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Remote detection of invasive plants: a review of spectral, textural and phenological approaches

BIOLOGICAL INVASIONS 2014年第7期16卷 1411-1425页

作者： Bradley, Bethany A. Univ Massachusetts Dept Environm Conservat Amherst MA 01003 USA

Remote sensing image analysis is increasingly being used as a tool for mapping invasive plant species. Resulting distribution maps can be used to target management of early infestations and to model future invasion risk. Remote identification of invasive plants based on differences in spectral signatures is the most common approach, typically using hyperspectral data. But several studies have found that textural and phenological differences are also effective approaches for identifying invasive plants. I review examples of remote detection of invasive plants based on spectral, textural and phenological analysis and highlight circumstances where the different approaches are likely to be most effective. I also review sources and availability of remotely sensed data that could be used for mapping and suggest field data collection approaches that would support the analysis of remotely sensed data. Remote mapping of biological invasions remains a relatively specialized research topic, but the distinct cover, morphology and/or seasonality of many invaded versus native ecosystems suggests that more species could be detected remotely. Remote sensing can sometimes support early detection and rapid response directly, however, accurately detecting small, nascent populations is a challenge. However, even maps of heavily infested areas can provide a valuable tool for risk assessment by increasing knowledge about temporal and spatial patterns and predictors of invasion.

关键词： Aerial photograph Hyperspectral Invasive plant object-based classification Phenology Satellite remote sensing

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Application of land use regression techniques for urban greening: An analysis of Tianjin, China

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URBAN FORESTRY & URBAN GREENING 2019年 38卷 11-21页

作者： Guo, Ying Su, Jason G. Dong, Ya Wolch, Jennifer Tianjin Univ Sch Architecture Tianjin 300072 Peoples R China Univ Calif Berkeley Sch Publ Hlth Environm Hlth Sci Berkeley CA 94720 USA Univ Calif Berkeley Coll Environm Design Berkeley CA 94720 USA

The land cover types in a 4 km by 4 km space centered around an air quality monitoring station in the city of Tianjin were classified using the e-Cognition software. We identified 23 air quality monitoring sites and their surrounding land cover types. The suitability of using land cover types to predict traffic-related air pollution Nitrogen Dioxide (NO2) was tested through a machine learning land use regression (LUR) modeling technique. We found that vegetation was significantly but negatively associated with air pollution levels (correlation coefficient r = -0.5, p < 0.01) while highways and major roadways were positively and significantly associated with air pollution levels (r = 0.60, p < 0.01). The LUR model explained 84% of the total variance in measured NO2 concentrations. The associations of the land cover types with NO2 concentrations were then used in land cover retrofitting strategies to quantify the potential for reducing traffic-related air pollution. We identified that the improvements in air quality could reach 25% if specific urban greening strategies were fully implemented. The results of the study can help policy makers and environmental designers adopt effective air pollution reduction policies to improve air quality and protect public health in Tianjin and other urban regions in China.

关键词： Land use regression Geographic information systems object-based classification Urban greening Nitrogen dioxide Tianjin

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Vegetation cover in relation to socioeconomic factors in a tropical city assessed from sub-meter resolution imagery

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ECOLOGICAL APPLICATIONS 2018年第3期28卷 681-693页

作者： Martinuzzi, Sebastian Ramos-Gonzalez, Olga M. Munoz-Erickson, Tischa A. Locke, Dexter H. Lugo, Ariel E. Radeloff, Volker C. Univ Wisconsin Dept Forest & Wildlife Ecol SILVIS Lab 1630 Linden Dr Madison WI 53706 USA US Forest Serv USDA Int Inst Trop Forestry Jardin Bot Sur 1201 Calle Ceiba Rio Piedras PR 00926 USA Natl Socioenvironm Synth Ctr SESYNC 1 Pk PlSuite 300 Annapolis MD 21401 USA

