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Vineyard Detection and Vine Variety Discrimination from Very High Resolution Satellite Data

REMOTE SENSING 2016年第3期8卷 235-235页

作者： Karakizi, Christina Oikonomou, Marios Karantzalos, Konstantinos Natl Tech Univ Athens Remote Sensing Lab Heroon Polytech 9 Zografos 15780 Greece

In order to exploit remote sensing data operationally for precision agriculture applications, efficient and automated methods are required for the accurate detection of vegetation, crops and different crop varieties. To this end, we have designed, developed and evaluated an object-based classification framework towards the detection of vineyards, the vine canopy extraction and the vine variety discrimination from very high resolution multispectral data. A novel set of spectral, spatial and textural features, as well as rules, segmentation scales and a set of parameters are proposed based on object-based image analysis. The validation of the developed methodology was carried out on multitemporal WorldView-2 satellite data at four different viticulture regions in Greece. Concurrent in situ canopy reflectance observations were acquired from a portable spectroradiometer during the field campaigns. The performed quantitative evaluation indicated that the developed approach managed in all cases to detect vineyards with high completeness and correctness detection rates, i.e., over 89%. The vine canopy extraction methodology was validated with overall accuracy (OA) rates of above 96%. The quantitative evaluation regarding the vine variety discrimination task, including experiments with up to six different varieties, reached OA rates above 85% at the parcel level. The combined analysis of the experimental results with the spectral signatures from the in situ reflectance data indicated that certain vine varieties (e.g., Merlot) presented distinct spectral patterns across the VNIR spectrum.

关键词： object-based image analysis features spectral signatures crops precision viticulture classification

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High-Resolution Classification of South Patagonian Peat Bog Microforms Reveals Potential Gaps in Up-Scaled CH₄ Fluxes by use of Unmanned Aerial System (UAS) and CIR imagery

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REMOTE SENSING 2016年第3期8卷 173-173页

作者： Lehmann, Jan R. K. Muenchberger, Wiebke Knoth, Christian Blodau, Christian Nieberding, Felix Prinz, Torsten Pancotto, Veronica A. Kleinebecker, Till Univ Munster Inst Landscape Ecol Heisenbergstr 2 D-48149 Munster Germany Univ Munster Inst Geoinformat Heisenbergstr 2 D-48149 Munster Germany Ctr Austral Invest Cient CADIC CONICET B Houssay 200 RA-9410 Ushuaia Tierra Del Fueg Argentina

South Patagonian peat bogs are little studied sources of methane (CH4). Since CH4 fluxes can vary greatly on a small scale of meters, high-quality maps are needed to accurately quantify CH4 fluxes from bogs. We used high-resolution color infrared (CIR) images captured by an Unmanned Aerial System (UAS) to investigate potential uncertainties in total ecosystem CH4 fluxes introduced by the classification of the surface area. An object-based approach was used to classify vegetation both on species and microform level. We achieved an overall Kappa Index of Agreement (KIA) of 0.90 for the species- and 0.83 for the microform-level classification, respectively. CH4 fluxes were determined by closed chamber measurements on four predominant microforms of the studied bog. Both classification approaches were employed to up-scale CH4 closed chamber measurements in a total area of around 1.8 hectares. Including proportions of the surface area where no chamber measurements were conducted, we estimated a potential uncertainty in ecosystem CH4 fluxes introduced by the classification of the surface area. This potential uncertainty ranged from 14.2 mg center dot m(-2)center dot day(-1) to 26.8 mg center dot m(-2)center dot day(-1). Our results show that a simple classification with only few classes potentially leads to pronounced bias in total ecosystem CH4 fluxes when plot-scale fluxes are up-scaled.

关键词： RPAS closed chamber object-based image analysis UAV peatland methane OBIA classification

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Mapping dynamic cover types in a large seasonally flooded wetland using extended principal component analysis and object-based classification

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REMOTE SENSING OF ENVIRONMENT 2015年 158卷 193-206页

作者： Dronova, Iryna Gong, Peng Wang, Lin Zhong, Liheng Univ Calif Berkeley Dept Landscape Architecture & Environm Planning Coll Environm Design Berkeley CA 94720 USA Univ Calif Berkeley Dept Environm Sci Policy & Management Div Ecosyst Sci Coll Nat Resources Berkeley CA 94720 USA Chinese Acad Sci Inst Remote Sensing Applicat State Key Lab Remote Sensing Sci Beijing 100101 Peoples R China Beijing Normal Univ Beijing 100101 Peoples R China Tsinghua Univ Ctr Earth Syst Sci Beijing 100084 Peoples R China Poyang Lake Ecol Res Stn Environm & Hlth Duchang 332600 Peoples R China Joint Ctr Global Change Studies Beijing 100875 Peoples R China Chinese Acad Fishery Sci Beijing 100141 Peoples R China

