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Remote Sens., Volume 3, Issue 10 (October 2011), Pages 2110-2304

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Research

Open AccessArticle Using Remote Sensing Products for Environmental Analysis in South America
Remote Sens. 2011, 3(10), 2110-2127; doi:10.3390/rs3102110
Received: 21 July 2011 / Revised: 2 September 2011 / Accepted: 12 September 2011 / Published: 26 September 2011
Cited by 4 | PDF Full-text (4193 KB) | HTML Full-text | XML Full-text
Abstract
Land cover plays a major role in many biogeochemical models that represent processes and connections with terrestrial systems; hence, it is a key component for public decisions in ecosystems management. The advance of remote sensing technology, combined with the emergence of new [...] Read more.
Land cover plays a major role in many biogeochemical models that represent processes and connections with terrestrial systems; hence, it is a key component for public decisions in ecosystems management. The advance of remote sensing technology, combined with the emergence of new operational products, offers alternatives to improve the accuracy of environmental monitoring and analysis. This work uses the GLOBCOVER, the Vegetation Continuous Field (VCF), MODIS Fire Radiative Power (FRP) and the Tropical Rainfall Measuring Mission (TRMM) remotely sensed databases to analyze the biomass burning distribution, the land use and land cover characteristics and the percent of tree cover in South America during the years 2000 to 2005. Initially, GLOBCOVER was assessed based on VCF product, and subsequently used for quantitative analysis of the spatial distribution of the South America fires with the fire radiative power (FRP). The results show that GLOBCOVER has a tendency to overestimate forest classes and to underestimate urban and mangroves areas. The fire quantification based on GLOBCOVER product shows that the highest incidence of fires can be observed in the arc of deforestation, located in the Amazon forest border, with vegetation cover composed mainly of broadleaved evergreen or semi-deciduous forest. A time series analysis of FRP database indicates that biomass burning occurs mainly in areas of broadleaved evergreen or semi-deciduous forest and in Brazilian Cerrado associated with grassland management, agricultural land clearing and with the deforestation of Amazon tropical rainforest. Also, variations in FRP intensity and spread can be attributed to rainfall anomalies, such as in 2004, when South America had a positive anomaly rainfall. Full article
(This article belongs to the Special Issue 100 Years ISPRS - Advancing Remote Sensing Science)
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Open AccessArticle Optimizing Spatial Resolution of Imagery for Urban Form Detection—The Cases of France and Vietnam
Remote Sens. 2011, 3(10), 2128-2147; doi:10.3390/rs3102128
Received: 1 August 2011 / Revised: 2 September 2011 / Accepted: 5 September 2011 / Published: 26 September 2011
Cited by 8 | PDF Full-text (1644 KB) | HTML Full-text | XML Full-text
Abstract
The multitude of satellite data products available offers a large choice for urban studies. Urban space is known for its high heterogeneity in structure, shape and materials. To approach this heterogeneity, finding the optimal spatial resolution (OSR) is needed for urban form [...] Read more.
The multitude of satellite data products available offers a large choice for urban studies. Urban space is known for its high heterogeneity in structure, shape and materials. To approach this heterogeneity, finding the optimal spatial resolution (OSR) is needed for urban form detection from remote sensing imagery. By applying the local variance method to our datasets (pan-sharpened images), we can identify OSR at two levels of observation: individual urban elements and urban districts in two agglomerations in West Europe (Strasbourg, France) and in Southeast Asia (Da Nang, Vietnam). The OSR corresponds to the minimal variance of largest number of spectral bands. We carry out three categories of interval values of spatial resolutions for identifying OSR: from 0.8 m to 3 m for isolated objects, from 6 m to 8 m for vegetation area and equal or higher than 20 m for urban district. At the urban district level, according to spatial patterns, form, size and material of elements, we propose the range of OSR between 30 m and 40 m for detecting administrative districts, new residential districts and residential discontinuous districts. The detection of industrial districts refers to a coarser OSR from 50 m to 60 m. The residential continuous dense districts effectively need a finer OSR of between 20 m and 30 m for their optimal identification. We also use fractal dimensions to identify the threshold of homogeneity/heterogeneity of urban structure at urban district level. It seems therefore that our approaches are robust and transferable to different urban contexts. Full article
(This article belongs to the Special Issue Urban Remote Sensing)
Open AccessArticle Urban Sprawl Analysis and Modeling in Asmara, Eritrea
Remote Sens. 2011, 3(10), 2148-2165; doi:10.3390/rs3102148
Received: 10 August 2011 / Revised: 7 September 2011 / Accepted: 8 September 2011 / Published: 26 September 2011
Cited by 20 | PDF Full-text (696 KB) | HTML Full-text | XML Full-text
Abstract
The extension of urban perimeter markedly cuts available productive land. Hence, studies in urban sprawl analysis and modeling play an important role to ensure sustainable urban development. The urbanization pattern of the Greater Asmara Area (GAA), the capital of Eritrea, was studied. [...] Read more.
