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Applicability of cadastral data to support the estimation of water use in private swimming pools

ENVIRONMENT AND PLANNING B-URBAN ANALYTICS AND CITY SCIENCE 2019年第6期46卷 1165-1181页

作者： Llausas, Albert Hof, Angela Wolf, Nils Sauri, David Siegmund, Alexander Univ Autonoma Barcelona Barcelona Spain Univ Salzburg Dept Geog & Geol Salzburg Austria Heidelberg Univ Educ Dept Geog RGEO Heidelberg Germany Univ Autonoma Barcelona Human Geog Barcelona Spain Heidelberg Univ Inst Geog Heidelberg Germany

Studies addressing outdoor water use in residential areas rely on surveying methods, the manual digitization of aerial imagery and remote sensing-based approaches to estimate water consumption by different land uses. Publicly available cadastral data potentially offer a more efficient avenue for researchers to obtain information on land use parameters, but few assessments of their quality and applicability have been conducted. A sample of three local areas, encompassing more than 12,000 plots in low-density residential areas and representative of different socioeconomic profiles, were selected in the metropolitan area of Barcelona and along the Mediterranean coast of Catalonia. The reliability and sufficiency of the Spanish cadastre were assessed for the identification of swimming pools against the ground truth evidence provided by high-resolution aerial imagery and the support of object-based image analysis. Omission and commission errors of the cadastre were measured, the delimitation of digitized pool areas was statistically tested for accuracy, and the condition of the facilities was compared in the three study sites. The results do not support the use of the Spanish cadastre as a source of data for uses requiring a high level of detail and completeness. Plot size, socioeconomic and cultural factors affect pool size, the frequency of empty pools and the installation of pool covers in different communities. The study demonstrates that researchers need to complement cadastral data with qualitative information about the condition of the swimming pools to successfully estimate their cumulative water consumption at larger scales. Implications for water use research and spatial planning are discussed.

关键词： Domestic water use geographic object-based image analysis low-density residential areas metropolitan area of Barcelona water resource management

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Stand age estimation of rubber (Hevea brasiliensis) plantations using an integrated pixel- and object-based tree growth model and annual Landsat time series

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2018年 144卷 94-104页

作者： Chen, Gang Thill, Jean-Claude Anantsuksomsri, Sutee Tontisirin, Nij Tao, Ran Univ N Carolina Dept Geog & Earth Sci LRSEC Charlotte NC 28223 USA Univ N Carolina Dept Geog & Earth Sci Charlotte NC USA Chulalongkorn Univ Dept Urban & Reg Planning Bangkok Thailand Thammasat Univ Fac Architecture & Planning Bangkok Thailand Univ Southern Calif Spatial Sci Inst Los Angeles CA USA

Rubber (Hevea brasiliensis) plantations are a rapidly increasing source of land cover change in mainland Southeast Asia. Stand age of rubber plantations obtained at fine scales provides essential baseline data, informing the pace of industrial and smallholder agricultural activities in response to the changing global rubber markets, and local political and socioeconomic dynamics. In this study, we developed an integrated pixel- and object-based tree growth model using Landsat annual time series to estimate the age of rubber plantations in a 21,115 km(2) tri-border region along the junction of China, Myanmar and Laos. We produced a rubber stand age map at 30 m resolution, with an accuracy of 87.00% for identifying rubber plantations and an average error of 1.53 years in age estimation. The integration of pixel- and object-based image analysis showed superior performance in building NDVI yearly time series that reduced spectral noises from background soil and vegetation in open-canopy, young rubber stands. The model parameters remained relatively stable during model sensitivity analysis, resulting in accurate age estimation robust to outliers. Compared to the typically weak statistical relationship between single-date spectral signatures and rubber tree age, Landsat image time series analysis coupled with tree growth modeling presents a viable alternative for fine-scale age estimation of rubber plantations.