Periodically inundated wetlands with high short-term surface variation require special approaches to assess their composition and long-term change. To circumvent high uncertainty in single-date analyses of such areas, we propose to characterize them as dynamic cover types (DCTs), or sequences of wetland states and transitions informed by physically and ecologically plausible surface processes. This study delineated DCTs for one 2007-2008 flood cycle at Poyang Lake, the largest freshwater wetland in China, using spatial and temporal orientation modes of extended principal components analysis (EPCA) and supervised object-based classification of multi-spectral and radar image series. Classification accuracy was compared among three sets of attributes selected by machine-learning optimization from object-level mean and standard deviations of: 1) image time series alone;2) the most informative EPCA outputs alone and 3) image time series and EPCA results together. Classification uncertainty was additionally assessed as low values of object's maximum class membership (<0.5). The highest accuracy was achieved with a larger set of 33 attributes selected from combined time series and EPCA results (overall accuracy 95.0%, kappa 0.94);however, accuracies with smaller sets of variables from input image series or EPCA results alone were comparably high (93.1% and 94.7%, respectively). All three selected attribute sets included standard deviations of image and/or EPCA values, suggesting the utility of object texture in dynamic class discrimination. The highest classification uncertainty was observed primarily along the mapped class boundaries, in some cases indicating minor change trajectories for which prior reference data were not available. Results indicate that DCTs provide a reasonable classification framework for complex and variable Poyang Lake wetlands that can be facilitated by EPCA transformation of complementary remote sensing time series. Future work should test this a

关键词： Dynamic cover types Wetland remote sensing object-based image analysis PR China Extended PCA Phenology Change trajectory analysis

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Rapid mapping and impact estimation of illegal charcoal production in southern Somalia based on WorldView-1 imagery

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ENERGY FOR SUSTAINABLE DEVELOPMENT 2015年 25卷 40-49页

作者： Bolognesi, M. Vrieling, A. Rembold, F. Gadain, H. FAO Somalia Water & Land Informat Management SWALIM P Food & Agr Org United Nations Nairobi Kenya Univ Twente Fac Geoinformat Sci & Earth Observat ITC NL-7500 AE Enschede Netherlands European Commiss Joint Res Ctr MARS Unit I-21027 Ispra VA Italy

Following more than 20 years of civil unrest, environmental information for southern Somalia is scarce. Wood charcoal production and export is a major activity supporting war regimes in this area such as the extreme Islamist group Al-Shabaab. However, little quantitative information exists on the extent of this charcoal production. In this study, we developed a semi-automatic detection method to identify charcoal production sites from very high resolution (0.5 m) satellite imagery. We then applied it to a 4700 km(2) area along the Juba River in southern Somalia using 2011 and 2013 WorldView-1 imagery. based on the sites detected exclusively for 2013 we estimated an average production of 24,000 tonnes of charcoal and 2.7% tree loss for the two-year interval, using literature- and local-knowledge-based assumptions on likely ranges of kiln and tree parameters. Our large-area assessment helps to better understand the dimension and impact of charcoal production in southern Somalia and reveals a rapid depletion of tree cover. The analysis provides a first step towards the development of a charcoal production monitoring system that could be extended to other parts of the country. (C) 2015 International Energy Initiative. Published by Elsevier Inc. All rights reserved.

关键词： Woodfuel Forest degradation Al-Shabaab Remote sensing object-based image analysis

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object-based Flood Mapping and Affected Rice Field Estimation with Landsat 8 OLI and MODIS Data

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REMOTE SENSING 2015年第5期7卷 5077-5097页

作者： Dao, Phuong D. Liou, Yuei-An Natl Cent Univ Ctr Space & Remote Sensing Res Taoyuan 320 Taiwan Taiwan Grp Earth Observat Zhubei City 30274 Hsinchu County Taiwan

Cambodia is one of the most flood-prone countries in Southeast Asia. It is geographically situated in the downstream region of the Mekong River with a lowland floodplain in the middle, surrounded by plateaus and high mountains. It usually experiences devastating floods induced by an overwhelming concentration of rainfall water over the Tonle Sap Lake's and Mekong River's banks during monsoon seasons. Flood damage assessment in the rice ecosystem plays an important role in this region as local residents rely heavily on agricultural production. This study introduced an object-based approach to flood mapping and affected rice field estimation in central Cambodia. In this approach, image segmentation processing was conducted with optimal scale parameter estimation based on the variation of objects' local variances. The inundated area was identified by using Landsat 8 images with an overall accuracy of higher than 95% compared to those derived from finer spatial resolution images. Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index products were utilized to identify the paddy rice field based on seasonal inter-variation between vegetation and water index during the transplanting stage. The rice classification result was well correlated with the statistical data at a commune level (R-2 = 0.675). The flood mapping and affected rice estimation results are useful to provide local governments with valuable information for flooding mitigation and post-flooding compensation and restoration.

