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Forty Years of Change in the Bulrush Marshes of the St. Lawrence Estuary and The Impact of the Greater Snow Goose

WETLANDS 2012年第6期32卷 1175-1188页

作者： Allard, Matthieu Fournier, Richard A. Grenier, Marcelle Lefebvre, Josee Giroux, Jean-Francois Univ Sherbrooke Dept Appl Geomat Sherbrooke PQ J1K 2R1 Canada Environm Canada Canadian Wildlife Serv Gatineau PQ K1A 0H3 Canada Environm Canada Canadian Wildlife Serv Quebec City PQ G1J 0C3 Canada Univ Quebec Dept Sci Biol Montreal PQ H3C 3P8 Canada

During its spring and fall migrations, the Greater Snow Goose (Chen caerulescens atlanticus) stages in the marshes along the St. Lawrence Estuary in southern Quebec, where it feeds on three-square bulrush (Schoenoplectus americanus) rhizomes. The goose population has grown from 70 000 birds to around one million over the last 40 years, thus increasing pressure on these tidal marshes. To determine the impact of geese on the ecological integrity of the marshes over this period, we used IKONOS satellite imagery and aerial photographs to classify vegetation types. We estimated changes in bulrush cover using the eCognition image analysis software (Trimble). We examined the spectral, textural, and contextual characteristics of the identified classes. The proportion of bulrush cover has declined significantly in the lower marsh since around 1980, and bulrush has been gradually replaced by wild rice (Zizania aquatica var. brevis). We also documented the erosion between the lower and upper marshes along most of the shoreline.

关键词： Remote sensing Bulrush Marsh Greater Snow Goose object-based classification Erosion

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Developing an Interpretation System for High-Resolution Remotely Sensed Images based on Hybrid Decision-Making Process in a Multi-scale Manner

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JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING 2020年第2期48卷 197-214页

作者： Kiani, Abbas Farnood Ahmadi, Farshid Ebadi, Hamid KN Toosi Univ Technol Fac Geodesy & Geomat Engn Dept Photogrammetry & Remote Sensing Tehran Iran Univ Tabriz Dept Geomat Engn Tabriz Iran

High spatial resolution (HSR) images are valuable data sources for urban applications. HSR images by high intra-class and low inter-class variabilities lead to a reduction in the statistical separability of the different land-cover classes in the spectral domain;therefore, conventional classification methods using merely spectral and textural information have proven to be inadequate for the HSR data. In this regard, interpretation systems by employing spatial and conceptual compatibility can be useful to closer a machining process to human analysis. In this research, a new framework has been introduced to classify remotely sensed imagery using a combination of ontological rules and geographic object-based image analysis. This paper to some degree attempts to untangle a few of the gaps in this field, especially by incorporating multi-scale analysis into the proposed framework, which is very advantageous in such an application. The present study used interactive segmentation and interpretation segmentation process with respect to the geometry of the image classes. In the proposed method, the segmentation process has been performed to avoid under-segmentation problems at several levels of the scale. The levels of scale are entered in the process of scoring and interpretation of the decision (not just applied at the level of results). This brings the process of labeling and interpretation closer to structural and natural reality. Additionally, a hybrid decision-making process (knowledge based and support vector machine) has been considered to achieve better results. The knowledge-based method is implemented to model the ontological relationships with the aim of labeling and controlling the decision-making process. To evaluate the efficiency of the method, the results of this research were assessed and compared with those of other methods in an urban area. The results showed that the proposed technique improved the overall accuracy and kappa coefficient by 9% and 11.5%,

关键词： Image interpretation Multi-scale object-based classification Remote sensing

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Performance evaluation of object based greenhouse detection from Sentinel-2 MSI and Landsat 8 OLI data: A case study from Almeria (Spain)

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2016年 52卷 403-411页

作者： Novelli, Antonio Aguilar, Manuel A. Nemmaoui, Abderrahim Aguilar, Fernando J. Tarantino, Eufemia Politecn Bari DICATECh Via Orabona 4CAP I-70125 Bari Italy Unversidad Almeria UAL Escuela Super Ingn Almeria Ctra Sacramento S-N I-04120 La Canana De San Urbano Spain