关键词： Stand age estimation Rubber plantation geographic object-based image analysis Landsat time series Tree growth model

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object-based urban landcover mapping methodology using high spatial resolution imagery and airborne laser scanning

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JOURNAL OF APPLIED REMOTE SENSING 2018年第4期12卷

作者： Williams, David A. R. Matasci, Giona Coops, Nicholas C. Gergel, Sarah E. Univ British Columbia Dept Forest Resource Management Vancouver BC Canada Univ British Columbia Dept Forest & Conservat Sci Vancouver BC Canada

Mapping landcover in cities is essential for urban ecology and landuse management, yet urban landcover is often highly heterogeneous at fine spatial scales. Pixel-based approaches are shown to be less successful for effectively mapping urban landcover due to high heterogeneity, with relatively low accuracies reported despite the use of high spatial resolution optical imagery. Alternatively, geographic object-based image analysis (GEOBIA) has yielded higher accuracies across a range of urban applications. We combine three-dimensional (3-D) information from airborne laser scanning (ALS) data with RapidEye high-spatial-resolution imagery in a GEOBIA approach to classify urban landcover in a large metropolitan region in Vancouver, Canada. Results indicate that 12 urban classes could be accurately mapped at 2-m spatial resolution across 150,000 ha with an overall accuracy of 88% (kappa 0.87). Though 5-m RapidEye multispectral pixels were often mixed in heterogeneous urban areas, the additional insight provided by the 3-D ALS information enabled accurate classification of fine spatial objects such as street trees and single-family dwellings. (C) 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： geographic object-based image analysis urban city random forests object-oriented light detection and ranging RapidEye data fusion

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Detection of Shelterbelt Density Change Using Historic APFO and NAIP Aerial imagery

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REMOTE SENSING 2019年第3期11卷 218-218页

作者： Burke, Morgen W. V. Rundquist, Bradley C. Zheng, Haochi Univ North Dakota Dept Earth Syst Sci & Policy Grand Forks ND 58202 USA Univ North Dakota Dept Geog & GISc Grand Forks ND 58202 USA

Grand Forks County, North Dakota, boasts the highest concentration of shelterbelts in the World. As trees age and reach their lifespan limits, renovations should have taken place with new trees being planted. However, in recent years, the rate of tree removal is thought to exceed the rate of replanting, which can result in a net loss of shelterbelts. Through manual digitization and geographic object-based image analysis (GEOBIA), we mapped shelterbelt densities in the Grand Forks County using historical and contemporary aerial photography, and estimated actual changes in density over 54 years. Our results showed a doubling in shelterbelt densities from 1962 to 2014, with an increase of 6402 m(2)/km(2) over the 52 years (or 123 m(2)/km(2)/year). From 2014 to 2016, we measured 1,040,178 m(2) of shelterbelt areas removed from the county, creating a density loss of -157 m(2)/km(2)/year. The total change over two years was relatively small compared with that seen over the previous 52 years. However, the fact that the rate of shelterbelt planting has slowed, and more removal is occurring, should be of concern for an increased risk of wind erosion, similar to that experienced in Midwestern U.S. during the 1930s. The reduction of shelterbelt density is likely related to changes in farming practices and a decline in the Conservation Reserve Program, resulting from the increased returns of growing other row crops. To encourage shelterbelt planting as a conservation practice, additional guidelines and financial support should be considered to balance the tradeoff between soil erosion and agricultural intensification.

关键词： geographic object-based image analysis shelterbelts Conservation Reserve Program

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Assessing agricultural damage by wild boar using drones

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WILDLIFE SOCIETY BULLETIN 2018年第4期42卷 568-576页

作者： Rutten, Anneleen Casaer, Jim Vogels, Marjolein F. A. Addink, Elisabeth A. Vanden Borre, Jeroen Leirs, Herwig Univ Antwerp Dept Biol Evolutionary Ecol Grp Univ Pl 1 B-2610 Antwerp Belgium Res Inst Nat & Forest INBO Res Grp Wildlife Management Havenlaan 88 Bus 73 B-1000 Brussels Belgium Univ Utrecht Fac Geosci Dept Phys Geog Princetonlaan 8a NL-3584 GB Utrecht Netherlands

