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Cultivated land information extraction from high-resolution unmanned aerial vehicle imagery data

JOURNAL OF APPLIED REMOTE SENSING 2014年第1期8卷 083673-083673页

作者： Ma, Lei Cheng, Liang Han, Wenquan Zhong, Lishan Li, Manchun Nanjing Univ Jiangsu Prov Key Lab Geog Informat Sci & Technol Nanjing 210097 Jiangsu Peoples R China Nanjing Univ Collaborat Innovat Ctr South China Sea Studies Nanjing 210097 Jiangsu Peoples R China Mapping & Geotechn Invest Co Ltd Nanjing Inst Surveying Nanjing 210019 Jiangsu Peoples R China

The development of precision agriculture demands high accuracy and efficiency of cultivated land information extraction. Simultaneously, unmanned aerial vehicles (UAVs) have been increasingly used for natural resource applications in recent years as a result of their greater availability, the miniaturization of sensors, and the ability to deploy UAVs relatively quickly and repeatedly at low altitudes. We examine the potential of utilizing a small UAV for the characterization, assessment, and monitoring of cultivated land. Because most UAV images lack spectral information, we propose a novel cultivated land information extraction method based on a triangulation for cultivated land information extraction (TCLE) method. Thus, the information on more spatial properties of a region is incorporated into the classification process. The TCLE comprises three main steps: image segmentation, triangulation construction, and triangulation clustering using AUTOCLUST. Experiments were conducted on three UAV images in Deyang, China, using TCLE and eCognition for cultivated land information extraction (ECLE). Experimental results show that TCLE, which does not require training samples and has a much higher level of automation, can obtain accuracies equivalent to ECLE. Comparing with ECLE, TCLE also extracts coherent cultivated land with much less noise. As such, cultivated land information extraction based on high-resolution UAV images can be effectively and efficiently conducted using the proposed method. (C) 2014 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： unmanned aerial vehicle AUTOCLUST cultivated land object-based classification Voronoi diagram

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From Chinese tourists to Taiwanese campers: Impacts of tourism policies on campsite land use/cover change

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2022年 310卷 114749页

作者： Chen, Ming-Wei Tu, Hung-Ming Tung, Chien-Hung Natl Chung Hsing Univ Program Landscape & Recreat 145 Singda Rd Taichung 402202 Taiwan Natl Chung Hsing Univ Dept Hort 145 Singda Rd Taichung 402202 Taiwan

The rapid increase in camping activities and campsites has had negative environmental impacts in mountainous areas. Tourism policies may be an important factor in changing recreational behavior and increasing campsites. The purpose of this study was to explore the effects of tourism policies on campsite-related landscape changes in Taiwan. The study area consisted of 276 campsites in the Jianshi and Wufeng Townships in Hsinchu County. The tourism policy periods were divided into 2001-2007 (Taiwan's agri-tourism policy), 2008-2015 (China and Taiwan's travel permit policy), and 2016-2019 (China's travel restriction policy), based on a reference review and relative theories. The 2000, 2008, 2016, and 2019 campsite landscapes were classified into forestland and non-forestland through object-based classification. This study established a general linear model to analyze the effect of tourism policy period on campsite forestland and non-forestland landscape change, according to the 50, 100, 250, 500, and 1000 m radii of 276 campsites. The results showed that tourism policies had a significant effect on campsite forestland and non-forestland landscape changes. The effect sizes ranged from medium to large. The Chinese tourist travel permit policy was significantly associated with increased non-forestland in campsites from 2008 to 2016. This policy likely affected recreational behavior indirectly, promoting camping and increasing non-forestland through the crowding-out effects of the many Chinese tourists, which was not the original purpose of the policy. Tourism policy decision-makers should consider the potential negative landscape change effects of changes in recreational behavior, and provide supporting measures to maintain recreational quality and avoid crowding-out effects. Campsite development should also be regulated to prevent forestland changes and achieve sustainable management.

