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Remote Sens., Volume 3, Issue 6 (June 2011), Pages 1067-1283

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Research

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Open AccessArticle Integrated Landsat Image Analysis and Hydrologic Modeling to Detect Impacts of 25-Year Land-Cover Change on Surface Runoff in a Philippine Watershed
Remote Sens. 2011, 3(6), 1067-1087; doi:10.3390/rs3061067
Received: 4 March 2011 / Revised: 4 May 2011 / Accepted: 6 May 2011 / Published: 26 May 2011
Cited by 9 | PDF Full-text (1928 KB) | HTML Full-text | XML Full-text
Abstract
Landsat MSS and ETM+ images were analyzed to detect 25-year land-cover change (1976–2001) in the critical Taguibo Watershed in Mindanao Island, Southern Philippines. This watershed has experienced historical modifications of its land-cover due to the presence of logging industries in the 1950s, and
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Landsat MSS and ETM+ images were analyzed to detect 25-year land-cover change (1976–2001) in the critical Taguibo Watershed in Mindanao Island, Southern Philippines. This watershed has experienced historical modifications of its land-cover due to the presence of logging industries in the 1950s, and continuous deforestation due to illegal logging and slash-and-burn agriculture in the present time. To estimate the impacts of land-cover change on watershed runoff, land-cover information derived from the Landsat images was utilized to parameterize a GIS-based hydrologic model. The model was then calibrated with field-measured discharge data and used to simulate the responses of the watershed in its year 2001 and year 1976 land-cover conditions. The availability of land-cover information on the most recent state of the watershed from the Landsat ETM+ image made it possible to locate areas for rehabilitation such as barren and logged-over areas. We then created a “rehabilitated” land-cover condition map of the watershed (re-forestation of logged-over areas and agro-forestation of barren areas) and used it to parameterize the model and predict the runoff responses of the watershed. Model results showed that changes in land-cover from 1976 to 2001 were directly related to the significant increase in surface runoff. Runoff predictions showed that a full rehabilitation of the watershed, especially in barren and logged-over areas, will be likely to reduce the generation of a huge volume of runoff during rainfall events. The results of this study have demonstrated the usefulness of multi-temporal Landsat images in detecting land-cover change, in identifying areas for rehabilitation, and in evaluating rehabilitation strategies for management of tropical watersheds through its use in hydrologic modeling. Full article
(This article belongs to the Special Issue 100 Years ISPRS - Advancing Remote Sensing Science)
Open AccessArticle Mapping Green Spaces in Bishkek—How Reliable can Spatial Analysis Be?
Remote Sens. 2011, 3(6), 1088-1103; doi:10.3390/rs3061088
Received: 19 April 2011 / Revised: 16 May 2011 / Accepted: 17 May 2011 / Published: 30 May 2011
Cited by 8 | PDF Full-text (2225 KB) | HTML Full-text | XML Full-text
Abstract
Within urban areas, green spaces play a critically important role in the quality of life. They have remarkable impact on the local microclimate and the regional climate of the city. Quantifying the ‘greenness’ of urban areas allows comparing urban areas at several levels,
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Within urban areas, green spaces play a critically important role in the quality of life. They have remarkable impact on the local microclimate and the regional climate of the city. Quantifying the ‘greenness’ of urban areas allows comparing urban areas at several levels, as well as monitoring the evolution of green spaces in urban areas, thus serving as a tool for urban and developmental planning. Different categories of vegetation have different impacts on recreation potential and microclimate, as well as on the individual perception of green spaces. However, when quantifying the ‘greenness’ of urban areas the reliability of the underlying information is important in order to qualify analysis results. The reliability of geo-information derived from remote sensing data is usually assessed by ground truth validation or by comparison with other reference data. When applying methods of object based image analysis (OBIA) and fuzzy classification, the degrees of fuzzy membership per object in general describe to what degree an object fits (prototypical) class descriptions. Thus, analyzing the fuzzy membership degrees can contribute to the estimation of reliability and stability of classification results, even when no reference data are available. This paper presents an object based method using fuzzy class assignments to outline and classify three different classes of vegetation from GeoEye imagery. The classification result, its reliability and stability are evaluated using the reference-free parameters Best Classification Result and Classification Stability as introduced by Benz et al. in 2004 and implemented in the software package eCognition (www.ecognition.com). To demonstrate the application potentials of results a scenario for quantifying urban ‘greenness’ is presented. Full article
(This article belongs to the Special Issue Urban Remote Sensing)
Open AccessArticle Heritage Recording and 3D Modeling with Photogrammetry and 3D Scanning
Remote Sens. 2011, 3(6), 1104-1138; doi:10.3390/rs3061104