This paper shows the first comparison between data from Sentinel-2 (S2) Multi Spectral Instrument (MSI) and Landsat 8 (L8) Operational Land Imager (OLI) headed up to greenhouse detection. Two closely related in time scenes, one for each sensor, were classified by using object based Image Analysis and Random Forest (RF). The RF input consisted of several object-based features computed from spectral bands and including mean values, spectral indices and textural features. S2 and L8 data comparisons were also extended using a common segmentation dataset extracted form VHR World-View 2 (WV2) imagery to test differences only due to their specific spectral contribution. The best band combinations to perform segmentation were found through a modified version of the Euclidian Distance 2 index. Four different RF classifications schemes were considered achieving 89.1%, 91.3%, 90.9% and 93.4% as the best overall accuracies respectively, evaluated over the whole study area. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Sentinel-2 MSI Landsat8 OLI WorldView-2 Greenhouses object-based classification Segmentation quality

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Understanding multi-temporal urban forest cover using high resolution images

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URBAN FORESTRY & URBAN GREENING 2018年 29卷 106-112页

作者： Canetti, Aline Garrastazu, Marilice Cordeiro de Mattos, Patricia Povoa Braz, Evaldo Munoz Netto, Sylvio Pellico Univ Fed Parana Av Pref Lothario Meissner 900 BR-80210170 Curitiba Parana Brazil Embrapa Florestas Estr Ribeira Km 111Caixa Postal 319 BR-83411000 Colombo PR Brazil

Urban forests offer city residents a better quality of life. They also act as barrier and filter to pollutants resulting from human activities. Mapping the distribution of forests in urban areas is therefore important for good urban planning. Currently, high resolution images are considered useful tools to quantify forested urban areas for large scale urban development. The objective of this work was to evaluate multi-temporal urban forest area changes over different land-use classes, using available high-resolution images obtained from different satellites. The municipality of Araucaria (Parana State) was chosen as study area because it is a large industrial zone of southern Brazil. The effects on the forests within this municipality caused by the increase in human activities between 2005 and 2012 were determined using high resolution images (5 m). We recorded a reduction of 22.8% in the forests surrounding urban areas of the municipality, as a result of deforestation of 791 ha and plantation of only 251 ha. The utility, commercial and residential zones which are more crowded the areas of highest population density were those which showed the greatest loss of tree cover. object-based classification accuracy using images from different satellites was sufficient to quantify the evolution of tree-cover over the studied period.

关键词： Urban forestry Image processing object-based classification

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Comparison of machine learning algorithms for mangrove species identification in Malad creek, Mumbai using WorldView-2 and Google Earth images

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JOURNAL OF COASTAL CONSERVATION 2022年第5期26卷 1-13页

作者： Nagarajan, Padmageetha Rajendran, Lakshana Pillai, Nithin D. Lakshmanan, Gnanappazham Anna Univ Coll Engn Guindy Dept Civil Engn Chennai 600025 Tamil Nadu India Indian Inst Space Sci & Technol Dept Earth & Space Sci Valiamala 695547 Kerala India

This study focuses on the comparison of machine learning algorithms for classifying the mangrove species of Malad creek, Mumbai, India using pixel-based and object-based approaches. As mangrove forests are complex in structure and require high resolution remotely sensed images for classifying them at the species level, traditional classification methods of remote sensing may not be suitable. In order to find an appropriate method, pixel-based random forest and K Nearest Neighbor classifiers and object-based random forest and K Nearest Neighbor classifiers were tested and compared using WorldView - 2 images. In addition, the best resolution RGB images from Google Earth were classified using random forest and K Nearest Neighbor algorithms in object-based approach and compared. The classification results showed that both object-oriented and pixel-based methods could identify the major mangrove species at the community level. However, the performance of object-based random forest classifier was better than other classification approaches in both WorldView-2 and Google Earth images with overall accuracies of 92.53% and 80.72% and kappa coefficients of 0.89 and 0.73. The overall results showed the potential of random forest classifier in object-based method for classifying the mangroves at the species level.

