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Applying object-based approach to monitor urban expansion in Ha Long city, Vietnam, during 2000-2023 from multi-date Landsat satellite imagery

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EUROPEAN JOURNAL OF REMOTE SENSING 2024年第1期57卷

作者： Vu, Anh Tuan Ngo, Duc Anh Nguyen, Thi Phuong Hao Nguyen, Cong Giang Vietnam Acad Sci & Technol VAST Vietnam Natl Space Ctr VNSC Earth Observat Syst Dept Hanoi Vietnam Vietnam Acad Sci & Technol VAST Vietnam Natl Space Ctr VNSC Spatial Informat Syst & Modelling Dept Hanoi Vietnam Hanoi Architectural Univ Fac Civil Engn Hanoi Vietnam

Rapid urban expansion in Vietnam presents significant management and environmental challenges. This study quantifies urban growth in the Ha Long City area using a time series of six Landsat images acquired from 2000 to 2023. To address the heterogeneity of the urban class, an object-based (OB) image analysis approach was employed. Instead of relying on spectral channels, Principal component analysis (PCA) was utilized during segmentation, followed by classification using the Random Forest (RF) algorithm. For post-classification processing, a logical filter was applied to confirm the classification results at a detailed level, using additional information from the general classification results. Unconfirmed objects were subsequently verified through visual interpretation. The accuracy of the post-classified process results of this study ranges from 83.44% to 96.64% (producer accuracy) and from 83.48% to 91.27% (user accuracy). The results show that the urban area expanded more than four times, with the most significant growth occurring between 2000 and 2005, and between 2015 and 2020. Notably, land reclamation contributed significantly to urban growth. Understanding these trends is crucial for informed urban planning and environmental management in the region.

关键词： Urban expansion Ha Long City Landsat image object-based classification RF PCA

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A Tailings Pond Identification Method based on Spatial Combination of objects

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2019年第8期12卷 2707-2717页

作者： Liu, Kun Liu, Ronggao Liu, Yang Chinese Acad Sci State Key Lab Resources & Environm Informat Syst Inst Geog Sci & Nat Resources Res Beijing 100101 Peoples R China Univ Chinese Acad Sci Coll Resource & Environm Beijing 100049 Peoples R China Minist Nat Resources PR China Land Satellite Remote Sensing Applicat Ctr Beijing 100048 Peoples R China Minist Land & Resources Key Lab Airborne Geophys & Remote Sensing Geol Beijing 100083 Peoples R China

Tailings ponds pose a significant risk to the safety of the surrounding residents and the local ecological environment. Therefore, it is necessary to efficiently and accurately monitor tailings ponds. However, the internal structures of tailings ponds are heterogeneous, and they are typically identified through manual interpretation. In this paper, an identification method for four main structures of tailings ponds, namely, starter dams, embankments, deposited beach, and water body, is proposed based on the spatial combinations among them. First, hierarchy objects were established based on GaoFen-2 imagery. Then, candidate objects (such as embankment-like) were identified using the spectral features and the number of parallel lines. Subsequently, rural settlement-like objects were eliminated as interfering objects based on their shapes and distributions. Finally, four structures of tailings ponds could be identified based on their spatial combination. Six cases of tailings ponds were selected for validation. Interference categories were eliminated step by step (79.64%, 38.21%, and 39.75% for case A), and all four structures were identified. The overall identification accuracies were 88.14%-96.21%. The average accuracy is 32.02% higher than that of comparison experiments using the random forest. The method was proved to be applicable to the automatic identification of mining areas, which is of great significance for efficient and accurate supervision of mining safety.

关键词： Context information object-based classification spatial combination tailings pond

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Improving Impervious Surface Estimation by Using Remote Sensed Imagery Combined With Open Street Map Points-of-Interest (POI) Data

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2019年第11期12卷 4265-4274页

作者： Fan, Wei Wu, Changshan Wang, Jin Univ Wisconsin Milwaukee WI 53211 USA Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China

As a key indicator of urban environments, the accurate mapping of impervious surface is essential. With the availability of high spatial resolution remote sensing imagery, such as Sentinel-2, it is feasible to produce fine-scale impervious surface maps. For mapping high-resolution impervious surfaces, object-based image analysis (OBIA) classification method demonstrated its efficiency and accuracy by combining spectral information and spatial information. Although with some success, impervious surface estimation remains challenging because different land cover types share similar spectral information. With the emergence of affordable GPS-enabled devices (such as a smartphone) and web 2.0 development, more and more people are getting involved in sharing their locations with others or posting on the Internet. These volunteered geographical information (VGI) data provide a brand-new prospect for mapping urban impervious surfaces. This research proposes an optimized method for impervious surface mapping based on Sentinel-2 multispectral imagery and open street map (OSM) points-of-interest (POI) data. The proposed method was tested in Milwaukee county, US, and the results show that the overall accuracy of the proposed OBIA increases from 82.57 to 87.02 compared with the conventional OBIA. Thus, this study provides an effective means of combining OBIA and the relatively new VGI POIs data to extract impervious surface with higher spatial resolution.