关键词： Travel permit Travel restriction Crowd-out effect Product shift theory Lipstick effect theory object-based classification

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classification of Very-High-Spatial-Resolution Aerial Images based on Multiscale Features with Limited Semantic Information

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REMOTE SENSING 2021年第3期13卷 364-364页

作者： Gao, Han Guo, Jinhui Guo, Peng Chen, Xiuwan Peking Univ Inst Remote Sensing & Geog Informat Syst Beijing 100871 Peoples R China

Recently, deep learning has become the most innovative trend for a variety of high-spatial-resolution remote sensing imaging applications. However, large-scale land cover classification via traditional convolutional neural networks (CNNs) with sliding windows is computationally expensive and produces coarse results. Additionally, although such supervised learning approaches have performed well, collecting and annotating datasets for every task are extremely laborious, especially for those fully supervised cases where the pixel-level ground-truth labels are dense. In this work, we propose a new object-oriented deep learning framework that leverages residual networks with different depths to learn adjacent feature representations by embedding a multibranch architecture in the deep learning pipeline. The idea is to exploit limited training data at different neighboring scales to make a tradeoff between weak semantics and strong feature representations for operational land cover mapping tasks. We draw from established geographic object-based image analysis (GEOBIA) as an auxiliary module to reduce the computational burden of spatial reasoning and optimize the classification boundaries. We evaluated the proposed approach on two subdecimeter-resolution datasets involving both urban and rural landscapes. It presented better classification accuracy (88.9%) compared to traditional object-based deep learning methods and achieves an excellent inference time (11.3 s/ha).

关键词： deep learning aerial imagery convolutional neural network object-based classification

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Bathymetry Derivatives and Habitat Data from Hyperspectral Imagery Establish a High-Resolution Baseline for Managing the Ningaloo Reef, Western Australia

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REMOTE SENSING 2022年第8期14卷 1827页

作者： Kobryn, Halina T. Beckley, Lynnath E. Wouters, Kristin Murdoch Univ Environm & Conservat Sci 90 South St Murdoch WA 6150 Australia

The Ningaloo Reef, Australia's longest fringing reef, is uniquely positioned in the NW region of the continent, with clear, oligotrophic waters, relatively low human impacts, and a high level of protection through the World Heritage Site and its marine park status. Non-invasive optical sensors, which seamlessly derive bathymetry and bottom reflectance, are ideally suited for mapping and monitoring shallow reefs such as Ningaloo. Using an existing airborne hyperspectral survey, we developed a new, geomorphic layer for the reef for depths down to 20 m, through an object-oriented classification that combines topography and benthic cover. We demonstrate the classification approach using three focus areas in the northern region of the Muiron Islands, the central part around Point Maud, and Gnaraloo Bay in the south. Topographic mapping combined aspect, slope, and depth into 18 classes and, unsurprisingly, allocated much of the area into shallow, flat lagoons, and highlighted narrow, deeper channels that facilitate water circulation. There were five distinct geomorphic classes of coral-algal mosaics in different topographic settings. Our classifications provide a useful baseline for stratifying ecological field surveys, designing monitoring programmes, and assessing reef resilience from current and future threats.

关键词： marine habitats coral reefs Ningaloo Marine Park object-based classification hyperspectral geomorphic classification topographic derivatives

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OBIA based hierarchical image classification for industrial lake water

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SCIENCE OF THE TOTAL ENVIRONMENT 2014年第1期487卷 565-573页

作者： Avci, Z. D. Uca Karaman, M. Ozelkan, E. Kumral, M. Budakoglu, M. Istanbul Tech Univ Dept Astronaut Engn Fac Aeronaut & Astronaut TR-80626 Istanbul Turkey Istanbul Tech Univ Geochem Res Grp TR-80626 Istanbul Turkey Istanbul Tech Univ JAL Labs Dept Geol Engn Fac Mines TR-80626 Istanbul Turkey Istanbul Tech Univ Agr & Environm Informat Res Ctr TR-80626 Istanbul Turkey

Water management is very important in water mining regions for the sustainability of the natural environment and for industrial activities. This study focused on Acigol Lake, which is an important wetland for sodium sulphate (Na2SO4) production, a significant natural protection area and habitat for local bird species and endemic species of this saline environment, and a stopover for migrating flamingos. By a hierarchical classification method, ponds representing the industrial part were classified according to in-situ measured Baume values, and lake water representing the natural part was classified according to in-situ measurements of water depth. The latter is directly related to the water level, which should not exceed a critical level determined by the regulatory authorities. The resulting data, produced at an accuracy of around 80%, illustrates the status in two main regions for a single date. The output of the analysis may be meaningful for firms and environmental researchers, and authorizations can provide a good perspective for decision making for sustainable resource management in the region which has uncommon and specific ecological characteristics. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Industrial lake Baume Hierarchical classification object-based classification ASTER

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object-based Land Cover Change Detection and Landscape Structure Analysis of Demilitarized Zone in Korea

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SENSORS AND MATERIALS 2019年第11期31卷 3733-3748页