关键词： Random forest K Nearest Neighbor Pixel-based classification object-based classification Malad creek

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Identifying a suitable combination of classification technique and bandwidth(s) for burned area mapping in tallgrass prairie with MODIS imagery

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2012年第1期14卷 103-111页

作者： Mohler, Rhett L. Goodin, Douglas G. Kansas State Univ Dept Geog Manhattan KS 66506 USA

Prescribed fire is crucial to the ecology and maintenance of tallgrass prairie, and its application affects a variety of human and natural systems. Consequently, maps showing the location and extent of these fires are critical to managing tallgrass prairies in a manner that balances the needs of all stakeholders. Satellite-based optical remote sensing can provide the necessary input for this mapping, but it requires the development mapping methods that are specific to tallgrass prairie. In this research, we devise and test a suitable mapping method by comparing the efficacy of seven combinations of bands and indices from the MODIS sensor using both pixel and object-based classification methods. Due to the relatively small size of many prescribed fires in tallgrass prairie, scenarios based on the 250 m spatial resolution red and NIR bands outperformed those based on the coarser 500 m spatial resolution bands, and a combination of both red and NIR performed better than each 250 m band individually. object-based classification offered no improvement over pixel-based classification, and performed poorer in some cases. Our results suggest that mapping burned areas in tallgrass prairie should be done at a minimum of 250 m spatial resolution, should used a pixel-based classification technique, and should use a combination of red and NIR. (C) 2011 Published by Elsevier B.V.

关键词： Burned area mapping MODIS object-based classification Tallgrass prairie Grasslands

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Unmanned Aircraft System Photogrammetry for Mapping Diverse Vegetation Species in a Heterogeneous Coastal Wetland

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WETLANDS 2020年第6期40卷 2621-2633页

作者： Durgan, Sara Denka Zhang, Caiyun Duecaster, Aaron Fourney, Francesca Su, Hongbo Florida Atlantic Univ Dept Geosci 777 Glades Rd Boca Raton FL 33431 USA Cyriacks Environm Consulting Serv 3001 SW 15th StSuite B Deerfield FL 33442 USA Florida Atlantic Univ Dept Civil Environm & Geomat Engn Boca Raton FL 33431 USA

Acquiring detailed information on wetland plant species is critical for monitoring wetland ecosystem restoration and management. The emerging technique of Unmanned Aircraft System (UAS) photogrammetry has immense potential for such applications. In this study, we assessed the capacity of UAS photogrammetric products for classifying and mapping a large number of wetland plant species using contemporary object-based Image Analysis (OBIA) and machine learning methods. Our testing results in a heterogeneous coastal wetland demonstrated the benefit of centimeter-level orthoimagery and vertical products from UAS photogrammetry for mapping 17 species compared with standard aerial photography products. We achieved an overall accuracy (OA) of 71.3% and 84.8% for mapping 17 species and 10 major species, respectively. Our study suggests that UAS photogrammetry is a valuable tool for mapping wetland species composition and distribution.

关键词： UAS Coastal wetland Structure from motion Species mapping object-based classification

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Determination of Opium Poppy (Papaver Somniferum) Parcels Using High-Resolution Satellite Imagery

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JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING 2019年第6期47卷 977-987页

作者： Demir, Sinan Basayigit, Levent Suleyman Demirel Univ Fac Agr Dept Soil Sci & Plant Nutr Isparta Turkey

Narcotic plants contain substances that cause unusual excitation and subsequent depression of the central nervous system. Many narcotic plants contain substances that have medicinal properties and are used primarily as pain relievers. Opium poppy (Papaver somniferum) is one of the most cultivated narcotic plants. The planning of Opium poppy growing is controlled by United Nations and Drugs and Medicines Control Program. This study was conducted to establish a basis for determining the traceability of poppy cultivating areas using remote sensing in the large plain. A very high-resolution QuickBird-2 satellite image was used in the study. The software of ERDAS Imagine and eCognition Developer was performed for image processing and classification. A variety of classification methods were performed on the satellite image. ArcGIS software was used for accuracy assessment, ground control, and map production of the poppy cultivating area. The accuracy of classification methods was compared. The producer-user accuracy was estimated as 97.99% using spectral difference sub-segmentation process of multiresolution segmentation algorithm in object-based classifications method. This algorithm can be a practical approach for determining of opium poppy parcels in the large plain.