The extension of urban perimeter markedly cuts available productive land. Hence, studies in urban sprawl analysis and modeling play an important role to ensure sustainable urban development. The urbanization pattern of the Greater Asmara Area (GAA), the capital of Eritrea, was studied. Satellite images and geospatial tools were employed to analyze the spatiotemporal urban landuse changes. Object-Based Image Analysis (OBIA), Landuse Cover Change (LUCC) analysis and urban sprawl analysis using Shannon Entropy were carried out. The Land Change Modeler (LCM) was used to develop a model of urban growth. The Multi-layer Perceptron Neural Network was employed to model the transition potential maps with an accuracy of 85.9% and these were used as an input for the ‘actual’ urban modeling with Markov chains. Model validation was assessed and a scenario of urban land use change of the GAA up to year 2020 was presented. The result of the study indicated that the built-up area has tripled in size (increased by 4,441 ha) between 1989 and 2009. Specially, after year 2000 urban sprawl in GAA caused large scale encroachment on high potential agricultural lands and plantation cover. The scenario for year 2020 shows an increase of the built-up areas by 1,484 ha (25%) which may cause further loss. The study indicated that the land allocation system in the GAA overrode the landuse plan, which caused the loss of agricultural land and plantation cover. The recommended policy options might support decision makers to resolve further loss of agricultural land and plantation cover and to achieve sustainable urban development planning in the GAA. Full article
(This article belongs to the Special Issue Remote Sensing in Support of Environmental Policy)
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Open AccessArticle Monitoring the Extent of Contamination from Acid Mine Drainage in the Iberian Pyrite Belt (SW Spain) Using Hyperspectral Imagery
Remote Sens. 2011, 3(10), 2166-2186; doi:10.3390/rs3102166
Received: 25 July 2011 / Revised: 6 September 2011 / Accepted: 2 October 2011 / Published: 14 October 2011
Cited by 10 | PDF Full-text (1237 KB) | HTML Full-text | XML Full-text
Abstract
Monitoring mine waste from sulfide deposits by hyperspectral remote sensing can be used to predict surface water quality by quantitatively estimating acid drainage and metal contamination on a yearly basis. In addition, analysis of the mineralogy of surface crusts rich in soluble [...] Read more.