关键词： object-based image analysis segmentation scale parameter estimation flood mapping paddy rice Landsat Moderate Resolution Imaging Spectroradiometer （MODIS） Cambodia

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Applying data fusion techniques for benthic habitat mapping and monitoring in a coral reef ecosystem

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2015年 104卷 213-223页

作者： Zhang, Caiyun Florida Atlantic Univ Dept Geosci Boca Raton FL 33431 USA

Accurate mapping and effective monitoring of benthic habitat in the Florida Keys are critical in developing management strategies for this valuable coral reef ecosystem. For this study, a framework was designed for automated benthic habitat mapping by combining multiple data sources (hyperspectral, aerial photography, and bathymetry data) and four contemporary imagery processing techniques (data fusion, object-based image analysis (OBIA), machine learning, and ensemble analysis). In the framework, 1-m digital aerial photograph was first merged with 17-m hyperspectral imagery and 10-m bathymetiy data using a pixel/feature-level fusion strategy. The fused dataset was then preclassified by three machine learning algorithms (Random Forest, Support Vector Machines, and k-Nearest Neighbor). Final object-based habitat maps were produced through ensemble analysis of outcomes from three classifiers. The framework was tested for classifying a group-level (3-class) and code-level (9-class) habitats in a portion of the Florida Keys. Informative and accurate habitat maps were achieved with an overall accuracy of 88.5% and 83.5% for the group-level and code-level classifications, respectively. (c) 2014 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.

关键词： Data fusion Machine learning Benthic habitat mapping Hyperspectral imagery object-based image analysis Coral reef ecosystem

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Stable Mean-Shift Algorithm and Its Application to the Segmentation of Arbitrarily Large Remote Sensing images

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2015年第2期53卷 952-964页

作者： Michel, Julien Youssefi, David Grizonnet, Manuel French Space Agcy Ctr Natl Etud Spatiales F-31400 Toulouse France

Segmentation of real-world remote sensing images is challenging because of the large size of those data, particularly for very high resolution imagery. However, a lot of high-level remote sensing methods rely on segmentation at some point and are therefore difficult to assess at full image scale, for real remote sensing applications. In this paper, we define a new property called stability of segmentation algorithms and demonstrate that piece- or tile-wise computation of a stable segmentation algorithm can be achieved with identical results with respect to processing the whole image at once. We also derive a technique to empirically estimate the stability of a given segmentation algorithm and apply it to four different algorithms. Among those algorithms, the mean-shift algorithm is found to be quite unstable. We propose a modified version of this algorithm enforcing its stability and thus allowing for tile-wise computation with identical results. Finally, we present results of this method and discuss the various trends and applications.

关键词： image processing image segmentation mean-shift object-based image analysis remote sensing stability of segmentation

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Automated classification of debris-covered glaciers combining optical, SAR and topographic data in an object-based environment

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REMOTE SENSING OF ENVIRONMENT 2015年 170卷 372-387页

作者： Robson, Benjamin Aubrey Nuth, Christopher Dahl, Svein Olaf Hoelbling, Daniel Strozzi, Tazio Nielsen, Pal Ringkjob Univ Bergen Dept Geog N-5007 Bergen Norway Univ Oslo Dept Geosci N-0316 Oslo Norway Salzburg Univ Dept Geoinfomat GIS Z A-5020 Salzburg Austria GAMMA Remote Sensing CH-3073 Gumlingen Switzerland

Satellite imagery is increasingly used to monitor glacier area changes and create glacier inventories. Robust and efficient pixel-based band ratios have proven to be accurate for automatically delineating clean glacier ice, however such classifications are restricted on debris-covered ice due to its spectral similarity with surrounding terrain. object-based image analysis (OBIA) has emerged as a new analysis technique within remote sensing. It offers many advantages over pixel-based classification techniques due to the ability to work with multiple data sources and handle data contextually and hierarchically. By making use of OBIA capabilities we automatically classify dean ice and debris-covered ice in the challenging area surrounding Mount Manaslu in Nepal using optical (Landsat 8), topographic (void-filled SRTM) and SAR coherence (ALOS PALSAR) data. Clean ice was classified with a mean accuracy of 93% whilst debris-covered ice was classified with an accuracy of 83% when compared to manually corrected outlines, providing a total glacier accuracy of 91%. With further developments in the classification, steep tributary sections of ice could be contextually included, raising the accuracy to over 94%. One prominent advantage of OBIA is that it allows some post-processing and correction of the glacier outlines automatically, reducing the amount of manual correction needed. OBIA incorporating SAR coherence data is recommended for future mapping of debris-covered ice. (C) 2015 Elsevier Inc. All rights reserved.