In Flanders (northern Belgium), wild boar (Sus scrofa) returned in 2006 after 50 years of absence and the population is increasing, both in abundance and geographic extent. In the absence of wild boar, Flanders' landscape structure changed into a dense, mosaic-like pattern of agricultural, natural, and urban areas. The return of the wild boar increasingly leads to human-wildlife conflicts, mainly linked to damage in agriculture. Hence, there is a growing need for a time-efficient, standardized, and accurate method to assess crop damage. We present an Unmanned Aerial Vehicle-based method, using geographic object-based image analysis and Random Forests to estimate the damaged area and associated yield losses, between 2015 and 2017, due to wild boar in individual fields in Flanders. Our approach resulted in an 84.50% overall accuracy in calculating damaged area for maize fields and 94.40% for grasslands. Damage levels ranged between 14.3% and 20.2% in maize fields and 16.5% to 25.4% in grasslands. Our method can provide objective base data for compensation schemes and guide management strategies based on damage assessments. (c) 2018 The Wildlife Society.

关键词： Belgium crop damage GEOBIA geographic object-based image analysis UAV wildlife damage

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Improved regional-scale Brazilian cropping systems' mapping based on a semi-automatic object-based clustering approach

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2018年 68卷 127-138页

作者： Bellon, Beatriz Begue, Agnes Lo Seen, Danny Lebourgeois, Valentine Evangelista, Balbino Antonio Simoes, Margareth Demonte Ferraz, Rodrigo Pecanha CIRAD UMR TETIS F-34398 Montpellier France Univ Montpellier TETIS AgroParisTech CIRADCNRSIRSTEA Montpellier France Embrapa Pesca & Aquicultura Prolongamento Ave NS 10Cruzamento Com Ave LO 18 BR-77008900 Palmas TO Brazil Embrapa Solos Rua Jardim Bot 1024 BR-22460000 Rio De Janeiro RI Brazil Rio de Janeiro State Univ UERJ Rua Sao Francisco Xavier 524 BR-20550900 Rio De Janeiro RJ Brazil

Cropping systems' maps at fine scale over large areas provide key information for further agricultural production and environmental impact assessments, and thus represent a valuable tool for effective land-use planning. There is, therefore, a growing interest in mapping cropping systems in an operational manner over large areas, and remote sensing approaches based on vegetation index time series analysis have proven to be an efficient tool. However, supervised pixel-based approaches are commonly adopted, requiring resource consuming field campaigns to gather training data. In this paper, we present a new object-based unsupervised classification approach tested on an annual MODIS 16-day composite Normalized Difference Vegetation Index time series and a Landsat 8 mosaic of the State of Tocantins, Brazil, for the 2014-2015 growing season. Two variants of the approach are compared: an hyperclustering approach, and a landscape-clustering approach involving a previous stratification of the study area into landscape units on which the clustering is then performed. The main cropping systems of Tocantins, characterized by the crop types and cropping patterns, were efficiently mapped with the landscape clustering approach. Results show that stratification prior to clustering significantly improves the classification accuracies for underrepresented and sparsely distributed cropping systems. This study illustrates the potential of unsupervised classification for large area cropping systems' mapping and contributes to the development of generic tools for supporting large-scale agricultural monitoring across regions.