Monitoring mine waste from sulfide deposits by hyperspectral remote sensing can be used to predict surface water quality by quantitatively estimating acid drainage and metal contamination on a yearly basis. In addition, analysis of the mineralogy of surface crusts rich in soluble salts can provide a record of annual humidity and temperature. In fact, temporal monitoring of salt efflorescence from mine wastes at a mine site in the Iberian Pyrite Belt (Huelva, Spain) has been achieved using hyperspectral airborne Hymap data. Furthermore, climate variability estimates are possible based on oxidation stages derived from well-known sequences of minerals, by tracing sulfide oxidation intensity using archive spectral libraries. Thus, airborne and spaceborne hyperspectral remote sensing data can be used to provide a short-term record of climate change, and represent a useful set of tools for assessing environmental geoindicators in semi-arid areas. Spectral and geomorphological indicators can be monitored on a regular basis through image processing, supported by field and laboratory spectral data. In fact, hyperspectral image analysis is one of the methods selected by the Joint Research Centre of the European Community (Ispra, Italy) to study abandoned mine sites, in order to assess the enforcement of the European Mine Waste Directive (2006/21/EC of the European Parliament and of the Council 15 March 2006) on the management of waste from extractive industries (Official Journal of the European Union, 11 April 2006). The pyrite belt in Andalucia has been selected as one of the core mission test sites for the PECOMINES II program (Cracow, November 2005), using imaging spectroscopy; and this technique is expected to be implemented as a monitoring tool by the Environmental Net of Andalucía (REDIAM, Junta de Andalucía, Spain). Full article
(This article belongs to the Special Issue Hyperspectral Remote Sensing)
Open AccessArticle Use of Orbital LIDAR in the Brazilian Cerrado Biome: Potential Applications and Data Availability
Remote Sens. 2011, 3(10), 2187-2206; doi:10.3390/rs3102187
Received: 11 August 2011 / Revised: 26 August 2011 / Accepted: 29 September 2011 / Published: 17 October 2011
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Abstract
This paper focuses on the Ice, Cloud and land Elevation Satellite (ICESat) Geoscience Laser Altimeter System (GLAS) data availability over the 2 million km2 Cerrado, the Brazilian central savanna biome and one of the world’s biodiversity hotspots. Overall, about 2.5 million [...] Read more.
This paper focuses on the Ice, Cloud and land Elevation Satellite (ICESat) Geoscience Laser Altimeter System (GLAS) data availability over the 2 million km2 Cerrado, the Brazilian central savanna biome and one of the world’s biodiversity hotspots. Overall, about 2.5 million laser shots, distributed along the seven years of ICESat operation (2003–2009) and comprising three major seasonal domains, were acquired, from which, 206,026 and 176,035 screened footprints are coincident with the remnant vegetation and cultivated pasture areas (the dominant land-use form in the Cerrado). Although these points are well distributed over the entire Cerrado, the ICESat track data collection results in substantial data gaps. In relation to the 15,612 Cerrado watersheds (6th order Otto basin system), 8,369 and 4,415 watersheds are completely deprived of data points over their remnant vegetation and pasture covers, respectively. Light Detection and Ranging (LIDAR) availability was also evaluated in relation to specific targets of interest, including both fully-protected conservation units as well as areas impacted by fire and deforestation. In spite of the very few occurrences, our assessments indicate that enough LIDAR data is available for retrieving structural and functional properties of a variety of Cerrado physiognomies, as well as to assess how these physiognomies respond to anthropogenic induced changes. In fact, the comprehensive data availability analysis conducted in this study corroborate the potential of GLAS LIDAR waveforms for the retrieval of biophysical properties at both local and regional scales, particularly concerning remnant carbon stocks and pasture conditions, key information for the conservation of the fast-changing and severely threatened Cerrado. Full article
Open AccessArticle Analysis of Incidence Angle and Distance Effects on Terrestrial Laser Scanner Intensity: Search for Correction Methods
Remote Sens. 2011, 3(10), 2207-2221; doi:10.3390/rs3102207
Received: 23 August 2011 / Revised: 3 October 2011 / Accepted: 12 October 2011 / Published: 20 October 2011
Cited by 45 | PDF Full-text (526 KB) | HTML Full-text | XML Full-text
Abstract
The intensity information from terrestrial laser scanners (TLS) has become an important object of study in recent years, and there are an increasing number of applications that would benefit from the addition of calibrated intensity data to the topographic information. In this [...] Read more.
The intensity information from terrestrial laser scanners (TLS) has become an important object of study in recent years, and there are an increasing number of applications that would benefit from the addition of calibrated intensity data to the topographic information. In this paper, we study the range and incidence angle effects on the intensity measurements and search for practical correction methods for different TLS instruments and targets. We find that the range (distance) effect is strongly dominated by instrumental factors, whereas the incidence angle effect is mainly caused by the target surface properties. Correction for both effects is possible, but more studies are needed for physical interpretation and more efficient use of intensity data for target characterization. Full article
(This article belongs to the Special Issue Terrestrial Laser Scanning)
Open AccessArticle Hyperspectral Data for Mangrove Species Mapping: A Comparison of Pixel-Based and Object-Based Approach
Remote Sens. 2011, 3(10), 2222-2242; doi:10.3390/rs3102222
Received: 12 August 2011 / Revised: 4 October 2011 / Accepted: 11 October 2011 / Published: 20 October 2011
Cited by 29 | PDF Full-text (1441 KB) | HTML Full-text | XML Full-text
Abstract
Visual image interpretation and digital image classification have been used to map and monitor mangrove extent and composition for decades. The presence of a high-spatial resolution hyperspectral sensor can potentially improve our ability to differentiate mangrove species. However, little research has explored the [...] Read more.