关键词： Debris-covered glacier object-based image analysis Landsat 8 SAR coherence Semi-automatic classification Himalayas

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Detection and Delineation of Localized Flooding from World View-2 Multispectral Data

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REMOTE SENSING 2015年第11期7卷 14853-14875页

作者： Malinowski, Radoslaw Groom, Geoff Schwanghart, Wolfgang Heckrath, Goswin Aarhus Univ Dept Agroecol DK-8830 Tjele Denmark Aarhus Univ Dept Biosci DK-8410 Ronde Denmark Univ Potsdam Inst Earth & Environm Sci D-14476 Potsdam Germany

Remote sensing technology serves as a powerful tool for analyzing geospatial characteristics of flood inundation events at various scales. However, the performance of remote sensing methods depends heavily on the flood characteristics and landscape settings. Difficulties might be encountered in mapping the extent of localized flooding with shallow water on riverine floodplain areas, where patches of herbaceous vegetation are interspersed with open water surfaces. To address the difficulties in mapping inundation on areas with complex water and vegetation compositions, a high spatial resolution dataset has to be used to reduce the problem of mixed pixels. The main objective of our study was to investigate the possibilities of using a single date WorldView-2 image of very high spatial resolution and supporting data to analyze spatial patterns of localized flooding on a riverine floodplain. We used a decision tree algorithm with various combinations of input variables including spectral bands of the WorldView-2 image, selected spectral indices dedicated to mapping water surfaces and vegetation, and topographic data. The overall accuracies of the twelve flood extent maps derived with the decision tree method and performed on both pixels and image objects ranged between 77% and 95%. The highest mapping overall accuracy was achieved with a method that utilized all available input data and the object-based image analysis. Our study demonstrates the possibility of using single date WorldView-2 data for analyzing flooding events at high spatial detail despite the absence of spectral bands from the short-waveform region that are frequently used in water related studies. Our study also highlights the importance of topographic data in inundation analyses. The greatest difficulties were met in mapping water surfaces under dense canopy herbaceous vegetation, due to limited water surface exposure and the dominance of vegetation reflectance.

关键词： decision tree floodplain inundation localized flooding object-based image analysis wetlands WorldView-2

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analysis of Unmanned Aerial System-based CIR images in Forestry-A New Perspective to Monitor Pest Infestation Levels

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FORESTS 2015年第3期6卷 594-612页

作者： Lehmann, Jan Rudolf Karl Nieberding, Felix Prinz, Torsten Knoth, Christian Univ Munster Inst Landscape Ecol D-48149 Munster Germany Univ Munster Inst Geoinformat D-48149 Munster Germany

The detection of pest infestation is an important aspect of forest management. In the case of the oak splendour beetle (Agrilus biguttatus) infestation, the affected oaks (Quercus sp.) show high levels of defoliation and altered canopy reflection signature. These critical features can be identified in high-resolution colour infrared (CIR) images of the tree crown and branches level captured by Unmanned Aerial Systems (UAS). In this study, we used a small UAS equipped with a compact digital camera which has been calibrated and modified to record not only the visual but also the near infrared reflection (NIR) of possibly infested oaks. The flight campaigns were realized in August 2013, covering two study sites which are located in a rural area in western Germany. Both locations represent small-scale, privately managed commercial forests in which oaks are economically valuable species. Our workflow includes the CIR/NIR image acquisition, mosaicking, georeferencing and pixel-based image enhancement followed by object-based image classification techniques. A modified Normalized Difference Vegetation Index (NDVImod) derived classification was used to distinguish between five vegetation health classes, i.e., infested, healthy or dead branches, other vegetation and canopy gaps. We achieved an overall Kappa Index of Agreement (KIA) of 0.81 and 0.77 for each study site, respectively. This approach offers a low-cost alternative to private forest owners who pursue a sustainable management strategy.

关键词： autonomous flying beetle infection drone GIS NDVI object-based image analysis OBIA UAV

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