作者： Park, Jeongmook Sim, Woodam Park, Jinwoo Lee, Jungsoo Kangwon Natl Univ Dept Forest Management Chunchon 24341 South Korea

The demilitarized zone (DMZ) in Korea, a 4-km-wide military-free zone at the border between South Korea and North Korea, has been well preserved for half a century and has a high ecological value. However, temporal and spatial data are lacking owing to poor accessibility. In this study, we detected object-based land cover changes using Landsat-5 TM, Landsat-7 ETM, and Landsat-8 OSI, and identified changes around the DMZ through landscape structure analysis. The land cover was classified into six categories, namely, settlements, cropland, wetlands, forest land, semiforest land, and bare land by object-based classification. The landscape structure was analyzed using five landscape metrics [i.e., number of patches (NumP), mean patch size (MPS), total edge (TE), edge density (ED), and area-weighted mean shape index (AWMSI)]. Results showed that, from 2000 to 2009, the forest land and bare land areas decreased, while the semiforest land area increased. In 2009-2019, the bare land area increased, while the forest land area decreased. Consequently, the restoration of wildfire-damaged areas decreased the fragmentation of Goseong-gun in 2000-2009 and wildfire caused more forest damage in 2009-2019.

关键词： demilitarized zone (DMZ) landsat image object-based classification land cover change landscape structure analysis

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Image Segmentation Parameter Selection and Ant Colony Optimization for Date Palm Tree Detection and Mapping from Very-High-Spatial-Resolution Aerial Imagery

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REMOTE SENSING 2018年第9期10卷 1413-1413页

作者： Al-Ruzouq, Rami Shanableh, Abdallah Gibril, Mohamed Barakat A. AL-Mansoori, Saeed Univ Sharjah Dept Civil & Environm Engn Sharjah 27272 U Arab Emirates Univ Sharjah Res Inst Sci & Engn Sharjah 27272 U Arab Emirates MBRSC Applicat Dev & Anal Sect Dubai 211833 U Arab Emirates

Accurate mapping of date palm trees is essential for their sustainable management, yield estimation, and environmental studies. In this study, we integrated geographic object-based image analysis, class-specific accuracy measures, fractional factorial design, metaheuristic feature-selection technique, and rule-based classification to detect and map date palm trees from very-high-spatial-resolution (VHSR) aerial images of two study areas. First, multiresolution segmentation was optimized through the synergy of the Fl-score accuracy measure and the robust Taguchi design. Second, ant colony optimization (ACO) was adopted to select the most significant features. Out of 31 features, only 12 significant color invariants and textural features were selected. Third, based on the selected features, the rule-based classification with the aid of a decision tree algorithm was applied to extract date palm trees. The proposed methodology was developed on a subset of the first study area, and ultimately applied to the second study area to investigate its efficiency and transferability. To evaluate the proposed classification scheme, various supervised object-based algorithms, namely random forest (RF), support vector machine (SVM), and k-nearest neighbor (k-NN), were applied to the first study area. The result of image segmentation optimization demonstrated that segmentation optimization based on an integrated Fl-score class-specific accuracy measure and Taguchi statistical design showed improvement compared with objective function, along with the Taguchi design. Moreover, the result of the feature selection by ACO outperformed, with almost 88% overall accuracy, several feature-selection techniques, such as chi-square, correlation-based feature selection, gain ratio, information gain, support vector machine, and principal component analysis. The integrated framework for palm tree detection outperformed RF, SVM, and k-NN classification algorithms with an overall accuracy of 91.88% a

关键词： object-based classification very-high-resolution aerial imagery image segmentation optimization feature selection ant colony optimization date palm tree

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Improvement of Low Level Bark Beetle Damage Estimates with Adaptive Cluster Sampling

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SILVA FENNICA 2010年第2期44卷 289-301页

作者： Coggins, Sam B. Coops, Nicholas C. Wulder, Michael A. Univ British Columbia Dept Forest Resources Management Vancouver BC V6T 1Z4 Canada Nat Resources Canada Canadian Forestry Serv Pacific Forestry Ctr Victoria BC V8Z 1M5 Canada

Detection of low level infestation in forest stands is of principle importance to determine effective control strategies before the attack spread to large areas. Of particular concern is the ongoing mountain pine beetle, Dendroctonus panderosae (Hopkins) epidemic, which has caused approximately 14 million hectares of damage to lodgepole pine (Pious comma Dougl. ex. Loud var. latifolia Engl.) forests in western Canada. At the stand level attacked trees are often difficult to locate and can remain undetected until the infestation has become established beyond a small number of trees. As such, methods are required to detect and characterise low levels of attack prior to infestation expansion, to inform management, and to aid mitigation activities. In this paper, an adaptive cluster sampling approach was applied to very fine-scale (20 cm) digital aerial imagery to locate mountain pine beetle damaged trees at the leading edge of the current infestation. Results indicated a mean number of 7.36 infested trees per hectare with a variance of 18.34. In contrast a non-adaptive approach estimated the mean number of infested trees in the same area to be 61.56 infested trees per hectare with a variance of 41.43. Using a relative efficiency estimator the adaptive cluster sampling approach was found to be over two times more efficient when compared to the non-adaptive approach.