Visual image interpretation and digital image classification have been used to map and monitor mangrove extent and composition for decades. The presence of a high-spatial resolution hyperspectral sensor can potentially improve our ability to differentiate mangrove species. However, little research has explored the use of pixel-based and object-based approaches on high-spatial hyperspectral datasets for this purpose. This study assessed the ability of CASI-2 data for mangrove species mapping using pixel-based and object-based approaches at the mouth of the Brisbane River area, southeast Queensland, Australia. Three mapping techniques used in this study: spectral angle mapper (SAM) and linear spectral unmixing (LSU) for the pixel-based approaches, and multi-scale segmentation for the object-based image analysis (OBIA). The endmembers for the pixel-based approach were collected based on existing vegetation community map. Nine targeted classes were mapped in the study area from each approach, including three mangrove species: Avicennia marina, Rhizophora stylosa, and Ceriops australis. The mapping results showed that SAM produced accurate class polygons with only few unclassified pixels (overall accuracy 69%, Kappa 0.57), the LSU resulted in a patchy polygon pattern with many unclassified pixels (overall accuracy 56%, Kappa 0.41), and the object-based mapping produced the most accurate results (overall accuracy 76%, Kappa 0.67). Our results demonstrated that the object-based approach, which combined a rule-based and nearest-neighbor classification method, was the best classifier to map mangrove species and its adjacent environments. Full article
(This article belongs to the Special Issue Hyperspectral Remote Sensing)
Open AccessArticle Monitoring Urban Tree Cover Using Object-Based Image Analysis and Public Domain Remotely Sensed Data
Remote Sens. 2011, 3(10), 2243-2262; doi:10.3390/rs3102243
Received: 11 August 2011 / Revised: 14 October 2011 / Accepted: 14 October 2011 / Published: 21 October 2011
Cited by 21 | PDF Full-text (1458 KB) | HTML Full-text | XML Full-text
Abstract
Urban forest ecosystems provide a range of social and ecological services, but due to the heterogeneity of these canopies their spatial extent is difficult to quantify and monitor. Traditional per-pixel classification methods have been used to map urban canopies, however, such techniques [...] Read more.
Urban forest ecosystems provide a range of social and ecological services, but due to the heterogeneity of these canopies their spatial extent is difficult to quantify and monitor. Traditional per-pixel classification methods have been used to map urban canopies, however, such techniques are not generally appropriate for assessing these highly variable landscapes. Landsat imagery has historically been used for per-pixel driven land use/land cover (LULC) classifications, but the spatial resolution limits our ability to map small urban features. In such cases, hyperspatial resolution imagery such as aerial or satellite imagery with a resolution of 1 meter or below is preferred. Object-based image analysis (OBIA) allows for use of additional variables such as texture, shape, context, and other cognitive information provided by the image analyst to segment and classify image features, and thus, improve classifications. As part of this research we created LULC classifications for a pilot study area in Seattle, WA, USA, using OBIA techniques and freely available public aerial photography. We analyzed the differences in accuracies which can be achieved with OBIA using multispectral and true-color imagery. We also compared our results to a satellite based OBIA LULC and discussed the implications of per-pixel driven vs. OBIA-driven field sampling campaigns. We demonstrated that the OBIA approach can generate good and repeatable LULC classifications suitable for tree cover assessment in urban areas. Another important finding is that spectral content appeared to be more important than spatial detail of hyperspatial data when it comes to an OBIA-driven LULC. Full article
(This article belongs to the Special Issue Urban Remote Sensing)
Open AccessArticle Machine Learning Comparison between WorldView-2 and QuickBird-2-Simulated Imagery Regarding Object-Based Urban Land Cover Classification
Remote Sens. 2011, 3(10), 2263-2282; doi:10.3390/rs3102263
Received: 19 August 2011 / Revised: 14 October 2011 / Accepted: 14 October 2011 / Published: 21 October 2011
Cited by 39 | PDF Full-text (4467 KB) | HTML Full-text | XML Full-text
Abstract
The objective of this study is to compare WorldView-2 (WV-2) and QuickBird-2-simulated (QB-2) imagery regarding their potential for object-based urban land cover classification. Optimal segmentation parameters were automatically found for each data set and the obtained results were quantitatively compared and discussed. [...] Read more.