Received: 7 April 2011 / Revised: 16 May 2011 / Accepted: 17 May 2011 / Published: 30 May 2011
Cited by 100 | PDF Full-text (1899 KB) | HTML Full-text | XML Full-text
Abstract
The importance of landscape and heritage recording and documentation with optical remote sensing sensors is well recognized at international level. The continuous development of new sensors, data capture methodologies and multi-resolution 3D representations, contributes significantly to the digital 3D documentation, mapping, conservation and
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The importance of landscape and heritage recording and documentation with optical remote sensing sensors is well recognized at international level. The continuous development of new sensors, data capture methodologies and multi-resolution 3D representations, contributes significantly to the digital 3D documentation, mapping, conservation and representation of landscapes and heritages and to the growth of research in this field. This article reviews the actual optical 3D measurement sensors and 3D modeling techniques, with their limitations and potentialities, requirements and specifications. Examples of 3D surveying and modeling of heritage sites and objects are also shown throughout the paper. Full article
(This article belongs to the Special Issue Remote Sensing in Natural and Cultural Heritage)
Open AccessArticle Automatic Geographic Object Based Mapping of Streambed and Riparian Zone Extent from LiDAR Data in a Temperate Rural Urban Environment, Australia
Remote Sens. 2011, 3(6), 1139-1156; doi:10.3390/rs3061139
Received: 12 April 2011 / Revised: 5 May 2011 / Accepted: 17 May 2011 / Published: 30 May 2011
Cited by 14 | PDF Full-text (1800 KB) | HTML Full-text | XML Full-text
Abstract
This research presents a time-effective approach for mapping streambed and riparian zone extent from high spatial resolution LiDAR derived products, i.e., digital terrain model, terrain slope and plant projective cover. Geographic object based image analysis (GEOBIA) has proven useful for feature extraction
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This research presents a time-effective approach for mapping streambed and riparian zone extent from high spatial resolution LiDAR derived products, i.e., digital terrain model, terrain slope and plant projective cover. Geographic object based image analysis (GEOBIA) has proven useful for feature extraction from high spatial resolution image data because of the capacity to reduce effects of reflectance variations of pixels making up individual objects and to include contextual and shape information. This functionality increases the likelihood of developing transferable and automated mapping approaches. LiDAR data covered parts of the Werribee Catchment in Victoria, Australia, which is characterized by urban, agricultural, and forested land cover types. Field data of streamside vegetation structure and physical form properties were used for both calibration of the mapping routines and validation of the mapping results. To improve the transferability of the rule set, the GEOBIA approach was developed for an area representing different riparian zone environments, i.e., urbanized, agricultural and hilly forested areas. Results show that mapping streambed extent (R2 = 0.93, RMSE = 3.6 m, n = 35) and riparian zone extent (R2 = 0.74, RMSE = 3.9, n = 35) from LiDAR derived products can be automated using GEOBIA to enable derivation of spatial information in an accurate and time-effective manner suited for natural resource management agencies. Full article
(This article belongs to the Special Issue Object-Based Image Analysis)
Open AccessArticle Strategies for Incorporating High-Resolution Google Earth Databases to Guide and Validate Classifications: Understanding Deforestation in Borneo
Remote Sens. 2011, 3(6), 1157-1176; doi:10.3390/rs3061157
Received: 31 March 2011 / Revised: 20 May 2011 / Accepted: 24 May 2011 / Published: 3 June 2011
Cited by 16 | PDF Full-text (1262 KB) | HTML Full-text | XML Full-text
Abstract
International climate change mitigation initiatives such as REDD-plus have fuelled the need for forest monitoring efforts that focus especially on the carbon rich natural ecosystems that are found in the humid tropics. Such monitoring efforts must tackle challenges intrinsic to these regions, such
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International climate change mitigation initiatives such as REDD-plus have fuelled the need for forest monitoring efforts that focus especially on the carbon rich natural ecosystems that are found in the humid tropics. Such monitoring efforts must tackle challenges intrinsic to these regions, such as high atmospheric contamination from particulates and persistent cloud cover. The emergence of new high-resolution platforms like Google Earth offers new potential scientific uses that can help meet these challenges. Using data from MODIS and detailed observation of Google Earth images, we have produced a yearly time series of deforestation hotspots for the island of Borneo for the 2000 to 2009 period. Our workflow and results demonstrate how multiple free data sources can be combined to greatly enhance the individual capacities of each. The methodology employed to produce this time series demonstrates simple, low-expense techniques that can be used to circumvent the obstacles that typically hinder systematic remote sensing in Borneo and other heavily clouded areas. Full article
(This article belongs to the Special Issue Remote Sensing in Support of Environmental Policy)