关键词： Cropping systems geographic object-based image analysis (GEOBIA) Stratification Clustering Moderate resolution imaging spectmradiometer (MODIS) Landsat

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Using GeoEye-1 imagery for Multi-Temporal object-based Detection of Canegrub Damage in Sugarcane Fields in Queensland, Australia

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GISCIENCE & REMOTE SENSING 2018年第2期55卷 285-305页

作者： Johansen, Kasper Sallam, Nader Robson, Andrew Samson, Peter Chandler, Keith Derby, Lisa Eaton, Allen Jennings, Jillian King Abdullah Univ Sci & Technol Div Biol & Environm Sci & Engn Hydrol & Land Observat Grp Water Desalinat & Reuse Ctr Thuwal 239556900 Saudi Arabia Dept Agr & Water Resources 114 Catalina Crescent Cairns Qld 4870 Australia Univ New England Sch Sci & Technol Precis Agr Res Grp Armidale NSW 2351 Australia Sugar Res Australia 50 Meiers Rd Indooroopilly Qld 4068 Australia Sugar Res Australia 26135 Peak Downs Highway Te Kowai Qld 4741 Australia Sugar Res Australia 71378 Bruce Highway Gordonvale Qld 4865 Australia

The greyback canegrub (Dermolepida albohirtum) is the main pest of sugarcane crops in all cane-growing regions between Mossman (16.5 degrees S) and Sarina (21.5 degrees S) in Queensland, Australia. In previous years, high infestations have cost the industry up to $40 million. However, identifying damage in the field is difficult due to the often impenetrable nature of the sugarcane crop. Satellite imagery offers a feasible means of achieving this by examining the visual characteristics of stool tipping, changed leaf color, and exposure of soil in damaged areas. The objective of this study was to use geographic object-based image analysis (GEOBIA) and high-spatial resolution GeoEye-1 satellite imagery for three years to map canegrub damage and develop two mapping approaches suitable for risk mapping. The GEOBIA mapping approach for canegrub damage detection was evaluated over three selected study sites in Queensland, covering a total of 254km(2) and included five main steps developed in the eCognition Developer software. These included: (1) initial segmentation of sugarcane block boundaries;(2) classification and subsequent omission of fallow/harvested fields, tracks, and other non-sugarcane features within the block boundaries;(3) identification of likely canegrub-damaged areas with low NDVI values and high levels of image texture within each block;(4) the further refining of canegrub damaged areas to low, medium, and high likelihood;and (5) risk classification. The validation based on field observations of canegrub damage at the time of the satellite image capture yielded producer's accuracies between 75% and 98.7%, depending on the study site. Error of commission occurred in some cases due to sprawling, drainage issues, wind, weed, and pig damage. The two developed risk mapping approaches were based on the results of the canegrub damage detection. This research will improve decision making by growers affected by canegrub damage.

关键词： geographic object-based image analysis Dermolepida Sugarcane geoeye-1 Queensland Australia damage mapping

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Combining automatic and manual image analysis in a web-mapping application for collaborative conflict damage assessment

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APPLIED GEOGRAPHY 2018年 97卷 25-34页

作者： Knoth, Christian Slimani, Sofian Appel, Marius Pebesma, Edzer Univ Munster Inst Geoinformat Heisenbergstr 2 D-48149 Munster Germany

Remote sensing is increasingly being used by non-profit organizations and international initiatives to localize and document combat impacts such as conflict damage. Most of the practical applications rely on labor-intensive and time-consuming manual image analysis. Even when using crowdsourcing or volunteer networks, the workload can quickly become challenging when larger areas have to be monitored over longer time periods. In this paper, we propose an approach that combines automatic change detection methods with collaborative mapping in a web application for conflict damage assessment in Darfur, Sudan. Settlement areas are automatically detected and searched for destructed dwelling structures by geographic object-based image analysis (GEOBIA). The web application prioritizes these areas based on the detected degree of destruction to guide human analysts to the most important locations. In a user experiment with 30 participants we evaluated the performance of volunteers with and without the automatic prioritization and investigated their mapping sequences. Participants who were guided by the prioritization detected 70.7% more target objects than participants mapping without guidance, who invested parts of their mapping time in examining locations that show little to no destruction.

关键词： Collaborative mapping Conflict damage assessment geographic object-based image analysis Crowdsourcing

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One-Dimensional Convolution Neural Networks for object-based Feature Selection 24

One-Dimensional Convolution Neural Networks for Object-Based...

