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Integration of LiDAR and QuickBird imagery for mapping riparian biophysical parameters and land cover types in Australian tropical savannas

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FOREST ECOLOGY AND MANAGEMENT 2010年第3期259卷 598-606页

作者： Arroyo, Lara A. Johansen, Kasper Armston, John Phinn, Stuart Univ Queensland Ctr Remote Sensing & Spatial Informat Sci Sch Geog Planning Environm Management Brisbane Qld 4072 Australia Ctr Remote Sensing Queensland Dept Environm & Resource Management Indooroopilly Qld 4068 Australia

Riparian zones are exposed to increasing pressures because of disturbance from agricultural and urban expansion and overgrazing. Accurate and cost-effective mapping of riparian environments is important for baseline inventories and monitoring and managing their functions associated with water quality, biodiversity, and wildlife habitats. In this study, we integrate remotely sensed light detection and ranging (LiDAR) data and high spatial resolution satellite imagery (QuickBird-2) to estimate riparian biophysical parameters and land cover types in the Fitzroy catchment in Queensland, Australia. An object based image analysis (OBIA) was adopted for the study. A digital terrain model (DTM), a tree canopy model (TCM) and a plant projective cover (PPC) map were first derived from the LiDAR data. A map of the streambed was then produced using the DTM information. Finally, all the LiDAR-derived biophysical map products and the QuickBird image bands were combined in an OBIA to (1) map the following land cover types: riparian vegetation, streambed, bare ground, woodlands and rangelands: (2) determine the distribution of overhang vegetation within the streambed;and (3) measure the width of both the riparian zone and the streambed. The combined use of both datasets allowed accurate land cover mapping, with an overall accuracy of 85.6%. The estimated widths of the riparian zone and the streambed showed strong correlation with the actual field measurements (r = 0.82 and 0.98 respectively). Our results show that the combined use of LiDAR and high spatial resolution imagery can potentially be used for the assessment of the riparian condition in a tropical savanna woodland riparian environment. This work also shows the capacity of OBIA to assist in the assessment of the composition of the riparian environment from multiple image datasets. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Riparian zone Riparian zone and streambed widths Riparian land cover object based image analysis LiDAR QuickBird Tropical savanna Australia

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Selecting patterns and features for between- and within-crop-row weed mapping using UAV-imagery

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EXPERT SYSTEMS WITH APPLICATIONS 2016年 47卷 85-94页

作者： Perez-Ortiz, Maria Manuel Pena, Jose Antonio Gutierrez, Pedro Torres-Sanchez, Jorge Hervas-Martinez, Cesar Lopez-Granados, Francisca CSIC Inst Sustainable Agr POB 4084 Cordoba 14080 Spain Univ Cordoba Dept Comp Sci & Numer Anal Rabanales CampusC2 Bldg E-14071 Cordoba Spain Univ Loyola Andalucia Dept Math & Engn Third Bldg Cordoba 14004 Spain

This paper approaches the problem of weed mapping for precision agriculture, using imagery provided by Unmanned Aerial Vehicles (UAVs) from sunflower and maize crops. Precision agriculture referred to weed control is mainly based on the design of early post -emergence site-specific control treatments according to weed coverage, where one of the most important challenges is the spectral similarity of crop and weed pixels in early growth stages. Our work tackles this problem in the context of object-based image analysis (OBIA) by means of supervised machine learning methods combined with pattern and feature selection techniques, devising a strategy for alleviating the user intervention in the system while not compromising the accuracy. This work firstly proposes a method for choosing a set of training patterns via clustering techniques so as to consider a representative set of the whole field data spectrum for the classification method. Furthermore, a feature selection method is used to obtain the best discriminating features from a set of several statistics and measures of different nature. Results from this research show that the proposed method for pattern selection is suitable and leads to the construction of robust sets of data. The exploitation of different statistical, spatial and texture metrics represents a new avenue with huge potential for between and within crop-row weed mapping via UAV-imagery and shows good synergy when complemented with OBIA. Finally, there are some measures (specially those linked to vegetation indexes) that are of great influence for weed mapping in both sunflower and maize crops. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Remote sensing Unmanned aerial vehicles (UAV) Weed detection object based image analysis

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Heuristic sample learning for complex urban scenes: Application to urban functional-zone mapping with VHR images and POI data

