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Combining object-based texture measures with a neural network for vegetation mapping in the Everglades from hyperspectral imagery

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REMOTE SENSING OF ENVIRONMENT 2012年 124卷 310-320页

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

An informative and accurate vegetation map for the Greater Everglades of South Florida is in an urgent need to assist with the Comprehensive Everglades Restoration Plan (CERP), a $10.5-billion mission to restore the south Florida ecosystem in 30 + years. In this study, we examined the capability of fine spatial resolution hyperspectral imagery collected from Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) for vegetation mapping in the Everglades. In order to obtain an efficient and accurate procedure for vegetation discrimination, we developed a neural network classifier first and then combined the object-based texture measures with the classifier to examine the contribution of the spatial information for vegetation mapping. The neural network is capable of modeling the characteristics of multiple spectral and spatial signatures within a class by an internally unsupervised engine and characterizing spectral and spatial differences between classes by an externally supervised system. The designed procedure was tested in a portion of the Everglades. An object-based vegetation map was generated with an overall classification accuracy averaged 94% and a kappa value averaged 0.94 in discriminating 15 classes. The results are significantly better than those obtained from conventional classifiers such as maximum likelihood and spectral angle mapper. The study illustrates that combining object-based texture measures in the neural network classifier can significantly improve the classification. It is concluded that fine spatial resolution hyperspectral data is an effective solution to accurate vegetation mapping in the Everglades which has a rich plant community with a high degree of spatial and spectral heterogeneity. (C) 2012 Elsevier Inc. All rights reserved.

关键词： Texture analysis object-based image analysis Neural network Hyperspectral imagery Everglades

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Reconstructing semi-arid wetland surface water dynamics through spectral mixture analysis of a time series of Landsat satellite images (1984-2011)

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REMOTE SENSING OF ENVIRONMENT 2016年 177卷 171-183页

作者： Halabisky, Meghan Moskal, L. Monika Gillespie, Alan Hannam, Michael Univ Washington Sch Environm & Forest Sci Seattle WA 98195 USA Univ Washington Dept Earth & Space Sci Seattle WA 98195 USA Smithsonian Environm Res Ctr POB 28 Edgewater MD 21037 USA

Wetlands are valuable ecosystems for maintaining biodiversity, but are vulnerable to climate change and land conversion. Despite their importance, wetland hydrology is poorly understood as few tools exist to monitor their hydrologic regime at a landscape scale. This is especially true when monitoring hydrologic change at scales below 30 m, the resolution of one Landsat pixel. To address this, we used spectral mixture analysis (SMA) of a time series of Landsat satellite imagery to reconstruct surface-water hydrographs for 750 wetlands in Douglas County, Washington State, USA, from 1984 to 2011. SMA estimates the fractional abundance of spectra representing physically meaningful materials, known as spectral endmembers, which comprise a mixed pixel, thus providing sub-pixel estimates of surface water extent Endmembers for water and sage steppe were selected directly from each image scene in the Landsat time series, whereas endmembers for salt and wetland vegetation were derived from a mean spectral signature of selected dates spanning the 1984-2011 timeframe. This method worked well (R-2 = 0.99) for even small wetlands (<1800 m(2)) providing a wall-to-wall dataset of reconstructed surface-water hydrographs for wetlands across our study area. We have validated this method only in semi-arid regions. Further research is necessary to extend its validity to other environments. This method can be used to better understand the role of hydrology in wetland ecosystems and as a monitoring tool to identify wetlands undergoing abnormal change. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Time series Landsat Wetlands Hydrology Hydroperiod High resolution OBIA object-based image analysis Hydrograph Monitoring Sub-pixel

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Mangrove Species Identification: Comparing WorldView-2 with Aerial Photographs

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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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Vegetation classification of Coffea on Hawaii Island using WorldView-2 satellite imagery

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JOURNAL OF APPLIED REMOTE SENSING 2017年第4期11卷

作者： Gaertner, Julie Genovese, Vanessa Brooks Potter, Christopher Sewake, Kelvin Manoukis, Nicholas C. ARS Daniel K Inouye US Pacific Basin Agr Res Ctr USDA Hilo HI 96720 USA Univ Hawaii Manoa Coll Trop Agr & Human Resources Hilo HI USA Calif State Univ Monterey Bay Div Sci & Environm Policy Seaside CA USA NASA Ames Res Ctr Earth Syst Sci Moffett Field CA 94035 USA Univ Hawaii Manoa Coll Trop Agr & Human Resources Honolulu HI 96822 USA

Coffee is an important crop in tropical regions of the world;about 125 million people depend on coffee agriculture for their livelihoods. Understanding the spatial extent of coffee fields is useful for management and control of coffee pests such as Hypothenemus hampei and other pests that use coffee fruit as a host for immature stages such as the Mediterranean fruit fly, for economic planning, and for following changes in coffee agroecosystems over time. We present two methods for detecting Coffea arabica fields using remote sensing and geospatial technologies on WorldView-2 high-resolution spectral data of the Kona region of Hawaii Island. The first method, a pixel-based method using a maximum likelihood algorithm, attained 72% producer accuracy and 69% user accuracy (68% overall accuracy) based on analysis of 104 ground truth testing polygons. The second method, an object-based image analysis (OBIA) method, considered both spectral and textural information and improved accuracy, resulting in 76% producer accuracy and 94% user accuracy (81% overall accuracy) for the same testing areas. We conclude that the OBIA method is useful for detecting coffee fields grown in the open and use it to estimate the distribution of about 1050 hectares under coffee agriculture in the Kona region in 2012. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License.