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Open AccessArticle Development of a New Ground Truth Database for Global Urban Area Mapping from a Gazetteer
Remote Sens. 2011, 3(6), 1177-1187; doi:10.3390/rs3061177
Received: 13 April 2011 / Revised: 21 May 2011 / Accepted: 31 May 2011 / Published: 3 June 2011
Cited by 7 | PDF Full-text (2492 KB) | HTML Full-text | XML Full-text
Abstract
We developed a ground truth database for urban areas from the Global Rural-Urban Mapping Project (GRUMP) Settlement Points gazetteer of populated place names by visually interpreting 3,734 urban points on satellite images, thus acquiring 2,144 urban and 1,388 non-urban data points. Our database
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We developed a ground truth database for urban areas from the Global Rural-Urban Mapping Project (GRUMP) Settlement Points gazetteer of populated place names by visually interpreting 3,734 urban points on satellite images, thus acquiring 2,144 urban and 1,388 non-urban data points. Our database contained many more urban data points than the existing databases, which had only 0 to 11 ground truth data points. We used our database in combination with the Degree Confluence Project database to assess the accuracy of eight satellite-derived urban area maps, among which the MODIS Terra + Aqua Land Cover Type Yearly L3 Global 500 m SIN Grid was the most accurate (84% overall accuracy; kappa coefficient, 0.63). Moreover, the most recently published maps were not necessarily the most accurate. We compared the accuracy assessment results of our database with those of another database and found that ours detected more errors of commission but included less chance agreement. Full article
(This article belongs to the Special Issue Urban Remote Sensing)
Open AccessArticle Evaluation of Automatic Building Detection Approaches Combining High Resolution Images and LiDAR Data
Remote Sens. 2011, 3(6), 1188-1210; doi:10.3390/rs3061188
Received: 24 March 2011 / Revised: 5 May 2011 / Accepted: 1 June 2011 / Published: 14 June 2011
Cited by 38 | PDF Full-text (1652 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, two main approaches for automatic building detection and localization using high spatial resolution imagery and LiDAR data are compared and evaluated: thresholding-based and object-based classification. The thresholding-based approach is founded on the establishment of two threshold values: one refers to
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In this paper, two main approaches for automatic building detection and localization using high spatial resolution imagery and LiDAR data are compared and evaluated: thresholding-based and object-based classification. The thresholding-based approach is founded on the establishment of two threshold values: one refers to the minimum height to be considered as building, defined using the LiDAR data, and the other refers to the presence of vegetation, which is defined according to the spectral response. The other approach follows the standard scheme of object-based image classification: segmentation, feature extraction and selection, and classification, here performed using decision trees. In addition, the effect of the inclusion in the building detection process of contextual relations with the shadows is evaluated. Quality assessment is performed at two different levels: area and object. Area-level evaluates the building delineation performance, whereas object-level assesses the accuracy in the spatial location of individual buildings. The results obtained show a high efficiency of the evaluated methods for building detection techniques, in particular the thresholding-based approach, when the parameters are properly adjusted and adapted to the type of urban landscape considered. Full article
(This article belongs to the Special Issue Urban Remote Sensing)
Open AccessArticle Post-Disaster Image Processing for Damage Analysis Using GENESI-DR, WPS and Grid Computing
Remote Sens. 2011, 3(6), 1234-1250; doi:10.3390/rs3061234
Received: 11 April 2011 / Revised: 2 May 2011 / Accepted: 3 June 2011 / Published: 14 June 2011
Cited by 6 | PDF Full-text (318 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The goal of the two year Ground European Network for Earth Science Interoperations-Digital Repositories (GENESI-DR) project was to build an open and seamless access service to Earth science digital repositories for European and world-wide science users. In order to showcase GENESI-DR, one of
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The goal of the two year Ground European Network for Earth Science Interoperations-Digital Repositories (GENESI-DR) project was to build an open and seamless access service to Earth science digital repositories for European and world-wide science users. In order to showcase GENESI-DR, one of the developed technology demonstrators focused on fast search, discovery, and access to remotely sensed imagery in the context of post-disaster building damage assessment. This paper describes the scenario and implementation details of the technology demonstrator, which was developed to support post-disaster damage assessment analyst activities. Once a disaster alert has been issued, response time is critical to providing relevant damage information to analysts and/or stakeholders. The presented technology demonstrator validates the GENESI-DR project data search, discovery and security infrastructure and integrates the rapid urban area mapping and the near real-time orthorectification web processing services to support a post-disaster damage needs assessment analysis scenario. It also demonstrates how the GENESI-DR SOA can be linked to web processing services that access grid computing resources for fast image processing and use secure communication to ensure confidentiality of information. Full article