Fine-scale information about urban vegetation and social-ecological relationships is crucial to inform both urban planning and ecological research, and high spatial resolution imagery is a valuable tool for assessing urban areas. However, urban ecology and remote sensing have largely focused on cities in temperate zones. Our goal was to characterize urban vegetation cover with sub-meter (<1 m) resolution aerial imagery, and identify social-ecological relationships of urban vegetation patterns in a tropical city, the San Juan Metropolitan Area, Puerto Rico. Our specific objectives were to (1) map vegetation cover using sub-meter spatial resolution (0.3-m) imagery, (2) quantify the amount of residential and non-residential vegetation, and (3) investigate the relationship between patterns of urban vegetation vs. socioeconomic and environmental factors. We found that 61% of the San Juan Metropolitan Area was green and that our combination of high spatial resolution imagery and object-based classification was highly successful for extracting vegetation cover in a moist tropical city (97% accuracy). In addition, simple spatial pattern analysis allowed us to separate residential from non-residential vegetation with 76% accuracy, and patterns of residential and non-residential vegetation varied greatly across the city. Both socioeconomic (e.g., population density, building age, detached homes) and environmental variables (e.g., topography) were important in explaining variations in vegetation cover in our spatial regression models. However, important socioeconomic drivers found in cities in temperate zones, such as income and home value, were not important in San Juan. Climatic and cultural differences between tropical and temperate cities may result in different social-ecological relationships. Our study provides novel information for local land use planners, highlights the value of high spatial resolution remote sensing data to advance ecological research and urban planni

关键词： airborne imagery green infrastructure land use planning object-based classification Puerto Rico residential vegetation social-ecological relationships tropical city urban ecology

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Water Requirement for Irrigation of Complicated Agricultural Land by Using Classified Airborne Digital Sensor Images

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JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING 2019年第8期47卷 1307-1314页

作者： Chen, Li Chien, Ta-Wei Hsu, Chia-Sheng Tan, Chih-Hung Hsu, Hsiang-Yi Kou, Chang-Huan Chung Hua Univ Dept Civil Engn 707Sect 2WuFu Rd Hsinchu 300 Taiwan Taiwan HsinChu Irrigat Assoc Engn Div 10F333 Wenxing Rd Zhubei City 302 Hsinchu County Taiwan Agr Engn Res Ctr 18 Hejiang Rd Taoyuan 320 Taiwan

Land use of irrigated areas nearby the metropolitan is complex. On fields, crop growth may differ with variations in the water demand. Among the image classification methods, combined object-oriented classification is currently preferred over conventional pixel-based classification. Compared with the traditional pixel-based method, which generally exhibits a spot-like salt-and-pepper effect, object-based classification can significantly reduce the salt-and-pepper effect and amount of data required for analysis. To obtain improved spectral recognition, maximum image information is described using color, texture, and shape to enhance image recognition. In this study, image information extraction and crop interpretation were performed using the airborne digital sensor ADS40 to obtain experimental data, and the traditional supervised image and image object classification methods were compared. The results indicate that both the image classification methods could yield an overall accuracy of more than 80%, and the accuracy of object-based classification (88.68%) was higher than that of the other classification. The daily water requirement of crops in the study area, calculated using a high-precision image object classification method, was approximately 2585 m(3). The current results may aid in the effective estimation of agricultural irrigation water consumption.

关键词： Land use Irrigated area Spectral recognition object-based classification ADS40

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An object-based approach for mapping forest structural types based on low-density LiDAR and multispectral imagery

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GEOCARTO INTERNATIONAL 2018年第5期33卷 443-457页

作者： Angel Ruiz, Luis Abel Recio, Jorge Crespo-Peremarch, Pablo Sapena, Marta Univ Politecn Valencia Dept Ingn Cartog Geodesia & Fotogrametria Geoenvironm Cartog & Remote Sensing Grp CGAT Valencia Spain

Mapping forest structure variables provides important information for the estimation of forest biomass, carbon stocks, pasture suitability or for wildfire risk prevention and control. The optimization of the prediction models of these variables requires an adequate stratification of the forest landscape in order to create specific models for each structural type or strata. This paper aims to propose and validate the use of an object-oriented classification methodology based on low-density LiDAR data (0.5m(-2)) available at national level, WorldView-2 and Sentinel-2 multispectral imagery to categorize Mediterranean forests in generic structural types. After preprocessing the data sets, the area was segmented using a multiresolution algorithm, features describing 3D vertical structure were extracted from LiDAR data and spectral and texture features from satellite images. objects were classified after feature selection in the following structural classes: grasslands, shrubs, forest (without shrubs), mixed forest (trees and shrubs) and dense young forest. Four classification algorithms (C4.5 decision trees, random forest, k-nearest neighbour and support vector machine) were evaluated using cross-validation techniques. The results show that the integration of low-density LiDAR and multispectral imagery provide a set of complementary features that improve the results (90.75% overall accuracy), and the object-oriented classification techniques are efficient for stratification of Mediterranean forest areas in structural- and fuel-related categories. Further work will be focused on the creation and validation of a different prediction model adapted to the various strata.