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2020年第0期161卷 1-12页

作者： Zhang, Xiuyuan Du, Shihong Zheng, Zhijia Peking Univ Inst Remote Sensing & GIS 5 Yiheyuan Rd Beijing 100871 Peoples R China McGill Univ Dept Nat Resource Sci 111 Lakeshore Rd Sainte Anne De Bellevue PQ H9X 3V9 Canada

Urban functional zones are basic units of urban planning and resource allocation, and contribute to a wide range of urban studies and investigations. Existing studies on functional-zone mapping with very-high-resolution (VHR) satellite images focused much on feature representations and classification techniques, but ignored zone sampling which however was fundamental to automatic zone classifications. Functional-zone sampling is much complicated and can hardly be resolved by classical sampling methods, as functional zones are complex urban scenes which consist of heterogeneous land covers and have highly abstract categories. To resolve the issue, this study presents a novel sampling paradigm, i.e., heuristic sample learning (HSL). It first proposes a sparse topic model to select representative functional zones, then uses deep forest to select confusing zones, and finally embraces Chinese restaurant process to label these selected zones. The presented method collects both representative and confusing zone samples and identifies their categories accurately, which makes the functionalzone classification process robust and the classification results accurate. Experiments conducted in Beijing indicate that HSL is effective and efficient for functional-zone sampling and classifications. Compared to traditional manual sampling, HSL reduces the time cost by 55% and improves the classification accuracy by 11.3% on average;furthermore, HSL can reduce the variation in sampling and classification results caused by different proficiency of operators. Accordingly, HSL significantly contributes to functional-zone mapping and plays an important role in urban studies.

关键词： Urban functional zones image classification Sample selection Heuristic sample learning object based image analysis

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Using orthoimages generated from oblique terrestrial photography to estimate and monitor vegetation cover

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ECOLOGICAL INDICATORS 2019年 101卷 91-101页

作者： Wallace, Luke Saldias, Daisy S. Reinke, Karin Hillman, Samuel Hally, Bryan Jones, Simon RMIT Univ Sch Sci Melbourne Vic Australia Bushfire & Nat Hazards Cooperat Res Ctr Melbourne Vic Australia

Percent vegetation cover is important variable used in understanding ecosystem processes, vegetation health and productivity. Downward looking images captured using a handheld camera have been demonstrated as a viable option for rapidly capturing in situ information to assess vegetation cover. This technique, however, is prone to perspective distortions biasing cover estimates towards taller vegetation elements. In this paper we present a new approach to generate imagery for use in vegetation cover estimation utilising multiple overlapping photographs and structure from motion algorithms to produce a 3D point cloud representation of the target plot. This point cloud is then converted into an orthoimage consisting of four bands-red, green, blue and vegetation height-which is free from perspective distortions. The approach is trialled in two Eucalypt forests in South Eastern Australia to produce an estimate of change in cover of all vegetation elements following a prescribed burn. Orthoimages are generated with 2.5 mm resolution and classified using object-based image analysis and random forests into broad vegetation and fuel classes. Utilising this approach an overall classification accuracy of 81% is achieved with the resulting estimates of cover agreeing with visual point based interpretation to within 6% across all classes.

关键词： Percent vegetation cover Orthoimage Structure from motion object based image analysis Prescribed burning

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Cascaded classification of high resolution remote sensing images using multiple contexts

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INFORMATION SCIENCES 2013年 221卷 84-97页

作者： Guo, Jun Zhou, Hui Zhu, Changren Natl Univ Def Technol Inst Elect Sci & Engn ATR Natl Lab Changsha Hunan Peoples R China Beijing Inst Tracking & Telecommun Technol Beijing Peoples R China

We present a novel cascaded classification approach by exploiting various contexts on different levels for high resolution remote sensing (HRRS) images. The contexts mentioned in our article are defined according to objects from a set of regions resulting from segmentation. The cascaded procedure comprises three stages: (1) initializing the classification using the object's inner context (i.e., the gray constraints of different pixels in an object), (2) correcting the classification using the object's neighbor context (i.e., the characteristic constraints of different objects adjacent to the concerned object), and (3) refining classification using the object's scene context (i.e., the distribution constraint of different objects' labels and their feature vectors in the whole scene). The proposed algorithm has the following distinctions. First, it uses an object's neighbor context to bridge the gap between its inner context and its scene context because the latter two types of contexts have inevitable drawbacks when being used for classification alone. Second, it carries on a cascaded classification procedure in which the previous stage provides a better initial classification for the following stage, and the result is gradually refined by integrating different contexts. The effectiveness and practicability of the proposed algorithm is demonstrated through a set of completely experimental results and substantiated using quantitative criteria. (C) 2012 Elsevier Inc. All rights reserved.