关键词： Opium poppy (Papaver Somniferum) Pixel-based classification object-based classification QuickBird-2 High-resolution satellite image

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Combined use of LiDAR data and multispectral earth observation imagery for wetland habitat mapping

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2015年 37卷 56-64页

作者： Rapinel, Sebastien Hubert-Moy, Laurence Clement, Bernard Univ Rennes 2 LETG RENNES COSTEL UMR CNRS 6554 F-35043 Rennes France Univ Rennes 1 ECOBIO UMR CNRS 6553 F-35042 Rennes France

Although wetlands play a key role in controlling flooding and nonpoint source pollution, sequestering carbon and providing an abundance of ecological services, the inventory and characterization of wetland habitats are most often limited to small areas. This explains why the understanding of their ecological functioning is still insufficient for a reliable functional assessment on areas larger than a few hectares. While LiDAR data and multispectral Earth Observation (EO) images are often used separately to map wetland habitats, their combined use is currently being assessed for different habitat types. The aim of this study is to evaluate the combination of multispectral and multiseasonal imagery and LiDAR data to precisely map the distribution of wetland habitats. The image classification was performed combining an object-based approach and decision-tree modeling. Four multispectral images with high (SPOT-5) and very high spatial resolution (Quickbird, KOMPSAT-2, aerial photographs) were classified separately. Another classification was then applied integrating summer and winter multispectral image data and three layers derived from LiDAR data: vegetation height, microtopography and intensity return. The comparison of classification results shows that some habitats are better identified on the winter image and others on the summer image (overall accuracies = 58.5 and 57.6%). They also point out that classification accuracy is highly improved (overall accuracy = 86.5%) when combining LiDAR data and multispectral images. Moreover, this study highlights the advantage of integrating vegetation height, microtopography and intensity parameters in the classification process. This article demonstrates that information provided by the synergetic use of multispectral images and LiDAR data can help in wetland functional assessment (C) 2014 Elsevier B.V. All rights reserved.

关键词： Wetland function Vegetation formations CORINE biotopes Very high spatial resolution object-based classification

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An automatic region-based image segmentation algorithm for remote sensing applications

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ENVIRONMENTAL MODELLING & SOFTWARE 2010年第10期25卷 1149-1165页

作者： Wang, Zhongwu Jensen, John R. Im, Jungho SUNY Syracuse Dept Environm Resources & Forest Engn Coll Environm Sci & Forestry Syracuse NY 13210 USA Sanborn Map Co Ann Arbor MI 48103 USA Univ S Carolina Dept Geog Columbia SC 29208 USA

object-based image analysis has proven its potentials for remote sensing applications, especially when using high-spatial resolution data. One of the first steps of object-based image analysis is to generate homogeneous regions from a pixel-based image, which is typically called the image segmentation process. This paper introduces a new automatic Region-based Image Segmentation Algorithm based on k-means clustering (RISA), specifically designed for remote sensing applications. The algorithm includes five steps: k-means clustering, segment initialization, seed generation, region growing, and region merging. RISA was evaluated using a case study focusing on land-cover classification for two sites: an agricultural area in the Republic of South Africa and a residential area in Fresno, CA. High spatial resolution SPOT 5 and QuickBird satellite imagery were used in the case study. RISA generated highly homogeneous regions based on visual inspection. The land-cover classification using the RISA-derived image segments resulted in higher accuracy than the classifications using the image segments derived from the Definiens software (eCognition) and original image pixels in combination with a minimum-distance classifier. Quantitative segmentation quality assessment using two object metrics showed RISA-derived segments successfully represented the reference objects. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Image segmentation k-means clustering Region growing Region merging object-based classification

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