关键词： coffee berry borer remote sensing object-based image analysis

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Establishing distribution maps and structural analysis of seagrass communities based on high-resolution remote sensing images and field surveys: a case study at Nam Yet Island, Truong Sa Archipelago, Vietnam

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LANDSCAPE AND ECOLOGICAL ENGINEERING 2022年第3期18卷 405-419页

作者： Dang Hoi Nguyen Trung Dung Ngo Viet Dung Vu Quan Vu Viet Du Vietnam Russian Trop Ctr Inst Trop Ecol 63 Nguyen Van Huyen Str Hanoi Vietnam Vietnam Russian Trop Ctr Coast Branch 30 Nguyen Thien Thuat Str Nha Trang City Vietnam Vietnam Natl Univ Univ Sci 334 Nguyen Trai Str Hanoi Vietnam

Seagrasses are among the most important ecosystems of continental and island coastal waters globally. Analyses of the species diversity, structure, and distribution mapping of seagrass communities provide a scientific basis for seagrass conservation and development and protection of the marine environment. The application of remote sensing, especially using very high-resolution remote sensing data, in mapping seagrass distribution confers high accuracy and detailed results. The distribution of seagrass communities and benthic biomes typical of Nam Yet Island, Truong Sa Archipelago, Vietnam was mapped using object-based classification of Pleiades satellite imagery with four spectral bands (red, blue, green, and near-infrared) and the Green-Blue Normalized Difference Vegetation Index, combined with handheld positioning devices (total field samples 100, overall accuracy 91%, kappa coefficient = 0.88). Besides, by sampling and measurement in the field, two seagrass species, Thalassia hemprichii and Halophila ovalis were identified with the same structure, density, and biomass in the northern and southern parts of the island. The application of high-resolution remote sensing images combined with field surveys contributes to improving the accuracy of seagrass distribution maps as well as benthic biome ones for shallow sea areas. Establishing a seagrass distribution map and assessing the diversity of seagrass is the basis for the conservation and development of the current seagrass community at Nam Yet Island.

关键词： Benthic habitat Green-blue normalized difference vegetation index Pleiades object-based image analysis Thalassia hemprichii Halophila ovalis

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Fate of pioneering vegetation patches in a dynamic meandering river

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EARTH SURFACE PROCESSES AND LANDFORMS 2019年第8期44卷 1618-1632页

作者： Kleinhans, Maarten Douma, Harke Addink, Elisabeth A. Univ Utrecht Fac Geosci POB 80115 NL-3508 TC Utrecht Netherlands

Fluvial biomorphodynamics in actively meandering rivers entail interactions between hydromorphodynamics and pioneering tree species that have eco-engineering effects. Here we study spatiotemporal patterns of vegetation patches smaller than 150 m(2) in a 4 km reach of the river Allier in France in order to unravel causes for tree persistence and mortality and identify spatial trends across the river valley. To this end we analysed aerial photographs by object-based image analysis over a period of 56 years and tracked individual patches through time. Furthermore the cover and surface age of the study reach were classified. The large-scale shifts of channels, bars and vegetation are consistent with the meandering process and chute cutoffs. However, the spatiotemporal patterns of the vegetation patches are surprising in that they are ubiquitous and have ages up to decades on the highly dynamic meander belt, but hardly expand into larger vegetation patches. Patches disappear exponentially as a function of their age, and faster so in the last decades. Causes are amalgamation into the riparian forest flanking the meander belt and mortality likely due to desiccation or erosion. Patches have a higher probability of survival when further away from the active channel and closer to high vegetation patches and valley boundary. The window of opportunity of vegetation settlement widens towards the valley boundaries and in floodplain lows of former channels and chutes. These results imply a gradual cross-valley gradient of riparian vegetation settling, survival and succession. (c) 2019 The Authors. Earth Surface Processes and Landforms Published by John Wiley & Sons Ltd.