关键词： Context-enabled classification object based image analysis Multiple contexts integration High resolution remote sensing images

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Assessing the potential applications of Landsat image archive in the ecological monitoring and management of a production mangrove forest in Malaysia

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WETLANDS ECOLOGY AND MANAGEMENT 2015年第6期23卷 1049-1066页

作者： Aziz, Ammar Abdul Phinn, Stuart Dargusch, Paul Omar, Hamdan Arjasakusuma, Sanjiwana Univ Queensland Sch Geog Planning & Environm Management Brisbane Qld 4072 Australia Forest Res Inst Malaysia Kepong 52109 Selangor Darul Malaysia

This paper explores the suitability of remotely sensed data obtained from Landsat imaging sensors, in combination with ancillary forest inventory data, for the ecological monitoring and management of a production mangrove forest in Malaysia. An assessment is presented on the capabilities and limitations of utilizing Landsat Thematic Mapper data for mapping mangrove species and stand age structure in the Matang Mangrove Forest Reserve. The cloud-free subset satellite images used in this study were acquired in May 1988, February 1995, December 1999, March 2001, December 2009 and November 2013. The December 1999 Landsat 7 ETM+ image was used for the error and accuracy assessment because it coincided with the year the reference map was produced. The image classification process included both pixel-based and object-based approaches conducted on a combination of software which included ENVI, ArcGIS 10.1 and eCognition. Results revealed that the pixel-based approach was not effective in delineating species and stand age structure due to the presence of the 'salt and pepper effect' in the image output. The object-based approach accurately delineated between commercial and non-commercial mangrove species. It also produced a reliable and accurate stand-age structure map based on three designated classes: clear-felled areas;young mangrove forest (less than 13 years old);and mature mangrove forest (13 years or older). The analysis highlights that the mapping and analysis of mangrove species and stand age structure with Landsat Thematic Mapper image datasets can play a useful role in achieving cost-efficient and ecologically sustainable outcomes in a production mangrove forest.

关键词： Landsat Species delineation Stand age delineation object based image analysis Matang mangrove forest reserve

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object-oriented mapping of urban trees using Random Forest classifiers

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2014年第1期26卷 235-245页

作者： Puissant, Anne Rougier, Simon Stumpf, Andre Univ Strasbourg CNRS UMR 7362 Lab Image Strasbourg France Univ Strasbourg CNRS UMR 7516 Inst Phys Globe Strasbourg Strasbourg France

Since vegetation in urban areas delivers crucial ecological services as a support to human well-being and to the urban population in general, its monitoring is a major issue for urban planners. Mapping and monitoring the changes in urban green spaces are important tasks because of their functions such as the management of air, climate and water quality, the reduction of noise, the protection of species and the development of recreational activities. In this context, the objective of this work is to propose a methodology to inventory and map the urban tree spaces from a mono-temporal very high resolution (VHR) optical image using a Random Forest classifier in combination with object-oriented approaches. The methodology is developed and its performance is evaluated on a dataset of the city of Strasbourg (France) for different categories of built-up areas. The results indicate a good accuracy and a high robustness for the classification of the green elements in terms of user and producer accuracies. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Urban trees VHR satellite images object based image analysis Feature selection, Random Forest classifier

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A comparison of three feature selection methods for object-based classification of sub-decimeter resolution UltraCam-L imagery

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2012年第1期15卷 70-78页

作者： Laliberte, A. S. Browning, D. M. Rango, A. New Mexico State Univ Las Cruces NM 88003 USA USDA Agr Res Serv Las Cruces NM 88003 USA