关键词： Fuel strata object-based classification LiDAR WorldView-2 Sentinel-2

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The use of WorldView-2 satellite imagery to model urban drainage system with low impact development (LID) Techniques

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GEOCARTO INTERNATIONAL 2016年第1期31卷 92-108页

作者： Khin, Moh Moh Lin Shaker, Ahmed Joksimovic, Darko Yan, Wai Yeung Ryerson Univ Dept Civil Engn Toronto ON Canada

Within a wide range of best management practices for stormwater management in urban areas, there has been an increasing interest in source control measures. Source controls such as low-impact development (LID) techniques are potentially attractive as retrofit options for older developed areas that lack available land to implement conventional measures such as stormwater management ponds. Hence, distributed urban drainage models requiring detailed representation of developed drainage areas should be developed to accurately estimate the benefits that LIDs may provide. This study (1) presents a two-stage classification process on a high-resolution WorldView-2 image, and (2) demonstrates how to use the extracted land cover information in the subsequent hydrologic modelling and assessment of different LIDs' performance. The proposed two-stage classification method achieved an overall accuracy of 80.6%, whereas a traditional pixel-based achieved 68.4% in classifying the same urban area into six land cover classes. From the classification results, the hydrologic properties of micro-subcatchments were imported in the United States Environmental Protection Agency Storm Water Management Model to assess the performance of LIDs. A reduction of run-off volume 18.2% and 37.1% was found with the implementation of porous pavement and bioretention, respectively, in a typical low-rise residential area located in the city of San Clemente, California, US. The study demonstrates the use of high-resolution remote sensing image to aid in evaluating LID retrofit options, and thus benefits in situations where detailed drainage area information is not available.

关键词： hydrologic modelling low-impact development object-based classification urban drainage WorldView-2

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Comparison of Vector Stacking, Multi-SVMs Fuzzy Output, and Multi-SVMs Voting Methods for Multiscale VHR Urban Mapping

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2010年第2期7卷 261-265页

作者： Huang, Xin Zhang, Liangpei Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Peoples R China

The objective of this letter is to integrate multiscale information for urban mapping using very high resolution (VHR) imagery. Three multiscale fusion methods were presented: 1) vector stacking (VS);2) multiple support vector machines (multi-SVMs) fuzzy output;and 3) multi-SVMs voting. Two kinds of spatial features were used to obtain multiscale representations of VHR images: morphological structural features and object-based approaches. In experiments, the Reflective Optics System Imaging Spectrometer-03 Pavia Center and University, the Hyperspectral Digital Imagery Collection Experiment Washington DC Mall, and the Quickbird Beijing data sets were used for algorithm validation. The experimental results revealed that, in most cases, the VS fusion outperformed other methods because it was able to create a new high-dimensional multiscale feature space and enhance the class separability. It was also shown that the multi-SVMs fuzzy fusion could optimize and reorganize the multiscale information effectively. Furthermore, multi-SVMs fuzzy output was better than multi-SVMs voting because the former was able to exploit the probabilistic output, while the latter only considered the crisp classification label. In addition, it is suggested that VS fusion is suitable for morphological features;however, for the object-based classification, the multiscale fusion methods do not necessarily yield better results than the single-scale classification in terms of accuracies.

关键词： Fusion high resolution morphological multiscale object-based classification support vector machines (SVMs)

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object-based change monitoring in mining areas-taking Pingshuo as an example 2nd

Object-based change monitoring in mining areas-taking Pingsh...