关键词： Remote sensing Image segmentation Estimation Spatial resolution Feature extraction Shape Buildings Impervious surface object-based classification open street map (OSM) points-of-interest (POI) data Sentinel-2

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Mapping Rural Areas with Widespread Plastic Covered Vineyards Using True Color Aerial Data

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REMOTE SENSING 2012年第7期4卷 1913-1928页

作者： Tarantino, Eufemia Figorito, Benedetto Politecn Bari I-70125 Bari Italy ISSIA CNR I-70126 Bari Italy

Plastic covering is used worldwide to protect crops against damaging growing conditions. This agricultural practice raises some controversial issues. While it significantly impacts on local economic vitality, plasticulture also shows several environmental affects. In the Apulia Region (Italy) the wide-spreading of artificial plastic coverings for vineyard protection has showed negative consequences on the hydrogeological balance of soils as well as on the visual quality of rural landscape. In order to monitor and manage this phenomenon, a detailed site mapping has become essential. In this study an efficient object-based classification procedure from Very High Spatial Resolution (VHSR) true color aerial data was developed on eight test areas located in the Ionian area of the Apulia Region in order to support the updating of the existing land use database aimed at plastic covered vineyard monitoring.

关键词： plastic covered vineyard object-based classification true color aerial data

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Using object-oriented classification and high-resolution imagery to map fuel types in a Mediterranean region

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JOURNAL OF GEOPHYSICAL RESEARCH-BIOGEOSCIENCES 2006年第G4期111卷

作者： Arroyo, Lara A. Healey, Sean P. Cohen, Warren B. Cocero, David Manzanera, Jose A. IMIDRA E-28800 Alcala De Henares Spain Univ Nacl Educ Distancia Dept Geog E-28040 Madrid Spain US Forest Serv Pacific NW Res Stn Corvallis OR 97331 USA US Forest Serv Rocky Mt Res Stn Ogden UT 84401 USA Univ Politecn Madrid ETSI Montes E-28040 Madrid Spain

[1] Knowledge of fuel load and composition is critical in fighting, preventing, and understanding wildfires. Commonly, the generation of fuel maps from remotely sensed imagery has made use of medium-resolution sensors such as Landsat. This paper presents a methodology to generate fuel type maps from high spatial resolution satellite data through object-oriented classification. Fuel maps were derived from QuickBird imagery, which offers a panchromatic and four multispectral bands ranging from 0.61 to 2.44 m resolution. The image used for this paper dated from July 2002 and is located in the NW region of Madrid, Spain. The Prometheus system, a fuel type classification adapted to the ecological characteristics of the European Mediterranean basin, was adopted for this study. Viewed with high-resolution imagery, fuel-related features are often aggregations of pixels exhibiting a variety of spectral properties. Correct identification and classification of these objects requires an explicit consideration of spatial context. We used an object-oriented approach, which allowed context consideration during the classification process, as a complement to traditional pixel-based methods. The map created with this approach was assessed to have greater than 80% accuracy for the prediction of six fuel classes. Results suggested that object-oriented classification of high-resolution imagery has the potential to create accurate and spatially precise fuel maps.

关键词： fuel mapping object-based classification Prometheus system high-resolution data

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Automatic Extraction of Grasses and Individual Trees in Urban Areas based on Airborne Hyperspectral and LiDAR Data

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REMOTE SENSING 2020年第17期12卷 2725-2725页

作者： Man, Qixia Dong, Pinliang Yang, Xinming Wu, Quanyuan Han, Rongqing Shandong Normal Univ Coll Geog & Environm Jinan 250358 Peoples R China Univ North Texas Dept Geog & Environm Denton TX 76203 USA Jinan Environm Res Acad Dept Soil Jinan 250102 Peoples R China