Open AccessArticle Toronto’s Urban Heat Island—Exploring the Relationship between Land Use and Surface Temperature
Remote Sens. 2011, 3(6), 1251-1265; doi:10.3390/rs3061251
Received: 15 April 2011 / Revised: 8 June 2011 / Accepted: 14 June 2011 / Published: 21 June 2011
Cited by 38 | PDF Full-text (947 KB) | HTML Full-text | XML Full-text
Abstract
The urban heat island effect is linked to the built environment and threatens human health during extreme heat events. In this study, we analyzed whether characteristic land uses within an urban area are associated with higher or lower surface temperatures, and whether concentrations
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The urban heat island effect is linked to the built environment and threatens human health during extreme heat events. In this study, we analyzed whether characteristic land uses within an urban area are associated with higher or lower surface temperatures, and whether concentrations of “hot” land uses exacerbate this relationship. Zonal statistics on a thermal remote sensing image for the City of Toronto revealed statistically significant differences between high average temperatures for commercial and resource/industrial land use (29.1 °C), and low average temperatures for parks and recreational land (25.1 °C) and water bodies (23.1 °C). Furthermore, higher concentrations of either of these land uses were associated with more extreme surface temperatures. We also present selected neighborhoods to illustrate these results. The paper concludes by recommending that municipal planners and decision-makers formulate policies and regulations that are specific to the problematic land uses, in order to mitigate extreme heat. Full article
(This article belongs to the Special Issue Urban Remote Sensing)
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Open AccessArticle Estimating Surface Soil Moisture from TerraSAR-X Data over Two Small Catchments in the Sahelian Part of Western Niger
Remote Sens. 2011, 3(6), 1266-1283; doi:10.3390/rs3061266
Received: 22 April 2011 / Revised: 15 June 2011 / Accepted: 15 June 2011 / Published: 23 June 2011
Cited by 13 | PDF Full-text (2157 KB) | HTML Full-text | XML Full-text
Abstract
The objective of this study is to validate an approach based on the change detection in multitemporal TerraSAR images (X-band) for mapping soil moisture in the Sahelian area. In situ measurements were carried out simultaneously with TerraSAR-X acquisitions on two study sites in
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The objective of this study is to validate an approach based on the change detection in multitemporal TerraSAR images (X-band) for mapping soil moisture in the Sahelian area. In situ measurements were carried out simultaneously with TerraSAR-X acquisitions on two study sites in Niger. The results show the need for comparing the difference between the rainy season image and a reference image acquired in the dry season. The use of two images enables a reduction of the roughness effects. The soils of plateaus covered with erosion crusts are dry throughout the year while the fallows show more significant moisture during the rainy season. The accuracy on the estimate of soil moisture is about 2.3% (RMSE) in comparison with in situ moisture contents. Full article

Review

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Open AccessReview Use of Remote Sensing to Support Forest and Wetlands Policies in the USA
Remote Sens. 2011, 3(6), 1211-1233; doi:10.3390/rs3061211
Received: 30 March 2011 / Revised: 24 May 2011 / Accepted: 31 May 2011 / Published: 14 June 2011
Cited by 11 | PDF Full-text (392 KB) | HTML Full-text | XML Full-text
Abstract
The use of remote sensing for environmental policy development is now quite common and well-documented, as images from remote sensing platforms are often used to focus attention on emerging environmental issues and spur debate on potential policy solutions. However, its use in policy
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The use of remote sensing for environmental policy development is now quite common and well-documented, as images from remote sensing platforms are often used to focus attention on emerging environmental issues and spur debate on potential policy solutions. However, its use in policy implementation and evaluation has not been examined in much detail. Here we examine the use of remote sensing to support the implementation and enforcement of policies regarding the conservation of forests and wetlands in the USA. Specifically, we focus on the “Roadless Rule” and “Travel Management Rules” as enforced by the US Department of Agriculture Forest Service on national forests, and the “No Net Loss” policy and Clean Water Act for wetlands on public and private lands, as enforced by the US Environmental Protection Agency and the US Army Corps of Engineers. We discuss several national and regional examples of how remote sensing for forest and wetland conservation has been effectively integrated with policy decisions, along with barriers to further integration. Some of these barriers are financial and technical (such as the lack of data at scales appropriate to policy enforcement), while others are political. Full article
(This article belongs to the Special Issue Remote Sensing in Support of Environmental Policy)

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