关键词： object-based classification high spatial resolution satellite digital aerial imagery mountain pine beetle forest inventory adaptive cluster sampling

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Towards Sustainable Land Uses within the Elbe River Biosphere Reserve in Lower Saxony, Germany by Means of TerraSAR-X Images

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Journal of Geoscience and Environment Protection 2016年第3期4卷 97-121页

作者： Dalia Farghaly Emad Elba Brigitte Urban Faculty of Sustainability Sciences Institute of Ecology Subject Area Landscape Change LEUPHANA University of Lüneburg Lüneburg Germany

Floods are one of the major hazards worldwide. They are the source of huge risks in rural and urban areas, resulting in severe impacts on the civil society, industry and the economy. The Elbe River has suffered from many severe floods during recent decades. In this study, the zones flooded during 2011 were analyzed using TerraSAR-X images and a digital elevation model for the area in order to identify possible ways to mitigate flood hazards in the future, regarding sustainable land-use. Two study areas are investigated, around the Walmsburg oxbow and the Wehningen oxbow. These are located between Elbe-Kilometer (505-520) and (533-543), respectively, within the Lower Saxonian Elbe River Biosphere Reserve. Those areas are characterized by several types of land use, with agricultural land use being predominant. The study investigated the possibility of using a Decision-Tree object-based classifier for determining the major land uses and the extent of the inundation areas. The inundation areas identify for 2011 submerged some agricultural fields that must be added to existing flood risk maps, and future cultivation activities there prevented to avoid the possible economic losses. Furthermore, part of the residential area is located within the high flood zone, and must be included in risk maps to avoid the possible human and economic losses, to achieve sustainable land use for the areas studied.

关键词： Elbe River Floods Land Use SAR TerraSAR-X Decision-Tree object-based classification Risk Maps

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Use of unmanned aircraft systems (UAS) and multispectral imagery for quantifying agricultural areas damaged by wild pigs

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CROP PROTECTION 2019年 125卷 104865-000页

作者： Fischer, Justin W. Greiner, Kelsey Lutman, Mark W. Webber, Bryson L. Vercauteren, Kurt C. Anim & Plant Hlth Inspect Serv USDA Wildlife Serv Natl Wildlife Res Ctr 4101 LaPorte Ave Ft Collins CO 80521 USA Anim & Plant Hlth Inspect Serv USDA Wildlife Serv Natl Feral Swine Damage Management Program 4101 LaPorte Ave Ft Collins CO 80521 USA Anim & Plant Hlth Inspect Serv USDA 4700 River Rd Unit 87 Riverdale MD 20737 USA

Wild pigs (Sus scrofa) cause extensive damage to agricultural crops, resulting in lost production and income. A major challenge associated with assessing damage to crops is locating and quantifying damaged areas within agricultural fields. We evaluated a novel method using multispectral high-resolution aerial imagery, collected from sensors mounted on unmanned aircraft systems (UAS), and feature extraction techniques to detect and map areas of corn fields damaged by wild pigs in southern Missouri, USA. Damaged areas were extracted from orthomosaics using visible and near-infrared band combinations, an object-based classification approach, and hierarchical learning cycles. To validate estimates we also collected ground reference data immediately following flights. Overall accuracy of damage estimates to corn fields were similar among band combinations evaluated, ranging from 74% to 98% when using visible and near-infrared information, compared to 72%-94% with visible information alone. By including near-infrared with visible information, though, we found higher average kappa values (0.76) than with visible information (0.60) alone. We demonstrated that UAS are an appropriate platform for collecting high-resolution multispectral imagery of corn fields and that object-oriented classifiers can be effectively used to delineate areas damaged by wild pigs. The proposed approach outlines a new monitoring technique that can efficiently estimate damage to entire corn fields caused by wild pigs and also has potential to be applied to other crop types.

关键词： Crop damage Damage assessment Drone Feral swine object-based classification Sus scrofa

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