Urban vegetation extraction is very important for urban biodiversity assessment and protection. However, due to the diversity of vegetation types and vertical structure, it is still challenging to extract vertical information of urban vegetation accurately with single remotely sensed data. Airborne light detection and ranging (LiDAR) can provide elevation information with high-precision, whereas hyperspectral data can provide abundant spectral information on ground objects. The complementary advantages of LiDAR and hyperspectral data could extract urban vegetation much more accurately. Therefore, a three-dimensional (3D) vegetation extraction workflow is proposed to extract urban grasses and trees at individual tree level in urban areas using airborne LiDAR and hyperspectral data. The specific steps are as follows: (1) airborne hyperspectral and LiDAR data were processed to extract spectral and elevation parameters, (2) random forest classification method and object-based classification method were used to extract the two-dimensional distribution map of urban vegetation, (3) individual tree segmentation was conducted on a canopy height model (CHM) and point cloud data separately to obtain three-dimensional characteristics of urban trees, and (4) the spatial distribution of urban vegetation and the individual tree delineation were assessed by validation samples and manual delineation results. The results showed that (1) both the random forest classification method and object-based classification method could extract urban vegetation accurately, with accuracies above 99%;(2) the watershed segmentation method based on the CHM could extract individual trees correctly, except for the small trees and the large tree groups;and (3) the individual tree segmentation based on point cloud data could delineate individual trees in three-dimensional space, which is much better than CHM segmentation as it can preserve the understory trees. All the results suggest that two- and thre

关键词： airborne LiDAR data hyperspectral data random forest classification object-based classification individual tree segmentation

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Analysis of forest tree dieback using UltraCam and UAV imagery

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SCANDINAVIAN JOURNAL OF FOREST RESEARCH 2023年第6期38卷 392-400页

作者： Naseri, Mohammad Hassan Shataee Jouibary, Shaban Habashi, Hashem Gorgan Univ Agr Sci & Nat Resources Dept Forestry Gorgan Golestan Iran Gorgan Univ Agr Sci & Nat Resources Dept Silviculture & Forest Ecol Gorgan Golestan Iran Gorgan Univ Agr Sci & Nat Resources Dept Forestry Gorgan 386 Golestan Iran

In recent years, increasing tree diebacks and mortality in some forests, particularly in forest parks, created a need amongst forest managers to find effective methods to gather information about the rate of dieback and mortality and their reasons. High-quality air and space-born remote sensing data has established as an alternative to field surveys for certain inventory tasks. This study used high-quality UltraCam-Xp and UAV drone images from 2016 and 2021 to map tree dieback and mortality in Daland Forest Park, Golestan Province, Iran. High-quality ortho mosaics and Digital Surface Models (DSMs) were generated from UltraCam (2016) and UAV (2021) images. The images were then classified through object-based classification by Nearest Neighbor (NN), Support Vector Machine (SVM), and Bayes algorithms using various input data sets including spectral bands, Canopy Height Model (CHM), vegetation indices, and texture analysis features. Our results indicate that the Bayes algorithm is more precise in mapping tree dieback for the two time steps compared to other algorithms. The best tree dieback map on UltraCam images was obtained using the spectral bands with CHM, texture analysis features, and vegetation indices. This combination resulted in an overall accuracy of 91.20% and a Kappa coefficient of 0.88. It was also found that combining the UAV main bands with CHM and texture features did produce a high-accuracy map with an overall accuracy of 88.46% and a Kappa coefficient of 0.84. Change detection analysis of tree dieback showed that between 2016 and 2021, the number of healthy trees decreased, and the number of gaps and open areas increased in the study area. We conclude that UltraCam and UAV photographs can serve to identify and map tree dieback and dead trees with good accuracies and can hence support forest health monitoring.

关键词： Aerial photos Forest health object-based classification Tree mortality

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Spatial Resolution as a Factor for Efficient UAV-based Weed Mapping-A Soybean Field Case Study

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REMOTE SENSING 2024年第10期16卷 1778页

作者： Ubben, Niklas Pukrop, Maren Jarmer, Thomas Univ Osnabrueck Inst Comp Sci Wachsble 27 D-49090 Osnabruck Germany