The availability of numerous spectral, spatial, and contextual features with object-based image analysis (OBIA) renders the selection of optimal features a time consuming and subjective process. While several feature selection methods have been used in conjunction with OBIA, a robust comparison of the utility and efficiency of approaches would facilitate broader and more effective implementation. In this study, we evaluated three feature selection methods, (1) Jeffreys-Matusita distance (JM), (2) classification tree analysis (CTA), and (3) feature space optimization (FSO) for object-based vegetation classifications with sub-decimeter digital aerial imagery in arid rangelands of the southwestern U.S. We assessed strengths, weaknesses, and best uses for each method using the criteria of ease of use, ability to rank and/or reduce input features, and classification accuracies. For the five sites tested. JM resulted in the highest overall classification accuracies for three sites, while CTA yielded highest accuracies for two sites. FSO resulted in the lowest accuracies. CTA offered ease of use and ability to rank and reduce features, while JM had the advantage of assessing class separation distances. FSO allowed for determining features relatively quickly, because it operates within the OBIA software used in this analysis (eCognition). However, the feature ranking in FSO is not transparent and accuracies were relatively low. While all methods offered an objective approach for determining suitable features for classifications of sub-decimeter resolution aerial imagery, we concluded that CTA was best suited for this particular application. We explore the limitations, assumptions, and appropriate uses for this and other datasets. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Feature selection object based image analysis Aerial imagery Very high resolution Classification

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Context-sensitive extraction of tree crown objects in urban areas using VHR satellite images

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2012年第1期15卷 57-69页

作者： Ardila, Juan P. Bijker, Wietske Tolpekin, Valentyn A. Stein, Alfred Univ Twente Fac Geoinformat Sci & Earth Observat ITC NL-7500 AA Enschede Netherlands

Municipalities need accurate and updated inventories of urban vegetation in order to manage green resources and estimate their return on investment in urban forestry activities. Earlier studies have shown that semi-automatic tree detection using remote sensing is a challenging task. This study aims to develop a reproducible geographic object-based image analysis (GEOBIA) methodology to locate and delineate tree crowns in urban areas using high resolution imagery. We propose a GEOBIA approach that considers the spectral, spatial and contextual characteristics of tree objects in the urban space. The study presents classification rules that exploit object features at multiple segmentation scales modifying the labeling and shape of image-objects. The GEOBIA methodology was implemented on QuickBird images acquired over the cities of Enschede and Delft (The Netherlands), resulting in an identification rate of 70% and 82% respectively. False negative errors concentrated on small trees and false positive errors in private gardens. The quality of crown boundaries was acceptable, with an overall delineation error <0.24 outside of gardens and backyards. (C) 2011 Elsevier B.V. All rights reserved.

关键词： GEOBIA object based image analysis Tree mapping Tree crown identification Context classification object classification

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Pocket parks in a compact city: how do birds respond to increasing residential density?

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LANDSCAPE ECOLOGY 2013年第1期28卷 45-56页

作者： Ikin, Karen Beaty, R. Matthew Lindenmayer, David B. Knight, Emma Fischer, Joern Manning, Adrian D. Australian Natl Univ Fenner Sch Environm & Soc Canberra ACT 0200 Australia CSIRO Ecosyst Sci Black Mt Labs Black Mt ACT 2601 Australia Australian Natl Univ Ctr Math & Its Applicat Canberra ACT 0200 Australia Univ Luneburg Dept Sustainabil Sci D-21335 Luneburg Germany

The desire to improve urban sustainability is motivating many city planners to adopt growth strategies that increase residential density, leading to substantial changes to urban landscapes. What effect this change will have on biodiversity remains unclear, but it is expected that the role of public greenspace in providing wildlife habitat will become critical. We explored the role of urban "pocket parks" as habitat for birds, and how this role changed with increasing residential density in the surrounding neighbourhood. We found that parks in neighbourhoods with high levels of public greenspace (corresponding to less residential land) supported more bird species and individuals overall, and more woodland-dependent species, insectivores and hollow-nesters. Total greenspace area was more important (included in the best ranked models for all bird responses) than the configuration (number, average size and connectivity) of greenspace patches. The majority of species were common suburban birds, indicating that species we assume are tolerant to urban areas will be negatively affected by increasing residential density. Parks form part of an interconnected network of urban open space. For parks to continue to support a diverse native bird community, the network must be viewed, managed, and maintained in its entirety. We suggest three key management actions to improve the bird diversity values of urban greenspaces in compact cities: (1) Increase urban greenspace cover in residential neighbourhoods. (2) Increase vegetation structure in greenspace. (3) Encourage homeowners to plant trees and shrubs.

关键词： Bird diversity object based image analysis Landscape composition Landscape configuration Planning and management Southeast Australia Spatial analysis Urban form Urban greenspace Urban sustainability

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