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Conference on image and Signal Processing for Remote Sensing XXIV

作者： Attaf, Dalila Djerriri, Khelifa Cheriguene, Sarah Rabia Karoui, Moussa Sofiane Ctr Tech Spatiales Dept Observat Terre Arzew 31200 Oran Algeria

ISBN: (纸本)9781510621626

Recently, the new geographic object-based image analysis (GEOBIA) was proposed as an alternative classification approach to pixel based ones. In GEOBIA, image segments can be depicted with various attributes such as spectral, texture, shape, deep features and context, and hence final classification can produce better land cover/use map. The presence of such a large number of features poses significant challenges to standard machine learning methods and has rendered many existing classification techniques impractical. In this work, we are interested to feature selection techniques, which are employed to reduce the dimensionality of the data while keeping the most of its expressive power. Inspired by recent works in remote sensing using Convolutional Neural Networks (CNNs), especially for hyperspectral band selection, a feature selection approach based on One-Dimensional Convolutional Neural Networks (1-D CNN) is proposed in this study. All object-based features are used to train the 1-D CNN to obtain well trained model. After testing different feature combinations, we use the well trained model to obtain their test classification accuracies, and finally we select the subset of features with the highest precision. In our experiments, we evaluate our feature selection approach on 30-cm resolution colour infrared (CIR) aerial orthoimagery. A multi-resolution segmentation is performed to segment the images into regions, which are characterized later using various spectral, textural and spatial attributes to form the final object-based feature dataset. The obtained experimental results show that the proposed method can achieve satisfactory results when compared with traditional feature selection approaches.

关键词： Very High Spatial Resolution imagery Feature Selection geographic object-based image analysis Deep Learning 1-D Convolutional Neural Network

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An Efficient Parallel Multi-Scale Segmentation Method for Remote Sensing imagery

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REMOTE SENSING 2018年第4期10卷 590-590页

作者： Gu, Haiyan Han, Yanshun Yang, Yi Li, Haitao Liu, Zhengjun Soergel, Uwe Blaschke, Thomas Cui, Shiyong Chinese Acad Surveying & Mapping Inst Photogrammetry & Remote Sensing 28 Lianhuachi Rd Beijing 100830 Peoples R China Univ Stuttgart Inst Photogrammetry Geschwister Scholl Str 24D D-70174 Stuttgart Germany Univ Salzburg Dept Geoinformat GIS Z Schillerstr 30 A-5020 Salzburg Austria German Aerosp Ctr DLR EOC Remote Sensing Technol Inst IMF D-82234 Wessling Germany

Remote sensing (RS) image segmentation is an essential step in geographic object-based image analysis (GEOBIA) to ultimately derive "meaningful objects". While many segmentation methods exist, most of them are not efficient for large data sets. Thus, the goal of this research is to develop an efficient parallel multi-scale segmentation method for RS imagery by combining graph theory and the fractal net evolution approach (FNEA). Specifically, a minimum spanning tree (MST) algorithm in graph theory is proposed to be combined with a minimum heterogeneity rule (MHR) algorithm that is used in FNEA. The MST algorithm is used for the initial segmentation while the MHR algorithm is used for object merging. An efficient implementation of the segmentation strategy is presented using data partition and the " reverse searching-forward processing" chain based on message passing interface (MPI) parallel technology. Segmentation results of the proposed method using images from multiple sensors (airborne, SPECIM AISA EAGLE II, WorldView-2, RADARSAT-2) and different selected landscapes (residential/industrial, residential/agriculture) covering four test sites indicated its efficiency in accuracy and speed. We conclude that the proposed method is applicable and efficient for the segmentation of a variety of RS imagery (airborne optical, satellite optical, SAR, high-spectral), while the accuracy is comparable with that of the FNEA method.

关键词： remote sensing image segmentation geographic object-based image analysis graph theory fractal net evolution approach minimum spanning tree minimum heterogeneity rule message passing interface

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