The objective of this study is to compare WorldView-2 (WV-2) and QuickBird-2-simulated (QB-2) imagery regarding their potential for object-based urban land cover classification. Optimal segmentation parameters were automatically found for each data set and the obtained results were quantitatively compared and discussed. Four different feature selection algorithms were used in order to verify to which data set the most relevant object-based features belong to. Object-based classifications were performed with four different supervised algorithms applied to each data set and the obtained accuracies and model performances indexes were compared. Segmentation experiments carried out involving bands exclusively available in the WV-2 sensor generated segments slightly more similar to our reference segments (only about 0.23 discrepancy). Fifty seven percent of the different selected features and 53% of all the 80 selections refer to features that can only be calculated with the additional bands of the WV-2 sensor. On the other hand, 57% of the most relevant features and 63% of the second most relevant features can also be calculated considering only the QB-2 bands. In 10 out of 16 classifications, higher Kappa values were achieved when features related to the additional bands of the WV-2 sensor were also considered. In most cases, classifications carried out with the 8-band-related features generated less complex and more efficient models than those generated only with QB-2 band-related features. Our results lead to the conclusion that spectrally similar classes like ceramic tile roofs and bare soil, as well as asphalt and dark asbestos roofs can be better distinguished when the additional bands of the WV-2 sensor are used throughout the object-based classification process. Full article
(This article belongs to the Special Issue Object-Based Image Analysis)
Open AccessArticle Feasibility of Invasive Grass Detection in a Desertscrub Community Using Hyperspectral Field Measurements and Landsat TM Imagery
Remote Sens. 2011, 3(10), 2283-2304; doi:10.3390/rs3102283
Received: 20 August 2011 / Revised: 3 October 2011 / Accepted: 9 October 2011 / Published: 21 October 2011
Cited by 9 | PDF Full-text (1494 KB) | HTML Full-text | XML Full-text
Abstract
Invasive species’ phenologies often contrast with those of native species, representing opportunities for detection of invasive species with multi-temporal remote sensing. Detection is especially critical for ecosystem-transforming species that facilitate changes in disturbance regimes. The African C4 grass, Pennisetum ciliare, is transforming [...] Read more.
Invasive species’ phenologies often contrast with those of native species, representing opportunities for detection of invasive species with multi-temporal remote sensing. Detection is especially critical for ecosystem-transforming species that facilitate changes in disturbance regimes. The African C4 grass, Pennisetum ciliare, is transforming ecosystems on three continents and a number of neotropical islands by introducing a grass-fire cycle. However, previous attempts at discriminating P. ciliare in North America using multi-spectral imagery have been unsuccessful. In this paper, we integrate field measurements of hyperspectral plant species signatures and canopy cover with multi-temporal spectral analysis to identify opportunities for detection using moderate-resolution multi-spectral imagery. By applying these results to Landsat TM imagery, we show that multi-spectral discrimination of P. ciliare in heterogeneous mixed desert scrub is feasible, but only at high abundance levels that may have limited value to land managers seeking to control invasion. Much higher discriminability is possible with hyperspectral shortwave infrared imagery because of differences in non-photosynthetic vegetation in uninvaded and invaded landscapes during dormant seasons but these spectra are unavailable in multispectral sensors. Therefore, we recommend hyperspectral imagery for distinguishing invasive grass-dominated landscapes from uninvaded desert scrub. Full article

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