关键词： meandering river pioneer vegetation object-based image analysis

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Discriminating crops/weeds in an upland rice field from UAV images with the SLIC-RF algorithm

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PLANT PRODUCTION SCIENCE 2021年第2期24卷 198-215页

作者： Kawamura, Kensuke Asai, Hidetoshi Yasuda, Taisuke Soisouvanh, Pheunphit Phongchanmixay, Sengthong Japan Int Res Ctr Agr Sci JIRCAS Tsukuba Ibaraki Japan Yamanashi Prefectural Govt Mt Fuji Res Inst Fujiyoshida Yamanashi Japan Natl Agr & Forestry Res Inst NAFRI Rice Res Ctr Viangchan Laos

In this study, we propose a method for discriminating crops/weeds in upland rice fields using a commercial unmanned aerial vehicles (UAVs) and red-green-blue (RGB) cameras with the simple linear iterative clustering (SLIC) algorithm and random forest (RF) classifier. In the SLIC-RF algorithm, we evaluated different combinations of input features: three color spaces (RGB, hue-saturation-brightness [HSV], CIE-L*a*b), canopy height model (CHM), spatial texture (Texture) and four vegetation indices (VIs) (excess green [ExG], excess red [ExR], green-red vegetation index [GRVI] and color index of vegetation extraction [CIVE]). Among the color spaces, the HSV-based SLIC-RF model showed the best performance with the highest out-of-bag (OOB) accuracy (0.904). The classification accuracy was improved by the combination of HSV with CHM, Texture, ExG, or CIVE. The highest OOB accuracy (0.915) was obtained from the HSV+Texture combination. The greatest errors from the confusion matrix occurred in the classification between crops and weeds, while soil could be classified with a very high accuracy. These results suggest that with the SLIC-RF algorithm developed in this study, rice and weeds can be discriminated by consumer-grade UAV images with acceptable accuracy to meet the needs of site-specific weed management (SSWM) even in the early growth stages of small rice plants..

关键词： Laos machine learning object-based image analysis weed management

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Comparing ultra-high spatial resolution remote-sensing methods in mapping peatland vegetation

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JOURNAL OF VEGETATION SCIENCE 2019年第5期30卷 1016-1026页

作者： Rasanen, Aleksi Juutinen, Sari Tuittila, Eeva-Stiina Aurela, Mika Virtanen, Tarmo Univ Helsinki Fac Biol & Environm Sci Ecosyst & Environm Res Programme Helsinki Finland Univ Helsinki Helsinki Inst Sustainabil Sci HELSUS Helsinki Finland Norwegian Univ Sci & Technol Dept Geog Trondheim Norway Univ Eastern Finland Sch Forest Sci Joensuu Finland Finnish Meteorol Inst Helsinki Finland

Questions How to map floristic variation in a patterned fen in an ecologically meaningfully way? Can plant communities be delineated with species data generalized into plant functional types? What are the benefits and drawbacks of the two selected remote-sensing approaches in mapping vegetation patterns, namely: (a) regression models of floristically defined fuzzy plant community clusters and (b) classification of predefined habitat types that combine vegetation and land cover information? Location Treeless 0.4 km(2) mesotrophic string-flark fen in Kaamanen, northern Finland. Methods We delineated plant community clusters with fuzzy c-means clustering based on two different inventories of plant species and functional type distribution. We used multiple optical remote-sensing data sets, digital elevation models and vegetation height models derived from drone, aerial and satellite platforms from ultra-high to very high spatial resolution (0.05-3 m) in an object-based approach. We mapped spatial patterns for fuzzy and crisp plant community clusters using boosted regression trees, and fuzzy and crisp habitat types using supervised random forest classification. Results Clusters delineated with species-specific data or plant functional type data produced comparable results. However, species-specific data for graminoids and mosses improved the accuracy of clustering in the case of flarks and string margins. Mapping accuracy was higher for habitat types (overall accuracy 0.72) than for fuzzy plant community clusters (R-2 values between 0.27 and 0.67). Conclusions For ecologically meaningful mapping of a patterned fen vegetation, plant functional types provide enough information. However, if the aim is to capture floristic variation in vegetation as realistically as possible, species-specific data should be used. Maps of plant community clusters and habitat types complement each other. While fuzzy plant communities appear to be floristically most accurate, crisp habitat type

关键词： digital elevation model drone floristic analysis fuzzy northern boreal object-based image analysis plant community plant functional types unmanned aerial system (UAS) unmanned aerial vehicle (UAV) very-high spatial resolution satellite imagery

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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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Urban expansion based on remote sensing extraction of impervious surface information

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INTERNATIONAL JOURNAL OF HYDROLOGY SCIENCE AND TECHNOLOGY 2024年第1期18卷 106-123页

作者： Kang, Weifeng Chen, Dayong Quanzhou Normal Univ Sch Resources & Environm Sci Quanzhou 362000 Fujian Peoples R China

Impervious surface is an important evaluation factor for the study of urban regional expansion and its social and environmental effects using remote sensing technology and the city has expanded rapidly, which is obviously reflected in the great changes in the impervious surface. 3S technology was relied on to study the classification method of urban impervious surfaces in this area and to explore the dynamic change law of urban impervious surfaces, and its driving force has the dual value of scientific research and social economy. This paper explores the computer automatic extraction method of impervious surface in my country in the past 20 years by using the time series Landsat TM images and object-based image analysis and processing technology and laws to explore the changes and internal causes of urban expansion. Moreover, this method can be utilised for hydrological modelling, flood mapping and monitoring, water quality monitoring, and urban heat island analysis.

关键词： impervious surface improved normalised difference impervious surface index object-based image analysis OBIA image segmentation

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