The influence of spatial resolution on classification accuracy strongly depends on the research object. With regard to unmanned aerial vehicle (UAV)-based weed mapping, contradictory results on the influence of spatial resolution have been attained so far. Thus, this study evaluates the effect of spatial resolution on the classification accuracy of weeds in a soybean field located in Belm, Lower Saxony, Germany. RGB imagery of four spatial resolutions (0.27, 0.55, 1.10, and 2.19 cm ground sampling distance) corresponding to flight altitudes of 10, 20, 40, and 80 m were assessed. Multinomial logistic regression was used to classify the study area, using both pixel- and object-based approaches. Additionally, the flight and processing times were monitored. For the purpose of an accuracy assessment, the producer's, user's, and overall accuracies as well as the F1 scores were computed and analyzed for statistical significance. Furthermore, McNemar's test was conducted to ascertain whether statistically significant differences existed between the classifications. A linear relationship between resolution and accuracy was found, with a diminishing accuracy as the resolution decreased. Pixel-based classification outperformed object-based classification across all the resolutions examined, with statistical significance (p < 0.05) for 10 and 20 m. The overall accuracies of the pixel-based approach ranged from 80 to 93 percent, while the accuracies of the object-based approach ranged from 75 to 87 percent. The most substantial drops in the weed-detection accuracy with regard to altitude occurred between 20 and 40 m for the pixel-based approach and between 10 and 20 m for the object-based approach. While the decline in accuracy was roughly linear as the flight altitude increased, the decrease in the total time required was exponential, providing guidance for the planning of future UAV-based weed-mapping missions.

关键词： weed mapping precision farming UAV multinomial logistic regression object-based classification

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Multiagent object-based Classifier for High Spatial Resolution Imagery

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2014年第2期52卷 841-857页

作者： Zhong, Yanfei Zhao, Bei Zhang, Liangpei Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Peoples R China

object-based classification, including object-based segmentation and classification, has been applied for the classification of high spatial resolution imagery due to the increase in the spatial resolution and the limited spectral resolution. Because of the independent design of the object-based segmentation and classification in many of the traditional object-based classification methods, additional work is required to select the appropriate segmentation algorithms to match the classification algorithms. The object-based segmentation algorithms, e. g., the fractal net evolution approach (FNEA), have been successfully utilized to provide the homogeneous regions, and are the basis of object-based classification. However, the traditional FNEA algorithm is greatly influenced by the global control strategy of the region-growing procedure. In addition, the existing object classification methods take little account of the object context information, which is important for high spatial-resolution image interpretation. To improve the accuracy of the object-based classification, in this paper, a multiagent object-based classification framework (MAOCF) for high-resolution remote sensing imagery is proposed. The proposed approach avoids the issue of segmentation algorithm selection by unifying the processing of object-based segmentation and classification through the use of a 4-tuple agent model. In the uniform framework, a multiagent object-based segmentation (MAOS) algorithm is proposed to optimally control the procedure of object merging. In addition, a MAOC is proposed to utilize the contextual information from the surrounding objects by taking advantage of the benefits of a multiagent system, e. g., strong interaction, high flexibility, and parallel global control capability. Due to the characteristics of a multiagent system, MAOCF has the potential for a parallel computing ability. Three experiments with different types of images were performed to evaluate the performanc

关键词： Global control high spatial resolution image multiagent systems object-based classification segmentation

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Mapping Changes in Land Cover Composition and Pattern for Comparing Mediterranean Rangeland Restoration Alternatives

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LAND DEGRADATION & DEVELOPMENT 2016年第3期27卷 671-681页

作者： Fava, Francesco Pulighe, Giuseppe Monteiro, Antonio T. Univ Milano Bicocca Dept Earth & Environm Sci Remote Sensing Environm Dynam Lab Piazza Sci 1 I-20126 Milan Italy Consiglio Ric Agr & Anal Econ Agr Via Nomentana 41 Rome Italy Univ Porto Res Ctr Biodivers & Genet Resources CIBIO Biol Conservat Grp Rua Campo Alegre 823 P-4100 Oporto Portugal

The limited amount of studies addressing the long-term effectiveness of restoration actions to combat land degradation is a constraint for current landscape restoration planning and implementation. Remote sensing data provide a unique opportunity for gathering information on the spatial and temporal variability of restoration processes and may contribute to fill this knowledge gap. The aim of this study is to compare different management actions undertaken in Mediterranean degraded rangelands (*** pine plantations, thinned pine plantations, protection and passive management) by analysing landscape composition and pattern change assessed from object-based classification of aerial photograph of 1954, 1977 and 2006. Results show a clear link between landscape composition and structure and the different restoration actions, allowing comparison between their spatial and temporal dynamics. All restoration actions promoted an increase in woody vegetation cover compared with non-restored areas. However, only protection and passive management favoured the restoration of dense, complex and well-connected patches of native Mediterranean maquis. The proposed methodology is discussed with respect to its applicability for future Mediterranean landscape restoration planning in a wider geographic and ecological perspective. Copyright (c) 2015 John Wiley & Sons, Ltd.

关键词： restoration land degradation aerial photographs object-based classification spatial pattern metrics Mediterranean

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