use a case study of Seattle, WA, USA to examine issues of uncertainty in canopy cover assessment. We find that uncertainty is often not reported, and when reported, may be biased. based on these findings, we provide a list of recommendations for those undertaking canopy cover assessment in complex urban environments. (C) 2013 Elsevier GmbH. All rights reserved.

关键词： Canopy cover Heterogeneity object-based image analysis Remote sensing Sampling

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object-based land-cover classification for metropolitan Phoenix, Arizona, using aerial photography

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2014年第1期33卷 321-330页

作者： Li, Xiaoxiao Myint, Soe W. Zhang, Yujia Galletti, Chritopher Zhang, Xiaoxiang Turner, Billie L., II Arizona State Univ Global Inst Sustainabil Tempe AZ 85287 USA Arizona State Univ Sch Geog Sci & Urban Planning Tempe AZ 85287 USA Hohai Univ Inst Geog Sci & Engn Nanjing 210098 Jiangsu Peoples R China

Detailed land-cover mapping is essential for a range of research issues addressed by the sustainability and land system sciences and planning. This study uses an object-based approach to create a 1 m land-cover classification map of the expansive Phoenix metropolitan area through the use of high spatial resolution aerial photography from National Agricultural imagery Program. It employs an expert knowledge decision rule set and incorporates the cadastral GIS vector layer as auxiliary data. The classification rule was established on a hierarchical image object network, and the properties of parcels in the vector layer were used to establish land cover types. image segmentations were initially utilized to separate the aerial photos into parcel sized objects, and were further used for detailed land type identification within the parcels. Characteristics of image objects from contextual and geometrical aspects were used in the decision rule set to reduce the spectral limitation of the four-band aerial photography. Classification results include 12 land-cover classes and subclasses that may be assessed from the sub-parcel to the landscape scales, facilitating examination of scale dynamics. The proposed object-based classification method provides robust results, uses minimal and readily available ancillary data, and reduces computational time. (C) 2014 Elsevier B.V. All rights reserved.

关键词： object-based image analysis Land cover Aerial photography Urban Phoenix Classification system

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A rule-based image analysis approach for calculating residues and vegetation cover under field conditions

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CATENA 2014年 113卷 363-369页

作者： Bauer, Th. Strauss, P. Fed Agcy Water Management Inst Land & Water Management Res A-3252 Petzenkirchen Austria

Estimation of soil cover by residues and vegetation is a fundamental issue for many agriculture-related topics, especially topics dealing with mulching practices and soil erosion, because the amount of cover is a basic driver for erosion risk. Soil cover measurement in the field is very time consuming and subjective. Our ambition for this study was to develop a quick and easy-to-handle field method for calculating the amount of different soil cover types, i.e. simultaneously dead and living biomass, in a single-step analysis. We used an object-based image analysis methodology (OBIA) to quantify different cover types. Classification of the images used resulted in the following classes: residues, vegetation, stones, shadow and uncertainty. The shadow and uncertainty classes were used as an image quality parameter. We compared this method to manual image analysis for the range of between 0 and 50% total cover and different catch crops and winter crops. To increase the accuracy of manual analysis, it was necessary to repeat the assessment five times per image. Degree of agreement between the OBIA method and manual assessment for each of the three different cover types was in the region of 0.8 (r(2) = 0.78 for total cover, r(2) = 0.75 for residue cover, r(2) = 0.82 for vegetation cover). Slopes of the regression intercepts between manual and automated analyses were not different from 1 for total cover and vegetation cover. 95% confidence intervals for the regression lines indicate that confidence limits at total soil cover of 25% (the mean of the investigated range of soil cover) are similar for both the manual evaluation (CI95% = 2.8) and the OBIA method (CI95% = 3.1). The time needed for evaluation was calculated at 115 min per manual image classification and 15 min per automated image classification, which we regard as a major advantage of the OBIA methodology. Finally we suggest that, while similar accuracies of evaluation for both methods have been obtained, the OBI

关键词： object-based image analysis Automated soil cover estimation Field method Residue cover estimation Vegetation cover estimation

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Combining object-based classification and data mining algorithm to classify urban surface materials from WorldView-2 satellite image

Combining object-based classification and data mining algori...

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5th International Conference on Information Science and Applications (ICISA)

作者： Hamedianfar, Alireza Shafri, Helmi Zulhaidi Mohd Univ Putra Malaysia Fac Engn Dept Civil Engn Serdang 43400 Selangor Malaysia Univ Putra Malaysia Fac Engn GISRC Serdang 43400 Selangor Malaysia

ISBN: (纸本)9781479944415

Although object-based image analysis (OBIA) has been used for detailed classification of urban areas, its attribute selection and knowledge discovery have been time consuming and subjective to analysts' performance. In this study, Data Mining was performed using C4.5 algorithm to select the appropriate attributes for object-based classification. This algorithm provides a decision tree output to represent the knowledge model, enabled a faster classification of intra-urban classes, and disabled the subjectivities which are related to the interaction of the analyst. The decision tree results were implemented in eCognition software to provide an effective and fast generation of semantic network in OBIA.

关键词： Data Mining object-based image analysis Intra-Urban Roofing materials

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