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2nd International Symposium on Land Reclamation and Ecological Restoration (LRER)

作者： Zhang, M. L. Zhou, W. Yuan, T. Xie, Y. H. Li, Y. F. China Univ Geosci Sch Land Sci & Technol Beijing Peoples R China Indiana State Univ Dept Earth & Environm Syst Ctr Urban & Environm Change Terre Haute IN 47809 USA Beijing Normal Univ Sch Environm Beijing Peoples R China

ISBN: (纸本)9781315166582;9781138051034

In this paper, RapidEye Remote Sensing Image (Map 2012) and SPOT7 Remote Sensing Image (Map 2015) in Pingshuo Mining Area are selected as the data. Combined with object-based classification and change vector analysis method, we studied the feasibility of high resolution remote sensing image for mining land classification and the accuracy of monitoring. The results show that the classification of reclaimed mining land has higher precision, the overall accuracy and kappa coefficient of the classification of Map2012 were 0.89 and 0.87, and the change region map were 0.87 and 0.84. It's obvious that object-based classification and change vector analysis which has a great significance to improve the monitoring accuracy can be used to monitor mining land, especially reclaiming mining land.

关键词： land use/cover monitoring object-based classification change vector analysis open-pit coal mine land reclamation

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Mapping of Debris-Covered Glaciers Using object-based Machine Learning Technique

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JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING 2024年第2期52卷 399-411页

作者： Sharda, Shikha Srivastava, Mohit IK Gujral Punjab Tech Univ Dept Elect & Commun Engn Jalandhar Punjab India Chandigarh Engn Coll Dept Elect & Commun Engn Mohali Punjab India

Debris-covered glaciers in High Mountain Asia are important indicators of climatic variability. The present study proposed an improved glacier mapping technique based on a TIRS/(RED/SWIR) band ratio that integrates thermal infrared (TIR), visible red, and shortwave infrared (SWIR) reflectance information with slope parameter to map debris-covered areas. The object-based machine learning technique comprising a hybrid feature selection model and a decision tree classifier was adopted to classify the glacierized region. The mapping results stated that the proposed band ratio combined with the slope parameter has better differentiated supraglacial debris from other glacier surfaces in comparison with the existing debris-covered glacier mapping approaches. The resulted debris-covered glacier boundaries were also validated with reference glacier inventories. The supraglacial debris-covered area was mapped with a high user's accuracy of approximate to 98%. In addition, a high overall classification accuracy in the range of 99.59-99.84% was achieved with the proposed technique that overcomes the challenges of the previous noteworthy studies, confirming that this technique is effective in detecting debris-covered glaciers.

关键词： Band ratio technique Debris-covered glacier Machine learning object-based classification

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Mapping invasive woody species in coastal dunes in the Netherlands: a remote sensing approach using LIDAR and high-resolution aerial photographs

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APPLIED VEGETATION SCIENCE 2012年第4期15卷 536-547页

作者： Hantson, Wouter Kooistra, Lammert Slim, Pieter A. Alterra Ctr Ecosyst Studies Wageningen Netherlands Wageningen Univ Lab Geo Informat Sci & Remote Sensing NL-6708 PB Wageningen Netherlands

Questions Does remote sensing improve classification of invasive woody species in dunes, useful for shrub management? Does additional height information and an object-based classifier increase woody species classification accuracy? Location The dunes of Vlieland, one of the Wadden Sea Islands, the Netherlands. Methods Extensive monitoring using optical remote sensing and LIDAR deliver large amounts of high-quality data to observe and manage coastal dunes as a defence against the sea in the Netherlands. Using these additional data could increase the accuracy of vegetation mapping and monitoring in coastal areas. In this study, a remote sensing approach has been developed to deliver detailed and standardized maps of (invasive) woody species in the dunes of Vlieland using multispectral aerial photographs and vegetation height derived from LIDAR. Three classification methods were used: maximum likelihood (ML) classification using aerial photographs, ML classification combined with vegetation heights derived from LIDAR (ML+) and object-based (OB) classification. Results The use of vegetation height from the LIDAR data increased the overall classification accuracy from 39% to 50%, but particularly improved classification of the taller woody species. The object-based classification increased the overall accuracy of the ML+ from 50% to 60%. The object-based results are comparable to human visual analysis while offering automated analysis. Conclusions Overall, the object-based classification delivers detailed maps of the woody species that are useful for management and evaluation of alien and invasive species in dune ecosystems.

关键词： Vegetation mapping alien species digital elevation model pixel-based classification object-based classification R osa rugosa P runus serotina H ippophae rhamnoides grey dunes

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